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Alliance formation with exclusion in the spatial public goods game | Detecting defection and alarming partners about the possible danger could be
essential to avoid being exploited. This act, however, may require a huge
individual effort from those who take this job, hence such a strategy seems to
be unfavorable. But structured populations can provide an opportunity where a
largely unselfish excluder strategy can form an effective alliance with other
cooperative strategies, hence they can sweep out defection. Interestingly, this
alliance is functioning even at the extremely high cost of exclusion where the
sole application of an exclusion strategy would be harmful otherwise. These
results may explain why the emergence of extreme selfless behavior is not
necessarily against individual selection but could be the result of an
evolutionary process.
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Fast and In Sync: Periodic Swarm Patterns for Quadrotors | This paper aims to design quadrotor swarm performances, where the swarm acts
as an integrated, coordinated unit embodying moving and deforming objects. We
divide the task of creating a choreography into three basic steps: designing
swarm motion primitives, transitioning between those movements, and
synchronizing the motion of the drones. The result is a flexible framework for
designing choreographies comprised of a wide variety of motions. The motion
primitives can be intuitively designed using few parameters, providing a rich
library for choreography design. Moreover, we combine and adapt existing goal
assignment and trajectory generation algorithms to maximize the smoothness of
the transitions between motion primitives. Finally, we propose a correction
algorithm to compensate for motion delays and synchronize the motion of the
drones to a desired periodic motion pattern. The proposed methodology was
validated experimentally by generating and executing choreographies on a swarm
of 25 quadrotors.
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Nonparametric estimation of locally stationary Hawkes processe | In this paper we consider multivariate Hawkes processes with baseline hazard
and kernel functions that depend on time. This defines a class of locally
stationary processes. We discuss estimation of the time-dependent baseline
hazard and kernel functions based on a localized criterion. Theory on
stationary Hawkes processes is extended to develop asymptotic theory for the
estimator in the locally stationary model.
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Comparison of Flow Scheduling Policies for Mix of Regular and Deadline Traffic in Datacenter Environments | Datacenters are the main infrastructure on top of which cloud computing
services are offered. Such infrastructure may be shared by a large number of
tenants and applications generating a spectrum of datacenter traffic. Delay
sensitive applications and applications with specific Service Level Agreements
(SLAs), generate deadline constrained flows, while other applications initiate
flows that are desired to be delivered as early as possible. As a result,
datacenter traffic is a mix of two types of flows: deadline and regular. There
are several scheduling policies for either traffic type with focus on
minimizing completion times or deadline miss rate. In this report, we apply
several scheduling policies to mix traffic scenario while varying the ratio of
regular to deadline traffic. We consider FCFS (First Come First Serve), SRPT
(Shortest Remaining Processing Time) and Fair Sharing as deadline agnostic
approaches and a combination of Earliest Deadline First (EDF) with either FCFS
or SRPT as deadline-aware schemes. In addition, for the latter, we consider
both cases of prioritizing deadline traffic (Deadline First) and prioritizing
regular traffic (Deadline Last). We study both light-tailed and heavy-tailed
flow size distributions and measure mean, median and tail flow completion times
(FCT) for regular flows along with Deadline Miss Rate (DMR) and average
lateness for deadline flows. We also consider two operation regimes of
lightly-loaded (low utilization) and heavily-loaded (high utilization). We find
that performance of deadline-aware schemes is highly dependent on fraction of
deadline traffic. With light-tailed flow sizes, we find that FCFS performs
better in terms of tail times and average lateness while SRPT performs better
in average times and deadline miss rate. For heavy-tailed flow sizes, except
for tail times, SRPT performs better in all other metrics.
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Getting around the Halting Problem | The Halting Theorem establishes that there is no program (or Turing machine)
H that can decide in all cases if an arbitrary program n halts on input m. The
conjecture of this paper is that nevertheless there exists a sound program H
such that if it halts it answers either yes or no, and can also in a certain
sense identify all the cases it is unable to decide. The Halting Theorem can be
proved by constructing a counterexample, i.e. a program that attempts to assert
that it itself does not halt. The thesis is that there exists a program that
proves about itself that its own attempt to prove, that the counterexample does
not halt, does not halt. This outcome can be interpreted as it is NOT TRUE that
the counterexample does not halt as opposed to it is FALSE that the
counterexample does not halt. This becomes possible when the Recursion Theorem
is reinterpreted as mutual necessitation rather than equivalence.
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Molecular Beam Epitaxy Growth of [CrGe/MnGe/FeGe] Superlattices: Toward Artificial B20 Skyrmion Materials with Tunable Interactions | Skyrmions are localized magnetic spin textures whose stability has been shown
theoretically to depend on material parameters including bulk Dresselhaus spin
orbit coupling (SOC), interfacial Rashba SOC, and magnetic anisotropy. Here, we
establish the growth of a new class of artificial skyrmion materials, namely
B20 superlattices, where these parameters could be systematically tuned.
Specifically, we report the successful growth of B20 superlattices comprised of
single crystal thin films of FeGe, MnGe, and CrGe on Si(111) substrates. Thin
films and superlattices are grown by molecular beam epitaxy and are
characterized through a combination of reflection high energy electron
diffraction, x-ray diffraction, and cross-sectional scanning transmission
electron microscopy (STEM). X-ray energy dispersive spectroscopy (XEDS)
distinguishes layers by elemental mapping and indicates good interface quality
with relatively low levels of intermixing in the [CrGe/MnGe/FeGe] superlattice.
This demonstration of epitaxial, single-crystalline B20 superlattices is a
significant advance toward tunable skyrmion systems for fundamental scientific
studies and applications in magnetic storage and logic.
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A comparative study of different exchange-correlation functionals in understanding structural, electronic and thermoelectric properties of Fe$_{2}$VAl and Fe$_{2}$TiSn compounds | Fe$_{2}$VAl and Fe$_{2}$TiSn are full Heusler compounds with non-magnetic
ground state. The two compouds are good thermoelectric materials. PBE and
LDA(PW92) are the two most commonly used density functionals to study the
Heusler compounds. Along with these two well studied exchange-correlation
functionals, recently developed PBEsol, mBJ and SCAN functionals are employed
to study the two compounds. Using the five functionals equilibrium lattice
parameter and bulk modulus are calculated. Obtained values are compared with
experimental reports wherever available. Electronic structure properties are
studied by calculating dispersion curves, total and partial density of states.
For Fe$_{2}$VAl, band gap of 0.22 eV is obtained from the mBJ potential which
is in reasonable agreement with experimental value while, for Fe$_{2}$TiSn band
gap of 0.68 eV is obtained. Fe$_{2}$VAl is predicted to be semimetallic with
different values of negative gaps from LDA,PBEsol,PBE and SCAN functionals.
Whereas, Fe$_{2}$TiSn is found to be semimetallic(semiconducting) from
LDA,PBEsol(PBE,SCAN) functionals employed calculations. From the dispersion
curve effective mass values are also computed to see the contribution to the
Seebeck coefficient. In Fe$_{2}$TiSn, a flat band is present along the
$\Gamma$-X direction with calculated value of effective mass $\sim$36 more than
the mass of electron. The improvements or inadequacies among the functionals in
explaining the properties of full Heusler alloys for thermoelectric application
are thus observed through this study.
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On a problem of Pillai with Fibonacci numbers and powers of 2 | In this paper, we find all integers c having at least two representations as
a difference between a Fibonacci number and a power of 2.
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Evidence from web-based dietary search patterns to the role of B12 deficiency in chronic pain | Profound vitamin B12 deficiency is a known cause of disease, but the role of
low or intermediate levels of B12 in the development of neuropathy and other
neuropsychiatric symptoms as well as the relationship of eating meat and B12
levels is unclear. Here we use food-related internet search patterns from a
sample of 8.5 million US-based people as a proxy to B12 intake and correlate
these searches with internet searches related to possible effects of B12
deficiency. Food-related search patterns are highly correlated with known
consumption and food-related searches (Spearman 0.69). Awareness of B12
deficiency was associated with a higher consumption of B12-rich foods and with
queries for B12 supplements. Searches for terms related to neurological
disorders were correlated with searches for B12-poor foods, in contrast with
control terms. Popular medicines, those having fewer indications, and those
which are predominantly used to treat pain are more strongly correlated with
the ability to predict neuropathic pain queries using the B12 contents of food.
Our findings provide evidence for the utility of using Internet search patterns
to investigate health questions in large populations and suggest that low B12
intake may be associated with a broader spectrum of neurological disorders than
currently appreciated.
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Non-Uniform Attacks Against Pseudoentropy | De, Trevisan and Tulsiani [CRYPTO 2010] show that every distribution over
$n$-bit strings which has constant statistical distance to uniform (e.g., the
output of a pseudorandom generator mapping $n-1$ to $n$ bit strings), can be
distinguished from the uniform distribution with advantage $\epsilon$ by a
circuit of size $O( 2^n\epsilon^2)$.
We generalize this result, showing that a distribution which has less than
$k$ bits of min-entropy, can be distinguished from any distribution with $k$
bits of $\delta$-smooth min-entropy with advantage $\epsilon$ by a circuit of
size $O(2^k\epsilon^2/\delta^2)$. As a special case, this implies that any
distribution with support at most $2^k$ (e.g., the output of a pseudoentropy
generator mapping $k$ to $n$ bit strings) can be distinguished from any given
distribution with min-entropy $k+1$ with advantage $\epsilon$ by a circuit of
size $O(2^k\epsilon^2)$.
Our result thus shows that pseudoentropy distributions face basically the
same non-uniform attacks as pseudorandom distributions.
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The Swift/BAT AGN Spectroscopic Survey (BASS) -- VI. The Gamma_X - L/L_Edd relation | We study the observed relation between accretion rate (in terms of L/L_Edd)
and shape of the hard X-ray spectral energy distribution (namely the photon
index Gamma_X) for a large sample of 228 hard X-ray selected, low-redshift
active galactic nuclei (AGN), drawn from the Swift/BAT AGN Spectroscopic Survey
(BASS). This includes 30 AGN for which black hole mass (and therefore L/L_Edd)
is measured directly through masers, spatially resolved gas or stellar
dynamics, or reverberation mapping. The high quality and broad energy coverage
of the data provided through BASS allow us to examine several alternative
determinations of both Gamma_X and L/L_Edd. For the BASS sample as a whole, we
find a statistically significant, albeit very weak correlation between Gamma_X
and L/L_Edd. The best-fitting relations we find, Gamma_X=0.15
log(L/L_Edd)+const., are considerably shallower than those reported in previous
studies. Moreover, we find no corresponding correlations among the subsets of
AGN with different M_BH determination methodology. In particular, we find no
robust evidence for a correlation when considering only those AGN with direct
or single-epoch M_BH estimates. This latter finding is in contrast to several
previous studies which focused on z>0.5 broad-line AGN. We discuss this tension
and conclude that it can be partially accounted for if one adopts a simplified,
power-law X-ray spectral model, combined with L/L_Edd estimates that are based
on the continuum emission and on single-epoch broad line spectroscopy in the
optical regime. We finally highlight the limitations on using Gamma_X as a
probe of supermassive black hole evolution in deep extragalactic X-ray surveys.
| 0 | 1 | 0 | 0 | 0 | 0 |
Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning | In deep reinforcement learning (RL) tasks, an efficient exploration mechanism
should be able to encourage an agent to take actions that lead to less frequent
states which may yield higher accumulative future return. However, both knowing
about the future and evaluating the frequentness of states are non-trivial
tasks, especially for deep RL domains, where a state is represented by
high-dimensional image frames. In this paper, we propose a novel informed
exploration framework for deep RL, where we build the capability for an RL
agent to predict over the future transitions and evaluate the frequentness for
the predicted future frames in a meaningful manner. To this end, we train a
deep prediction model to predict future frames given a state-action pair, and a
convolutional autoencoder model to hash over the seen frames. In addition, to
utilize the counts derived from the seen frames to evaluate the frequentness
for the predicted frames, we tackle the challenge of matching the predicted
future frames and their corresponding seen frames at the latent feature level.
In this way, we derive a reliable metric for evaluating the novelty of the
future direction pointed by each action, and hence inform the agent to explore
the least frequent one.
| 1 | 0 | 0 | 1 | 0 | 0 |
The infrared to X-ray correlation spectra of unobscured type 1 active galactic nuclei | We use new X-ray data obtained with the Nuclear Spectroscopic Telescope Array
(NuSTAR), near-infrared (NIR) fluxes, and mid-infrared (MIR) spectra of a
sample of 24 unobscured type 1 active galactic nuclei (AGN) to study the
correlation between various hard X-ray bands between 3 and 80 keV and the
infrared (IR) emission. The IR to X-ray correlation spectrum (IRXCS) shows a
maximum at ~15-20 micron, coincident with the peak of the AGN contribution to
the MIR spectra of the majority of the sample. There is also a NIR correlation
peak at ~2 micron, which we associate with the NIR bump observed in some type 1
AGN at ~1-5 micron and is likely produced by nuclear hot dust emission. The
IRXCS shows practically the same behaviour in all the X-ray bands considered,
indicating a common origin for all of them. We finally evaluated correlations
between the X-ray luminosities and various MIR emission lines. All the lines
show a good correlation with the hard X-rays (rho>0.7), but we do not find the
expected correlation between their ionization potentials and the strength of
the IRXCS.
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Quantum ensembles of quantum classifiers | Quantum machine learning witnesses an increasing amount of quantum algorithms
for data-driven decision making, a problem with potential applications ranging
from automated image recognition to medical diagnosis. Many of those algorithms
are implementations of quantum classifiers, or models for the classification of
data inputs with a quantum computer. Following the success of collective
decision making with ensembles in classical machine learning, this paper
introduces the concept of quantum ensembles of quantum classifiers. Creating
the ensemble corresponds to a state preparation routine, after which the
quantum classifiers are evaluated in parallel and their combined decision is
accessed by a single-qubit measurement. This framework naturally allows for
exponentially large ensembles in which -- similar to Bayesian learning -- the
individual classifiers do not have to be trained. As an example, we analyse an
exponentially large quantum ensemble in which each classifier is weighed
according to its performance in classifying the training data, leading to new
results for quantum as well as classical machine learning.
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Recurrent Additive Networks | We introduce recurrent additive networks (RANs), a new gated RNN which is
distinguished by the use of purely additive latent state updates. At every time
step, the new state is computed as a gated component-wise sum of the input and
the previous state, without any of the non-linearities commonly used in RNN
transition dynamics. We formally show that RAN states are weighted sums of the
input vectors, and that the gates only contribute to computing the weights of
these sums. Despite this relatively simple functional form, experiments
demonstrate that RANs perform on par with LSTMs on benchmark language modeling
problems. This result shows that many of the non-linear computations in LSTMs
and related networks are not essential, at least for the problems we consider,
and suggests that the gates are doing more of the computational work than
previously understood.
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Nematic phase with colossal magnetoresistance and orbital polarons in manganite La$_{1-x}$Sr$_x$MnO$_3$ | The origin of colossal magnetoresistance (CMR) is still controversial. The
spin dynamics of La$_{1-x}$Sr$_x$MnO$_3$ is revisited along the Mn-O-Mn
direction at $x\leq 0.5$, $T\leq T_C$ with a new study at $x$=0.4. A new
lattice dynamics study is also reported at $x_0$=0.2,representative of the
optimal doping for CMR. In large-$q$ wavevector range, typical of the scale of
polarons, spin dynamics exhibits a discrete spectrum, $E^n_{\rm mag}$ with $n$
equal to the degeneracy of orbital-pseudospin transitions and energy values in
coincidence with the phonon ones. It corresponds to the spin-orbital excitation
spectrum of short life-time polarons, in which the orbital pseudospin
degeneracy is lift by phonons. For $x\neq x_0$, its q-range reveals a $\ell
\approx 1.7a$ size of polarons with a dimension $2d$ at $x=1/8$ partly
increasing to $\approx$ $3d$ at $x=0.3$. At $x_0=0.2$ ($T<T_C$) two distinct
$q$ and energy ranges appear separated by $\Delta E(q_0\approx 0.35)=3meV$. The
same $\Delta E(q_0)$ value separates two unusual transverse acoustic branches
($T>T_C$). Both characterize a nematic-phase defined by chains of "orbital
polarons" of $2a$ size, distant from $3a$, typical of $x_0=1/6$. It could
explain CMR.
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The generalized optical memory effect | The optical memory effect is a well-known type of wave correlation that is
observed in coherent fields that scatter through thin and diffusive materials,
like biological tissue. It is a fundamental physical property of scattering
media that can be harnessed for deep-tissue microscopy or 'through-the-wall'
imaging applications. Here we show that the optical memory effect is a special
case of a far more general class of wave correlation. Our new theoretical
framework explains how waves remain correlated over both space and angle when
they are jointly shifted and tilted inside scattering media of arbitrary
geometry. We experimentally demonstrate the existence of such coupled
correlations and describe how they can be used to optimize the scanning range
in adaptive optics microscopes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Geometry of Policy Improvement | We investigate the geometry of optimal memoryless time independent decision
making in relation to the amount of information that the acting agent has about
the state of the system. We show that the expected long term reward, discounted
or per time step, is maximized by policies that randomize among at most $k$
actions whenever at most $k$ world states are consistent with the agent's
observation. Moreover, we show that the expected reward per time step can be
studied in terms of the expected discounted reward. Our main tool is a
geometric version of the policy improvement lemma, which identifies a
polyhedral cone of policy changes in which the state value function increases
for all states.
| 1 | 0 | 1 | 0 | 0 | 0 |
Modelling and characterization of a pneumatically actuated peristaltic micropump | There is an emerging class of microfluidic bioreactors which possess
long-term, closed circuit perfusion under sterile conditions with in vivo-like
flow parameters. Integrated into microfluidics, peristaltic-like pneumatically
actuated displacement micropumps are able to meet these requirements. We
present both a theoretical and experimental characterization of such pumps. In
order to examine volume flow rate, we have developed a mathemati- cal model
describing membrane motion under external pressure. The viscoelasticity of the
membrane and hydrodynamic resistance of the microfluidic channel have been
taken into account. Unlike other models, the developed model includes only the
physical parameters of the pump and allows the estimation of their impact on
the resulting flow. The model has been validated experimentally.
| 0 | 1 | 0 | 0 | 0 | 0 |
Semi-algebraic triangulation over p-adically closed fields | We prove a triangulation theorem for semi-algebraic sets over a p-adically
closed field, quite similar to its real counterpart. We derive from it several
applications like the existence of flexible retractions and splitting for
semi-algebraic sets.
| 0 | 0 | 1 | 0 | 0 | 0 |
Improving the Performance of OTDOA based Positioning in NB-IoT Systems | In this paper, we consider positioning with
observed-time-difference-of-arrival (OTDOA) for a device deployed in
long-term-evolution (LTE) based narrow-band Internet-of-things (NB-IoT)
systems. We propose an iterative expectation-maximization based successive
interference cancellation (EM-SIC) algorithm to jointly consider estimations of
residual frequency-offset (FO), fading-channel taps and time-of-arrival (ToA)
of the first arrival-path for each of the detected cells. In order to design a
low complexity ToA detector and also due to the limits of low-cost analog
circuits, we assume an NB-IoT device working at a low-sampling rate such as
1.92 MHz or lower. The proposed EM-SIC algorithm comprises two stages to detect
ToA, based on which OTDOA can be calculated. In a first stage, after running
the EM-SIC block a predefined number of iterations, a coarse ToA is estimated
for each of the detected cells. Then in a second stage, to improve the ToA
resolution, a low-pass filter is utilized to interpolate the correlations of
time-domain PRS signal evaluated at a low sampling-rate to a high sampling-rate
such as 30.72 MHz. To keep low-complexity, only the correlations inside a small
search window centered at the coarse ToA estimates are upsampled. Then, the
refined ToAs are estimated based on upsampled correlations. If at least three
cells are detected, with OTDOA and the locations of detected cell sites, the
position of the NB-IoT device can be estimated. We show through numerical
simulations that, the proposed EM-SIC based ToA detector is robust against
impairments introduced by inter-cell interference, fading-channel and residual
FO. Thus significant signal-to-noise (SNR) gains are obtained over traditional
ToA detectors that do not consider these impairments when positioning a device.
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OGLE-2015-BLG-1459L: The Challenges of Exo-Moon Microlensing | We show that dense OGLE and KMTNet $I$-band survey data require four bodies
(sources plus lenses) to explain the microlensing light curve of
OGLE-2015-BLG-1459. However, these can equally well consist of three lenses and
one source (3L1S), two lenses and two sources (2L2S) or one lens and three
sources (1L3S). In the 3L1S and 2L2S interpretations, the host is a brown dwarf
and the dominant companion is a Neptune-class planet, with the third body (in
the 3L1S case) being a Mars-class object that could have been a moon of the
planet. In the 1L3S solution, the light curve anomalies are explained by a
tight (five stellar radii) low-luminosity binary source that is offset from the
principal source of the event by $\sim 0.17\,\au$. These degeneracies are
resolved in favor of the 1L3S solution by color effects derived from comparison
to MOA data, which are taken in a slightly different ($R/I$) passband. To
enable current and future ($WFIRST$) surveys to routinely characterize exomoons
and distinguish among such exotic systems requires an observing strategy that
includes both a cadence faster than 9 min$^{-1}$ and observations in a second
band on a similar timescale.
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Particle-flow reconstruction and global event description with the CMS detector | The CMS apparatus was identified, a few years before the start of the LHC
operation at CERN, to feature properties well suited to particle-flow (PF)
reconstruction: a highly-segmented tracker, a fine-grained electromagnetic
calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an
excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned
to the CMS detector was therefore developed and has been consistently used in
physics analyses for the first time at a hadron collider. For each collision,
the comprehensive list of final-state particles identified and reconstructed by
the algorithm provides a global event description that leads to unprecedented
CMS performance for jet and hadronic tau decay reconstruction, missing
transverse momentum determination, and electron and muon identification. This
approach also allows particles from pileup interactions to be identified and
enables efficient pileup mitigation methods. The data collected by CMS at a
centre-of-mass energy of 8 TeV show excellent agreement with the simulation and
confirm the superior PF performance at least up to an average of 20 pileup
interactions.
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Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts | This paper proposes a discontinuity-sensitive approach to learn the solutions
of parametric optimal control problems with high accuracy. Many tasks, ranging
from model predictive control to reinforcement learning, may be solved by
learning optimal solutions as a function of problem parameters. However,
nonconvexity, discrete homotopy classes, and control switching cause
discontinuity in the parameter-solution mapping, thus making learning difficult
for traditional continuous function approximators. A mixture of experts (MoE)
model composed of a classifier and several regressors is proposed to address
such an issue. The optimal trajectories of different parameters are clustered
such that in each cluster the trajectories are continuous function of problem
parameters. Numerical examples on benchmark problems show that training the
classifier and regressors individually outperforms joint training of MoE. With
suitably chosen clusters, this approach not only achieves lower prediction
error with less training data and fewer model parameters, but also leads to
dramatic improvements in the reliability of trajectory tracking compared to
traditional universal function approximation models (e.g., neural networks).
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Ergodicity analysis and antithetic integral control of a class of stochastic reaction networks with delays | Delays are an important phenomenon arising in a wide variety of real world
systems. They occur in biological models because of diffusion effects or as
simplifying modeling elements. We propose here to consider delayed stochastic
reaction networks. The difficulty here lies in the fact that the state-space of
a delayed reaction network is infinite-dimensional, which makes their analysis
more involved. We demonstrate here that a particular class of stochastic
time-varying delays, namely those that follow a phase-type distribution, can be
exactly implemented in terms of a chemical reaction network. Hence, any
delay-free network can be augmented to incorporate those delays through the
addition of delay-species and delay-reactions. Hence, for this class of
stochastic delays, which can be used to approximate any delay distribution
arbitrarily accurately, the state-space remains finite-dimensional and,
therefore, standard tools developed for standard reaction network still apply.
In particular, we demonstrate that for unimolecular mass-action reaction
networks that the delayed stochastic reaction network is ergodic if and only if
the non-delayed network is ergodic as well. Bimolecular reactions are more
difficult to consider but an analogous result is also obtained. These results
tell us that delays that are phase-type distributed, regardless of their
distribution, are not harmful to the ergodicity property of reaction networks.
We also prove that the presence of those delays adds convolution terms in the
moment equation but does not change the value of the stationary means compared
to the delay-free case. Finally, the control of a certain class of delayed
stochastic reaction network using a delayed antithetic integral controller is
considered. It is proven that this controller achieves its goal provided that
the delay-free network satisfy the conditions of ergodicity and
output-controllability.
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The three-dimensional standard solution to the Ricci flow is modeled by the Bryant soliton | It came to my attention after posting this paper that Yu Ding has proved the
same result before. I would like to apologize to Yu Ding for the appearance of
this paper.
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Dynamic classifier chains for multi-label learning | In this paper, we deal with the task of building a dynamic ensemble of chain
classifiers for multi-label classification. To do so, we proposed two concepts
of classifier chains algorithms that are able to change label order of the
chain without rebuilding the entire model. Such modes allows anticipating the
instance-specific chain order without a significant increase in computational
burden. The proposed chain models are built using the Naive Bayes classifier
and nearest neighbour approach as a base single-label classifiers. To take the
benefits of the proposed algorithms, we developed a simple heuristic that
allows the system to find relatively good label order. The heuristic sort
labels according to the label-specific classification quality gained during the
validation phase. The heuristic tries to minimise the phenomenon of error
propagation in the chain. The experimental results showed that the proposed
model based on Naive Bayes classifier the above-mentioned heuristic is an
efficient tool for building dynamic chain classifiers.
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Big Data Regression Using Tree Based Segmentation | Scaling regression to large datasets is a common problem in many application
areas. We propose a two step approach to scaling regression to large datasets.
Using a regression tree (CART) to segment the large dataset constitutes the
first step of this approach. The second step of this approach is to develop a
suitable regression model for each segment. Since segment sizes are not very
large, we have the ability to apply sophisticated regression techniques if
required. A nice feature of this two step approach is that it can yield models
that have good explanatory power as well as good predictive performance.
Ensemble methods like Gradient Boosted Trees can offer excellent predictive
performance but may not provide interpretable models. In the experiments
reported in this study, we found that the predictive performance of the
proposed approach matched the predictive performance of Gradient Boosted Trees.
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Real-time Road Traffic Information Detection Through Social Media | In current study, a mechanism to extract traffic related information such as
congestion and incidents from textual data from the internet is proposed. The
current source of data is Twitter. As the data being considered is extremely
large in size automated models are developed to stream, download, and mine the
data in real-time. Furthermore, if any tweet has traffic related information
then the models should be able to infer and extract this data.
Currently, the data is collected only for United States and a total of
120,000 geo-tagged traffic related tweets are extracted, while six million
geo-tagged non-traffic related tweets are retrieved and classification models
are trained. Furthermore, this data is used for various kinds of spatial and
temporal analysis. A mechanism to calculate level of traffic congestion,
safety, and traffic perception for cities in U.S. is proposed. Traffic
congestion and safety rankings for the various urban areas are obtained and
then they are statistically validated with existing widely adopted rankings.
Traffic perception depicts the attitude and perception of people towards the
traffic.
It is also seen that traffic related data when visualized spatially and
temporally provides the same pattern as the actual traffic flows for various
urban areas. When visualized at the city level, it is clearly visible that the
flow of tweets is similar to flow of vehicles and that the traffic related
tweets are representative of traffic within the cities. With all the findings
in current study, it is shown that significant amount of traffic related
information can be extracted from Twitter and other sources on internet.
Furthermore, Twitter and these data sources are freely available and are not
bound by spatial and temporal limitations. That is, wherever there is a user
there is a potential for data.
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The stable Picard group of $\mathcal{A}(2)$ | Using a form of descent in the stable category of $\mathcal{A}(2)$-modules,
we show that there are no exotic elements in the stable Picard group of
$\mathcal{A}(2)$, \textit{i.e.} that the stable Picard group of
$\mathcal{A}(2)$ is free on $2$ generators.
| 0 | 0 | 1 | 0 | 0 | 0 |
Understanding kernel size in blind deconvolution | Most blind deconvolution methods usually pre-define a large kernel size to
guarantee the support domain. Blur kernel estimation error is likely to be
introduced, and is proportional to kernel size. In this paper, we
experimentally and theoretically show the reason of noises introduction in
oversized kernel by demonstrating that sizeable kernels lead to lower
optimization cost. To eliminate this adverse effect, we propose a low
rank-based regularization on blur kernel by analyzing the structural
information in degraded kernels. Compared with the sparsity prior, e.g.,
$\ell_\alpha$-norm, our regularization term can effectively suppress random
noises in oversized kernels. On benchmark test dataset, the proposed method is
compared with several state-of-the-art methods, and can achieve better
quantitative score. Especially, the improvement margin is much more significant
for oversized blur kernels. We also validate the proposed method on real-world
blurry images.
| 1 | 0 | 0 | 0 | 0 | 0 |
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions | We provide new results for noise-tolerant and sample-efficient learning
algorithms under $s$-concave distributions. The new class of $s$-concave
distributions is a broad and natural generalization of log-concavity, and
includes many important additional distributions, e.g., the Pareto distribution
and $t$-distribution. This class has been studied in the context of efficient
sampling, integration, and optimization, but much remains unknown about the
geometry of this class of distributions and their applications in the context
of learning. The challenge is that unlike the commonly used distributions in
learning (uniform or more generally log-concave distributions), this broader
class is not closed under the marginalization operator and many such
distributions are fat-tailed. In this work, we introduce new convex geometry
tools to study the properties of $s$-concave distributions and use these
properties to provide bounds on quantities of interest to learning including
the probability of disagreement between two halfspaces, disagreement outside a
band, and the disagreement coefficient. We use these results to significantly
generalize prior results for margin-based active learning, disagreement-based
active learning, and passive learning of intersections of halfspaces. Our
analysis of geometric properties of $s$-concave distributions might be of
independent interest to optimization more broadly.
| 1 | 0 | 0 | 1 | 0 | 0 |
Connectivity Properties of Factorization Posets in Generated Groups | We consider three notions of connectivity and their interactions in partially
ordered sets coming from reduced factorizations of an element in a generated
group. While one form of connectivity essentially reflects the connectivity of
the poset diagram, the other two are a bit more involved: Hurwitz-connectivity
has its origins in algebraic geometry, and shellability in topology. We propose
a framework to study these connectivity properties in a uniform way. Our main
tool is a certain total order of the generators that is compatible with the
chosen element.
| 0 | 0 | 1 | 0 | 0 | 0 |
Boundary-sum irreducible finite order corks | We prove for any positive integer $n$ there exist boundary-sum irreducible
${\mathbb Z}_n$-corks with Stein structure. Here `boundary-sum irreducible'
means the manifold is indecomposable with respect to boundary-sum. We also
verify that some of the finite order corks admit hyperbolic boundary by HIKMOT.
| 0 | 0 | 1 | 0 | 0 | 0 |
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem | As neural networks grow deeper and wider, learning networks with
hard-threshold activations is becoming increasingly important, both for network
quantization, which can drastically reduce time and energy requirements, and
for creating large integrated systems of deep networks, which may have
non-differentiable components and must avoid vanishing and exploding gradients
for effective learning. However, since gradient descent is not applicable to
hard-threshold functions, it is not clear how to learn networks of them in a
principled way. We address this problem by observing that setting targets for
hard-threshold hidden units in order to minimize loss is a discrete
optimization problem, and can be solved as such. The discrete optimization goal
is to find a set of targets such that each unit, including the output, has a
linearly separable problem to solve. Given these targets, the network
decomposes into individual perceptrons, which can then be learned with standard
convex approaches. Based on this, we develop a recursive mini-batch algorithm
for learning deep hard-threshold networks that includes the popular but poorly
justified straight-through estimator as a special case. Empirically, we show
that our algorithm improves classification accuracy in a number of settings,
including for AlexNet and ResNet-18 on ImageNet, when compared to the
straight-through estimator.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems | This paper presents a novel data-driven approach for predicting the number of
vegetation-related outages that occur in power distribution systems on a
monthly basis. In order to develop an approach that is able to successfully
fulfill this objective, there are two main challenges that ought to be
addressed. The first challenge is to define the extent of the target area. An
unsupervised machine learning approach is proposed to overcome this difficulty.
The second challenge is to correctly identify the main causes of
vegetation-related outages and to thoroughly investigate their nature. In this
paper, these outages are categorized into two main groups: growth-related and
weather-related outages, and two types of models, namely time series and
non-linear machine learning regression models are proposed to conduct the
prediction tasks, respectively. Moreover, various features that can explain the
variability in vegetation-related outages are engineered and employed. Actual
outage data, obtained from a major utility in the U.S., in addition to
different types of weather and geographical data are utilized to build the
proposed approach. Finally, a comprehensive case study is carried out to
demonstrate how the proposed approach can be used to successfully predict the
number of vegetation-related outages and to help decision-makers to detect
vulnerable zones in their systems.
| 0 | 0 | 0 | 1 | 0 | 0 |
Structural subnetwork evolution across the life-span: rich-club, feeder, seeder | The impact of developmental and aging processes on brain connectivity and the
connectome has been widely studied. Network theoretical measures and certain
topological principles are computed from the entire brain, however there is a
need to separate and understand the underlying subnetworks which contribute
towards these observed holistic connectomic alterations. One organizational
principle is the rich-club - a core subnetwork of brain regions that are
strongly connected, forming a high-cost, high-capacity backbone that is
critical for effective communication in the network. Investigations primarily
focus on its alterations with disease and age. Here, we present a systematic
analysis of not only the rich-club, but also other subnetworks derived from
this backbone - namely feeder and seeder subnetworks. Our analysis is applied
to structural connectomes in a normal cohort from a large, publicly available
lifespan study. We demonstrate changes in rich-club membership with age
alongside a shift in importance from 'peripheral' seeder to feeder subnetworks.
Our results show a refinement within the rich-club structure (increase in
transitivity and betweenness centrality), as well as increased efficiency in
the feeder subnetwork and decreased measures of network integration and
segregation in the seeder subnetwork. These results demonstrate the different
developmental patterns when analyzing the connectome stratified according to
its rich-club and the potential of utilizing this subnetwork analysis to reveal
the evolution of brain architectural alterations across the life-span.
| 0 | 0 | 0 | 0 | 1 | 0 |
No Silk Road for Online Gamers!: Using Social Network Analysis to Unveil Black Markets in Online Games | Online game involves a very large number of users who are interconnected and
interact with each other via the Internet. We studied the characteristics of
exchanging virtual goods with real money through processes called "real money
trading (RMT)." This exchange might influence online game user behaviors and
cause damage to the reputation of game companies. We examined in-game
transactions to reveal RMT by constructing a social graph of virtual goods
exchanges in an online game and identifying network communities of users.
We analyzed approximately 6,000,000 transactions in a popular online game and
inferred RMT transactions by comparing the RMT transactions crawled from an
out-game market. Our findings are summarized as follows: (1) the size of the
RMT market could be approximately estimated; (2) professional RMT providers
typically form a specific network structure (either star-shape or chain) in the
trading network, which can be used as a clue for tracing RMT transactions; and
(3) the observed RMT market has evolved over time into a monopolized market
with a small number of large-sized virtual goods providers.
| 1 | 0 | 0 | 0 | 0 | 0 |
Parameter-dependent Stochastic Optimal Control in Finite Discrete Time | We prove a general existence result in stochastic optimal control in discrete
time where controls take values in conditional metric spaces, and depend on the
current state and the information of past decisions through the evolution of a
recursively defined forward process. The generality of the problem lies beyond
the scope of standard techniques in stochastic control theory such as random
sets, normal integrands and measurable selection theory. The main novelty is a
formalization in conditional metric space and the use of techniques in
conditional analysis. We illustrate the existence result by several examples
including wealth-dependent utility maximization under risk constraints with
bounded and unbounded wealth-dependent control sets, utility maximization with
a measurable dimension, and dynamic risk sharing. Finally, we discuss how
conditional analysis relates to random set theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator | A monocular visual-inertial system (VINS), consisting of a camera and a
low-cost inertial measurement unit (IMU), forms the minimum sensor suite for
metric six degrees-of-freedom (DOF) state estimation. However, the lack of
direct distance measurement poses significant challenges in terms of IMU
processing, estimator initialization, extrinsic calibration, and nonlinear
optimization. In this work, we present VINS-Mono: a robust and versatile
monocular visual-inertial state estimator.Our approach starts with a robust
procedure for estimator initialization and failure recovery. A tightly-coupled,
nonlinear optimization-based method is used to obtain high accuracy
visual-inertial odometry by fusing pre-integrated IMU measurements and feature
observations. A loop detection module, in combination with our tightly-coupled
formulation, enables relocalization with minimum computation overhead.We
additionally perform four degrees-of-freedom pose graph optimization to enforce
global consistency. We validate the performance of our system on public
datasets and real-world experiments and compare against other state-of-the-art
algorithms. We also perform onboard closed-loop autonomous flight on the MAV
platform and port the algorithm to an iOS-based demonstration. We highlight
that the proposed work is a reliable, complete, and versatile system that is
applicable for different applications that require high accuracy localization.
We open source our implementations for both PCs and iOS mobile devices.
| 1 | 0 | 0 | 0 | 0 | 0 |
Equation of State Effects on Gravitational Waves from Rotating Core Collapse | Gravitational waves (GWs) generated by axisymmetric rotating collapse,
bounce, and early postbounce phases of a galactic core-collapse supernova will
be detectable by current-generation gravitational wave observatories. Since
these GWs are emitted from the quadrupole-deformed nuclear-density core, they
may encode information on the uncertain nuclear equation of state (EOS). We
examine the effects of the nuclear EOS on GWs from rotating core collapse and
carry out 1824 axisymmetric general-relativistic hydrodynamic simulations that
cover a parameter space of 98 different rotation profiles and 18 different EOS.
We show that the bounce GW signal is largely independent of the EOS and
sensitive primarily to the ratio of rotational to gravitational energy, and at
high rotation rates, to the degree of differential rotation. The GW frequency
of postbounce core oscillations shows stronger EOS dependence that can be
parameterized by the core's EOS-dependent dynamical frequency
$\sqrt{G\bar{\rho}_c}$. We find that the ratio of the peak frequency to the
dynamical frequency follows a universal trend that is obeyed by all EOS and
rotation profiles and that indicates that the nature of the core oscillations
changes when the rotation rate exceeds the dynamical frequency. We find that
differences in the treatments of low-density nonuniform nuclear matter, of the
transition from nonuniform to uniform nuclear matter, and in the description of
nuclear matter up to around twice saturation density can mildly affect the GW
signal. We find that approximations and uncertainties in electron capture rates
can lead to variations in the GW signal that are of comparable magnitude to
those due to different nuclear EOS. This emphasizes the need for reliable
nuclear electron capture rates and for self-consistent multi-dimensional
neutrino radiation-hydrodynamic simulations of rotating core collapse.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quantum and thermal fluctuations in a Raman spin-orbit coupled Bose gas | We theoretically study a three-dimensional weakly-interacting Bose gas with
Raman-induced spin-orbit coupling at finite temperature. By employing a
generalized Hartree-Fock-Bogoliubov theory with Popov approximation, we
determine a complete finite-temperature phase diagram of three exotic
condensation phases (i.e., the stripe, plane-wave and zero-momentum phases),
against both quantum and thermal fluctuations. We find that the plane-wave
phase is significantly broadened by thermal fluctuations. The phonon mode and
sound velocity at the transition from the plane-wave phase to the zero-momentum
phase are thoughtfully analyzed. At zero temperature, we find that quantum
fluctuations open an unexpected gap in sound velocity at the phase transition,
in stark contrast to the previous theoretical prediction of a vanishing sound
velocity. At finite temperature, thermal fluctuations continue to significantly
enlarge the gap, and simultaneously shift the critical minimum. For a Bose gas
of $^{87}$Rb atoms at the typical experimental temperature, $T=0.3T_{0}$, where
$T_{0}$ is the critical temperature of an ideal Bose gas without spin-orbit
coupling, our results of gap opening and critical minimum shifting in the sound
velocity, are qualitatively consistent with the recent experimental observation
{[}S.-C. Ji \textit{et al.}, Phys. Rev. Lett. \textbf{114}, 105301 (2015){]}.
| 0 | 1 | 0 | 0 | 0 | 0 |
San Pedro Meeting on Wide Field Variability Surveys: Some Concluding Comments | This is a written version of the closing talk at the 22nd Los Alamos Stellar
pulsation conference on wide field variability surveys. It comments on some of
the issues which arise from the meeting. These include the need for attention
to photometric standardization (especially in the infrared) and the somewhat
controversial problem of statistical bias in the use of parallaxes (and other
methods of distance determination). Some major advances in the use of pulsating
variables to study Galactic structure are mentioned. The paper includes a
clarification of apparently conflicting results from classical Cepheids and RR
Lyrae stars in the inner Galaxy and bulge. The importance of understanding
non-periodic phenomena in variable stars,particularly AGB variables and RCB
stars is stressed, especially for its relevance to mass-loss, in which
pulsation may only play a minor role.
| 0 | 1 | 0 | 0 | 0 | 0 |
Weighted Community Detection and Data Clustering Using Message Passing | Grouping objects into clusters based on similarities or weights between them
is one of the most important problems in science and engineering. In this work,
by extending message passing algorithms and spectral algorithms proposed for
unweighted community detection problem, we develop a non-parametric method
based on statistical physics, by mapping the problem to Potts model at the
critical temperature of spin glass transition and applying belief propagation
to solve the marginals corresponding to the Boltzmann distribution. Our
algorithm is robust to over-fitting and gives a principled way to determine
whether there are significant clusters in the data and how many clusters there
are. We apply our method to different clustering tasks and use extensive
numerical experiments to illustrate the advantage of our method over existing
algorithms. In the community detection problem in weighted and directed
networks, we show that our algorithm significantly outperforms existing
algorithms. In the clustering problem when the data was generated by mixture
models in the sparse regime we show that our method works to the theoretical
limit of detectability and gives accuracy very close to that of the optimal
Bayesian inference. In the semi-supervised clustering problem, our method only
needs several labels to work perfectly in classic datasets. Finally, we further
develop Thouless-Anderson-Palmer equations which reduce heavily the computation
complexity in dense-networks but gives almost the same performance as belief
propagation.
| 1 | 0 | 0 | 1 | 0 | 0 |
Edge Control of Graphene Domains Grown on Hexagonal Boron Nitride | Edge structure of graphene has a significant influence on its electronic
properties. However, control over the edge structure of graphene domains on
insulating substrates is still challenging. Here we demonstrate edge control of
graphene domains on hexagonal boron nitride (h-BN) by modifying ratio of
working-gases. Edge directions were determined with the help of both moiré
pattern and atomic-resolution image obtained via atomic force microscopy
measurement. It is believed that the variation on graphene edges mainly
attributes to different growth rates of armchair and zigzag edges. This work
demonstrated here points out a potential approach to fabricate graphene ribbons
on h-BN.
| 0 | 1 | 0 | 0 | 0 | 0 |
On MASAs in $q$-deformed von Neumann algebras | We study certain $q$-deformed analogues of the maximal abelian subalgebras of
the group von Neumann algebras of free groups. The radial subalgebra is defined
for Hecke deformed von Neumann algebras of the Coxeter group
$(\mathbb{Z}/{2\mathbb{Z}})^{\star k}$ and shown to be a maximal abelian
subalgebra which is singular and with Pukánszky invariant $\{\infty\}$.
Further all non-equal generator masas in the $q$-deformed Gaussian von Neumann
algebras are shown to be mutually non-unitarily conjugate.
| 0 | 0 | 1 | 0 | 0 | 0 |
A note on computing range space bases of rational matrices | We discuss computational procedures based on descriptor state-space
realizations to compute proper range space bases of rational matrices. The main
computation is the orthogonal reduction of the system matrix pencil to a
special Kronecker-like form, which allows to extract a full column rank factor,
whose columns form a proper rational basis of the range space. The computation
of several types of bases can be easily accommodated, such as minimum-degree
bases, stable inner minimum-degree bases, etc. Several straightforward
applications of the range space basis computation are discussed, such as, the
computation of full rank factorizations, normalized coprime factorizations,
pseudo-inverses, and inner-outer factorizations.
| 1 | 0 | 0 | 0 | 0 | 0 |
Directed-Loop Quantum Monte Carlo Method for Retarded Interactions | The directed-loop quantum Monte Carlo method is generalized to the case of
retarded interactions. Using the path integral, fermion-boson or spin-boson
models are mapped to actions with retarded interactions by analytically
integrating out the bosons. This yields an exact algorithm that combines the
highly-efficient loop updates available in the stochastic series expansion
representation with the advantages of avoiding a direct sampling of the bosons.
The application to electron-phonon models reveals that the method overcomes the
previously detrimental issues of long autocorrelation times and exponentially
decreasing acceptance rates. For example, the resulting dramatic speedup allows
us to investigate the Peierls quantum phase transition on chains of up to
$1282$ sites.
| 0 | 1 | 0 | 0 | 0 | 0 |
Towards CNN map representation and compression for camera relocalisation | This paper presents a study on the use of Convolutional Neural Networks for
camera relocalisation and its application to map compression. We follow state
of the art visual relocalisation results and evaluate the response to different
data inputs. We use a CNN map representation and introduce the notion of map
compression under this paradigm by using smaller CNN architectures without
sacrificing relocalisation performance. We evaluate this approach in a series
of publicly available datasets over a number of CNN architectures with
different sizes, both in complexity and number of layers. This formulation
allows us to improve relocalisation accuracy by increasing the number of
training trajectories while maintaining a constant-size CNN.
| 1 | 0 | 0 | 0 | 0 | 0 |
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning | In this paper, we unravel a fundamental connection between weighted finite
automata~(WFAs) and second-order recurrent neural networks~(2-RNNs): in the
case of sequences of discrete symbols, WFAs and 2-RNNs with linear activation
functions are expressively equivalent. Motivated by this result, we build upon
a recent extension of the spectral learning algorithm to vector-valued WFAs and
propose the first provable learning algorithm for linear 2-RNNs defined over
sequences of continuous input vectors. This algorithm relies on estimating low
rank sub-blocks of the so-called Hankel tensor, from which the parameters of a
linear 2-RNN can be provably recovered. The performances of the proposed method
are assessed in a simulation study.
| 0 | 0 | 0 | 1 | 0 | 0 |
Evaluating Predictive Models of Student Success: Closing the Methodological Gap | Model evaluation -- the process of making inferences about the performance of
predictive models -- is a critical component of predictive modeling research in
learning analytics. We survey the state of the practice with respect to model
evaluation in learning analytics, which overwhelmingly uses only naive methods
for model evaluation or statistical tests which are not appropriate for
predictive model evaluation. We conduct a critical comparison of both null
hypothesis significance testing (NHST) and a preferred Bayesian method for
model evaluation. Finally, we apply three methods -- the na{ï}ve average
commonly used in learning analytics, NHST, and Bayesian -- to a predictive
modeling experiment on a large set of MOOC data. We compare 96 different
predictive models, including different feature sets, statistical modeling
algorithms, and tuning hyperparameters for each, using this case study to
demonstrate the different experimental conclusions these evaluation techniques
provide.
| 0 | 0 | 0 | 1 | 0 | 0 |
A vertex and edge deletion game on graphs | Starting with a graph, two players take turns in either deleting an edge or
deleting a vertex and all incident edges. The player removing the last vertex
wins. We review the known results for this game and extend the computation of
nim-values to new families of graphs. A conjecture of Khandhawit and Ye on the
nim-values of graphs with one odd cycle is proved. We also see that, for wheels
and their subgraphs, this game exhibits a surprising amount of unexplained
regularity.
| 1 | 0 | 0 | 0 | 0 | 0 |
Determinants of cyclization-decyclization kinetics of short DNA with sticky ends | Cyclization of DNA with sticky ends is commonly used to construct DNA
minicircles and to measure DNA bendability. The cyclization probability of
short DNA (< 150 bp) has a strong length dependence, but how it depends on the
rotational positioning of the sticky ends around the helical axis is less
clear. To shed light upon the determinants of the cyclization probability of
short DNA, we measured cyclization and decyclization rates of ~100-bp DNA with
sticky ends over two helical periods using single-molecule Fluorescence
Resonance Energy Transfer (FRET). The cyclization rate increases monotonically
with length, indicating no excess twisting, while the decyclization rate
oscillates with length, higher at half-integer helical turns and lower at
integer helical turns. The oscillation profile is kinetically and
thermodynamically consistent with a three-state cyclization model in which
sticky-ended short DNA first bends into a torsionally-relaxed teardrop, and
subsequently transitions to a more stable loop upon terminal base stacking. We
also show that the looping probability density (the J factor) extracted from
this study is in good agreement with the worm-like chain model near 100 bp. For
shorter DNA, we discuss various experimental factors that prevent an accurate
measurement of the J factor.
| 0 | 0 | 0 | 0 | 1 | 0 |
Remark on a theorem of H. Hauser on textile maps | We give a counter example to the new theorem that appeared in the survey
\cite{H} on Artin approximation. We then provide a correct statement and a
proof of it.
| 0 | 0 | 1 | 0 | 0 | 0 |
Faster and Simpler Distributed Algorithms for Testing and Correcting Graph Properties in the CONGEST-Model | In this paper we present distributed testing algorithms of graph properties
in the CONGEST-model [Censor-Hillel et al. 2016]. We present one-sided error
testing algorithms in the general graph model.
We first describe a general procedure for converting $\epsilon$-testers with
a number of rounds $f(D)$, where $D$ denotes the diameter of the graph, to
$O((\log n)/\epsilon)+f((\log n)/\epsilon)$ rounds, where $n$ is the number of
processors of the network. We then apply this procedure to obtain an optimal
tester, in terms of $n$, for testing bipartiteness, whose round complexity is
$O(\epsilon^{-1}\log n)$, which improves over the $poly(\epsilon^{-1} \log
n)$-round algorithm by Censor-Hillel et al. (DISC 2016). Moreover, for
cycle-freeness, we obtain a \emph{corrector} of the graph that locally corrects
the graph so that the corrected graph is acyclic. Note that, unlike a tester, a
corrector needs to mend the graph in many places in the case that the graph is
far from having the property.
In the second part of the paper we design algorithms for testing whether the
network is $H$-free for any connected $H$ of size up to four with round
complexity of $O(\epsilon^{-1})$. This improves over the
$O(\epsilon^{-2})$-round algorithms for testing triangle freeness by
Censor-Hillel et al. (DISC 2016) and for testing excluded graphs of size $4$ by
Fraigniaud et al. (DISC 2016).
In the last part we generalize the global tester by Iwama and Yoshida (ITCS
2014) of testing $k$-path freeness to testing the exclusion of any tree of
order $k$. We then show how to simulate this algorithm in the CONGEST-model in
$O(k^{k^2+1}\cdot\epsilon^{-k})$ rounds.
| 1 | 0 | 0 | 0 | 0 | 0 |
Reflexive Regular Equivalence for Bipartite Data | Bipartite data is common in data engineering and brings unique challenges,
particularly when it comes to clustering tasks that impose on strong structural
assumptions. This work presents an unsupervised method for assessing similarity
in bipartite data. Similar to some co-clustering methods, the method is based
on regular equivalence in graphs. The algorithm uses spectral properties of a
bipartite adjacency matrix to estimate similarity in both dimensions. The
method is reflexive in that similarity in one dimension is used to inform
similarity in the other. Reflexive regular equivalence can also use the
structure of transitivities -- in a network sense -- the contribution of which
is controlled by the algorithm's only free-parameter, $\alpha$. The method is
completely unsupervised and can be used to validate assumptions of
co-similarity, which are required but often untested, in co-clustering
analyses. Three variants of the method with different normalizations are tested
on synthetic data. The method is found to be robust to noise and well-suited to
asymmetric co-similar structure, making it particularly informative for cluster
analysis and recommendation in bipartite data of unknown structure. In
experiments, the convergence and speed of the algorithm are found to be stable
for different levels of noise. Real-world data from a network of malaria genes
are analyzed, where the similarity produced by the reflexive method is shown to
out-perform other measures' ability to correctly classify genes.
| 1 | 0 | 0 | 1 | 0 | 0 |
Structural and magnetic properties of core-shell Au/Fe3O4 nanoparticles | We present a systematic study of core-shell Au/Fe_3O_4 nanoparticles produced
by thermal decomposition under mild conditions. The morphology and crystal
structure of the nanoparticles revealed the presence of Au core of <d> =
(6.9\pm 1.0) nm surrounded by Fe_3O_4 shell with a thickness of ~3.5 nm,
epitaxially grown onto the Au core surface. The Au/Fe_3O_4 core-shell structure
was demonstrated by high angle annular dark field scanning transmission
electron microscopy analysis. The magnetite shell grown on top of the Au
nanoparticle displayed a thermal blocking state at temperatures below T_B = 59
K and a relaxed state well above T_B. Remarkably, an exchange bias effect was
observed when cooling down the samples below room temperature under an external
magnetic field. Moreover, the exchange bias field (H_{EX}) started to appear at
T~40 K and its value increased by decreasing the temperature. This effect has
been assigned to the interaction of spins located in the magnetically
disordered regions (in the inner and outer surface of the Fe_3O_4 shell) and
spins located in the ordered region of the Fe_3O_4 shell.
| 0 | 1 | 0 | 0 | 0 | 0 |
Boron-doped diamond | Boron-doped diamond undergoes an insulator-metal transition at some critical
value (around 2.21 at %) of the dopand concentration. Here, we report a simple
method for the calculation of its bulk modulus, based on the thermodynamical
model, by Varotsos and Alexopoulos, that has been originally suggested for the
interconnection between the defect formation parameters in solids and bulk
properties. The results obtained at the doping level of 2.6 at %, which was
later improved at the level 0.5 at %, are in agreement with the experimental
values.
| 0 | 1 | 0 | 0 | 0 | 0 |
The kinematics of the white dwarf population from the SDSS DR12 | We use the Sloan Digital Sky Survey Data Release 12, which is the largest
available white dwarf catalog to date, to study the evolution of the
kinematical properties of the population of white dwarfs in the Galactic disc.
We derive masses, ages, photometric distances and radial velocities for all
white dwarfs with hydrogen-rich atmospheres. For those stars for which proper
motions from the USNO-B1 catalog are available the true three-dimensional
components of the stellar space velocity are obtained. This subset of the
original sample comprises 20,247 objects, making it the largest sample of white
dwarfs with measured three-dimensional velocities. Furthermore, the volume
probed by our sample is large, allowing us to obtain relevant kinematical
information. In particular, our sample extends from a Galactocentric radial
distance $R_{\rm G}=7.8$~kpc to 9.3~kpc, and vertical distances from the
Galactic plane ranging from $Z=-0.5$~kpc to 0.5~kpc. We examine the mean
components of the stellar three-dimensional velocities, as well as their
dispersions with respect to the Galactocentric and vertical distances. We
confirm the existence of a mean Galactocentric radial velocity gradient,
$\partial\langle V_{\rm R}\rangle/\partial R_{\rm
G}=-3\pm5$~km~s$^{-1}$~kpc$^{-1}$. We also confirm North-South differences in
$\langle V_{\rm z}\rangle$. Specifically, we find that white dwarfs with $Z>0$
(in the North Galactic hemisphere) have $\langle V_{\rm z}\rangle<0$, while the
reverse is true for white dwarfs with $Z<0$. The age-velocity dispersion
relation derived from the present sample indicates that the Galactic population
of white dwarfs may have experienced an additional source of heating, which
adds to the secular evolution of the Galactic disc.
| 0 | 1 | 0 | 0 | 0 | 0 |
Geometric tracking control of thrust vectoring UAVs | In this paper a geometric approach to the trajectory tracking control of
Unmanned Aerial Vehicles with thrust vectoring capabilities is proposed. The
control design is suitable for aerial systems that allow to effectively
decouple position and orientation tracking tasks. The control problem is
developed within the framework of geometric control theory on the group of
rigid displacements SE(3), yielding a control law that is independent of any
parametrization of the configuration space. The proposed design works seamlessy
when the thrust vectoring capability is limited, by prioritizing position over
orientation tracking. A characterization of the region of attraction and of the
convergence properties is explicitly derived. Finally, a numerical example is
presented to test the proposed control law. The generality of the control
scheme can be exploited for a broad class of aerial vehicles.
| 1 | 0 | 1 | 0 | 0 | 0 |
Two scenarios of advective washing-out of localized convective patterns under frozen parametric disorder | The effect of spatial localization of states in distributed parameter systems
under frozen parametric disorder is well known as the Anderson localization and
thoroughly studied for the Schrödinger equation and linear dissipation-free
wave equations. Some similar (or mimicking) phenomena can occur in dissipative
systems such as the thermal convection ones. Specifically, many of these
dissipative systems are governed by a modified Kuramoto-Sivashinsky equation,
where the frozen spatial disorder of parameters has been reported to lead to
excitation of localized patterns. Imposed advection in the modified
Kuramoto-Sivashinsky equation can affect the localized patterns in a nontrivial
way; it changes the localization properties and suppresses the pattern. The
latter effect is considered in this paper by means of both numerical simulation
and model reduction, which turns out to be useful for a comprehensive
understanding of the bifurcation scenarios in the system. Two possible
bifurcation scenarios of advective suppression ("washing-out") of localized
patterns are revealed and characterised.
| 0 | 1 | 0 | 0 | 0 | 0 |
Neural Models for Key Phrase Detection and Question Generation | We propose a two-stage neural model to tackle question generation from
documents. First, our model estimates the probability that word sequences in a
document are ones that a human would pick when selecting candidate answers by
training a neural key-phrase extractor on the answers in a question-answering
corpus. Predicted key phrases then act as target answers and condition a
sequence-to-sequence question-generation model with a copy mechanism.
Empirically, our key-phrase extraction model significantly outperforms an
entity-tagging baseline and existing rule-based approaches. We further
demonstrate that our question generation system formulates fluent, answerable
questions from key phrases. This two-stage system could be used to augment or
generate reading comprehension datasets, which may be leveraged to improve
machine reading systems or in educational settings.
| 1 | 0 | 0 | 0 | 0 | 0 |
Radio Tomography for Roadside Surveillance | Radio tomographic imaging (RTI) has recently been proposed for tracking
object location via radio waves without requiring the objects to transmit or
receive radio signals. The position is extracted by inferring which voxels are
obstructing a subset of radio links in a dense wireless sensor network. This
paper proposes a variety of modeling and algorithmic improvements to RTI for
the scenario of roadside surveillance. These include the use of a more
physically motivated weight matrix, a method for mitigating negative
(aphysical) data due to noisy observations, and a method for combining frames
of a moving vehicle into a single image. The proposed approaches are used to
show improvement in both imaging (useful for human-in-the-loop target
recognition) and automatic target recognition in a measured data set.
| 1 | 0 | 0 | 0 | 0 | 0 |
But How Does It Work in Theory? Linear SVM with Random Features | We prove that, under low noise assumptions, the support vector machine with
$N\ll m$ random features (RFSVM) can achieve the learning rate faster than
$O(1/\sqrt{m})$ on a training set with $m$ samples when an optimized feature
map is used. Our work extends the previous fast rate analysis of random
features method from least square loss to 0-1 loss. We also show that the
reweighted feature selection method, which approximates the optimized feature
map, helps improve the performance of RFSVM in experiments on a synthetic data
set.
| 0 | 0 | 0 | 1 | 0 | 0 |
Adaptive Bayesian nonparametric regression using kernel mixture of polynomials with application to partial linear model | We propose a kernel mixture of polynomials prior for Bayesian nonparametric
regression. The regression function is modeled by local averages of polynomials
with kernel mixture weights. We obtain the minimax-optimal rate of contraction
of the full posterior distribution up to a logarithmic factor that adapts to
the smoothness level of the true function by estimating metric entropies of
certain function classes. We also provide a frequentist sieve maximum
likelihood estimator with a near-optimal convergence rate. We further
investigate the application of the kernel mixture of polynomials to the partial
linear model and obtain both the near-optimal rate of contraction for the
nonparametric component and the Bernstein-von Mises limit (i.e., asymptotic
normality) of the parametric component. The proposed method is illustrated with
numerical examples and shows superior performance in terms of computational
efficiency, accuracy, and uncertainty quantification compared to the local
polynomial regression, DiceKriging, and the robust Gaussian stochastic process.
| 0 | 0 | 1 | 1 | 0 | 0 |
Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing | Mobile edge computing is a new computing paradigm, which pushes cloud
computing capabilities away from the centralized cloud to the network edge.
However, with the sinking of computing capabilities, the new challenge incurred
by user mobility arises: since end-users typically move erratically, the
services should be dynamically migrated among multiple edges to maintain the
service performance, i.e., user-perceived latency. Tackling this problem is
non-trivial since frequent service migration would greatly increase the
operational cost. To address this challenge in terms of the performance-cost
trade-off, in this paper we study the mobile edge service performance
optimization problem under long-term cost budget constraint. To address user
mobility which is typically unpredictable, we apply Lyapunov optimization to
decompose the long-term optimization problem into a series of real-time
optimization problems which do not require a priori knowledge such as user
mobility. As the decomposed problem is NP-hard, we first design an
approximation algorithm based on Markov approximation to seek a near-optimal
solution. To make our solution scalable and amenable to future 5G application
scenario with large-scale user devices, we further propose a distributed
approximation scheme with greatly reduced time complexity, based on the
technique of best response update. Rigorous theoretical analysis and extensive
evaluations demonstrate the efficacy of the proposed centralized and
distributed schemes.
| 1 | 0 | 0 | 0 | 0 | 0 |
Assessing student's achievement gap between ethnic groups in Brazil | Achievement gaps refer to the difference in the performance on examinations
of students belonging to different social groups. Achievement gaps between
ethnic groups have been observed in several countries with heterogeneous
populations. In this paper, we analyze achievement gaps between ethnic
populations in Brazil by studying the performance of a large cohort of senior
high-school students in a standardized national exam. We separate ethnic groups
into the Brazilian states to remove potential biases associated to
infrastructure and financial resources, cultural background and ethnic
clustering. We focus on the disciplines of mathematics and writing that involve
different cognitive functions. We estimate the gaps and their statistical
significance through the Welch's t-test and study key socio-economic variables
that may explain the existence or absence of gaps. We identify that gaps
between ethnic groups are either statistically insignificant (p<.01) or small
(2%-6%) if statistically significant, for students living in households with
low income. Increasing gaps however may be observed for higher income. On the
other hand, while higher parental education is associated to higher
performance, it may either increase, decrease or maintain the gaps between
White and Black, and between White and Pardo students. Our results support that
socio-economic variables have major impact on student's performance in both
mathematics and writing examinations irrespectively of ethnic backgrounds,
giving evidence that genetic factors have little or no effect on ethnic group
performance when students are exposed to similar cultural and financial
contexts.
| 0 | 0 | 0 | 1 | 0 | 0 |
SONS: The JCMT legacy survey of debris discs in the submillimetre | Debris discs are evidence of the ongoing destructive collisions between
planetesimals, and their presence around stars also suggests that planets exist
in these systems. In this paper, we present submillimetre images of the thermal
emission from debris discs that formed the SCUBA-2 Observations of Nearby Stars
(SONS) survey, one of seven legacy surveys undertaken on the James Clerk
Maxwell telescope between 2012 and 2015. The overall results of the survey are
presented in the form of 850 microns (and 450 microns, where possible) images
and fluxes for the observed fields. Excess thermal emission, over that expected
from the stellar photosphere, is detected around 49 stars out of the 100
observed fields. The discs are characterised in terms of their flux density,
size (radial distribution of the dust) and derived dust properties from their
spectral energy distributions. The results show discs over a range of sizes,
typically 1-10 times the diameter of the Edgeworth-Kuiper Belt in our Solar
System. The mass of a disc, for particles up to a few millimetres in size, is
uniquely obtainable with submillimetre observations and this quantity is
presented as a function of the host stars' age, showing a tentative decline in
mass with age. Having doubled the number of imaged discs at submillimetre
wavelengths from ground-based, single dish telescope observations, one of the
key legacy products from the SONS survey is to provide a comprehensive target
list to observe at high angular resolution using submillimetre/millimetre
interferometers (e.g., ALMA, SMA).
| 0 | 1 | 0 | 0 | 0 | 0 |
Visual Search at eBay | In this paper, we propose a novel end-to-end approach for scalable visual
search infrastructure. We discuss the challenges we faced for a massive
volatile inventory like at eBay and present our solution to overcome those. We
harness the availability of large image collection of eBay listings and
state-of-the-art deep learning techniques to perform visual search at scale.
Supervised approach for optimized search limited to top predicted categories
and also for compact binary signature are key to scale up without compromising
accuracy and precision. Both use a common deep neural network requiring only a
single forward inference. The system architecture is presented with in-depth
discussions of its basic components and optimizations for a trade-off between
search relevance and latency. This solution is currently deployed in a
distributed cloud infrastructure and fuels visual search in eBay ShopBot and
Close5. We show benchmark on ImageNet dataset on which our approach is faster
and more accurate than several unsupervised baselines. We share our learnings
with the hope that visual search becomes a first class citizen for all large
scale search engines rather than an afterthought.
| 1 | 0 | 0 | 0 | 0 | 0 |
Isomorphism and classification for countable structures | We introduce a topology on the space of all isomorphism types represented in
a given class of countable models, and use this topology as an aid in
classifying the isomorphism types. This mixes ideas from effective descriptive
set theory and computable structure theory, extending concepts from the latter
beyond computable structures to examine the isomorphism problem on arbitrary
countable structures. We give examples using specific classes of fields and of
trees, illustrating how the new concepts can yield classifications that reveal
differences between seemingly similar classes. Finally, we use a computable
homeomorphism to define a measure on the space of isomorphism types of
algebraic fields, and examine the prevalence of relative computable
categoricity under this measure.
| 0 | 0 | 1 | 0 | 0 | 0 |
Complex Networks Unveiling Spatial Patterns in Turbulence | Numerical and experimental turbulence simulations are nowadays reaching the
size of the so-called big data, thus requiring refined investigative tools for
appropriate statistical analyses and data mining. We present a new approach
based on the complex network theory, offering a powerful framework to explore
complex systems with a huge number of interacting elements. Although interest
on complex networks has been increasing in the last years, few recent studies
have been applied to turbulence. We propose an investigation starting from a
two-point correlation for the kinetic energy of a forced isotropic field
numerically solved. Among all the metrics analyzed, the degree centrality is
the most significant, suggesting the formation of spatial patterns which
coherently move with similar vorticity over the large eddy turnover time scale.
Pattern size can be quantified through a newly-introduced parameter (i.e.,
average physical distance) and varies from small to intermediate scales. The
network analysis allows a systematic identification of different spatial
regions, providing new insights into the spatial characterization of turbulent
flows. Based on present findings, the application to highly inhomogeneous flows
seems promising and deserves additional future investigation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Convexity in scientific collaboration networks | Convexity in a network (graph) has been recently defined as a property of
each of its subgraphs to include all shortest paths between the nodes of that
subgraph. It can be measured on the scale [0, 1] with 1 being assigned to fully
convex networks. The largest convex component of a graph that emerges after the
removal of the least number of edges is called a convex skeleton. It is
basically a tree of cliques, which has been shown to have many interesting
features. In this article the notions of convexity and convex skeletons in the
context of scientific collaboration networks are discussed. More specifically,
we analyze the co-authorship networks of Slovenian researchers in computer
science, physics, sociology, mathematics, and economics and extract convex
skeletons from them. We then compare these convex skeletons with the residual
graphs (remainders) in terms of collaboration frequency distributions by
various parameters such as the publication year and type, co-authors' birth
year, status, gender, discipline, etc. We also show the top-ranked scientists
by four basic centrality measures as calculated on the original networks and
their skeletons and conclude that convex skeletons may help detect influential
scholars that are hardly identifiable in the original collaboration network. As
their inherent feature, convex skeletons retain the properties of collaboration
networks. These include high-level structural properties but also the fact that
the same authors are highlighted by centrality measures. Moreover, the most
important ties and thus the most important collaborations are retained in the
skeletons.
| 1 | 0 | 0 | 0 | 0 | 0 |
Contact Localization through Spatially Overlapping Piezoresistive Signals | Achieving high spatial resolution in contact sensing for robotic manipulation
often comes at the price of increased complexity in fabrication and
integration. One traditional approach is to fabricate a large number of taxels,
each delivering an individual, isolated response to a stimulus. In contrast, we
propose a method where the sensor simply consists of a continuous volume of
piezoresistive elastomer with a number of electrodes embedded inside. We
measure piezoresistive effects between all pairs of electrodes in the set, and
count on this rich signal set containing the information needed to pinpoint
contact location with high accuracy using regression algorithms. In our
validation experiments, we demonstrate submillimeter median accuracy in
locating contact on a 10mm by 16mm sensor using only four electrodes (creating
six unique pairs). In addition to extracting more information from fewer wires,
this approach lends itself to simple fabrication methods and makes no
assumptions about the underlying geometry, simplifying future integration on
robot fingers.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the Parallel Parameterized Complexity of the Graph Isomorphism Problem | In this paper, we study the parallel and the space complexity of the graph
isomorphism problem (\GI{}) for several parameterizations. Let
$\mathcal{H}=\{H_1,H_2,\cdots,H_l\}$ be a finite set of graphs where
$|V(H_i)|\leq d$ for all $i$ and for some constant $d$. Let $\mathcal{G}$ be an
$\mathcal{H}$-free graph class i.e., none of the graphs $G\in \mathcal{G}$
contain any $H \in \mathcal{H}$ as an induced subgraph. We show that \GI{}
parameterized by vertex deletion distance to $\mathcal{G}$ is in a
parameterized version of $\AC^1$, denoted $\PL$-$\AC^1$, provided the colored
graph isomorphism problem for graphs in $\mathcal{G}$ is in $\AC^1$. From this,
we deduce that \GI{} parameterized by the vertex deletion distance to cographs
is in $\PL$-$\AC^1$.
The parallel parameterized complexity of \GI{} parameterized by the size of a
feedback vertex set remains an open problem. Towards this direction we show
that the graph isomorphism problem is in $\PL$-$\TC^0$ when parameterized by
vertex cover or by twin-cover.
Let $\mathcal{G}'$ be a graph class such that recognizing graphs from
$\mathcal{G}'$ and the colored version of \GI{} for $\mathcal{G}'$ is in
logspace ($\L$). We show that \GI{} for bounded vertex deletion distance to
$\mathcal{G}'$ is in $\L$. From this, we obtain logspace algorithms for \GI{}
for graphs with bounded vertex deletion distance to interval graphs and graphs
with bounded vertex deletion distance to cographs.
| 1 | 0 | 0 | 0 | 0 | 0 |
Direct measurement of superdiffusive and subdiffusive energy transport in disordered granular chains | The study of energy transport properties in heterogeneous materials has
attracted scientific interest for more than a century, and it continues to
offer fundamental and rich questions. One of the unanswered challenges is to
extend Anderson theory for uncorrelated and fully disordered lattices in
condensed-matter systems to physical settings in which additional effects
compete with disorder. Specifically, the effect of strong nonlinearity has been
largely unexplored experimentally, partly due to the paucity of testbeds that
can combine the effect of disorder and nonlinearity in a controllable manner.
Here we present the first systematic experimental study of energy transport and
localization properties in simultaneously disordered and nonlinear granular
crystals. We demonstrate experimentally that disorder and nonlinearity ---
which are known from decades of studies to individually favor energy
localization --- can in some sense "cancel each other out", resulting in the
destruction of wave localization. We also report that the combined effect of
disorder and nonlinearity can enable the manipulation of energy transport speed
in granular crystals from subdiffusive to superdiffusive ranges.
| 0 | 1 | 0 | 0 | 0 | 0 |
Reading the Sky and The Spiral of Teaching and Learning in Astronomy | This theoretical paper introduces a new way to view and characterize teaching
and learning astronomy. It describes a framework, based on results from
empirical data, analyzed through standard qualitative research methodology, in
which a theoretical model for vital competencies of learning astronomy is
proposed: Reading the Sky. This model takes into account not only disciplinary
knowledge but also disciplinary discernment and extrapolating
three-dimensionality. Together, these constitute the foundation for the
competency referred to as Reading the Sky. In this paper, I describe these
concepts and how I see them being connected and intertwined to form a new
competency model for learning astronomy and how this can be used to inform
astronomy education to better match the challenges students face when entering
the discipline of astronomy: The Spiral of Teaching and Learning. Two examples
are presented to highlight how this model can be used in teaching situations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization | Decades of research on the neural code underlying spatial navigation have
revealed a diverse set of neural response properties. The Entorhinal Cortex
(EC) of the mammalian brain contains a rich set of spatial correlates,
including grid cells which encode space using tessellating patterns. However,
the mechanisms and functional significance of these spatial representations
remain largely mysterious. As a new way to understand these neural
representations, we trained recurrent neural networks (RNNs) to perform
navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find
that grid-like spatial response patterns emerge in trained networks, along with
units that exhibit other spatial correlates, including border cells and
band-like cells. All these different functional types of neurons have been
observed experimentally. The order of the emergence of grid-like and border
cells is also consistent with observations from developmental studies.
Together, our results suggest that grid cells, border cells and others as
observed in EC may be a natural solution for representing space efficiently
given the predominant recurrent connections in the neural circuits.
| 0 | 0 | 0 | 1 | 1 | 0 |
Random walks on the discrete affine group | We introduce the discrete affine group of a regular tree as a finitely
generated subgroup of the affine group. We describe the Poisson boundary of
random walks on it as a space of configurations. We compute isoperimetric
profile and Hilbert compression exponent of the group. We also discuss metric
relationship with some lamplighter groups and lamplighter graphs.
| 0 | 0 | 1 | 0 | 0 | 0 |
Spectroscopic evidence of odd frequency superconducting order | Spin filter superconducting S/I/N tunnel junctions (NbN/GdN/TiN) show a
robust and pronounced zero bias conductance peak at low temperatures, the
magnitude of which is several times the normal state conductance of the
junction. Such a conductance anomaly is representative of unconventional
superconductivity and is interpreted as a direct signature of an odd frequency
superconducting order.
| 0 | 1 | 0 | 0 | 0 | 0 |
Double spend races | We correct the double spend race analysis given in Nakamoto's foundational
Bitcoin article and give a closed-form formula for the probability of success
of a double spend attack using the Regularized Incomplete Beta Function. We
give a proof of the exponential decay on the number of confirmations, often
cited in the literature, and find an asymptotic formula. Larger number of
confirmations are necessary compared to those given by Nakamoto. We also
compute the probability conditional to the known validation time of the blocks.
This provides a finer risk analysis than the classical one.
| 1 | 0 | 1 | 0 | 0 | 0 |
An Asymptotic Analysis of Queues with Delayed Information and Time Varying Arrival Rates | Understanding how delayed information impacts queueing systems is an
important area of research. However, much of the current literature neglects
one important feature of many queueing systems, namely non-stationary arrivals.
Non-stationary arrivals model the fact that customers tend to access services
during certain times of the day and not at a constant rate. In this paper, we
analyze two two-dimensional deterministic fluid models that incorporate
customer choice behavior based on delayed queue length information with time
varying arrivals. In the first model, customers receive queue length
information that is delayed by a constant Delta. In the second model, customers
receive information about the queue length through a moving average of the
queue length where the moving average window is Delta. We analyze the impact of
the time varying arrival rate and show using asymptotic analysis that the time
varying arrival rate does not impact the critical delay unless the frequency of
the time varying arrival rate is twice that of the critical delay. When the
frequency of the arrival rate is twice that of the critical delay, then the
stability is enlarged by a wedge that is determined by the model parameters. As
a result, this problem allows us to combine the theory of nonlinear dynamics,
parametric excitation, delays, and time varying queues together to provide
insight on the impact of information in queueing systems.
| 0 | 0 | 1 | 0 | 0 | 0 |
Effect of mixed pinning landscapes produced by 6 MeV Oxygen irradiation on the resulting critical current densities J$_c$ in 1.3 $μ$m thick GdBa$_2$Cu$_3$O$_{7-d}$ coated conductors grown by co-evaporation | We report the influence of crystalline defects introduced by 6 MeV
$^{16}$O$^{3+}$ irradiation on the critical current densities J$_c$ and flux
creep rates in 1.3 $\mu$m thick GdBa$_2$Cu$_3$O$_{7-d}$ coated conductor
produced by co-evaporation. Pristine films with pinning produced mainly by
random nanoparticles with diameter close to 50 nm were irradiated with doses
between 2x10$^{13}$ cm$^{-2}$ and 4x10$^{14}$ cm$^{-2}$. At temperatures below
40 K with the magnetic field applied parallel (H//c) and at 45°
(H//45°) to the c-axis, the in-field J$_c$ dependences can be
significantly improved by irradiation. For doses of 1x10$^{14}$ cm$^{-2}$ the
J$_c$ values at $\mu$$_0$H = 5 T are doubled without affecting significantly
the J$_c$ at small fields. Analyzing the flux creep rates as function of the
temperature in both magnetic field configurations, it can be observed that the
irradiation suppresses the peak associated with double-kink relaxation and
increases the flux creep rates at intermediate and high temperatures. Under 0.5
T, the flux relaxation for H//c and H//45° in pristine films presents
characteristic glassy exponents $\mu$ = 1.63 and $\mu$ = 1.45, respectively.
For samples irradiated with 1x10$^{14}$ cm$^{-2}$, these values drop to $\mu$ =
1.45 and $\mu$ =1.24, respectively.
| 0 | 1 | 0 | 0 | 0 | 0 |
Merge or Not? Learning to Group Faces via Imitation Learning | Given a large number of unlabeled face images, face grouping aims at
clustering the images into individual identities present in the data. This task
remains a challenging problem despite the remarkable capability of deep
learning approaches in learning face representation. In particular, grouping
results can still be egregious given profile faces and a large number of
uninteresting faces and noisy detections. Often, a user needs to correct the
erroneous grouping manually. In this study, we formulate a novel face grouping
framework that learns clustering strategy from ground-truth simulated behavior.
This is achieved through imitation learning (a.k.a apprenticeship learning or
learning by watching) via inverse reinforcement learning (IRL). In contrast to
existing clustering approaches that group instances by similarity, our
framework makes sequential decision to dynamically decide when to merge two
face instances/groups driven by short- and long-term rewards. Extensive
experiments on three benchmark datasets show that our framework outperforms
unsupervised and supervised baselines.
| 1 | 0 | 0 | 0 | 0 | 0 |
Entropic Causality and Greedy Minimum Entropy Coupling | We study the problem of identifying the causal relationship between two
discrete random variables from observational data. We recently proposed a novel
framework called entropic causality that works in a very general functional
model but makes the assumption that the unobserved exogenous variable has small
entropy in the true causal direction.
This framework requires the solution of a minimum entropy coupling problem:
Given marginal distributions of m discrete random variables, each on n states,
find the joint distribution with minimum entropy, that respects the given
marginals. This corresponds to minimizing a concave function of nm variables
over a convex polytope defined by nm linear constraints, called a
transportation polytope. Unfortunately, it was recently shown that this minimum
entropy coupling problem is NP-hard, even for 2 variables with n states. Even
representing points (joint distributions) over this space can require
exponential complexity (in n, m) if done naively.
In our recent work we introduced an efficient greedy algorithm to find an
approximate solution for this problem. In this paper we analyze this algorithm
and establish two results: that our algorithm always finds a local minimum and
also is within an additive approximation error from the unknown global optimum.
| 1 | 0 | 0 | 1 | 0 | 0 |
When few survive to tell the tale: thymus and gonad as auditioning organs: historical overview | Unlike other organs, the thymus and gonads generate non-uniform cell
populations, many members of which perish, and a few survive. While it is
recognized that thymic cells are 'audited' to optimize an organism's immune
repertoire, whether gametogenesis could be orchestrated similarly to favour
high quality gametes is uncertain. Ideally, such quality would be affirmed at
early stages before the commitment of extensive parental resources. A case is
here made that, along the lines of a previously proposed lymphocyte quality
control mechanism, gamete quality can be registered indirectly through
detection of incompatibilities between proteins encoded by the grandparental
DNA sequences within the parent from which haploid gametes are meiotically
derived. This 'stress test' is achieved in the same way that thymic screening
for potential immunological incompatibilities is achieved - by 'promiscuous'
expression, under the influence of the AIRE protein, of the products of genes
that are not normally specific for that organ. Consistent with this, the Aire
gene is expressed in both thymus and gonads, and AIRE deficiency impedes
function in both organs. While not excluding the subsequent emergence of hybrid
incompatibilities due to the intermixing of genomic sequences from parents
(rather than grandparents), many observations, such as the number of proteins
that are aberrantly expressed during gametogenesis, can be explained on this
basis. Indeed, promiscuous expression could have first evolved in
gamete-forming cells where incompatible proteins would be manifest as aberrant
protein aggregates that cause apoptosis. This mechanism would later have been
co-opted by thymic epithelial cells which display peptides from aggregates to
remove potentially autoreactive T cells.
| 0 | 0 | 0 | 0 | 1 | 0 |
SfMLearner++: Learning Monocular Depth & Ego-Motion using Meaningful Geometric Constraints | Most geometric approaches to monocular Visual Odometry (VO) provide robust
pose estimates, but sparse or semi-dense depth estimates. Off late, deep
methods have shown good performance in generating dense depths and VO from
monocular images by optimizing the photometric consistency between images.
Despite being intuitive, a naive photometric loss does not ensure proper pixel
correspondences between two views, which is the key factor for accurate depth
and relative pose estimations. It is a well known fact that simply minimizing
such an error is prone to failures.
We propose a method using Epipolar constraints to make the learning more
geometrically sound. We use the Essential matrix, obtained using Nister's Five
Point Algorithm, for enforcing meaningful geometric constraints on the loss,
rather than using it as labels for training. Our method, although simplistic
but more geometrically meaningful, using lesser number of parameters, gives a
comparable performance to state-of-the-art methods which use complex losses and
large networks showing the effectiveness of using epipolar constraints. Such a
geometrically constrained learning method performs successfully even in cases
where simply minimizing the photometric error would fail.
| 1 | 0 | 0 | 0 | 0 | 0 |
Approximate Bayesian inference as a gauge theory | In a published paper [Sengupta, 2016], we have proposed that the brain (and
other self-organized biological and artificial systems) can be characterized
via the mathematical apparatus of a gauge theory. The picture that emerges from
this approach suggests that any biological system (from a neuron to an
organism) can be cast as resolving uncertainty about its external milieu,
either by changing its internal states or its relationship to the environment.
Using formal arguments, we have shown that a gauge theory for neuronal dynamics
-- based on approximate Bayesian inference -- has the potential to shed new
light on phenomena that have thus far eluded a formal description, such as
attention and the link between action and perception. Here, we describe the
technical apparatus that enables such a variational inference on manifolds.
Particularly, the novel contribution of this paper is an algorithm that utlizes
a Schild's ladder for parallel transport of sufficient statistics (means,
covariances, etc.) on a statistical manifold.
| 1 | 0 | 0 | 0 | 0 | 0 |
DROPWAT: an Invisible Network Flow Watermark for Data Exfiltration Traceback | Watermarking techniques have been proposed during the last 10 years as an
approach to trace network flows for intrusion detection purposes. These
techniques aim to impress a hidden signature on a traffic flow. A central
property of network flow watermarking is invisibility, i.e., the ability to go
unidentified by an unauthorized third party. Although widely sought after, the
development of an invisible watermark is a challenging task that has not yet
been accomplished.
In this paper we take a step forward in addressing the invisibility problem
with DROPWAT, an active network flow watermarking technique developed for
tracing Internet flows directed to the staging server that is the final
destination in a data exfiltration attack, even in the presence of several
intermediate stepping stones or an anonymous network. DROPWAT is a timing-based
technique that indirectly modifies interpacket delays by exploiting network
reaction to packet loss. We empirically demonstrate that the watermark embedded
by means of DROPWAT is invisible to a third party observing the watermarked
traffic. We also validate DROPWAT and analyze its performance in a controlled
experimental framework involving the execution of a series of experiments on
the Internet, using Web proxy servers as stepping stones executed on several
instances in Amazon Web Services, as well as the TOR anonymous network in the
place of the stepping stones. Our results show that the detection algorithm is
able to identify an embedded watermark achieving over 95% accuracy while being
invisible.
| 1 | 0 | 0 | 0 | 0 | 0 |
Outage Analysis of Offloading in Heterogeneous Networks: Composite Fading Channels | Small cells deployment is one of the most significant long-term strategic
policies of the mobile network operators. In heterogeneous networks (HetNets),
small cells serve as offloading spots in the radio access network to offload
macro users (MUs) and their associated traffic from congested macrocells. In
this paper, we perform analytical analysis and investigate how the radio
propagation effects such as multipath and shadowing and small cell base station
density affect MUs' offloading to small cell network (SCN). In particular, we
exploit composite fading channels in our evaluation when an MU is offloaded to
SCN with varying small and macro cell densities in the stochastic HetNets
framework. We derive the expressions for outage probability (equivalently
success probability) of the MU in macro network and SCN for two different
cases, viz.: i) Nakagami-lognormal channel fading; ii) time-shared (combined)
shadowed/unshadowed channel fading. We propose efficient approximations for the
probability density functions of the channel fading (power) for the
above-mentioned fading distributions that do not have closed-form expressions
employing Gauss-Hermite integration and finite exponential series,
respectively. Finally, the outage probability performance of MU with and
without offloading options/services is analyzed for various settings of fading
channels.
| 1 | 0 | 0 | 0 | 0 | 0 |
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks | Although Generative Adversarial Networks (GANs) have shown remarkable success
in various tasks, they still face challenges in generating high quality images.
In this paper, we propose Stacked Generative Adversarial Networks (StackGAN)
aiming at generating high-resolution photo-realistic images. First, we propose
a two-stage generative adversarial network architecture, StackGAN-v1, for
text-to-image synthesis. The Stage-I GAN sketches the primitive shape and
colors of the object based on given text description, yielding low-resolution
images. The Stage-II GAN takes Stage-I results and text descriptions as inputs,
and generates high-resolution images with photo-realistic details. Second, an
advanced multi-stage generative adversarial network architecture, StackGAN-v2,
is proposed for both conditional and unconditional generative tasks. Our
StackGAN-v2 consists of multiple generators and discriminators in a tree-like
structure; images at multiple scales corresponding to the same scene are
generated from different branches of the tree. StackGAN-v2 shows more stable
training behavior than StackGAN-v1 by jointly approximating multiple
distributions. Extensive experiments demonstrate that the proposed stacked
generative adversarial networks significantly outperform other state-of-the-art
methods in generating photo-realistic images.
| 1 | 0 | 0 | 1 | 0 | 0 |
Towards Secure and Safe Appified Automated Vehicles | The advancement in Autonomous Vehicles (AVs) has created an enormous market
for the development of self-driving functionalities,raising the question of how
it will transform the traditional vehicle development process. One adventurous
proposal is to open the AV platform to third-party developers, so that AV
functionalities can be developed in a crowd-sourcing way, which could provide
tangible benefits to both automakers and end users. Some pioneering companies
in the automotive industry have made the move to open the platform so that
developers are allowed to test their code on the road. Such openness, however,
brings serious security and safety issues by allowing untrusted code to run on
the vehicle. In this paper, we introduce the concept of an Appified AV platform
that opens the development framework to third-party developers. To further
address the safety challenges, we propose an enhanced appified AV design schema
called AVGuard, which focuses primarily on mitigating the threats brought about
by untrusted code, leveraging theory in the vehicle evaluation field, and
conducting program analysis techniques in the cybersecurity area. Our study
provides guidelines and suggested practice for the future design of open AV
platforms.
| 1 | 0 | 0 | 0 | 0 | 0 |
Equidimensional adic eigenvarieties for groups with discrete series | We extend Urban's construction of eigenvarieties for reductive groups $G$
such that $G(\mathbb{R})$ has discrete series to include characteristic $p$
points at the boundary of weight space. In order to perform this construction,
we define a notion of "locally analytic" functions and distributions on a
locally $\mathbb{Q}_p$-analytic manifold taking values in a complete Tate
$\mathbb{Z}_p$-algebra in which $p$ is not necessarily invertible. Our
definition agrees with the definition of locally analytic distributions on
$p$-adic Lie groups given by Johansson and Newton.
| 0 | 0 | 1 | 0 | 0 | 0 |
Latent Association Mining in Binary Data | We consider the problem of identifying groups of mutually associated
variables in moderate or high dimensional data. In many cases, ordinary Pearson
correlation provides useful information concerning the linear relationship
between variables. However, for binary data, ordinary correlation may lose
power and may lack interpretability. In this paper, we develop and investigate
a new method called Latent Association Mining in Binary Data (LAMB). The LAMB
method is built on the assumption that the binary observations represent a
random thresholding of a latent continuous variable that may have a complex
correlation structure. We consider a new measure of association, latent
correlation, that is designed to assess association in the underlying
continuous variable, without bias due to the mediating effects of the
thresholding procedure. The full LAMB procedure makes use of iterative
hypothesis testing to identify groups of latently correlated variables. LAMB is
shown to improve power over existing methods in simulated settings, to be
computationally efficient for large datasets, and to uncover new meaningful
results from common real data types.
| 0 | 0 | 0 | 1 | 0 | 0 |
The Supernova -- Supernova Remnant Connection | Many aspects of the progenitor systems, environments, and explosion dynamics
of the various subtypes of supernovae are difficult to investigate at
extragalactic distances where they are observed as unresolved sources.
Alternatively, young supernova remnants in our own galaxy and in the Large and
Small Magellanic Clouds offer opportunities to resolve, measure, and track
expanding stellar ejecta in fine detail, but the handful that are known exhibit
widely different properties that reflect the diversity of their parent
explosions and local circumstellar and interstellar environments. A way of
complementing both supernova and supernova remnant research is to establish
strong empirical links between the two separate stages of stellar explosions.
Here we briefly review recent progress in the development of
supernova---supernova remnant connections, paying special attention to
connections made through the study of "middle-aged" (10-100 yr) supernovae and
young (< 1000 yr) supernova remnants. We highlight how this approach can
uniquely inform several key areas of supernova research, including the origins
of explosive mixing, high-velocity jets, and the formation of dust in the
ejecta.
| 0 | 1 | 0 | 0 | 0 | 0 |
DSVO: Direct Stereo Visual Odometry | This paper proposes a novel approach to stereo visual odometry without stereo
matching. It is particularly robust in scenes of repetitive high-frequency
textures. Referred to as DSVO (Direct Stereo Visual Odometry), it operates
directly on pixel intensities, without any explicit feature matching, and is
thus efficient and more accurate than the state-of-the-art
stereo-matching-based methods. It applies a semi-direct monocular visual
odometry running on one camera of the stereo pair, tracking the camera pose and
mapping the environment simultaneously; the other camera is used to optimize
the scale of monocular visual odometry. We evaluate DSVO in a number of
challenging scenes to evaluate its performance and present comparisons with the
state-of-the-art stereo visual odometry algorithms.
| 1 | 0 | 0 | 0 | 0 | 0 |
Urban Analytics: Multiplexed and Dynamic Community Networks | In the past decade, cities have experienced rapid growth, expansion, and
changes in their community structure. Many aspects of critical urban
infrastructure are closely coupled with the human communities that they serve.
Urban communities are composed of a multiplex of overlapping factors which can
be distinguished into cultural, religious, social-economic, political, and
geographical layers. In this paper, we review how increasingly available
heterogeneous mobile big data sets can be leveraged to detect the community
interaction structure using natural language processing and machine learning
techniques. A number of community layer and interaction detection algorithms
are then reviewed, with a particular focus on robustness, stability, and
causality of evolving communities. The better understanding of the structural
dynamics and multiplexed relationships can provide useful information to inform
both urban planning policies and shape the design of socially coupled urban
infrastructure systems.
| 1 | 1 | 0 | 0 | 0 | 0 |
A quantum phase transition induced by a microscopic boundary condition | Quantum phase transitions are sudden changes in the ground-state wavefunction
of a many-body system that can occur as a control parameter such as a
concentration or a field strength is varied. They are driven purely by the
competition between quantum fluctuations and mutual interactions among
constituents of the system, not by thermal fluctuations; hence they can occur
even at zero temperature. Examples of quantum phase transitions in many-body
physics may be found in systems ranging from high-temperature superconductors
to topological insulators. A quantum phase transition usually can be
characterized by nonanalyticity/discontinuity in certain order parameters or
divergence of the ground state energy eigenvalue and/or its derivatives with
respect to certain physical quantities. Here in a circular one-dimensional spin
model with Heisenberg XY interaction and no magnetic field, we observe critical
phenomena for the $n_0=1/N\rightarrow0$ Mott insulator caused by a qualitative
change of the boundary condition. We demonstrate in the vicinity of the
transition point a sudden change in ground-state properties accompanied by an
avoided level-crossing between the ground and the first excited states.
Notably, our result links conventional quantum phase transitions to microscopic
boundary conditions, with significant implications for quantum information,
quantum control, and quantum computing.
| 0 | 1 | 0 | 0 | 0 | 0 |
Asymptotic Goodness-of-Fit Tests for Point Processes Based on Scaled Empirical K-Functions | We study sequences of scaled edge-corrected empirical (generalized)
K-functions (modifying Ripley's K-function) each of them constructed from a
single observation of a $d$-dimensional fourth-order stationary point process
in a sampling window W_n which grows together with some scaling rate
unboundedly as n --> infty. Under some natural assumptions it is shown that the
normalized difference between scaled empirical and scaled theoretical
K-function converges weakly to a mean zero Gaussian process with simple
covariance function. This result suggests discrepancy measures between
empirical and theoretical K-function with known limit distribution which allow
to perform goodness-of-fit tests for checking a hypothesized point process
based only on its intensity and (generalized) K-function. Similar test
statistics are derived for testing the hypothesis that two independent point
processes in W_n have the same distribution without explicit knowledge of their
intensities and K-functions.
| 0 | 0 | 1 | 1 | 0 | 0 |
Effects of Arrival Type and Degree of Saturation on Queue Length Estimation at Signalized Intersections | Purpose of this study is evaluation of the relationship between different
arrival types and degree of saturation (X) with overestimations of HCM 2010
procedure for estimating the back of queue within a study area. Further
analysis is performed to establish the relationship between queue length and
delay and also between each of them individually and X in cases with
overestimation. The analyses are based on the 50th percentile queue lengths for
data collected at four signalized intersections along a corridor in 4 time
periods (off peak period and AM, Noon and PM peak periods). Based on the
statistical test results, arrival type did not play a role in overestimations.
However, there is a significant relationship between the overestimations on
minor and major street and different ranges of X. On minor streets, about 59%
of the overestimations are at X values less than half; while near 23% of the
overestimations are at oversaturation condition with X values greater than 1.
The relationship between amount of overestimations and degree of saturation
should be established based on the numerical amount of overestimations versus X
values rather than the relative amounts; since the statistical comparison
between the relative amount of overestimations and X values, resulted in a
wrong idea of the real world condition. There was a significant correlation
between field queue and delay data of the cases with overestimated queue length
in all cases on major and minor streets. Also, field queue is correlated to X,
in all cases on minor and major streets.
| 0 | 0 | 0 | 1 | 0 | 0 |
Sourcerer's Apprentice and the study of code snippet migration | On the worldwide web, not only are webpages connected but source code is too.
Software development is becoming more accessible to everyone and the licensing
for software remains complicated. We need to know if software licenses are
being maintained properly throughout their reuse and evolution. This motivated
the development of the Sourcerer's Apprentice, a webservice that helps track
clone relicensing, because software typically employ software licenses to
describe how their software may be used and adapted. But most developers do not
have the legal expertise to sort out license conflicts. In this paper we put
the Apprentice to work on empirical studies that demonstrate there is much
sharing between StackOverflow code and Python modules and Python documentation
that violates the licensing of the original Python modules and documentation:
software snippets shared through StackOverflow are often being relicensed
improperly to CC-BY-SA 3.0 without maintaining the appropriate attribution. We
show that many snippets on StackOverflow are inappropriately relicensed by
StackOverflow users, jeopardizing the status of the software built by companies
and developers who reuse StackOverflow snippets.
| 1 | 0 | 0 | 0 | 0 | 0 |
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