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Time-series forecasting has been an important research domain for so many
years. Its applications include ECG predictions, sales forecasting, weather
conditions, even COVID-19 spread predictions. These applications have motivated
many researchers to figure out an optimal forecasting approach, but the
modeling approach also changes as the application domain changes. This work has
focused on reviewing different forecasting approaches for telemetry data
predictions collected at data centers. Forecasting of telemetry data is a
critical feature of network and data center management products. However, there
are multiple options of forecasting approaches that range from a simple linear
statistical model to high capacity deep learning architectures. In this paper,
we attempted to summarize and evaluate the performance of well known time
series forecasting techniques. We hope that this evaluation provides a
comprehensive summary to innovate in forecasting approaches for telemetry data.
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In recent years, the performance of Scientifc Complementary Metal Oxide
Semiconductor (sCMOS) sensors has been improved signifcantly. Compared with CCD
sensors, sCMOS sensors have various advantages, making them potentially better
devices for optical and X-ray detection, especially in time-domain astronomy.
After a series of tests of sCMOS sensors, we proposed a new dedicated
high-speed, large-format X-ray detector in 2016 cooperating with Gpixel Inc.
This new sCMOS sensor has a physical size of 6 cm by 6 cm, with an array of
4096 by 4096 pixels and a pixel size of 15 um. The frame rate is 20.1 fps under
current condition and can be boosted to a maximum value around 100 fps. The
epitaxial thickness is increased to 10 um compared to the previous sCMOS
product. We show the results of its frst taped-out product in this work. The
dark current of this sCMOS is lower than 10 e/pixel/s at 20C, and lower than
0.02 e/pixel/s at -30C. The Fixed Pattern Noise (FPN) and the readout noise are
lower than 5 e in high-gain situation and show a small increase at low
temperature. The energy resolution reaches 180.1 eV (3.1%) at 5.90 keV for
single-pixel events and 212.3 eV (3.6%) for all split events. The continuous
X-ray spectrum measurement shows that this sensor is able to response to X-ray
photons from 500 eV to 37 keV. The excellent performance, as demonstrated from
these test results, makes sCMOS sensor an ideal detector for X-ray imaging and
spectroscopic application.
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In this paper, we have demonstrated the large-size free-standing
single-crystal b-Ga2O3 NMs fabricated by the hydrogen implantation and lift-off
process directly from MOCVD grown b-Ga2O3 epifilms on native substrates. The
optimum implantation conditions were simulated with a Monte-Carlo simulation to
obtain the high hydrogen concentration with a narrow ion distribution at the
desired depth. Two as grown b-Ga2O3 samples with different orientation ([100]
and [001]) were used and successfully create 1.2 um thick b-Ga2O3 NMs without
any physical damages. These b-Ga2O3 NMs were then transfer-printed onto rigid
and flexible substrates such as SiC substrate and polyimide substrate. Various
material characterizations were performed to investigate the crystal quality,
surface morphology, optical property, mechanical property, and bandgap before
and after the lift-off and revealed that good material quality is maintained.
This result offers several benefits in that the thickness, doping, and size of
b-Ga2O3 NMs can be fully controlled. Moreover, more advanced b-Ga2O3-based NM
structures such as (AlxGa1-x)2O3/Ga2O3 heterostructure NMs can be directly
created from their bulk epitaxy substrates thus this result provides a viable
route for the realization of high performance b-Ga2O3 NM-based electronics and
optoelectronics that can be built on various substrates and platforms.
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Controlling infectious diseases is a major health priority because they can
spread and infect humans, thus evolving into epidemics or pandemics. Therefore,
early detection of infectious diseases is a significant need, and many
researchers have developed models to diagnose them in the early stages. This
paper reviewed research articles for recent machine-learning (ML) algorithms
applied to infectious disease diagnosis. We searched the Web of Science,
ScienceDirect, PubMed, Springer, and IEEE databases from 2015 to 2022,
identified the pros and cons of the reviewed ML models, and discussed the
possible recommendations to advance the studies in this field. We found that
most of the articles used small datasets, and few of them used real-time data.
Our results demonstrated that a suitable ML technique depends on the nature of
the dataset and the desired goal.
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We propose a scheme for digital quantum simulation of lattice gauge theories
with dynamical fermions. Using a layered optical lattice with ancilla atoms
that can move and interact with the other atoms (simulating the physical
degrees of freedom), we obtain a stroboscopic dynamics which yields the
four-body plaquette interactions, arising in models with $2+1$ and higher
dimensions, without the use of perturbation theory. As an example we show how
to simulate a $\mathbb{Z}_2$ model in $2+1$ dimensions.
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We show that the lower bound for the optimal directional discrepancy with
respect to the class of rectangles in $\mathbb{R}^2$ rotated in a restricted
interval of directions $[-\theta, \theta]$ with $\theta < \frac{\pi}{4}$ is of
the order at least $N^{1/5}$ with a constant depending on $\theta$.
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A constant need to increase the network capacity for meeting the growing
demands of the subscribers has led to the evolution of cellular communication
networks from the first generation (1G) to the fifth generation (5G). There
will be billions of connected devices in the near future. Such a large number
of connections are expected to be heterogeneous in nature, demanding higher
data rates, lesser delays, enhanced system capacity and superior throughput.
The available spectrum resources are limited and need to be flexibly used by
the mobile network operators (MNOs) to cope with the rising demands. An
emerging facilitator of the upcoming high data rate demanding next generation
networks (NGNs) is device-to-device (D2D) communication. An extensive survey on
device-to-device (D2D) communication has been presented in this paper,
including the plus points it offers, the key open issues associated with it
like peer discovery, resource allocation etc, demanding special attention of
the research community, some of its integrant technologies like millimeter wave
D2D (mmWave), ultra dense networks (UDNs), cognitive D2D, handover procedure in
D2D and its numerous use cases. Architecture is suggested aiming to fulfill all
the subscriber demands in an optimal manner. The Appendix mentions some ongoing
standardization activities and research projects of D2D communication.
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As cellular networks are turning into a platform for ubiquitous data access,
cellular operators are facing a severe data capacity crisis due to the
exponential growth of traffic generated by mobile users. In this work, we
investigate the benefits of sharing infrastructure and spectrum among two
cellular operators. Specifically, we provide a multi-cell analytical model
using stochastic geometry to identify the performance gain under different
sharing strategies, which gives tractable and accurate results. To validate the
performance using a realistic setting, we conduct extensive simulations for a
multi-cell OFDMA system using real base station locations. Both analytical and
simulation results show that even a simple cooperation strategy between two
similar operators, where they share spectrum and base stations, roughly
quadruples capacity as compared to the capacity of a single operator. This is
equivalent to doubling the capacity per customer, providing a strong incentive
for operators to cooperate, if not actually merge.
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In this paper we introduce and study uniform bases for the ideal
arrangements. In particular, explicit uniform bases are presented on each Lie
type. Combining the explicit uniform bases with the work of
Abe-Horiguchi-Masuda-Murai-Sato, we also obtain explicit presentations of the
cohomology rings of regular nilpotent Hessenberg varieties in all Lie types.
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TLAPS, the TLA+ proof system, is a platform for the development and
mechanical verification of TLA+ proofs written in a declarative style requiring
little background beyond elementary mathematics. The language supports
hierarchical and non-linear proof construction and verification, and it is
independent of any verification tool or strategy. A Proof Manager uses backend
verifiers such as theorem provers, proof assistants, SMT solvers, and decision
procedures to check TLA+ proofs. This paper documents the first public release
of TLAPS, distributed with a BSD-like license. It handles almost all the
non-temporal part of TLA+ as well as the temporal reasoning needed to prove
standard safety properties, in particular invariance and step simulation, but
not liveness properties.
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This paper proposes a novel topology for grid connected photovoltaic (PV)
system based on modular multilevel converter (MMC). In this topology, a PV
array is connected to capacitors of each submodule (SM) of the MMC through a
DC-DC boost converter with maximum power point tracking (MPPT) control. This
topology will maximize the efficiency of the system in the case of partial
shading conditions, as it can regulate the SM capacitor voltages independently
from each other to realize distributed MPPT. A model predictive control is used
to track the AC output current, balance the SMs capacitor voltages, and to
mitigate the circulating current. The proposed PV generation topology with 7
level MMC system validity has been verified by simulations via MATLAB/Simulink
toolbox under normal operation, partial shading and PV array failure.
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These lecture notes give a short review of methods such as the matrix ansatz,
the additivity principle or the macroscopic fluctuation theory, developed
recently in the theory of non-equilibrium phenomena. They show how these
methods allow to calculate the fluctuations and large deviations of the density
and of the current in non-equilibrium steady states of systems like exclusion
processes. The properties of these fluctuations and large deviation functions
in non-equilibrium steady states (for example non-Gaussian fluctuations of
density or non-convexity of the large deviation function which generalizes the
notion of free energy) are compared with those of systems at equilibrium.
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Given a nonnegative integer weight $f(v)$ for each vertex $v$ in a multigraph
$G$, an {\it $f$-bounded subgraph} of $G$ is a multigraph $H$ contained in $G$
such that $d_H(v)\le f(v)$ for all $v\in V(G)$. Using Tutte's $f$-Factor
Theorem, we give a new proof of the min-max relation for the maximum size of an
$f$-bounded subgraph of $G$. When $f(v)=1$ for all $v$, the formula reduces to
the classical Tutte--Berge Formula for the maximum size of a matching.
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Kepler-78b is a transiting planet that is 1.2 times the radius of Earth and
orbits a young, active K dwarf every 8 hours. The mass of Kepler-78b has been
independently reported by two teams based on radial velocity measurements using
the HIRES and HARPS-N spectrographs. Due to the active nature of the host star,
a stellar activity model is required to distinguish and isolate the planetary
signal in radial velocity data. Whereas previous studies tested parametric
stellar activity models, we modeled this system using nonparametric Gaussian
process (GP) regression. We produced a GP regression of relevant Kepler
photometry. We then use the posterior parameter distribution for our
photometric fit as a prior for our simultaneous GP + Keplerian orbit models of
the radial velocity datasets. We tested three simple kernel functions for our
GP regressions. Based on a Bayesian likelihood analysis, we selected a
quasi-periodic kernel model with GP hyperparameters coupled between the two RV
datasets, giving a Doppler amplitude of 1.86 $\pm$ 0.25 m s$^{-1}$ and
supporting our belief that the correlated noise we are modeling is
astrophysical. The corresponding mass of 1.87 $^{+0.27}_{-0.26}$ M$_{\oplus}$
is consistent with that measured in previous studies, and more robust due to
our nonparametric signal estimation. Based on our mass and the radius
measurement from transit photometry, Kepler-78b has a bulk density of
6.0$^{+1.9}_{-1.4}$ g cm$^{-3}$. We estimate that Kepler-78b is 32$\pm$26% iron
using a two-component rock-iron model. This is consistent with an Earth-like
composition, with uncertainty spanning Moon-like to Mercury-like compositions.
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We compute the three-loop helicity amplitudes for $q\bar{q} \to gg$ and its
crossed partonic channels, in massless QCD. Our analytical results provide a
non-trivial check of the color quadrupole contribution to the infrared poles
for external states in different color representations. At high energies, the
$qg \to qg$ amplitude shows the predicted factorized form from Regge theory and
confirms previous results for the gluon Regge trajectory extracted from $qq'
\to qq'$ and $gg \to gg$ scattering.
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We investigate local quantum field theories for one-dimensional (1D) Bose and
Fermi gases with contact interactions, which are closely connected with each
other by Girardeau's Bose-Fermi mapping. While the Lagrangian for bosons
includes only a two-body interaction, a marginally relevant three-body
interaction term is found to be necessary for fermions. Because of this
three-body coupling, the three-body contact characterizing a local triad
correlation appears in the energy relation for fermions, which is one of the
sum rules for a momentum distribution. In addition, we apply in both systems
the operator product expansion to derive large-energy and momentum asymptotics
of a dynamic structure factor and a single-particle spectral density. These
behaviors are universal in the sense that they hold for any 1D scattering
length at any temperature. The asymptotics for the Tonks-Girardeau gas, which
is a Bose gas with a hardcore repulsion, as well as the Bose-Fermi
correspondence in the presence of three-body attractions are also discussed.
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Let $F$ be a field, let $V$ be a valuation ring of $F$ of arbitrary Krull
dimension (rank), let $K$ be a finite Galois extension of $F$ with group $G$,
and let $S$ be the integral closure of $V$ in $K$. Let $f:G\times G\mapsto
K\setminus \{0\}$ be a normalized two-cocycle such that $f(G\times G)\subseteq
S\setminus \{0\}$, but we do not require that $f$ should take values in the
group of multiplicative units of $S$. One can construct a crossed-product
$V$-order $A_f=\sum_{\sigma\in G}Sx_{\sigma}$ with multiplication given by
$x_{\sigma}sx_{\tau}=\sigma(s)f(\sigma,\tau)x_{\sigma\tau}$ for $s\in S$,
$\sigma,\tau\in G$. We characterize semihereditary and Dubrovin crossed-product
orders, under mild valuation-theoretic assumptions placed on the nature of the
extension $K/F$.
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We present a pedagogical introduction to the problem of evolving a head on
collision of two Aichelburg-Sexl gravitational shock waves in D-dimensions,
using perturbative techniques. We follow a constructive approach with examples,
going in some detail through: the set up of the exact initial conditions and
their properties; perturbative methods in flat space-time with Green function
solutions; and numerical strategies to evaluate the integral solutions. We also
discuss, briefly, radiation extraction methods adapted to this problem,
together with some of the results for this system.
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We present a new scheme for quantum interfaces to accomplish the
interconversion of photonic qubits and spin qubits based on optomechanical
resonators and the spin-orbit-induced interactions in suspended carbon nanotube
quantum dots. This interface implements quantum spin transducers and further
enables electrical manipulation of local electron spin qubits, which lays the
foundation for all-electrical control of state transfer protocols between two
distant quantum nodes in a quantum network. We numerically evaluate the state
transfer processes and proceed to estimate the effect of each coupling strength
on the operation fidelities.
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This study investigates the effects on the connected and disconnected
correlations for the scalar density that are induced by created monopoles and
instantons in the QCD vacuum. To reveal the effects, we add a monopole and
anti-monopole pair in the gauge field configurations in \textit{SU}(3) by
applying the monopole creation operator to the vacuum. We vary the magnetic
charges of the monopole and anti-monopole and increase the number of monopoles
and anti-monopoles in the configurations. The Dirac operator of overlap
fermions preserves the exact chiral symmetry in lattice gauge theory and exact
zero-modes exist in its spectrum. The eigenvalues and eigenvectors of the
overlap Dirac operator have been calculated using these configurations, and the
numbers of instantons and anti-instantons which are created by these additional
monopoles and anti-monopoles have been estimated from the numbers of
topological charges in our previous studies. In this study, we demonstrate the
preliminary results that instantons and monopoles influence the masses that are
evaluated from the connected and disconnected correlation functions for the
scalar density using low-lying eigenvalues and eigenvectors of the overlap
Dirac operator.
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We give a div-curl type lemma for the wedge product of closed differential
forms on R^n when they have coefficients respectively in a Hardy space and
L^infinity or BMO. In this last case, the wedge product belongs to an
appropriate Hardy-Orlicz space.
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A numerical method to efficiently solve for mixing and reaction of scalars in
a two-dimensional flow field at large P\'eclet numbers but otherwise arbitrary
Damk\"ohler numbers is reported. We consider a strip of one reactant in a pool
of another reactant, both of which are advected with the known velocity field.
We first establish that the system evolution for such a system under certain
conditions is described by a locally one-dimensional reaction-diffusion
problem. The approximation of a locally one-dimensional dynamics is true for
cases where the strip thickness is smaller than the local radius of curvature
and also when the strip thickness is smaller than the distance between adjacent
strips. We first demonstrate the method for the transport of a conservative
scalar under a linear shear flow, point vortex and a chaotic sine flow. We then
proceed to consider the situation with a simple bimolecular reaction between
two reactants to yield a single product. The methodology presented herewith
essentially generalizes nontrivially the Diffusive Strip Method developed by
Meunier and Villermaux (J. Fluid Mech. 662, 134-172 (2010)) to address passive
scalar transport, to the generalized situation with multiple reacting species.
In essence, the reduction of dimensionality of the problem, which renders the
2D problem 1D, allows one to efficiently model reactive transport under high
P\'eclet numbers which are otherwise prohibitively difficult to resolve from
classical finite difference or finite element based methods.
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Quantum Optical Coherence Tomography (Q-OCT) is the non-classical counterpart
of Optical Coherence Tomography (OCT) - a high-resolution 3D imaging technique
based on white-light interferometry. Because Q-OCT uses a source of
frequency-entangled photon pairs, not only is the axial resolution not affected
by dispersion mismatch in the interferometer, but is also inherently improved
by a factor of square root of two. Unfortunately, practical applications of
Q-OCT are hindered by image-scrambling artefacts and slow acquisition times.
Here, we present a theoretical analysis of a novel approach that is free of
these problems: Q-OCT with joint spectrum detection (JS-Q-OCT). Based on a
photon pair coincidence detection as in the standard Q-OCT configuration, it
also discerns, each photon pair by their wavelength. We show that all the
information about the internal structures of the object is encoded in the joint
spectrum and can be easily retrieved through Fourier transformation. No depth
scanning is required, making our technique potentially faster than standard
Q-OCT. Finally, we show that the data available in the joint spectrum enables
artefact removal and discuss prospective algorithms for doing so.
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Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules,
are good predictors of lung cancer malignancy and hence, are routinely assessed
and reported by radiologists as part of the standardized Lung-RADS clinical
scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice
assessment by radiologists, manual spiculation/lobulation annotation is a
tedious task and thus no public datasets exist to date for probing the
importance of these clinically-reported features in the SOTA malignancy
prediction algorithms. As part of this paper, we release a large-scale
Clinically-Interpretable Radiomics Dataset, CIRDataset, containing 956
radiologist QA/QC'ed spiculation/lobulation annotations on segmented lung
nodules from two public datasets, LIDC-IDRI (N=883) and LUNGx (N=73). We also
present an end-to-end deep learning model based on multi-class Voxel2Mesh
extension to segment nodules (while preserving spikes), classify spikes
(sharp/spiculation and curved/lobulation), and perform malignancy prediction.
Previous methods have performed malignancy prediction for LIDC and LUNGx
datasets but without robust attribution to any clinically reported/actionable
features (due to known hyperparameter sensitivity issues with general
attribution schemes). With the release of this comprehensively-annotated
CIRDataset and end-to-end deep learning baseline, we hope that malignancy
prediction methods can validate their explanations, benchmark against our
baseline, and provide clinically-actionable insights. Dataset, code, pretrained
models, and docker containers are available at
https://github.com/nadeemlab/CIR.
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The Interstellar Boundary Explorer (IBEX) has been measuring fluxes of the
Energetic Neutral Atoms (ENAs) using the IBEX-Hi (0.3 -- 6 keV) instrument
since 2008. We have developed a numerical time-depended code to calculate
globally distributed flux (GDF) of hydrogen ENAs employing both 1) 3D
kinetic-MHD model of the global heliosphere and 2) reconstruction of atom
trajectories from 1 au, where they are observed by IBEX, to the point of their
origin in the inner heliosheath (IHS). The key factor in the simulation is a
detailed kinetic consideration of the pickup ions (PUIs), the supra-thermal
component of protons in the heliosphere, which is "parental" to the ENAs and
originates in the region of the supersonic solar wind being picked by the
heliospheric magnetic field. As a result of our study, we have concluded that
(1) the developed model is able to reproduce the geometry of the multi-lobe
structure seen in the IBEX-Hi GDF maps, (2) the GDF is extremely sensitive to
the form of the velocity distribution function of PUIs in the IHS, and the
accounting for the existence of an additional energetic population of PUIs is
essential to explain the data, (3) despite a relatively good agreement, there
are some quantitative differences between the model calculations and IBEX-Hi
data. Possible reasons for these differences are discussed.
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The approximative calculation of iterated nested expectations is a recurring
challenging problem in applications. Nested expectations appear, for example,
in the numerical approximation of solutions of backward stochastic differential
equations (BSDEs), in the numerical approximation of solutions of semilinear
parabolic partial differential equations (PDEs), in statistical physics, in
optimal stopping problems such as the approximative pricing of American or
Bermudan options, in risk measure estimation in mathematical finance, or in
decision-making under uncertainty. Nested expectations which arise in the above
named applications often consist of a large number of nestings. However, the
computational effort of standard nested Monte Carlo approximations for iterated
nested expectations grows exponentially in the number of nestings and it
remained an open question whether it is possible to approximately calculate
multiply iterated high-dimensional nested expectations in polynomial time. In
this article we tackle this problem by proposing and studying a new class of
full-history recursive multilevel Picard (MLP) approximation schemes for
iterated nested expectations. In particular, we prove under suitable
assumptions that these MLP approximation schemes can approximately calculate
multiply iterated nested expectations with a computational effort growing at
most polynomially in the number of nestings $ K \in \mathbb{N} = \{1, 2, 3,
\ldots \} $, in the problem dimension $ d \in \mathbb{N} $, and in the
reciprocal $\frac{1}{\varepsilon}$ of the desired approximation accuracy $
\varepsilon \in (0, \infty) $.
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A doped semiconductor double-quantum-dot molecule is proposed as a qubit
realization. The quantum information is encoded in the electron spin, thus
benefiting from the long relevant decoherence times; the enhanced flexibility
of the molecular structure allows to map the spin degrees of freedom onto the
orbital ones and vice versa, and opens the possibility for high-finesse
(conditional and unconditional) quantum gates by means of stimulated Raman
adiabatic passage.
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The types of constraints encountered in black-box and simulation-based
optimization problems differ significantly from those treated in nonlinear
programming. We introduce a characterization of constraints to address this
situation. We provide formal definitions for several constraint classes and
present illustrative examples in the context of the resulting taxonomy. This
taxonomy, denoted QRAK, is useful for modeling and problem formulation, as well
as optimization software development and deployment. It can also be used as the
basis for a dialog with practitioners in moving problems to increasingly
solvable branches of optimization.
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We use LINUS, a procedure developed by Srinivasan and Rose, to provide a
physical interpretation of and to predict the secondary structures of proteins.
The secondary structure type at a given site is identified by the largest
conformational bias during short time simulations. We examine the rate of
successful prediction as a function of temperature and the interaction window.
At high temperatures, there is a large propensity for the establishment of
$\beta$-strands whereas $\alpha$-helices appear only when the temperature is
lower than a certain threshold value. It is found that there exists an optimal
temperature at which the correct secondary structures are predicted most
accurately. We find that this temperature is close to the peak temperature of
the specific heat. Changing the interaction window or carrying out longer
simulations approaching equilibrium lead to little change in the optimal
success rate. Our findings are in accord with the observation by Srinivasan and
Rose that the secondary structures are mainly determined by local interactions
and they appear in the early stage of folding.
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This work addresses cross-view camera pose estimation, i.e., determining the
3-Degrees-of-Freedom camera pose of a given ground-level image w.r.t. an aerial
image of the local area. We propose SliceMatch, which consists of ground and
aerial feature extractors, feature aggregators, and a pose predictor. The
feature extractors extract dense features from the ground and aerial images.
Given a set of candidate camera poses, the feature aggregators construct a
single ground descriptor and a set of pose-dependent aerial descriptors.
Notably, our novel aerial feature aggregator has a cross-view attention module
for ground-view guided aerial feature selection and utilizes the geometric
projection of the ground camera's viewing frustum on the aerial image to pool
features. The efficient construction of aerial descriptors is achieved using
precomputed masks. SliceMatch is trained using contrastive learning and pose
estimation is formulated as a similarity comparison between the ground
descriptor and the aerial descriptors. Compared to the state-of-the-art,
SliceMatch achieves a 19% lower median localization error on the VIGOR
benchmark using the same VGG16 backbone at 150 frames per second, and a 50%
lower error when using a ResNet50 backbone.
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A half-mirror that divides a spin-polarized electron into two parallel
copropagating spin-resolved quantum Hall edge channels one half each is
presented in this study. The partition process was coherent, as confirmed by
observing the Aharonov-Bohm oscillation at a high visibility of up to 60% in a
Mach-Zehnder interferometer, which comprised two such half-mirrors. The device
characteristics were highly stable, making the device promising in the
application of quantum information processing. The beam-splitting process is
theoretically modelled, and the numerical simulation successfully reproduces
the experimental observation. The partition of the electron accompanied by the
spin rotation is explained by the angular momentum transfer from the orbital to
the spin via spin-orbit interactions.
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The stochastic block model (SBM) is a widely used framework for community
detection in networks, where the network structure is typically represented by
an adjacency matrix. However, conventional SBMs are not directly applicable to
an adjacency matrix that consists of non-negative zero-inflated continuous edge
weights. To model the international trading network, where edge weights
represent trading values between countries, we propose an innovative SBM based
on a restricted Tweedie distribution. Additionally, we incorporate nodal
information, such as the geographical distance between countries, and account
for its dynamic effect on edge weights. Notably, we show that given a
sufficiently large number of nodes, estimating this covariate effect becomes
independent of community labels of each node when computing the maximum
likelihood estimator of parameters in our model. This result enables the
development of an efficient two-step algorithm that separates the estimation of
covariate effects from other parameters. We demonstrate the effectiveness of
our proposed method through extensive simulation studies and an application to
real-world international trading data.
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The lytic polysaccharide monooxygenases (LPMOs) are copper metalloenzymes
that can enhance polysaccharide depolymerization through an oxidative mechanism
and hence boost generation of biofuel from e.g. cellulose. By employing density
functional theory in a combination of quantum mechanics and molecular mechanics
(QM/MM), we report a complete description of the molecular mechanism of LPMOs.
The QM/MM scheme allows us to describe all reaction steps with a detailed
protein environment and we show that this is necessary. Several active species
capable of abstracting a hydrogen from the substrate have been proposed
previously. We investigate previously suggested paths as well as new ones. We
describe the generation of the reactive intermediates, the abstraction of a
hydrogen atom from the polysaccharide substrate, as well as the final
recombination step in which OH is transferred back to the substrate. We show
that a superoxo [CuO2]+ complex can be protonated by a nearby histidine residue
(suggested by recent mutagenesis studies and crystallographic work) and,
provided an electron source is available, leads to formation of an oxyl-complex
after cleavage of the O-O bond and dissociation of water. The oxyl complex
either reacts with the substrate or is further protonated to a hydroxyl
complex. Both the oxyl and hydroxyl complexes are also readily generated from a
reaction with H2O2. The C-H abstraction by the oxyl and hydroxy complexes is
overall favorable with activation barriers of 69 and 94 kJ/mol, compared to the
much higher barrier (156 kJ/mol) obtained for the copper-superoxo species. We
obtain good structural agreement for intermediates for which structural data
are available and the estimated reaction energies agree with experimental rate
constants. Thus, our suggested mechanism is the most complete to date and
concur with available experimental evidence.
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A general procedure for studying finite-N effects in quantum phase
transitions of finite systems is presented and applied to the critical-point
dynamics of nuclei undergoing a shape-phase transition of second-order
(continuous), and of first-order with an arbitrary barrier.
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The decoupling limit of the D1-D5 system compactified on T^4\times S^1 has a
rich spectrum of U(1) charged excitations. Even though these states are not BPS
in the limit, BPS considerations determine the mass and the semiclassical
entropy for a given charge vector. The dependence of the mass formula on the
compactification moduli situates the symmetric orbifold Sym^N(T^4) x T^4
conformal field theory in the moduli space. A detailed analysis of the global
identifications of the moduli space yields a picture of multiple weak-coupling
limits - one for each factorization of N into D1 and D5 charges d1 and d5=N/d1
- joined through regions of strong coupling in the CFT moduli space.
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Classical chaotic dynamics is characterized by the exponential sensitivity to
initial conditions. Quantum mechanics, however, does not show this feature. We
consider instead the sensitivity of quantum evolution to perturbations in the
Hamiltonian. This is observed as an atenuation of the Loschmidt Echo, $M(t)$,
i.e. the amount of the original state (wave packet of width $\sigma$) which is
recovered after a time reversed evolution, in presence of a classically weak
perturbation. By considering a Lorentz gas of size $L$, which for large $L$ is
a model for an {\it unbounded} classically chaotic system, we find numerical
evidence that, if the perturbation is within a certain range, $M(t)$ decays
exponentially with a rate $1/\tau_{\phi}$ determined by the Lyapunov exponent
$\lambda$ of the corresponding classical dynamics. This exponential decay
extends much beyond the Eherenfest time $t_{E}$ and saturates at a time
$t_{s}\simeq \lambda^{-1}\ln (\widetilde{N})$, where $\widetilde{N}\simeq
(L/\sigma)^2$ is the effective dimensionality of the Hilbert space. Since $\tau
_{\phi}$ quantifies the increasing uncontrollability of the quantum phase
(decoherence) its characterization and control has fundamental interest.
|
The goal of this paper is twofold. First we prove a rigidity estimate, which
generalises the theorem on geometric rigidity of Friesecke, James and M\"uller
to 1-forms with non-vanishing exterior derivative.
Second we use this estimate to prove a kind of spontaneous breaking of
rotational symmetry for some models of crystals, which allow almost all kinds
of defects, including unbounded defects as well as edge, screw and mixed
dislocations, i.e. defects with Burgers vectors.
|
When two planar atomic membranes are placed within the van der Waals
distance, the charge and heat transport across the interface are coupled by the
rules of momentum conservation and structural commensurability, leading to
outstanding thermoelectric properties. Here we show that an effective
"interlayer phonon drag" determines the Seebeck coefficient (S) across the van
der Waals gap formed in twisted bilayer graphene (tBLG). The cross-plane
thermovoltage, which is non-monotonic in both temperature and density, is
generated through scattering of electrons by the out-of-plane layer breathing
(ZO'/ZA2) phonon modes and differs dramatically from the expected
Landauer-Buttiker formalism in conventional tunnel junctions. The tunability of
the cross-plane Seebeck effect in van der Waals junctions may be valuable in
creating a new genre of versatile thermoelectric systems with layered solids.
|
This paper considers the problem of hub-based platoon coordination for a
large-scale transport system, where trucks have individual utility functions to
optimize. An event-triggered distributed model predictive control method is
proposed to solve the optimal scheduling of waiting times at hubs for
individual trucks. In this distributed framework, trucks are allowed to decide
their waiting times independently and only limited information is shared
between trucks. Both the predicted reward gained from platooning and the
predicted cost for waiting at hubs are included in each truck's utility
function. The performance of the coordination method is demonstrated in a
simulation with one hundred trucks over the Swedish road network.
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Women have been shown to be effective leaders in many team-based situations.
However, it is also well-recognized that women are underrepresented in
engineering and technology areas, which leads to wasted efforts and a lack of
diversity in professional organizations. Although studies about gender and
leadership are rich, research focusing on engineering-specific activities, are
scarce. To react on this gap, we explored the experience of female leaders of
software development projects and possible context factors that influence
leadership effectiveness. The study was conducted as a longitudinal multiple
case study. Data was collected from survey, interviews, observation and project
reports. In this work, we reported some preliminary findings related to
leadership style, team perception on leadership and team-task context factors.
We found a strong correlation between perceived team leadership and task
management. We also observed a potential association between human-oriented
leading approach in low customer involvement scenarios and task-oriented
leading approach in high customer involvement situations.
|
We investigate conductances and current correlations in a system consisting
of a normal multichannel conductor connected to one superconducting and two
ferromagnetic electrodes. For antiparallel orientation of the ferromagnet
polarizations, current injection from one ferromagnet can, due to Andreev
reflection, lead to a net drag of current from the second ferromagnet toward
the superconductor. We present the conditions for the Andreev drag in terms of
the degree of lead polarizations, contact conductances and spin-flip
scattering. Remarkably, both equilibrium and nonequilibrium zero-frequency
current correlations between the ferromagnets become positive even in the
presence of spin relaxation.
|
Over the past two decades, the research of (Ga,Mn)As has led to a deeper
understanding of relativistic spin-dependent phenomena in magnetic systems. It
has also led to discoveries of new effects and demonstrations of unprecedented
functionalities of experimental spintronic devices with general applicability
to a wide range of materials. In this article we review the basic material
properties that make (Ga,Mn)As a favorable test-bed system for spintronics
research and discuss contributions of (Ga,Mn)As studies in the general context
of the spin-dependent phenomena and device concepts. Special focus is on the
spin-orbit coupling induced effects and the reviewed topics include the
interaction of spin with electrical current, light, and heat.
|
We elaborate the theory of the stable Bernstein center of a $p$-adic group
$G$, and apply it to state a general conjecture on test functions for Shimura
varieties due to the author and R. Kottwitz. This conjecture provides a
framework by which one might pursue the Langlands-Kottwitz method in a very
general situation: not necessarily PEL Shimura varieties with arbitrary level
structure at $p$. We give a concrete reinterpretation of the test function
conjecture in the context of parahoric level structure. We also use the stable
Bernstein center to formulate some of the transfer conjectures (the
"fundamental lemmas") that would be needed if one attempts to use the test
function conjecture to express the local Hasse-Weil zeta function of a Shimura
variety in terms of automorphic $L$-functions.
|
As a neurophysiological response to threat or adverse conditions, stress can
affect cognition, emotion and behaviour with potentially detrimental effects on
health in the case of sustained exposure. Since the affective content of speech
is inherently modulated by an individual's physical and mental state, a
substantial body of research has been devoted to the study of paralinguistic
correlates of stress-inducing task load. Historically, voice stress analysis
(VSA) has been conducted using conventional digital signal processing (DSP)
techniques. Despite the development of modern methods based on deep neural
networks (DNNs), accurately detecting stress in speech remains difficult due to
the wide variety of stressors and considerable variability in the individual
stress perception. To that end, we introduce a set of five datasets for task
load detection in speech. The voice recordings were collected as either
cognitive or physical stress was induced in the cohort of volunteers, with a
cumulative number of more than a hundred speakers. We used the datasets to
design and evaluate a novel self-supervised audio representation that leverages
the effectiveness of handcrafted features (DSP-based) and the complexity of
data-driven DNN representations. Notably, the proposed approach outperformed
both extensive handcrafted feature sets and novel DNN-based audio
representation learning approaches.
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The class number divisibility problem for number fields is one of the
classical problems in algebraic number theory, which originated from Gauss'
class number conjectures. The relation between the points on an elliptic curve
and class number divisibility of a number field has been explored through the
works of various mathematicians. Here, we explicitly construct an unramified
abelian extension of a bi-quadratic field generated from points of a certain
type of elliptic curve. Moreover, showing the $2$-Selmer rank of the said
elliptic curve as $1$, we also construct an infinite family of bi-quadratic
fields of even class number.
|
A measurement of the phase difference between the short- and long-distance
contributions to the $B^{+}\to K^{+}\mu^{+}\mu^{-}$ decay is performed by
analysing the dimuon mass distribution. The analysis is based on $pp$ collision
data corresponding to an integrated luminosity of 3 $\rm fb^{-1}$ collected by
the LHCb experiment in 2011 and 2012. The long-distance contribution to the
$B^{+}\to K^{+}\mu^{+}\mu^{-}$ decay is modelled as a sum of relativistic
Breit--Wigner amplitudes representing different vector meson resonances
decaying to muon pairs, each with their own magnitude and phase. The measured
phases of the $J/\psi$ and $\psi(2S)$ resonances are such that the interference
with the short-distance component in dimuon mass regions far from their pole
masses is small. In addition, constraints are placed on the Wilson
coefficients, $\mathcal{C}_{9}$ and $\mathcal{C}_{10}$, and the branching
fraction of the short-distance component is measured.
|
The forward-backward operator splitting algorithm is one of the most
important methods for solving the optimization problem of the sum of two convex
functions, where one is differentiable with a Lipschitz continuous gradient and
the other is possibly nonsmooth but proximable. It is convenient to solve some
optimization problems in the form of dual or primal-dual problems. Both methods
are mature in theory. In this paper, we construct several efficient first-order
splitting algorithms for solving a multi-block composite convex optimization
problem. The objective function includes a smooth function with a Lipschitz
continuous gradient, a proximable convex function that may be nonsmooth, and a
finite sum of a composition of a proximable function and a bounded linear
operator. To solve such an optimization problem, we transform it into the sum
of three convex functions by defining an appropriate inner product space. On
the basis of the dual forward-backward splitting algorithm and the primal-dual
forward-backward splitting algorithm, we develop several iterative algorithms
that involve only computing the gradient of the differentiable function and
proximity operators of related convex functions. These iterative algorithms are
matrix-inversion-free and completely splitting algorithms. Finally, we employ
the proposed iterative algorithms to solve a regularized general prior image
constrained compressed sensing (PICCS) model that is derived from computed
tomography (CT) image reconstruction under sparse sampling of projection
measurements. Numerical results show that the proposed iterative algorithms
outperform other algorithms.
|
Perovskites have attracted much attention due to their remarkable optical
properties. While it is well established that excitons dominate their optical
response, the impact of higher excitonic states and formation of phonon
sidebands in optical spectra still need to be better understood. Here, we
perform a theoretical study on excitonic properties of monolayered hybrid
organic perovskites -- supported by temperature-dependent photoluminescence
measurements. Solving the Wannier equation, we obtain microscopic access to the
Rydberg-like series of excitonic states including their wavefunctions and
binding energies. Exploiting the generalized Elliot formula, we calculate the
photoluminescence spectra demonstrating a pronounced contribution of a phonon
sideband for temperatures up to 50 K -- in agreement with experimental
measurements. Finally, we predict temperature-dependent linewidths of the three
energetically lowest excitonic transitions and identify the underlying
phonon-driven scattering processes.
|
The ever-growing computational demands of increasingly complex machine
learning models frequently necessitate the use of powerful cloud-based
infrastructure for their training. Binary neural networks are known to be
promising candidates for on-device inference due to their extreme compute and
memory savings over higher-precision alternatives. However, their existing
training methods require the concurrent storage of high-precision activations
for all layers, generally making learning on memory-constrained devices
infeasible. In this article, we demonstrate that the backward propagation
operations needed for binary neural network training are strongly robust to
quantization, thereby making on-the-edge learning with modern models a
practical proposition. We introduce a low-cost binary neural network training
strategy exhibiting sizable memory footprint reductions while inducing little
to no accuracy loss vs Courbariaux & Bengio's standard approach. These
decreases are primarily enabled through the retention of activations
exclusively in binary format. Against the latter algorithm, our drop-in
replacement sees memory requirement reductions of 3--5$\times$, while reaching
similar test accuracy in comparable time, across a range of small-scale models
trained to classify popular datasets. We also demonstrate from-scratch ImageNet
training of binarized ResNet-18, achieving a 3.78$\times$ memory reduction. Our
work is open-source, and includes the Raspberry Pi-targeted prototype we used
to verify our modeled memory decreases and capture the associated energy drops.
Such savings will allow for unnecessary cloud offloading to be avoided,
reducing latency, increasing energy efficiency, and safeguarding end-user
privacy.
|
In this paper we set out general principles and develop geostatistical
methods for the analysis of data from spatio-temporally referenced prevalence
surveys. Our objective is to provide a tutorial guide that can be used in order
to identify parsimonious geostatistical models for prevalence mapping. A
general variogram-based Monte Carlo procedure is proposed to check the validity
of the modelling assumptions. We describe and contrast likelihood-based and
Bayesian methods of inference, showing how to account for parameter uncertainty
under each of the two paradigms. We also describe extensions of the standard
model for disease prevalence that can be used when stationarity of the
spatio-temporal covariance function is not supported by the data. We discuss
how to define predictive targets and argue that exceedance probabilities
provide one of the most effective ways to convey uncertainty in prevalence
estimates. We describe statistical software for the visualization of
spatio-temporal predictive summaries of prevalence through interactive
animations. Finally, we illustrate an application to historical malaria
prevalence data from 1334 surveys conducted in Senegal between 1905 and 2014.
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Producing synthetic voice, similar to human-like sound, is an emerging
novelty of modern interactive media systems. Text-To-Speech (TTS) systems try
to generate synthetic and authentic voices via text input. Besides, well known
and familiar dubbing, announcing and narrating voices, as valuable possessions
of any media organization, can be kept forever by utilizing TTS and Voice
Conversion (VC) algorithms . The emergence of deep learning approaches has made
such TTS systems more accurate and accessible. To understand TTS systems
better, this paper investigates the key components of such systems including
text analysis, acoustic modelling and vocoding. The paper then provides details
of important state-of-the-art TTS systems based on deep learning. Finally, a
comparison is made between recently released systems in term of backbone
architecture, type of input and conversion, vocoder used and subjective
assessment (MOS). Accordingly, Tacotron 2, Transformer TTS, WaveNet and
FastSpeech 1 are among the most successful TTS systems ever released. In the
discussion section, some suggestions are made to develop a TTS system with
regard to the intended application.
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The scaled factorial moments $F_q$ are studied for a second-order
quark-hadron phase transition within the Ginzburg-Landau description. The role
played by the ground state of the system under low temperature is emphasized.
After a local shift of the order parameter the fluctuations are around the
ground state, and a perturbative calculation for $F_q$ can be carried out.
Power scaling between $F_q$'s is shown, and a universal scaling exponent
$\nu\simeq 1.75$ is given for the case with weak correlations and weak
self-interactions.
|
Metaverse provides an alternative platform for human interaction in the
virtual world. Since virtual platform holds few restrictions in changing the
surrounding environments or the appearance of the avatars, it can serve as a
platform that reflects human thoughts or even dreams at least in the metaverse
world. When it is merged together with the current brain-computer interface
(BCI) technology, which enables system control via brain signals, a new
paradigm of human interaction through mind may be established in the metaverse
conditions. Recent BCI systems are aiming to provide user-friendly and
intuitive means of communication using brain signals. Imagined speech has
become an alternative neuro-paradigm for communicative BCI since it relies
directly on a person's speech production process, rather than using
speech-unrelated neural activity as the means of communication. In this paper,
we propose a brain-to-speech (BTS) system for real-world smart communication
using brain signals. Also, we show a demonstration of imagined speech based
smart home control through communication with a virtual assistant, which can be
one of the future applications of brain-metaverse system. We performed
pseudo-online analysis using imagined speech electroencephalography data of
nine subjects to investigate the potential use of virtual BTS system in the
real-world. Average accuracy of 46.54 % (chance level = 7.7 %) and 75.56 %
(chance level = 50 %) was acquired in the thirteen-class and binary
pseudo-online analysis, respectively. Our results support the potential of
imagined speech based smart communication to be applied in the metaverse world.
|
A new type of high-energy binary systems has been revealed by the INTEGRAL
satellite. These sources are in the course of being unveiled by means of
multi-wavelength optical, near- and mid-infrared observations. Among these
sources, two distinct classes are appearing: the first one is constituted of
intrinsically obscured high-energy sources, of which IGR J16318-4848 seems to
be the most extreme example. The second one is populated by the so-called
supergiant fast X-ray transients, with IGR J17544-2619 being the archetype. We
report here on multi-wavelength optical to mid-infrared observations of a
sample of 21 INTEGRAL sources. We show that in the case of the obscured sources
our observations suggest the presence of absorbing material (dust and/or cold
gas) enshrouding the whole binary system. We finally discuss the nature of
these two different types of sources, in the context of high energy binary
systems.
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Graphene is known to be non-superconducting. However, surprising
superconductivity is recently discovered in a flat-band in a twisted bi-layer
graphene. Here we show that superconductivity can be more easily realized in
topological flat-bands induced by strain in graphene through periodic ripples.
Specifically, it is shown that by including correlation effects, the chiral
d-wave superconductivity can be stabilized under strain even for slightly doped
graphene. The chiral d-wave superconductivity generally coexists with charge
density waves (CDW) and pair density waves (PDW) of the same period.
Remarkably, a pure PDW state with doubled period that coexists with the CDW
state is found to emerge at a finite temperature region under reasonable strain
strength. The emergent PDW state is shown to be superconducting with
non-vanishing superfluid density, and it realizes the long searched
superconducting states with non-vanishing center of mass momentum for Cooper
pairs.
|
We investigate the dineutron in the $2^+_1$ state of $^6$He via analysis of
its decay mode by using the complex scaling method. In this letter, we propose
the cross section for the resonant state to distinguish the resonant
contributions from the nonresonant ones. As the results, it is found that the
shoulder peak appears in the cross section for the resonant state as a function
of $\varepsilon_{n\text{-}n}$. Furthermore, we show that the $S$ = 0 component
of the cross section, where $S$ is the total spin of the valence two neutrons,
has a peak around the shoulder peak, which comes from the dineutron
configuration in the $2^+_1$ state. Thus we conclude that the shoulder peak is
expected to indicate the existence of the dineutron in the $2^+_1$ state.
|
Consider power utility maximization of terminal wealth in a 1-dimensional
continuous-time exponential Levy model with finite time horizon. We discretize
the model by restricting portfolio adjustments to an equidistant discrete time
grid. Under minimal assumptions we prove convergence of the optimal
discrete-time strategies to the continuous-time counterpart. In addition, we
provide and compare qualitative properties of the discrete-time and
continuous-time optimizers.
|
The combinatorial theory of rotor-routers has connections with problems of
statistical mechanics, graph theory, chaos theory, and computer science. A
rotor-router network defines a deterministic walk on a digraph G in which a
particle walks from a source vertex until it reaches one of several target
vertices. Motivated by recent results due to Giacaglia et al., we study
rotor-router networks in which all non-target vertices have the same type. A
rotor type r is universal if every hitting sequence can be achieved by a
homogeneous rotor-router network consisting entirely of rotors of type r. We
give a conjecture that completely classifies universal rotor types. Then, this
problem is simplified by a theorem we call the Reduction Theorem that allows us
to consider only two-state rotors. A rotor-router network called the
compressor, because it tends to shorten rotor periods, is introduced along with
an associated algorithm that determines the universality of almost all rotors.
New rotor classes, including boppy rotors, balanced rotors, and BURD rotors,
are defined to study this algorithm rigorously. Using the compressor the
universality of new rotor classes is proved, and empirical computer results are
presented to support our conclusions. Prior to these results, less than 100 of
the roughly 260,000 possible two-state rotor types of length up to 17 were
known to be universal, while the compressor algorithm proves the universality
of all but 272 of these rotor types.
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Let $A \subset \mathbb{Z}^d$ be a finite set. It is known that $NA$ has a
particular size ($\vert NA\vert = P_A(N)$ for some $P_A(X) \in \mathbb{Q}[X]$)
and structure (all of the lattice points in a cone other than certain
exceptional sets), once $N$ is larger than some threshold. In this article we
give the first effective upper bounds for this threshold for arbitrary $A$.
Such explicit results were only previously known in the special cases when
$d=1$, when the convex hull of $A$ is a simplex or when $\vert A\vert = d+2$,
results which we improve.
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We present a numerical study of multi-commodity transport in a noisy,
nonlinear network. The nonlinearity determines the dynamics of the edge
capacities, which can be amplified or suppressed depending on the local current
flowing across an edge. We consider network self-organization for three
different nonlinear functions: For all three we identify parameter regimes
where noise leads to self-organization into more robust topologies, that are
not found by the sole noiseless dynamics. Moreover, the interplay between noise
and specific functional behavior of the nonlinearity gives rise to different
features, such as (i) continuous or discontinuous responses to the demand
strength and (ii) either single or multi-stable solutions. Our study shows the
crucial role of the activation function on noise-assisted phenomena.
|
The chameleon model is a scalar field theory with a screening mechanism that
explains how a cosmologically relevant light scalar can avoid the constraints
of intra-solar-system searches for fifth-forces. The chameleon is a popular
dark energy candidate and also arises in $f(R)$ theories of gravity. Whilst the
chameleon is designed to avoid historical searches for fifth-forces it is not
unobservable and much effort has gone into identifying the best observables and
experiments to detect it. These results are not always presented for the same
models or in the same language, a particular problem when comparing
astrophysical and laboratory searches making it difficult to understand what
regions of parameter space remain. Here we present combined constraints on the
chameleon model from astrophysical and laboratory searches for the first time
and identify the remaining windows of parameter space. We discuss the
implications for cosmological chameleon searches and future small-scale probes.
|
The analysis of the CoRoT space mission data was performed aiming to test a
method that selects, among the several light curves observed, the transiting
systems that likely host a low-mass star orbiting the main target. The method
identifies stellar companions by fitting a model to the observed transits.
Applying this model, that uses equations like Kepler's third law and an
empirical mass-radius relation, it is possible to estimate the mass and radius
of the primary and secondary objects as well as the semimajor axis and
inclination angle of the orbit. We focus on how the method can be used in the
characterisation of transiting systems having a low-mass stellar companion with
no need to be monitored with radial-velocity measurements or ground-based
photometric observations. The model, which provides a good estimate of the
system parameters, is also useful as a complementary approach to select
possible planetary candidates. A list of confirmed binaries together with our
estimate of their parameters are presented. The characterisation of the first
twelve detected CoRoT exoplanetary systems was also performed and agrees very
well with the results of their respective announcement papers. The comparison
with confirmed systems validates our method, specially when the radius of the
secondary companion is smaller than 1.5 Rjup, in the case of planets, or larger
than 2 Rjup, in the case of low-mass stars. Intermediate situations are not
conclusive.
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We examine groups whose resonance varieties, characteristic varieties and
Sigma-invariants have a natural arithmetic group symmetry, and we explore
implications on various finiteness properties of subgroups. We compute
resonance varieties, characteristic varieties and Alexander polynomials of
Torelli groups, and we show that all subgroups containing the Johnson kernel
have finite first Betti number, when the genus is at least four. We also prove
that, in this range, the $I$-adic completion of the Alexander invariant is
finite-dimensional, and the Kahler property for the Torelli group implies the
finite generation of the Johnson kernel.
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Computing the size of maximum independent sets is a NP-hard problem for fixed
graphs. Characterizing and designing efficient algorithms to estimate this
independence number for random graphs are notoriously difficult and still
largely open issues. In a companion paper, we showed that a low complexity
degree-greedy exploration is actually asymptotically optimal on a large class
of sparse random graphs. Encouraged by this result, we present and study two
variants of sequential exploration algorithms: static and dynamic degree-aware
explorations. We derive hydrodynamic limits for both of them, which in turn
allow us to compute the size of the resulting independent set. Whereas the
former is simpler to compute, the latter may be used to arbitrarily approximate
the degree-greedy algorithm. Both can be implemented in a distributed manner.
The corresponding hydrodynamic limits constitute an efficient method to compute
or bound the independence number for a large class of sparse random graphs. As
an application, we then show how our method may be used to estimate the
capacity of a large 802.11-based wireless network. We finally consider further
indicators such as the fairness of the resulting configuration, and show how an
unexpected trade-off between fairness and capacity can be achieved.
|
I argue that measurements of Au+Au collisions at 20, 130 and 200 GeV of the
centrality dependence of the mean p_t together with p_t and net-charge
fluctuations reflect the approach to local thermal equilibrium.
|
We perform an updated model-independent analysis using all the latest solar
neutrino data, including the one coming from remarkably high statistics
SuperKamiokande experiment. We confirm that the astrophysical solutions to the
solar neutrino problem are extremely disfavored. We also present a new way of
illuminating the suppression pattern of various solar neutrino flux, which
indicates that the strong suppression of $^{7}$Be neutrinos is no more true
once the neutrino flavor conversion is taken into account.
|
A sensor network is a collection of wireless devices that are able to monitor
physical or environmental conditions. These devices (nodes) are expected to
operate autonomously, be battery powered and have very limited computational
capabilities. This makes the task of protecting a sensor network against
misbehavior or possible malfunction a challenging problem. In this document we
discuss performance of Artificial immune systems (AIS) when used as the
mechanism for detecting misbehavior.
We show that (i) mechanism of the AIS have to be carefully applied in order
to avoid security weaknesses, (ii) the choice of genes and their interaction
have a profound influence on the performance of the AIS, (iii) randomly created
detectors do not comply with limitations imposed by communications protocols
and (iv) the data traffic pattern seems not to impact significantly the overall
performance.
We identified a specific MAC layer based gene that showed to be especially
useful for detection; genes measure a network's performance from a node's
viewpoint. Furthermore, we identified an interesting complementarity property
of genes; this property exploits the local nature of sensor networks and moves
the burden of excessive communication from normally behaving nodes to
misbehaving nodes. These results have a direct impact on the design of AIS for
sensor networks and on engineering of sensor networks.
|
In a recent paper [Phys. Rev. Lett. 125, 043201 (2020)] (Ref.1) Liao et al.
propose a theory of the interferometric photoemission delay based on the
concepts of the photoelectron phase and photoelectron effective mass. The
present comment discusses the applicability and limitations of the proposed
approach based on an ab initio analysis supported by vast literature. Two
central assumptions of the paper are questioned, namely that the photoelectron
can be characterized by a phase (have a well-defined phase velocity), and that
it can always be ascribed an effective mass Theories based on these concepts
are concluded to be inapplicable to real solids, which is illustrated by the
example of the system addressed in Ref. 1. That the basic assumptions of the
theory are never fulfilled in nature discredits the underlying idea of the
"time-domain interferometric solid-state energy-momentum-dispersion imaging
method" suggested in Ref. 1. Apart from providing a necessary caution to
experimentalists, the present comment also gives an insight into the
photoelectron wave function and points out problems and pitfalls inherent in
modeling real crystals.
|
A random chaotic interval map with noise which causes coarse-graining induces
a finite-state Markov chain. For a map topologically conjugate to a
piecewise-linear map with the Lebesgue measure being ergodic, we prove that the
Shannon entropy for the induced Markov chain possesses a finite limit as the
noise level tends to zero. In most cases, the limit turns out to be strictly
greater than the Lyapunov exponent of the original map without noise.
|
We compare the observed probability distribution function of the transmission
in the \HI\ Lyman-alpha forest, measured from the UVES 'Large Programme' sample
at redshifts z=[2,2.5,3], to results from the GIMIC cosmological simulations.
Our measured values for the mean transmission and its PDF are in good agreement
with published results. Errors on statistics measured from high-resolution data
are typically estimated using bootstrap or jack-knife resampling techniques
after splitting the spectra into chunks. We demonstrate that these methods tend
to underestimate the sample variance unless the chunk size is much larger than
is commonly the case. We therefore estimate the sample variance from the
simulations. We conclude that observed and simulated transmission statistics
are in good agreement, in particular, we do not require the temperature-density
relation to be 'inverted'.
|
This work deals with two pressing issues in the design and operation of
Josephson qubits -- loss of coherence and measurement. (Longer abstract follows
in the work.)
|
We construct a new cylinder object for semifree differential graded (dg)
categories in the category of dg categories. Using this, we give a practical
formula computing homotopy colimits of semifree dg categories. Combining it
with the result of Ganatra, Pardon, and Shende, we get a formula computing
wrapped Fukaya categories of Weinstein manifolds using their sectorial
coverings. This formula has lots of applications including a practical
computation of the wrapped Fukaya category of any cotangent bundle or plumbing
space. In this paper, we compute wrapped Fukaya categories of cotangent bundles
of lens spaces using their Heegaard decomposition. From the computation, we
show that the endomorphism algebra of the cotangent fibre is a full invariant
of the homotopy type of lens spaces.
|
Most approaches for goal recognition rely on specifications of the possible
dynamics of the actor in the environment when pursuing a goal. These
specifications suffer from two key issues. First, encoding these dynamics
requires careful design by a domain expert, which is often not robust to noise
at recognition time. Second, existing approaches often need costly real-time
computations to reason about the likelihood of each potential goal. In this
paper, we develop a framework that combines model-free reinforcement learning
and goal recognition to alleviate the need for careful, manual domain design,
and the need for costly online executions. This framework consists of two main
stages: Offline learning of policies or utility functions for each potential
goal, and online inference. We provide a first instance of this framework using
tabular Q-learning for the learning stage, as well as three measures that can
be used to perform the inference stage. The resulting instantiation achieves
state-of-the-art performance against goal recognizers on standard evaluation
domains and superior performance in noisy environments.
|
The relationship between bipolar magnetic regions (BMRs) and their sunspots
is an important property of the solar magnetic field, but it is not well
constrained. One consequence is that it is a challenge for surface flux
transport models (SFTMs) based on sunspot observations to determine the details
of BMR emergence, which they require as input, from such data. We aimed to
establish the relationship between the amount of magnetic flux in newly emerged
BMRs and the area of the enclosed sunspots. Earlier attempts to constrain BMR
magnetic flux were hindered by the fact that there is no proper database of the
magnetic and physical properties of newly emerged BMRs currently available. We
made use of the empirical model of the relationship between the disc-integrated
facular and network magnetic flux and the total surface coverage by sunspots
reported in a recent study. The structure of the model is such that it enabled
us to establish, from these disc-integrated quantities, an empirical
relationship between the magnetic flux and sunspot area of individual newly
emerged BMRs, circumventing the lack of any proper BMR database. Applying the
constraint on BMR magnetic flux derived here to an established SFTM retained
its ability to replicate various independent datasets and the correlation
between the model output polar field at the end of each cycle and the observed
strength of the following cycle. The SFTM output indicates that facular and
network magnetic flux rises with increasing sunspot magnetic flux at a slowing
rate such that it appears to gradually saturate, analogous to earlier studies.
The activity dependence of the ratio of facular and network flux to sunspot
flux is consistent with the findings of recent studies: although the Sun is
faculae-dominated, it is only marginally so as facular and network brightening
and sunspot darkening appear to be closely balanced.
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This is a survey paper on algorithms that have been developed during the last
25 years for the explicit computation of the structure of an associative
algebra of finite dimension over either a finite field or an algebraic number
field. This constructive approach was initiated in 1985 by Friedl and Ronyai
and has since been developed by Cohen, de Graaf, Eberly, Giesbrecht, Ivanyos,
Kuronya and Wales. I illustrate these algorithms with the case n = 2 of the
rational semigroup algebra of the partial transformation semigroup PT_n on n
elements; this generalizes the full transformation semigroup and the symmetric
inverse semigroup, and these generalize the symmetric group S_n.
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We detected the formation of p-wave Feshbach molecules for all three
combinations of the two lowest atomic spin states of 6Li. By creating a pure
molecular sample in an optical trap, we measured the inelastic collision rates
of p-wave molecules. The elastic collision rate was measured from the
thermalization rate of a breathing mode excited spontaneously upon molecular
formation.
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Using a simple tight-binding model, we compare the limitations of the
tunnelling predictions coming out of the complex band structure of a
semiconductor with the output of thin film calculations done for the same
semiconducting spacer but considering it to be of finite width, and sandwiched
by metallic electrodes. The comparison is made as a function of spacer width
and interfacial roughness.
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This paper presents two models that exemplify psychological factors as a
determinant and as a consequence of social network characteristics. There is an
endogeneity considered in network formation: while the social experiences have
impacts on people, their current psychological states and traits affect network
evolution. The first model is an agent-based model over Bianconi-Barabasi
networks, used to explain the relation between network size, extroversion, and
age of individuals. The second model deals with the emergence of urban tribes
as a consequence of a smaller propensity to communicate with different with
different traits and opinions.
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The solar atmosphere is filled with clusters of hot small-scale loops
commonly known as Coronal Bright Points (CBPs). These ubiquitous structures
stand out in the Sun by their strong X-ray and/or extreme-ultraviolet (EUV)
emission for hours to days, which makes them a crucial piece when solving the
solar coronal heating puzzle. In addition, they can be the source of coronal
jets and small-scale filament eruptions. Here we present a novel 3D numerical
model using the Bifrost code that explains the sustained CBP heating for
several hours. We find that stochastic photospheric convective motions alone
significantly stress the CBP magnetic field topology, leading to important
Joule and viscous heating concentrated around the CBP's inner spine at a few
megameters above the solar surface. We also detect continuous upflows with
faint EUV signal resembling observational dark coronal jets and small-scale
eruptions when H$_{\alpha}$ fibrils interact with the reconnection site. We
validate our model by comparing simultaneous CBP observations from SDO and SST
with observable diagnostics calculated from the numerical results for EUV
wavelengths as well as for the H$_{\alpha}$ line using the Multi3D synthesis
code. Additionally, we provide synthetic observables to be compared with
Hinode, Solar Orbiter, and IRIS. Our results constitute a step forward in the
understanding of the many different facets of the solar coronal heating
problem.
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Aims. We present a multiwavelength study of the Be/X-ray binary system SAX
J2103.5+4545 with the goal of better characterizing the transient behaviour of
this source.
Methods. SAX J2103.5+4545 was observed by Swift-XRT four times in 2007 from
April 25 to May 5, and during quiescence in 2012 August 31. In addition, this
source has been monitored from the ground-based astronomical observatories of
El Teide (Tenerife, Spain), Roque de los Muchachos (La Palma, Spain) and Sierra
Nevada (Granada, Spain) since 2011 August, and from the TUBITAK National
Observatory (Antalya, Turkey) since 2009 June. We have performed spectral and
photometric temporal analyses in order to investigate the different states
exhibited by SAX J2103.5+4545.
Results. In X-rays, an absorbed power law model provided the best fit for all
the XRT spectra. An iron-line feature at ~6.42 keV was present in all the
observations except for that taken during quiescence in 2012. The photon
indexes are consistent with previous studies of SAX J2103.5+4545 in high/low
luminosity states. Pulsations were found in all the XRT data from 2007
(2.839(2) mHz; MJD 54222.02), but not during quiescence. Both optical outbursts
in 2010 and 2012 lasted for about 8/9 months (as the one in 2007 probably did
and the current one in 2014 might do) and were most probably caused by mass
ejection events from the Be star that eventually fed the circumstellar disc.
All of these outbursts started about 3 months before the triggering of the
X-ray activity, and about the same period before the maximum of the H_alpha
line equivalent width (in emission) was reached at only ~ -5 \AA. In this work
we found that the global correlation between the BV variability and the X-ray
intensity was also observed at longer wavelengths in the IR domain.
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The linear amplification of disturbances is critical in setting up transition
scenarios in viscoelastic channel and Couette flow, and may also play an
important role when such flows are fully turbulent. As such, it is of interest
to assess how this amplification, defined as the steady-state variance
maintained under Gaussian white noise forcing, scales with the main
nondimensional parameters: the Reynolds ($Re$) and Weissenberg ($Wi$) numbers.
This scaling is derived analytically in the two limits of strong and weak
elasticity for when the forcing is streamwise-constant. The latter is the
relevant forcing for capturing the overall behaviour because it was previously
shown to have the dominant contribution to amplification. The final expressions
show that for weak elasticity the scaling retains a form similar to the
well-known O($Re^3$) relationship with an added elastic correction. For strong
elasticity, however, the scaling is O($Wi^3$) with a viscous correction. The
key factor leading to such a mirroring in the scaling is the introduction of
forcing in the polymer stress. The results demonstrate that energy
amplification in a viscoelastic flow can be very sensitive to the model
parameters even at low $Re$. They also suggest that energy amplification can be
significantly increased by forcing the polymer stress, thereby opening up
possibilities such as flow control using systematically designed polymer stress
perturbations.
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Solitary stars that wander too close to their galactic centres can become
tidally disrupted, if the tidal forces due to the supermassive black hole
(SMBH) residing there overcome the self-gravity of the star. If the star is
only partially disrupted, so that a fraction survives as a self-bound object,
this remaining core will experience a net gain in specific orbital energy,
which translates into a velocity "kick" of up to $\sim 10^3$ km/s. In this
paper, we present the result of smoothed particle hydrodynamics (SPH)
simulations of such partial disruptions, and analyse the velocity kick imparted
on the surviving core. We compare $\gamma$ = 5/3 and $\gamma$ = 4/3 polytropes
disrupted in both a Newtonian potential, and a generalized potential that
reproduces most relativistic effects around a Schwarzschild black hole either
exactly or to excellent precision. For the Newtonian case, we confirm the
results of previous studies that the kick velocity of the surviving core is
virtually independent of the ratio of the black hole to stellar mass, and is a
function of the impact parameter $\beta$ alone, reaching at most the escape
velocity of the original star. For a given $\beta$, relativistic effects become
increasingly important for larger black hole masses. In particular, we find
that the kick velocity increases with the black hole mass, making larger kicks
more common than in the Newtonian case, as low-$\beta$ encounters are
statistically more likely than high-$\beta$ encounters. The analysis of the
tidal tensor for the generalized potential shows that our results are robust
lower limits on the true relativistic kick velocities, and are generally in
very good agreement with the exact results.
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Off-axis electron holography can measure the mean inner electric potential of
materials. The theory of hole superconductivity predicts that when a material
is cooled into the superconducting state it expels electrons from its interior
to the surface, giving rise to a mean inner potential that increases with
sample thickness. Instead, in a normal metal and in a conventional BCS
superconductor the mean inner potential is expected to be independent of sample
thickness and temperature. Thus, this experiment can provide a definitive test
of the validity of the theory of hole superconductivity.
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Berry phases and quantum fidelities for interacting spins have attracted
considerable attention, in particular in relation to entanglement properties of
spin systems and quantum phase transitions. These efforts mainly focus either
on spin pairs or the thermodynamic infinite spin limit, while studies of the
multipartite case of a finite number of spins are rare. Here, we analyze Berry
phases and quantum fidelities of the energetic ground state of a
Lipkin-Meshkov-Glick (LMG) model consisting of three spin-1/2 particles
(qubits). We find explicit expressions for the Berry phase and fidelity
susceptibility of the full system as well as the mixed state Berry phase and
partial-state fidelity susceptibility of its one- and two-qubit subsystems. We
demonstrate a realization of a nontrivial magnetic monopole structure
associated with local, coordinated rotations of the three-qubit system around
the external magnetic field.
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The Bershadsky-Polyakov algebras are the minimal quantum hamiltonian
reductions of the affine vertex algebras associated to $\mathfrak{sl}_3$ and
their simple quotients have a long history of applications in conformal field
theory and string theory. Their representation theories are therefore quite
interesting. Here, we classify the simple relaxed highest-weight modules, with
finite-dimensional weight spaces, for all admissible but nonintegral levels,
significantly generalising the known highest-weight classifications
[arxiv:1005.0185, arxiv:1910.13781]. In particular, we prove that the simple
Bershadsky-Polyakov algebras with admissible nonintegral $\mathsf{k}$ are
always rational in category $\mathscr{O}$, whilst they always admit
nonsemisimple relaxed highest-weight modules unless $\mathsf{k}+\frac{3}{2} \in
\mathbb{Z}_{\ge0}$.
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The electrical energy system has attracted much attention from an
increasingly diverse research community. Many theoretical predictions have been
made, from scaling laws of fluctuations to propagation velocities of
disturbances. However, to validate any theory, empirical data from large-scale
power systems are necessary but are rarely shared openly. Here, we analyse an
open data base of measurements of electric power grid frequencies across 17
locations in 12 synchronous areas on three continents. The power grid frequency
is of particular interest, as it indicates the balance of supply and demand and
carries information on deterministic, stochastic, and control influences. We
perform a broad analysis of the recorded data, compare different synchronous
areas and validate a previously conjectured scaling law. Furthermore, we show
how fluctuations change from local independent oscillations to a homogeneous
bulk behaviour. Overall, the presented open data base and analyses constitute a
step towards more shared, collaborative
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Different methods have been recently put forward and implemented
experimentally to inverse engineer the time dependent Hamiltonian of a quantum
system and accelerate slow adiabatic processes via non-adiabatic shortcuts. In
the "transitionless tracking algorithm" proposed by Berry, shortcut
Hamiltonians are designed so that the system follows exactly, in an arbitrarily
short time, the approximate adiabatic path defined by a reference Hamiltonian.
A different approach is based on designing first a Lewis-Riesenfeld invariant
to carry the eigenstates of a Hamiltonian from specified initial to final
configurations, again in an arbitrary time, and then constructing from the
invariant the transient Hamiltonian connecting these boundary configurations.
We show that the two approaches, apparently quite different in form and so far
in results, are in fact strongly related and potentially equivalent, so that
the inverse-engineering operations in one of them can be reinterpreted and
understood in terms of the concepts and operations of the other one. We study
as explicit examples the expansions of time-dependent harmonic traps and state
preparation of two level systems.
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Recommender systems aim to predict user interest based on historical
behavioral data. They are mainly designed in sequential pipelines, requiring
lots of data to train different sub-systems, and are hard to scale to new
domains. Recently, Large Language Models (LLMs) have demonstrated remarkable
generalized capabilities, enabling a singular model to tackle diverse
recommendation tasks across various scenarios. Nonetheless, existing LLM-based
recommendation systems utilize LLM purely for a single task of the
recommendation pipeline. Besides, these systems face challenges in presenting
large-scale item sets to LLMs in natural language format, due to the constraint
of input length. To address these challenges, we introduce an LLM-based
end-to-end recommendation framework: UniLLMRec. Specifically, UniLLMRec
integrates multi-stage tasks (e.g. recall, ranking, re-ranking) via
chain-of-recommendations. To deal with large-scale items, we propose a novel
strategy to structure all items into an item tree, which can be dynamically
updated and effectively retrieved. UniLLMRec shows promising zero-shot results
in comparison with conventional supervised models. Additionally, it boasts high
efficiency, reducing the input token need by 86% compared to existing LLM-based
models. Such efficiency not only accelerates task completion but also optimizes
resource utilization. To facilitate model understanding and to ensure
reproducibility, we have made our code publicly available.
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Object oriented constraint programs (OOCPs) emerge as a leading evolution of
constraint programming and artificial intelligence, first applied to a range of
industrial applications called configuration problems. The rich variety of
technical approaches to solving configuration problems (CLP(FD), CC(FD), DCSP,
Terminological systems, constraint programs with set variables ...) is a source
of difficulty. No universally accepted formal language exists for communicating
about OOCPs, which makes the comparison of systems difficult. We present here a
Z based specification of OOCPs which avoids the falltrap of hidden object
semantics. The object system is part of the specification, and captures all of
the most advanced notions from the object oriented modeling standard UML. The
paper illustrates these issues and the conciseness and precision of Z by the
specification of a working OOCP that solves an historical AI problem : parsing
a context free grammar. Being written in Z, an OOCP specification also supports
formal proofs. The whole builds the foundation of an adaptative and evolving
framework for communicating about constrained object models and programs.
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In this paper, we present a Riemannian Motion Policy (RMP)flow-based
whole-body control framework for improved dynamic legged locomotion. RMPflow is
a differential geometry-inspired algorithm for fusing multiple task-space
policies (RMPs) into a configuration space policy in a geometrically consistent
manner. RMP-based approaches are especially suited for designing simultaneous
tracking and collision avoidance behaviors and have been successfully deployed
on serial manipulators. However, one caveat of RMPflow is that it is designed
with fully actuated systems in mind. In this work, we, for the first time,
extend it to the domain of dynamic-legged systems, which have unforgiving
under-actuation and limited control input. Thorough push recovery experiments
are conducted in simulation to validate the overall framework. We show that
expanding the valid stepping region with an RMP-based collision-avoidance swing
leg controller improves balance robustness against external disturbances by up
to 53\% compared to a baseline approach using a restricted stepping region.
Furthermore, a point-foot biped robot is purpose-built for experimental studies
of dynamic biped locomotion. A preliminary unassisted in-place stepping
experiment is conducted to show the viability of the control framework and
hardware.
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Recent results on optimization and generalization properties of neural
networks showed that in a simple two-layer network, the alignment of the labels
to the eigenvectors of the corresponding Gram matrix determines the convergence
of the optimization during training. Such analyses also provide upper bounds on
the generalization error. We experimentally investigate the implications of
these results to deeper networks via embeddings. We regard the layers preceding
the final hidden layer as producing different representations of the input data
which are then fed to the two-layer model. We show that these representations
improve both optimization and generalization. In particular, we investigate
three kernel representations when fed to the final hidden layer: the Gaussian
kernel and its approximation by random Fourier features, kernels designed to
imitate representations produced by neural networks and finally an optimal
kernel designed to align the data with target labels. The approximated
representations induced by these kernels are fed to the neural network and the
optimization and generalization properties of the final model are evaluated and
compared.
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Metadevices based on dielectric nanostructured surfaces with both electric
and magnetic Mie-type resonances have resulted in the best efficiency to date
for functional flat optics with only one disadvantage: a narrow operational
bandwidth. Here we experimentally demonstrate broadband transparent
all-dielectric metasurfaces for highly efficient polarization manipulation. We
utilize the generalized Huygens principle, with a superposition of the
scattering contributions from several electric and magnetic multipolar modes of
the constituent meta-atoms, to achieve destructive interference in reflection
over a large spectral bandwidth. By employing this novel concept, we
demonstrate reflectionless (~90% transmission) half-wave plates, quarter-wave
plates, and vector beam q-plates that can operate across multiple telecom bands
with ~99% polarization conversion efficiency.
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We show that media with inhomogeneous defocusing cubic nonlinearity growing
toward the periphery can support a variety of stable vortex clusters nested in
a common localized envelope. Nonrotating symmetric clusters are built of an
even number of vortices with opposite topological charges, located at equal
distances from the origin. Rotation makes the clusters strongly asymmetric, as
the centrifugal force shifts some vortices to the periphery, while others
approach the origin, depending on the topological charge. We obtain such
asymmetric clusters as stationary states in the rotating coordinate frame,
identify their existence domains, and show that the rotation may stabilize some
of them.
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Three postulates asserting the validity of conventional quantum theory,
semi-classical general relativity and the statistical basis for thermodynamics
are introduced as a foundation for the study of black hole evolution. We
explain how these postulates may be implemented in a ``stretched horizon'' or
membrane description of the black hole, appropriate to a distant observer. The
technical analysis is illustrated in the simplified context of 1+1 dimensional
dilaton gravity. Our postulates imply that the dissipative properties of the
stretched horizon arise from a course graining of microphysical degrees of
freedom that the horizon must possess. A principle of black hole
complementarity is advocated. The overall viewpoint is similar to that
pioneered by 't~Hooft but the detailed implementation is different.
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Learning how to adapt to complex and dynamic environments is one of the most
important factors that contribute to our intelligence. Endowing artificial
agents with this ability is not a simple task, particularly in competitive
scenarios. In this paper, we present a broad study on how popular reinforcement
learning algorithms can be adapted and implemented to learn and to play a
real-world implementation of a competitive multiplayer card game. We propose
specific training and validation routines for the learning agents, in order to
evaluate how the agents learn to be competitive and explain how they adapt to
each others' playing style. Finally, we pinpoint how the behavior of each agent
derives from their learning style and create a baseline for future research on
this scenario.
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We study a d-wave superconductor with dominant $d_{x^2-y^2}$-wave order
parameter and subdominant pairing in either the $s$- or the $d_{xy}$-wave
channel near a surface. In particular we analyze the influence of surface
roughness on the mixed order parameter which may break the time-reversal
symmetry. We find that the subdominant component is suppressed by the roughness
independent of its pairing symmetry; for very rough surfaces the subdominant
component may even vanish completely. Additionally we discuss a possible
real-valued admixture which counteracts the suppression of the
$d_{x^2-y^2}$-wave order parameter at the surface.
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A simple geometrical model is presented for the gravity-driven motion of a
single particle on a rough inclined surface. Adopting a simple restitution law
for the collisions between the particle and the surface, we arrive at a model
in which the dynamics is described by a one-dimensional map. This map is
studied in detail and it is shown to exhibit several dynamical regimes (steady
state, chaotic behavior, and accelerated motion) as the model parameters vary.
A phase diagram showing the corresponding domain of existence for these regimes
is presented. The model is also found to be in good qualitative agreement with
recent experiments on a ball moving on a rough inclined line.
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WebAssembly is a compilation target for cross-platform applications that is
increasingly being used. In this paper, we investigate whether one can
transparently cross-compile C programs to WebAssembly, and if not, what impact
porting can have on their security. We compile 17,802 programs that exhibit
common vulnerabilities to 64-bit x86 and to WebAssembly binaries, and we
observe that the execution of 4,911 binaries produces different results across
these platforms. Through manual inspection, we identify three classes of root
causes for such differences: the use of a different standard library
implementation, the lack of security measures in WebAssembly, and the different
semantics of the execution environments. We describe our observations and
discuss the ones that are critical from a security point of view and need most
attention from developers. We conclude that compiling an existing C program to
WebAssembly for cross-platform distribution may require source code
adaptations; otherwise, the security of the WebAssembly application may be at
risk.
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In the mobile communication services, users wish to subscribe to high quality
service with a low price level, which leads to competition between mobile
network operators (MNOs). The MNOs compete with each other by service prices
after deciding the extent of investment to improve quality of service (QoS).
Unfortunately, the theoretic backgrounds of price dynamics are not known to us,
and as a result, effective network planning and regulative actions are hard to
make in the competitive market. To explain this competition more detail, we
formulate and solve an optimization problem applying the two-stage Cournot and
Bertrand competition model. Consequently, we derive a price dynamics that the
MNOs increase and decrease their service prices periodically, which completely
explains the subsidy dynamics in the real world. Moving forward, to avoid this
instability and inefficiency, we suggest a simple regulation rule which leads
to a Pareto-optimal equilibrium point. Moreover, we suggest regulator's optimal
actions corresponding to user welfare and the regulator's revenue.
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We simultaneously and successfully fit the multi-epoch X-ray spectra of the
tidal disruption event (TDE) 3XMM J215022.4-055108 using a modified version of
our relativistic slim disk model that now accounts for angular momentum losses
from radiation. We explore the effects of different disk properties and of
uncertainties in the spectral hardening factor fc and redshift z on the
estimation of the black hole mass M and spin a. Across all choices of
theoretical priors, we constrain M to less than 2.2e4 Ms at 1 sigma confidence.
Assuming that the TDE host is a star cluster associated with the adjacent,
brighter, barred lenticular galaxy at z=0.055, we constrain M and a to be
(1.75+0.45-0.05)e4 Ms and 0.8+0.12-0.02, respectively, at 1 sigma confidence.
The high, but sub-extremal, spin suggests that, if this intermediate mass black
hole (IMBH) has grown significantly since formation, it has acquired its last
e-fold in mass in a way incompatible with both the standard and chaotic limits
of gas accretion. Ours is the first clear IMBH with a spin measurement. As
such, this object represents a novel laboratory for astro-particle physics; its
M and a place tight limits on the existence of ultralight bosons, ruling out
those with masses 1.0e-15 to 1.0e-16 eV.
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