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Topological connections in the single-streaming voids and multistreaming filaments and walls reveal a cosmic web structure different from traditional mass density fields. A single void structure not only percolates the multistream field in all the directions, but also occupies over 99 per cent of all the single-streaming regions. Sub-grid analyses on scales smaller than simulation resolution reveal tiny pockets of voids that are isolated by membranes of the structure. For the multistreaming excursion sets, the percolating structure is significantly thinner than the filaments in over-density excursion approach. Hessian eigenvalues of the multistream field are used as local geometrical indicators of dark matter structures. Single-streaming regions have most of the zero eigenvalues. Parameter-free conditions on the eigenvalues in the multistream region may be used to delineate primitive geometries with concavities corresponding to filaments, walls and haloes.
Network embedding, which maps graphs to distributed representations, is a unified framework for various graph inference tasks. According to the topology properties (e.g., structural roles and community memberships of nodes) to be preserved, it can be categorized into the identity and position embedding. However, existing methods can only capture one type of property. Some approaches can support the inductive inference that generalizes the embedding model to new nodes or graphs but relies on the availability of attributes. Due to the complicated correlations between topology and attributes, it is unclear for some inductive methods which type of property they can capture. In this study, we explore a unified framework for the joint inductive inference of identity and position embeddings without attributes. An inductive random walk embedding (IRWE) method is proposed, which combines multiple attention units to handle the random walk on graph topology and simultaneously derives identity and position embeddings that are jointly optimized. In particular, we demonstrate that some random walk statistics can be informative features to characterize node identities and positions while supporting the inductive embedding inference. Experiments validate the superior performance of IRWE beyond various baselines for the transductive and inductive inference of identity and position embeddings.
As of today, the main business application of onomastics is naming, or branding: finding the proper name for your company or your product to stand out in the world. Meaningfully, Onoma, the Greek root for name, is also a registered trademark of Nomen, the naming agency founded by Marcel Botton in 1981. Nomen initially licensed one of Roland Moreno's inventions, the Radoteur name generator, and created many distinctive and global brand names such as: Vinci, Clio or Amundi. But once your business has a name, should you forget about onomastics? Not anymore. Globalization, digitalization and the Big Data open new fields to experiment disruptive applications in Sales and Marketing, Communication, HR and Risk Management. Though discriminating names carries a high risk of abuse, it can also drive new, unexpected ways for developing poor areas.
We present a novel deep learning-based framework: Embedded Feature Similarity Optimization with Specific Parameter Initialization (SOPI) for 2D/3D medical image registration which is a most challenging problem due to the difficulty such as dimensional mismatch, heavy computation load and lack of golden evaluation standard. The framework we design includes a parameter specification module to efficiently choose initialization pose parameter and a fine-registration module to align images. The proposed framework takes extracting multi-scale features into consideration using a novel composite connection encoder with special training techniques. We compare the method with both learning-based methods and optimization-based methods on a in-house CT/X-ray dataset as well as simulated data to further evaluate performance. Our experiments demonstrate that the method in this paper has improved the registration performance, and thereby outperforms the existing methods in terms of accuracy and running time. We also show the potential of the proposed method as an initial pose estimator. The code is available at https://github.com/m1nhengChen/SOPI
A restless multi-armed bandit problem that arises in multichannel opportunistic communications is considered, where channels are modeled as independent and identical Gilbert-Elliot channels and channel state observations are subject to errors. A simple structure of the myopic policy is established under a certain condition on the false alarm probability of the channel state detector. It is shown that the myopic policy has a semi-universal structure that reduces channel selection to a simple round-robin procedure and obviates the need to know the underlying Markov transition probabilities. The optimality of the myopic policy is proved for the case of two channels and conjectured for the general case based on numerical examples.
Local maxima of random processes are useful for finding important regions and are routinely used, for summarising features of interest (e.g. in neuroimaging). In this work we provide confidence regions for the location of local maxima of the mean and standardized effect size (i.e. Cohen's d) given multiple realisations of a random process. We prove central limit theorems for the location of the maximum of mean and t-statistic random fields and use these to provide asymptotic confidence regions for the location of peaks of the mean and Cohen's d. Under the assumption of stationarity we develop Monte Carlo confidence regions for the location of peaks of the mean that have better finite sample coverage than regions derived based on classical asymptotic normality. We illustrate our methods on 1D MEG data and 2D fMRI data from the UK Biobank.
A recent preprint [arxiv:1807.08572] has reported the observation of room temperature supercondutivity in a nanostructured solid composed of gold and silver nanocrystals. Given the extraordinary and exciting nature of this claim, it is worth examining the reported data closely. In this short comment I point out a very surprising feature in the data: an identical pattern of noise for two presumably independent measurements of the magnetic susceptibility as a function of temperature.
Working within the post-Minkowskian approach to General Relativity, we prove that the radiation-reaction to the emission of gravitational waves during the large-impact-parameter scattering of two (classical) point masses modifies the conservative scattering angle by an additional contribution of order $G^3$ which involves a high-energy (or massless) logarithmic divergence of opposite sign to the one contained in the third-post-Minkowskian result of Bern et al. [Phys. Rev. Lett. {\bf 122}, 201603 (2019)]. The high-energy limit of the resulting radiation-reaction-corrected (classical) scattering angle is finite, and is found to agree with the one following from the (quantum) eikonal-phase result of Amati, Ciafaloni and Veneziano [ Nucl. Phys. B {\bf 347}, 550 (1990)].
We consider the properties of the ground state of bottomium. The $\Upsilon$ mass is evaluated to two loops, and including leading higher order [$O(\alpha_s^5\log\alpha_s)$] and $m_c^2/m_b^2$ corrections. This allows us to present updated values for the pole mass and $\bar{MS}$ mass of the $b$ quark: $m_b=5022\pm58$ MeV, for the pole mass, and $\bar{m}_b(\bar{m}_b)=4286\pm36$ MeV for the $\bar{MS}$ one. The value for the \msbar mass is accurate including and $O(\alpha_s^3)$ corrections and leading orders in the ratio $m_c^2/m_b^2$. We then consider the wave function for the ground state of $\bar{b}b$, which is calculated to two loops in the nonrelativistic approximation. Taking into account the evaluation of the matching coefficients by Beneke and Signer one can calculate, in principle, the width for the decay $\Upsilon\to e^+e^-$ to order $\alpha_s^5$. Unfortunately, given the size of the corrections it is impossible to produce reliable numbers. The situation is slightly better for the ground state of toponium, where a decay width into $e^+e^-$ of 11 -- 14 keV is predicted.
Brain tumor segmentation is a critical task for patient's disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly with deep supervision and stochastic weight averaging, on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. Two independent ensembles of models from two different training pipelines were trained, and each produced a brain tumor segmentation map. These two labelmaps per patient were then merged, taking into account the performance of each ensemble for specific tumor subregions. Our performance on the online validation dataset with test time augmentation were as follows: Dice of 0.81, 0.91 and 0.85; Hausdorff (95%) of 20.6, 4,3, 5.7 mm for the enhancing tumor, whole tumor and tumor core, respectively. Similarly, our solution achieved a Dice of 0.79, 0.89 and 0.84, as well as Hausdorff (95%) of 20.4, 6.7 and 19.5mm on the final test dataset, ranking us among the top ten teams. More complicated training schemes and neural network architectures were investigated without significant performance gain at the cost of greatly increased training time. Overall, our approach yielded good and balanced performance for each tumor subregion. Our solution is open sourced at https://github.com/lescientifik/open_brats2020.
Deep neural networks (DNNs) have shown vulnerability to adversarial attacks, i.e., carefully perturbed inputs designed to mislead the network at inference time. Recently introduced localized attacks, Localized and Visible Adversarial Noise (LaVAN) and Adversarial patch, pose a new challenge to deep learning security by adding adversarial noise only within a specific region without affecting the salient objects in an image. Driven by the observation that such attacks introduce concentrated high-frequency changes at a particular image location, we have developed an effective method to estimate noise location in gradient domain and transform those high activation regions caused by adversarial noise in image domain while having minimal effect on the salient object that is important for correct classification. Our proposed Local Gradients Smoothing (LGS) scheme achieves this by regularizing gradients in the estimated noisy region before feeding the image to DNN for inference. We have shown the effectiveness of our method in comparison to other defense methods including Digital Watermarking, JPEG compression, Total Variance Minimization (TVM) and Feature squeezing on ImageNet dataset. In addition, we systematically study the robustness of the proposed defense mechanism against Back Pass Differentiable Approximation (BPDA), a state of the art attack recently developed to break defenses that transform an input sample to minimize the adversarial effect. Compared to other defense mechanisms, LGS is by far the most resistant to BPDA in localized adversarial attack setting.
In this study, we present EventRL, a reinforcement learning approach developed to enhance event extraction for large language models (LLMs). EventRL utilizes outcome supervision with specific reward functions to tackle prevalent challenges in LLMs, such as instruction following and hallucination, manifested as the mismatch of event structure and the generation of undefined event types. We evaluate EventRL against existing methods like Few-Shot Prompting (FSP) (based on GPT4) and Supervised Fine-Tuning (SFT) across various LLMs, including GPT-4, LLaMa, and CodeLLaMa models. Our findings show that EventRL significantly outperforms these conventional approaches by improving the performance in identifying and structuring events, particularly in handling novel event types. The study emphasizes the critical role of reward function selection and demonstrates the benefits of incorporating code data for better event extraction. While increasing model size leads to higher accuracy, maintaining the ability to generalize is essential to avoid overfitting.
Infrastructure systems play a critical role in providing essential products and services for the functioning of modern society; however, they are vulnerable to disasters and their service disruptions can cause severe societal impacts. To protect infrastructure from disasters and reduce potential impacts, great achievements have been made in modeling interdependent infrastructure systems in past decades. In recent years, scholars have gradually shifted their research focus to understanding and modeling societal impacts of disruptions considering the fact that infrastructure systems are critical because of their role in societal functioning, especially under situations of modern societies. Exploring how infrastructure disruptions impair society to enhance resilient city has become a key field of study. By comprehensively reviewing relevant studies, this paper demonstrated the definition and types of societal impact of infrastructure disruptions, and summarized the modeling approaches into four types: extended infrastructure modeling approaches, empirical approaches, agent-based approaches, and big data-driven approaches. For each approach, this paper organized relevant literature in terms of modeling ideas, advantages, and disadvantages. Furthermore, the four approaches were compared according to several criteria, including the input data, types of societal impact, and application scope. Finally, this paper illustrated the challenges and future research directions in the field.
Complete positivity of quantum dynamics is often viewed as a litmus test for physicality, yet it is well known that correlated initial states need not give rise to completely positive evolutions. This observation spurred numerous investigations over the past two decades attempting to identify necessary and sufficient conditions for complete positivity. Here we describe a complete and consistent mathematical framework for the discussion and analysis of complete positivity for correlated initial states of open quantum systems. This formalism is built upon a few simple axioms and is sufficiently general to contain all prior methodologies going back to Pechakas, PRL (1994). The key observation is that initial system-bath states with the same reduced state on the system must evolve under all admissible unitary operators to system-bath states with the same reduced state on the system, in order to ensure that the induced dynamical maps on the system are well-defined. Once this consistency condition is imposed, related concepts like the assignment map and the dynamical maps are uniquely defined. In general, the dynamical maps may not be applied to arbitrary system states, but only to those in an appropriately defined physical domain. We show that the constrained nature of the problem gives rise to not one but three inequivalent types of complete positivity. Using this framework we elucidate the limitations of recent attempts to provide conditions for complete positivity using quantum discord and the quantum data-processing inequality. The problem remains open, and may require fresh perspectives and new mathematical tools. The formalism presented herein may be one step in that direction.
Optically active artificial structures have attracted tremendous research attention. Such structures must meet two requirements: Lack of spatial inversion symmetries and, a condition usually not explicitly considered, the structure shall preserve the helicity of light, which implies that there must be a vanishing coupling between the states of opposite polarization handedness among incident and scattered plane waves. Here, we put forward and demonstrate that a unit cell made from chiraly arranged electromagnetically dual scatterers serves exactly this purpose. We prove this by demonstrating optical activity of such unit cell in general scattering directions.
In the paper we propose a theoretical model that takes into account Vegard strains and perform a detailed quantitative comparison of the theoretical results with experimental ones for quasispherical nanoparticles, which reveal the essential (about 100 K) increase of the transition temperature in spherical nanoparticles in comparison with bulk crystals. The average radius of nanoparticles was about 25 nm, they consist of K(Ta,Nb)O3 solid solution, where KTaO3 is a quantum paraelectric, while KNbO3 is a ferroelectric.From the comparison between the theory and experiment we unambiguously established the leading contribution of Vegard strains into the extrinsic size effect in ferroelectric nanoparticles. We determined the dependence of Vegard strains on the content of Nb and reconstructed the Curie temperature dependence on the content of Nb using this dependence. Appeared that the dependence of the Curie temperature on the Nb content becomes nonmonotonic one for the small (< 20 nm) elongated K(Ta,Nb)O3 nanoparticles. We established that the accumulation of intrinsic and extrinsic defects near the surface can play the key role in the physical origin of extrinsic size effect in ferroelecric nanoparticles and govern its main features.
Stochastic gradient descent (SGD) exhibits strong algorithmic regularization effects in practice, which has been hypothesized to play an important role in the generalization of modern machine learning approaches. In this work, we seek to understand these issues in the simpler setting of linear regression (including both underparameterized and overparameterized regimes), where our goal is to make sharp instance-based comparisons of the implicit regularization afforded by (unregularized) average SGD with the explicit regularization of ridge regression. For a broad class of least squares problem instances (that are natural in high-dimensional settings), we show: (1) for every problem instance and for every ridge parameter, (unregularized) SGD, when provided with logarithmically more samples than that provided to the ridge algorithm, generalizes no worse than the ridge solution (provided SGD uses a tuned constant stepsize); (2) conversely, there exist instances (in this wide problem class) where optimally-tuned ridge regression requires quadratically more samples than SGD in order to have the same generalization performance. Taken together, our results show that, up to the logarithmic factors, the generalization performance of SGD is always no worse than that of ridge regression in a wide range of overparameterized problems, and, in fact, could be much better for some problem instances. More generally, our results show how algorithmic regularization has important consequences even in simpler (overparameterized) convex settings.
With the advent of space-based precision photometry missions the quantity and quality of starspot light curves has greatly increased. This paper presents a large number of starspot models and their resulting light curves to: 1) better determine light curve metrics and methods that convey useful physical information, 2) understand how the underlying degeneracies of the translation from physical starspot distributions to the resulting light curves obscure that information. We explore models of relatively active stars at several inclinations while varying the number of (dark) spots, random spot distributions in position and time, timescales of growth and decay, and differential rotation. We examine the behavior of absolute and differential variations of individual intensity dips and overall light curves, and demonstrate how complex spot distributions and behaviors result in light curves that typically exhibit only one or two dips per rotation. Unfortunately simplistic "one or two spot" or "active longitude" descriptions or modeling of light curves can often be highly misleading. We also show that short "activity cycles" can easily be simply due to random processes. It turns out to be quite difficult to disentangle the competing effects of spot lifetime and differential rotation, but under most circumstances spot lifetime is the more influential of the two. Many of the techniques tried to date only work when spots live for many rotations. These include autocorrelation degradation for spot lifetimes and periodograms for both global and differential rotation. Differential rotation may be nearly impossible to accurately infer from light curves alone unless spots live for many rotations. The Sun and solar-type stars its age or older are unfortunately the most difficult type of case. Further work is needed to have increased confidence in light curve inferences.
We predict that a novel bias-voltage assisted magnetization reversal process will occur in Mn doped II-VI semiconductor quantum wells or heterojunctions with carrier induced ferromagnetism. The effect is due to strong exchange-coupling induced subband mixing that leads to electrically tunable hysteresis loops. Our model calculations are based on the mean-field theory of carrier induced ferromagnetism in Mn-doped quantum wells and on a semi-phenomenological description of the host II-VI semiconductor valence bands.
Location-aware networks are of great importance and interest in both civil and military applications. This paper determines the localization accuracy of an agent, which is equipped with an antenna array and localizes itself using wireless measurements with anchor nodes, in a far-field environment. In view of the Cram\'er-Rao bound, we first derive the localization information for static scenarios and demonstrate that such information is a weighed sum of Fisher information matrices from each anchor-antenna measurement pair. Each matrix can be further decomposed into two parts: a distance part with intensity proportional to the squared baseband effective bandwidth of the transmitted signal and a direction part with intensity associated with the normalized anchor-antenna visual angle. Moreover, in dynamic scenarios, we show that the Doppler shift contributes additional direction information, with intensity determined by the agent velocity and the root mean squared time duration of the transmitted signal. In addition, two measures are proposed to evaluate the localization performance of wireless networks with different anchor-agent and array-antenna geometries, and both formulae and simulations are provided for typical anchor deployments and antenna arrays.
In this paper we study the theoretical properties of the simultaneous multiscale change point estimator (SMUCE) proposed by Frick et al. (2014) in regression models with dependent error processes. Empirical studies show that in this case the change point estimate is inconsistent, but it is not known if alternatives suggested in the literature for correlated data are consistent. We propose a modification of SMUCE scaling the basic statistic by the long run variance of the error process, which is estimated by a difference-type variance estimator calculated from local means from different blocks. For this modification we prove model consistency for physical dependent error processes and illustrate the finite sample performance by means of a simulation study.
Time-dependent protocols that perform irreversible logical operations, such as memory erasure, cost work and produce heat, placing bounds on the efficiency of computers. Here we use a prototypical computer model of a physical memory to show that it is possible to learn feedback-control protocols to do fast memory erasure without input of work or production of heat. These protocols, which are enacted by a neural-network ``demon'', do not violate the second law of thermodynamics because the demon generates more heat than the memory absorbs. The result is a form of nonlocal heat exchange in which one computation is rendered energetically favorable while a compensating one produces heat elsewhere, a tactic that could be used to rationally design the flow of energy within a computer.
I present a review of Smoothed Particle Hydrodynamics (SPH), with the aim of providing a mathematically rigorous, clear derivation of the algorithms from first principles. The method of discretising a continuous field into particles using a smoothing kernel is considered, and also the errors associated with this approach. A fully conservative form of SPH is then derived from the Lagrangian, demonstrating the explicit conservation of mass, linear and angular momenta and energy/entropy. The method is then extended to self-consistently include spatially varying smoothing lengths, (self) gravity and various forms of artificial viscosity, required for the correct treatment of shocks. Finally two common methods of time integration are discussed, the Runge-Kutta-Fehlberg and leapfrog integrators, along with an overview of time-stepping criteria.
Despite progress developing experimentally-consistent models of insect in-flight sensing and feedback for individual agents, a lack of systematic understanding of the multi-agent and group performance of the resulting bio-inspired sensing and feedback approaches remains a barrier to robotic swarm implementations. This study introduces the small-target motion reactive (STMR) swarming approach by designing a concise engineering model of the small target motion detector (STMD) neurons found in insect lobula complexes. The STMD neuron model identifies the bearing angle at which peak optic flow magnitude occurs, and this angle is used to design an output feedback switched control system. A theoretical stability analysis provides bi-agent stability and state boundedness in group contexts. The approach is simulated and implemented on ground vehicles for validation and behavioral studies. The results indicate despite having the lowest connectivity of contemporary approaches (each agent instantaneously regards only a single neighbor), collective group motion can be achieved. STMR group level metric analysis also highlights continuously varying polarization and decreasing heading variance.
In our comprehensive experiments and evaluations, we show that it is possible to generate multiple contrast (even all synthetically) and use synthetically generated images to train an image segmentation engine. We showed promising segmentation results tested on real multi-contrast MRI scans when delineating muscle, fat, bone and bone marrow, all trained on synthetic images. Based on synthetic image training, our segmentation results were as high as 93.91\%, 94.11\%, 91.63\%, 95.33\%, for muscle, fat, bone, and bone marrow delineation, respectively. Results were not significantly different from the ones obtained when real images were used for segmentation training: 94.68\%, 94.67\%, 95.91\%, and 96.82\%, respectively.
We present further arguments that the Hipparcos parallaxes for some of the clusters and associations represented in the Hipparcos catalog should be used with caution in the study of the Galactic structure. It has been already shown that the discrepancy between the Hipparcos and ground based parallaxes for several clusters including the Pleiades, Coma Ber and NGC 6231 can be resolved by recomputing the Hipparcos astrometric solutions with an improved algorithm diminishing correlated errors in the attitude parameters. Here we present new parallaxes obtained with this algorithm for another group of stars with discrepant data - the galactic cluster Cr 121. The original Hipparcos parallaxes led de Zeeuw et al. to conclude that Cr 121 and the surrounding association of OB stars form a relatively compact and coherent moving group at a distance of 550 -- 600 pc. Our corrected parallaxes reveal a different spatial distribution of young stellar populace in this area. Both the cluster Cr 121 and the extended OB association are considerably more distant (750 -- 1000 pc), and the latter has a large depth probably extending beyond 1 kpc. Therefore, not only are the recalculated parallaxes in complete agreement with the photometric uvbybeta parallaxes, but the structure of the field they reveal is no longer in discrepancy with that found by the photometric method.
We apply the notion of 2-extensions of algebras to the deformation theory of algebras. After standard results on butterflies between 2-extensions, we use this (2, 0)-category to give three perspectives on the deformation theory of algebras. We conclude by fixing an error in the literature.
Thermal escape out of a metastable well is considered in the weak friction regime, where the bottleneck for decay is energy diffusion, and at lower temperatures, where quantum tunneling becomes relevant. Within a systematic semiclassical formalism an extension of the classical diffusion equation is derived starting from a quantum mechanical master equation. In contrast to previous approaches finite barrier transmission also affects transition probabilities. The decay rate is obtained from the stationary non-equilibrium solution and captures the intimate interplay between thermal and quantum fluctuations above the crossover to the deep quantum regime.
One may define a complex system as a system in which phenomena emerge as a consequence of multiscale interaction among the system's components and their environments. The field of Complex Systems is the study of such systems--usually naturally occurring, either bio-logical or social. Systems Engineering may be understood to include the conceptualising and building of systems that consist of a large number of concurrently operating and interacting components--usually including both human and non-human elements. It has become increasingly apparent that the kinds of systems that systems engineers build have many of the same multiscale characteristics as those of naturally occurring complex systems. In other words, systems engineering is the engineering of complex systems. This paper and the associated panel will explore some of the connections between the fields of complex systems and systems engineering.
Three-dimensional (3D) topological Weyl semimetals (TWSs) represent a novel state of quantum matter with unusual electronic structures that resemble both a "3D graphene" and a topological insulator by possessing pairs of Weyl points (through which the electronic bands disperse linearly along all three momentum directions) connected by topological surface states, forming the unique "Fermi-arc" type Fermi-surface (FS). Each Weyl point is chiral and contains half of the degrees of freedom of a Dirac point, and can be viewed as a magnetic monopole in the momentum space. Here, by performing angle-resolved photoemission spectroscopy on non-centrosymmetric compound TaAs, we observed its complete band structures including the unique "Fermi-arc" FS and linear bulk band dispersion across the Weyl points, in excellent agreement with the theoretical calculations. This discovery not only confirms TaAs as the first 3D TWS, but also provides an ideal platform for realizing exotic physical phenomena (e.g. negative magnetoresistance, chiral magnetic effects and quantum anomalous Hall effect) which may also lead to novel future applications.
Random matrix models based on an integral over supermatrices are proposed as a natural extension of bosonic matrix models. The subtle nature of superspace integration allows these models to have very different properties from the analogous bosonic models. Two choices of integration slice are investigated. One leads to a perturbative structure which is reminiscent of, and perhaps identical to, the usual Hermitian matrix models. Another leads to an eigenvalue reduction which can be described by a two component plasma in one dimension. A stationary point of the model is described.
In this paper we consider minimal Lagrangian submanifolds in $n$-dimensional complex space forms. More precisely, we study such submanifolds which, endowed with the induced metrics, write as a Riemannian product of two Riemannian manifolds, each having constant sectional curvature. As the main result, we give a complete classification of these submanifolds.
We prove that any non zero inertia, however small, is able to change the nature of the synchronization transition in Kuramoto-like models, either from continuous to discontinuous, or from discontinuous to continuous. This result is obtained through an unstable manifold expansion in the spirit of J.D. Crawford, which features singularities in the vicinity of the bifurcation. Far from being unwanted artifacts, these singularities actually control the qualitative behavior of the system. Our numerical tests fully support this picture.
The India-based Neutrino Observatory (INO) is a project aimed at building a large underground laboratory to explore the Earth's mater effects on the atmospheric neutrinos in multi-GeV range. INO will host a 50 kton magnetized iron calorimeter detector (ICAL) in which Resistive Plate Chambers(RPCs) will be the active detector elements. In ICAL, 28,800 glass RPCs of 2 m $\times$ 2 m size will be operated in the avalanche mode. A small variation in the compositions of ionizing gaseous medium in the RPC affects its performance. Study of the charge distribution of the RPC at different gas compositions is necessary to optimize the gas mixture. An RPC made with glass plates of dimension 30 cm $\times$ 30 cm was operated in avalanche mode with a gas mixture of $C_2H_2F_4$/$iC_4H_{10}$/$SF_6$. We have studied the performance of these RPCs at the same ambient conditions. The percentages of the $iC_4H_{10}$ or $SF_6$ were varied and its effect on the performance of RPC were studied. The study of the charge distribution and time resolution of the RPC signals at different gas compositions is presented in this paper.
We present a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also describe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single and multiple document summaries. Finally, we describe two user studies that test our models of multi-document summarization.
We review here the main contributions of Einstein to the quantum theory. To put them in perspective we first give an account of Physics as it was before him. It is followed by a brief account of the problem of black body radiation which provided the context for Planck to introduce the idea of quantum. Einstein's revolutionary paper of 1905 on light-quantum hypothesis is then described as well as an application of this idea to the photoelectric effect. We next take up a discussion of Einstein's other contributions to old quantum theory. These include (i) his theory of specific heat of solids, which was the first application of quantum theory to matter, (ii) his discovery of wave-particle duality for light and (iii) Einstein's A and B coefficients relating to the probabilities of emission and absorption of light by atomic systems and his discovery of radiation stimulated emission of light which provides the basis for laser action. We then describe Einstein's contribution to quantum statistics viz Bose-Einstein Statistics and his prediction of Bose-Einstein condensation of a boson gas. Einstein played a pivotal role in the discovery of Quantum mechanics and this is briefly mentioned. After 1925 Einstein's contributed mainly to the foundations of Quantum Mechanics. We choose to discuss here (i) his Ensemble (or Statistical) Interpretation of Quantum Mechanics and (ii) the discovery of Einstein-Podolsky-Rosen (EPR) correlations and the EPR theorem on the conflict between Einstein-Locality and the completeness of the formalism of Quantum Mechanics. We end with some comments on later developments.
The detailed characterization of scaling laws relating the observables of cluster of galaxies to their mass is crucial for obtaining accurate cosmological constraints with clusters. In this paper, we present a comparison between the hydrostatic and lensing mass profiles of the cluster \psz\ at $z=0.59$. The hydrostatic mass profile is obtained from the combination of high resolution NIKA2 thermal Sunyaev-Zel'dovich (tSZ) and \xmm\ X-ray observations of the cluster. Instead, the lensing mass profile is obtained from an analysis of the CLASH lensing data based on the lensing convergence map. We find significant variation on the cluster mass estimate depending on the observable, the modelling of the data and the knowledge of the cluster dynamical state. This {\bf might} lead to significant systematic effects on cluster cosmological analyses for which only a single observable is generally used. From this pilot study, we conclude that the combination of high resolution SZ, X-ray and lensing data could allow us to identify and correct for these systematic effects. This would constitute a very interesting extension of the NIKA2 SZ Large Program.
A comprehensive numerical investigation has been conducted on the angular distribution and spectrum of radiation emitted by 855 MeV electron and positron beams while traversing a 'quasi-mosaic' bent silicon (111) crystal. This interaction of charged particles with a bent crystal gives rise to various phenomena such as channeling, dechanneling, volume reflection, and volume capture. The crystal's geometry, emittance of the collimated particle beams, as well as their alignment with respect to the crystal, have been taken into account as they are essential for an accurate quantitative description of the processes. The simulations have been performed using a specialized relativistic molecular dynamics module implemented in the MBN Explorer package. The angular distribution of the particles after traversing the crystal has been calculated for beams of different emittances as well as for different anticlastic curvatures of the bent crystals. For the electron beam, the angular distributions of the deflected particles and the spectrum of radiation obtained in the simulations are compared with the experimental data collected at the Mainz Microtron facility. For the positron beam such calculations have been performed for the first time. We predict significant differences in the angular distributions and the radiation spectra for positrons versus electrons.
Let $(R,\frak m)$ be a commutative Noetherian local ring and let $M$ and $N$ be finitely generated $R$-modules of finite injective dimension and finite Gorenstein injective dimension, respectively. In this paper we prove a generalization of Ischebeck Formula, that is $\depth_RM+\sup\{i| {0.1cm}\Ext_R^i(M,N)\neq 0\}=\depth R.$
This paper presents a multilevel convergence framework for multigrid-reduction-in-time (MGRIT) as a generalization of previous two-grid estimates. The framework provides a priori upper bounds on the convergence of MGRIT V- and F-cycles, with different relaxation schemes, by deriving the respective residual and error propagation operators. The residual and error operators are functions of the time stepping operator, analyzed directly and bounded in norm, both numerically and analytically. We present various upper bounds of different computational cost and varying sharpness. These upper bounds are complemented by proposing analytic formulae for the approximate convergence factor of V-cycle algorithms that take the number of fine grid time points, the temporal coarsening factors, and the eigenvalues of the time stepping operator as parameters. The paper concludes with supporting numerical investigations of parabolic (anisotropic diffusion) and hyperbolic (wave equation) model problems. We assess the sharpness of the bounds and the quality of the approximate convergence factors. Observations from these numerical investigations demonstrate the value of the proposed multilevel convergence framework for estimating MGRIT convergence a priori and for the design of a convergent algorithm. We further highlight that observations in the literature are captured by the theory, including that two-level Parareal and multilevel MGRIT with F-relaxation do not yield scalable algorithms and the benefit of a stronger relaxation scheme. An important observation is that with increasing numbers of levels MGRIT convergence deteriorates for the hyperbolic model problem, while constant convergence factors can be achieved for the diffusion equation. The theory also indicates that L-stable Runge-Kutta schemes are more amendable to multilevel parallel-in-time integration with MGRIT than A-stable Runge-Kutta schemes.
Integration testing is one the important phase in software testing life cycle (STLC). With the fast growth of internet and web services, web-based applications are also growing rapidly and their importance and complexity is also increasing. Heterogeneous and diverse nature of distributed components, applications, along with their multi-platform support and cooperativeness make these applications more complex and swiftly increasing in their size. Quality assurance of these applications is becoming more crucial and important. Testing is one of the key processes to achieve and ensure the quality of these software or Webbased products. There are many testing challenges involved in Web-based applications. But most importantly integration is the most critical testing associated with Web-based applications. There are number of challenging factors involved in integration testing efforts. These factors have almost 70 percent to 80 percent impact on overall quality of Web-based applications. In software industry different kind of testing approaches are used by practitioners to solve the issues associated with integration which are due to ever increasing complexities of Web-based applications.
We construct the Green current for a random iteration of "horizontal-like" mappings in two complex dimensions. This is applied to the study of a polynomial map $f:\mathbb{C}^2\to\mathbb{C}^2$ with the following properties: 1. infinity is $f$-attracting, 2. $f$ contracts the line at infinity to a point not in the indeterminacy set. Then the Green current of $f$ can be decomposed into pieces associated with an itinerary determined by the indeterminacy points. We also study the set of escape rates near infinity, i.e. the possible values of the function $\limsup \frac{1}{n}\log^+\log^+ \norm{f^n}$. We exhibit examples for which this set contains an interval.
The noncovariant duality symmetric action put forward by Schwarz-Sen is quantized by means of the Dirac bracket quantization procedure. The resulting quantum theory is shown to be, nevertheless, relativistically invariant.
We fit an isothermal oscillatory density model of Neptune's protoplanetary disk to the surviving regular satellites and its innermost ring and we determine the radial scale length of the disk, the equation of state and the central density of the primordial gas, and the rotational state of the Neptunian nebula. Neptune's regular moons suffered from the retrograde capture of Triton that disrupted the system. Some moons may have been ejected, while others may have survived inside their potential minima. For this reason, the Neptunian nebula does not look like any of the nebulae that we modeled previously. In particular, there must be two density maxima deep inside the core of the nebula where no moons or rings are found nowadays. Even with this strong assumption, the recent discovery of the minor moon N XIV complicates further the modeling effort. With some additional assumptions, the Neptunian nebula still shares many similarities with the Uranian nebula, as was expected from the relative proximity and similar physical conditions of the two systems. For Neptune's primordial disk, we find a steep power-law index ($k=-3.0$), needed to accommodate the arrangement of the outer moons Larissa, N XIV, and Proteus. The rotation parameter that measures centrifugal support against self-gravity is quite small ($\beta_0=0.00808$), as is its radial scale length (13.6 km). The extent of the disk ($R_{\rm max}=0.12$ Gm) is a lot smaller than that of Uranus ($R_{\rm max}=0.60$ Gm) and Triton appears to be responsible for the truncation of the disk. The central density of the compact Neptunian core and its angular velocity are higher than but comparable to those of Uranus' core. In the end, we compare the models of the protoplanetary disks of the four gaseous giants.
We study the properties of the relativistic, steady, axisymmetric, low angular momentum, inviscid, advective, geometrically thin accretion flow in a Kerr-Taub-NUT (KTN) spacetime which is characterized by the Kerr parameter ($a_{\rm k}$) and NUT parameter ($n$). Depending on $a_{\rm k}$ and $n$ values, KTN spacetime represents either a black or a naked singularity. We solve the governing equations that describe the relativistic accretion flow in KTN spacetime and obtain all possible global transonic accretion solutions around KTN black hole in terms of the energy $({\cal E})$ and angular momentum $(\lambda)$ of the flow. We identify the region of the parameter space in $\lambda-{\cal E}$ plane that admits the flow to possess multiple critical points for KTN black hole. We examine the modification of the parameter space due to $a_{\rm k}$ and $n$ and find that the role of $a_{\rm k}$ and $n$ in determining the parameter space is opposite to each other. This clearly indicates that the NUT parameter $n$ effectively mitigates the effect of black hole rotation in deciding the accretion flow structure. Further, we calculate the maximum disc luminosity ($L_{\rm max}$) corresponding to the accretion solutions around the KTN black hole and for a given set of $a_{\rm k}$ and $n$. In addition, we also investigate all possible flow topologies around the naked singularity and find that there exists a region around the naked singularity which remains inaccessible to the flow. We study the critical point properties for naked singularities and find that the flow possesses maximum of four critical points. Finally, we obtain the parameter space for multiple critical points for naked singularity and find that parameter space is shrunk and shifted to lower $\lambda$ and higher ${\cal E}$ side as $a_{\rm k}$ is increased which ultimately disappears.
We demonstrate an atom laser using all-optical techniques. A Bose-Einstein condensate of rubidium atoms is created by direct evaporative cooling in a quasistatic dipole trap realized with a single, tightly focused CO$_{2}$-laser beam. An applied magnetic field gradient allows formation of the condensate in a field-insensitive $m_{F} = 0$ spin projection only, which suppresses fluctuations of the chemical potential from stray magnetic fields. A collimated and monoenergetic beam of atoms is extracted from the Bose-Einstein condensate by continuously lowering the dipole trapping potential in a controlled way to form a novel type of atom laser.
Many young, massive stars are found in close binaries. Using population synthesis simulations we predict the likelihood of a companion star being present when these massive stars end their lives as core-collapse supernovae (SNe). We focus on stripped-envelope SNe, whose progenitors have lost their outer hydrogen and possibly helium layers before explosion. We use these results to interpret new Hubble Space Telescope observations of the site of the broad-lined Type Ic SN 2002ap, 14 years post-explosion. For a subsolar metallicity consistent with SN 2002ap, we expect a main-sequence companion present in about two thirds of all stripped-envelope SNe and a compact companion (likely a stripped helium star or a white dwarf/neutron star/black hole) in about 5% of cases. About a quarter of progenitors are single at explosion (originating from initially single stars, mergers or disrupted systems). All the latter scenarios require a massive progenitor, inconsistent with earlier studies of SN 2002ap. Our new, deeper upper limits exclude the presence of a main-sequence companion star $>8$-$10$ Msun, ruling out about 40% of all stripped-envelope SN channels. The most likely scenario for SN 2002ap includes nonconservative binary interaction of a primary star initially $\lesssim 23$ Msun. Although unlikely ($<$1% of the scenarios), we also discuss the possibility of an exotic reverse merger channel for broad-lined Type Ic events. Finally, we explore how our results depend on the metallicity and the model assumptions and discuss how additional searches for companions can constrain the physics that governs the evolution of SN progenitors.
We analyse forward-jet production at HERA in the framework of the Golec-Biernat and Wusthoff saturation models. We obtain a good description of the forward jet cross sections measured by the H1 and ZEUS collaborations in the two-hard-scale region kT ~ Q >> Lambda_QCD with two different parametrisations with either significant or weak saturation effects. The weak saturation parametrization gives a scale compatible with the one found for the proton structure function F_2. We argue that Mueller-Navelet jets at the Tevatron and the LHC could help distinguishing between both options.
A paradoxist Smarandache geometry combines Euclidean, hyperbolic, and elliptic geometry into one space along with other non-Euclidean behaviors of lines that would seem to require a discrete space. A class of continuous spaces is presented here together with specific exmples that exhibit almost all of these phenomena and suggest the prospect of a continuous paradoxist geometry.
In this paper, we consider the following problem $$ -\Delta u -\zeta \frac{u}{|x|^{2}} = \sum_{i=1}^{k} \left( \int_{\mathbb{R}^{N}} \frac{|u|^{2^{*}_{\alpha_{i}}}}{|x-y|^{\alpha_{i}}} \mathrm{d}y \right) |u|^{2^{*}_{\alpha_{i}}-2}u + |u|^{2^{*}-2}u , \mathrm{~in~} \mathbb{R}^{N}, $$ where $N\geqslant3$, $\zeta\in(0,\frac{(N-2)^{2}}{4})$, $2^{*}=\frac{2N}{N-2}$ is the critical Sobolev exponent, and $2^{*}_{\alpha_{i}}=\frac{2N-\alpha_{i}}{N-2}$ ($i=1,\ldots,k$) are the critical Hardy--Littlewood--Sobolev upper exponents. The parameters $\alpha_{i}$ ($i=1,\ldots,k$) satisfy some suitable assumptions. By using Coulomb--Sobolev space, endpoint refined Sobolev inequality and variational methods, we establish the existence of nontrivial solutions. Our result generalizes the result obtained by Yang and Wu [Adv. Nonlinear Stud. (2017)].
Exclusive neutral-pion electroproduction ($ep\to e^\prime p^\prime \pi^0$) was measured at Jefferson Lab with a 5.75-GeV electron beam and the CLAS detector. Differential cross sections $d^4\sigma/dtdQ^2dx_Bd\phi_\pi$ and structure functions $\sigma_T+\epsilon\sigma_L, \sigma_{TT}$ and $\sigma_{LT}$ as functions of $t$ were obtained over a wide range of $Q^2$ and $x_B$. The data are compared with Regge and handbag theoretical calculations. Analyses in both frameworks find that a large dominance of transverse processes is necessary to explain the experimental results. For the Regge analysis it is found that the inclusion of vector meson rescattering processes is necessary to bring the magnitude of the calculated and measured structure functions into rough agreement. In the handbag framework, there are two independent calculations, both of which appear to roughly explain the magnitude of the structure functions in terms of transversity generalized parton distributions.
Influence of magic numbers on nuclear radii is investigated via the Hartree-Fock-Bogolyubov calculations and available experimental data. With the $\ell s$ potential including additional density-dependence suggested from the chiral effective field theory, kinks are universally predicted at the $jj$-closed magic numbers and anti-kinks (\textit{i.e.} inverted kinks) are newly predicted at the $\ell s$-closed magic numbers, both in the charge radii and in the matter radii along the isotopic and isotonic chains where nuclei stay spherical. These results seem consistent with the kinks of the charge radii observed in Ca, Sn and Pb and the anti-kink in Ca. The kinks and the anti-kinks could be a peculiar indicator for magic numbers, discriminating $jj$-closure and $\ell s$-closure.
In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours that trade-off between multiple, possibly conflicting, objectives. MORL based on decomposition is a family of solution methods that employ a number of utility functions to decompose the multi-objective problem into individual single-objective problems solved simultaneously in order to approximate a Pareto front of policies. We focus on the case of linear utility functions parameterised by weight vectors w. We introduce a method based on Upper Confidence Bound to efficiently search for the most promising weight vectors during different stages of the learning process, with the aim of maximising the hypervolume of the resulting Pareto front. The proposed method is shown to outperform various MORL baselines on Mujoco benchmark problems across different random seeds. The code is online at: https://github.com/SYCAMORE-1/ucb-MOPPO.
The problem to establish not only the asymptotic distribution results for statistical estimators but also the moment convergence of the estimators has been recognized as an important issue in advanced theories of statistics. One of the main goals of this paper is to present a metod to derive the moment convergence of $Z$-estimators as it has been done for $M$-estimators. Another goal of this paper is to develop a general, unified approach, based on some partial estimation functions which we call "$Z$-process", to the change point problems for ergodic models as well as some models where the Fisher information matrix is random and inhomogeneous in time. Applications to some diffusion process models and Cox's regression model are also discussed.
Various materials are made of long thin fibers that are randomly oriented to form a complex network in which drops of wetting liquid tend to accumulate at the nodes. The capillary force exerted by the liquid can bend flexible fibers, which in turn influences the morphology adopted by the liquid. In this paper, we investigate, the role of the fiber flexibility on the shape of a small volume of liquid on a pair of crossed flexible fibers, through a model situation. We characterize the liquid morphologies as we vary the volume of liquid, the angle between the fibers, and the length of the fibers. The drop morphologies previously reported for rigid crossed fibers, i.e., a drop, a column and a mixed morphology, are also observed on flexible crossed fibers with modified domains of existence. In addition, at small tilting angles between the fibers, a new behavior is observed: the fibers bend and collapse. Depending on the volume of liquid, a thin column with or without a drop is reported on the collapsed fibers. Our study suggests that the fiber flexibility adds a rich variety of behaviors that may be important for some applications.
Gaussian beams are often used in optical systems. The fundamental Gaussian TEM00 mode is the most common of the Gaussian modes present in various optical devices, systems and equipment. Within an optical system, it is common that this Gaussian TEM00 beam passes through a circular aperture of a finite diameter. Such circular apertures include irises, spatial filters, circular Photo-Detectors (PDs) and optical mounts with circular rims. The magnitude of optical power passing through a finite-sized circular aperture is well-documented for cases where the Gaussian beam passes through the center of the clear circular aperture, and is chopped off symmetrically in all radial directions on a given plane. More often than not, a non-axial incident Gaussian Beam is not blocked in a radially uniform manner by a circular aperture. Such situations arise due to a lateral displacement of the beam from tilted glass blocks, manufacturing errors and imperfect surface flatness or parallelness of surfaces. The fraction of optical power of a laterally-shifted Gaussian Beam passing through a circular aperture is calculated in this paper through conventional integration techniques.
We measure empirical relationships between the local star formation rate (SFR) and properties of the star-forming molecular gas on 1.5 kpc scales across 80 nearby galaxies. These relationships, commonly referred to as "star formation laws," aim at predicting the local SFR surface density from various combinations of molecular gas surface density, galactic orbital time, molecular cloud free-fall time, and the interstellar medium dynamical equilibrium pressure. Leveraging a multiwavelength database built for the PHANGS survey, we measure these quantities consistently across all galaxies and quantify systematic uncertainties stemming from choices of SFR calibrations and the CO-to-H$_2$ conversion factors. The star formation laws we examine show 0.3-0.4 dex of intrinsic scatter, among which the molecular Kennicutt-Schmidt relation shows a $\sim$10% larger scatter than the other three. The slope of this relation ranges $\beta\approx0.9{-}1.2$, implying that the molecular gas depletion time remains roughly constant across the environments probed in our sample. The other relations have shallower slopes ($\beta\approx0.6{-}1.0$), suggesting that the star formation efficiency (SFE) per orbital time, the SFE per free-fall time, and the pressure-to-SFR surface density ratio (i.e., the feedback yield) may vary systematically with local molecular gas and SFR surface densities. Last but not least, the shapes of the star formation laws depend sensitively on methodological choices. Different choices of SFR calibrations can introduce systematic uncertainties of at least 10-15% in the star formation law slopes and 0.15-0.25 dex in their normalization, while the CO-to-H$_2$ conversion factors can additionally produce uncertainties of 20-25% for the slope and 0.10-0.20 dex for the normalization.
Using a new state-dependent, $\lambda$-deformable, linear functional operator, ${\cal Q}_{\psi}^{\lambda}$, which presents a natural $C^{\infty}$ deformation of quantization, we obtain a uniquely selected non--linear, integro--differential Generalized Schr\"odinger equation. The case ${\cal Q}_{\psi}^{1}$ reproduces linear quantum mechanics, whereas ${\cal Q}_{\psi}^{0}$ admits an exact dynamic, energetic and measurement theoretic {\em reproduction} of classical mechanics. All solutions to the resulting classical wave equation are given and we show that functionally chaotic dynamics exists.
We study the effect of symmetry breaking perturbations in the one-dimensional SU(4) spin-orbital model. We allow the exchange in spin ($J_1$) and orbital ($J_2$) channel to be different and thus reduce the symmetry to SU(2) $\otimes$ SU(2). A magnetic field $h$ along the $S^z$ direction is also applied. Using the formalism developped by Azaria et al we extend their analysis of the isotropic $J_1=J_2$, h=0 case and obtain the low-energy effective theory near the SU(4) point in the asymmetric case. An accurate analysis of the renormalization group flow is presented with a particular emphasis on the effect of the anisotropy. In zero magnetic field, we retrieve the same qualitative low-energy physics than in the isotropic case. In particular, the massless behavior found on the line $J_1=J_2>K/4$ extends in a large anisotropic region. We discover though that the anisotropy plays its trick in allowing non trivial scaling behaviors of the physical quantities. When a magnetic field is present the effect of the anisotropy is striking. In addition to the usual commensurate-incommensurate phase transition that occurs in the spin sector of the theory, we find that the field may induce a second transition of the KT type in the remaining degrees of freedom to which it does not couple directly. In this sector, we find that the effective theory is that of an SO(4) Gross-Neveu model with an h-dependent coupling that may change its sign as h varies.
We investigate the properties of the standard perturbative expansions which describe the early stages of the dynamics of gravitational clustering. We show that for hierarchical scenarios with no small-scale cutoff perturbation theory always breaks down beyond a finite order $q_+$. Besides, the degree of divergence increases with the order of the perturbative terms so that renormalization procedures cannot be applied. Nevertheless, we explain that despite the divergence of these subleading terms the results of perturbation theory are correct at leading order because they can be recovered through a steepest-descent method which does not use such perturbative expansions. Finally, we investigate the simpler cases of the Zel'dovich and Burgers dynamics. In particular, we show that the standard Burgers equation exhibits similar properties. This analogy suggests that the results of the standard perturbative expansions are valid up to the order $q_+$ (i.e. until they are finite). Moreover, the first ``non-regular'' term of a large-scale expansion of the two-point correlation function should be of the form $R^{-2} \sigma^2(R)$. At higher orders the large-scale expansion should no longer be over powers of $\sigma^2$ but over a different combination of powers of 1/R. However, its calculation requires new non-perturbative methods.
A team of identical and oblivious ant-like agents - a(ge)nts - leaving pheromone traces, are programmed to jointly patrol an area modeled as a graph. They perform this task using simple local interactions, while also achieving the important byproduct of partitioning the graph into roughly equal-sized disjoint sub-graphs. Each a(ge)nt begins to operate at an arbitrary initial location, and throughout its work does not acquire any information on either the shape or size of the graph, or the number or whereabouts of other a(ge)nts. Graph partitioning occurs spontaneously, as each of the a(ge)nts patrols and expands its own pheromone-marked sub-graph, or region. This graph partitioning algorithm is inspired by molecules hitting the borders of air filled elastic balloons: an a(ge)nt that hits a border edge from the interior of its region more frequently than an external a(ge)nt hits the same edge from an adjacent vertex in the neighboring region, may conquer that adjacent vertex, expanding its region at the expense of the neighbor. Since the rule of patrolling a region ensures that each vertex is visited with a frequency inversely proportional to the size of the region, in terms of vertex count, a smaller region will effectively exert higher "pressure" at its borders, and conquer adjacent vertices from a larger region, thereby increasing the smaller region and shrinking the larger. The algorithm, therefore, tends to equalize the sizes of the regions patrolled, resembling a set of perfectly elastic physical balloons, confined to a closed volume and filled with an equal amount of air. The pheromone based local interactions of agents eventually cause the system to evolve into a partition that is close to balanced rather quickly, and if the graph and the number of a(ge)nts remain unchanged, it is guaranteed that the system settles into a stable and balanced partition.
We generalize the ZX calculus to quantum systems of dimension higher than two. The resulting calculus is sound and universal for quantum mechanics. We define the notion of a mutually unbiased qudit theory and study two particular instances of these theories in detail: qudit stabilizer quantum mechanics and Spekkens-Schreiber toy theory for dits. The calculus allows us to analyze the structure of qudit stabilizer quantum mechanics and provides a geometrical picture of qudit stabilizer theory using D-toruses, which generalizes the Bloch sphere picture for qubit stabilizer quantum mechanics. We also use our framework to describe generalizations of Spekkens toy theory to higher dimensional systems. This gives a novel proof that qudit stabilizer quantum mechanics and Spekkens-Schreiber toy theory for dits are operationally equivalent in three dimensions. The qudit pictorial calculus is a useful tool to study quantum foundations, understand the relationship between qubit and qudit quantum mechanics, and provide a novel, high level description of quantum information protocols.
A probabilistic expert system emulates the decision-making ability of a human expert through a directional graphical model. The first step in building such systems is to understand data generation mechanism. To this end, one may try to decompose a multivariate distribution into product of several conditionals, and evolving a blackbox machine learning predictive models towards transparent cause-and-effect discovery. Most causal models assume a single homogeneous population, an assumption that may fail to hold in many applications. We show that when the homogeneity assumption is violated, causal models developed based on such assumption can fail to identify the correct causal direction. We propose an adjustment to a commonly used causal direction test statistic by using a $k$-means type clustering algorithm where both the labels and the number of components are estimated from the collected data to adjust the test statistic. Our simulation result show that the proposed adjustment significantly improves the performance of the causal direction test statistic for heterogeneous data. We study large sample behaviour of our proposed test statistic and demonstrate the application of the proposed method using real data.
Prior attacks on graph neural networks have mostly focused on graph poisoning and evasion, neglecting the network's weights and biases. Traditional weight-based fault injection attacks, such as bit flip attacks used for convolutional neural networks, do not consider the unique properties of graph neural networks. We propose the Injectivity Bit Flip Attack, the first bit flip attack designed specifically for graph neural networks. Our attack targets the learnable neighborhood aggregation functions in quantized message passing neural networks, degrading their ability to distinguish graph structures and losing the expressivity of the Weisfeiler-Lehman test. Our findings suggest that exploiting mathematical properties specific to certain graph neural network architectures can significantly increase their vulnerability to bit flip attacks. Injectivity Bit Flip Attacks can degrade the maximal expressive Graph Isomorphism Networks trained on various graph property prediction datasets to random output by flipping only a small fraction of the network's bits, demonstrating its higher destructive power compared to a bit flip attack transferred from convolutional neural networks. Our attack is transparent and motivated by theoretical insights which are confirmed by extensive empirical results.
Deep Neural Networks (DNNs) are prone to learning spurious features that correlate with the label during training but are irrelevant to the learning problem. This hurts model generalization and poses problems when deploying them in safety-critical applications. This paper aims to better understand the effects of spurious features through the lens of the learning dynamics of the internal neurons during the training process. We make the following observations: (1) While previous works highlight the harmful effects of spurious features on the generalization ability of DNNs, we emphasize that not all spurious features are harmful. Spurious features can be "benign" or "harmful" depending on whether they are "harder" or "easier" to learn than the core features for a given model. This definition is model and dataset-dependent. (2) We build upon this premise and use instance difficulty methods (like Prediction Depth (Baldock et al., 2021)) to quantify "easiness" for a given model and to identify this behavior during the training phase. (3) We empirically show that the harmful spurious features can be detected by observing the learning dynamics of the DNN's early layers. In other words, easy features learned by the initial layers of a DNN early during the training can (potentially) hurt model generalization. We verify our claims on medical and vision datasets, both simulated and real, and justify the empirical success of our hypothesis by showing the theoretical connections between Prediction Depth and information-theoretic concepts like V-usable information (Ethayarajh et al., 2021). Lastly, our experiments show that monitoring only accuracy during training (as is common in machine learning pipelines) is insufficient to detect spurious features. We, therefore, highlight the need for monitoring early training dynamics using suitable instance difficulty metrics.
We present a joint theoretical and experimental study to investigate polymorphism in $\alpha$-sexithiophene (6T) crystals. By means of density-functional theory calculations, we clarify that the low-temperature phase is favorable over the high-temperature one, with higher relative stability by about 50 meV/molecule. This result is in agreement with our thermal desorption measurements. We also propose a transition path between the high- and low-temperature 6T polymorphs, estimating an upper bound for the energy barrier of about 1 eV/molecule. The analysis of the electronic properties of the investigated 6T crystal structures complements our study.
The advancement of large language models (LLMs) brings notable improvements across various applications, while simultaneously raising concerns about potential private data exposure. One notable capability of LLMs is their ability to form associations between different pieces of information, but this raises concerns when it comes to personally identifiable information (PII). This paper delves into the association capabilities of language models, aiming to uncover the factors that influence their proficiency in associating information. Our study reveals that as models scale up, their capacity to associate entities/information intensifies, particularly when target pairs demonstrate shorter co-occurrence distances or higher co-occurrence frequencies. However, there is a distinct performance gap when associating commonsense knowledge versus PII, with the latter showing lower accuracy. Despite the proportion of accurately predicted PII being relatively small, LLMs still demonstrate the capability to predict specific instances of email addresses and phone numbers when provided with appropriate prompts. These findings underscore the potential risk to PII confidentiality posed by the evolving capabilities of LLMs, especially as they continue to expand in scale and power.
We examine the weak noise limit of an overdamped dissipative system within a semiclassical description and show how quantization influences the growth and decay of fluctuations of the thermally equilibrated systems. We trace its origin in a semiclassical counterpart of the generalized potential for the dissipative system.
We count the number of occurrences of restricted patterns of length 3 in permutations with respect to length and the number of cycles. The main tool is a bijection between permutations in standard cycle form and weighted Motzkin paths.
A polynomial $f(x)$ over a field $K$ is said to be stable if all its iterates are irreducible over $K$. L. Danielson and B. Fein have shown that over a large class of fields $K$, if $f(x)$ is an irreducible monic binomial, then it is stable over $K$. In this paper it is proved that this result no longer holds over finite fields. Necessary and sufficient conditions are given in order that a given binomial is stable over $\mathbb{F}_q$. These conditions are used to construct a table listing the stable binomials over $\mathbb{F}_q$ of the form $f(x)=x^d-a$, $a\in\mathbb{F}_q\setminus\{0,1\}$, for $q \leq 27$ and $d \leq 10$. The paper ends with a brief link with Mersenne primes.
We construct a 3-dimensional cell complex that is the 3-skeleton for an Eilenberg--MacLane classifying space for the symmetric group $\mathfrak{S}_n$. Our complex starts with the presentation for $\mathfrak{S}_n$ with $n-1$ adjacent transpositions with squaring, commuting, and braid relations, and adds seven classes of 3-cells that fill in certain 2-spheres bounded by these relations. We use a rewriting system and a combinatorial method of K. Brown to prove the correctness of our construction. Our main application is a computation of the second cohomology of $\mathfrak{S}_n$ in certain twisted coefficient modules; we use this computation in a companion paper to study splitting of extensions related to braid groups. As another application, we give a concrete description of the third homology of $\mathfrak{S}_n$ with untwisted coefficients in $\mathbb{Z}$.
The goal in this paper is to demonstrate a new method for constructing global-in-time approximate (asymptotic) solutions of (pseudodifferential) parabolic equations with a small parameter. We show that, in the leading term, such a solution can be constructed by using characteristics, more precisely, by using solutions of the corresponding Hamiltonian system and without using any integral representation. For completeness, we also briefly describe the well-known scheme developed by V.P.Maslov for constructing global-in-time solutions.
The growth of a finitely generated group is an important geometric invariant which has been studied for decades. It can be either polynomial, for a well-understood class of groups, or exponential, for most groups studied by geometers, or intermediate, that is between polynomial and exponential. Despite recent spectacular progresses, the class of groups with intermediate growth remains largely mysterious. Many examples of such groups are constructed using Mealy automata. The aim of this paper is to give an algorithmic procedure to study the growth of such automata groups, and more precisely to provide numerical upper bounds on their exponents. Our functions retrieve known optimal bounds on the famous first Grigorchuk group. They also improve known upper bounds on other automata groups and permitted us to discover several new examples of automata groups of intermediate growth. All the algorithms described are implemented in GAP, a language dedicated to computational group theory.
Crystal seeding enables a deeper understanding of phase behavior, leading to the development of methods for controlling and manipulating phase transitions in various applications such as materials synthesis, crystallization processes, and phase transformation engineering. How to seed a crystalline in time domain is an open question, which is of great significant and may provide an avenue to understand and control time-dependent quantum many-body physics. Here, we utilize a microwave pulse as a seed to induce the formation of a discrete time crystal in Floquet driven Rydberg atoms. In the experiment, the periodic driving on Rydberg states acts as a seeded crystalline order in subspace, which triggers the time-translation symmetry breaking across the entire ensemble. The behavior of the emergent time crystal is elaborately linked to alterations in the seed, such as the relative phase shift and the frequency difference, which result in phase dependent seeding and corresponding shift in periodicity of the time crystal, leading to embryonic synchronization. This result opens up new possibilities for studying and harnessing time-dependent quantum many-body phenomena, offering insights into the behavior of complex many-body systems under seeding.
We study the general evolution of spherical over-densities for thawing class of dark energy models. We model dark energy with scalar fields having canonical as well as non-canonical kinetic energy. For non-canonical case, we consider models where the kinetic energy is of the Born-Infeld Form. We study various potentials like linear, inverse-square, exponential as well as PNGB-type. We also consider the case when dark energy is homogeneous as well as the case when it is inhomogeneous and virializes together with matter. Our study shows that models with linear potential in particular with Born-Infeld type kinetic term can have significant deviation from the $\Lambda$CDM model in terms of density contrast at the time of virialization. Although our approach is a simplified one to study the nonlinear evolution of matter overdensities inside the cluster and is not applicable to actual physical situation, it gives some interesting insights into the nonlinear clustering of matter in the presence of thawing class of dark energy models.
This paper introduces an enhanced meta-heuristic (ML-ACO) that combines machine learning (ML) and ant colony optimization (ACO) to solve combinatorial optimization problems. To illustrate the underlying mechanism of our ML-ACO algorithm, we start by describing a test problem, the orienteering problem. In this problem, the objective is to find a route that visits a subset of vertices in a graph within a time budget to maximize the collected score. In the first phase of our ML-ACO algorithm, an ML model is trained using a set of small problem instances where the optimal solution is known. Specifically, classification models are used to classify an edge as being part of the optimal route, or not, using problem-specific features and statistical measures. The trained model is then used to predict the probability that an edge in the graph of a test problem instance belongs to the corresponding optimal route. In the second phase, we incorporate the predicted probabilities into the ACO component of our algorithm, i.e., using the probability values as heuristic weights or to warm start the pheromone matrix. Here, the probability values bias sampling towards favoring those predicted high-quality edges when constructing feasible routes. We have tested multiple classification models including graph neural networks, logistic regression and support vector machines, and the experimental results show that our solution prediction approach consistently boosts the performance of ACO. Further, we empirically show that our ML model trained on small synthetic instances generalizes well to large synthetic and real-world instances. Our approach integrating ML with a meta-heuristic is generic and can be applied to a wide range of optimization problems.
Starting from a finite-dimensional representation of the Yangian $Y(\mathfrak{g})$ for a simple Lie algebra $\mathfrak{g}$ in Drinfeld's original presentation, we construct a Hopf algebra $X_\mathcal{I}(\mathfrak{g})$, called the extended Yangian, whose defining relations are encoded in a ternary matrix relation built from a specific $R$-matrix $R(u)$. We prove that there is a surjective Hopf algebra morphism $X_\mathcal{I}(\mathfrak{g})\twoheadrightarrow Y(\mathfrak{g})$ whose kernel is generated as an ideal by the coefficients of a central matrix $\mathcal{Z}(u)$. When the underlying representation is irreducible, we show that this matrix becomes a grouplike central series, thereby making available a proof of a well-known theorem stated by Drinfeld in the 1980's. We then study in detail the algebraic structure of the extended Yangian, and prove several generalizations of results which are known to hold for Yangians associated to classical Lie algebras in their $R$-matrix presentations.
We introduce a method to synthesize animator guided human motion across 3D scenes. Given a set of sparse (3 or 4) joint locations (such as the location of a person's hand and two feet) and a seed motion sequence in a 3D scene, our method generates a plausible motion sequence starting from the seed motion while satisfying the constraints imposed by the provided keypoints. We decompose the continual motion synthesis problem into walking along paths and transitioning in and out of the actions specified by the keypoints, which enables long generation of motions that satisfy scene constraints without explicitly incorporating scene information. Our method is trained only using scene agnostic mocap data. As a result, our approach is deployable across 3D scenes with various geometries. For achieving plausible continual motion synthesis without drift, our key contribution is to generate motion in a goal-centric canonical coordinate frame where the next immediate target is situated at the origin. Our model can generate long sequences of diverse actions such as grabbing, sitting and leaning chained together in arbitrary order, demonstrated on scenes of varying geometry: HPS, Replica, Matterport, ScanNet and scenes represented using NeRFs. Several experiments demonstrate that our method outperforms existing methods that navigate paths in 3D scenes.
From a perspective of feature matching, optical flow estimation for event cameras involves identifying event correspondences by comparing feature similarity across accompanying event frames. In this work, we introduces an effective and robust high-dimensional (HD) feature descriptor for event frames, utilizing Vector Symbolic Architectures (VSA). The topological similarity among neighboring variables within VSA contributes to the enhanced representation similarity of feature descriptors for flow-matching points, while its structured symbolic representation capacity facilitates feature fusion from both event polarities and multiple spatial scales. Based on this HD feature descriptor, we propose a novel feature matching framework for event-based optical flow, encompassing both model-based (VSA-Flow) and self-supervised learning (VSA-SM) methods. In VSA-Flow, accurate optical flow estimation validates the effectiveness of HD feature descriptors. In VSA-SM, a novel similarity maximization method based on the HD feature descriptor is proposed to learn optical flow in a self-supervised way from events alone, eliminating the need for auxiliary grayscale images. Evaluation results demonstrate that our VSA-based method achieves superior accuracy in comparison to both model-based and self-supervised learning methods on the DSEC benchmark, while remains competitive among both methods on the MVSEC benchmark. This contribution marks a significant advancement in event-based optical flow within the feature matching methodology.
Conceptual reasoning, the ability to reason in abstract and high-level perspectives, is key to generalization in human cognition. However, limited study has been done on large language models' capability to perform conceptual reasoning. In this work, we bridge this gap and propose a novel conceptualization framework that forces models to perform conceptual reasoning on abstract questions and generate solutions in a verifiable symbolic space. Using this framework as an analytical tool, we show that existing large language models fall short on conceptual reasoning, dropping 9% to 28% on various benchmarks compared to direct inference methods. We then discuss how models can improve since high-level abstract reasoning is key to unbiased and generalizable decision-making. We propose two techniques to add trustworthy induction signals by generating familiar questions with similar underlying reasoning paths and asking models to perform self-refinement. Experiments show that our proposed techniques improve models' conceptual reasoning performance by 8% to 11%, achieving a more robust reasoning system that relies less on inductive biases.
We consider a model combining technicolor with the top quark condensation. As a concrete model for Technicolor we use the Minimal Walking Technicolor, and this will result in the appearance of a novel fourth generation whose leptons constitute a usual weak doublet while the QCD quarks are vectorlike singlets under the weak interactions. We carry out an analysis of the mass spectra and precision measurement constraints, and find the model viable. We contrast the model with present LHC data and discuss the future prospects.
In this paper, we study bijections on strictly convex sets of $\mathbf R \mathbf P^n$ for $n \geq 2$ and closed convex projective surfaces equipped with the Hilbert metric that map complete geodesics to complete geodesics as sets. Hyperbolic $n$-space with its standard metric is a special example of the spaces we consider, and it is known that these bijections in this context are precisely the isometries. We first prove that this result generalizes to an arbitrary strictly convex set. For the surfaces setting, we prove the equivalence of mapping simple closed geodesics to simple closed geodesics and mapping closed geodesics to closed geodesics. We also outline some future directions and questions to further explore these topics.
This paper reviews the experimental and theoretical state of the art in ballistic hot electron transistors that utilize two-dimensional base contacts made from graphene, i.e. graphene base transistors (GBTs). Early performance predictions that indicated potential for THz operation still hold true today, even with improved models that take non-idealities into account. Experimental results clearly demonstrate the basic functionality, with on/off current switching over several orders of magnitude, but further developments are required to exploit the full potential of the GBT device family. In particular, interfaces between graphene and semiconductors or dielectrics are far from perfect and thus limit experimental device integrity, reliability and performance.
The article discusses carbocatalysis provided with amorphous carbons. The discussion is conducted from the standpoint of the spin chemistry of graphene molecules, in the framework of which the amorphous carbocatalysts are a conglomerate of graphene-oxynitrothiohydride stable radicals presenting the basic structural units (BSUs) of the species. The chemical activity of the BSUs atoms is reliably determined computationally, which allows mapping the distribution of active sites in these molecular catalysts. The presented maps reliably evidence the BSUs radicalization provided with carbon atoms only, the non-terminated edge part of which presents a set of active cites. Spin mapping of carbocatalysts active cites is suggested as the first step towards the spin carbocatalysis of the species.
Zipf's law predicts a power-law relationship between word rank and frequency in language communication systems, and is widely reported in texts yet remains enigmatic as to its origins. Computer simulations have shown that language communication systems emerge at an abrupt phase transition in the fidelity of mappings between symbols and objects. Since the phase transition approximates the Heaviside or step function, we show that Zipfian scaling emerges asymptotically at high rank based on the Laplace transform. We thereby demonstrate that Zipf's law gradually emerges from the moment of phase transition in communicative systems. We show that this power-law scaling behavior explains the emergence of natural languages at phase transitions. We find that the emergence of Zipf's law during language communication suggests that the use of rare words in a lexicon is critical for the construction of an effective communicative system at the phase transition.
The essence of quadrupeds' movements is the movement of the center of gravity, which has a pattern in the action of quadrupeds. However, the gait motion planning of the quadruped robot is time-consuming. Animals in nature can provide a large amount of gait information for robots to learn and imitate. Common methods learn animal posture with a motion capture system or numerous motion data points. In this paper, we propose a video imitation adaptation network (VIAN) that can imitate the action of animals and adapt it to the robot from a few seconds of video. The deep learning model extracts key points during animal motion from videos. The VIAN eliminates noise and extracts key information of motion with a motion adaptor, and then applies the extracted movements function as the motion pattern into deep reinforcement learning (DRL). To ensure similarity between the learning result and the animal motion in the video, we introduce rewards that are based on the consistency of the motion. DRL explores and learns to maintain balance from movement patterns from videos, imitates the action of animals, and eventually, allows the model to learn the gait or skills from short motion videos of different animals and to transfer the motion pattern to the real robot.
Although the Sun's polar magnetic fields are thought to provide important clues for understanding the 11-year sunspot cycle, including the observed variations of its amplitude and period, the current database of high-quality polar-field measurements spans relatively few sunspot cycles. In this paper we address this deficiency by consolidating Mount Wilson Observatory polar faculae data from four data reduction campaigns, validating it through a comparison with facular data counted automatically from MDI intensitygrams, and calibrating it against polar field measurements taken by the Wilcox Solar Observatory and average polar field and total polar flux calculated using MDI line-of-sight magnetograms. Our results show that the consolidated polar facular measurements are in excellent agreement with both polar field and polar flux estimates, making them an ideal proxy to study the evolution of the polar magnetic field. Additionally, we combine this database with sunspot area measurements to study the role of the polar magnetic flux in the evolution of the heliospheric magnetic field (HMF). We find that there is a strong correlation between HMF and polar flux at solar minimum and that, taken together, polar flux and sunspot area are better at explaining the evolution of the HMF during the last century than sunspot area alone.
We analyze X-ray spectra and images of a sample of Seyfert 2 galaxies that unambiguously contain starbursts, based on their optical and UV characteristics. Although all sample members contain active galactic nuclei (AGNs), supermassive black holes or other related processes at the galactic centers alone cannot account for the total X-ray emission in all instances. Eleven of the twelve observed galaxies are significantly resolved with the ROSAT HRI, while six of the eight sources observed with the lower-resolution PSPC also appear extended on larger scales. The X-ray emission is extended on physical scales of 10 kpc and greater, which we attribute to starburst-driven outflows and supernova-heating of the interstellar medium. Spectrally, a physically-motivated composite model of the X-ray emission that includes a heavily absorbed (N_H > 10^{23} cm^{-2}) nuclear component (the AGN), power-law like scattered AGN flux, and a thermal starburst describes this sample well. Half the sample exhibit iron K alpha lines, which are typical of AGNs.
Reduced dimensionality has long been regarded as an important strategy for increasing thermoelectric performance, for example in superlattices and other engineered structures. Here we point out and illustrate by examples that three dimensional bulk materials can be made to behave as if they were two dimensional from the point of view of thermoelectric performance. Implications for the discovery of new practical thermoelectrics are discussed.
Aims: We examine the recoverability and completeness limits of the dense core mass functions (CMFs) derived for a molecular cloud using extinction data and a core identification scheme based on two-dimensional thresholding. Methods: We performed simulations where a population of artificial cores was embedded into the variable background extinction field of the Pipe nebula. We extracted the cores from the simulated extinction maps, constructed the CMFs, and compared them to the input CMFs. The simulations were repeated using a variety of extraction parameters and several core populations with differing input mass functions and differing degrees of crowding. Results: The fidelity of the observed CMF depends on the parameters selected for the core extraction algorithm for our background. More importantly, it depends on how crowded the core population is. We find that the observed CMF recovers the true CMF reliably when the mean separation of cores is larger than their mean diameter (f>1). If this condition holds, the derived CMF is accurate and complete above M > 0.8-1.5 Msun, depending on the parameters used for the core extraction. In the simulations, the best fidelity was achieved with the detection threshold of 1 or 2 times the rms-noise of the extinction data, and with the contour level spacings of 3 times the rms-noise. Choosing larger threshold and wider level spacings increases the limiting mass. The simulations show that when f>1.5, the masses of individual cores are recovered with a typical uncertainty of 25-30 %. When f=1 the uncertainty is ~60 %. In very crowded cases where f<1 the core identification algorithm is unable to recover the masses of the cores adequately. For the cores of the Pipe nebula f~2.0 and therefore the use of the method in that region is justified.
Bode integrals of sensitivity and sensitivity-like functions along with complementary sensitivity and complementary sensitivity-like functions are conventionally used for describing performance limitations of a feedback control system. In this paper, we show that in the case when the disturbance is a wide sense stationary process the (complementary) sensitivity Bode integral and the (complementary) sensitivity-like Bode integral are identical. A lower bound of the continuous-time complementary sensitivity-like Bode integral is also derived and examined with the linearized flight-path angle tracking control problem of an F-16 aircraft.
Track functions describe the collective effect of the fragmentation of quarks and gluons into charged hadrons, making them a key ingredient for jet substructure measurements at hadron colliders, where track-based measurements offer superior angular resolution. The first moment of the track function, describing the average energy deposited in charged particles, is a simple and well-studied object. However, measurements of higher-point correlations of energy flow necessitate a characterization of fluctuations in the hadronization process, described theoretically by higher moments of the track function. In this paper we derive the structure of the renormalization group (RG) evolution equations for track function moments. We show that energy conservation gives rise to a shift symmetry that allows the evolution equations to be written in terms of cumulants, $\kappa(N)$, and the difference between the first moment of quark and gluon track functions, $\Delta$. The uniqueness of the first three cumulants then fixes their all-order evolution to be DGLAP, up to corrections involving powers of $\Delta$, that are numerically suppressed by an effective order in the perturbative expansion for phenomenological track functions. However, at the fourth cumulant and beyond there is non-trivial RG mixing into products of cumulants such as $\kappa(4)$ into $\kappa(2)^2$. We analytically compute the evolution equations up to the sixth moment at $\mathcal{O}(\alpha_s^2)$, and study the associated RG flows. These results allow for the study of up to six-point correlations in energy flow using tracks, paving the way for precision jet substructure at the LHC.
We study spin glass clusters ("shards") in a random transverse magnetic field, and determine the regime where quantum chaos and random matrix level statistics emerge from the integrable limits of weak and strong field. Relations with quantum phase transition are also discussed.
We study higher critical points of the variational functional associated with a free boundary problem related to plasma confinement. Existence and regularity of minimizers in elliptic free boundary problems have already been studied extensively. But because the functionals are not smooth, standard variational methods cannot be used directly to prove the existence of higher critical points. Here we find a nontrivial critical point of mountain pass type and prove many of the same estimates known for minimizers, including Lipschitz continuity and nondegeneracy. We then show that the free boundary is smooth in dimension 2 and prove partial regularity in higher dimensions.
Monte Carlo integration is typically interpreted as an estimator of the expected value using stochastic samples. There exists an alternative interpretation in calculus where Monte Carlo integration can be seen as estimating a \emph{constant} function -- from the stochastic evaluations of the integrand -- that integrates to the original integral. The integral mean value theorem states that this \emph{constant} function should be the mean (or expectation) of the integrand. Since both interpretations result in the same estimator, little attention has been devoted to the calculus-oriented interpretation. We show that the calculus-oriented interpretation actually implies the possibility of using a more \emph{complex} function than a \emph{constant} one to construct a more efficient estimator for Monte Carlo integration. We build a new estimator based on this interpretation and relate our estimator to control variates with least-squares regression on the stochastic samples of the integrand. Unlike prior work, our resulting estimator is \emph{provably} better than or equal to the conventional Monte Carlo estimator. To demonstrate the strength of our approach, we introduce a practical estimator that can act as a simple drop-in replacement for conventional Monte Carlo integration. We experimentally validate our framework on various light transport integrals. The code is available at \url{https://github.com/iribis/regressionmc}.
Using covariant quantization of the electromagnetic field, the Casimir force per unit area experienced by a long conducting cylindrical shell, under both Dirichlet and Neumann boundary conditions, is calculated. The renormalization procedure is based on the plasma cut-off frequency for real conductors. The real case of a gold (silver) cylindrical shell is considered and the corresponding electromagnetic Casimir pressure is computed. It is discussed that the Dirichlet and Neumann problems should be considered separately without adding their corresponding results.
Deep clustering has exhibited remarkable performance; however, the over-confidence problem, i.e., the estimated confidence for a sample belonging to a particular cluster greatly exceeds its actual prediction accuracy, has been overlooked in prior research. To tackle this critical issue, we pioneer the development of a calibrated deep clustering framework. Specifically, we propose a novel dual-head (calibration head and clustering head) deep clustering model that can effectively calibrate the estimated confidence and the actual accuracy. The calibration head adjusts the overconfident predictions of the clustering head, generating prediction confidence that match the model learning status. Then, the clustering head dynamically select reliable high-confidence samples estimated by the calibration head for pseudo-label self-training. Additionally, we introduce an effective network initialization strategy that enhances both training speed and network robustness. The effectiveness of the proposed calibration approach and initialization strategy are both endorsed with solid theoretical guarantees. Extensive experiments demonstrate the proposed calibrated deep clustering model not only surpasses state-of-the-art deep clustering methods by 10 times in terms of expected calibration error but also significantly outperforms them in terms of clustering accuracy.
Shell galaxies make a class of tidally distorted galaxies, characterised by wide concentric arc(s), extending out to large galactocentric distances with sharp outer edges. Recent observations of young massive star clusters in the prominent outer shell of NGC 474 suggest that such systems host extreme conditions of star formation. In this paper, we present a hydrodynamic simulation of a galaxy merger and its transformation into a shell galaxy. We analyse how the star formation activity evolves with time, location-wise within the system, and what are the physical conditions for star formation. During the interaction, an excess of dense gas appears, triggering a starburst, i.e. an enhanced star formation rate and a reduced depletion time. Star formation coincides with regions of high molecular gas fraction, such as the galactic nucleus, spiral arms, and occasionally the tidal debris during the early stages of the merger. Tidal interactions scatter stars into a stellar spheroid, while the gas cools down and reforms a disc. The morphological transformation after coalescence stabilises the gas and thus quenches star formation, without the need for feedback from an active galactic nucleus. This evolution shows similarities with a compaction scenario for compact quenched spheroids at high-redshift, yet without a long red nugget phase. Shells appear after coalescence, during the quenched phase, implying that they do not host the conditions necessary for in situ star formation. The results suggest that shell-forming mergers might be part of the process of turning blue late-type galaxies into red and dead early-types.
Clinical trials in specific indications require the administration of rescue medication in case a patient does not sufficiently respond to investigational treatment. The application of additional treatment on an as needed basis causes problems to the analysis and interpretation of the results of these studies since the effect of the investigational treatment can be confounded by the additional medication. Following-up all patients until study end and capturing all data is not fully addressing the issue. We present an analysis that takes care of the fact that rescue is a study outcome and not a covariate when rescue medication is administered according to a deterministic rule. This approach allows to clearly define a biological effect. For normally distributed longitudinal data a practically unbiased estimator of the biological effect can be obtained. The results are compared to an ITT analysis and an analysis on all patients not receiving rescue.
We construct a triangulation of a compactification of the Moduli space of a surface with at least one puncture that is closely related to the Deligne-Mumford compactification. Specifically, there is a surjective map from the compactification we construct to the Deligne-Mumford compactification so that the inverse image of each point is contractible. In particular our compactification is homotopy equivalent to the Deligne-Mumford compactification.