December 2020 arXiv papers — page 43

Zhifeng Hao, Di Lv, Zijian Li, Ruichu Cai

Named entity recognition (NER) for identifying proper nouns in unstructured text is one of the most important and fundamental tasks in natural language processing. However, despite the widespread use of NER models, they still require a large-scale labeled data set, which incurs a heavy burden due to manual annotation. Domain adaptation is one of the most pro

Liang Li, Dian Shi, Ronghui Hou, Hui Li

Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications. Despite the potential benefits, FL imposes huge communication and computation burdens on participating devices due to

Bingyao Huang, Ruyi Lian, Dimitris Samaras, Haibin Ling

Mail privacy protection aims to prevent unauthorized access to hidden content within an envelope since normal paper envelopes are not as safe as we think. In this paper, for the first time, we show that with a well designed deep learning model, the hidden content may be largely recovered without opening the envelope. We start by modeling deep learning-based

Juan Zhang, Cheng Wang, Steven M. Wise, Zhengru Zhang

In this paper, two finite difference numerical schemes are proposed and analyzed for the droplet liquid film model, with a singular Leonard-Jones energy potential involved. Both first and second order accurate temporal algorithms are considered. In the first order scheme, the convex potential and the surface diffusion terms are implicitly, while the concave

C. Itoi

It is proven rigorously that the ground state in the Edwards-Anderson spin glass model is unique in any dimension for almost all continuous random exchange interactions under a condition that a single spin breaks the global ${\mathbb Z}_2$ symmetry. This theorem implies that replica symmetry breaking does not occur at zero temperature. The site- and bond-ove

W. J. Zuluaga Botero

In this paper we use the theory of central elements in order to provide a characterization for coextensive varieties. In particular, if the variety is of finite type, congruence-permutable and its class of directly indecomposable members is universal, then coextensivity is equivalent to be a variety of shells.

Nathaniel Trask, Andy Huang, Xiaozhe Hu

As traditional machine learning tools are increasingly applied to science and engineering applications, physics-informed methods have emerged as effective tools for endowing inferences with properties essential for physical realizability. While promising, these methods generally enforce physics weakly via penalization. To enforce physics strongly, we turn to

Jialei Chen, Zhehui Chen, Chuck Zhang, C. F. Jeff Wu

Kriging (or Gaussian process regression) is a popular machine learning method for its flexibility and closed-form prediction expressions. However, one of the key challenges in applying kriging to engineering systems is that the available measurement data is scarce due to the measurement limitations and high sensing costs. On the other hand, physical knowledg

Ruichu Cai, Jiawei Chen, Zijian Li, Wei Chen

Domain adaptation on time series data is an important but challenging task. Most of the existing works in this area are based on the learning of the domain-invariant representation of the data with the help of restrictions like MMD. However, such extraction of the domain-invariant representation is a non-trivial task for time series data, due to the complex

Zann Koh, Yuren Zhou, Billy Pik Lik Lau, Chau Yuen

Information about the spatiotemporal flow of humans within an urban context has a wide plethora of applications. Currently, although there are many different approaches to collect such data, there lacks a standardized framework to analyze it. The focus of this paper is on the analysis of the data collected through passive Wi-Fi sensing, as such passively col

Primitivo B. Acosta-Humánez, David Blázquez-Sanz, Henock Venegas-Gómez

This paper is devoted to a complete parametric study of Liouvillian solutions of the general trace-free second order differential equation with a Laurent polynomial coefficient. This family of equations, for fixed orders at $0$ and $\infty$ of the Laurent polynomial, is seen as an affine algebraic variety. We proof that the set of Picard-Vessiot integrable d

Meng Xu, Shun Zhang, Caijun Zhong, Jianpeng Ma

The reconfigurable intelligent surface (RIS) is considered as a promising new technology for reconfiguring wireless communication environments. To acquire the channel information accurately and efficiently, we only turn on a fraction of all the RIS elements, formulate a sub-sampled RIS channel, and design a deep learning based scheme to extrapolate the full

Yuting Fang, Saman Atapattu, Hazer Inaltekin, Jamie Evans

The reconfigurable intelligent surface (RIS) is a promising technology that is anticipated to enable high spectrum and energy efficiencies in future wireless communication networks. This paper investigates optimum location-based RIS selection policies in RIS-aided wireless networks to maximize the end-to-end signal-to-noise ratio for product-scaling and sum-

Mehrdad Zakershahrak, Samira Ghodratnama

Providing explanations is considered an imperative ability for an AI agent in a human-robot teaming framework. The right explanation provides the rationale behind an AI agent's decision-making. However, to maintain the human teammate's cognitive demand to comprehend the provided explanations, prior works have focused on providing explanations in a specific o

Naser Ahmadiniaz, Cristhiam Lopez-Arcos, Misha A. Lopez-Lopez, Christian Schubert

The QED four-photon amplitude has been well-studied by many authors, and on-shell is treated in many textbooks. However, a calculation with all four photons off-shell is presently still lacking, despite of the fact that this amplitude appears off-shell as a subprocess in many different contexts, in vacuum as well as with some photons connecting to external f

Haeun Yoo, Victor M. Zavala, Jay H. Lee

Reinforcement learning (RL) is attracting attention as an effective way to solve sequential optimization problems that involve high dimensional state/action space and stochastic uncertainties. Many such problems involve constraints expressed by inequality constraints. This study focuses on using RL to solve constrained optimal control problems. Most RL appli

Chengcheng Cheng, Zuohuan Zheng

In current paper, we put forward a reaction-diffusion system for West Nile virus in spatial heterogeneous and time almost periodic environment with free boundaries to investigate the influences of the habitat differences and seasonal variations on the propagation of West Nile virus. The existence, uniqueness and regularity estimates of the global solution fo

Kaivalya Rawal, Ece Kamar, Himabindu Lakkaraju

As predictive models are increasingly being deployed to make a variety of consequential decisions, there is a growing emphasis on designing algorithms that can provide recourse to affected individuals. Existing recourse algorithms function under the assumption that the underlying predictive model does not change. However, models are regularly updated in prac

K. G. D. Sulalitha Priyankara, Sanjeeva Balasuriya, Erik Bollt

We present a Melnikov method to analyze two-dimensional stable or unstable manifolds associated with a saddle point in three-dimensional non-volume preserving autonomous systems. The time-varying perturbed locations of such manifolds is obtained under very general, non-volume preserving and with arbitrary time-dependence, perturbations. In unperturbed situat

S. J. Kuhn, S. McKay, J. Shen, N. Geerits

The development of direct probes of entanglement is integral to the rapidly expanding field of complex quantum materials. Here we test the robustness of entangled neutrons as a quantum probe by measuring the Clauser-Horne-Shimony-Holt contextuality witness while varying the beam properties. Specifically, we prove that the entanglement of the spin and path su

Mohameden Ahmedou, Lina Wu, Lei Zhang

On $(M,g)$ a compact riemannian $4-$manifold we consider the prescribed $Q-$curvature equation defined on $M$ with finite singular sources. We first prove a classification theorem for singular Liouville equations defined on $\mathbb R^4$ and perform a concentration compactness analysis. Then we derive a quantization result for bubbling solutions and establis

Hyunwoo Lee, Chris H. Greene

A variational basis set motivated by mean-field theory is utilized to describe the Bose-Einstein condensate within the adiabatic hyperspherical coordinate framework. The simplest single-orbital variant of this treatment reproduces many of the ground state properties predicted by the Gross-Pitaevskii equation. But a multi-orbital improvement to the basis set

Wei Cui, Wei Yu

This paper proposes a novel scalable reinforcement learning approach for simultaneous routing and spectrum access in wireless ad-hoc networks. In most previous works on reinforcement learning for network optimization, the network topology is assumed to be fixed, and a different agent is trained for each transmission node -- this limits scalability and genera

Ryan E. Langendorf, Matthew G. Burgess

Network models are used to study interconnected systems across many physical, biological, and social disciplines. Such models often assume a particular network-generating mechanism, which when fit to data produces estimates of mechanism-specific parameters that describe how systems function. For instance, a social network model might assume new individuals c

Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike

Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result. Given a perturbation vector, a user can interpret it as an

Xingyu Shen, Yi Ji

The relation between the Kondo spin relaxation rate $ {\tau_{sK}}^{-1} $ and the Kondo momentum relaxation rate $ {\tau_{eK}}^{-1} $ is explored by using nonlocal spin valves with submicron copper channels that contain dilute iron impurities. A linear relation between $ {\tau_{sK}}^{-1} $ and $ {\tau_{eK}}^{-1} $ is established under varying temperatures. Ho

J. Kissi-Ameyaw, K. McIsaac, X. Wang, G. R. Osinski

In geology, a key activity is the characterisation of geological structures (surface formation topology and rock units) using Planar Orientation measurements such as Strike, Dip and Dip Direction. In general these measurements are collected manually using basic equipment; usually a compass/clinometer and a backboard, recorded on a map by hand. Various comput

P. J. "Eddie'' Edwards, Dimitris Psychogyios, Stefanie Speidel, Lena Maier-Hein

In computer vision, reference datasets have been highly successful in promoting algorithmic development in stereo reconstruction. Surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surfaces and the presence of blood and smoke. Publicly available datasets have been produced using CT and either phantom

Paul M. Chesler, Lindy Blackburn, Sheperd S. Doeleman, Michael D. Johnson

The Event Horizon Telescope recently produced the first images of a black hole. These images were synthesized by measuring the coherent correlation function of the complex electric field measured at telescopes located across the Earth. This correlation function corresponds to the Fourier transform of the image under the assumption that the source emits spati

Andrey V Chubukov, Artem Abanov

We consider pairing of itinerant fermions in a metal near a quantum-critical point (QCP) towards some form of particle-hole order (nematic, spin-density-wave, charge-density-wave, etc). At a QCP, the dominant interaction between fermions comes from exchanging massless fluctuations of a critical order parameter. At low energies, this physics can be described

Ali Eshaghian Dorche, Ali Adibi

An integrated photonic approach for complex quantum state generation through dynamical Casimir effect (DCE) is demonstrated. This approach provides a scheme to realize multipartite high-dimensional entangled states in the microwave (MW) and terahertz (THz) regimes, through the modulation in a MW-resonator coupled to an optical microresonator supporting tempo

Wei Wang, Emily Sallenback, Zeyu Ning, Hugues Nelson Iradukunda

The following work presents a new algorithm for character recognition from obfuscated images. The presented method is an example of a potential threat to current postal services. This paper both analyzes the efficiency of the given algorithm and suggests countermeasures to prevent such threats from occurring.

Kuo Yang, Emad A. Mohammed

Alzheimer's Disease (AD) is a severe brain disorder, destroying memories and brain functions. AD causes chronically, progressively, and irreversibly cognitive declination and brain damages. The reliable and effective evaluation of early dementia has become essential research with medical imaging technologies and computer-aided algorithms. This trend has move

Amirsina Torfi, Edward A. Fox, Chandan K. Reddy

Deep learning models have demonstrated superior performance in several application problems, such as image classification and speech processing. However, creating a deep learning model using health record data requires addressing certain privacy challenges that bring unique concerns to researchers working in this domain. One effective way to handle such priv

Leonardo N. Coregliano, Alexander A. Razborov

The theory of quasirandomness has greatly expanded from its inaugural graph theoretical setting to several different combinatorial objects such as hypergraphs, tournaments, permutations, etc. However, these quasirandomness variants have been done in an ad-hoc case-by-case manner. In this paper, we propose three new hierarchies of quasirandomness properties t

Maximilian Fiedler, Andreas Alpers

Superpixel algorithms grouping pixels with similar color and other low-level properties are increasingly used for pre-processing in image segmentation. In recent years, a focus has been placed on developing geometric superpixel methods that facilitate the extraction and analysis of geometric image features. Diagram-based superpixel methods are important amon

Naomi Sweeting

Let $E/\mathbb{Q}$ be an elliptic curve and let $K$ be an imaginary quadratic field. Under a certain Heegner hypothesis, Kolyvagin constructed cohomology classes for $E$ using $K$-CM points and conjectured they did not all vanish. Conditional on this conjecture, he described the Selmer rank of $E$ using his system of classes. We extend work of Wei Zhang to p

Benjamín A. Itzá-Ortiz, Roberto López Hernández, Pedro Miramontes

Given a point $(p,q)$ with nonnegative integer coordinates and $p\not=q$, we prove that the quadratic B\'ezier curve relative to the points $(p,q)$, $(0,0)$ and $(q,p)$ is approximately the envelope of a family of segments whose endpoints are the B\'ezout coefficients of coprime numbers belonging to neighborhoods of $(p,q)$ and $(q,p)$, respectively.

Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang

Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems. Current robust training methods such as adversarial training explicitly uses an "attack" (e.g., $\ell_{\infty}$-norm bounded perturbation) to generate adversarial examples during model training for improving adve

Jeffrey D. Michler, Anna Josephson, Talip Kilic, Siobhan Murray

This paper quantifies the significance and magnitude of the effect of measurement error in remote sensing weather data in the analysis of smallholder agricultural productivity. The analysis leverages 17 rounds of nationally-representative, panel household survey data from six countries in Sub-Saharan Africa. These data are spatially-linked with a range of ge

Yawen Guan, Garritt L. Page, Brian J Reich, Massimo Ventrucci

Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. In this paper, we derive necessary conditions on the coherence between the treatment variable of interest and the unmeasured confounder that ensure the causal effect of the treatment is estimable. We specify our

Raffaele Tito D'Agnolo, Di Liu, Joshua T. Ruderman, Po-Jen Wang

We present kinematically forbidden dark matter annihilations into Standard Model leptons. This mechanism precisely selects the dark matter mass that gives the observed relic abundance. This is qualitatively different from existing models of thermal dark matter, where fixing the relic density typically leaves open orders of magnitude of viable dark matter mas

Andreas Thomasen, Karlo Penc, Nic Shannon, Judit Romhányi

The discovery by Kane and Mele of a model of spinful electrons characterized by a $\mathcal{Z}_2$ topological invariant had a lasting effect on the study of electronic band structures. Given this, it is natural to ask whether similar topology can be found in the band-like excitations of magnetic insulators, and recently models supporting $\mathcal{Z}_2$ topo

Albert Mollén, M. F. Adams, M. G. Knepley, R. Hager

The global total-$f$ gyrokinetic particle-in-cell code XGC, used to study transport in magnetic fusion plasmas, implements a continuum grid to perform the dissipative operations, such as plasma collisions. To transfer the distribution function between marker particles and a rectangular velocity-space grid, XGC employs a bilinear mapping. The conservation of

Agniva Roychowdhury, Sebastian Deffner

Only very recently, rescaling time has been recognized as a way to achieve adiabatic dynamics in fast processes. The advantage of time-rescaling over other shortcuts to adiabaticity is that it does not depend on the eigenspectrum and eigenstates of the Hamiltonian. However, time-rescaling requires that the original dynamics are adiabatic, and in the rescaled

Tian Xia, Wei-Shinn Ku

Graphs as a type of data structure have recently attracted significant attention. Representation learning of geometric graphs has achieved great success in many fields including molecular, social, and financial networks. It is natural to present proteins as graphs in which nodes represent the residues and edges represent the pairwise interactions between res

James Ferlez, Yasser Shoukry

In this paper, we consider the computational complexity of formally verifying the behavior of Rectified Linear Unit (ReLU) Neural Networks (NNs), where verification entails determining whether the NN satisfies convex polytopic specifications. Specifically, we show that for two different NN architectures -- shallow NNs and Two-Level Lattice (TLL) NNs -- the v

Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen, Walter Chang

Acronyms are the short forms of longer phrases and they are frequently used in writing, especially scholarly writing, to save space and facilitate the communication of information. As such, every text understanding tool should be capable of recognizing acronyms in text (i.e., acronym identification) and also finding their correct meaning (i.e., acronym disam

Andrine Elsetrønning, Adil Rasheed, Jon Bekker, Omer San

Lung sounds refer to the sound generated by air moving through the respiratory system. These sounds, as most biomedical signals, are non-linear and non-stationary. A vital part of using the lung sound for disease detection is discrimination between normal lung sound and abnormal lung sound. In this paper, several approaches for classifying between no-crackle

Fabian Lehmann

We clarify the global geometry of two 1-parameter families of cohomogeneity one Spin(7) holonomy metrics with generic orbit the Aloff--Wallach space $N(1,-1) \cong \mathrm{SU}(3)/\mathrm{U}(1)$ and singular orbits $S^5$ and $\mathbb{C}P^2$, which at short distance were shown to exist by Reidegeld. The two families fit into the geography of previously known f

Nora Yujia Payne, Johann A. Gagnon-Bartsch

Genomic datasets contain the effects of various unobserved biological variables in addition to the variable of primary interest. These latent variables often affect a large number of features (e.g., genes) and thus give rise to dense latent variation, which presents both challenges and opportunities for classification. Some of these latent variables may be p

Darrell Cox, Sourangshu Ghosh, Eldar Sultanow

In this paper, we derive new properties of the Mertens function and discuss a likely upper bound of the absolute value of the Mertens function $\sqrt{\log{x!}}>|M(x)|$ when $x>1$. Using this likely bound we show that we have a sufficient condition to prove the Riemann Hypothesis.

Yasushi Ieno

A newly-generalized problem from a problem initially thought for the Mathematical Olympiad and the methods to solve it.

Eric Bonnetier, Sergio Gaete, Alejandro Jofre, Rodrigo Lecaros

Block caving is an ore extraction technique used in the copper mines of Chile. It uses gravity to ease the breaking of rocks, and to facilitate the extraction from the mine of the resulting mixture of ore and waste. To simulate this extraction process numerically and better understand its impact on the mine environment, we study 3 variational models for dama

Elena Khlebnikova, Kaarthik Sundar, Anatoly Zlotnik, Russell Bent

The majority of overland transport needs for crude petroleum and refined petroleum products are met using pipelines. Numerous studies have developed optimization methods for design of these systems in order to minimize construction costs while meeting capacity requirements. Here, we formulate problems to optimize the operations of existing single liquid comm

G. Lamer, A. D. Schwope, P. Predehl, I. Traulsen

We report the discovery of a giant dust scattering ring around the Black Hole transient MAXI J1348-630 with SRG/eROSITA during its first X-ray all-sky survey. During the discovery observation in February 2020 the ring had an outer diameter of 1.3 deg, growing to 1.6 deg by the time of the second all sky survey scan in August 2020. This makes the new dust rin

Qian Wang, Toby P. Breckon

Automatic prohibited object detection within 2D/3D X-ray Computed Tomography (CT) has been studied in literature to enhance the aviation security screening at checkpoints. Deep Convolutional Neural Networks (CNN) have demonstrated superior performance in 2D X-ray imagery. However, there exists very limited proof of how deep neural networks perform in materia

Jeffrey A. Hogan, Joseph D. Lakey

We study certain spaces of vertex functions on the Cayley graphs corresponding to N-fold products of the group of integers modulo m, where m=3, 4, or 5, that are invariant under the adjacency operator that maps a value at a given vertex to each of its neighbors. An application to spatio-spectral limiting, an analogue of time and band limiting, is also discus

V. G. Baryshevsky, P. I. Porshnev

The spin dynamics in constant electromagnetic fields is described by the Bargmann-Michel-Telegdi equation which can be upgraded with anomalous magnetic and electric dipole moments. The upgraded equation remains self-consistent, Lorentz-covariant and gauge-invariant. It and its different forms have been confirmed in numerous experiments to high degree of accu

Juan L. Varona

It is well known that the arithmetic nature of Mills' prime-representing constant is uncertain: we do not know if Mills' constant is a rational or irrational number. In the case of other prime-representing constants, irrationality can be proved, but it is not known whether these constants are algebraic or transcendental numbers. By using Liouville or Roth's

CUORE Collaboration, D. Q. Adams, C. Alduino, K. Alfonso

We measured two-neutrino double beta decay of $^{130}$Te using an exposure of 300.7 kg$\cdot$yr accumulated with the CUORE detector. Using a Bayesian analysis to fit simulated spectra to experimental data, it was possible to disentangle all the major background sources and precisely measure the two-neutrino contribution. The half-life is in agreement with pa

Lukas Baumgärtner, Ronny Bergmann, Roland Herzog, Stephan Schmidt

We present a novel approach to denoising and inpainting problems for surface meshes. The purpose of these problems is to remove noise or fill in missing parts while preserving important features such as sharp edges. A discrete variant of the total variation of the unit normal vector field serves as a regularizing functional to achieve these goals. In order t

Ruining He, Anirudh Ravula, Bhargav Kanagal, Joshua Ainslie

Transformer is the backbone of modern NLP models. In this paper, we propose RealFormer, a simple and generic technique to create Residual Attention Layer Transformer networks that significantly outperform the canonical Transformer and its variants (BERT, ETC, etc.) on a wide spectrum of tasks including Masked Language Modeling, GLUE, SQuAD, Neural Machine Tr

Teruaki Hayashi, Hiroki Sakaji, Hiroyasu Matsushima, Yoshiaki Fukami

In recent years, rather than enclosing data within a single organization, exchanging and combining data from different domains has become an emerging practice. Many studies have discussed the economic and utility value of data and data exchange, but the characteristics of data that contribute to problem solving through data combination have not been fully un

Tien Chu, Kamil Mykitiuk, Miron Szewczyk, Adam Wiktor

This work presents a method for reducing memory consumption to a constant complexity when training deep neural networks. The algorithm is based on the more biologically plausible alternatives of the backpropagation (BP): direct feedback alignment (DFA) and feedback alignment (FA), which use random matrices to propagate error. The proposed method, memory-effi

W. Aniszewski, T. Arrufat, M. Crialesi-Esposito, S. Dabiri

Paris (PArallel, Robust, Interface Simulator) is a finite volume code for simulations of immiscible multifluid or multiphase flows. It is based on the "one-fluid" formulation of the Navier-Stokes equations where different fluids are treated as one material with variable properties, and surface tension is added as a singular interface force. The fluid equatio

Sulaf Elshaar, Samira Sadaoui

This research explores Cost-Sensitive Learning (CSL) in the fraud detection domain to decrease the fraud class's incorrect predictions and increase its accuracy. Notably, we concentrate on shill bidding fraud that is challenging to detect because the behavior of shill and legitimate bidders are similar. We investigate CSL within the Semi-Supervised Classific

Jana Cslovjecsek, Friedrich Eisenbrand, Michał Pilipczuk, Moritz Venzin

We consider the problem of solving integer programs of the form $\min \{\,c^\intercal x\ \colon\ Ax=b, x\geq 0\}$, where $A$ is a multistage stochastic matrix in the following sense: the primal treedepth of $A$ is bounded by a parameter $d$, which means that the columns of $A$ can be organized into a rooted forest of depth at most $d$ so that columns not bou

Anton Solovev, Benjamin M. Friedrich

Carpets of actively bending cilia represent arrays of biological oscillators that can exhibit self-organized metachronal synchronization in the form of traveling waves of cilia phase. This metachronal coordination supposedly enhances fluid transport by cilia carpets. Using a multi-scale model calibrated by an experimental cilia beat pattern, we predict multi

Thomas van Dongen, Gideon Maillette de Buy Wenniger, Lambert Schomaker

Predicting the number of citations of scholarly documents is an upcoming task in scholarly document processing. Besides the intrinsic merit of this information, it also has a wider use as an imperfect proxy for quality which has the advantage of being cheaply available for large volumes of scholarly documents. Previous work has dealt with number of citations

Lucas Kocia

We find a scaling reduction in the stabilizer rank of the twelve-qubit tensored $T$ gate magic state. This lowers its asymptotic bound to $2^{\sim 0.463 t}$ for multi-Pauli measurements on $t$ magic states, improving over the best previously found bound of $2^{\sim 0.468 t}$. We numerically demonstrate this reduction. This constructively produces the most ef

Dennis Tilipman, Mariela Vieytes, Jeffrey L. Linsky, Andrea P. Buccino

Stellar ultraviolet (UV) radiation drives photochemistry, and extreme-ultraviolet (EUV) radiation drives mass loss in exoplanet atmospheres. However, the UV flux is partly unobservable due to interstellar absorption, particularly in the EUV range (100--912 A). It is therefore necessary to reconstruct the unobservable spectra in order to characterize the radi

Samantha K. Trumbo, Michael E. Brown, Kevin P. Hand

The composition of Europa's trailing hemisphere reflects the combined influences of endogenous geologic resurfacing and exogenous sulfur radiolysis. Using spatially resolved visible-wavelength spectra of Europa obtained with the Hubble Space Telescope, we map multiple spectral features across the trailing hemisphere and compare their geographies with the dis

Quang Nhat Le, Van-Dinh Nguyen, Octavia A. Dobre, Ruiqin Zhao

Integrating the reconfigurable intelligent surface in a cell-free (RIS-CF) network is an effective solution to improve the capacity and coverage of future wireless systems with low cost and power consumption. The reflecting coefficients of RISs can be programmed to enhance signals received at users. This letter addresses a joint design of transmit beamformer

Pushpinder Singh, Abhijit Mandal, Ayanendranath Basu

Density-based minimum divergence procedures represent popular techniques in parametric statistical inference. They combine strong robustness properties with high (sometimes full) asymptotic efficiency. Among density-based minimum distance procedures, the methods based on the Bregman-divergence have the attractive property that the empirical formulation of th

Ryan Mohr, Maria Fonoberova, Zlatko Drmač, Iva Manojlović

Hierarchical support vector regression (HSVR) models a function from data as a linear combination of SVR models at a range of scales, starting at a coarse scale and moving to finer scales as the hierarchy continues. In the original formulation of HSVR, there were no rules for choosing the depth of the model. In this paper, we observe in a number of models a

Matthijs H. J. de Jong, Jie Li, Claus Gärtner, Richard A. Norte

Studying the interplay between multiple coupled mechanical resonators is a promising new direction in the field of optomechanics. Understanding the dynamics of the interaction can lead to rich new effects, such as enhanced coupling and multi-body physics. In particular, multi-resonator optomechanical systems allow for distinct dynamical effects due to the op

G. P. Zhang, Y. H. Bai

High harmonic generation (HHG) has unleashed the power of strong laser physics in solids. Here we investigate HHG from a large system, solid C$_{60}$, with 240 valence electrons engaging harmonic generation at each crystal momentum, the first of this kind. We employ the density functional theory and the time-dependent Liouville equation of the density matrix

Richard Olaniyan, Muthucumaru Maheswaran

Real-time artificial intelligence (AI) applications mapped onto edge computing need to perform data capture, process data, and device actuation within given bounds while using the available devices. Task synchronization across the devices is an important problem that affects the timely progress of an AI application by determining the quality of the captured

Florence H. Vermeire, William H. Green

Data scarcity, bias, and experimental noise are all frequently encountered problems in the application of deep learning to chemical and material science disciplines. Transfer learning has proven effective in compensating for the lack in data. The use of quantum calculations in machine learning enables the generation of a diverse dataset and ensures that lear

Beybin Ilhan, Frieder Mugele, Michael H. G. Duits

Roughening the surface of spherical colloids can drastically change their translational and rotational dynamics in dense suspensions. Using 3D confocal microscopy, we show that roughness not only lowers the concentration of the translational colloidal glass transition, but also generates a broad concentration range in which the rotational Brownian motion cha

Mohameden Ahmedou, Mohamed Ben Ayed

In this paper we study a Nirenberg type problem on standard half spheres $(\mathbb{S}^n_+,g_0)$ consisting of finding conformal metrics of prescribed scalar curvature and zero boundary mean curvature on the boundary $\partial \mathbb{S}^n_+$. This problem amounts to solve the following boundary value problem involving the critical Sobolev exponent: \begin{eq

Jing Shi, Marcel Ausloos, Tingting Zhu

This paper investigates the heterogeneous impacts of either Global or Local Investor Sentiments on stock returns. We study 10 industry sectors through the lens of 6 (so called) emerging countries: China, Brazil, India, Mexico, Indonesia and Turkey, over the 2000 to 2014 period. Using a panel data framework, our study sheds light on a significant effect of Lo

Jinyong Hou, Jeremiah D. Deng, Stephen Cranefield, Xuejie Ding

We propose a cross-domain latent modulation mechanism within a variational autoencoders (VAE) framework to enable improved transfer learning. Our key idea is to procure deep representations from one data domain and use it as perturbation to the reparameterization of the latent variable in another domain. Specifically, deep representations of the source and t

C. Tiburzi, G. M. Shaifullah, C. G. Bassa, P. Zucca

High-precision pulsar timing requires accurate corrections for dispersive delays of radio waves, parametrized by the dispersion measure (DM), particularly if these delays are variable in time. In a previous paper we studied the Solar-wind (SW) models used in pulsar timing to mitigate the excess of DM annually induced by the SW, and found these to be insuffic

Bartłomiej Nowak, Bartosz Stoń, Katarzyna Sznajd-Weron

We introduce a generalized version of the noisy $q$-voter model, one of the most popular opinion dynamics models, in which voters can be in one of $s \ge 2$ states. As in the original binary $q$-voter model, which corresponds to $s=2$, at each update randomly selected voter can conform to its $q$ randomly chosen neighbors (copy their state) only if all $q$ n

Rostislav Grigorchuk, Supun Samarakoon

Fractal groups (also called self-similar groups) is the class of groups discovered by the first author in the 80-s of the last century with the purpose to solve some famous problems in mathematics, including the question raising to von Neumann about non-elementary amenability (in the association with studies around the Banach-Tarski Paradox) and John Milnor'

F. Lemic, S. Abadal, J. Famaey

Software-defined metamaterials (SDMs) represent a novel paradigm for real-time control of metamaterials. SDMs are envisioned to enable a variety of exciting applications in the domains such as smart textiles and sensing in challenging conditions. Many of these applications envisage deformations of the SDM structure (e.g., rolling, bending, stretching). This

Jean Walrand, Max Turner, Roy Myers

As vehicles get equipped with increasingly complex sensors and processors, the communication requirements become more demanding. Traditionally, vehicles have used specialized networking technologies designed to guarantee bounded latencies, such as the Controller Area Network (CAN) bus. Recently, some have used dedicated technologies to transport signals from

Tan H. Cao, Giovanni Colombo, Boris S. Mordukhovich, Dao Nguyen

The paper is devoted to deriving necessary optimality conditions in a general optimal control problem for dynamical systems governed by controlled sweeping processes with hard-constrained control actions entering both polyhedral moving sets and additive perturbations. By using the first-order and mainly second-order tools of variational analysis and generali

Samuel Schwab, William Christopherson, Michael Crescimanno, Kenneth Singer

The nonlinear optical response of organic polaritonic matter has received increasing attention due to their enhanced and controllable nonlinear response and their potential for novel optical devices such as compact photon sources and optical and quantum information devices. Using z-scans at different wavelengths and incident powers we have studied the nonlin

O. V. Teryaev

The origin of Lam-Tung relation for the angular asymmetry of Drell-Yan dileptons is analyzed. The asymmetry constrained by this relation is shown to belongto the class of kinematic azimuthal asymmetries, emerging due to the deviation of the reference axis from the natural physical one. The validity and violation of Lam-Tung relation due to radiative and powe

N Kruger, M Kurtulik, N Revivo, A Manor

The radiance of thermal emission, as described by Planck law, depends only on the emissivity and temperature of a body, and increases monotonically with the temperature rise at any emitted wavelength. Nonthermal radiation, such as photoluminescence, is a fundamental light matter interaction that conventionally involves the absorption of an energetic photon,

Akaki Tikaradze

Given the spherical subalgebra $B$ of a rational Cherednik algebra, we aim to classify all finite groups $\Gamma$ for which there exists a domain $R$ on which $\Gamma$ acts by ring automorphisms, such that $B=R^{\Gamma}.$ We describe such groups in terms of geometry of the center of the reduction of $B$ modulo a large prime.

Yuejiang Liu, Qi Yan, Alexandre Alahi

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks still struggle to generalize in closed-loop predictions (e.g., output colliding trajectories). This issue l

Nymisha Bandi, Theja Tulabandhula

The dynamic portfolio optimization problem in finance frequently requires learning policies that adhere to various constraints, driven by investor preferences and risk. We motivate this problem of finding an allocation policy within a sequential decision making framework and study the effects of: (a) using data collected under previously employed policies, w

Felipe Rosso

We define and study the $T\bar{T}$ deformation of a random matrix model, showing a consistent definition requires the inclusion of both the perturbative and non-perturbative solutions to the flow equation. The deformed model is well defined for arbitrary values of the coupling, exhibiting a phase transition for the critical value in which the spectrum comple

Albert Ammerman

One of the last chapters in the long course of human evolution was the shift from hunting and gathering to the production of food or strategies of subsistence based on farming and the herding of animals. In Southwest Asia, the first steps towards the origins of agriculture began some 12,000 years ago and then spread over most regions of Europe during the spa

Vaidy Sivaraman, Daniel Slilaty

We characterize the 3-connected members of the intersection of the class of bicircular and cobicircular matroids. Aside from some exceptional matroids with rank and corank at most 5, this class consists of just the free swirls and their minors.

Taro Kimura

The aim of this memoir for "Habilitation \`a Diriger des Recherches" is to present quantum geometric and algebraic aspects of supersymmetric gauge theory, which emerge from non-perturbative nature of the vacuum structure induced by instantons. We start with a brief summary of the equivariant localization of the instanton moduli space, and show how to obtain