April 2023 arXiv papers — page 123

Bing Huang, Guido Falk von Rudorff, O. Anatole von Lilienfeld

Density functional theory (DFT) plays a pivotal role for the chemical and materials science due to its relatively high predictive power, applicability, versatility and computational efficiency. We review recent progress in machine learning model developments which has relied heavily on density functional theory for synthetic data generation and for the desig

Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren

The growing carbon footprint of artificial intelligence (AI) has been undergoing public scrutiny. Nonetheless, the equally important water (withdrawal and consumption) footprint of AI has largely remained under the radar. For example, training the GPT-3 language model in Microsoft's state-of-the-art U.S. data centers can directly evaporate 700,000 liters of

Riddhipratim Basu, Manan Bhatia

For the directed landscape, the putative universal space-time scaling limit object in the (1+1) dimensional Kardar-Parisi-Zhang (KPZ) universality class, consider the geodesic tree -- the tree formed by the coalescing semi-infinite geodesics in a given direction. As shown in Bhatia '23, this tree comes interlocked with a dual tree, which (up to a reflection)

Dmitrii I. Zavershinskii, Nonna E. Molevich, Dmitrii S. Riashchikov, Sergey A. Belov

Magnetoacoustic oscillations are nowadays routinely observed in various regions of the solar corona. This allows them to be used as means of diagnosing plasma parameters and processes occurring in it. Plasma diagnostics, in turn, requires a sufficiently reliable MHD model to describe the wave evolution. In our paper, we focus on obtaining the exact analytica

Savinien Kreczman, Luca Prigioniero, Eric Rowland, Manon Stipulanti

In formal languages and automata theory, the magic number problem can be formulated as follows: for a given integer n, is it possible to find a number d in the range [n,2^n] such that there is no minimal deterministic finite automaton with d states that can be simulated by an optimal nondeterministic finite automaton with exactly n states? If such a number d

Camille L. Latune, Graeme Pleasance, Francesco Petruccione

While strong system-bath coupling produces rich and interesting phenomena, applications to quantum thermal engines have been so far pointing mainly at detrimental effects. The delicate trade-off between efficiency loss due to strong coupling and power increase due to faster equilibration, while acknowledged, remained largely unexplored owing to the challenge

Zian Wang, Tianchang Shen, Jun Gao, Shengyu Huang

Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion. Recent NeRF based methods achieve impressive fidelity of 3D reconstruction, but bake the lighting and shadows into the radiance field, while mesh-based methods that facilitate intrinsic decomposition throu

Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang

Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability. Moreover additional restrictions are often imposed in order to simplify the inference task: this is the case for the Gaussian noise assumption on additive non-linear models, which is common to many causal discovery approaches. In this

Adwait Datar, Antonio Mendez Gonzalez, Herbert Werner

This paper extends recent results on the exponential performance analysis of gradient based cooperative control dynamics using the framework of exponential integral quadratic constraints ($\alpha-$IQCs). A cooperative source-seeking problem is considered as a specific example where one or more vehicles are embedded in a strongly convex scalar field and are r

Jorge Ovalle, Roberto Casadio, Andrea Giusti

Static and stationary regular black holes are examined under a minimal set of requirements consisting of (i) the existence of a well defined event horizon and (ii) the weak energy condition for matter sourcing the geometry. We perform our analysis by means of the gravitational decoupling approach and find hairy solutions free of curvature singularities. We i

Jiuhai Chen, Lichang Chen, Heng Huang, Tianyi Zhou

Chain-of-Thought (CoT) prompting can effectively elicit complex multi-step reasoning from Large Language Models~(LLMs). For example, by simply adding CoT instruction ``Let's think step-by-step'' to each input query of MultiArith dataset, GPT-3's accuracy can be improved from 17.7\% to 78.7\%. However, it is not clear whether CoT is still effective on more re

A. Sheykhi, S. Ghaffari

We revisit agegraphic dark energy (ADE) model when the entropy associated with the apparent horizon is in the form of Barrow entropy, $S\sim A^{1+\delta/2}$, where $0\leq\delta\leq1$ indicates the amount of the quantum-gravitational deformation effects of the horizon. The modification to the entropy expression, not only change the energy density of ADE, but

Haruhisa Enomoto

Let C be a subcategory of the category of finitely generated R-modules over a commutative noetheian ring R. We prove that, if C is closed under images and extensions (which we call an IE-closed subcategory), then C is closed under submodules, and hence is a torsion-free class. This result complements Stanley--Wang's result in some sense and, furthermore, pro

Rodrigo A. González, Cristian R. Rojas, Siqi Pan, James S. Welsh

The identification of electrical, mechanical, and biological systems using data can benefit greatly from prior knowledge extracted from physical modeling. Parametric continuous-time identification methods can naturally incorporate this knowledge, which leads to interpretable and parsimonious models. However, some applications lead to model structures that la

Oscar A. Chavez Ortiz, Steven L. Finkelstein, Dustin Davis, Gene Leung

We present the Texas Euclid Survey for Lyman-Alpha (TESLA), a spectroscopic survey in the 10 square degree of the Euclid North Ecliptic Pole (NEP) field. Using TESLA, we study how the physical properties of Lyman-alpha emitters (LAEs) correlate with Lyman-alpha emission to understand the escape of Lyman alpha from galaxies at redshifts 2 -- 3.5. We present a

Rajat Bhattacharjya, Biswadip Maity, Nikil Dutt

Viterbi decoders are widely used in communication systems, natural language processing (NLP), and other domains. While Viterbi decoders are compute-intensive and power-hungry, we can exploit approximations for early design space exploration (DSE) of trade-offs between accuracy, power, and area. We present Locate, a DSE framework that uses approximate adders

Agnijo Banerjee, João Pedro Marciano, Adva Mond, Jan Petr

Deciding whether a graph can be edge-decomposed into a matching and a $k$-bounded linear forest was recently shown by Campbell, H{\"o}rsch and Moore to be NP-complete for every $k \ge 9$, and solvable in polynomial time for $k=1,2$. In the first part of this paper, we close this gap by showing that this problem is in NP-complete for every $k \ge 3$. In the s

Konstantinos Bessas, Matteo Novaga, Fumihiko Onoue

We study the equilibrium shape of liquid drops minimizing the fractional perimeter under the action of a potential energy. We prove, with a quantitative estimate, that the small volume minimizers are convex and uniformly close to a ball.

Sergio Moreschi, Gilberto Recupito, Valentina Lenarduzzi, Fabio Palomba

MLOps tools enable continuous development of machine learning, following the DevOps process. Different MLOps tools have been presented on the market, however, such a number of tools often create confusion on the most appropriate tool to be used in each DevOps phase. To overcome this issue, we conducted a multivocal literature review mapping 84 MLOps tools id

Celeste Barnaby, Qiaochu Chen, Roopsha Samanta, Isil Dillig

This paper presents a new synthesis-based approach for batch image processing. Unlike existing tools that can only apply global edits to the entire image, our method can apply fine-grained edits to individual objects within the image. For example, our method can selectively blur or crop specific objects that have a certain property. To facilitate such fine-g

Paul Bressler, Diego A. Robayo Bargans

N.C.Leung and V.Reiner showed that certain convexity conditions on a complete rational simplicial fan determine the sign of the signature of the Poincar\'e pairing on the cohomology of the associated toric variety. The purpose of the present article is to give an "elementary" proof of their result.

Björn Michele, Alexandre Boulch, Gilles Puy, Tuan-Hung Vu

Learning models on one labeled dataset that generalize well on another domain is a difficult task, as several shifts might happen between the data domains. This is notably the case for lidar data, for which models can exhibit large performance discrepancies due for instance to different lidar patterns or changes in acquisition conditions. This paper addresse

Madhumita Saha, Manas Kulkarni, Bijay Kumar Agarwalla

We investigate the emergence and corresponding nature of exceptional points located on exceptional hyper-surfaces of non-hermitian transfer matrices for finite-range one-dimensional lattice models. We unravel the non-trivial role of these exceptional points in determining the system size scaling of electrical conductance in non-equilibrium steady state. We o

Purbesh Mitra, Sennur Ulukus

We consider a fully-connected wireless gossip network which consists of a source and $n$ receiver nodes. The source updates itself with a Poisson process and also sends updates to the nodes as Poisson arrivals. Upon receiving the updates, the nodes update their knowledge about the source. The nodes gossip the data among themselves in the form of Poisson arri

Andrew Ridden-Harper, Ernst de Mooij, Ray Jayawardhana, Neale Gibson

The transmission spectrum of the ultrahot Jupiter KELT-9b ($T_{eq}$ $\sim$ 4000 K) exhibits absorption by several metal species. We searched for atomic and molecular lines in its emission spectrum by observing partial phase curves with the CARMENES spectrograph ($R$ $\sim$ 80,000 $-$ 95,000). We find evidence for emission by Si I in the atmosphere of KELT-9b

Arman Oganisian, Anthony Girard, Jon A. Steingrimsson, Patience Moyo

Observational studies of recurrent event rates are common in biomedical statistics. Broadly, the goal is to estimate differences in event rates under two treatments within a defined target population over a specified followup window. Estimation with observational data is challenging because, while membership in the target population is defined in terms of el

Ahmet Burak Yildirim, Vedat Baday, Erkut Erdem, Aykut Erdem

Image inpainting task refers to erasing unwanted pixels from images and filling them in a semantically consistent and realistic way. Traditionally, the pixels that are wished to be erased are defined with binary masks. From the application point of view, a user needs to generate the masks for the objects they would like to remove which can be time-consuming

Marzena Karpinska, Mohit Iyyer

Large language models (LLMs) are competitive with the state of the art on a wide range of sentence-level translation datasets. However, their ability to translate paragraphs and documents remains unexplored because evaluation in these settings is costly and difficult. We show through a rigorous human evaluation that asking the Gpt-3.5 (text-davinci-003) LLM

Eadom Dessalene, Michael Maynord, Cornelia Fermuller, Yiannis Aloimonos

In this paper we introduce a rule-based, compositional, and hierarchical modeling of action using Therbligs as our atoms. Introducing these atoms provides us with a consistent, expressive, contact-centered representation of action. Over the atoms we introduce a differentiable method of rule-based reasoning to regularize for logical consistency. Our approach

S. S. Agaev, K. Azizi, B. Barsbay, H. Sundu

The masses, current couplings and widths of the fully heavy scalar tetraquarks $X_{\mathrm{4Q}}=QQ\overline{Q}\overline{Q}$, $Q=c, b$ are calculated by modeling them as four-quark systems composed of axial-vector diquark and antidiquark. The masses $m^{(\prime)}$ and couplings $ f^{(\prime)}$ of these tetraquarks are computed in the context of the QCD sum ru

Daniel McDuff, Theodore Curran, Achuta Kadambi

Synthetic data are becoming a critical tool for building artificially intelligent systems. Simulators provide a way of generating data systematically and at scale. These data can then be used either exclusively, or in conjunction with real data, for training and testing systems. Synthetic data are particularly attractive in cases where the availability of ``

Peter Korn, Edriss S. Titi

We prove global well-posedness of the ocean primitive equations coupled to advection-diffusion equations of the oceanic tracers temperature and salinity that are supplemented by the eddy parametrization model due to Gent-McWilliams and Redi. This parametrization forms a milestone in global ocean modelling and constitutes a central part of any general ocean c

Margaret E. Carrington, Wade N. Cowie, Bryce T. Friesen, Stanislaw Mrowczynski

In a series of works by two of us, various characteristics of the glasma from the earliest phase of relativistic heavy-ion collisions have been studied using a proper time expansion. These characteristics include: energy density, longitudinal and transverse pressures, collective flow, angular momentum and parameters of jet quenching. In this paper we extend

Annie Ray, Raymond Laflamme, Aleksander Kubica

Probabilistic cellular automata describe the dynamics of classical spin models, which, for sufficiently small temperature $T$, can serve as classical memory capable of storing information even in the presence of nonzero external magnetic field $h$. In this article, we study a recently-introduced probabilistic cellular automaton, the sweep rule, and map out a

Ali Amelia, Lourdes Pena-Castillo, Hamid Usefi

Genome-Wide Association Studies (GWAS) help identify genetic variations in people with diseases such as Parkinson's disease (PD), which are less common in those without the disease. Thus, GWAS data can be used to identify genetic variations associated with the disease. Feature selection and machine learning approaches can be used to analyze GWAS data and ide

Joeri Brackenhoff, Kees Wapenaar

We show the capability of the Marchenko method to retrieve not only propagating waves, but also evanescent waves, based on a recent derivation of the Marchenko method that does not depend on up-down decomposition inside the medium of interest. We show how these wave fields can be easily retrieved in the slowness-intercept-time domain and what the wave fields

Chih-Ting Lu, Huifang Lv, Wei Shen, Lei Wu

The QCD-like dark sector with GeV-scale dark hadrons has the potential to generate new signatures at the Large Hadron Collider (LHC). In this paper, we consider a singlet scalar mediator in the tens of GeV-scale that connects the dark sector and the Standard Model (SM) sector via the Higgs portal. We focus on the Higgs-strahlung process, $q\overline{q}'\righ

Luca V. Delacretaz, Ruchira Mishra

Nonintegrable systems thermalize, leading to the emergence of fluctuating hydrodynamics. Typically, this hydrodynamics is diffusive. We use the effective field theory (EFT) of diffusion to compute higher-point functions of conserved densities. We uncover a simple scaling behavior of correlators at late times, and, focusing on three and four-point functions,

William B. Langdon, Justyna Petke, Aymeric Blot, David Clark

By their very name caches are often overlooked and yet play a vital role in the performance of modern and indeed future hardware. Using MAGPIE (Machine Automated General Performance Improvement via Evolution of software) we show genetic improvement GI can reduce the cache load of existing computer programs. Operating on lines of C and C++ source code using l

Jop Briët, Davi Castro-Silva

Using recent developments on the theory of locally decodable codes, we prove that the critical size for Szemer\'edi's theorem with random differences is bounded from above by $N^{1-\frac{2}{k} + o(1)}$ for length-$k$ progressions. This gives polynomial improvements over the previous best bounds for all odd $k$.

Sriram Bhyravarapu, Satyabrata Jana, Lawqueen Kanesh, Saket Saurabh

Given an undirected graph $G=(V,E)$ and an integer $\ell$, the Eccentricity Shortest Path (ESP) asks to find a shortest path $P$ such that for every vertex $v\in V(G)$, there is a vertex $w\in P$ such that $d_G(v,w)\leq \ell$, where $d_G(v,w)$ represents the distance between $v$ and $w$ in $G$. Dragan and Leitert [Theor. Comput. Sci. 2017] showed that the op

Akhil Chadha, Vishrut Jain, Andrea Michelle Rios Lazcano, Barys Shyrokau

Driving simulators have been used in the automotive industry for many years because of their ability to perform tests in a safe, reproducible and controlled immersive virtual environment. The improved performance of the simulator and its ability to recreate in-vehicle experience for the user is established through motion cueing algorithms (MCA). Such algorit

Giovanni Alberto Ummarino

The phenomenology of overdoped high Tc superconductors can be described by a one band d wave Eliashberg theory where the mechanism of superconducting coupling is mediated by antiferromagnetic spin fluctuations and whose characteristic energy Omega0 scales with Tc according to the empirical law Omega0 = 5.8 kBTc. This model presents universal characteristics

David Cowburn, Michael Rout

Nuclear pore complexes (NPCs) mediate the exchange of materials between the nucleoplasm and cytoplasm, playing a key role in the separation of nucleic acids and proteins into their required compartments. The static structure of the NPC is relatively well defined by recent cryo EM and other studies. The functional roles of dynamic components in the pore of th

Suthakaran Ratnasingam, Anton Butenko

A Lorenz curve is a graphical representation of the distribution of income or wealth within a population. The generalized Lorenz curve can be created by scaling the values on the vertical axis of a Lorenz curve by the average output of the distribution. In this paper, we propose two non-parametric methods for testing the equality of two generalized Lorenz cu

Gurtej Kanwar, Alessandro Lovato, Noemi Rocco, Michael Wagman

The Green's function Monte Carlo (GFMC) method provides accurate solutions to the nuclear many-body problem and predicts properties of light nuclei starting from realistic two- and three-body interactions. Controlling the GFMC fermion-sign problem is crucial, as the signal-to-noise ratio decreases exponentially with Euclidean time, requiring significant comp

D. S. Riashchikov, N. E. Molevich, D. I. Zavershinskii

The joint effect of gravity and thermal misbalance on the dynamics of acoustic-gravity waves (AGW) in the solar atmosphere is considered. It is shown that the heating and cooling taken in the form of power functions lead to the linear dependence of stationary temperature profile. Estimates of the ratio of the characteristic length associated with thermal pro

Nicolas Lenner, Stephan Eule, Jörg Großhans, Fred Wolf

Mesoscopic bio-systems typically evolve towards functionally important target states, such as cell-cycle checkpoints or decision boundaries for the release of specific behaviors. For the data-driven inference of the underlying directional out-of-equilibrium dynamics, we here develop a theory of target state aligned (TSA) ensembles. Target state alignment all

C. F. Silva, A. E. Bernardini

Considering the parity symmetry related to the Dirac equation, the interplay between energy localization and the temporal evolution of parity-defined quantum superpositions is investigated for fermions in a magnetic field. The unitary evolution of Dirac cat states is obtained by initializing either even or odd principal quantum numbers in the equivalent harm

Tobias J. Osborne, Alexander Stottmeister

We analyze the renormalization group fixed point of the two-dimensional Ising model at criticality. In contrast with expectations from tensor network renormalization (TNR), we show that a simple, explicit analytic description of this fixed point using operator-algebraic renormalization (OAR) is possible. Specifically, the fixed point is characterized in term

W. Hare, G. Jarry-Bolduc, C. Planiden

This work presents a novel matrix-based method for constructing an approximation Hessian using only function evaluations. The method requires less computational power than interpolation-based methods and is easy to implement in matrix-based programming languages such as MATLAB. As only function evaluations are required, the method is suitable for use in deri

Tamás Kálmán, Lilla Tóthmérész

The interior polynomial of a directed graph is defined as the $h^*$-polynomial of the graph's (extended) root polytope, and it displays several attractive properties. Here we express its degree in terms of the minimum cardinality of a directed join, and give a formula for the leading coefficient. We present natural generalizations of these results to oriente

Ahsan Mahmood, Junier Oliva, Martin Styner

We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to detect anomalies in categorical data. GNSM accomplishes this by estimating the scores, i.e. the gradients of log likelihoods w.r.t.~inputs, of continuously relaxed categorical distributions. We test our method on a suite of anomaly detection tabular datasets. GNSM achieves a consis

Linjin Zheng

It is pointed out that the conventional Lie transform perturbation theory for the guiding center motion of charged particles in a magnetic field needs to be modified for ordering inconsistency. There are two reasons. First, the ordering difference between the temporal variation of gyrophase and that of the other phase space coordinates needs to be taken into

Mitchell Pavlak, Nathan Drenkow, Nicholas Petrick, Mohammad Mehdi Farhangi

To safely deploy deep learning-based computer vision models for computer-aided detection and diagnosis, we must ensure that they are robust and reliable. Towards that goal, algorithmic auditing has received substantial attention. To guide their audit procedures, existing methods rely on heuristic approaches or high-level objectives (e.g., non-discrimination

Yongqiang Wen, Philip L. Diggle, Neil McN. Alford, Daan M. Arroo

Masers - the microwave analogue of lasers - are coherent microwave sources that can act as oscillators or quantum-limited amplifiers. Masers have historically required high vacuum and cryogenic temperatures to operate, but recently masers based on diamond have been demonstrated to operate at room temperature and pressure, opening a route to new applications

Liang Chen, Shuming Ma, Dongdong Zhang, Furu Wei

In this work, we study how the performance of a given direction changes with its sampling ratio in Multilingual Neural Machine Translation (MNMT). By training over 200 multilingual models with various model sizes, data sizes, and language directions, we find it interesting that the performance of certain translation direction does not always improve with the

Ali Taghibakhshi, Mingyuan Ma, Ashwath Aithal, Onur Yilmaz

Cross-device user matching is a critical problem in numerous domains, including advertising, recommender systems, and cybersecurity. It involves identifying and linking different devices belonging to the same person, utilizing sequence logs. Previous data mining techniques have struggled to address the long-range dependencies and higher-order connections bet

Moritz Hartlieb

We study special subvarieties, i.e., subvarieties containing a dense subset of CM points, of the moduli space $A_5$ of principally polarized abelian varieties of dimension five, generically contained in the locus of intermediate Jacobians of cubic threefolds. The analogous question for Jacobians of curves is related to a conjecture of Coleman-Ort and has bee

Camilla Beneduce, Diogo E. P. Pinto, Petr Sulc, Francesco Sciortino

Nucleation in systems with a metastable liquid-gas critical point is the prototypical example of a two-step nucleation process, in which the appearance of the critical nucleus is preceded by the formation of a liquid-like density fluctuation. So far, the majority of studies on colloidal and protein crystallization have focused on one-component systems, and w

Daniel Kressner, Tingting Ni, André Uschmajew

Given a Hilbert space $\mathcal H$ and a finite measure space $\Omega$, the approximation of a vector-valued function $f: \Omega \to \mathcal H$ by a $k$-dimensional subspace $\mathcal U \subset \mathcal H$ plays an important role in dimension reduction techniques, such as reduced basis methods for solving parameter-dependent partial differential equations.

Hernán A. González, Oriana Labrin, Olivera Miskovic

We discuss the emergence of a new symmetry generator in a Hamiltonian realisation of four-dimensional gauge theories in the flat space foliated by retarded (advanced) time. It generates an asymptotic symmetry that acts on the asymptotic fields in a way different from the usual large gauge transformations. The improved canonical generators, corresponding to g

Ashka Shah, Arvind Ramanathan, Valerie Hayot-Sasson, Rick Stevens

Causal discovery of genome-scale networks is important for identifying pathways from genes to observable traits - e.g. differences in cell function, disease, drug resistance and others. Causal learners based on graphical models rely on interventional samples to orient edges in the network. However, these models have not been shown to scale up the size of the

Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib

Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation. Towards this end, recent deep learning-based medical segmentation methods have shown great promise in better modeling such information. However, convolution operators for medical segmentation typically operate on r

Nolan Dey, Gurpreet Gosal, Zhiming, Chen

We study recent research advances that improve large language models through efficient pre-training and scaling, and open datasets and tools. We combine these advances to introduce Cerebras-GPT, a family of open compute-optimal language models scaled from 111M to 13B parameters. We train Cerebras-GPT models on the Eleuther Pile dataset following DeepMind Chi

Chitres Guria, Qi Zhong, Sahin K. Ozdemir, Yogesh S. S. Patil

Non-Hermiticity has emerged as a new paradigm for controlling coupled-mode systems in ways that cannot be achieved with conventional techniques. One aspect of this control that has received considerable attention recently is the encircling of exceptional points (EPs). To date, most work has focused on systems consisting of two modes that are tuned by two con

Haoye Qin, Zhe Zhang, Qiaolu Chen, Romain Fleury

Recently, non-reciprocal two-dimensional unitary scattering networks have gained considerable interest due to the possibility of obtaining robust edge wave propagation in the anomalous Floquet phase. Conversely, zero-dimensional topological states in such networks have been left uncharted. Here, we demonstrate the existence of Floquet disclination states in

Enrico Le Donne, Nicola Paddeu

In the setting of subFinsler Carnot groups, we consider curves that satisfy the normal equation coming from the Pontryagin Maximum Principle. We show that, unless it is constant, each such a curve leaves every compact set, quantitatively. Namely, the distance between the points at time 0 and time $t$ grows at least of the order of $t^{1/s}$, where $s$ denote

Vytene Janiukstyte, Thomas W Owen, Umair J Chaudhary, Beate Diehl

A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential cl

Lina Li, Gweneth McKinley, Jinyoung Park

For an odd integer $n = 2d-1$, let $\mathcal B_d$ be the subgraph of the hypercube $Q_n$ induced by the two largest layers. In this paper, we describe the typical structure of proper $q$-colorings of $V(\mathcal B_d)$ and give asymptotics on the number of them. The proofs use various tools including information theory (entropy), Sapozhenko's graph container

Yihe Dong, Sercan O. Arik

Feature selection has been widely used to alleviate compute requirements during training, elucidate model interpretability, and improve model generalizability. We propose SLM -- Sparse Learnable Masks -- a canonical approach for end-to-end feature selection that scales well with respect to both the feature dimension and the number of samples. At the heart of

Nayana Das, Goutam Paul

Quantum Secure Direct Communication (QSDC) is an important branch of quantum cryptography, which enables the secure transmission of messages without prior key encryption. However, traditional quantum communication protocols rely on the security and trustworthiness of the devices employed to implement the protocols, which can be susceptible to attacks. Device

Ramona Anton, Nicolae Mihalache, François Vigneron

We propose a short proof of the Fundamental Theorem of Algebra based on the ODE that describes the Newton flow and the fact that the value $|P(z)|$ is a Lyapunov function. It clarifies an idea that goes back to Cauchy.

Matheus Campos Fernandes, Fabrício Olivetti de França, Emilio Francesquini

Program synthesis is the process of generating a computer program following a set of specifications, which can be a high-level description of the problem and/or a set of input-output examples. The synthesis can be modeled as a search problem in which the search space is the set of all the programs valid under a grammar. As the search space is vast, brute for

Bohan Zeng, Xuhui Liu, Sicheng Gao, Boyu Liu

Face animation has achieved much progress in computer vision. However, prevailing GAN-based methods suffer from unnatural distortions and artifacts due to sophisticated motion deformation. In this paper, we propose a Face Animation framework with an attribute-guided Diffusion Model (FADM), which is the first work to exploit the superior modeling capacity of

K. Au-Yeung, A. Quintanas-Corominas, E. Martínez-Pañeda, W. Tan

This paper investigates the effect of moisture content upon the degradation behaviour of composite materials. A coupled phase field framework considering moisture diffusion, hygroscopic expansion, and fracture behaviour is developed. This multi-physics framework is used to explore the damage evolution of composite materials, spanning the micro-, meso- and ma

Xin Zhang, Chen Liu, Degang Yang, Tingting Song

In the realm of deep learning, spatial attention mechanisms have emerged as a vital method for enhancing the performance of convolutional neural networks. However, these mechanisms possess inherent limitations that cannot be overlooked. This work delves into the mechanism of spatial attention and reveals a new insight. It is that the mechanism essentially ad

Diana Barseghyan, Baruch Schneider

In this paper we study a bounded domain with a small hole removed. Our main result concerns the spectrum of the Laplace operator with the Robin conditions imposed at the hole boundary. Moreover we prove that under some suitable assumptions on the parameter in the boundary condition the spectrum of the Laplacian converges in the Hausdorff distance sense to th

Ethan Romary, Alexander Zupan

An R-link is an $n$-component link $L$ in $S^3$ such that Dehn surgery on $L$ yields $\#^n(S^1 \times S^2)$. Every R-link $L$ gives rise to a geometrically simply-connected homotopy 4-sphere $X_L$, which in turn can be used to produce a balanced presentation of the trivial group. Adapting work of Gompf, Scharlemann, and Thompson, Meier and Zupan produced a f

Xuan-Bac Nguyen, Chi Nhan Duong, Xin Li, Susan Gauch

Micro-expression recognition is one of the most challenging topics in affective computing. It aims to recognize tiny facial movements difficult for humans to perceive in a brief period, i.e., 0.25 to 0.5 seconds. Recent advances in pre-training deep Bidirectional Transformers (BERT) have significantly improved self-supervised learning tasks in computer visio

Ayaboe K. Edoh, Timothy P. Gallagher, Venkateswaran Sankaran

Residual and solution filtering procedures are studied with respect to inhibiting the accumulation of small-scale (i.e., high wavenumber) content. Assessing each method in terms of an ``equivalent residual equation" reveals fundamental differences in their behaviors, such as how the underlying solution can be constrained to a target filter width. The residua

Francisco Lopez-Tiro, Elias Villalvazo-Avila, Juan Pablo Betancur-Rengifo, Ivan Reyes-Amezcua

This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints, with the aim to produce more discriminant object features for the identification of the type of kidney stones seen in endoscopic images. The model was further improved with a two-step transfer learning approach and by attention bl

Jonathan J Horsley, Rhys H Thomas, Fahmida A Chowdhury, Beate Diehl

When investigating suitability for surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalit

Ainesh Bakshi, Shyam Narayanan

We consider the problem of rank-$1$ low-rank approximation (LRA) in the matrix-vector product model under various Schatten norms: $$ \min_{\|u\|_2=1} \|A (I - u u^\top)\|_{\mathcal{S}_p} , $$ where $\|M\|_{\mathcal{S}_p}$ denotes the $\ell_p$ norm of the singular values of $M$. Given $\varepsilon>0$, our goal is to output a unit vector $v$ such that $$ \|A(I

David Bolin, Alexandre B. Simas, Jonas Wallin

There has recently been much interest in Gaussian fields on linear networks and, more generally, on compact metric graphs. One proposed strategy for defining such fields on a metric graph $\Gamma$ is through a covariance function that is isotropic in a metric on the graph. Another is through a fractional-order differential equation $L^{\alpha/2} (\tau u) = \

K. Fredrik Karlsson

The concept of a recently proposed Forward-Forward learning algorithm for fully connected artificial neural networks is applied to a single multi output perceptron for classification. The parameters of the system are trained with respect to increased (decreased) "goodness" for correctly (incorrectly) labelled input samples. Basic numerical tests demonstrate

Anuj Kumar, Tarek Echekki

A combustion chemistry acceleration scheme is developed based on deep operator networks (DeepONets). The scheme is based on the identification of combustion reaction dynamics through a modified DeepOnet architecture such that the solutions of thermochemical scalars are projected to new solutions in small and flexible time increments. The approach is designed

Akshay Gadi Patil, Supriya Gadi Patil, Manyi Li, Matthew Fisher

This report surveys advances in deep learning-based modeling techniques that address four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We describe different kinds of representations for indoor scenes, various indoor scene datasets available for research in the aforementioned areas, and discuss notable works employing ma

Hadi Mohammadigoushki, Kourosh Shoele

Measuring surface tension of yield stress fluids has remained a critical challenge due to limitations of the traditional tensiometry techniques. Here, we overcome those limits and successfully measure the surface tension and mechanical properties of a model yield stress fluid based on Carbopol gels via a needle-induced cavitation (NIC) technique. Our results

Elena Angelini

The new identifiable case appeared in \cite{AGMO}, together with the analysis on simultaneous identifiability of pairs of ternary forms recently developed in \cite{BG}, suggested the following conjecture towards a complete classification of all simultaneous Waring identifiable cases: for any $ d \geq 2 $, the general polynomial vectors consisting of $ d-1 $

Guanhang Lei, Lei Shi

Regularized pairwise ranking with Gaussian kernels is one of the cutting-edge learning algorithms. Despite a wide range of applications, a rigorous theoretical demonstration still lacks to support the performance of such ranking estimators. This work aims to fill this gap by developing novel oracle inequalities for regularized pairwise ranking. With the help

Yuheng Jiang, Kaixin Yao, Zhuo Su, Zhehao Shen

Convenient 4D modeling of human-object interactions is essential for numerous applications. However, monocular tracking and rendering of complex interaction scenarios remain challenging. In this paper, we propose Instant-NVR, a neural approach for instant volumetric human-object tracking and rendering using a single RGBD camera. It bridges traditional non-ri

Sougata Bose, Thomas A. Henzinger, Karoliina Lehtinen, Sven Schewe

We explore the notion of history-determinism in the context of timed automata (TA) over infinite timed words. History-deterministic (HD) automata are those in which nondeterminism can be resolved on the fly, based on the run constructed thus far. History-determinism is a robust property that admits different game-based characterisations, and HD specification

Antonio Blanca, Reza Gheissari

Sampling from the $q$-state ferromagnetic Potts model is a fundamental question in statistical physics, probability theory, and theoretical computer science. On general graphs, this problem may be computationally hard, and this hardness holds at arbitrarily low temperatures. At the same time, in recent years, there has been significant progress showing the e

Sian Jin, Pu Perry Wang, Petros Boufounos, Philip V. Orlik

This paper considers mutual interference mitigation among automotive radars using frequency-modulated continuous wave (FMCW) signal and multiple-input multiple-output (MIMO) virtual arrays. For the first time, we derive a general interference signal model that fully accounts for not only the time-frequency incoherence, e.g., different FMCW configuration para

Pritam Choudhury

Linearity and dependency analyses are key to several applications in computer science, especially, in resource management and information flow control. What connects these analyses is that both of them need to model at least two different worlds with constrained mutual interaction. Though linearity and dependency analyses address similar problems, the analys

Weihang Mao, Bo Han, Zihao Wang

Sketch-guided image editing aims to achieve local fine-tuning of the image based on the sketch information provided by the user, while maintaining the original status of the unedited areas. Due to the high cost of acquiring human sketches, previous works mostly relied on edge maps as a substitute for sketches, but sketches possess more rich structural inform

Chin-Yao Chang

Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in th

Ricardo Espinosa, Carlos Axel Garcia-Vega, Gilberto Ochoa-Ruiz, Dominique Lamarque

This contribution shows how an appropriate image pre-processing can improve a deep-learning based 3D reconstruction of colon parts. The assumption is that, rather than global image illumination corrections, local under- and over-exposures should be corrected in colonoscopy. An overview of the pipeline including the image exposure correction and a RNN-SLAM is

G. Wade McDonald, Nathaniel D. Osgood

Agent-based modeling is a computational dynamic modeling technique that may be less familiar to some readers. Agent-based modeling seeks to understand the behaviour of complex systems by situating agents in an environment and studying the emergent outcomes of agent-agent and agent-environment interactions. In comparison with compartmental models, agent-based