October 2023 arXiv papers — page 155

Zhiting Ma, Juntao Huang, Wen-An Yong

This work is concerned with the uniform accuracy of implicit-explicit backward differentiation formulas for general linear hyperbolic relaxation systems satisfying the structural stability condition proposed previously by the third author. We prove the uniform stability and accuracy of a class of IMEX-BDF schemes discretized spatially by a Fourier spectral m

Chang'an Yi, Haotian Chen, Yifan Zhang, Yonghui Xu

Test-time adaptation (TTA) aims to adapt a model, initially trained on training data, to test data with potential distribution shifts. Most existing TTA methods focus on classification problems. The pronounced success of classification might lead numerous newcomers and engineers to assume that classic TTA techniques can be directly applied to the more challe

Giulio Foggi Rota, Christian Amor, Soledad Le Clainche, Marco Edoardo Rosti

The chaotic flow of elastic fluids at low Reynolds number (Re) is typically distinguished into elasto-inertial and elastic turbulence (EIT/ET). However, the clear separation among these two turbulent regimes in parallel flows with a gradual Re decrease remains elusive. Here, spanning Re over four orders of magnitude, we investigate the statistical and struct

Chao-Ming Lin

We prove that if there exists a $C$-subsolution to a constant coefficients strictly $\Upsilon$-stable general inverse $\sigma_k$ equation, then there exists a unique solution. As a consequence, this result covers all the analytical results of the classical strictly $\Upsilon$-stable general inverse $\sigma_k$ equations, for example, the complex Monge--Amp\`e

Holy Lovenia, Wenliang Dai, Samuel Cahyawijaya, Ziwei Ji

Object hallucination poses a significant challenge in vision-language (VL) models, often leading to the generation of nonsensical or unfaithful responses with non-existent objects. However, the absence of a general measurement for evaluating object hallucination in VL models has hindered our understanding and ability to mitigate this issue. In this work, we

Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon

The success of modern neural networks has prompted study of the connection between memorisation and generalisation: overparameterised models generalise well, despite being able to perfectly fit (memorise) completely random labels. To carefully study this issue, Feldman proposed a metric to quantify the degree of memorisation of individual training examples,

Samet Bayram, Kenneth Barner

This paper presents GReAT (Graph Regularized Adversarial Training), a novel regularization method designed to enhance the robust classification performance of deep learning models. Adversarial examples, characterized by subtle perturbations that can mislead models, pose a significant challenge in machine learning. Although adversarial training is effective i

Yu Cui, Xin-Jun Ma, Jia-Pei Deng, Shao-Juan Li

We propose the rotational dynamics of the intralayer and interlayer excitons with their inherent momenta of inertia in the monolayer and bilayer transition metal dichalcogenides, respectively, where the new chirality of exciton is endowed by the rotational angular momentum, namely, the formations of left- and right-handed excitons at the +K and -K valleys, r

Jürg Kramer, Antareep Mandal

Let $\Gamma\subsetneq \mathrm{Sp}_n(\mathbb{R})$ be an arithmetic subgroup of the symplectic group $\mathrm{Sp}_n(\mathbb{R})$ acting on the Siegel upper half-space $\mathbb{H}_n$ of degree $n$. Consider the $d$-dimensional space of Siegel cusp forms $\mathcal{S}_{\kappa}^n(\Gamma)$ of weight $\kappa$ for $\Gamma$ and let $\{f_j\}_{1\leq j\leq d}$ be a basis

Longxiang He, Li Shen, Linrui Zhang, Junbo Tan

Constrained policy search (CPS) is a fundamental problem in offline reinforcement learning, which is generally solved by advantage weighted regression (AWR). However, previous methods may still encounter out-of-distribution actions due to the limited expressivity of Gaussian-based policies. On the other hand, directly applying the state-of-the-art models wit

Brayan Melchor, Rolando Perca, Wilfredo Yupanqui

We propose a new lapse function that simplifies the Hamiltonian constraint, describing the interior of the black hole in terms of the Ashtekar-Barbero variables, into a more straightforward form. The new Hamiltonian leads to different equations of motion than those found in the literature, but through a suitable transformation between temporal parameters, it

Yufang Liu, Changzhi Sun, Yuanbin Wu, Aimin Zhou

Machine unlearning aims to revoke some training data after learning in response to requests from users, model developers, and administrators. Most previous methods are based on direct fine-tuning, which may neither remove data completely nor retain full performances on the remain data. In this work, we find that, by first masking some important parameters be

Yang Wang, Jiaogen Zhou, Jihong Guan

Video anomaly detection is to determine whether there are any abnormal events, behaviors or objects in a given video, which enables effective and intelligent public safety management. As video anomaly labeling is both time-consuming and expensive, most existing works employ unsupervised or weakly supervised learning methods. This paper focuses on weakly supe

Andrey Pototsky, Sergey A. Suslov

We consider a thin horizontal layer of a non-magnetic electrolyte containing a bulk solution of salt and carrying an electric current. The layer is bounded by two deformable free surfaces loaded with an insoluble surfactant and is placed in a vertical magnetic field. The arising Lorentz force drives the electrolyte in the plane of the layer. We employ the lo

Terence Tao

The classical Maclaurin inequality asserts that the elementary symmetric means $$ s_k(y) = \frac{1}{\binom{n}{k}} \sum_{1 \leq i_1 < \dots < i_k \leq n} y_{i_1} \dots y_{i_k}$$ obey the inequality $s_\ell(y)^{1/\ell} \leq s_k(y)^{1/k}$ whenever $1 \leq k \leq \ell \leq n$ and $y = (y_1,\dots,y_n)$ consists of non-negative reals. We establish a variant $$ |s_

Thaddäus Wiedemer, Jack Brady, Alexander Panfilov, Attila Juhos

Learning representations that generalize to novel compositions of known concepts is crucial for bridging the gap between human and machine perception. One prominent effort is learning object-centric representations, which are widely conjectured to enable compositional generalization. Yet, it remains unclear when this conjecture will be true, as a principled

Jinrong Hu

In this paper, we deal with the torsion log-Minkowski problem without symmetry assumptions via an approximation argument.

Gonzalo Cao-Labora, Javier Gómez-Serrano, Jia Shi, Gigliola Staffilani

In this paper we construct smooth, non-radial solutions of the compressible Euler and Navier-Stokes equation that develop an imploding finite time singularity. Our construction is motivated by the works [Merle, Rapha\"{e}l, Rodnianski, and Szeftel, Ann. of Math., 196(2):567-778, 2022, Ann. of Math., 196(2):779-889, 2022], [Buckmaster, Cao-Labora, and G\'{o}m

Andrew Starnes, Anton Dereventsov, Clayton Webster

In this effort, we consider the impact of regularization on the diversity of actions taken by policies generated from reinforcement learning agents trained using a policy gradient. Policy gradient agents are prone to entropy collapse, which means certain actions are seldomly, if ever, selected. We augment the optimization objective function for the policy wi

Haojie Hou, Yiyang Jiang, Yan-Xia Ren, Renming Song

Consider a critical branching L\'{e}vy process $\{X_t, t\ge 0\}$ with branching rate $\beta>0, $ offspring distribution $\{p_k:k\geq 0\}$ and spatial motion $\{\xi_t, \Pi_x\}$. For any $t\ge 0$, let $N_t$ be the collection of particles alive at time $t$, and, for any $u\in N_t$, let $X_u(t)$ be the position of $u$ at time $t$. We study the tail probability o

Leilei Shi, Bing Han, Yingzi Zhu, Liyan Han

It has been long that literature in financial academics focuses mainly on price and return but much less on trading volume. In the past twenty years, it has already linked both price and trading volume to economic fundamentals, and explored the behavioral implications of trading volume such as investor's attitude toward risks, overconfidence, disagreement, a

Abdulateef Daud, Mark Amo-Boateng, Neema Jakisa Owor, Armstrong Aboah

Real-time pavement condition monitoring provides highway agencies with timely and accurate information that could form the basis of pavement maintenance and rehabilitation policies. Existing technologies rely heavily on manual data processing, are expensive and therefore, difficult to scale for frequent, networklevel pavement condition monitoring. Additional

Zdenek Sekanina

A vast majority of bright comets between the late 2nd century and the early 18th century, moving in potentially Kreutz orbits according to Hasegawa & Nakano (2001), was first sighted between 2 and 16 days after perihelion, thanks to the spectacular tails that they were then displaying. In this paper I examine the basic properties of the post-perihelion tails

Wenqiang Zhou, Jing Ding, Jiannan Hua, Le Zhang

The topological electronic structure of crystalline materials often gives rise to intriguing surface states, such as Dirac surface states in topological insulators, Fermi arc surface states in Dirac semimetals, and topological superconductivity in iron-based superconductors. Recently, rhombohedral multilayer graphene has emerged as a promising platform for e

Xunxin Cai, Meng Xiao, Zhiyuan Ning, Yuanchun Zhou

In addressing the imbalanced issue of data within the realm of Natural Language Processing, text data augmentation methods have emerged as pivotal solutions. This data imbalance is prevalent in the research proposals submitted during the funding application process. Such imbalances, resulting from the varying popularity of disciplines or the emergence of int

Athul Pradeepkumar Girija

The choice of the launch vehicle is an important consideration during the preliminary planning of interplanetary missions. The launch vehicle must be highly reliable, capable of imparting sufficient energy to the spacecraft to inject it on to an Earth-escape trajectory, and must fit within the cost constraints of the mission. Over the recent past, the most c

Siyang Liu, Naihao Deng, Sahand Sabour, Yilin Jia

We propose task-adaptive tokenization as a way to adapt the generation pipeline to the specifics of a downstream task and enhance long-form generation in mental health. Inspired by insights from cognitive science, our task-adaptive tokenizer samples variable segmentations from multiple outcomes, with sampling probabilities optimized based on task-specific da

Leroy Cronin

Assembly Theory (AT) is a theory that explains how to determine if a complex object is the product of evolution. Here we explain why attempts to compare AT to compression algorithms, ref 1, does not help identify if the object is the product of selection or not. Specifically, we show why aims to perform benchmark comparisons of different compression schemes

Jaewoo Park, Jacky Chen Long Chai, Jaeho Yoon, Andrew Beng Jin Teoh

A neural network trained on a classification dataset often exhibits a higher vector norm of hidden layer features for in-distribution (ID) samples, while producing relatively lower norm values on unseen instances from out-of-distribution (OOD). Despite this intriguing phenomenon being utilized in many applications, the underlying cause has not been thoroughl

Alexandr Andoni, Hengjie Zhang

We study graph spanners for point-set in the high-dimensional Euclidean space. On the one hand, we prove that spanners with stretch <\sqrt{2} and subquadratic size are not possible, even if we add Steiner points. On the other hand, if we add extra nodes to the graph (non-metric Steiner points), then we can obtain (1+\eps)-approximate spanners of subquadratic

Hanzi Huang, Haoshuo Chen, Qi Gao, Yetian Huang

We experimentally investigate transmitting high-order quadrature amplitude modulation (QAM) signals with carrierless and intensity-only measurements with phase retrieval (PR) receiving techniques. The intensity errors during measurement, including noise and distortions, are found to be a limiting factor for the precise convergence of the PR algorithm. To imp

Yuan Meng, Qian Wang, Tianxin Zu, Viktor Prasanna

Deep Neural Network guided Monte-Carlo Tree Search (DNN-MCTS) is a powerful class of AI algorithms. In DNN-MCTS, a Deep Neural Network model is trained collaboratively with a dynamic Monte-Carlo search tree to guide the agent towards actions that yields the highest returns. While the DNN operations are highly parallelizable, the search tree operations involv

Tinghui Ouyang, Hoang-Quoc Nguyen-Son, Huy H. Nguyen, Isao Echizen

Large Language Models (LLMs) have been garnering significant attention of AI researchers, especially following the widespread popularity of ChatGPT. However, due to LLMs' intricate architecture and vast parameters, several concerns and challenges regarding their quality assurance require to be addressed. In this paper, a fine-tuned GPT-based sentiment analys

L. Massai, B. Hetényi, M. Mergenthaler, F. J. Schupp

Hole spins in Ge/SiGe heterostructure quantum dots have emerged as promising qubits for quantum computation. The strong spin-orbit coupling (SOC), characteristic of heavy-hole states in Ge, enables fast and all-electrical qubit control. However, SOC also increases the susceptibility of spin qubits to charge noise. While qubit coherence can be significantly i

Fabiana Morales Alvarez, María Belén Vigna, Mariano Quintero, Diego G. Lamas

In this study, we investigate the magnetic and magnetocaloric properties of nanostructured La0.6Sr0.4CoO3 (LSC) samples synthesized under confinement conditions within porous templates. Using this method, we obtained de-agglomerated nanoparticles, which provide us with the feasibility of applying them in nanoparticle films that can be tailored to intricate g

Paavo Sattler, Georg Zimmermann

A widely used formulation for null hypotheses in the analysis of multivariate $d$-dimensional data is $\mathcal{H}_0: \boldsymbol{H} \boldsymbolθ =\boldsymbol{y}$ with $\boldsymbol{H}$ $\in\mathbb{R}^{m\times d}$, $\boldsymbolθ$ $\in \mathbb{R}^d$ and $\boldsymbol{y}\in\mathbb{R}^m$, where $m\leq d$. Here the unknown parameter vector $\boldsymbolθ$ can, for

Leon Bungert, Tim Roith, Philipp Wacker

In this paper we propose polarized consensus-based dynamics in order to make consensus-based optimization (CBO) and sampling (CBS) applicable for objective functions with several global minima or distributions with many modes, respectively. For this, we ``polarize&#39;&#39; the dynamics with a localizing kernel and the resulting model can be viewed as a boun

Manu Navjeevan, Rodrigo Pinto, Andres Santos

This paper develops a class of potential outcomes models characterized by three main features: (i) Unobserved heterogeneity can be represented by a vector of potential outcomes and a type describing the manner in which an instrument determines the choice of treatment; (ii) The availability of an instrumental variable that is conditionally independent of unob

Bruce Brewer, Jake Daniels, Nghiem V. Nguyen

Some systems were recently put forth by Nguyen et. al. as models for studying the interaction of long and short waves in dispersive media. These systems were shown to possess synchronized Jacobi elliptic solutions as well as synchronized solitary wave solutions under certain constraints, i.e., vector solutions where the two components are proportional to one

Constantine Caramanis, Dimitris Fotakis, Alkis Kalavasis, Vasilis Kontonis

Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems. In those methods a deep neural network is used as a solution generator which is then trained by gradient-based methods (e.g., policy gradient) to successively obtain better solution distributions. In this work we introd

Rishab Balasubramanian, Jiawei Li, Prasad Tadepalli, Huazheng Wang

We study reward poisoning attacks on Combinatorial Multi-armed Bandits (CMAB). We first provide a sufficient and necessary condition for the attackability of CMAB, a notion to capture the vulnerability and robustness of CMAB. The attackability condition depends on the intrinsic properties of the corresponding CMAB instance such as the reward distributions of

Jian-Zhang Wu, Gleb Beliakov

Graphs serve as efficient tools for visualizing mathematical concepts and their interrelationships. In this paper, focusing on the discrete case with universal set with finite elements, we first introduce the rules and characteristics of graph representation of fuzzy measure and discuss graphic properties of fuzzy measure's duality, symmetry, nonadditivity a

Benjamin Kalfon, Soumaya Cherkaoui, Jean-Frédéric Laprade, Ola Ahmad

Classical GAN architectures have shown interesting results for solving anomaly detection problems in general and for time series anomalies in particular, such as those arising in communication networks. In recent years, several quantum GAN architectures have been proposed in the literature. When detecting anomalies in time series using QGANs, huge challenges

Ruiqi Wang, Hanyang Liu, Jiaming Qiu, Moran Xu

IoT devices are increasingly the source of data for machine learning (ML) applications running on edge servers. Data transmissions from devices to servers are often over local wireless networks whose bandwidth is not just limited but, more importantly, variable. Furthermore, in cyber-physical systems interacting with the physical environment, image offloadin

Hossein Rastgoftar

The usability of ride-sharing services like Uber and Lyft has been considerably improved by advancements in cellular communications. Such a tech-driven transportation system can reduce the number of private cars, in roads with limited physical capacity, effectively match drivers with passengers requesting service, and save drivers and passengers time by opti

Sindhu B Hegde, Andrew Zisserman

In this paper we introduce a new synchronisation task, Gesture-Sync: determining if a person's gestures are correlated with their speech or not. In comparison to Lip-Sync, Gesture-Sync is far more challenging as there is a far looser relationship between the voice and body movement than there is between voice and lip motion. We introduce a dual-encoder model

Wenwen Li

Multiparameter persistence modules come up naturally in topological data analysis and topological robotics. Given a metric graph $(X,\delta)$, the second configuration space of $(X,\delta)$ with proximity parameters (for example, the minimum distance allowed between each pair of robots) can be interpreted as a collection of all possible configurations of two

Zaid Jangda, Hannah Menke, Andreas Busch, Sebastian Geiger

Subsurface porous rocks have the potential to store large volumes of hydrogen (H$_2$) required for transitioning towards a H$_2$-based energy future. Understanding the flow and trapping behavior of H$_2$ in subsurface storage systems, which is influenced by pore-scale heterogeneities inherent to subsurface rocks, is crucial to reliably evaluate the storage e

Martin Brandenburg

A classical theorem by Jacobson says that a ring in which every element $x$ satisfies the equation $x^n=x$ for some $n>1$ is commutative. According to Birkhoff's Completeness Theorem, if $n$ is fixed, there must be an equational proof of this theorem. But equational proofs have only appeared for some values of $n$ so far. This paper is about finding such a p

Reka A. Horvath, Kesha Sorathia, Isabelle Saint, David P. Tew

Low-scaling electron correlation theory based on the pair natural orbital approximation, PNO-CCSD(T), has become a powerful computational tool. Motivated by the recent discovery of large errors for organometallic molecules, we assess the role of the model density used to discard unimportant contributions. We find that second-order perturbation theory provide

InChan Hwang, MinJae Woo

This research presents a novel framework for the compression and decompression of medical images utilizing the Latent Diffusion Model (LDM). The LDM represents advancement over the denoising diffusion probabilistic model (DDPM) with a potential to yield superior image quality while requiring fewer computational resources in the image decompression process. A

Alexander Slastin, Dan Alistarh, Vitaly Aksenov

Self-adjusting data structures are a classic approach to adapting the complexity of operations to the data access distribution. While several self-adjusting variants are known for both binary search trees and B-Trees, existing constructions come with limitations. For instance, existing works on self-adjusting B-Trees do not provide static-optimality and tend

Jason Yim, Andrew Campbell, Andrew Y. K. Foong, Michael Gastegger

We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching. Specifically, we adapt FrameDiff, a state-of-the-art diffusion model, to the flow-matching generative modeling paradigm. We show how flow matching can be applied on SE(3) and propose modifications during training to effectively learn the vector field. Compared to F

Siyuan Huang, Yunchong Song, Jiayue Zhou, Zhouhan Lin

Attention mechanisms have made significant strides in graph learning, yet they still exhibit notable limitations: local attention faces challenges in capturing long-range information due to the inherent problems of the message-passing scheme, while global attention cannot reflect the hierarchical neighborhood structure and fails to capture fine-grained local

Danyang Liu, Mirella Lapata, Frank Keller

Visual storytelling aims to generate compelling narratives from image sequences. Existing models often focus on enhancing the representation of the image sequence, e.g., with external knowledge sources or advanced graph structures. Despite recent progress, the stories are often repetitive, illogical, and lacking in detail. To mitigate these issues, we presen

Andrea Piergentili, Beatrice Savoldi, Dennis Fucci, Matteo Negri

Gender inequality is embedded in our communication practices and perpetuated in translation technologies. This becomes particularly apparent when translating into grammatical gender languages, where machine translation (MT) often defaults to masculine and stereotypical representations by making undue binary gender assumptions. Our work addresses the rising d

Ilya Kokorin, Dan Alistarh, Vitaly Aksenov

Tree data structures, such as red-black trees, quad trees, treaps, or tries, are fundamental tools in computer science. A classical problem in concurrency is to obtain expressive, efficient, and scalable versions of practical tree data structures. We are interested in concurrent trees supporting range queries, i.e., queries that involve multiple consecutive

Qianou Ma, Hua Shen, Kenneth Koedinger, Tongshuang Wu

Large Language Models (LLMs) now excel at generative skills and can create content at impeccable speeds. However, they are imperfect and still make various mistakes. In a Computer Science education context, as these models are widely recognized as "AI pair programmers," it becomes increasingly important to train students on evaluating and debugging the LLM-g

Richard Evans, Antonio Pozzi

Sample size calculations play a central role in study design because sample size affects study interpretability, costs, hospital resources, and staff time. For most veterinary orthopaedic risk-factor studies, either the sample size calculation or the post-hoc power calculation assumes the disease status of control subjects is perfectly ascertained, when it m

Rusheng Zhang, Depu Meng, Shengyin Shen, Zhengxia Zou

As vehicular communication and networking technologies continue to advance, infrastructure-based roadside perception emerges as a pivotal tool for connected automated vehicle (CAV) applications. Due to their elevated positioning, roadside sensors, including cameras and lidars, often enjoy unobstructed views with diminished object occlusion. This provides the

Haolin Liu, Christian Gobert, Kevin Ferguson, Brandon Abranovic

With a growing demand for high-quality fabrication, the interest in real-time process and defect monitoring of laser powder bed fusion (LPBF) has increased, leading manufacturers to incorporate a variety of online sensing methods including acoustic sensing, photodiode sensing, and high-speed imaging. However, real-time acquisition of high-resolution melt poo

Katharine M. Clark, Paul D. McNicholas

Matrix-variate distributions are a relatively recent addition to the model-based clustering literature, thereby making it possible to analyze data in matrix form with complex structure such as images and time series. Due to its recent appearance, there is limited literature on matrix-variate data, with even less on dealing with outliers in these models. An a

Sergey Khoperskov, Ivan Minchev, Matthias Steinmetz, Julien Marabotto

The Gaia-Sausage-Enceladus~(GSE) stands out as the largest known ancient accretion event in the Milky Way~(MW) history. Despite this significance, the parameters of its progenitor galaxy are still poorly constrained. We identify GSE stars from the APOGEE DR17 using Gaussian mixture models and recover a negative radial metallicity gradient for the GSE debris

Heinrich Peters, Alireza Hashemi, James Rae

Machine learning (ML) and artificial intelligence (AI) systems rely heavily on human-annotated data for training and evaluation. A major challenge in this context is the occurrence of annotation errors, as their effects can degrade model performance. This paper presents a predictive error model trained to detect potential errors in search relevance annotatio

Malena Sabate Landman, Jiahua Jiang, Jianru Zhang, Wuwei Ren

This paper presents two new augmented flexible (AF)-Krylov subspace methods, AF-GMRES and AF-LSQR, to compute solutions of large-scale linear discrete ill-posed problems that can be modeled as the sum of two independent random variables, exhibiting smooth and sparse stochastic characteristics respectively. Following a Bayesian modelling approach, this corres

Mykola Matviichuk

Let $\mathsf{X}$ be the product of a complex projective space and a polydisc. We study Poisson brackets on $\mathsf{X}$ that are log symplectic, that is, generically symplectic and such that the inverse two-form has only first order poles. We propose a method of constructing such Poisson brackets that additionally are elliptic, in a precise sense. Our method

Victor Bohdanskyi, Vladyslav Bohun, Alexander Marynych, Igor Samoilenko

Let $(\xi_1, \eta_1)$, $(\xi_2, \eta_2),\ldots$ be independent identically distributed $\mathbb{N}^2$-valued random vectors with arbitrarily dependent components. The sequence $(\Theta_k)_{k\in\mathbb{N}}$ defined by $\Theta_k=\Pi_{k-1}\cdot\eta_k$, where $\Pi_0=1$ and $\Pi_k=\xi_1\cdot\ldots\cdot \xi_{k}$ for $k\in\mathbb{N}$, is called a multiplicative per

Matěj Doležálek, Martin Raška, Ester Sgallová, Eric Nathan Stucky

We consider the enumeration of states in the Brubaker-Bump-Friedberg six-vertex model, whose boundary conditions are determined by an integer partition. In general, we find the number of states is a polynomial in the largest part of the partition. By explicating this technique, we also enumerate the states completely for hook shapes and staircases.

Yixin Wan, Jieyu Zhao, Aman Chadha, Nanyun Peng

Recent advancements in Large Language Models empower them to follow freeform instructions, including imitating generic or specific demographic personas in conversations. We define generic personas to represent demographic groups, such as "an Asian person", whereas specific personas may take the form of specific popular Asian names like "Yumi". While the adop

Penghua Ying, Zheyong Fan

Machine-learned potentials (MLPs) have become a popular approach of modelling interatomic interactions in atomistic simulations, but to keep the computational cost under control, a relatively short cutoff must be imposed, which put serious restrictions on the capability of the MLPs for modelling relatively long-ranged dispersion interactions. In this paper,

Yasser Bakhouch, Silvio Buchner, Rafael Abel Silveira, Leonardo Resende

Lithium disilicate glasses and glass-ceramics are good potential candidates for biomedical applications and solid-state batteries, and serve as models of nucleation and crystal growth. Moreover, these glasses exhibit a phase separation that influences their nucleation and crystallization behavior. The atomistic mechanisms of the phase separation and their pr

N. Fialko, M. Olshevets, V. D. Lakhno

When modeling charge dynamics in a chain of N sites at a temperature T, a Langevin thermostat and a Hamiltonian system, i.e., a chain heated to a given temperature before charge is injected, are compared. It is shown that the polaron disruption occurs in the same range of values of the thermal energy NT, however, T is not given by the initial data, but obtai

Anas Belfathi, Nicolas Hernandez, Laura Monceaux

The legal domain is a vast and complex field that involves a considerable amount of text analysis, including laws, legal arguments, and legal opinions. Legal practitioners must analyze these texts to understand legal cases, research legal precedents, and prepare legal documents. The size of legal opinions continues to grow, making it increasingly challenging

Apoorva Lal, Daniel M Thompson

Private donors contributed more than $350 million to local election officials to support the administration of the 2020 election. Supporters argue these grants were neutral and necessary to maintain normal election operations during the pandemic, while critics worry these grants mostly went to Democratic strongholds and tilted election outcomes. These concer

Laura DeMarco, Niki Myrto Mavraki

We study algebraic relations among postcritically finite (PCF) parameters in the family $f_c(z) = z^2 + c$. Ghioca, Krieger, Nguyen and Ye proved that an algebraic curve in $\mathbb{C}^2$ contains infinitely many PCF pairs $(c_1, c_2)$ if and only if the curve is special (i.e., the curve is a vertical or horizontal line through a PCF parameter, or the curve

Adrian Groza

I shall quantify the logical faults generated by ChatGPT when applied to reasoning tasks. For experiments, I use the 144 puzzles from the library \url{https://users.utcluj.ro/~agroza/puzzles/maloga}~\cite{groza:fol}. The library contains puzzles of various types, including arithmetic puzzles, logical equations, Sudoku-like puzzles, zebra-like puzzles, truth-

Wentao Yu, Eslam Abdelaleem, Ilya Nemenman, Justin C. Burton

Dusty plasma is a mixture of ions, electrons, and macroscopic charged particles that is commonly found in space and planetary environments. The particles interact through Coulomb forces mediated by the surrounding plasma, and as a result, the effective forces between particles can be non-conservative and non-reciprocal. Machine learning (ML) models are a pro

Kendric Schefers

We develop an elementary formalism of functional calculus for entire holomorphic functions in the setting of Clausen and Scholze's $p$-liquid vector spaces.

Vincent Mastain, Fabio Petrillo

Testing plays a vital role in software development, but in the realm of video games, the process differs from traditional software development practices. Game developers typically rely on human testers who are provided with checklists to evaluate various elements. While major game developers already employ automated testing using script-based bots, the incre

Walaa Alqwider, Aly Sabri Abdalla, Vuk Marojevic

The coexistence between active wireless communications and passive RF spectrum use becomes an increasingly important requirement for coordinated spectrum access supporting critical services. The ongoing research and technological progress are focused on effective spectrum utilization including large-scale MIMO and energy efficient and low-power communication

Sankar B., Mukil Saravanan, Kalaivanan Kumar, Siri Dubbaka

Art restoration is crucial for preserving cultural heritage, but traditional methods have limitations in faithfully reproducing original artworks while addressing issues like fading, staining, and damage. We present an innovative approach using deep learning, specifically Convolutional Neural Networks (CNNs), and Computer Vision techniques to revolutionize a

Azim Akhtarshenas, Mohammad Ali Vahedifar, Navid Ayoobi, Behrouz Maham

Robust machine learning (ML) models can be developed by leveraging large volumes of data and distributing the computational tasks across numerous devices or servers. Federated learning (FL) is a technique in the realm of ML that facilitates this goal by utilizing cloud infrastructure to enable collaborative model training among a network of decentralized dev

Thanh-Tri Châu, Giovanni Zocchi

We report measurements on the dynamics of the Debye layer at a gold electrode in several electrolytes. In the experiments, the Debye layer transmits a damped voltage wave along the electrode, which we use to probe the dynamics. We compare the measurements with traditional impedance models, which schematize the Debye layer as a capacitance. We find good agree

Neville Harnew

The Detector R&D Roadmap for European Particle Physics was published in February 2022. The outcome of the Roadmap process relating to particle identification and photon detectors is summarised.

Zilin Si, Kevin Zhang, Oliver Kroemer, F. Zeynep Temel

Dexterous robotic manipulation in unstructured environments can aid in everyday tasks such as cleaning and caretaking. Anthropomorphic robotic hands are highly dexterous and theoretically well-suited for working in human domains, but their complex designs and dynamics often make them difficult to control. By contrast, parallel-jaw grippers are easy to contro

Zahraa Khaled

The first goal of this article is to give a complete classification (up to Real biholomorphisms) of Real primary Hopf surfaces $(H,s)$, and, for any such pair, to describe in detail the following naturally associated objects : the group $\mathrm{Aut}_h(H,s)$ of Real automorphisms, the Real Picard group $(\mathrm{Pic}(H),\hat s^*)$, and the Picard group of Re

Pengfei He, Yue Xing, Han Xu, Jie Ren

Backdoor attacks aim to surreptitiously insert malicious triggers into DNN models, granting unauthorized control during testing scenarios. Existing methods lack robustness against defense strategies and predominantly focus on enhancing trigger stealthiness while randomly selecting poisoned samples. Our research highlights the overlooked drawbacks of random s

Zudi Lin, Donglai Wei, Aarush Gupta, Xingyu Liu

Objects with complex structures pose significant challenges to existing instance segmentation methods that rely on boundary or affinity maps, which are vulnerable to small errors around contacting pixels that cause noticeable connectivity change. While the distance transform (DT) makes instance interiors and boundaries more distinguishable, it tends to overl

Amirsaeid Safari, Jesse B. Hoagg

This paper presents a time-varying soft-maximum composite control barrier function (CBF) that can be used to ensure safety in an a priori unknown environment, where local perception information regarding the safe set is periodically obtained. We consider the scenario where the periodically obtained perception feedback can be used to construct a local CBF tha

James A. Tummon Simmons, Sarah J. Werezak, Benjamin R. Ecclestone, James E. D. Tweel

Vascular imaging is critical for understanding human health and disease. Most established non-contact and label-free optical techniques capture predominantly structural information about vasculature. However, in many pathologies, functional changes often precede visible morphological changes. This limits the ability of established modalities to prevent negat

J. Aponte, D. Carrasco-Olivera, H. Villavicencio

An inner-distal homeomorphism is one such that each of its proximal cells has empty interior. In locally connected spaces, we prove these homeomorphisms have the following properties: Every $cw$-distal homeomorphism is inner-distal but not conversely. The inner-distal homeomorphisms are precisely those for which the diameters of the iterates of every connect

Mayank Kejriwal, Hamid Haidarian, Min-Hsueh Chiu, Andy Xiang

Efficiently finding doctors and locations is an important search problem for patients in the healthcare domain, for which traditional information retrieval methods tend not to work optimally. In the last ten years, knowledge graphs (KGs) have emerged as a powerful way to combine the benefits of gleaning insights from semi-structured data using semantic model

Marianne Akian, Stephane Gaubert, Louis Rowen

This is part of an ongoing project to find a general algebraic framework for semiring theory. The structure theory of semirings is quite challenging, largely because of the lack of negation, and such basic properties such as unique factorization of polynomials, multiplicativity of determinants, and the characteristic polynomial of a matrix, all fail. (In fac

Tobias Wolfgruber, Marco Knöll, Robert Roth

For light nuclei, ab initio many-body methods such as the no-core shell model are the tools of choice for predictive, high-precision nuclear structure calculations. The applicability and the level of precision of these methods, however, is limited by the model-space truncation that has to be employed to make such computations feasible. We present a universal

Mehrdad Mohammadian, Neda Maleki, Tobias Olsson, Fredrik Ahlgren

What happens if we encounter a suitable font for our design work but do not know its name? Visual Font Recognition (VFR) systems are used to identify the font typeface in an image. These systems can assist graphic designers in identifying fonts used in images. A VFR system also aids in improving the speed and accuracy of Optical Character Recognition (OCR) s

H. Bigdeli, S. M. S. Mirdamadi, A. Deaconu, J. Tayyebi

The vehicle routing problem has great importance and application in transportation and supply chain management. In this case, there are several supply requests in a transportation network. The main goal is to allocate customers to available vehicles and find the sequence of customer visits on each route. It is possible to attack the arcs of the network when

Haoran Wang, Kai Shu

Claim verification plays a crucial role in combating misinformation. While existing works on claim verification have shown promising results, a crucial piece of the puzzle that remains unsolved is to understand how to verify claims without relying on human-annotated data, which is expensive to create at a large scale. Additionally, it is important for models

Pinaki Mandal

This paper studies the (group) strategy-proofness aspect of two-sided matching markets under stability. For a one-to-one matching market, we show an equivalence between individual and group strategy-proofness under stability. We obtain this equivalence assuming the domain satisfies a richness condition. However, the result cannot be extended to the many-to-o

Noam Krupnik, Abraham Berman

For natural numbers $k<n$ we study the graphs $T_{n,k}:=K_{k}\lor\overline{K_{n-k}}$. For $k=1$, $T_{n,1}$ is the star $S_{n-1}$. For $k>1$ we refer to $T_{n,k}$ as a \emph{graph of pyramids}. We prove that the graphs of pyramids are determined by their spectrum, and that a star $S_{n}$ is determined by its spectrum iff $n$ is prime. We also show that the gr

Luciano Vinas, Arash A. Amini

We revisit recent spectral GNN approaches to semi-supervised node classification (SSNC). We posit that state-of-the-art (SOTA) GNN architectures may be over-engineered for common SSNC benchmark datasets (citation networks, page-page networks, etc.). By replacing feature aggregation with a non-parametric learner we are able to streamline the GNN design proces