December 2024 arXiv papers — page 39

Katrina Brown, Marton Havasi, Finale Doshi-Velez

Concept bottleneck models are interpretable predictive models that are often used in domains where model trust is a key priority, such as healthcare. They identify a small number of human-interpretable concepts in the data, which they then use to make predictions. Learning relevant concepts from data proves to be a challenging task. The most predictive conce

Martha Du Preez, William Q. Erickson, Jonathan Feigert, Markus Hunziker

In the symmetric group $S_n$, each element $\sigma$ has an associated cycle type $\alpha$, a partition of $n$ that identifies the conjugacy class of $\sigma$. The Robinson-Schensted (RS) correspondence links each $\sigma$ to another partition $\lambda$ of $n$, representing the shape of the pair of Young tableaux produced by applying the RS row-insertion algo

Kaidi Xu, Shan-Shan Wang, Rong Yu, Shuai Dong

Stabilizing exotic quantum phases of matter, e.g. spin liquid, is an attractive topic in condensed matter. Here, by a Monte Carlo study of a two-orbital spin-fermion model on a honeycomb lattice, we show the cooperative effects of the orbital degeneracy of itinerant electrons and the exchange interaction of localized spins can significantly suppress both fer

Alexander D. Kaiser, Jing Wang, Aaron L. Brown, Enbo Zhu

The zebrafish is a valuable model organism for studying cardiac development and diseases due to its many shared aspects of genetics and anatomy with humans and ease of experimental manipulations. Computational fluid-structure interaction (FSI) simulations are an efficient and highly controllable means to study the function of cardiac valves in development an

S. Carey, V. Pandey

The interactions of low-energy neutrinos with nuclei provide a unique window to explore various Standard Model (SM) and Beyond the Standard Model (BSM) processes. In particular, the recent observation of coherent elastic neutrino-nucleus scattering (CEvNS), predicted over five decades ago, has generated significant interest across disciplines. With its high

Martin Bojowald, Erick I. Duque, Dennis Hartmann

Models of gravitational collapse provide important means to test whether non-classical space-time effects motivated for instance by quantum gravity can be realized in generic ways in physically relevant situations. Here, a detailed analysis of marginally bound Lemaitre-Tolman-Bondi space-times is given in emergent modified gravity, which in particular includ

Xin Zhao, Zehui Jiang, Naoki Yoshinaga

While feed-forward neurons in pre-trained language models (PLMs) can encode knowledge, past research targeted a small subset of neurons that heavily influence outputs. This leaves the broader role of neuron activations unclear, limiting progress in areas like knowledge editing. We uncover a global linear relationship between neuron activations and outputs us

Miguel Esparza, Bo Li, Junwei Ma, Ali Mostafavi

Our society is on the verge of a revolution powered by Artificial Intelligence (AI) technologies. With increasing advancements in AI, there is a growing expansion in data centers (DCs) serving as critical infrastructure for this new wave of technologies. This technological wave is also on a collision course with exacerbating climate hazards which raises the

Francois Chaubard, Duncan Eddy, Mykel J. Kochenderfer

We introduce Gradient Agreement Filtering (GAF) to improve on gradient averaging in distributed deep learning optimization. Traditional distributed data-parallel stochastic gradient descent involves averaging gradients of microbatches to calculate a macrobatch gradient that is then used to update model parameters. We find that gradients across microbatches a

Youshen Zhao, Keiji Iramina

Electroencephalogram (EEG) signals are critical for detecting abnormal brain activity, but their high dimensionality and complexity pose significant challenges for effective analysis. In this paper, we propose CwA-T, a novel framework that combines a channelwise CNN-based autoencoder with a single-head transformer classifier for efficient EEG abnormality det

Chiyi Luo, Wenhui Ma, Yun Zhao

For a $C^{r}$ $(r>1)$ diffeomorphism on a compact manifold that admits a dominated splitting, this paper establishes the upper semi-continuity of the entropy map. More precisely, this paper establishes the upper semi-continuity of the entropy map in the following two cases: (1) if a sequence of invariant measures has only positive Lyapunov exponents along a

Maya Patel, Aditi Anand

Benchmarking modern large language models (LLMs) on complex and realistic tasks is critical to advancing their development. In this work, we evaluate the factual accuracy and citation performance of state-of-the-art LLMs on the task of Question Answering (QA) in ambiguous settings with source citations. Using three recently published datasets-DisentQA-DupliC

Abdellatif Elgrou, Federica Gregorio, Abdelaziz Rhandi

We investigate the null and approximate controllability of coupled linear forward stochastic reaction--convection--diffusion systems under suitable cascade coupling conditions. The model consists of two forward stochastic parabolic equations governed by general second-order differential operators with time-, space-, and random-dependent coefficients. We cons

Zhipeng Guan, Chi Zhang

Let $\mathcal{R}$ be a commutative ring with unity, and let $P$ be a locally finite poset. The aim of the paper is to provide an explicit description of the additive biderivations of the incidence algebra $I(P, \mathcal{R})$. We demonstrate that every additive biderivation is the sum of several inner biderivations and extremal biderivations. Furthermore, if

Weitao Tang, Guanliang Chen, Shuaishuai Zu, Jiangyi Luo

In second-language acquisition, predictive modeling aids educators in implementing diverse teaching strategies, attracting significant research attention. However, while model accuracy is widely explored, model fairness remains under-examined. Model fairness ensures equitable treatment of groups, preventing unintentional biases based on attributes such as ge

Lo Pang-Yun Ting, Ali Şenol, Huan-Yang Wang, Hsu-Chao Lai

Recent advances in bidirectional EV charging and discharging systems have spurred interest in workplace applications. However, real-world deployments face various dynamic factors, such as fluctuating electricity prices and uncertain EV departure times, that hinder effective energy management. To address these issues and minimize building electricity costs wh

Shuqi Cui, Nirmalya Thakur, Audrey Poon

Emojis are widely used across social media platforms but are often lost in noisy or garbled text, posing challenges for data analysis and machine learning. Conventional preprocessing approaches recommend removing such text, risking the loss of emojis and their contextual meaning. This paper proposes a three-step reverse-engineering methodology to retrieve em

Daniel Barrera Salazar, Luis Santiago Palacios

Let $K$ be an imaginary quadratic field. In this article, we study the local geometry of the Bianchi eigenvariety around non-cuspidal classical points, in particular, ordinary non-cuspidal base change points. To perform this study we introduce Bianchi Eisenstein eigensystems and prove strong multiplicity-one results on the cohomology of the corresponding Bia

O. J. Franca, Fabian Spallek, Steffen M. Giesen, Robert Berger

We investigate how the quantum friction experienced by a polarizable atom moving with constant velocity parallel to a planar interface is modified when the latter consists of chiral or nonreciprocal media, with special focus on topological insulators. We use macroscopic quantum electrodynamics to obtain the velocity-dependent Casimir-Polder frequency shift a

Yidong Gan, Maciej Rybinski, Ben Hachey, Jonathan K. Kummerfeld

Clinical coding is crucial for healthcare billing and data analysis. Manual clinical coding is labour-intensive and error-prone, which has motivated research towards full automation of the process. However, our analysis, based on US English electronic health records and automated coding research using these records, shows that widely used evaluation methods

Hangli Ge, Hirotsugu Seike, Noboru Koshizuka

Smart buildings incorporate various emerging Internet of Things (IoT) applications for comprehensive management of energy efficiency, human comfort, automation, and security. However, the development of a knowledge extraction framework is fundamental. Currently, there is a lack of a unified and practical framework for modeling heterogeneous sensor data withi

S. J. Watts, L. Crow

In recent years generative artificial intelligence has been used to create data to support science analysis. For example, Generative Adversarial Networks (GANs) have been trained using Monte Carlo simulated input and then used to generate data for the same problem. This has the advantage that a GAN creates data in a significantly reduced computing time. N tr

Xiaoyu Li, Yingyu Liang, Zhenmei Shi, Zhao Song

Tensor Attention extends traditional attention mechanisms by capturing high-order correlations across multiple modalities, addressing the limitations of classical matrix-based attention. Meanwhile, Rotary Position Embedding ($\mathsf{RoPE}$) has shown superior performance in encoding positional information in long-context scenarios, significantly enhancing t

David Jewitt, Jane Luu, Jing Li

Most Kreutz family sungrazing comets are discovered only days before perihelion, severely limiting observational opportunities to study their physical nature and decay. Kreutz sungrazer C/2024 S1 (ATLAS) was discovered a month before reaching its perihelion distance of 0.008 au, allowing physical observations from both ground- and space-based telescopes. We

Mirko Zaffaroni, Federico Signoretta, Marco Grangetto, Attilio Fiandrotti

Accurately predicting pedestrian trajectories is crucial in applications such as autonomous driving or service robotics, to name a few. Deep generative models achieve top performance in this task, assuming enough labelled trajectories are available for training. To this end, large amounts of synthetically generated, labelled trajectories exist (e.g., generat

P. Yin, A. M. Shirokov, H. Li, B. Zhou

The state-of-the-art ab initio nuclear many-body approaches with modern nuclear forces are challenged by the recent experimental measurement of the monopole form factor of the $0^+_1\rightarrow 0^+_2$ transition in the $\alpha$ particle [Kegel et al., Phys. Rev. Lett. 130, 152502 (2023)]. We investigate the elastic and inelastic $0^+_1\rightarrow 0^+_2$ tran

Melkamu Mersha, Mingiziem Bitewa, Tsion Abay, Jugal Kalita

Neural networks are widely regarded as black-box models, creating significant challenges in understanding their inner workings, especially in natural language processing (NLP) applications. To address this opacity, model explanation techniques like Local Interpretable Model-Agnostic Explanations (LIME) have emerged as essential tools for providing insights i

Indranil Palit, Tushar Sharma

Automated source code refactoring, particularly extract method refactoring, is a crucial and frequently employed technique during software development. Despite its importance and frequent use by practitioners, current automated techniques face significant limitations. These approaches often rely on developers to identify the precise bounds of refactoring opp

Alan Kostelecky, Ralf Lehnert, Marco Schreck, Babak Seradjeh

The physical intepretation of effective field theories of fundamental interactions incorporating large Lorentz violation is a long-standing challenge, known as the concordance problem. In condensed-matter physics, certain Weyl semimetals with emergent Lorentz invariance exhibit large Lorentz violation, thereby offering prospective laboratory analogues for ex

Adel Aghajan, Miguel Jimenez-Aparicio, Michael E. Ropp, Jorge I. Poveda

We study the problem of distributed optimal resource allocation on networks with actions defined on discrete spaces, with applications to adaptive under-frequency load-shedding in power systems. In this context, the primary objective is to identify an optimal subset of loads (i.e., resources) in the grid to be shed to maintain system stability whenever there

Rebecca Ramnauth, Dražen Brščić, Brian Scassellati

As foundation models increasingly permeate sensitive domains such as healthcare, finance, and mental health, ensuring their behavior meets desired outcomes and social expectations becomes critical. Given the complexities of these high-dimensional models, traditional techniques for constraining agent behavior, which typically rely on low-dimensional, discrete

Chiyu Wei, Sean Noh, Ho-Chun Herbert Chang

Sentiment analysis of textual content has become a well-established solution for analyzing social media data. However, with the rise of images and videos as primary modes of expression, more information on social media is conveyed visually. Among these, facial expressions serve as one of the most direct indicators of emotional content in images. This study a

William Bialek

The 2024 Nobel Prize in Physics was awarded to John Hopfield and Geoffrey Hinton, "for foundational discoveries and inventions that enable machine learning with artificial neural networks." As noted by the Nobel committee, their work moved the boundaries of physics. This is a brief reflection on Hopfield's work, its implications for the emergence of biologic

Ding Yu, Zhuo Liu, Hangfeng He

Post-earnings volatility prediction is critical for investors, with previous works often leveraging earnings call transcripts under the assumption that their rich semantics contribute significantly. To further investigate how transcripts impact volatility, we introduce DEC, a dataset featuring accurate volatility calculations enabled by the previously overlo

M. M. Shen, J. R. Szalay, P. Pokorný, J. G. Mitchell

The Integrated Science Investigation of the Sun (IS$\odot$IS) energetic particle instrument suite on Parker Solar Probe is dedicated to measuring energetic ions and electrons in the near-Sun environment. It includes a half-sky-viewing time-of-flight mass spectrometer (EPI-Lo) and five high-energy silicon solid-state detector-telescopes (EPI-Hi). To August 20

Evgeny Hershkovitch Neiterman, Gil Ben-Artzi

Training very deep convolutional networks is challenging, requiring significant computational resources and time. Existing acceleration methods often depend on specific architectures or require network modifications. We introduce LayerDropBack (LDB), a simple yet effective method to accelerate training across a wide range of deep networks. LDB introduces ran

Ada Amendola

This paper develops an analytic study on the existence and properties of solitary waves on 1D chains of lumped masses and nonlinear springs, which exhibit a mechanical response similar to that of tensegrity prisms with locking-type response under axial loading. Making use of the Weierstrass' theory of 1D Lagrangian conservative systems, we show that such wav

Hana Schiff, Paul McClarty, Jeffrey G. Rau, Judit Romhanyi

Altermagnets exhibit spontaneously spin-split electronic bands in the zero spin-orbit coupling (SOC) limit arising from the presence of collinear compensated magnetic order. The distinctive magneto-crystalline symmetries of altermagnets ensure that these spin splittings have a characteristic anisotropy in crystal momentum space. These systems have attracted

Grigor Bezirganyan, Sana Sellami, Laure Berti-Équille, Sébastien Fournier

Multimodal AI models are increasingly used in fields like healthcare, finance, and autonomous driving, where information is drawn from multiple sources or modalities such as images, texts, audios, videos. However, effectively managing uncertainty - arising from noise, insufficient evidence, or conflicts between modalities - is crucial for reliable decision-m

Rebecca Ramnauth, Dražen Brščić, Brian Scassellati

Beyond mere formality, small talk plays a pivotal role in social dynamics, serving as a verbal handshake for building rapport and understanding. For conversational AI and social robots, the ability to engage in small talk enhances their perceived sociability, leading to more comfortable and natural user interactions. In this study, we evaluate the capacity o

Kyoungmin Kim, Anastasia Ailamaki

In the rapidly evolving AI era with large language models (LLMs) at the core, making LLMs more trustworthy and efficient, especially in output generation (inference), has gained significant attention. This is to reduce plausible but faulty LLM outputs (a.k.a hallucinations) and meet the highly increased inference demands. This tutorial explores such efforts

X. Aguerre, A. Barabash, A. Basharina-Freshville, M. Bongrand

The SuperNEMO experiment is searching for neutrinoless double beta decay of \textsuperscript{82}Se, with the unique combination of a tracking detector and a segmented calorimeter. This feature allows to detect the two electrons emitted in the decay and measure their individual energy and angular distribution. The SuperNEMO calorimeter consists of 712 plastic

Antonija Grubišić-Čabo, Marcos H. D. Guimarães, Dmytro Afanasiev, Jose H. Garcia Aguilar

Fundamental research on two-dimensional (2D) magnetic systems based on van der Waals materials has been gaining traction rapidly since their recent discovery. With the increase of recent knowledge, it has become clear that such materials have also a strong potential for applications in devices that combine magnetism with electronics, optics, and nanomechanic

Alejandro Martinez, Yuyan Chen, Riya Anilkumar

Equipment used for site investigation activities like drill rigs are typically large and heavy to provide sufficient reaction mass to overcome the soil's penetration resistance. The need for large and heavy equipment creates challenges for performing site investigations at sites with limited accessibility, such as urban centres, vegetated areas, locations wi

Veronika E. Hubeny, Massimiliano Rota

We continue the study of the quantum marginal independence problem, namely the question of which faces of the subadditivity cone are achievable by quantum states. We introduce a new representation of the patterns of marginal independence (PMIs, corresponding to faces of the subadditivity cone) based on certain correlation hypergraphs, and demonstrate that th

Pouyan Pourhadi, Nima Tashakor, Mahdi Bayati, Stefan Goetz

Integrating power electronics with batteries can offer many advantages, including load sharing and balancing with parallel connectivity. However, parallel batteries with differing voltages and power profiles can cause large circulating currents and uncontrolled energy transfers, risking system instability. To overcome these challenges, we propose a novel mod

Laura C. Slivinski, Jeffrey S. Whitaker, Sergey Frolov, Timothy A. Smith

There has been a recent surge in development of accurate machine learning (ML) weather prediction models, but evaluation of these models has mainly been focused on medium-range forecasts, not their performance in cycling data assimilation (DA) systems. Cycling DA provides a statistically optimal estimate of model initial conditions, given observations and pr

Pratik Lonare, Michele Cantiello, Marco Mirabile, Marilena Spavone

Globular clusters (GCs) are fossil tracer of formation and evolution of galaxies and studying their properties provides crucial insights into past formation and interaction events. We study the properties of GC candidates in 1.25 $\times$ 1.03 sq. degrees area centred on NGC 5018 group of galaxies using deep, wide field and multi-band observations obtained w

Houssam El Cheairi, David Gamarnik

Universality, namely distributional invariance, is a well-known property for many random structures. For example, it is known to hold for a broad range of variational problems with random input. Much less is known about the algorithmic universality of specific methods for solving such variational problems. Namely, whether algorithms tuned to specific variati

Edward L. Chapin, Theodore Grosson, Tim Hardy, Jordan Lothrop

Persistence effects in HgCdTe infrared detectors cause significant artifacts that can impact the quality of science observations for up to many hours after exposure to bright/saturating sources. This problem will have a substantially greater impact on viable observing modes for infrared cameras on future ELTs due to the leap in sensitivities that is expected

Ali Suleiman, Gamal Kassem

Process mining has grown popular today given their ability to provide managers with insights into the actual business process as executed by employees. Process mining depends on event logs found in process aware information systems to model business processes. This has raised the need to develop event log standards given that event logs are the entry point t

Hailin Chen, Fangkai Jiao, Mathieu Ravaut, Nawshad Farruque

The rapid advancement of large language models (LLMs) demands robust, unbiased, and scalable evaluation methods. However, human annotations are costly to scale, model-based evaluations are susceptible to stylistic biases, and target-answer-based benchmarks are vulnerable to data contamination and cheating. To address these limitations, we propose StructTest,

P. Grèbol-Tomàs, J. Ibáñez-Insa, J. M. Trigo-Rodríguez, E. Peña-Asensio

The mechanical properties of minerals in planetary materials are not only interesting from a fundamental point of view but also critical to the development of future space missions. Here we present nanoindentation experiments to evaluate the hardness and reduced elastic modulus of olivine, (Mg, Fe)2SiO4, in meteorite NWA 12008, a lunar basalt. Our experiment

Shihan Kanungo

We examine space-filling curves, which are surjective continuous maps from $[0,1]$ to some higher-dimensional space, usually the unit square $[0,1]^2$. In particular, we define Peano's curve and Lebesgue's curve, and state some of their properties. We also discuss the Hahn-Mazurkiewicz theorem, which characterizes those subsets of $\mathbb{R}^n$ that are the

Gopinath Sahoo

We introduce the notion of tensor t-structures on the bounded derived categories of schemes. For a Noetherian scheme $X$ admitting a dualizing complex, Bezrukavnikov-Deligne, and then independently Gabber and Kashiwara have shown that given a monotone comonotone perversity function on $X$ one can construct a t-structure on $\mathbf{D}^b (X)$. We show that su

Zijin Wan, Xiaojun Dong, Letong Wang, Enzuo Zhu

Contraction Hierarchies (CH) (Geisberger et al., 2008) is one of the most widely used algorithms for shortest-path queries on road networks. Compared to Dijkstra's algorithm, CH enables orders of magnitude faster query performance through a preprocessing phase, which iteratively categorizes vertices into hierarchies and adds shortcuts. However, constructing

Marine Demarty, James Mills, Kenza Hammam, Raul Garcia-Patron

Understanding the limitations imposed by noise on current and next-generation quantum devices is a crucial step toward demonstrating practical quantum advantage. In this work, we investigate the accumulation of entropy density as a benchmark to monitor the performance of quantum processing units (QPUs). We provide a proof-of-principle demonstration of our me

Edward L. Chapin, Jennifer Dunn, Dan Kerley, Lianne Mueller

The Herzberg Extensible Adaptive optics Real-Time Toolkit (HEART) is a complete framework written in C and Python for building next-generation adaptive optics (AO) system real-time controllers, with the performance needed for extremely large telescopes. With numerous HEART-based RTCs now in their design or build phases, each with different AO algorithms, tar

Robyn Brooks, Marissa Masden

One common function class in machine learning is the class of ReLU neural networks. ReLU neural networks induce a piecewise linear decomposition of their input space called the canonical polyhedral complex. It has previously been established that it is decidable whether a ReLU neural network is piecewise linear Morse. In order to expand computational tools f

Jonas Wallat, Maria Heuss, Maarten de Rijke, Avishek Anand

Retrieving relevant context is a common approach to reduce hallucinations and enhance answer reliability. Explicitly citing source documents allows users to verify generated responses and increases trust. Prior work largely evaluates citation correctness - whether cited documents support the corresponding statements. But citation correctness alone is insuffi

Imran Pervez, Ricardo Pinto Lima, Omar Knio

We develop novel integrated learning and optimization (ILO) methodologies to solve economic dispatch (ED) and DC optimal power flow (DCOPF) problems for better economic operation. The optimization problem for ED is formulated with load being an unknown parameter while DCOPF consists of load and power transfer distribution factor (PTDF) matrix as unknown para

Igor Balla, Marek Filakovský, Bartłomiej Kielak, Daniel Kráľ

Aougab and Gaster [Math. Proc. Cambridge Philos. Soc. 174 (2023), 569-584] proved that any set of simple closed curves on the torus, where any two are non-homotopic and intersect at most k times, has a maximum size of $k+O(\sqrt{k}\log k)$. We determine the maximum size of such a set for every k. In particular, the maximum never exceeds k+6, and it does not

Jingru Zhang

We consider a generalized version of the (weighted) one-center problem on graphs. Given an undirected graph $G$ of $n$ vertices and $m$ edges and a positive integer $k\leq n$, the problem aims to find a point in $G$ so that the maximum (weighted) distance from it to $k$ connected vertices in its shortest path tree(s) is minimized. No previous work has been p

Christoph Adam, Hadrien Duprez, Natalie Lehmann, Antoni Yglesias

We measure the entropy change of charge transitions in an electrostatically defined quantum dot in bilayer graphene. Entropy provides insights into the equilibrium thermodynamic properties of both ground and excited states beyond transport measurements. For the one-carrier regime, the obtained entropy shows that the ground state has a two-fold degeneracy lif

Tao Wen, Yu-Hua Yao, Song-Zhan Chen, Ben-Zhong Dai

The recent and brightest GRB 221009A observed by LHAASO marked the first detection of the onset of TeV afterglow, with a total of 7 GRBs exhibiting very high energy (VHE) afterglow radiation. However, consensus on VHE radiation of GRBs is still lacking. Multi-wavelength studies are currently a primary research method for investigating high-energy $\gamma$-ra

Govind Mittal, Sarthak Gupta, Shruti Wagle, Chirag Chopra

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in rea

Jason M. Altschuler, Sinho Chewi

Coupling arguments are a central tool for bounding the deviation between two stochastic processes, but traditionally have been limited to Wasserstein metrics. In this paper, we apply the shifted composition rule--an information-theoretic principle introduced in our earlier work--in order to adapt coupling arguments to the Kullback-Leibler (KL) divergence. Ou

J. Z. Kamiński, K. Krajewska

We study nondipole effects in multiphoton ionization of a two-dimensional hydrogen-like atom by a flat-top laser pulse of varied intensity. For this purpose, we solve numerically a two-dimensional Schr\"odinger equation treating a propagating laser pulse exactly. The resulting distributions are then compared to those calculated in the dipole approximation. A

Peter Halldestam, Paul Heinrich, Gergely Papp, Mathias Hoppe

Plasma-terminating disruptions represent a critical outstanding issue for reactor-relevant tokamaks. ITER will use shattered pellet injection (SPI) as its disruption mitigation system to reduce heat loads, vessel forces, and to suppress the formation of runaway electrons. In this paper we demonstrate that reduced kinetic modelling of SPI is capable of captur

Debendro Mookerjee, Sarah Kostinski

We report on a closed-form expression for the survival probability of a discrete 1D biased random walk to not return to its origin after N steps. Our expression is exact for any N, including the elusive intermediate range, thereby allowing one to study its convergence to the large N limit. In that limit we recover Polya's recurrence probability, i.e. the sur

Chengyue Huang, Yahe Yang

With the widespread application of machine learning in financial risk management, conventional wisdom suggests that longer training periods and more feature variables contribute to improved model performance. This paper, focusing on mortgage default prediction, empirically discovers a phenomenon that contradicts traditional knowledge: in time series predicti

Jinhao Liang, Jacob K. Christopher, Sven Koenig, Ferdinando Fioretto

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics, requiring the computation of collision-free paths for multiple agents moving from their respective start to goal positions. Coordinating multiple agents in a shared environment poses significant challenges, especially in continuous spaces where traditional optimization algorithms struggle

Khen Elimelech, Morteza Lahijanian, Lydia E. Kavraki, Moshe Y. Vardi

Validating the behavior of autonomous Cyber-Physical Systems (CPS) and Artificial Intelligence (AI) agents, which rely on automated controllers, is an objective of great importance. In recent years, Neural-Network (NN) controllers have been demonstrating great promise. Unfortunately, such learned controllers are often not certified and can cause the system t

Joseph L. Betthauser, Rebecca Greene, Ananya Dhawan, John T. Krall

One of the most elusive goals in myographic prosthesis control is the ability to reliably decode continuous positions simultaneously across multiple degrees-of-freedom. Goal: To demonstrate dexterous, natural, biomimetic finger and wrist control of the highly advanced robotic Modular Prosthetic Limb. Methods: We combine sequential temporal regression models

Reuben Tate, Quinn Langfitt, Elijah Pelofske, Ammar Kirmani

We present Snapshot-QAOA, a variation of the Quantum Approximate Optimization Algorithm (QAOA) that finds approximate minimum energy eigenstates of a large set of quantum Hamiltonians (i.e. Hamiltonians with non-diagonal terms). Traditionally, QAOA targets the task of approximately solving combinatorial optimization problems; Snapshot-QAOA enables a signific

Dmitriy Y. Anistratov, Terry S. Haut

Thermal radiative transfer (TRT) is an essential piece of physics in inertial confinement fusion, high-energy density physics, astrophysics etc. The physical models of this type of problem are defined by strongly coupled differential equations describing multiphysics phenomena. This paper presents a new nonlinear multilevel iterative method with two photon e

Roussel Rahman, Jane Shtalenkova, Aashwin Ananda Mishra, Wan-Lin Hu

We implement a network-based approach to study expertise in a complex real-world task: operating particle accelerators. Most real-world tasks we learn and perform (e.g., driving cars, operating complex machines, solving mathematical problems) are difficult to learn because they are complex, and the best strategies are difficult to find from many possibilitie

Olav Geil

In this work we revisit the fundamental findings by Chen et al. in [5] on general information transfer in linear ramp secret sharing schemes to conclude that their method not only gives a way to establish worst case leakage [5, 25] and best case recovery [5, 19], but can also lead to additional insight on non-qualifying sets for any prescribed amount of info

Alireza Geravand, Zibo Zheng, Farshid Shateri, Simon Levasseur

Next-generation computing clusters require ultra-high-bandwidth optical interconnects to support large-scale artificial-intelligence applications. In this context, microring modulators (MRMs) emerge as a promising solution. Nevertheless, their potential is curtailed by inherent challenges, such as pronounced frequency chirp and dynamic non-linearity. Moreove

Paige Bright, Caleb Marshall, Steven Senger

We study a variant of the Falconer distance problem for dot products. In particular, for fractal subsets $A\subset \mathbb{R}^n$ and $a,x\in \mathbb{R}^n$, we study sets of the form \[ \Pi_x^a(A) := \{\alpha \in \mathbb{R} : (a-x)\cdot y= \alpha, \text{ for some $y\in A$}\}. \] We discuss some of what is already known to give a picture of the current state o

Sofia Marino, Jennifer Vandoni, Emanuel Aldea, Ichraq Lemghari

In this companion paper for the DAGECC (Domain Adaptation and GEneralization for Character Classification) competition organized within the frame of the ICPR 2024 conference, we present the general context of the tasks we proposed to the community, we introduce the data that were prepared for the competition and we provide a summary of the results along with

Samir Llamazares-Elias, Angel Tocino

The ability of $\theta$-Milstein methods with $\theta\ge 1$ to capture the non-negativity and the mean-reversion property of the exact solution of the CIR model is shown. In addition, the order of convergence and the preservation of the long-term variance is studied. These theoretical results are illustrated with numerical examples.

Junyu Chen, Shuwen Wei, Yihao Liu, Zhangxing Bian

Spatially varying regularization accommodates the deformation variations that may be necessary for different anatomical regions during deformable image registration. Historically, optimization-based registration models have harnessed spatially varying regularization to address anatomical subtleties. However, most modern deep learning-based models tend to gra

Camilla Baratto, Egit Musaev, Valeria Demontis, Stefano Luin

Epitaxially grown InAs NWs are relevant for electrical sensing applications due to Fermi level pinning at NW surface, thus very sensitive to surrounding environment. While a single NW growth batch consists of millions virtually identical replicas of the same NW, real samples display subtle differences in NW size, shape, structure which may affect the detecti

Dazhi Zhou, Scott C. Chapman, Nikolaus Sulzenauer, Ryley Hill

We present Atacama Compact Array (ACA) Band-3 observations of the protocluster SPT2349$-$56, an extreme system hosting ${\gtrsim}\,12$ submillimeter galaxies (SMGs) at $z\,{=}\,4.3$, to study its integrated molecular gas content via CO(4-3) and long-wavelength dust continuum. The $\sim$30-hour integration represents one of the longest exposures yet taken on

Guang Yang, Peter Trinh, Sannan Iqbal, Justin Zhang

PHICOIN (PHI) is a high-performance cryptocurrency based on the Proof-of-Work (PoW) mechanism. It aims to provide ordinary users with decentralized participation opportunities through an improved and innovative mining algorithm and fair design principles. PHI addresses the challenges of centralization in cryptocurrency mining by enhancing resistance to ASIC

Abdolvahhab Rostamijavanani, Shanwu Li, Yongchao Yang

This work presents a data-driven solution to accurately predict parameterized nonlinear fluid dynamical systems using a dynamics-generator conditional GAN (Dyn-cGAN) as a surrogate model. The Dyn-cGAN includes a dynamics block within a modified conditional GAN, enabling the simultaneous identification of temporal dynamics and their dependence on system param

Prabhu Vellaisamy, Harideep Nair, Vamsikrishna Ratnakaram, Dhruv Gupta

Temporal Neural Networks (TNNs), a special class of spiking neural networks, draw inspiration from the neocortex in utilizing spike-timings for information processing. Recent works proposed a microarchitecture framework and custom macro suite for designing highly energy-efficient application-specific TNNs. These recent works rely on manual hardware design, a

David Gluck

Let G be a finite solvable permutation group. Then modulo a possibly trivial normal elementary abelian 3-subgroup, some set-stabilizer in G is a 2-group.

Victor Júnio Alcântara Cardoso, Rodrigo Moreira, João Fernando Mari, Larissa Ferreira Rodrigues Moreira

Sickle cell anemia, which is characterized by abnormal erythrocyte morphology, can be detected using microscopic images. Computational techniques in medicine enhance the diagnosis and treatment efficiency. However, many computational techniques, particularly those based on Convolutional Neural Networks (CNNs), require high resources and time for training, hi

Zhenjie Li

We discuss how to compute maximal-helicity-violating (MHV) form factors for the chiral part of the stress-tensor supermultiplet from periodic light-like polygon Wilson loops in planar $\mathcal N=4$ super Yang-Mills theory beyond the one-loop level. We show that the periodicity imposes path ordering on points on different edges, which explains the appearance

Abhijat Sarma

In this work we analyze a class of Moir\'e models consisting of an active honeycomb monolayer such as graphene or a hexagonal transition-metal dichalcogenide (TMD) on top of a substrate, in which the K and K' valleys of the active layer are folded near each other by a suitably chosen substrate geometry. Generalizing the so-called ``coupled-valley'' model of

Travis Leadbetter, Prashant K. Purohit, Celia Reina

Over the past several decades, phase field modeling has been established as a standard simulation technique for mesoscopic science, allowing for seamless boundary tracking of moving interfaces and relatively easy coupling to other physical phenomena. However, despite its widespread success, phase field modeling remains largely driven by phenomenological just

Marc Vidal, Marc Leman, Ana M. Aguilera

We develop a theory for functional independent component analysis in an infinite-dimensional framework using Sobolev spaces that accommodate smoother functions. The notion of penalized kurtosis is introduced motivated by Silverman's method for smoothing principal components. This approach allows for a classical definition of independent components obtained v

Ruibo Tu, Hedvig Kjellström, Gustav Eje Henter, Cheng Zhang

Causal reasoning capabilities are essential for large language models (LLMs) in a wide range of applications, such as education and healthcare. But there is still a lack of benchmarks for a better understanding of such capabilities. Current LLM benchmarks are mainly based on conversational tasks, academic math tests, and coding tests. Such benchmarks evaluat

Mikaela Irene Fudolig, Laura S. P. Bloomfield, Matthew Price, Yoshi M. Bird

To optimize interventions for improving wellness, it is essential to understand habits, which wearable devices can measure with greater precision. Using high temporal resolution biometric data taken from the Oura Gen3 ring, we examine daily and weekly sleep and activity patterns of a cohort of young adults (N=582) in their first semester of college. A high c

Ravi Datta Rachuri, Duoduo Liao, Samhita Sarikonda, Datha Vaishnavi Kondur

This paper presents a pilot study introducing a multimodal fusion framework for the detection and analysis of bridge defects, integrating Non-Destructive Evaluation (NDE) techniques with advanced image processing to enable precise structural assessment. By combining data from Impact Echo (IE) and Ultrasonic Surface Waves (USW) methods, this preliminary inves

Larissa F. Rodrigues Moreira, Rodrigo Moreira, Flávio de Oliveira Silva, André R. Backes

Artificial Intelligence (AI) is pivotal in advancing mobile network systems by facilitating smart capabilities and automation. The transition from 4G to 5G has substantial implications for AI in consolidating a network predominantly geared towards business verticals. In this context, 3GPP has specified and introduced the Network Data Analytics Function (NWDA

Harideep Nair, Prabhu Vellaisamy, Albert Chen, Joseph Finn

General matrix multiplication (GEMM) is a ubiquitous computing kernel/algorithm for data processing in diverse applications, including artificial intelligence (AI) and deep learning (DL). Recent shift towards edge computing has inspired GEMM architectures based on unary computing, which are predominantly stochastic and rate-coded systems. This paper proposes

Osama Abdellatif, Ahmed Ayman, Ali Hamdi

Automating high-volume unstructured data processing is essential for operational efficiency. Optical Character Recognition (OCR) is critical but often struggles with accuracy and efficiency in complex layouts and ambiguous text. These challenges are especially pronounced in large-scale tasks requiring both speed and precision. This paper introduces LMV-RPA,