February 2024 arXiv papers — page 78

Rémi Di Guardia, Olivier Laurent

We characterize type isomorphisms in the multiplicative-additive fragment of linear logic (MALL), and thus in *-autonomous categories with finite products, extending a result for the multiplicative fragment by Balat and Di Cosmo. This yields a much richer equational theory involving distributivity and cancellation laws. The unit-free case is obtained by rely

Naian Liao

A set of pointwise estimates are established for local solutions to nonlocal diffusion equations with a drift term. In particular, our Harnack estimates are the first ones for such equations, and our H\"older regularity refines certain known result in several aspects. The approach is measure theoretical in the spirit of DeGiorgi classes. It yields novel nonl

Philip Müller, Felix Meissen, Georgios Kaissis, Daniel Rueckert

Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successes of multiple instance learning in this task for natural images, however, do not translate well to medical images due to the very different characteristics of their objects (i.e. p

Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He

Neuromorphic computing with spiking neural networks is promising for energy-efficient artificial intelligence (AI) applications. However, different from humans who continually learn different tasks in a lifetime, neural network models suffer from catastrophic forgetting. How could neuronal operations solve this problem is an important question for AI and neu

Alexander Felix, Silvio Mandelli, Marcus Henninger, Stephan ten Brink

Mono-static sensing operations in ISAC perform joint beamforming between transmitter and receiver. However, in contrast to pure radar systems, ISAC requires to fulfill communications tasks and to retain the corresponding design constraints for at least one half-duplex antenna array. This shifts the available degrees of freedom to the design of the second hal

Vladimir Damljanovic

It is well known that a single Dirac cone at high-symmetry point (HSP) of a Brillouin zone, akin to the one in graphenes' band structure, can not appear as the only quasiparticle at the Fermi level in two-dimensional (2D), non-magnetic materials. Here we found two layer groups with time-reversal symmetry, among all possible both without- and with spin-orbit

Tam Le, Jérôme Malick

Distributionally robust optimization has emerged as an attractive way to train robust machine learning models, capturing data uncertainty and distribution shifts. Recent statistical analyses have proved that generalization guarantees of robust models based on the Wasserstein distance have generalization guarantees that do not suffer from the curse of dimensi

Adil Chhabra, Marcelo Fonseca Faraj, Christian Schulz, Daniel Seemaier

Addressing the challenges of processing massive graphs, which are prevalent in diverse fields such as social, biological, and technical networks, we introduce HeiStreamE and FreightE, two innovative (buffered) streaming algorithms designed for efficient edge partitioning of large-scale graphs. HeiStreamE utilizes an adapted Split-and-Connect graph model and

Frédéric Chapoton

We introduce a q-analogue of the classical Zeta polynomial of finite partially ordered sets, as a polynomial in one variable x with coefficients depending on the indeterminate q. We prove some properties of this polynomial invariant, including its behaviour with respect to duality, product and disjoint union. The leading term is a q-analogue of the number of

Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang

Diffusion models have demonstrated exceptional capability in generating high-quality images, videos, and audio. Due to their adaptiveness in iterative refinement, they provide a strong potential for achieving better non-autoregressive sequence generation. However, existing text diffusion models still fall short in their performance due to a challenge in hand

Miaoer Liu, Fangqiu Li, Hao Cheng, Endao Li

In this study, thermal-fluid-solid coupled simulations on the gas-phase pre-cooling operation of the corrugated cryogenic hoses were performed. Attention was focused on the temporal evolution and spatial distribution of transient thermal stress in the hose structure caused by convective heat transfer of the cooling medium, Liquefied Natural Gas Boil-Off Gas

Alexander Franke, Nils Sültmann, Christoph Reinhardt, Sandy Croatto

Next-generation gravitational wave observatories are expected to use cryogenically cooled, pendulum-suspended 200 kg test mass mirrors from a crystalline material such as crystalline silicon. During operation of the observatories, these mirrors undergo heating due to the absorption of laser radiation of up to a watt. Low noise cooling techniques need to be d

Domenico Lippolis

Chaotic dynamics is always characterized by swarms of unstable trajectories, unpredictable individually, and thus generally studied statistically. It is often the case that such phase-space densities relax exponentially fast to a limiting distribution, that rules the long-time average of every observable of interest. Before that asymptotic timescale, the sta

Nuo Chen, Hongguang Li, Juhua Huang, Baoyuan Wang

Existing retrieval-based methods have made significant strides in maintaining long-term conversations. However, these approaches face challenges in memory database management and accurate memory retrieval, hindering their efficacy in dynamic, real-world interactions. This study introduces a novel framework, COmpressive Memory-Enhanced Dialogue sYstems (COMED

Tahajuddin Sk, Kaushik Bal, Santosh Biswas, Tridip Sardar

The current manuscript introduce a single-strain dengue model developed from stochastic processes incorporating fractional order transmission and recovery. The fractional derivative has been introduced within the context of transmission and recovery process, displaying characteristics similar to tempered fractional ($TF$) derivatives. It has been established

Frederik Boe Hüttel, Christoffer Riis, Filipe Rodrigues, Francisco Câmara Pereira

Bayesian active learning is based on information theoretical approaches that focus on maximising the information that new observations provide to the model parameters. This is commonly done by maximising the Bayesian Active Learning by Disagreement (BALD) acquisitions function. However, we highlight that it is challenging to estimate BALD when the new data p

Michele Ancona, Damien Gayet

In this paper, we study the curvature properties of random complex plane curves. We bound from below the probability that a uniform proportion of the area of a random complex degree $d$ plane curve has a curvature smaller than $-d/8$. Our lower bound is uniform, in the sense that it does not depend on $d$. We also provide uniform upper bounds for similar pro

Yongming Li, Xikui Ma, Xuchen Wang, Sergei A. Tretyakov

In the context of electromagnetic absorption, it is obvious that for an infinite planar periodic structure illuminated by a plane wave, the maximum attainable absorptance, i.e., perfect absorption, is theoretically limited to 100% of the incident power. Here we show that an intriguing possibility of overcoming this limit arises in finite-size resonant absorb

Nelson Cisneros, Yongxin Wu, Kanty Rabenorosoa, Yann Le Gorrec

This paper is concerned with the modeling and control of a curling Hydraulically Amplified Self-healing Electrostatic (HASEL) actuator using the port-Hamiltonian (PH) approach. For that purpose, we use a modular approach and consider the HASEL actuator as an interconnection of elementary subsystems. Each subsystem is modeled by an electrical component consis

Sejoong Kim

We present optimized tight-binding models with atomic orbitals to improve \textit{ab initio} tight-binding models constructed by truncating full density functional theory (DFT) Hamiltonian based on localized orbitals. Retaining qualitative features of the original Hamiltonian, the optimization reduces quantitative deviations in overall band structures betwee

Verena Blaschke, Christoph Purschke, Hinrich Schütze, Barbara Plank

Natural language processing (NLP) has largely focused on modelling standardized languages. More recently, attention has increasingly shifted to local, non-standardized languages and dialects. However, the relevant speaker populations' needs and wishes with respect to NLP tools are largely unknown. In this paper, we focus on dialects and regional languages re

Frédéric Charve

In our previous work dedicated to the strongly stratified Boussinesq system, we obtained for the first time a limit system (when the froude number $\epsilon$ goes to zero) that depends on the thermal diffusivity $\nu$ ' (other works obtained a limit system only depending on the visosity $\nu$). To reach those richer asymptotics we had to consider an unusual

Hemanshul Garg, Pier Giuseppe Ledda, Jon Skov Pedersen, Matteo Pezzulla

We study the buckling of a clamped beam immersed in a creeping flow within a rectangular channel. Via a combination of precision experiments, simulations, and theoretical modeling, we show how the instability depends on a pressure feedback mechanism and rationalize it in terms of dimensionless parameters. As the beam can bend until touching the wall above a

Qinggang Zhang, Hao Chen, Junnan Dong, Shengyuan Chen

Recent advancements in large language models (LLMs) have shown promise in bridging the gap between natural language queries and database management systems, enabling users to interact with databases without the background of SQL. However, LLMs often struggle to comprehend complex database structures and accurately interpret user intentions. Decomposition-bas

Hao Chen, Anu Dhochak, Pradip Kumar, Sai Rasmi Ranjan Mohanty

The node-opening technique, originally designed for constructing minimal surfaces, is adapted to construct a rich variety of new maxfaces of high genus that are embedded outside a compact set and have arbitrarily many catenoid or planar ends, thus removing the scarcity of examples of maxfaces. The surfaces look like spacelike planes connected by small necks.

Shashank Prakash, Urna Basu, Sanjib Sabhapandit

We study the tagged particle dynamics in a harmonic chain of direction reversing active Brownian particles, with spring constant $k$, rotation diffusion coefficient $D_{\text{r}}$, and directional reversal rate $\gamma$. We exactly compute the tagged particle position variance for quenched and annealed initial orientations of the particles. For well-separate

Daniel Kowatsch, Nicolas M. Müller, Kilian Tscharke, Philip Sperl

For classification, the problem of class imbalance is well known and has been extensively studied. In this paper, we argue that imbalance in regression is an equally important problem which has so far been overlooked: Due to under- and over-representations in a data set's target distribution, regressors are prone to degenerate to naive models, systematically

Mariano Suárez-Álvarez

We consider the algebra $A_N=k\langle x, y\rangle/(yx-xy-x^N)$, with $k$ a field of characteristic zero and $N$ a positive integer. Our main result is a complete description of the first Hochschild cohomology $\operatorname{HH}^1(A_N)$ of $A_N$ that consists both of explicit derivations of $A_N$ whose cohomology classes span it and a description of its Lie a

Shangen Li

Does a more transparent climate disclosure policy induce lower emissions? This paper examines the welfare implications of transparency in climate disclosure regulation. Increased disclosure transparency could result in a larger equilibrium externality, but never leaves the firm worse off. Consequently, mandating full disclosure is no different from maximizin

Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin

Large language models (LLMs) have significantly advanced the field of natural language processing, while the expensive memory and computation consumption impede their practical deployment. Quantization emerges as one of the most effective methods for improving the computational efficiency of LLMs. However, existing ultra-low-bit quantization always causes se

Matteo Rinaldi, Vicente Vento

In this analysis, the so-called holographic graviton soft-wall model (GSW), first developed to investigate the glueball spectrum, has been adopted to predict the masses of hybrids with different quantum numbers. Results have been compared with other models and lattice calculations. We have extended the GSW model by introducing two modifications based on anom

Anqi Li, Yu Lu, Nirui Song, Shuai Zhang

Robust therapeutic relationships between counselors and clients are fundamental to counseling effectiveness. The assessment of therapeutic alliance is well-established in traditional face-to-face therapy but may not directly translate to text-based settings. With millions of individuals seeking support through online text-based counseling, understanding the

Yutian Chen, Shi Guo, Fangzheng Yu, Feng Zhang

Detecting and magnifying imperceptible high-frequency motions in real-world scenarios has substantial implications for industrial and medical applications. These motions are characterized by small amplitudes and high frequencies. Traditional motion magnification methods rely on costly high-speed cameras or active light sources, which limit the scope of their

Yue Fang, Huanjun Chen, Zhibing Li, Weiliang Wang

Hyperbolic phonon polariton is important in precisely controlling photons at the nanoscale. It was common practice to calculate the dielectric function of the phonon polariton system with the Drude-Lorenz model. We considered the impact of LO-TO splitting while applying the Drude-Lorenz model. Then the dielectric functions become wave vector direction depend

Tohida Rehman, Raghubir Bose, Soumik Dey, Samiran Chattopadhyay

This paper explores the realm of abstractive text summarization through the lens of the SEASON (Salience Allocation as Guidance for Abstractive SummarizatiON) technique, a model designed to enhance summarization by leveraging salience allocation techniques. The study evaluates SEASON's efficacy by comparing it with prominent models like BART, PEGASUS, and Pr

Xiaohui Zhang, Wenjie Fu, Mangui Liang

Speech Emotion Recognition (SER) is still a complex task for computers with average recall rates usually about 70% on the most realistic datasets. Most SER systems use hand-crafted features extracted from audio signal such as energy, zero crossing rate, spectral information, prosodic, mel frequency cepstral coefficient (MFCC), and so on. More recently, using

Shubhi Shukla, Manaar Alam, Pabitra Mitra, Debdeep Mukhopadhyay

Machine learning, with its myriad applications, has become an integral component of numerous technological systems. A common practice in this domain is the use of transfer learning, where a pre-trained model's architecture, readily available to the public, is fine-tuned to suit specific tasks. As Machine Learning as a Service (MLaaS) platforms increasingly u

N. I. Stoilova, J. Van der Jeugt

A $Z_2\times Z_2$-graded Lie superalgebra $g$ is a $Z_2\times Z_2$-graded algebra with a bracket $[.,.]$ that satisfies certain graded versions of the symmetry and Jacobi identity. In particular, despite the common terminology, $g$ is not a Lie superalgebra. We construct the most general orthosymplectic $Z_2\times Z_2$-graded Lie superalgebra $osp(2m_1+1,2m_

Ziyu Huang, Bo Jiang, Yuntian Jiang

In this paper, we investigate the convergence behavior of the Accelerated Newton Proximal Extragradient (A-NPE) method when employing inexact Hessian information. The exact A-NPE method was the pioneer near-optimal second-order approach, exhibiting an oracle complexity of $\Tilde{O}(\epsilon^{-2/7})$ for convex optimization. Despite its theoretical optimalit

Yasuhiro Yoshikai, Tadahaya Mizuno, Shumpei Nemoto, Hiroyuki Kusuhara

Recently, molecule generation using deep learning has been actively investigated in drug discovery. In this field, Transformer and VAE are widely used as powerful models, but they are rarely used in combination due to structural and performance mismatch of them. This study proposes a model that combines these two models through structural and parameter optim

Xin-Ke Hu, Yu-Wei Yu, Jin Zhang, Xiang-Gao Wang

Mrk 501 is a prototypical high-synchrotron-peaked blazar (HBL) and serves as one of the primary targets for the {\it Imaging X-ray Polarimetry Explorer} ({\it IXPE}). In this study, we report X-ray polarization measurements of Mrk 501 based on six {\it IXPE} observations. The detection of X-ray polarization at a confidence level exceeding 99\% is achieved in

Jialiang Wang, Weiling Li, Yurong Zhong, Xin Luo

Interactions among large number of entities is naturally high-dimensional and incomplete (HDI) in many big data related tasks. Behavioral characteristics of users are hidden in these interactions, hence, effective representation of the HDI data is a fundamental task for understanding user behaviors. Latent factor analysis (LFA) model has proven to be effecti

Antonio Enea Romano

We study the effects of the time evolution of the matter-gravity coupling on the luminosity distance, showing it can provide a natural explanation to the apparent Hubble tension. The gravitational coupling evolution induces a modification of the Friedman equation with respect to the $\Lambda$CDM model, which we study in both the Einstein and Jordan frame. We

Zengqiang Lin, Xiuping Su

Associated to a symmetrisable Cartan matrix $C$, Geiss-Lerclerc-Schr\"{o}er constructed and studied a class of Iwanaga-Gorenstein algebras $H$. They proved a generalised version of Gabriel's Theorem, that is, the rank vectors of $\tau$-locally free $H$-modules are the positive roots of type $C$ when $C$ is of finite type, and conjectured that this is true fo

Yugo Takanashi, Satoshi Wakatsuki

In this paper, we study the asymptotic behavior of the sum of twisted traces of self-dual or conjugate self-dual discrete automorphic representations of $\mathrm{GL}_n$ for the level aspect of principal congruence subgroups under some conditions. Our asymptotic formula is derived from the Arthur twisted trace formula, and it is regarded as a twisted version

Unai Muniain, Javier Aizpurua, Rainer Hillenbrand, Luis Martín-Moreno

Classical coupled harmonic oscillator models are capable of describing the optical and infrared response of nanophotonic systems where a cavity photon couples to dipolar matter excitations. The distinct forms of coupling adopted in these classical models leads to different results in the ultrastrong coupling regime. To clarify the specific classical model re

Keyang Xuan, Li Yi, Fan Yang, Ruochen Wu

The rise of multimodal misinformation on social platforms poses significant challenges for individuals and societies. Its increased credibility and broader impact compared to textual misinformation make detection complex, requiring robust reasoning across diverse media types and profound knowledge for accurate verification. The emergence of Large Vision Lang

Yinglong Guo, Shaohan Li, Gilad Lerman

We investigate the training and generalization errors of overparameterized neural networks (NNs) with a wide class of leaky rectified linear unit (ReLU) functions. More specifically, we carefully upper bound both the convergence rate of the training error and the generalization error of such NNs and investigate the dependence of these bounds on the Leaky ReL

Xinbei Ma, Zhuosheng Zhang, Hai Zhao

Multimodal large language models (MLLMs) have shown remarkable potential as human-like autonomous language agents to interact with real-world environments, especially for graphical user interface (GUI) automation. However, those GUI agents require comprehensive cognition ability including exhaustive perception and reliable action response. We propose a Compr

Kailai Sun, Tianxiang Lan, Say Hong Kam, Yang Miang Goh

There is a rising interest in using artificial intelligence (AI)-powered safety analytics to predict accidents in the trucking industry. Companies may face the practical challenge, however, of not having enough data to develop good safety analytics models. Although pretrained models may offer a solution for such companies, existing safety research using tran

Jiyao Li, Mingze Ni, Yifei Dong, Tianqing Zhu

Recent advances in deep learning research have shown remarkable achievements across many tasks in computer vision (CV) and natural language processing (NLP). At the intersection of CV and NLP is the problem of image captioning, where the related models' robustness against adversarial attacks has not been well studied. This paper presents a novel adversarial

Marcus Henninger, Silvio Mandelli, Artjom Grudnitsky, Stephan ten Brink

The mitigation of clutter is an important research branch in Integrated Sensing and Communication (ISAC), one of the emerging technologies of future cellular networks. In this work, we extend our previously introduced method Clutter Removal with Acquisitions Under Phase Noise (CRAP) by means to track clutter over time. This is necessary in scenarios that req

Jan Haltermanna, Marie-Christine Jakobs, Cedric Richter, Heike Wehrheim

Symbolic execution is a software verification technique symbolically running programs and thereby checking for bugs. Ranged symbolic execution performs symbolic execution on program parts, so called path ranges, in parallel. Due to the parallelism, verification is accelerated and hence scales to larger programs. In this paper, we discuss a generalization of

Ayesha Nanda, Sudeep Bhattacharjee

The polytropic index of electrons in a magnetized plasma is experimentally investigated in the presence of heating and anisotropic work done, incorporating the effective dimensionality arising from temperature anisotropy. The study is performed in a plasma confined by a permanent cylindrical dipole magnet using a compact device. The measurements clearly demo

Johannes Buchner

Nested sampling is widely used in astrophysics for reliably inferring model parameters and comparing models within a Bayesian framework. To address models with many parameters, Markov Chain Monte Carlo (MCMC) random walks are incorporated within nested sampling to advance a live point population. Diagnostic tools for nested sampling are crucial to ensure the

Suprajo Das, Saipriya Dubey, Sudeshna Roy, Jugal K. Verma

This article investigates the computational aspects of the $\varepsilon$-multiplicity. Primarily, we show that the $\varepsilon$-multiplicity of a homogeneous ideal $I$ in a two-dimensional standard graded domain of finite type over an algebraically closed field of arbitrary characteristic, is always a rational number. In this situation, we produce a formula

Xiaoman Xu, Xiangrun Li, Taihang Wang, Jianxiang Tian

This paper presents the participation of team QUST in Task 8 SemEval 2024. We first performed data augmentation and cleaning on the dataset to enhance model training efficiency and accuracy. In the monolingual task, we evaluated traditional deep-learning methods, multiscale positive-unlabeled framework (MPU), fine-tuning, adapters and ensemble methods. Then,

Jongha Lee, Sunwoo Kim, Kijung Shin

To detect anomalies in real-world graphs, such as social, email, and financial networks, various approaches have been developed. While they typically assume static input graphs, most real-world graphs grow over time, naturally represented as edge streams. In this context, we aim to achieve three goals: (a) instantly detecting anomalies as they occur, (b) ada

Ali Pedram, Vira R. Besaga, Frank Setzpfandt, Özgür E. Müstecaplıoğlu

A nonlocal quantum approach is presented to polarimetry, leveraging the phenomenon of entanglement in photon pairs to enhance the precision in sample property determination. By employing two distinct channels, one containing the sample of interest and the other serving as a reference, the conditions are explored under which the inherent correlation between e

Yang Li, Wenhao Zhang, Jianhong Wang, Shao Zhang

Among the research topics in multi-agent learning, mixed-motive cooperation is one of the most prominent challenges, primarily due to the mismatch between individual and collective goals. The cutting-edge research is focused on incorporating domain knowledge into rewards and introducing additional mechanisms to incentivize cooperation. However, these approac

Xiaohui Zhang, Wenjie Fu, Mangui Liang

Alzheimer's disease is a common cognitive disorder in the elderly. Early and accurate diagnosis of Alzheimer's disease (AD) has a major impact on the progress of research on dementia. At present, researchers have used machine learning methods to detect Alzheimer's disease from the speech of participants. However, the recognition accuracy of current methods i

Yaoyue Tang, Karina Arias-Calluari, M. N. Najafi, Michael S. Harré

This paper analyses the high-frequency intraday Bitcoin dataset from 2019 to 2022. During this time frame, the Bitcoin market index exhibited two distinct periods, 2019-20 and 2021-22, characterized by an abrupt change in volatility. The Bitcoin price returns for both periods can be described by an anomalous diffusion process, transitioning from subdiffusion

Chong Zeng, Yue Dong, Pieter Peers, Youkang Kong

This paper presents a novel method for exerting fine-grained lighting control during text-driven diffusion-based image generation. While existing diffusion models already have the ability to generate images under any lighting condition, without additional guidance these models tend to correlate image content and lighting. Moreover, text prompts lack the nece

Robin Louiset, Edouard Duchesnay, Antoine Grigis, Pietro Gori

Contrastive Analysis is a sub-field of Representation Learning that aims at separating common factors of variation between two datasets, a background (i.e., healthy subjects) and a target (i.e., diseased subjects), from the salient factors of variation, only present in the target dataset. Despite their relevance, current models based on Variational Auto-Enco

Edmond Orignac

We consider an antiferromagnetic four leg spin-1/2 tube using abelian and non-abelian bosonization. We show that in the limit of weak interchain coupling, the most relevant interaction gives rise to an emergent SU(3)} symmetry, broken only by marginal interactions that can be canceled by diagonal interchain couplings. We discuss the low energy spectrum in th

Arpit Babbar, Praveen Chandrashekar

Lax-Wendroff Flux Reconstruction (LWFR) is a single-stage, high order, quadrature free method for solving hyperbolic conservation laws. This work extends the LWFR scheme to solve conservation laws on curvilinear meshes with adaptive mesh refinement (AMR). The scheme uses a subcell based blending limiter to perform shock capturing and exploits the same subcel

Sujin Kook, Won-Yong Shin, Seong-Lyun Kim, Seung-Woo Ko

The vision of pervasive artificial intelligence (AI) services can be realized by training an AI model on time using real-time data collected by internet of things (IoT) devices. To this end, IoT devices require offloading their data to an edge server in proximity. However, transmitting high-dimensional and voluminous data from energy-constrained IoT devices

Jian Wu, Linyi Yang, Zhen Wang, Manabu Okumura

While Large Language Models (LLMs) excel in question-answering (QA) tasks, their real reasoning abilities on multiple evidence retrieval and integration on Multi-hop QA tasks remain less explored. Firstly, LLMs sometimes generate answers that rely on internal memory rather than retrieving evidence and reasoning in the given context, which brings concerns abo

Susanta Ghosh, Achintya Low, Susmita Changdar, Shubham Purwar

We report on the magnetic and Hall effect measurements of the magnetic Weyl semimetal, Mn$_{2.94}$Ge (Ge-rich) single crystal. From the magnetic properties study, we identify unusual multiple magnetic transitions below the N$\acute{e}$el temperature of 353 K, such as the spin-reorientation ($T_{SR}$) and ferromagnetic-like transitions. Consistent with the ma

Yuan Yuan, Chenyang Shao, Jingtao Ding, Depeng Jin

Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions. To bridge this gap, we propose a novel generative pre-training framework, GPD, for spatio-temporal few-shot learning with urban knowledge transfer. Unlike conventional approaches that heavily rely on common feature extra

Lu Xu, Linjie Zhao

We consider an open interacting particle system on a finite lattice. The particles perform asymmetric simple exclusion and are randomly created or destroyed at all sites, with rates that grow rapidly near the boundaries. We study the hydrodynamic limit for the particle density at the hyperbolic space-time scale and obtain the entropy solution to a boundary-d

Melody Qiming Xuan, Jorge Nocedal

This paper explores a method for solving constrained optimization problems when the derivatives of the objective function are unavailable, while the derivatives of the constraints are known. We allow the objective and constraint function to be nonconvex. The method constructs a quadratic model of the objective function via interpolation and computes a step b

Yuan Xie, Jiawei Ren, Ji Xu

Underwater acoustic target recognition is a difficult task owing to the intricate nature of underwater acoustic signals. The complex underwater environments, unpredictable transmission channels, and dynamic motion states greatly impact the real-world underwater acoustic signals, and may even obscure the intrinsic characteristics related to targets. Consequen

Naoyuki Kamiyama

Super-stability is one of the stability concepts in the stable matching problem with ties. It is known that there may not exist a super-stable matching, and the existence of a super-stable matching can be checked in polynomial time. In this paper, we consider the problem of modifying an instance of the super-stable matching problem by deleting some bounded n

Jannik Brinkmann, Abhay Sheshadri, Victor Levoso, Paul Swoboda

Transformers demonstrate impressive performance on a range of reasoning benchmarks. To evaluate the degree to which these abilities are a result of actual reasoning, existing work has focused on developing sophisticated benchmarks for behavioral studies. However, these studies do not provide insights into the internal mechanisms driving the observed capabili

Elisa Cabana

A primary challenge for the deaf and hearing-impaired community stems from the communication gap with the hearing society, which can greatly impact their daily lives and result in social exclusion. To foster inclusivity in society, our endeavor focuses on developing a cost-effective, resource-efficient, and open technology based on Artificial Intelligence, d

Jonas B. Profe, Lennart Klebl, Francesco Grandi, Hendrik Hohmann

The recent discovery of a variety of intricate electronic order in kagome metals has sprouted significant theoretical and experimental interest. From an electronic perspective on the potential microscopic origin of these phases, the most basic model is given by a Hubbard model on the kagome lattice. We employ functional renormalization group (FRG) to analyze

Ashish Dwivedi, Zeyu Guo, Ben Lee Volk

We construct explicit pseudorandom generators that fool $n$-variate polynomials of degree at most $d$ over a finite field $\mathbb{F}_q$. The seed length of our generators is $O(d \log n + \log q)$, over fields of size exponential in $d$ and characteristic at least $d(d-1)+1$. Previous constructions such as Bogdanov's (STOC 2005) and Derksen and Viola's (FOC

V. G. Krechet, V. B. Oshurko, A. E. Kisser

The dynamics of particles with intrinsic angular momentum (spin) described by the Dirac equation is considered in a homogeneous space with rotation in the presence of a homogeneous vortex gravitational field. The effects of the interaction between the spin of Dirac particles and the vortex gravitational field, as well as a possible mechanism for the appearan

Marko Savic, Guoying Zhao

Remote photoplethysmography (rPPG) is a promising technology that consists of contactless measuring of cardiac activity from facial videos. Most recent approaches utilize convolutional networks with limited temporal modeling capability or ignore long temporal context. Supervised rPPG methods are also severely limited by scarce data availability. In this work

Christoph Hoyer, Raimund Girwidz

Vector fields are a highly abstract physical concept that is often taught using visualizations. Although vector representations are particularly suitable for visualizing quantitative data, they are often confusing, especially when describing real fields such as magnetic and electric fields, as the vector arrows can overlap. The present study examines vector

Xin Chen, Todd Karin, Anubhav Jain

Solar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar

Saranya Alagarsamy, Chakkrit Tantithamthavorn, Wannita Takerngsaksiri, Chetan Arora

Context: Test-driven development (TDD) is a widely employed software development practice that involves developing test cases based on requirements prior to writing the code. Although various methods for automated test case generation have been proposed, they are not specifically tailored for TDD, where requirements instead of code serve as input. Objective:

Zhixuan Yu, Ziqian Bai, Abhimitra Meka, Feitong Tan

Traditional methods for constructing high-quality, personalized head avatars from monocular videos demand extensive face captures and training time, posing a significant challenge for scalability. This paper introduces a novel approach to create high quality head avatar utilizing only a single or a few images per user. We learn a generative model for 3D anim

Sayeh Gholipour Picha, Dawood Al Chanti, Alice Caplier

Medical language processing and deep learning techniques have emerged as critical tools for improving healthcare, particularly in the analysis of medical imaging and medical text data. These multimodal data fusion techniques help to improve the interpretation of medical imaging and lead to increased diagnostic accuracy, informed clinical decisions, and impro

Tianheng Zhu, Ling Wang, Yiheng Feng, Wanjing Ma

Previous studies in predicting crash risks primarily associated the number or likelihood of crashes on a road segment with traffic parameters or geometric characteristics, usually neglecting the impact of vehicles' continuous movement and interactions with nearby vehicles. Recent technology advances, such as Connected and Automated Vehicles (CAVs) and Unmann

Aiwei Liu, Haoping Bai, Zhiyun Lu, Xiang Kong

Aligning large language models (LLMs) with human expectations without human-annotated preference data is an important problem. In this paper, we propose a method to evaluate the response preference by using the output probabilities of response pairs under contrastive prompt pairs, which could achieve better performance on LLaMA2-7B and LLaMA2-13B compared to

K. Anaswara Das, M. Praveen Kumar, Surajit Dhara

Dielectric relaxation of materials provides important information on the polarisation dynamics at different time scales. We study the dielectric relaxation of a nematic liquid crystal under steady rotational shear and simultaneously measure the viscosity. The dielectric anisotropy of the nematic is positive and the applied field is parallel to the velocity g

Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong

Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention. However, existing methods predominantly rely on memorizing the updated knowledge, impeding LLMs from effectively combining the new knowledg

Zhijian Duan, Haoran Sun, Yichong Xia, Siqiang Wang

Identifying high-revenue mechanisms that are both dominant strategy incentive compatible (DSIC) and individually rational (IR) is a fundamental challenge in auction design. While theoretical approaches have encountered bottlenecks in multi-item auctions, there has been much empirical progress in automated designing such mechanisms using machine learning. How

Yu Zhang, Hui-Ling Zhen, Zehua Pei, Yingzhao Lian

Considering the challenges faced by large language models (LLMs) in logical reasoning and planning, prior efforts have sought to augment LLMs with access to external solvers. While progress has been made on simple reasoning problems, solving classical constraint satisfaction problems, such as the Boolean Satisfiability Problem (SAT) and Graph Coloring Proble

Kentaro Kasai, Masahiro Kawasaki, Kai Murai

Sterile neutrino is a fascinating candidate for dark matter. In this paper, we examine the Affleck-Dine (AD) leptogenesis scenario generating a large lepton asymmetry, which can induce the resonant production of sterile neutrino dark matter via the Shi-Fuller (SF) mechanism. We also revisit the numerical calculation of the SF mechanism and the constraints fr

Wiktor Piotrowski, Alexandre Perez

Real-world applications of AI Planning often require a highly expressive modeling language to accurately capture important intricacies of target systems. Hybrid systems are ubiquitous in the real-world, and PDDL+ is the standardized modeling language for capturing such systems as planning domains. PDDL+ enables accurate encoding of mixed discrete-continuous

Tianjie Ju, Yijin Chen, Xinwei Yuan, Zhuosheng Zhang

Recent work has showcased the powerful capability of large language models (LLMs) in recalling knowledge and reasoning. However, the reliability of LLMs in combining these two capabilities into reasoning through multi-hop facts has not been widely explored. This paper systematically investigates the possibilities for LLMs to utilize shortcuts based on direct

Jee Won Lee, Sang-Sung Lee, Jeffrey Hodgson, Algaba Juan-Carlos

We present the results of a spectral analysis using simultaneous multifrequency (22, 43, 86, and 129 GHz) very long baseline interferometry (VLBI) observations of the Korean VLBI Network (KVN) on BL Lac object, Markarian 421 (Mrk 421). The data we used was obtained from January 2013 to June 2018. The light curves showed several flux enhancements with global

Lingyan Zhang, Junlin Huang, Tingting Zhang, Qinyu Zhang

Wireless fingerprint-based localization has become one of the most promising technologies for ubiquitous location-aware computing and intelligent location-based services. However, due to RF vulnerability to environmental dynamics over time, continuous radio map updates are time-consuming and infeasible, resulting in severe accuracy degradation. To address th

Kazuhiro Sekiguchi, Jess McIver, Annapurni Subramaniam, Eswar Reddy

Policy Brief on "Regional and Global Collaborations in Astronomy", distilled from the corresponding panel that was part of the discussions during S20 Policy Webinar on Astroinformatics for Sustainable Development held on 6-7 July 2023. Astronomy brings together advanced scientific research, state-of-the-art technology, and educational initiatives, all while

Baojie Li, Xin Chen, Anubhav Jain

Power prediction is crucial to the efficiency and reliability of Photovoltaic (PV) systems. For the model-chain-based (also named indirect or physical) power prediction, the conversion of ground environmental data (plane-of-array irradiance and module temperature) to the output power is a fundamental step, commonly accomplished through physical modeling. The

Yogesh Wadadekar, Shriharsh Tendulkar, Sanjit Mitra, Eswar Reddy

Policy Brief on "Large Projects in Astronomy: An Indian Endeavour", distilled from the corresponding panel that was part of the discussions during S20 Policy Webinar on Astroinformatics for Sustainable Development held on 6-7 July 2023. Cutting-edge astronomy initiatives often entail substantial investment and require a high level of expertise. Even the most

Zhihao Wen, Jie Zhang, Yuan Fang

Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and LoRA, allow for adjustments to only a minor fraction of the parameters of these LLMs. Concurrently, it has been noted that the issue of