October 2024 arXiv papers — page 18

Yu-Fei Sun, Yue Mao, Qing-Feng Sun

We theoretically propose a mechanism to realize the superconducting diode effect (SDE): The current can generate a magnetic field, affecting the magnetic moment of magnetic impurity. When the connection region of the Josephson junction is coupled with the magnetic impurity, the supercurrents in positive and negative directions have different influences on th

Takuto Yamamoto, Hirosato Akahoshi, Shigeru Kitazawa

Many models of visual attention have been proposed so far. Traditional bottom-up models, like saliency models, fail to replicate human gaze patterns, and deep gaze prediction models lack biological plausibility due to their reliance on supervised learning. Vision Transformers (ViTs), with their self-attention mechanisms, offer a new approach but often produc

Sejin Lee, Dongha Kim, Min Song

Goal-oriented chatbots are essential for automating user tasks, such as booking flights or making restaurant reservations. A key component of these systems is Dialogue State Tracking (DST), which interprets user intent and maintains the dialogue state. However, existing DST methods often rely on fixed ontologies and manually compiled slot values, limiting th

Florentiana Yuwono, Gan Pang Yen, Jason Christopher

This paper explores the application of deep reinforcement learning (RL) techniques in the domain of autonomous self-driving car racing. Motivated by the rise of AI-driven mobility and autonomous racing events, the project aims to develop an AI agent that efficiently drives a simulated car in the OpenAI Gymnasium CarRacing environment. We investigate various

Xuanyu Li, Miao Li, Yuhan Cao, Dengji Zhao

Diffusion auction design for combinatorial settings is a long-standing challenge. One difficulty is that we cannot directly extend the solutions for simpler settings to combinatorial settings (like extending the Vickrey auction to VCG in the traditional settings). In this paper, we propose a different approach to leverage the diffusion auctions for single-it

Jan Martens, Anton Wijs

We study parallel algorithms for the minimization of Deterministic Finite Automata (DFAs). In particular, we implement four different massively parallel algorithms on Graphics Processing Units (GPUs). Our results confirm the expectations that the algorithm with the theoretically best time complexity is not practically suitable to run on GPUs due to the large

Xiaolong Liang, Yì N. Wáng

We present a type of epistemic logics that encapsulates both the dynamics of acquiring knowledge (knowing) and losing information (forgetting), alongside the integration of group knowledge concepts. Our approach is underpinned by a system of weighted models, which introduces an "epistemic skills" metric to effectively represent the epistemic abilities associ

Dylan Léveillé, Jason Jaskolka

Selecting the combination of security controls that will most effectively protect a system's assets is a difficult task. If the wrong controls are selected, the system may be left vulnerable to cyber-attacks that can impact the confidentiality, integrity and availability of critical data and services. In practical settings, it is not possible to select and i

R Keerthan, B Srivathsan, R Venkatesh, Sagar Verma

We introduce deterministic suffix-reading automata (DSA), a new automaton model over finite words. Transitions in a DSA are labeled with words. From a state, a DSA triggers an outgoing transition on seeing a word ending with the transition's label. Therefore, rather than moving along an input word letter by letter, a DSA can jump along blocks of letters, wit

Linda Feeken, Martin Fränzle

The synthesis of reactive systems aims for the automated construction of strategies for systems that interact with their environment. Whereas the synthesis approach has the potential to change the development of reactive systems significantly due to the avoidance of manual implementation, it still suffers from a lack of efficient synthesis algorithms for man

Emanuele Chini, Pietro Sala, Andrea Simonetti, Omid Zare

As business processes become increasingly complex, effectively modeling decision points, their likelihood, and resource consumption is crucial for optimizing operations. To address this challenge, this paper introduces a formal extension of the Business Process Model and Notation (BPMN) that incorporates choices, probabilities, and impacts, referred to as BP

Min Wang, Zhimin Zhang

The Fredholm-Hammerstein integral equations (FHIEs) with weakly singular kernels exhibit multi-point singularity at the endpoints or boundaries. The dense discretized matrices result in high computational complexity when employing numerical methods. To address this, we propose a novel class of mapped Hermite functions, which are constructed by applying a map

Yuki Fujimoto, Toru Kojo, Larry McLerran

We study the evolution of matter composition from nuclear to quark densities in the confining regime, by extending an ideal model of Quarkyonic matter, IdylliQ model, to multi-flavor systems including strangeness. The model provides a dual description of quark and baryon occupation probabilities which are determined by minimizing the energy of the system. Sa

Renato Acampora, Dario Della Monica, Luca Geatti, Nicola Gigante

Qualitative timeline-based planning models domains as sets of independent, but interacting, components whose behaviors over time, the timelines, are governed by sets of qualitative temporal constraints (ordering relations), called synchronization rules. Its plan-existence problem has been shown to be PSPACE-complete; in particular, PSPACE-membership has been

Stephan Spengler

We consider games played on the transition graph of concurrent programs running under the Total Store Order (TSO) weak memory model. Games are frequently used to model the interaction between a system and its environment, in this case between the concurrent processes and the nondeterministic TSO buffer updates. In our formulation, the game is played by two p

Faisal Etminan

Very recently the Wood-Saxon (WS) type interaction in the single-folding potential approach are constructed to simulate the $ \phi{\text -}\alpha $ potentials. One set of the $ \phi{\text -}\alpha $ potentials are based on the first principle HAL QCD $\phi{\text -}N $ interactions in $^{4}S_{3/2}$ channel, and in another set, the $\phi$-meson-nucleus potenti

Yan Lyu, Takumi Doi, Tetsuo Hatsuda, Takuya Sugiura

We present a realistic lattice QCD study on low-energy $N$-$J/\psi$ and $N$-$\eta_c$ interactions based on (2+1) flavor configurations with nearly physical pion mass $m_\pi=146$ MeV. The interactions, extracted from the spacetime correlations of nucleon and charmonium system by using the HAL QCD method, are found to be attractive in all distances and possess

Dino Sejdinovic

Kernel embeddings have emerged as a powerful tool for representing probability measures in a variety of statistical inference problems. By mapping probability measures into a reproducing kernel Hilbert space (RKHS), kernel embeddings enable flexible representations of complex relationships between variables. They serve as a mechanism for efficiently transfer

Benjamin Church

We prove several conjectures relating the existence of nonvanishing 1- forms to smooth morphisms over abelian varieties, assuming the existence of good minimal models. The proof involves a decomposition result for a family of Calabi-Yau varieties equipped with a surjective map to an abelian scheme. In the uniruled case, supposing the MRC base admits a good m

Minh Tri Huynh, Duc Dung Nguyen

In recent years, motion planning for urban self-driving cars (SDV) has become a popular problem due to its complex interaction of road components. To tackle this, many methods have relied on large-scale, human-sampled data processed through Imitation learning (IL). Although effective, IL alone cannot adequately handle safety and reliability concerns. Combini

Yixiao Ruan, Zan Li, Zhaohui Li, Dennis K. J. Lin

Computational capability often falls short when confronted with massive data, posing a common challenge in establishing a statistical model or statistical inference method dealing with big data. While subsampling techniques have been extensively developed to downsize the data volume, there is a notable gap in addressing the unique challenge of handling exten

Julian Asilis, Mikael Møller Høgsgaard, Grigoris Velegkas

Multiclass learnability is known to exhibit a properness barrier: there are learnable classes which cannot be learned by any proper learner. Binary classification faces no such barrier for learnability, but a similar one for optimal learning, which can in general only be achieved by improper learners. Fortunately, recent advances in binary classification hav

Andoni Cortés, Clemente Rodríguez, Gorka Velez, Javier Barandiarán

A major challenges of deep learning (DL) is the necessity to collect huge amounts of training data. Often, the lack of a sufficiently large dataset discourages the use of DL in certain applications. Typically, acquiring the required amounts of data costs considerable time, material and effort. To mitigate this problem, the use of synthetic images combined wi

Saranya P., Sunoj S. M

This article introduces novel measures of inaccuracy and divergence based on survival extropy and their dynamic forms and explores their properties and applications. To address the drawbacks of asymmetry and range limitations, we introduce two measures: the survival extropy inaccuracy ratio and symmetric divergence measures. The inaccuracy ratio is utilized

Zhuoran Li, Zhen Gao, Kuiyu Wang, Yikun Mei

To ensure the thriving development of low-altitude economy, countering unauthorized unmanned aerial vehicles (UAVs) is an essential task. The existing widely deployed base stations hold great potential for joint communication and jamming. In light of this, this paper investigates the joint design of beamforming to simultaneously support communication with le

Gunter Malle, A. A. Schaeffer Fry

The Eaton--Moret\'o conjecture extends the recently-proven Brauer height zero conjecture to blocks with non-abelian defect group, positing equality between the minimal positive heights of a block of a finite group and its defect group. Here we provide further evidence for the inequality in this conjecture that is not implied by Dade's conjecture. Specificall

Ahmed M Salih

Explainable Artificial Intelligence (XAI) emerged to reveal the internal mechanism of machine learning models and how the features affect the prediction outcome. Collinearity is one of the big issues that XAI methods face when identifying the most informative features in the model. Current XAI approaches assume the features in the models are independent and

Nima Zargham, Mateusz Dubiel, Smit Desai, Thomas Mildner

In the rapidly evolving field of artificial intelligence (AI) agents, designing the agent's characteristics is crucial for shaping user experience. This workshop aims to establish a research community focused on AI agent persona design for various contexts, such as in-car assistants, educational tools, and smart home environments. We will explore critical as

Mingtang Deng, Chunlin Yu, Guangyao Huang, P. Caroff

Semiconductor InSb nanowires present a highly intriguing platform with immense potential for applications in spintronics and topological quantum devices. The narrow band gap exhibited by InSb allows for precise tuning of these nanowires, facilitating smooth transitions between the electron transport region and the hole transport region. In this study, we dem

Conghui Li, Yuting Tian

In this note, we extend the inductions and restrictions of modules over finite groups to non-injective group homomorphisms, establishing transitivity, Frobenius reciprocity, Mackey's formula, etc.

Xin, Ma, Dong Si

Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), have spurred the development of sophisticated map-to-model tools like DeepTracer and ModelAngelo, their efficacy nota

Masahiro Kawamata, Xiaoqi Pang, Hiroshi Murakawa, Seiko Ohira-Kawamura

Magnetic excitations in the noncentrosymmetric magnet Sr$_2$MnSi$_2$O$_7$ were investigated through inelastic neutron scattering measurements. Major magnetic excitations are limited up to the energy transfer of 0.5 meV, and two magnon branches under zero magnetic field were well explained in the framework of linear spin-wave theory. The magnitudes of the squ

Kyrie Zhixuan Zhou, Royta Iftakher, Sean P. Mullen, Rachel F. Adler

Cancer survivors experience a wide range of impairments arising from cancer or its treatment, such as chemo brain, visual impairments, and physical impairments. These impairments degrade their quality of life and potentially make software use more challenging for them. However, there has been limited research on designing accessible software for cancer survi

Cong Fu, Kun Wang, Jiahua Wu, Yizhou Chen

Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services. Consequently, multi-task learning has become an integral part of their ranking systems. However, existing multi-task learning methods encounter two main challenges: some lack explicit modeling of task relationships, resulting in inferior performance,

Jing-Yun Fang, Yu-Chen Zhuang, Ai-Min Guo, Qing-Feng Sun

Quantum spin Hall effect is characterized by topologically protected helical edge states. Here we study the thermal dissipation of helical edge states by considering two types of dissipation sources. The results show that the helical edge states are dissipationless for normal dissipation sources with or without Rashba spin-orbit coupling in the system, but t

Jing-Yun Fang, Yu-Chen Zhuang, Qing-Feng Sun

Nonuniform strain in graphene can induce a pseudo-magnetic field (PMF) preserving time-reversal symmetry, generating pseudo-Landau levels under zero real magnetic field (MF). The different natures between PMF and real MF lead to the counterpropagating valley-polarized edge states under the PMF and unidirectionally chiral edge states under the real MF. In thi

Areli Castrejon, Michiel Min, Inga Kamp, Uffe Gråe Jørgensen

Context. To date, more than 5000 exoplanets have been discovered. The large majority of these planets have a mass between 1 and 17 {M_\oplus}, making them so-called super-Earths and mini-Neptunes. The exact formation process for this abundant planet population has not yet been fully constrained. Aims. Recent studies on the formation of these planets make var

Keito Sasagawa, Koki Maeda, Issa Sugiura, Shuhei Kurita

To develop high-performing Visual Language Models (VLMs), it is essential to prepare multimodal resources, such as image-text pairs, interleaved data, and instruction data. While multimodal resources for English are abundant, there is a significant lack of corresponding resources for non-English languages, such as Japanese. To address this problem, we take J

Minha Kim, Kishor Kumar Bhaumik, Amin Ahsan Ali, Simon S. Woo

For modern industrial applications, accurately detecting and diagnosing anomalies in multivariate time series data is essential. Despite such need, most state-of-the-art methods often prioritize detection performance over model interpretability. Addressing this gap, we introduce MIXAD (Memory-Induced Explainable Time Series Anomaly Detection), a model design

Yuping Li, Mingfeng Liu, Jiangxu Li, Jiantao Wang

Recently, a first-order phase transition associated with charge density wave (CDW) has been observed at low temperatures in intermetallic compound BaFe$_2$Al$_9$. However, this transition is absent in its isostructural sister compound BaCo$_2$Al$_9$. Consequently, an intriguing question arises as to the underlying factors that differentiate BaFe$_2$Al$_9$ fr

Junjie Ni, Guofeng Zhang, Guanglin Li, Yijin Li

We tackle the efficiency problem of learning local feature matching. Recent advancements have given rise to purely CNN-based and transformer-based approaches, each augmented with deep learning techniques. While CNN-based methods often excel in matching speed, transformer-based methods tend to provide more accurate matches. We propose an efficient transformer

Ran Hong, Yuxia Huang, Lei Liu, Zhonghui Wu

PET imaging is widely employed for observing biological metabolic activities within the human body. However, numerous benign conditions can cause increased uptake of radiopharmaceuticals, confounding differentiation from malignant tumors. Several studies have indicated that dual-time PET imaging holds promise in distinguishing between malignant and benign tu

Hang Sheng, Qinji Shu, Hui Feng, Bo Hu

Time-varying data with irregular structures can be described by finite time-vertex graph signals (FTVGS), which represent potential temporal and spatial relationships among multiple sources. While sampling and corresponding reconstruction of FTVGS with known spectral support are well investigated, methods for the case of unknown spectral support remain under

Evgeny Dantsin, Alexander Wolpert

A property of a recurrent neural network (RNN) is called \emph{extensional} if, loosely speaking, it is a property of the function computed by the RNN rather than a property of the RNN algorithm. Many properties of interest in RNNs are extensional, for example, robustness against small changes of input or good clustering of inputs. Given an RNN, it is natura

Vincent Guan, Joseph Janssen, Hossein Rahmani, Andrew Warren

Stochastic differential equations (SDEs) are a fundamental tool for modelling dynamic processes, including gene regulatory networks (GRNs), contaminant transport, financial markets, and image generation. However, learning the underlying SDE from data is a challenging task, especially if individual trajectories are not observable. Motivated by burgeoning rese

Shiye Lei, Sen Zhang, Dacheng Tao

Massive reinforcement learning (RL) data are typically collected to train policies offline without the need for interactions, but the large data volume can cause training inefficiencies. To tackle this issue, we formulate offline behavior distillation (OBD), which synthesizes limited expert behavioral data from sub-optimal RL data, enabling rapid policy lear

Ying-Fei Li, Emily M. Been, Sudhaman Balguri, Chun-Jing Jia

Recent experiments suggest a new paradigm towards novel colossal magnetoresistance (CMR) in a family of materials EuM$_2$X$_2$(M=Cd, In, Zn; X=P, As), distinct from the traditional avenues involving Kondo-RKKY crossovers, magnetic phase transitions with structural distortions, or topological phase transitions. Here, we use angle-resolved photoemission spectr

Man Yang

In this study, we investigate the convergence rates for the homogenization of elliptic equations with lower-order terms under the spectral gap assumption, in both bounded domains and the entire space. Our analysis demonstrates that lower-order terms significantly affect the convergence rate, particularly in the full space, where the rate changes from \(O(\ep

Fulai Yang, Di Wu, Yi He, Li Tao

Cognitive diagnosis (CD) utilizes students' existing studying records to estimate their mastery of unknown knowledge concepts, which is vital for evaluating their learning abilities. Accurate CD is extremely challenging because CD is associated with complex relationships and mechanisms among students, knowledge concepts, studying records, etc. However, exist

Ji Guo, Wenbo Jiang, Rui Zhang, Guoming Lu

Recently, various types of Text-to-Image (T2I) models have emerged (such as DALL-E and Stable Diffusion), and showing their advantages in different aspects. Therefore, some third-party service platforms collect different model interfaces and provide cheaper API services and more flexibility in T2I model selections. However, this also raises a new security co

Qingmin Zhang, Yudi Ou, Zhenghua Huang, Yongliang Song

In this paper, we carry out multiwavelength and multiview observations of the prominence eruption, which generates a C2.3 class flare and a coronal mass ejection (CME) on 2023 March 7. For the first time, we apply the revised cone model to three-dimension reconstruction and tracking of the eruptive prominence for ~4 hrs. The prominence propagates non-radiall

George Biswas, Sayan Sengupta, Anindya Biswas

We propose a method to design a magnetic field detector using a noisy two-qubit system. The system evolves under a noisy Heisenberg interaction Hamiltonian, and we investigate its behavior by calculating both the $l_1$-norm of quantum coherence and the return probability in the presence and absence of an external magnetic field. We allow for decoherence mode

Wei Wu, Liang Tang, Zhongjie Zhao, Chung-Piaw Teo

Stacking, a potent ensemble learning method, leverages a meta-model to harness the strengths of multiple base models, thereby enhancing prediction accuracy. Traditional stacking techniques typically utilize established learning models, such as logistic regression, as the meta-model. This paper introduces a novel approach that integrates computational geometr

Mimi Sun, Chaitanya Kamath, Mohit Agarwal, Arbaaz Muslim

Aggregated relative search frequencies offer a unique composite signal reflecting people's habits, concerns, interests, intents, and general information needs, which are not found in other readily available datasets. Temporal search trends have been successfully used in time series modeling across a variety of domains such as infectious diseases, unemploymen

Xinyi Wei, Yangshuai Wang, Kai Jiang, Lei Zhang

Crystalline defects critically influence material properties, necessitating accurate simulation methods. Existing approaches, from atomic-scale configurations to continuum elasticity, face inherent limitations in modeling dislocation-induced lattice deformation. The amplitude expansion of the phase field crystal (APFC) model bridges this gap with a mesoscopi

Woohyeok Jo, Jongil Park, Kyungbae Park

We study which lens spaces can bound smooth 4-manifolds with second Betti number one under various topological conditions. Specifically, we show that there are infinite families of lens spaces that bound compact, simply-connected, smooth 4-manifolds with second Betti number one, yet cannot bound a 4-manifold consisting of a single 0-handle and 2-handle. Addi

Tomohito Amano, Tamio Yamazaki, Naoki Matsumura, Yuta Yoshimoto

We conducted a first-principles study of the dielectric properties of liquid propylene glycol (PG) and polypropylene glycol (PPG) using a recently developed chemical bond-based machine learning (ML) model for dipole moments [T. Amano et al. Phys. Rev. B 110, 165159 (2024)]. The ML dipole models successfully predict the dipole moment of various liquid configu

HyunJun Jung, Weihang Li, Shun-Cheng Wu, William Bittner

Traditionally, 3d indoor datasets have generally prioritized scale over ground-truth accuracy in order to obtain improved generalization. However, using these datasets to evaluate dense geometry tasks, such as depth rendering, can be problematic as the meshes of the dataset are often incomplete and may produce wrong ground truth to evaluate the details. In t

Anthony Kling, Ben Savoie

We develop a graph-theoretic algorithm to compute the $\varphi$-Selmer group of the elliptic curve $E_b: y^2 = x^3 + bx$ over $\mathbb{Q}(i)$, where $b \in \mathbb{Z}[i]$ and $\varphi$ is a degree 2 isogeny of $E_b$. We associate to $E_b$ a weighted graph $G_b$, whose vertices are the odd Gaussian primes dividing $b$, and whose edge weights are determined by

Rozhin Yousefjani, Angelo Carollo, Krzysztof Sacha, Saif Al-Kuwari

Discrete time crystals (DTC) exhibit a special non-equilibrium phase of matter in periodically driven many-body systems with spontaneous breaking of time translational symmetry. The presence of decoherence generally enhances thermalization and destroys the coherence required for the existence of DTC. In this letter, we devise a mechanism for establishing a s

Pablo Cubides Kovacsics, Silvain Rideau-Kikuchi, Mariana Vicaría

We present a unifying framework of residual domination for (expansions of) henselian valued fields of equicharacteristic zero, encompassing some valued fields with operators. We show that the class of residually dominated types coincides with the types that are orthogonal to the value group, and with the class of types whose reduct to ACVF (the theory of alg

Neea Palojärvi, Aleksander Simonič

Assuming the Generalized Riemann Hypothesis, we provide uniform upper and lower bounds with explicit main terms for $\log{\left|\cL(s)\right|}$ for $\sigma \in (1/2,1)$ and for functions in the Selberg class. In particular, we focus on the region $0\leq\sigma-1/2\ll 1/\log{\log{\left(\sq|t|^{\sdeg}\right)}}$. We also provide estimates under additional assump

Naijian Cao, Renjie He, Yuchao Dai, Mingyi He

Local feature matching is an essential technique in image matching and plays a critical role in a wide range of vision-based applications. However, existing Transformer-based detector-free local feature matching methods encounter challenges due to the quadratic computational complexity of attention mechanisms, especially at high resolutions. However, while e

Bohang Sun

In this study, we introduce an enhanced version of ViT that conducts attention-based QKV operations during the initial stages of downsampling. Performing attention directly on high-resolution feature maps is computationally demanding due to the large size and numerous tokens. To mitigate this, we propose a filter attention mechanism that uses a Filter Block

Woohyeok Jo, Jongil Park, Kyungbae Park

In this article, we study the effects of topological and smooth obstructions on the existence of rational homology complex projective planes that admit quotient singularities of small indices. In particular, we provide a classification of the types of quotient singularities that can be realized on rational homology complex projective planes with indices up t

Kento Kawaharazuka, Yoshiki Obinata, Naoaki Kanazawa, Kei Okada

State recognition of the environment and objects, such as the open/closed state of doors and the on/off of lights, is indispensable for robots that perform daily life support and security tasks. Until now, state recognition methods have been based on training neural networks from manual annotations, preparing special sensors for the recognition, or manually

Zhengyang Chi, Junbin Gao, Chao Wang

This paper introduces an innovative realized volatility (RV) forecasting framework that extends the conventional Heterogeneous autoregressive (HAR) model via integrating Graph Signal Processing (GSP). The study first evaluates various constructions of volatility-interrelationship networks by analyzing how the associated graph signal energy tracks global fina

Qi Song, Ziyuan Luo, Ka Chun Cheung, Simon See

Single-view 3D reconstruction methods like Triplane Gaussian Splatting (TGS) have enabled high-quality 3D model generation from just a single image input within seconds. However, this capability raises concerns about potential misuse, where malicious users could exploit TGS to create unauthorized 3D models from copyrighted images. To prevent such infringemen

A. Verma, R. Vedin, J. Jesudasan, J. Lidmar

We present a study of the Berezinskii-Kosterlitz-Thouless (BKT) transition in mildly disordered NbN nanoporous (NP) films. The measured superfluid stiffness, Js, is found to be much lower than that predicted by considering the reduction in the geometric area. This effect is also reproduced theoretically via Monte Carlo simulations on a 2D XY model with diffe

Daniel Cirkovic, Tiandong Wang, Daren B. H. Cline

Both empirical and theoretical investigations of scale-free network models have found that large degrees in a network exert an outsized impact on its structure. However, the tools used to infer the tail behavior of degree distributions in scale-free networks often lack a strong theoretical foundation. In this paper, we introduce a new framework for analyzing

Ujjwal Basumatary, Nirmal Raj, Anupam Ray

The memory burden effect is an explicit resolution to the information paradox by which an evaporating black hole acquires quantum hair, which then suppresses its rate of mass loss with respect to the semi-classical Hawking rate. We show that this has significant implications for particle dark matter that captures in neutron stars and forms black holes that g

Peter Burton, Jane Panangaden

Furstenberg's $\times 2 \times 3$ conjecture has remained a central open problem in ergodic theory for over $50$ years, and it serves as the basic test case for a broad class of rigidity phenomena which are believed to hold in number-theoretic dynamics. More recently, two related statements have appeared in the literature: a question about periodic approxima

Mohamed Aboudonia, Csaba Balazs, Andreas Papaefstathiou, Graham White

A strong first-order electroweak phase transition (SFOEWPT) is essential for explaining baryogenesis and for potentially generating observable gravitational waves. This study investigates the potential of a high-energy muon collider to examine the occurrence of SFOEWPT within the context of a Standard Model extended by a real scalar singlet (xSM). We analyze

Steven E. Pav

We generalize the non-negative matrix factorization algorithm of Lee and Seung to accept a weighted norm, and to support ridge and Lasso regularization. We recast the Lee and Seung multiplicative update as an additive update which does not get stuck on zero values. We apply the companion R package rnnmf to the problem of finding a reduced rank representation

Aishwarya Sarkar, Sayan Ghosh, Nathan R. Tallent, Ali Jannesari

Graph Neural Networks (GNN) are indispensable in learning from graph-structured data, yet their rising computational costs, especially on massively connected graphs, pose significant challenges in terms of execution performance. To tackle this, distributed-memory solutions such as partitioning the graph to concurrently train multiple replicas of GNNs are in

Kohtaro Kato

Matrix product density operator (MPDO) provides an efficient tensor network representation of mixed states on one-dimensional quantum many-body systems. We study a real-space renormalization group transformation of MPDOs represented by a circuit of local quantum channels. We require that the renormalization group flow is exact, in the sense that it exactly p

Akhilan Boopathy, Aneesh Muppidi, Peggy Yang, Abhiram Iyer

Sequential learning in deep models often suffers from challenges such as catastrophic forgetting and loss of plasticity, largely due to the permutation dependence of gradient-based algorithms, where the order of training data impacts the learning outcome. In this work, we introduce a novel permutation-invariant learning framework based on high-dimensional pa

Mrunal Kamble, Evan Humberd, Tian Li, Girish S. Agarwal

Analyzing the kinetics of biological processes plays a significant role in understanding fundamental cellular functions. Many physics-based technologies used to study such processes are limited by the shot noise inherent to the coherent states of light. These technologies can greatly benefit by leveraging quantum probes to improve the sensitivity of measurem

Koki Iwakura

In this paper, we study the non-singular extension problem of horizontal stable fold maps. This problem asks what conditions ensure the existence of a submersion whose restriction to the boundary coincides with a given map, called a non-singular extension. By defining a combinatorial object called a pairing map, we prove that the existence of a non-singular

Daniele Bartoli, Mohit Pal, Pantelimon Stanica

In this paper we use algebraic curves and other algebraic number theory methods to show the validity of a permutation polynomial conjecture regarding $f(X)=X^{q(p-1)+1} +\alpha X^{pq}+X^{q+p-1}$, on finite fields $\mathbb{F}_{q^2}, q=p^k$, from [A. Rai, R. Gupta, {\it Further results on a class of permutation trinomials}, Cryptogr. Commun. 15 (2023), 811--82

Jiedong Lang, Zhehao Guo, Shuyu Huang

Large Language Models (LLMs) have been extensively researched and used in both academia and industry since the rise in popularity of the Transformer model, which demonstrates excellent performance in AI. However, the computational demands of LLMs are immense, and the energy resources required to run them are often limited. For instance, popular models like G

Wanlin Li, Zihang Zhao, Leiyao Cui, Weiyi Zhang

Robot-assisted minimally invasive surgery (RAMIS) provides substantial benefits over traditional open and laparoscopic methods. However, a significant limitation of RAMIS is the surgeon's inability to palpate tissues, a crucial technique for examining tissue properties and detecting abnormalities, restricting the widespread adoption of RAMIS. To overcome thi

Xuesong Li, Zeeshan Hayder, Ali Zia, Connor Cassidy

Crop biomass offers crucial insights into plant health and yield, making it essential for crop science, farming systems, and agricultural research. However, current measurement methods, which are labor-intensive, destructive, and imprecise, hinder large-scale quantification of this trait. To address this limitation, we present a biomass prediction network (B

Henrik Marklund, Benjamin Van Roy

As AI agents generate increasingly sophisticated behaviors, manually encoding human preferences to guide these agents becomes more challenging. To address this, it has been suggested that agents instead learn preferences from human choice data. This approach requires a model of choice behavior that the agent can use to interpret the data. For choices between

Théodor Lemerle, Téo Guichoux, Axel Roebel, Nicolas Obin

Neural codec language models, built on transformer architecture, have revolutionized text-to-speech (TTS) synthesis, excelling in voice cloning by treating it as a prefix continuation task. However, their limited context length hinders their effectiveness to short speech samples. As a result, the voice cloning ability is restricted to a limited coverage and

Huihan Liu, Yu Zhang, Vaarij Betala, Evan Zhang

Recent advancements in large-scale multi-task robot learning offer the potential for deploying robot fleets in household and industrial settings, enabling them to perform diverse tasks across various environments. However, AI-enabled robots often face challenges with generalization and robustness when exposed to real-world variability and uncertainty. We int

Katerina Saettone, Alexandru Zaharescu, Zhuo Zhang

Let $p$ be an odd prime, and let $\omega$ be a primitive $p$th root of unity. In this paper, we introduce a metric on the cyclotomic field $K=\mathbb{Q}(\omega)$. We prove that this metric has several remarkable properties, such as invariance under the action of the Galois group. Furthermore, we show that points in the ring of integers $\mathcal{O}_K$ behave

Sean Y. Hon, Po Yin Fung, Xue-lei Lin

In this work, we propose a novel diagonalization-based preconditioner for the all-at-once linear system arising from the optimal control problem of parabolic equations. The proposed preconditioner is constructed based on an $\epsilon$-circulant modification to the rotated block diagonal (RBD) preconditioning technique and can be efficiently diagonalized by f

Yashvir S. Grewal, Edwin V. Bonilla, Thang D. Bui

Accurately quantifying uncertainty in large language models (LLMs) is crucial for their reliable deployment, especially in high-stakes applications. Current state-of-the-art methods for measuring semantic uncertainty in LLMs rely on strict bidirectional entailment criteria between multiple generated responses and also depend on sequence likelihoods. While ef

Wu-zhong Guo, Jin Xu

In this paper, we explore the imaginary part of the timelike entanglement entropy. In the context of field theory, it is more appropriate to obtain the timelike entanglement entropy through the Wick rotation of the twist operators. It is found that, in certain special cases, the imaginary part of the timelike entanglement entropy is related to the commutator

Feng-Yi Liao, Lijun Ding, Yang Zheng

Augmented Lagrangian Methods (ALMs) are widely employed in solving constrained optimizations, and some efficient solvers are developed based on this framework. Under the quadratic growth assumption, it is known that the dual iterates and the Karush-Kuhn-Tucker (KKT) residuals of ALMs applied to semidefinite programs (SDPs) converge linearly. In contrast, the

Su-Chan Park

We explore the physical implications of applying principal component analysis (PCA) to translationally invariant classical systems defined on a $d$-dimensional hypercubic lattice. Using Rayleigh-Schr\"odinger perturbation theory, we demonstrate that the principal components are related to the reciprocal lattice vectors of the hypercubic lattice, and the corr

Zhiwei Zhang, Shaojun E, Fandong Meng, Jie Zhou

Traffic forecasting plays a key role in Intelligent Transportation Systems, and significant strides have been made in this field. However, most existing methods can only predict up to four hours in the future, which doesn't quite meet real-world demands. we identify that the prediction horizon is limited to a few hours mainly due to the separation of tempora

Zhehui Liao, Maria Antoniak, Inyoung Cheong, Evie Yu-Yen Cheng

The rise of large language models (LLMs) has led many researchers to consider their usage for scientific work. Some have found benefits using LLMs to augment or automate aspects of their research pipeline, while others have urged caution due to risks and ethical concerns. Yet little work has sought to quantify and characterize how researchers use LLMs and wh

Debanjali Bhattacharya, Rajneet Kaur, Ninad Aithal, Neelam Sinha

Mild cognitive impairment (MCI), often linked to early neurodegeneration, is characterized by subtle cognitive declines and disruptions in brain connectivity. The present study offers a detailed analysis of topological changes associated with MCI, focusing on two subtypes: Early MCI and Late MCI. This analysis utilizes fMRI time series data from two distinct

Atharv Deshmukh

Federated Learning (FL) paradigms enable large numbers of clients to collaboratively train Machine Learning models on private data. However, due to their multi-party nature, traditional FL schemes are left vulnerable to Byzantine attacks that attempt to hurt model performance by injecting malicious backdoors. A wide variety of prevention methods have been pr

Reza Moravej, Saurabh Bodhe, Zhanguang Zhang, Didier Chetelat

Logic synthesis is a crucial phase in the circuit design process, responsible for transforming hardware description language (HDL) designs into optimized netlists. However, traditional logic synthesis methods are computationally intensive, restricting their iterative use in refining chip designs. Recent advancements in large language models (LLMs), particula

Parisha Joshi, Daljit Singh J. Dhillon

Inverse rendering pipelines are gaining prominence in realizing photo-realistic reconstruction of real-world objects for emulating them in virtual reality scenes. Apart from material reflectances, spectral rendering and in-scene illuminants' spectral power distributions (SPDs) play important roles in producing photo-realistic images. We present a simple, low

Ji Guo, Hongwei Li, Wenbo Jiang, Guoming Lu

Vision Transformers (ViTs) have outperformed traditional Convolutional Neural Networks (CNN) across various computer vision tasks. However, akin to CNN, ViTs are vulnerable to backdoor attacks, where the adversary embeds the backdoor into the victim model, causing it to make wrong predictions about testing samples containing a specific trigger. Existing back

Rebecca Saul, Chang Liu, Noah Fleischmann, Richard Zak

Binary analysis is a core component of many critical security tasks, including reverse engineering, malware analysis, and vulnerability detection. Manual analysis is often time-consuming, but identifying commonly-used or previously-seen functions can reduce the time it takes to understand a new file. However, given the complexity of assembly, and the NP-hard