December 2024 arXiv papers — page 53

Luo Ji, Feixiang Guo, Teng Chen, Qingqing Gu

Despite the recent advancement in Retrieval-Augmented Generation (RAG) systems, most retrieval methodologies are often developed for factual retrieval, which assumes query and positive documents are semantically similar. In this paper, we instead propose and study a more challenging type of retrieval task, called hidden rationale retrieval, in which query an

Nanda Rani, Divyanshu Singh, Bikash Saha, Sandeep Kumar Shukla

The rise in cybercrime and the complexity of multilingual and code-mixed complaints present significant challenges for law enforcement and cybersecurity agencies. These organizations need automated, scalable methods to identify crime types, enabling efficient processing and prioritization of large complaint volumes. Manual triaging is inefficient, and tradit

David Lacoste, Michele Castellana

We present an improvement of the Gillespie Exact Stochastic Simulation Algorithm, which leverages a bitwise representation of variables to perform independent simulations in parallel. We show that the subsequent gain in computational yield is significant, and it may allow to perform simulations of non-well mixed chemical systems. We illustrate this idea with

Dmitry Chistikov, Wojciech Czerwiński, Filip Mazowiecki, Łukasz Orlikowski

Vector Addition Systems with States (VASS), equivalent to Petri nets, are a well-established model of concurrency. The central algorithmic challenge in VASS is the reachability problem: is there a run from a given starting state and counter values to a given target state and counter values? When the input is encoded in binary, reachability is computationally

Muhammad Ali Jamshed, Aryan Kaushik, Sanaullah Manzoor, Muhammad Zeeshan Shakir

The International Mobile Telecommunications (IMT)-2030 framework recently adopted by the International Telecommunication Union Radiocommunication Sector (ITU-R) envisions 6G networks to deliver intelligent, seamless connectivity that supports reliable, sustainable, and resilient communications. Recent developments in the 3rd Generation Partnership Project (3

Alireza Maleki, Ahmad Sheykhi

We study the effects of quantum fluctuations on the event horizon area and their implications for corrections to the Bekenstein-Hawking entropy. These quantum corrections are incorporated into the framework of large-scale gravitational systems, utilizing the holographic principle to derive modified Friedmann equations. By redefining the Bekenstein-Hawking en

Jiayi Zhu, Qing Guo, Felix Juefei-Xu, Yihao Huang

Co-salient object detection (Co-SOD) aims to identify common salient objects across a group of related images. While recent methods have made notable progress, they typically rely on low-level visual patterns and lack semantic priors, limiting their detection performance. We propose ConceptCoSOD, a concept-guided framework that introduces high-level semantic

Devesh Sawant, Manjesh K. Hanawal, Atul Kabra

Software vulnerabilities are commonly exploited as attack vectors in cyberattacks. Hence, it is crucial to identify vulnerable software configurations early to apply preventive measures. Effective vulnerability detection relies on identifying software vulnerabilities through standardized identifiers such as Common Platform Enumeration (CPE) strings. However,

Timothy Sun

We show that for all $n \equiv 0 \pmod{6}$, $n \geq 18$, there is an orientable triangular embedding of the octahedral graph on $n$ vertices that can be augmented with handles to produce a genus embedding of the complete graph of the same order. For these values of $n$, the intermediate embeddings of the construction also determine some surface crossing numb

Isaac Harris, Victor Hughes, Andreas Kleefeld

In this paper, we consider the inverse scattering problem associated with an anisotropic medium with a conductive boundary condition. We will assume that the corresponding far--field pattern or Cauchy data is either known or measured. The conductive boundary condition models a thin coating around the boundary of the scatterer. We will develop two direct samp

Suyoung Lee, Jaeyoung Chung, Kihoon Kim, Jaeyoo Huh

Feed-forward 3D Gaussian splatting (3DGS) models have gained significant popularity due to their ability to generate scenes immediately without needing per-scene optimization. Although omnidirectional images are becoming more popular since they reduce the computation required for image stitching to composite a holistic scene, existing feed-forward models are

Chunming Zheng

Thermodynamic uncertainty relation (TUR) bounds coherence in stochastic oscillatory systems. In this paper, we show that both dynamical and thermodynamic bounds play important roles for the excitable oscillators, e.g. neurons. Firstly, we investigate the trade-off between coherence and dissipation both in the sub and super-threshold regions for a single exci

Tien-Yu Chi, Hung-Yueh Chiang, Chi-Chih Chang, Ning-Chi Huang

Vision transformers dominate image processing tasks due to their superior performance. However, the quadratic complexity of self-attention limits the scalability of these systems and their deployment on resource-constrained devices. State Space Models (SSMs) have emerged as a solution by introducing a linear recurrence mechanism, which reduces the complexity

Célio Terra

This thesis investigates critical phenomena and equilibrium states in various stochastic models through three interconnected studies. In the first chapter, we analyze the Activated Random Walk model on a one-dimensional ring in the high-density regime. We introduce a toppling procedure that incrementally constructs an environment demonstrating the sustained

Itai Benjamini, Gady Kozma

We construct a coupling of two random walks in 4 dimensions so that their traces do not intersect with positive probability.

Hang Yin, Zhifeng Lin, Xin Liu, Bin Sun

Direction reasoning is essential for intelligent systems to understand the real world. While existing work focuses primarily on spatial reasoning, compass direction reasoning remains underexplored. To address this, we propose the Compass Direction Reasoning (CDR) benchmark, designed to evaluate the direction reasoning capabilities of multimodal language mode

Yixiong Ren, Jianhui Zhou

Monte Carlo methods are widely used importance sampling techniques for studying complex physical systems. Integrating these methods with deep learning has significantly improved efficiency and accuracy in high-dimensional problems and complex system simulations. However, these neural network-enhanced Monte Carlo methods still face challenges such as slow sam

Hamraz Javaheri, Omid Ghamarnejad, Paul Lukowicz, Gregor Alexander Stavrou

Wearable Augmented Reality (AR) technologies are gaining recognition for their potential to transform surgical navigation systems. As these technologies evolve, selecting the right interaction method to control the system becomes crucial. Our work introduces a voice-controlled user interface (VCUI) for surgical AR assistance systems (ARAS), designed for panc

Denis Yagodkin, Kenneth Burfeindt, Zakhar A. Iakovlev, Abhijeet M. Kumar

Excitons in Transition Metal Dichalcogenides (TMDs) acquire a spin-like quantum number, a pseudospin, originating from the crystal's discrete rotational symmetry. Here, we break this symmetry using a tunable uniaxial strain, effectively generating a pseudomagnetic field exceeding 40 Tesla. Under this large field, we demonstrate pseudospin analogs of spintron

Prakhar Paliwal, Arjun Sable, Manjesh K. Hanawal

As cyber threats continue to evolve and diversify, it has become increasingly challenging to identify the root causes of security breaches that occur between periodic security assessments. This paper explores the fundamental importance of system fingerprinting as a proactive and effective approach to addressing this issue. By capturing a comprehensive host's

Basak Demirok, Mucahid Kutlu

While large language models provide significant convenience for software development, they can lead to ethical issues in job interviews and student assignments. Therefore, determining whether a piece of code is written by a human or generated by an artificial intelligence (AI) model is a critical issue. In this study, we present AIGCodeSet, which consists of

Athanasios Beslikas

In the present article, composition operators induced by Rational Inner Functions on the bidisc $\mathbb{D}^2$ are studied, acting on the weighted Bergman space $A^2_{\beta}(\mathbb{D}^2).$ We prove that under mild conditions that Rational Inner Functions with one singularity on $\mathbb{T}^2$ induce unbounded composition operator on $A^2(\mathbb{D}^2).$ We

Javier Montalvo, Roberto Alcover-Couso, Pablo Carballeira, Álvaro García-Martín

This paper introduces a novel synthetic dataset that captures urban scenes under a variety of weather conditions, providing pixel-perfect, ground-truth-aligned images to facilitate effective feature alignment across domains. Additionally, we propose a method for domain adaptation and generalization that takes advantage of the multiple versions of each scene,

Marco Pangallo, R. Maria del Rio-Chanona

Economic agent-based models (ABMs) are becoming more and more data-driven, establishing themselves as increasingly valuable tools for economic research and policymaking. We propose to classify the extent to which an ABM is data-driven based on whether agent-level quantities are initialized from real-world micro-data and whether the ABM's dynamics track empir

Carlos Galindo, Fernando Hernando, Helena Martín-Cruz, Ryutaroh Matsumoto

Classical $(r,\delta)$-locally recoverable codes are designed for avoiding loss of information in large scale distributed and cloud storage systems. We introduce the quantum counterpart of those codes by defining quantum $(r,\delta)$-locally recoverable codes which are quantum error-correcting codes capable of correcting $\delta -1$ qudit erasures from sets

Hitesh Sagtani, Rishabh Mehrotra, Beyang Liu

Fill-in-the-Middle (FIM) models play a vital role in code completion tasks, leveraging both prefix and suffix context to provide more accurate and contextually relevant suggestions. This paper presents approaches to improve FIM code completion while addressing the challenge of maintaining low latency for real-time coding assistance. We enhance FIM code compl

Jonghyeon Lee, Boumediene Hamzi, Boya Hou, Houman Owhadi

The Koopman operator provides a linear framework to study nonlinear dynamical systems. Its spectra offer valuable insights into system dynamics, but the operator can exhibit both discrete and continuous spectra, complicating direct computations. In this paper, we introduce a kernel-based method to construct the principal eigenfunctions of the Koopman operato

Valentin Benedetti, Horacio Casini, Javier M. Magan

Minimal d=2 CFTs are usually classified through modular invariant partition functions. There is a finer classification of ``non complete'' models when S-duality is not imposed. We approach this classification by starting with the local chiral algebra and adding primaries sequentially. At each step, we only impose locality (T-duality) and closure of the opera

Minbo Gao, Zhengfeng Ji, Qisheng Wang

Quantum $k$-minimum finding is a fundamental subroutine with numerous applications in combinatorial problems and machine learning. Previous approaches typically assume oracle access to exact function values, making it challenging to integrate this subroutine with other quantum algorithms. In this paper, we propose an (almost) optimal quantum $k$-minimum find

Robert Ganian, Fionn Mc Inerney, Dimitra Tsigkari

The fundamental caching problem in networks asks to find an allocation of contents to a network of caches with the aim of maximizing the cache hit rate. Despite the problem's importance to a variety of research areas -- including not only content delivery, but also edge intelligence and inference -- and the extensive body of work on empirical aspects of cach

Souvik Ghosh, Kallol Paul, Debmalya Sain

In this article we study the difference between orthogonality induced by the norm derivatives (known as $\rho$-orthogonality) and Birkhoff-James orthogonality in a normed linear space $ \mathbb X$ by introducing a new geometric constant, denoted by $\Gamma(\mathbb{X}).$ We explore the relation between various geometric properties of the space and the constan

Xizhe Xue, Guoting Wei, Hao Chen, Haokui Zhang

The rapid evolution of Vision Language Models (VLMs) has catalyzed significant advancements in artificial intelligence, expanding research across various disciplines, including Earth Observation (EO). While VLMs have enhanced image understanding and data processing within EO, their applications have predominantly focused on image content description. This li

Chenguang Xiao, Zheming Zuo, Shuo Wang

Federated learning (FL) triggers intra-client and inter-client class imbalance, with the latter compared to the former leading to biased client updates and thus deteriorating the distributed models. Such a bias is exacerbated during the server aggregation phase and has yet to be effectively addressed by conventional re-balancing methods. To this end, differe

Shuo Liu, Wenbin Li, Di Yao, Jingping Bi

Flight trajectory data plays a vital role in the traffic management community, especially for downstream tasks such as trajectory prediction, flight recognition, and anomaly detection. Existing works often utilize handcrafted features and design models for different tasks individually, which heavily rely on domain expertise and are hard to extend. We argue t

Louis Garénaux, Hermen Jan Hupkes

We study the existence of traveling wave solutions for a numerical counterpart of the KPP equation. We obtain the existence of monostable fronts for all super-critical speeds in the regime where the spatial step size is small. The key strategy is to transfer the invertibility of certain linear operators related to the front solutions from the continuous sett

José Andrés Armario, Ronan Egan, Hadi Kharaghani, Padraig Ó Catháin

An $n\times n$ complex matrix $M$ with entries in the $k^{\textrm{th}}$ roots of unity which satisfies $MM^{\ast} = nI_{n}$ is called a Butson Hadamard matrix. While a matrix with entries in the $k^{\textrm{th}}$ roots typically does not have an eigenvector with entries in the same set, such vectors and their generalisations turn out to have multiple applica

Joshua F. Robinson, Patrick B. Warren

The titular ordinary differential equation (ODE) is encountered in the theory of on-axis inertial particle capture by a blunt stationary collector at a viscous-flow stagnation point. Phase space for the ODE divides into two attractor basins, representing particle trajectories which do or do not collide with the collector in a finite time. Written as $\ddot{x

Anish Dhir, Matthew Ashman, James Requeima, Mark van der Wilk

Discovering a unique causal structure is difficult due to both inherent identifiability issues, and the consequences of finite data. As such, uncertainty over causal structures, such as those obtained from a Bayesian posterior, are often necessary for downstream tasks. Finding an accurate approximation to this posterior is challenging, due to the large numbe

Daichi Yashima, Ryosuke Korekata, Komei Sugiura

Growing labor shortages are increasing the demand for domestic service robots (DSRs) to assist in various settings. In this study, we develop a DSR that transports everyday objects to specified pieces of furniture based on open-vocabulary instructions. Our approach focuses on retrieving images of target objects and receptacles from pre-collected images of in

Xuhua He, Qingchao Yu

In this paper, we consider the geometric special fibers of local models of Shimura varieties and of moduli of $\bG$-Shtukas with parahoric level structure. We investigate two problems with respect to the irreducibility of local models. First, we classify the cases where the local models are irreducible. Next, we show that the fibers of the level-changing map

Mathias Van Den Bossche, Philippe Grangier

In previous papers we have explained how a sequence of theorems by John von Neumann on infinite tensor products (ITP) can be understood as providing elements to support both sectorisation of the Hilbert space of large quantum systems, and a mechanism of self decoherence thereof. These two effects may help understanding the articulation of the classical and q

Huidong Xie, Weijie Gan, Wei Ji, Xiongchao Chen

Myocardial perfusion imaging using SPECT is widely utilized to diagnose coronary artery diseases, but image quality can be negatively affected in low-dose and few-view acquisition settings. Although various deep learning methods have been introduced to improve image quality from low-dose or few-view SPECT data, previous approaches often fail to generalize ac

Sayan Goswami

This article resolves two related problems in Ramsey theory on the integers. We show that for any finite coloring of the set of natural numbers, there exist numbers $a$ and $b$ for which the configuration $\{a, b, ab, a(b+1)\}$ is monochromatic. By redefining the variables $a=x$ and $ab=y,$ our configurations transforms into $\{x,y,x+y,\frac{y}{x}\}.$ This f

Chao Ma, Yikai Hou, Xiang Li, Yinggang Sun

Long-term time-series forecasting is essential for planning and decision-making in economics, energy, and transportation, where long foresight is required. To obtain such long foresight, models must be both efficient and effective in processing long sequence. Recent advancements have enhanced the efficiency of these models; however, the challenge of effectiv

Subhrajit Modak, Pieter Kok

Quantum entanglement can be used to extend the baseline of telescope arrays in order to increase the spatial resolution. In one proposal by Marchese and Kok [Phys. Rev. Lett. 130, 160801 (2023)], identical single photons are shared between receivers, and interfere with a star photon. In this paper we consider two outstanding questions: i) what is the precise

Claus Hertling, Khadija Larabi

This monograph starts with an upper triangular matrix with integer entries and 1's on the diagonal. It develops from this a spectrum of structures, which appear in different contexts, in algebraic geometry, representation theory and the theory of irregular meromorphic connections. It provides general tools to study these structures, and it studies sytematica

Ella Nikodem, Jakob Schluck, Max Geier, Michal Papaj

A Josephson diode passes current with zero resistance in one direction but is resistive in the other direction. While such an effect has been observed in several platforms, a large and tunable Josephson diode effect has been rare. Here we report that a simple device consisting of a topological-insulator (TI) nanowire side-contacted by superconductors to form

Margarida Fernandes, José Soares, Matheus Silva, Crystian Saraiva

Brain metastases are a common diagnosis that affects between 20% and 40% of cancer patients. Subsequent to radiation therapy, patients with brain metastases undergo follow-up sessions during which the response to treatment is monitored. In this study, a dataset of medical images from 44 patients with at least one brain metastasis and different primary tumor

Jung-Hua Wang, Huai-Wen Chang, Rong-Yu Wu, Ting-Yuan Wang

This study addresses the issue of leveraging federated learning to improve data privacy and performance in IVF embryo selection. The EM (Expectation-Maximization) algorithm is incorporated into deep learning models to form a federated learning framework for quality evaluation of blastomere cleavage using two-dimensional images. The framework comprises a serv

Yikai Zhao, Yuhan Wu, Tong Yang

In the realm of data stream processing, the advent of SET-INCREMENT Mixed (SIM) data streams necessitates algorithms that efficiently handle both SET and INCREMENT operations. We present Carbonyl4, an innovative algorithm designed specifically for SIM data streams, ensuring accuracy, unbiasedness, and adaptability. Carbonyl4 introduces two pioneering techniq

Chong Zheng, Shiwen He, Yongming Huang, Tony Q. S. Quek

Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate. However, the technical bottlenecks of traditional methods pose significant challenges to cross-layer optimization. In this paper, joint subcarrier allocation and beamforming opti

Thomas A. Henzinger, Fabian Kresse, Kaushik Mallik, Emily Yu

We study the problem of predictive runtime monitoring of black-box dynamical systems with quantitative safety properties. The black-box setting stipulates that the exact semantics of the dynamical system and the controller are unknown, and that we are only able to observe the state of the controlled (aka, closed-loop) system at finitely many time points. We

Xiangyue Zhang, Jianfang Li, Jiaxu Zhang, Ziqiang Dang

Co-speech gesture generation must carefully integrate common rhythmic motion with rare yet essential semantic gestures. In this work, we propose SemTalk for holistic co-speech gesture generation with frame-level semantic emphasis. Our key insight is to separately learn base motions and sparse motions, and then adaptively fuse them. In particular, coarse2fine

Ziming Guo, Chao Ma, Yinggang Sun, Tiancheng Zhao

Recent advancements in large language models (LLMs) have significantly advanced text-to-SQL systems. However, most LLM-based methods often narrowly focus on SQL generation, neglecting the complexities of real-world conversational queries. This oversight can lead to unreliable responses, particularly for ambiguous questions that cannot be directly addressed w

Shintaro Nakamura

The origin of the magnetic quadrupole (MQ) interactions in Gd$_{3}$Ru$_{4}$Al$_{12}$ which is known as a frustrated spin system and as a host material of skyrmion with a sentrosymmetric crystal structure are discussed. The MQ interactions between ferromagnetic (FM) spin trimers with imperfect FM directivity are deduced from synthesis of dipole Ruderman-Kitte

Tingting Ni, Maryam Kamgarpour

We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a safe set while reaching a target set within a finite time horizon. Due to the time-dependent nature of these constraints, we show that, in general, the optimal policy for this constra

Philippe Schnoebelen, Isa Vialard

The piecewise complexity $h(u)$ of a word is the minimal length of subwords needed to exactly characterise $u$. Its piecewise minimality index $\rho(u)$ is the smallest length $k$ such that $u$ is minimal among its order-$k$ class $[u]_k$ in Simon's congruence. We initiate a study of these two descriptive complexity measures. Among other results we provide e

Ben Goertzel

We articulate here a series of specific metagoals designed to address the challenge of creating AGI systems that possess the ability to flexibly self-modify yet also have the propensity to maintain key invariant properties of their goal systems 1) a series of goal-stability metagoals aimed to guide a system to a condition in which goal-stability is compatibl

Víctor Elvira, Émilie Chouzenoux, O. Deniz Akyildiz

Adaptive importance sampling (AIS) algorithms are a rising methodology in signal processing, statistics, and machine learning. An effective adaptation of the proposals is key for the success of AIS. Recent works have shown that gradient information about the involved target density can greatly boost performance, but its applicability is restricted to differe

Wei Chen, Yuting Wu, Shuhan Wu, Zhiyu Zhang

Reasoning future unknowable facts on temporal knowledge graphs (TKGs) is a challenging task, holding significant academic and practical values for various fields. Existing studies exploring explainable reasoning concentrate on modeling comprehensible temporal paths relevant to the query. Yet, these path-based methods primarily focus on local temporal paths a

Chenxi Fu, Xiaoyi Wang, Zaijiang Man, Caimei Yang

As recursion has been underlying any linguistic work for the last 60 years, the acquisition of recursive structures by children during language learning has become a focal point of inquiry. This study delves into the developmental trajectory of Mandarin-speaking children's acquisition of recursive possessives and locatives, assessing the impact of structural

Yanxu Mao, Peipei Liu, Tiehan Cui, Zhaoteng Yan

Large language models (LLMs) are widely applied in various fields of society due to their powerful reasoning, understanding, and generation capabilities. However, the security issues associated with these models are becoming increasingly severe. Jailbreaking attacks, as an important method for detecting vulnerabilities in LLMs, have been explored by research

Quoc-Anh Hoang Nguyen, The Hung Tran

Bayesian Optimization (BO) is a popular approach to optimizing expensive-to-evaluate black-box functions. Despite the success of BO, its performance may decrease exponentially as the dimensionality increases. A common framework to tackle this problem is to assume that the objective function depends on a limited set of features that lie on a low-dimensional m

Yunshan Zhong, Yuyao Zhou, Yuxin Zhang, Wanchen Sui

Data-free quantization (DFQ) enables model quantization without accessing real data, addressing concerns regarding data security and privacy. With the growing adoption of Vision Transformers (ViTs), DFQ for ViTs has garnered significant attention. However, existing DFQ methods exhibit two limitations: (1) semantic distortion, where the semantics of synthetic

Jiarui Yang, Tao Dai, Yufei Zhu, Naiqi Li

Diffusion models represent the state-of-the-art in generative modeling. Due to their high training costs, many works leverage pre-trained diffusion models' powerful representations for downstream tasks, such as face super-resolution (FSR), through fine-tuning or prior-based methods. However, relying solely on priors without supervised training makes it chall

Antoine Loew, Dewen Sun, Hai-Chen Wang, Silvana Botti

There has been an ongoing race for the past several years to develop the best universal machinelearning interatomic potential. This progress has led to increasingly accurate models for predictingenergy, forces, and stresses, combining innovative architectures with big data. Here, we benchmarkthese models on their ability to predict harmonic phonon properties

Colin Christopher, Sebastian Walcher

We consider complex rational vector fields that admit a first integral whose logarithmic derivative lies in a finite extension of the rational function field $K$. In view of the Prelle-Singer theorem, these are the rational vector fields that admit an elementary first integral. Elementary integrable vector fields which are not Darboux integrable -- thus the

Jiawen Zhang, Jingming Zhu

In this short note, we give a complete answer to the question of when the generalised F\o lner sets exhibiting property A can be chosen to be subsets of the space itself. More precisely, we prove that this holds for any discrete metric space of bounded geometry.

C. Condeescu, D. M. Ghilencea, A. Micu

We study Weyl conformal geometry as a general gauge theory of the Weyl group (of Poincar\'e and dilatations symmetries) in a manifestly Weyl gauge covariant formalism in which this geometry is automatically metric and physically relevant. This gives a realistic (quadratic) gauge theory of gravity, with Einstein-Hilbert gravity recovered in its spontaneously

Ben Goertzel

We explore a novel paradigm (labeled ActPC-Chem) for biologically inspired, goal-guided artificial intelligence (AI) centered on a form of Discrete Active Predictive Coding (ActPC) operating within an algorithmic chemistry of rewrite rules. ActPC-Chem is envisioned as a foundational "cognitive kernel" for advanced cognitive architectures, such as the OpenCog

Daisuke Ikegami, Matteo Viale

We generalize the basic theory of universally Baire sets of $2^\omega$ to a theory of universally Baire subsets of $2^\kappa$. We show that the fundamental characterizations of the property of being universally Baire have natural generalizations that can be formulated also for subsets of $2^\kappa$, in particular we provide four equivalent uniform definition

Zhisong Zhang, Yan Wang, Xinting Huang, Tianqing Fang

Large language models have shown remarkable performance across a wide range of language tasks, owing to their exceptional capabilities in context modeling. The most commonly used method of context modeling is full self-attention, as seen in standard decoder-only Transformers. Although powerful, this method can be inefficient for long sequences and may overlo

Doruk Aksoy, Alex A. Gorodetsky

We present two new algorithms for approximating and updating the hierarchical Tucker decomposition of tensor streams. The first algorithm, Batch Hierarchical Tucker - leaf to root (BHT-l2r), proposes an alternative and more efficient way of approximating a batch of similar tensors in hierarchical Tucker format. The second algorithm, Hierarchical Tucker - Rap

Shizhe Liang, Wei Zhang, Tianyang Zhong, Tianming Liu

This paper presents a comprehensive overview on the applications of artificial intelligence (AI) in mathematical research, highlighting the transformative role AI has begun to play in this domain. Traditionally, AI advancements have heavily relied on theoretical foundations provided by mathematics and statistics. However, recent developments in AI, particula

Yiqin Luo, Tianlong Gu

With the rapid advancement of deep learning technologies, artificial intelligence has become increasingly prevalent in the research and application of dermatological disease diagnosis. However, this data-driven approach often faces issues related to decision bias. Existing fairness enhancement techniques typically come at a substantial cost to accuracy. This

Alexander D. Rathcke, Lars A. Buchhave, Julien De Wit, Benjamin V. Rackham

Stellar surface heterogeneities, such as spots and faculae, often contaminate exoplanet transit spectra, hindering precise atmospheric characterization. We demonstrate a novel, epoch-based, model-independent method to mitigate stellar contamination, applicable to multi-planet systems with at least one airless planet. We apply this method using quasi-simultan

Ziheng Sun

Inspired by the iconic movie Back to the Future, this paper explores an innovative adaptive nowcasting approach that reimagines the relationship between present actions and future outcomes. In the movie, characters travel through time to manipulate past events, aiming to create a better future. Analogously, our framework employs predictive insights about the

S Divakar Bhat, Amit More, Mudit Soni, Surbhi Agrawal

Learning-based solutions for long-tailed recognition face difficulties in generalizing on balanced test datasets. Due to imbalanced data prior, the learned \textit{a posteriori} distribution is biased toward the most frequent (head) classes, leading to an inferior performance on the least frequent (tail) classes. In general, the performance can be improved b

Mohammad Hajiesmaili, Shaolei Ren, Ramesh K. Sitaraman, Adam Wierman

The skyrocketing demand for artificial intelligence (AI) has created an enormous appetite for globally deployed power-hungry servers. As a result, the environmental footprint of AI systems has come under increasing scrutiny. More crucially, the current way that we exploit AI workloads' flexibility and manage AI systems can lead to wildly different environmen

Chang Liu, Hongtao Fan, Yajing Li

This paper addresses a class of two-person zero-sum stochastic differential equations, which encompass Markov chains and fractional Brownian motion, and satisfy some monotonicity conditions over an infinite time horizon. Within the framework of forward-backward stochastic differential equations (FBSDEs) that describe system evolution, we extend the classical

Yuntian Chen, Zhanyong Tang, Tianpei Lu, Bingsheng Zhang

Homomorphic encryption (HE) and secret sharing (SS) enable computations on encrypted data, providing significant privacy benefits for large transformer-based models (TBM) in sensitive sectors like medicine and finance. However, private TBM inference incurs significant costs due to the coarse-grained application of HE and SS. We present FASTLMPI, a new approa

Kosuke Fukui, Peter van Loock

Quantum computation with light, compared with other platforms, offers the unique benefit of natural high-speed operations at room temperature and large clock rate, but a big obstacle of photonics is the lack of strong nonlinearities which also makes loss-tolerant or generally fault-tolerant quantum computation (FTQC) complicated in an all-optical setup. Typi

Hong Zhu

In this paper, we propose an inexact proximal Newton-type method for nonconvex composite problems. We establish the global convergence rate of the order $\mathcal{O}(k^{-1/2})$ in terms of the minimal norm of the KKT residual mapping and the local superlinear convergence rate in terms of the sequence generated by the proposed algorithm under the higher-order

Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen

Deep Neural Networks (DNNs) have revolutionized artificial intelligence, achieving impressive results on diverse data types, including images, videos, and texts. However, DNNs still lag behind Gradient Boosting Decision Trees (GBDT) on tabular data, a format extensively utilized across various domains. In this paper, we propose DOFEN, short for \textbf{D}eep

Chao-Chi Chen, Chin-Yuan Yeh, Hsi-Wen Chen, De-Nian Yang

We introduce Knowledgeable Network of Thoughts (kNoT): a prompt scheme that advances the capabilities of large language models (LLMs) beyond existing paradigms like Chain-of-Thought (CoT), Tree of Thoughts (ToT), and Graph of Thoughts (GoT). The key innovation of kNoT is the LLM Workflow Template (LWT), which allows for an executable plan to be specified by

Theresa Lange, Marco Rehmeier, Andre Schenke

For the $3D$ fractional Navier--Stokes equations perturbed by transport noise, we prove the existence of infinitely many H\"older continuous analytically weak, probabilistically strong Leray--Hopf solutions starting from the same deterministic initial velocity field. Our solutions are global in time and satisfy the energy inequality pathwise on a non-empty r

Atticus Sims

The age of Artificial Intelligence (AI) is marked by its transformative "generative" capabilities, distinguishing it from prior iterations. This burgeoning characteristic of AI has enabled it to produce new and original content, inherently showcasing its creative prowess. This shift challenges and requires a recalibration in the realm of arts education, urgi

Lucas Goncalves, Prashant Mathur, Xing Niu, Brady Houston

Audio-Visual Speech-to-Speech Translation typically prioritizes improving translation quality and naturalness. However, an equally critical aspect in audio-visual content is lip-synchrony-ensuring that the movements of the lips match the spoken content-essential for maintaining realism in dubbed videos. Despite its importance, the inclusion of lip-synchrony

Koushik R. Das, Sudipta Dutta

We investigate the electronic transport behavior of Fano-Anderson (FA) systems, consisting of a one-dimensional finite backbone chain and an attached side-group of varying length. The tight-binding model within the non-equilibrium Green's function (NEGF) reveals distinct interference patterns between the frontier orbitals of odd and even side-group systems t

Iskander Gazizov, Davide Pinto, Harald Moser, Savda Sam

This paper investigates three spectroscopic techniques - Tunable Diode Laser Absorption Spectroscopy (TDLAS), Wavelength Modulation Spectroscopy (WMS), and Heterodyne Phase Sensitive Dispersion Spectroscopy (HPSDS) - to evaluate their performance in detecting carbon monoxide (CO) in nitrogen in a 31 m Herriot cell using an Interband Cascade Laser (ICL) emitt

Aravind Aikot, Bindusar Sahoo

We study various N=2 multiplets in four dimensions by looking at the supersymmetric truncation of four dimensional N=3 multiplets. Under supersymmetric truncation, the off-shell N=3 Weyl multiplet reduces to the off-shell N=2 Weyl multiplet and the off-shell N=2 vector multiplet (which we will refer to as the central charge multiplet). Under the same truncat

Keshav Bhandari, Abhinaba Roy, Kyra Wang, Geeta Puri

This paper introduces text2midi, an end-to-end model to generate MIDI files from textual descriptions. Leveraging the growing popularity of multimodal generative approaches, text2midi capitalizes on the extensive availability of textual data and the success of large language models (LLMs). Our end-to-end system harnesses the power of LLMs to generate symboli

Jinwei Xu, He Zhang, Yanjing Yang, Lanxin Yang

Large language model-generated code (LLMgCode) has become increasingly common in software development. So far LLMgCode has more quality issues than human-authored code (HaCode). It is common for LLMgCode to mix with HaCode in a code change, while the change is signed by only human developers, without being carefully examined. Many automated methods have been

Han Liang, Chengyu Huang, Yuecheng Xu, Cheng Tang

In the realm of Sign Language Translation (SLT), reliance on costly gloss-annotated datasets has posed a significant barrier. Recent advancements in gloss-free SLT methods have shown promise, yet they often largely lag behind gloss-based approaches in terms of translation accuracy. To narrow this performance gap, we introduce LLaVA-SLT, a pioneering Large Mu

Erhu He, Declan Kutscher, Yiqun Xie, Jacob Zwart

This work introduces a novel graph neural networks (GNNs)-based method to predict stream water temperature and reduce model bias across locations of different income and education levels. Traditional physics-based models often have limited accuracy because they are necessarily approximations of reality. Recently, there has been an increasing interest of usin

Yudong Zhang, Ruobing Xie, Jiansheng Chen, Xingwu Sun

Contrastive learning is a prevalent technique in self-supervised vision representation learning, typically generating positive pairs by applying two data augmentations to the same image. Designing effective data augmentation strategies is crucial for the success of contrastive learning. Inspired by the story of the blind men and the elephant, we introduce Jo

Ao Zhou, Bin Liu, Jin Wang, Grigorios Tsoumakas

The accuracy of deep neural networks is significantly influenced by the effectiveness of mini-batch construction during training. In single-label scenarios, such as binary and multi-class classification tasks, it has been demonstrated that batch selection algorithms preferring samples with higher uncertainty achieve better performance than difficulty-based m

Joan Hernández, Joan Mateu, Laura Prat

In the present paper we characterize the removable sets for solutions of the fractional heat equation satisfying some parabolic $\text{BMO}$ or $\text{Lip}_\alpha$ normalization conditions. We do this by introducing associated fractional caloric capacities, that we show to be comparable to a certain parabolic Hausdorff content.

Yawei Chen, Huibing Wang, Jinjia Peng, Yang Wang

Anchor-based multi-view clustering (MVC) has received extensive attention due to its efficient performance. Existing methods only focus on how to dynamically learn anchors from the original data and simultaneously construct anchor graphs describing the relationships between samples and perform clustering, while ignoring the reality of anchors, i.e., high-qua

Subhrajit Sahoo, Praveen C. Srivastava, Noritaka Shimizu, Yutaka Utsuno

Large-scale shell-model calculations have been performed to study the nuclear structure properties of Hg isotopes with mass varying from $A=193$ to $A=200$. The shell-model calculations are carried out in the 50 $\leq Z \leq$ 82 and 82 $ \leq N \leq$ 126 model space using monopole-based truncation. We present detailed studies on low-energy excitation spectra