October 2024 arXiv papers — page 110

Lei Huang, Xiaocheng Feng, Weitao Ma, Liang Zhao

Teaching large language models (LLMs) to generate text with citations to evidence sources can mitigate hallucinations and enhance verifiability in information-seeking systems. However, improving this capability requires high-quality attribution data, which is costly and labor-intensive. Inspired by recent advances in self-improvement that enhance LLMs withou

L. Ya. Glozman, A. V. Nefediev, R. F. Wagenbrunn

We investigate properties of the quark--antiquark mesons at zero and finite temperature in the framework of a solvable chirally symmetric quark model with linear confining potential. The interquark interaction in the model is reminiscent of that derived in Coulomb gauge QCD, with the string tension being the only model parameter. We demonstrate that while th

Janine Strotherm, Barbara Hammer

As relevant examples such as the future criminal detection software [1] show, fairness of AI-based and social domain affecting decision support tools constitutes an important area of research. In this contribution, we investigate the applications of AI to socioeconomically relevant infrastructures such as those of water distribution networks (WDNs), where fa

Mingda Lu, Zitian Ao, Chao Wang, Sudhakar Prasad

For the 3D localization problem using point spread function (PSF) engineering, we propose a novel enhancement of our previously introduced localization neural network, LocNet. The improved network is a physics-informed neural network (PINN) that we call PiLocNet. Previous works on the localization problem may be categorized separately into model-based optimi

Xuexun Liu, Xiaoxu Xu, Jinlong Li, Qiudan Zhang

Referring 3D Segmentation is a visual-language task that segments all points of the specified object from a 3D point cloud described by a sentence of query. Previous works perform a two-stage paradigm, first conducting language-agnostic instance segmentation then matching with given text query. However, the semantic concepts from text query and visual cues a

Prakhar Dixit, Tim Oates

Many students struggle with math word problems (MWPs), often finding it difficult to identify key information and select the appropriate mathematical operations. Schema-based instruction (SBI) is an evidence-based strategy that helps students categorize problems based on their structure, improving problem-solving accuracy. Building on this, we propose a Sche

Davide Belluomo, Tiziana Calamoneri, Giacomo Paesani, Ivano Salvo

We present a new unified graph-based representation of medical data, combining genetic information and medical records of patients with medical knowledge via a unique knowledge graph. This approach allows us to infer meaningful information and explanations that would be unavailable by looking at each data set separately. The systematic use of different datab

Minhyun Kim, Se-Chan Lee

We study the local behavior of weak solutions, with possible singularities, of nonlocal nonlinear equations. We first prove that sets of capacity zero are removable for weak solutions under certain integrability conditions. We then characterize the asymptotic behavior of singular solutions near an isolated singularity in terms of the fundamental solution.

Cong Li

We investigated the impact parameter dependence of the background field's effect on the coupling constant of the $\gamma \gamma \rightarrow l^{+} l^{-} \gamma$ process in heavy-ion collisions. The peripheral electric fields of heavy ions collide, and after photon annihilation into lepton pairs, the subsequent emission of radiation photons will be affected by

Cristina G. Fernandes, Tássio Naia, Giovanne Santos, Maya Stein

We prove that for every ${\gamma > 0}$ there exists $n_0 \in \mathbb{N}$ such that for every ${n \geq n_0}$ any family of up to $\lfloor{n^{\frac12+\gamma}}\rfloor$ trees having at most $(1-\gamma)n$ vertices in each bipartition class can be packed into $K_{n,n}$. As a tool for our proof, we show an approximate bipartite version of the Koml\'os-S\'ark\"ozy-S

Satoru Tadokoro, Akihiro Yamaguchi, Takao Namiki, Ichiro Tsuda

By extending the extreme learning machine by additional control inputs, we achieved almost complete reproduction of bifurcation structures of dynamical systems. The learning ability of the proposed neural network system is striking in that the entire structure of the bifurcations of a target one-parameter family of dynamical systems can be nearly reproduced

Yu Gu, Qiushi Zhu, Guangzhi Lei, Chao Weng

This paper proposes an improved version of DurIAN-E (DurIAN-E 2), which is also a duration informed attention neural network for expressive and high-fidelity text-to-speech (TTS) synthesis. Similar with the DurIAN-E model, multiple stacked SwishRNN-based Transformer blocks are utilized as linguistic encoders and Style-Adaptive Instance Normalization (SAIN) l

Xiaoyan Hu, Ho-fung Leung, Farzan Farnia

Selecting a sample generation scheme from multiple prompt-based generative models, including large language models (LLMs) and prompt-guided image and video generation models, is typically addressed by choosing the model that maximizes an averaged evaluation score. However, this score-based selection overlooks the possibility that different models achieve the

Jake Robertson, Thorsten Schmidt, Frank Hutter, Noor Awad

Fairness-aware Machine Learning (FairML) applications are often characterized by complex social objectives and legal requirements, frequently involving multiple, potentially conflicting notions of fairness. Despite the well-known Impossibility Theorem of Fairness and extensive theoretical research on the statistical and socio-technical trade-offs between fai

Shuyin Xia, Bolun Shi, Yifan Wang, Jiang Xie

Traditional clustering algorithms often focus on the most fine-grained information and achieve clustering by calculating the distance between each pair of data points or implementing other calculations based on points. This way is not inconsistent with the cognitive mechanism of "global precedence" in human brain, resulting in those methods' bad performance

Chuyu Zhang, Peiyan Gu, Xueyang Yu, Xuming He

We tackle the generalized category discovery (GCD) problem, which aims to discover novel classes in unlabeled datasets by leveraging the knowledge of known classes. Previous works utilize the known class knowledge through shared representation spaces. Despite their progress, our analysis experiments show that novel classes can achieve impressive clustering r

Yu-Neng Chuang, Prathusha Kameswara Sarma, Parikshit Gopalan, John Boccio

Large language models (LLMs) have demonstrated impressive performance on several tasks and are increasingly deployed in real-world applications. However, especially in high-stakes settings, it becomes vital to know when the output of an LLM may be unreliable. Depending on whether an answer is trustworthy, a system can then choose to route the question to ano

Eaman Eftekhary

We show that the torsion order $\mathrm{Ord}(K)$ of a knot $K$ in knot Floer homology gives a lower bound on the minimum number $n$ such that an oriented $(n+1)$-tangle replacement unknots $K$. This generalizes earlier results by Alishahi and the author and by Juhasz, Miller and Zemke, that $\mathrm{Ord}(K)$ is a lower bound for both the unknotting number $u

Natsuo Yamashita, Masaaki Yamamoto, Yohei Kawaguchi

Speech Emotion Recognition (SER) often operates on speech segments detected by a Voice Activity Detection (VAD) model. However, VAD models may output flawed speech segments, especially in noisy environments, resulting in degraded performance of subsequent SER models. To address this issue, we propose an end-to-end (E2E) method that integrates VAD and SER usi

Fabiha Haider, Fariha Tanjim Shifat, Md Farhan Ishmam, Deeparghya Dutta Barua

The proliferation of transliterated texts in digital spaces has emphasized the need for detecting and classifying hate speech in languages beyond English, particularly in low-resource languages. As online discourse can perpetuate discrimination based on target groups, e.g. gender, religion, and origin, multi-label classification of hateful content can help i

Yanan Guo, Ying Xie, Ying Chang, Benkui Zhang

Novel view synthesis has made significant progress in the field of 3D computer vision. However, the rendering of view-consistent novel views from imperfect camera poses remains challenging. In this paper, we introduce a hybrid bundle-adjusting 3D Gaussians model that enables view-consistent rendering with pose optimization. This model jointly extract image-b

Ki-Hoon Hong, Hyun-Chul Kim, M. M. Musakhanov, N. Rakhimov

We investigate heavy-light quark systems within the framework of the QCD instanton vacuum, focusing on the $N_f = 1$ light flavor case. We derive an effective heavy-light quark interaction from the low-energy QCD partition function and construct a heavy-meson effective Lagrangian. The physical residual mass of heavy mesons, $\Lambda$, is determined by employ

Atsuki Sato, Yusuke Matsui

Bloom filter is a widely used classic data structure for approximate membership queries. Learned Bloom filters improve memory efficiency by leveraging machine learning, with the partitioned learned Bloom filter (PLBF) being among the most memory-efficient variants. However, PLBF suffers from high computational complexity during construction, specifically $O(

Stefanos Chaliasos, Nicolas Mohnblatt, Assimakis Kattis, Benjamin Livshits

ZK-Rollups have emerged as a leading solution for blockchain scalability, leveraging succinct proofs primarily based on ZKP protocols. This paper explores the design of transaction fee mechanisms (TFMs) for ZK-Rollups, focusing on how key components like sequencing, data availability~(DA), and ZK proving interact to influence cost structures. We outline the

Yizhao Gao, Zhichen Zeng, Dayou Du, Shijie Cao

Attention is the cornerstone of modern Large Language Models (LLMs). Yet its quadratic complexity hinders efficiency and scalability, especially for long-context processing. A promising approach is to leverage sparsity in attention. However, existing sparsity-based solutions predominantly rely on predefined patterns or heuristics at the attention head level,

Francois Hennecart

Kneser's theorem in the integers asserts that denoting by $ \underline{\mathrm{d}}$ the lower asymptotic density, if $\underline{\mathrm{d}}(X_1+\cdots+X_k)<\sum_{i=1}^k\underline{\mathrm{d}}(X_i)$ then the sumset $X_1+\cdots+X_k$ is \emph{periodic} for some positive integer $q$. In this article we establish a similar statement for upper Buck density and com

Minseok Choi, ChaeHun Park, Dohyun Lee, Jaegul Choo

Large language models (LLMs) serve as giant information stores, often including personal or copyrighted data, and retraining them from scratch is not a viable option. This has led to the development of various fast, approximate unlearning techniques to selectively remove knowledge from LLMs. Prior research has largely focused on minimizing the probabilities

Alessandro Giacchetto, Danilo Lewański

In these lecture notes, we provide an introduction to the moduli space of Riemann surfaces, a fundamental concept in the theories of 2D quantum gravity, topological string theory, and matrix models. We begin by reviewing some basic results concerning the recursive boundary structure of the moduli space and the associated cohomology theory. We then present Wi

Dongfang Zhao

This paper introduces \textit{Federated Retrieval-Augmented Generation (FRAG)}, a novel database management paradigm tailored for the growing needs of retrieval-augmented generation (RAG) systems, which are increasingly powered by large-language models (LLMs). FRAG enables mutually-distrusted parties to collaboratively perform Approximate $k$-Nearest Neighbo

Kexuan Shi, Hai Chen, Leheng Zhang, Shuhang Gu

Implicit Neural Representations (INRs), as a versatile representation paradigm, have achieved success in various computer vision tasks. Due to the spectral bias of the vanilla multi-layer perceptrons (MLPs), existing methods focus on designing MLPs with sophisticated architectures or repurposing training techniques for highly accurate INRs. In this paper, we

Andrew G. Semenov, Alex Latyshev, Andrei D. Zaikin

We predict two novel quantum drag effects which can occur in macroscopically quantum coherent Josephson circuits. We demonstrate that biasing one resistively shunted Josephson junction by an external current one can induce a non-zero voltage drop across another such junction capacitively coupled to the first one. This quantum Coulomb drag is caused by cotunn

R Mitteau, M Diez, M Firdaouss

A possible design rule for preventing surface damage from thermal transients to solid tungsten armour is proposed and formulated for the plasma facing components (divertor, first wall) of magnetic fusion machines. The rule is based on combined results from laboratory experiments and operating fusion machines, and fundamental engineering principles such as th

Zhuohan Xie, Rui Xing, Yuxia Wang, Jiahui Geng

Fact-checking long-form text is challenging, and it is therefore common practice to break it down into multiple atomic claims. The typical approach to fact-checking these atomic claims involves retrieving a fixed number of pieces of evidence, followed by a verification step. However, this method is usually not cost-effective, as it underutilizes the verifica

Fan Bu, Yuhao Zhang, Xidong Wang, Benyou Wang

The success of large language models (LLMs) has prompted efforts to integrate speech and audio data, aiming to create general foundation models capable of processing both textual and non-textual inputs. Recent advances, such as GPT-4o, highlight the potential for end-to-end speech LLMs, which preserves non-semantic information and world knowledge for deeper

Shangda Wu, Yashan Wang, Ruibin Yuan, Zhancheng Guo

Challenges in managing linguistic diversity and integrating various musical modalities are faced by current music information retrieval systems. These limitations reduce their effectiveness in a global, multimodal music environment. To address these issues, we introduce CLaMP 2, a system compatible with 101 languages that supports both ABC notation (a text-b

Chuan-Zhi Thomas Xie, Tie-Qiao Tang, Alexandre Nicolas

Pushes, falls, stampedes, and crushes are safety hazards that emerge from the collective motion of crowds, but might be avoided by better design and guidance. While pedestrian dynamics are now getting better understood on the whole, complex heterogeneous flows involvinge.g. adult-child pairs, though widely found at e.g. crowded Chinese training schools, stil

Vasily Tolstikov

An automated market maker where the price can cross the zero bound into the negative price domain with applications in electricity, energy, and derivatives markets is presented. A unique feature involves the ability to swap both negatively and positively priced assets between one another, which unlike traditional markets requires a numeraire in the form of a

Xu Han, Yuancheng Sun, Kai Chen, Yuxuan Ren

Coarse-grained (CG) molecular dynamics simulations enable efficient exploration of protein conformational ensembles. However, reconstructing atomic details from CG structures (backmapping) remains a challenging problem. Current approaches face an inherent trade-off between maintaining atomistic accuracy and exploring diverse conformations, often necessitatin

Weishan Cai, Wenjun Ma, Yuncheng Jiang

The success of current Entity Alignment (EA) task depends largely on the supervision information provided by labeled data. Considering the cost of labeled data, most supervised methods are difficult to apply in practical scenarios. Therefore, more and more works based on contrastive learning, active learning or other deep learning techniques have been develo

Yifei Huang, Matin Amini, Alexis Le Glaunec, Konstantinos Mamouras

SMORE (Chen et al., 2023) recently proposed the concept of semantic regular expressions that extend the classical formalism with a primitive to query external oracles such as databases and large language models (LLMs). Such patterns can be used to identify lines of text containing references to semantic concepts such as cities, celebrities, political entitie

James Cohen Gabor, Clara Grazian

This paper presents a comparative study of two Bayesian approaches - Markov Chain Monte Carlo (MCMC) and Approximate Bayesian Computation (ABC) - for estimating the parameters of autoregressive fractionally-integrated moving average (ARFIMA) models, which are widely used to capture long-memory in time series data. We propose a novel MCMC algorithm that filte

Luyao Zou, Quang Hieu Vo, Kitae Kim, Huy Q. Le

In this paper, cyber-attack prevention for the prosumer-based electric vehicle (EV) charging stations (EVCSs) is investigated, which covers two aspects: 1) cyber-attack detection on prosumers' network traffic (NT) data, and 2) cyber-attack intervention. To establish an effective prevention mechanism, several challenges need to be tackled, for instance, the N

Qiyuan Yang, Pengda Wang, Luke D. Plonsky, Frederick L. Oswald

We examine the language capabilities of language models (LMs) from the critical perspective of human language acquisition. Building on classical language development theories, we propose a three-stage framework to assess the abilities of LMs, ranging from preliminary word understanding to complex grammar and complex logical reasoning. Using this framework, w

Xiangci Li, Jessica Ouyang

Retrieval-augmented generation (RAG) is a powerful method for enhancing natural language generation by integrating external knowledge into a model's output. While prior work has demonstrated the importance of improving knowledge retrieval for boosting generation quality, the role of knowledge selection, a.k.a. reranking or filtering, remains less clear. This

Dian Meng, Bohao Xing, Xinlei Huang, Yanran Liu

Single-cell multi-omics (scMulti-omics) refers to the paired multimodal data, such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), where the regulation of each cell was measured from different modalities, i.e. genes and proteins. scMulti-omics can reveal heterogeneity inside tumors and understand the distinct genetic properties

James W. Johnson, David H. Weinberg, Guillermo A. Blanc, Ana Bonaca

Metallicities of both gas and stars decline toward large radii in spiral galaxies, a trend known as the radial metallicity gradient. We quantify the evolution of the metallicity gradient in the Milky Way as traced by APOGEE red giants with age estimates from machine learning algorithms. Stars up to ages of $\sim$9 Gyr follow a similar relation between metall

Maria Levchenko

This paper investigates the application of translation alignment algorithms in the creation of a Multilingual Digital Edition (MDE) of Alessandro Manzoni's Italian novel "I promessi sposi" ("The Betrothed"), with translations in eight languages (English, Spanish, French, German, Dutch, Polish, Russian and Chinese) from the 19th and 20th centuries. We identif

Namrata Roy, Timothy Heckman, Alaina Henry, John Chisholm

The origin of Lyman Continuum (LyC) photons responsible for reionizing the universe remains a mystery, with the fraction of escaping LyC photons from galaxies at z$\sim$ 6 to 12 being highly uncertain. While direct detection of LyC photons from this epoch is hindered by absorption from the intergalactic medium, lower redshift analogs offer a promising avenue

Jintao Zhang, Mingyue Cheng, Xiaoyu Tao, Zhiding Liu

Time series forecasting models are becoming increasingly prevalent due to their critical role in decision-making across various domains. However, most existing approaches represent the coupled temporal patterns, often neglecting the distinction between their specific components. In particular, fluctuating patterns and smooth trends within time series exhibit

H. P. Zhang, Z. Song

We study the one-dimensional Bose-Hubbard model under the resonant condition, where a series of quantum slinky oscillations occur in a two-site system for boson numbers $n\in \lbrack 2,\infty )$. In the strong interaction limit, it can be shown that the quantum slinky motions become the dominant channels for boson propagation, which are described by a set of

Aleena Thomas, Abhijith Ajayakumar, Raju K. George

In this paper we extend the work in the conference paper 'On the Controllability and Observability of Heterogeneous Networked Systems with distinct node dimensions and inner-coupling matrices' wherein the controllability and observability of a heterogeneous networked system with distinct node dimensions were studied. This paper adds to the conference paper a

Soojong Kim

This study investigates how personal differences (digital self-efficacy, technical knowledge, belief in equality, political ideology) and demographic factors (age, education, and income) are associated with perceptions of artificial intelligence (AI) outcomes exhibiting gender and racial bias and with general attitudes towards AI. Analyses of a large-scale e

Miguel Corrales, Sean Berti, Bertrand Denel, Paul Williamson

In recent years, Full-Waveform Inversion (FWI) has been extensively used to derive high-resolution subsurface velocity models from seismic data. However, due to the nonlinearity and ill-posed nature of the problem, FWI requires a good starting model to avoid producing non-physical solutions. Moreover, conventional optimization methods fail to quantify the un

Ryotaro Shimizu, Takashi Wada, Yu Wang, Johannes Kruse

Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between the predicted and ground-truth explanations. However, this approach fails to consider one crucial aspect of the systems: whether their outputs accurately reflect the users' (post-pu

Chaofeng Zhang, Jia Hou, Xueting Tan, Gaolei Li

The advancement of large language model (LLM) based artificial intelligence technologies has been a game-changer, particularly in sentiment analysis. This progress has enabled a shift from highly specialized research environments to practical, widespread applications within the industry. However, integrating diverse AI models for processing complex multimoda

Caiqi Zhang, Ruihan Yang, Zhisong Zhang, Xinting Huang

Large language models (LLMs) often suffer from hallucinations, posing significant challenges for real-world applications. Confidence calibration, as an effective indicator of hallucination, is thus essential to enhance the trustworthiness of LLMs. Prior work mainly focuses on short-form tasks using a single response-level score (macro calibration), which is

Davide Batic, Denys Dutykh, Breno Loureiro Giacchini

In this paper, we undertake a comprehensive examination of quasinormal modes linked to Lee-Wick black holes, delving into scalar, electromagnetic, and gravitational perturbations using the spectral method. Such black holes can display a rich structure of horizons, and our analysis considers all the representative scenarios, including extremal and non-extrema

Ka Wai Ho, Ka Ho Yuen, Raphael Flauger, Alexei G. Kritsuk

We present results from a high-resolution interstellar turbulence simulation and show that it closely reproduces recent $Planck$ measurements. Our model captures the scaling of $EE$ and $BB$ spectra, and the $EE/BB$ ratio in the inertial range. The probability density function of the dust polarization fraction is also consistent with observations. The $TE$ c

Bijoy Dalal, Dibyendu Chakrabarty, Christina M. S. Cohen, Nandita Srivastava

Origin of energetic upstream ions propagating towards the Sun from the Earth's bow shock is not understood clearly. In this letter, relationship between solar wind suprathermal and upstream ions has been investigated by analyzing fluxes of H, 4He, and CNO obtained from multidirectional in-situ measurements at the first Lagrange point of the Sun-Earth system

Weiyi Zhang, Jiancheng Yang, Ruoyu Chen, Siyu Huang

Fundus fluorescein angiography (FFA) is crucial for diagnosing and monitoring retinal vascular issues but is limited by its invasive nature and restricted accessibility compared to color fundus (CF) imaging. Existing methods that convert CF images to FFA are confined to static image generation, missing the dynamic lesional changes. We introduce Fundus2Video,

Xiao-Dong Lin, Long Zhang

While non-Hermitian (NH) topological phases and phenomena have been observed across various quantum systems, directly measuring NH topological invariants remains a significant challenge. In this study, we present a generic and unified framework for the direct measurement of various NH topological invariants in odd-dimensional systems through quench dynamics.

Ting-Rui Chiang, Joshua Robinson, Xinyan Velocity Yu, Dani Yogatama

The ability to locate an object in an image according to natural language instructions is crucial for many real-world applications. In this work we propose LocateBench, a high-quality benchmark dedicated to evaluating this ability. We experiment with multiple prompting approaches, and measure the accuracy of several large vision language models. We find that

Yanpeng Jia, Ting Wang, Xieyuanli Chen, Shiliang Shao

Simultaneous state estimation and mapping is an essential capability for mobile robots working in dynamic urban environment. The majority of existing SLAM solutions heavily rely on a primarily static assumption. However, due to the presence of moving vehicles and pedestrians, this assumption does not always hold, leading to localization accuracy decreased an

Shashi Ranjan Sinha

Celikbas, Liang and Sadeghi established a one-sided inequality for the relative version of Jorgensen's dependency formula and questioned whether it would be an equality. In this paper, we show that the inequality can be indeed strict, and prove a relative dependency formula. Along the way, we obtain some bounds on s(M,N), a notion related to the vanishing of

Xuan Mao, Yuxiang Li

This paper is concerned with a quasilinear chemotaxis model with indirect signal production, $u_t = \nabla\cdot(D(u)\nabla u - S(u)\nabla v)$, $v_t = \Delta v - v + w$ and $w_t = \Delta w - w + u$, posed on a bounded smooth domain $\Omega\subset\mathbb R^n$, subjected to homogenerous Neumann boundary conditions, where nonlinear diffusion $D$ and sensitivity

Yiyi Chen, Qiongxiu Li, Russa Biswas, Johannes Bjerva

Language Confusion is a phenomenon where Large Language Models (LLMs) generate text that is neither in the desired language, nor in a contextually appropriate language. This phenomenon presents a critical challenge in text generation by LLMs, often appearing as erratic and unpredictable behavior. We hypothesize that there are linguistic regularities to this

Hani Alers, Aleksandra Malinowska, Mathis Mourey, Jasper Waaijer

This paper introduces SELAR, a framework designed to effectively help teachers integrate artificial intelligence (AI) into their curriculum. The framework was designed by running workshops organized to gather lecturers' feedback. In this paper, we assess the effectiveness of the framework through additional workshops organized with lecturers from the Hague U

Leon Zhou, Junfeng Yang, Chengzhi Mao

Large Language Models (LLMs) are increasingly used in a variety of important applications, yet their safety and reliability remain as major concerns. Various adversarial and jailbreak attacks have been proposed to bypass the safety alignment and cause the model to produce harmful responses. We introduce Self-supervised Prompt INjection (SPIN) which can detec

Kung-Yi Su, Priyamvada Natarajan, Hyerin Cho, Ramesh Narayan

Coupling black hole (BH) feeding and feedback involves interactions across vast spatial and temporal scales that is computationally challenging. Tracking gas inflows and outflows from kilo-parsec scales to the event horizon for non-spinning BHs in the presence of strong magnetic fields, Cho et al. (2023, 2024) report strong suppression of accretion on horizo

Nguyen Hoang Phuc, Nguyen Tri Toan Phuc, Do Cong Cuong

We perform a systematic study of inelastic nuclear rainbow scattering for the \oc system to the 2$^+$ (4.44 MeV) state of $^{12}$C at incident energies of 100--608 MeV with the coupled-channels method. The recently generalized nearside-farside decomposition for inelastic scattering was applied in combination with the multichannel deflection function analysis

Li Tan, Shizhong Hu, Shengrong Wang

In this article, we study the McKean-Vlasov neutral stochastic differential delay equations driven by fractional Brownian motion with super-linearly growing coefficients, where the Hurst exponent $H\in(1/2,1)$. The existence and uniqueness of the exact solution were shown by the Picard iteration. Besides, we propose a tamed theta Euler-Maruyama scheme for th

Hyungjoo Chae, Namyoung Kim, Kai Tzu-iunn Ong, Minju Gwak

Large language models (LLMs) have recently gained much attention in building autonomous agents. However, the performance of current LLM-based web agents in long-horizon tasks is far from optimal, often yielding errors such as repeatedly buying a non-refundable flight ticket. By contrast, humans can avoid such an irreversible mistake, as we have an awareness

Yuliya Mishura, Kostiantyn Ralchenko, Svitlana Kushnirenko

This paper studies two related stochastic processes driven by Brownian motion: the Cox-Ingersoll-Ross (CIR) process and the Bessel process. We investigate their shared and distinct properties, focusing on time-asymptotic growth rates, distance between the processes in integral norms, and parameter estimation. The squared Bessel process is shown to be a phase

Shengyao Zhuang, Shuai Wang, Fabio Zheng, Bevan Koopman

2D Matryoshka training enables a single embedding model to generate sub-network representations across different layers and embedding dimensions, offering adaptability to diverse computational and task constraints. However, its effectiveness remains well below that of individually trained models of equivalent sizes. To address this, we propose Starbucks, a n

Hung-Yueh Chiang, Chi-Chih Chang, Natalia Frumkin, Kai-Chiang Wu

State Space Models (SSMs) have emerged as an appealing alternative to Transformers for large language models, achieving state-of-the-art accuracy with constant memory complexity which allows for holding longer context lengths than attention-based networks. The superior computational efficiency of SSMs in long sequence modeling positions them favorably over T

Juan Diego Toscano, Vivek Oommen, Alan John Varghese, Zongren Zou

Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspe

Navin Agrawal-Chung, Zohran Moin

Landmine detection using traditional methods is slow, dangerous and prohibitively expensive. Using deep learning-based object detection algorithms drone videos is promising but has multiple challenges due to the small, soda-can size of recently prevalent surface landmines. The literature currently lacks scientific evaluation of optimal ML models for this pro

Rittwika Kansabanik, Adrian Barbu

This paper introduces a Video Quality Assessment (VQA) problem that has received little attention in the literature, called the latent resolution prediction problem. The problem arises when images or videos are upscaled from their native resolution and are reported as having a higher resolution than their native resolution. This paper formulates the problem,

Yiquan Wang

The route planning problem based on the greedy algorithm represents a method of identifying the optimal or near-optimal route between a given start point and end point. In this paper, the PCA method is employed initially to downscale the city evaluation indexes, extract the key principal components, and then downscale the data using the KMO and TOPSIS algori

Si-Yi Qiao, Qin-Tao Song

The tensor-polarized structures of the deuteron can be probed through the proton-deuteron Drell-Yan process, where the proton is unpolarized and the deuteron is tensor polarized. This measurement will be conducted at Fermilab in the near future. In this reaction, the twist-3 contribution is not negligible compared to the twist-2 contribution due to the limit

Matthew Ho, Vincent Zhu, Xiaoyin Chen, Moksh Jain

Reasoning is a fundamental substrate for solving novel and complex problems. Deliberate efforts in learning and developing frameworks around System 2 reasoning have made great strides, yet problems of sufficient complexity remain largely out of reach for open models. To address this gap, we examine the potential of Generative Flow Networks as a fine-tuning m

Bohan Zhang, Zhongkai Yi, Ying Xu, Zhenghong Tu

The complexity and nonlinearity of active distribution network (ADN), coupled with the fast-changing renewable energy (RE), necessitate advanced real-time and safe dispatch approach. This paper proposes a complementary reinforcement learning (RL) and optimization approach, namely SA2CO, to address the coordinated dispatch of the energy storage systems (ESSs)

Hongzhe Yu, Diana Frias Franco, Aaron M. Johnson, Yongxin Chen

This work addresses the problem of optimally steering the state covariance of a linear stochastic system from an initial to a target, subject to hybrid transitions. The nonlinear and discontinuous jump dynamics complicate the control design for hybrid systems. Under uncertainties, stochastic jump timing and state variations further intensify this challenge.

Chao Tan, Sheng Li, Yang Cao, Zhao Ren

Federated Learning (FL) is a privacy-preserving approach that allows servers to aggregate distributed models transmitted from local clients rather than training on user data. More recently, FL has been applied to Speech Emotion Recognition (SER) for secure human-computer interaction applications. Recent research has found that FL is still vulnerable to infer

Parthiban Santhanam, Daniel Cui, Jae Seung Hwang, David Abraham

Voltage conversion is a fundamental electronic process critical to engineered systems across a wide spectrum of applications and spanning many orders of magnitude in scale. Conventional approaches like transformers and charge pumps perform well in specific contexts but face fundamental limitations to miniaturization, electromagnetic interference, and voltage

Fan Liu, Tingting Zhang, Zenan Zhang, Bin Cao

This paper investigates a bi-static integrated sensing and communication (ISAC) system for multi-target scenarios using impulse radio ultra-wideband (IR-UWB) signals, which offer fine temporal resolution, low power consumption, and strong resistance to multipath interference. Two typical modulation schemes, namely pulse position modulation (PPM) and binary p

Mian Zhang, Xianjun Yang, Xinlu Zhang, Travis Labrum

There is a significant gap between patient needs and available mental health support today. In this paper, we aim to thoroughly examine the potential of using Large Language Models (LLMs) to assist professional psychotherapy. To this end, we propose a new benchmark, CBT-BENCH, for the systematic evaluation of cognitive behavioral therapy (CBT) assistance. We

Ruohan Wang, Zilong Wang, Ziyang Song, David Buckeridge

Automatic subphenotyping from electronic health records (EHRs)provides numerous opportunities to understand diseases with unique subgroups and enhance personalized medicine for patients. However, existing machine learning algorithms either focus on specific diseases for better interpretability or produce coarse-grained phenotype topics without considering nu

Luis Felipe Villa-Arenas, Ata Nizamoglu, Qianli Wang, Sebastian Möller

In this work, we introduce a methodology for alignment designed to enhance the ability of large language models (LLMs) to articulate their reasoning (self-explanation) even in the absence of annotated rationale explanations. Our alignment methodology comprises three key components: explanation quality assessment, self-instruction dataset generation, and mode

Alex Mallen, Nora Belrose

Scalable oversight studies methods of training and evaluating AI systems in domains where human judgment is unreliable or expensive, such as scientific research and software engineering in complex codebases. Most work in this area has focused on methods of improving the quality of labels. Recent work by Burns et al. (2023) considers the complementary problem

Kim Jae-Dong

The rapid expansion of the Internet of Things (IoT) has revolutionized various domains, offering significant benefits through enhanced interconnectivity and data exchange. However, the security challenges associated with IoT networks have become increasingly prominent owing to their inherent vulnerability. This paper provides an in-depth analysis of the netw

Shubham, Keichi Takahashi, Hiroyuki Takizawa

In the rapidly evolving domain of high-performance computing (HPC), heterogeneous architectures such as the SX-Aurora TSUBASA (SX-AT) system architecture, which integrate diverse processor types, present both opportunities and challenges for optimizing resource utilization. This paper investigates workload interference within an SX-AT system, with a specific

Caigao Jiang, Xiang Shu, Hong Qian, Xingyu Lu

Optimization problems are prevalent across various scenarios. Formulating and then solving optimization problems described by natural language often requires highly specialized human expertise, which could block the widespread application of optimization-based decision making. To automate problem formulation and solving, leveraging large language models (LLM

Qian Tao, Wenyuan Yu, Jingren Zhou

Large language models have shown exceptional capabilities in a wide range of tasks, such as text generation and video generation, among others. However, due to their massive parameter count, these models often require substantial storage space, imposing significant constraints on the machines deploying LLMs. To overcome this limitation, one research directio

Gabriel Wu, Jacob Hilton

We consider the problem of low probability estimation: given a machine learning model and a formally-specified input distribution, how can we estimate the probability of a binary property of the model's output, even when that probability is too small to estimate by random sampling? This problem is motivated by the need to improve worst-case performance, whic

Forrest Sheng Bao, Miaoran Li, Renyi Qu, Ge Luo

Summarization is one of the most common tasks performed by large language models (LLMs), especially in applications like Retrieval-Augmented Generation (RAG). However, existing evaluations of hallucinations in LLM-generated summaries, and evaluations of hallucination detection models both suffer from a lack of diversity and recency in the LLM and LLM familie

Soichi Tatsumi, Yutaka Nakamura, Takashi Miyazaki, Tomohiro Kayano

We have performed differential scanning calorimetric and synchrotron x-ray diffraction studies to elucidate the nature of vapor-deposited ultrastable phenolphthalein glass. As a result, we found that phenolphthalein forms the ultrastable glass by depositing at 313 K, which is about 0.86 times the ordinal glass transition temperature of 361 K. As previous ult

Jialin Li, Haowu Wang

Let $J_{1,m}(N)$ be the vector space of Jacobi forms of weight one and index $m$ on $\Gamma_0(N)$. In 1985, Skoruppa proved that $J_{1,m}(1)=0$ for all $m$. In 2007, Ibukiyama and Skoruppa proved that $J_{1,m}(N)=0$ for all $m$ and all squarefree $N$ with $\mathrm{gcd}(m,N)=1$. This paper aims to extend their results. We determine all levels $N$ separately,

Yan Dai, Aojia Xu, Gang Li, Mao Song

We conducted a theoretical study on the process $\Sigma^{+} p \to\Lambda a_0^{+} p$ based on an effective Lagrangian approach. This model encompasses the excitation of intermediate states leading to the production of $\Delta(1920)$ through $\pi^{+}$ and $K^{+}$ meson exchanges between the initial $\Sigma^{+}$ baryon and the initial proton $p$, as well as the

Daegene Song

Quantum superposition, a cornerstone of quantum mechanics, enables systems to exist in multiple states simultaneously, giving rise to probabilistic outcomes. In quantum information science, conditional entropy has become a key metric for quantifying uncertainty in one system given information about another, revealing non-classical correlations that transcend