December 2024 arXiv papers — page 54

Cristian Cobeli, Mihai Prunescu, Alexandru Zaharescu

Let ${\nu}_q(n)$ be the p-adic valuation of $n$. We show that the power series with coefficients ${\nu}_q(n)$, respectively ${\nu}_p(n)(\mathrm{ mod\;} k)$, are non-holonomic and not algebraic in characteristic 0. We find infinitely many rational numbers and infinitely many algebraic irrational numbers for which the values of these series are transcendental.

Tomoyuki Tokuue, Tomoaki Ishiyama, Ken Osato, Satoshi Tanaka

MPI-Rockstar is a massively parallel halo finder based on the Rockstar phase-space temporal halo finder code, which is one of the most extensively used halo finding codes. Compared to the original code, parallelized by a primitive socket communication library, we parallelized it in a hybrid way using MPI and OpenMP, which is suitable for analysis on the hybr

Jun Wang, Jiamu Zhou, Muning Wen, Xiaoyun Mo

Evaluating the performance of LLMs in multi-turn human-agent interactions presents significant challenges, particularly due to the complexity and variability of user behavior. In this paper, we introduce HammerBench, a novel benchmark framework for assessing LLMs' function-calling capabilities in real-world, multi-turn dialogues. HammerBench simulates divers

Wenjie Xi, Rundong Zuo, Alejandro Alvarez, Jie Zhang

Multivariate time series classification is a crucial task in data mining, attracting growing research interest due to its broad applications. While many existing methods focus on discovering discriminative patterns in time series, real-world data does not always present such patterns, and sometimes raw numerical values can also serve as discriminative featur

Ammar Daskin

Since the final quantum state in the Grover search algorithm is the normalized marked quantum state from the Gram-Schmidt process, Abrams and Lloyd [1] has shown that we can generate this vector by using a non-unitary gate. Following their ideas, in this paper, we present multiple explicit unitary implementations by using the square root of the non-unitary m

Jia-Jie Li, Yu Tian, Armen Sedrakian

Recent advancements in astrophysical observations of compact stars, particularly the new and updated NICER constraints, have provided mass-radius ($M$-$R$) data for pulsars spanning masses from 1 to $2\,M_{\odot }$. These data offer a unique opportunity to test modern theories of dense matter using multi-messenger constraints. Covariant density functional (C

Zhengyang Qi, Xiaohua Xu

Flow-based generative models (FMs) have rapidly advanced as a method for mapping noise to data, its efficient training and sampling process makes it widely applicable in various fields. FMs can be viewed as a variant of diffusion models (DMs). At the same time, previous studies have shown that DMs are vulnerable to Trojan/Backdoor attacks, a type of output m

Keon Moradi, Ethan Haque, Jasmeen Kaur, Alexandra B. Bentz

This paper presents a novel approach for robust 3D tracking of multiple birds in an outdoor aviary using a multi-camera system. Our method addresses the challenges of visually similar birds and their rapid movements by leveraging environmental landmarks for enhanced feature matching and 3D reconstruction. In our approach, outliers are rejected based on their

Søren Toxvaerd

A recent article in J. Chem. Phys. argues that the two algorithms, the velocity-Verlet, and position-Verlet integrators, commonly used in Molecular Dynamics (MD) simulations, are different \cite{Ni2024}. But not only are the two algorithms just different formulations of the same discrete algorithm, but so are other simple discrete algorithms used in MD in th

Taushif Ahmed, Saurav Goyal, Syed Mehedi Hasan, Roman N. Lee

We evaluate the phase-space integrals that arise in double real emission diagrams for semi-inclusive deep-inelastic scattering at next-to-next-to-leading order (NNLO) in QCD. Utilizing the reverse unitarity technique, we convert these integrals into loop integrals, allowing us to employ integration-by-parts identities and reduce them to a set of master integ

Xingsheng Li, Jing Li

Worthington jets ejected after the impact of a solid or liquid object on a liquid surface have extensive applications in natural, industrial, and scientific contexts. Here, we present a combined experimental and theoretical investigation of the jet generated by sphere impact with no cavity formed. Experiments identify three distinct pinch-off modes, whose re

Jiahui Zhao, Hao Shi, Chenrui Cui, Tianrui Wang

Code-switching (CS) automatic speech recognition (ASR) faces challenges due to the language confusion resulting from accents, auditory similarity, and seamless language switches. Adaptation on the pre-trained multi-lingual model has shown promising performance for CS-ASR. In this paper, we adapt Whisper, which is a large-scale multilingual pre-trained speech

Linfeng Qi, Huibing Wang, Jiqing Zhang, Jinjia Peng

Unsupervised Domain Adaptive (UDA) person search focuses on employing the model trained on a labeled source domain dataset to a target domain dataset without any additional annotations. Most effective UDA person search methods typically utilize the ground truth of the source domain and pseudo-labels derived from clustering during the training process for dom

Jifu Lin, Zenan Du, Xinghui Zhao, Lihua You

Let $G$ be a connected graph of order $n$. A spanning $k$-tree of $G$ is a spanning tree with the maximum degree at most $k$, and a spanning $k$-ended-tree of $G$ is a spanning tree at most $k$ leaves, where $k\geq2$ is an integer. This paper establishes some spectral conditions for the existence of spanning $k$-trees or spanning $k$-ended-trees in $t$-conne

Hao Du, Shang Liu, Lele Zheng, Yang Cao

Fine-tuning has emerged as a critical process in leveraging Large Language Models (LLMs) for specific downstream tasks, enabling these models to achieve state-of-the-art performance across various domains. However, the fine-tuning process often involves sensitive datasets, introducing privacy risks that exploit the unique characteristics of this stage. In th

Qiang Hu, Mei Liu, Qiang Li, Zhiwei Wang

Automatic video polyp segmentation plays a critical role in gastrointestinal cancer screening, but the cost of frameby-frame annotations is prohibitively high. While sparse-frame supervised methods have reduced this burden proportionately, the cost remains overwhelming for long-duration videos and large-scale datasets. In this paper, we, for the first time,

Shuyuan Zhao, Wei Chen, Boyan Shi, Liyong Zhou

The takeaway recommendation system aims to recommend users' future takeaway purchases based on their historical purchase behaviors, thereby improving user satisfaction and boosting merchant sales. Existing methods focus on incorporating auxiliary information or leveraging knowledge graphs to alleviate the sparsity issue of user purchase sequences. However, t

Jin-Xing Cai, Wenfei Liu

Let $S$ be a regular minimal surface of general type over the field of complex numbers, and $\mathrm{Aut}_\mathbb{Q}(S)$ the subgroup of automorphisms acting trivially on $H^*(S,\mathbb{Q})$. It has been known since twenty years that $|\mathrm{Aut}_\mathbb{Q}(S)|\leq 4$ if the invariants of $S$ are sufficiently large. Under the assumption that $K_S$ is ample

Do June Min, Karel Mundnich, Andy Lapastora, Erfan Soltanmohammadi

One common approach for question answering over speech data is to first transcribe speech using automatic speech recognition (ASR) and then employ text-based retrieval-augmented generation (RAG) on the transcriptions. While this cascaded pipeline has proven effective in many practical settings, ASR errors can propagate to the retrieval and generation steps.

Chinmay Makarand Pimpalkhare, D. N. Pawaskar

In a lot of scientific problems, there is the need to generate data through the running of an extensive number of experiments. Further, some tasks require constant human intervention. We consider the problem of crack detection in steel plates. The way in which this generally happens is through humans looking at an image of the thermogram generated by heating

J. P. Velasquez-Rodriguez

Let p> 2 be a prime number, and let G be a compact nilpotent p-adic Lie group with nilpotency class N<p. In this note we calculate explicitly the unitary dual and the matrix coefficients of every compact nilpotent-adic Lie group with dimension less or equal than 5. As an application, we provide the corresponding spectral theorem for the Vladimirov sub-Laplac

Rotan Hawlader Pranto, Shahnewaz Siddique

The human body communicates through various meaningful gestures, with sign language using hands being a prominent example. Bangla Sign Language Translation (BSLT) aims to bridge communication gaps for the deaf and mute community. Our approach involves using Mediapipe Holistic to gather key points, LSTM architecture for data training, and Computer Vision for

Chenwei Gu, Qian Wu, Zeqi Lai, Hewu Li

Emerging satellite Internet constellations such as SpaceX's Starlink will deploy thousands of broadband satellites and construct Low-Earth Orbit(LEO) satellite networks(LSNs) in space, significantly expanding the boundaries of today's terrestrial Internet. However, due to the unique global LEO dynamics, satellite routers will inevitably pass through uncontro

Xin Huang, Tarun Kumar Vangani, Minh Duc Pham, Xunlong Zou

Multilingual large language models (MLLMs) have shown impressive capabilities across a variety of languages. However, efficacy can differ greatly between different language families, especially for those with limited linguistic resources. This report presents MERaLiON-TextLLM, a series of open-source language models specifically tailored to improve understan

Beiyuan Zhang, Yue Ma, Chunlei Fu, Xinyang Song

Text-editable and pose-controllable character video generation is a challenging but prevailing topic with practical applications. However, existing approaches mainly focus on single-object video generation with pose guidance, ignoring the realistic situation that multi-character appear concurrently in a scenario. To tackle this, we propose a novel multi-char

Mohsen Nejad-Asghar

Thermal instability (TI) is a trigger mechanism, which can explain the formation of small condensations through some regions of the interstellar clouds. The instability criterion for flat geometry approximations has been investigated in previous works. Here, we focus on spherical perturbations in the spherical clouds. Our goal here is to examine the conditio

Weijia Zhang, Dongnan Liu, Weidong Cai, Chao Ma

Knowledge distillation (KD) is an established paradigm for transferring privileged knowledge from a cumbersome model to a lightweight and efficient one. In recent years, logit-based KD methods are quickly catching up in performance with their feature-based counterparts. However, previous research has pointed out that logit-based methods are still fundamental

Sangwook Tae

We prove that twisting and filamentation occur near a family of stable steady states for one dimensional periodic Vlasov-Poisson system, describing the electron dynamics under a fixed ion background. More precisely, we establish the growth in time of the L1 norm of the gradient for the electron distribution function and the corresponding flow map in the phas

Seungdong Yoa, Seungjun Lee, Hyeseung Cho, Bumsoo Kim

Vision Transformers (ViTs) have achieved remarkable success in various computer vision tasks. However, ViTs have a huge computational cost due to their inherent reliance on multi-head self-attention (MHSA), prompting efforts to accelerate ViTs for practical applications. To this end, recent works aim to reduce the number of tokens, mainly focusing on how to

Aidan Toner-Rodgers

This paper studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. Thes

Jiayi Chen, Yubin Ke, He Wang

Robotic dexterous grasping is important for interacting with the environment. To unleash the potential of data-driven models for dexterous grasping, a large-scale, high-quality dataset is essential. While gradient-based optimization offers a promising way for constructing such datasets, previous works suffer from limitations, such as inefficiency, strong ass

Abedin Sherifi

Recent advancements in artificial intelligence (AI) applications within aerospace have demonstrated substantial growth, particularly in the context of control systems. As High Performance Computing (HPC) platforms continue to evolve, they are expected to replace current flight control or engine control computers, enabling increased computational capabilities

Wentao Ma, Zhiping Chen, Huifu Xu

Inspired by Shapiro et al.~\cite{shapiro2023episodic}, we consider a stochastic optimal control (SOC) and Markov decision process (MDP) where the risks arising from epistemic and aleatoric uncertainties are assessed using Bayesian composite risk (BCR) measures (Qian et al.~\cite{qian2019composite}). The time dependence of the risk measures allows us to captu

Jian Zhu, Xin Zou, Lei Liu, Zhangmin Huang

Multi-view clustering can partition data samples into their categories by learning a consensus representation in an unsupervised way and has received more and more attention in recent years. However, there is an untrusted fusion problem. The reasons for this problem are as follows: 1) The current methods ignore the presence of noise or redundant information

Tanjim Bin Faruk

The pervasive influence of social media during the COVID-19 pandemic has been a double-edged sword, enhancing communication while simultaneously propagating misinformation. This \textit{Digital Infodemic} has highlighted the urgent need for automated tools capable of discerning and disseminating factual content. This study evaluates the efficacy of Large Lan

Xiaowei Ye, Rong-Hua Li, Longlong Lin, Shaojie Qiao

Counting the number of $(p, q)$-bicliques (complete bipartite subgraphs) in a bipartite graph is a fundamental problem which plays a crucial role in numerous bipartite graph analysis applications. However, existing algorithms for counting $(p, q)$-bicliques often face significant computational challenges, particularly on large real-world networks. In this pa

Tanjim Bin Faruk

The vast majority of cybersecurity information is unstructured text, including critical data within databases such as CVE, NVD, CWE, CAPEC, and the MITRE ATT&CK Framework. These databases are invaluable for analyzing attack patterns and understanding attacker behaviors. Creating a knowledge graph by integrating this information could unlock significant insig

Jingjing Hu, Dan Guo, Zhan Si, Deguang Liu

Molecular representation learning plays a crucial role in various downstream tasks, such as molecular property prediction and drug design. To accurately represent molecules, Graph Neural Networks (GNNs) and Graph Transformers (GTs) have shown potential in the realm of self-supervised pretraining. However, existing approaches often overlook the relationship b

Shyam Venkatasubramanian, Vahid Tarokh

Accelerating model convergence in resource-constrained environments is essential for fast and efficient neural network training. This work presents learn2mix, a new training strategy that adaptively adjusts class proportions within batches, focusing on classes with higher error rates. Unlike classical training methods that use static class proportions, learn

Liyan Chen, Gregory P. Meyer, Zaiwei Zhang, Eric M. Wolff

Recent efforts recognize the power of scale in 3D learning (e.g. PTv3) and attention mechanisms (e.g. FlashAttention). However, current point cloud backbones fail to holistically unify geometric locality, attention mechanisms, and GPU architectures in one view. In this paper, we introduce Flash3D Transformer, which aligns geometric locality and GPU tiling th

Kai Wu, Zhihua Chen, Zhen-Peng Xu, Zhihao Ma

Multipartite entanglement is a crucial resource for a wide range of quantum information processing tasks, including quantum metrology, quantum computing, and quantum communication. The verification of multipartite entanglement, along with an understanding of its intrinsic structure, is of fundamental importance, both for the foundations of quantum mechanics

Xing Gao, Nannan Li, Dominique Manchon

The universal limit theorem is a central result in rough path theory, which has been proved for: (i) rough paths with roughness $\frac{1}{3}< \alpha \leq \frac{1}{2}$; (ii) geometric rough paths with roughness $0< \alpha \leq 1$; (iii) branched rough paths with roughness $0< \alpha \leq 1$. Planarly branched rough paths are natural generalizations of both ro

Yunxiang Yang, Hao Zhen, Yongcan Huang, Jidong J. Yang

Existing deep learning-based object detection models perform well under daytime conditions but face significant challenges at night, primarily because they are predominantly trained on daytime images. Additionally, training with nighttime images presents another challenge: even human annotators struggle to accurately label objects in low-light conditions. Th

Motohiko Ezawa

The bulk photovoltaic effect is a photocurrent generation from alternating electric field, which is a promising candidate for future efficient solar cell technology. It is the second-order optical current, which is the injection current or the shift current. We focus on the direct current generation. By employing a simple two-band model of the $d$-wave alter

Sijia Jiang, Jing Hua, Zhizhong Han

Neural implicit representations have shown remarkable abilities in jointly modeling geometry, color, and camera poses in simultaneous localization and mapping (SLAM). Current methods use coordinates, positional encodings, or other geometry features as input to query neural implicit functions for signed distances and color which produce rendering errors to dr

Yuchen Zhu, Daniel Augusto de Souza, Zhengyan Shi, Mengyue Yang

We address the problem of reward hacking, where maximising a proxy reward does not necessarily increase the true reward. This is a key concern for Large Language Models (LLMs), as they are often fine-tuned on human preferences that may not accurately reflect a true objective. Existing work uses various tricks such as regularisation, tweaks to the reward mode

Shao-Syuan Huang, Kuan-Po Huang, Andy T. Liu, Hung-yi Lee

Multilingual Automatic Speech Recognition (ASR) aims to recognize and transcribe speech from multiple languages within a single system. Whisper, one of the most advanced ASR models, excels in this domain by handling 99 languages effectively, leveraging a vast amount of data and incorporating language tags as prefixes to guide the recognition process. However

Jon Crall

Small, amorphous waste objects such as biological droppings and microtrash can be difficult to see, especially in cluttered scenes, yet they matter for environmental cleanliness, public health, and autonomous cleanup. We introduce "ScatSpotter": a new dataset of images annotated with polygons around dog feces, collected to train and study object detection an

Hugo A. Camargo, Yichao Fu, Viktor Jahnke, Keun-Young Kim

In quantum many-body systems, time-evolved states typically remain confined to a smaller region of the Hilbert space known as the $\textit{Krylov subspace}$. The time evolution can be mapped onto a one-dimensional problem of a particle moving on a chain, where the average position $\langle n \rangle$ defines Krylov state complexity or spread complexity. Gene

Noella D'Souza, Kieren A. Harkins, Cooper Selco, Ushoshi Basumallick

Optical dynamic nuclear polarization (DNP) offers an attractive approach to enhancing the sensitivity of nuclear magnetic resonance (NMR) spectroscopy. Efficient, optically-generated electron polarization can be leveraged to operate across a broad range of temperatures and magnetic fields, making it particularly appealing for applications requiring high DNP

Belle, Belle II Collaborations, :, I. Adachi

We present the first search for the lepton flavor-violating decay modes $B^0 \rightarrow K_S^0 \tau^\pm \ell^\mp~(\ell=\mu, e)$ using the 711 fb$^{-1}$ and 365 fb$^{-1}$ data samples recorded by the Belle and Belle II detectors, respectively. We use a hadronic $B$-tagging technique, and search for the signal decay in the system recoiling against the fully re

Megan Ung, Alicia Sun, Samuel J. Bell, Bhaktipriya Radharapu

Large language models (LLMs) are often fine-tuned for use on downstream tasks, though this can degrade capabilities learned during previous training. This phenomenon, often referred to as catastrophic forgetting, has important potential implications for the safety of deployed models. In this work, we first show that models trained on downstream tasks forget

Sijia Jiang, Tong Wu, Jing Hua, Zhizhong Han

It is vital to recover 3D geometry from multi-view RGB images in many 3D computer vision tasks. The latest methods infer the geometry represented as a signed distance field by minimizing the rendering error on the field through volume rendering. However, it is still challenging to explicitly impose constraints on surfaces for inferring more geometry details

Hao Wu, Xin Li, Wang Gao, Qing Jiang

Solute segregation at grain boundaries (GBs) of polycrystals strongly impacts the mechanical properties of metals including strength, fracture, embrittlement, and corrosion. However, the complexity of GB structures and the large chemical space of solutes and matrices impede the understanding of segregation. Herein, we identify a physical-based determinant, b

Xiaoling He, Fuliang Lu, Jinxin Xue

A connected graph G with at least two vertices is matching covered if each of its edges lies in a perfect matching. We say that an edge e in a matching covered graph G is removable if G-e is matching covered. A pair {e; f} of edges of a matching covered graph G is a removable doubleton if G-e-f is matching covered, but neither G-e nor G-f is. Removable edges

Keqi Deng, Jinxi Guo, Yingyi Ma, Niko Moritz

While large language models (LLMs) have been applied to automatic speech recognition (ASR), the task of making the model streamable remains a challenge. This paper proposes a novel model architecture, Transducer-Llama, that integrates LLMs into a Factorized Transducer (FT) model, naturally enabling streaming capabilities. Furthermore, given that the large vo

Xinpeng Wang, Xiao-Han Ma, Misao Sasaki

We conduct a thorough study of the comoving curvature perturbation $\mathcal{R}$ in single-field inflation with two stages, represented by a piecewise quadratic potential, where both the first and second derivatives are allowed to be discontinuous at the transition point. We calculate the evolution of $\mathcal{R}$ by combining the perturbative and non-pertu

Govinda Anantha Padmanabha, Cosmin Safta, Nikolaos Bouklas, Reese E. Jones

We propose a Stein variational gradient descent method to concurrently sparsify, train, and provide uncertainty quantification of a complexly parameterized model such as a neural network. It employs a graph reconciliation and condensation process to reduce complexity and increase similarity in the Stein ensemble of parameterizations. Therefore, the proposed

Tetsuya Takahashi, Christopher Batty

We propose a parameter optimization method for achieving static equilibrium of discrete elastic rods. Our method simultaneously optimizes material stiffness and rest shape parameters under box constraints to exactly enforce zero net force while avoiding stability issues and violations of physical laws. For efficiency, we split our constrained optimization pr

Tong Li, Lizhi Wang, Zhiyuan Xu, Lin Zhu

Image denoising enhances image quality, serving as a foundational technique across various computational photography applications. The obstacle to clean image acquisition in real scenarios necessitates the development of self-supervised image denoising methods only depending on noisy images, especially a single noisy image. Existing self-supervised image den

Tong Li, Lizhi Wang, Hansen Feng, Lin Zhu

Low-light image enhancement (LLIE) is a fundamental task in computational photography, aiming to improve illumination, reduce noise, and enhance the image quality of low-light images. While recent advancements primarily focus on customizing complex neural network models, we have observed significant redundancy in these models, limiting further performance im

Amir Ayati, Hugh G. A. Burton, Stijn De Baerdemacker

We introduce an electronic structure approach for spin symmetry breaking and restoration from the mean-field level. The spin-projected constrained-unrestricted Hartree-Fock (SPcUHF) method restores the broken spin symmetry inherent in spin-constrained-UHF determinants by employing a non-orthogonal Configuration Interaction (NOCI) projection method. This meth

Xuemin Chi, Yiming Li, Jihao Huang, Bolun Dai

Generating collision-free motions in dynamic environments is a challenging problem for high-dimensional robotics, particularly under real-time constraints. Control Barrier Functions (CBFs), widely utilized in safety-critical control, have shown significant potential for motion generation. However, for high-dimensional robot manipulators, existing QP formulat

Yongsheng Yang, Xiaoying Wang

In the digital age of today, the internet has become an indispensable platform for people's lives, work, and information exchange. However, the problem of violent text proliferation in the network environment has arisen, which has brought about many negative effects. In view of this situation, it is particularly important to build an effective system for cut

Yu Luo, Lina Pu, Junming Diao, Chun-Hung Liu

This article explores the potential of underwater acoustic reconfigurable intelligent surfaces (UA-RIS) for facilitating long-range and eco-friendly communication in marine environments. Unlike radio frequency-based RIS (RF-RIS), which have been extensively investigated in terrestrial contexts, UA-RIS is an emerging field of study. The distinct characteristi

Christopher Lai, Jason Mo, Haotian Xia, Yuan-fang Wang

Classifying fine-grained actions in fast-paced, close-combat sports such as fencing and boxing presents unique challenges due to the complexity, speed, and nuance of movements. Traditional methods reliant on pose estimation or fancy sensor data often struggle to capture these dynamics accurately. We introduce FACTS, a novel transformer-based approach for fin

Irene Hou, Hannah Vy Nguyen, Owen Man, Stephen MacNeil

Help-seeking is a critical aspect of learning and problem-solving for computing students. Recent research has shown that many students are aware of generative AI (GenAI) tools; however, there are gaps in the extent and effectiveness of how students use them. With over two years of widespread GenAI usage, it is crucial to understand whether students' help-see

Flora C. Shi, Stephen Bates, Martin J. Wainwright

Statistical protocols are often used for decision-making involving multiple parties, each with their own incentives, private information, and ability to influence the distributional properties of the data. We study a game-theoretic version of hypothesis testing in which a statistician, also known as a principal, interacts with strategic agents that can gener

Han Zhang, Zhuo Zhang, Yi Zhang, Yuanzhao Zhai

Recent advancements in AI alignment techniques have significantly improved the alignment of large language models (LLMs) with static human preferences. However, the dynamic nature of human preferences can render some prior training data outdated or even erroneous, ultimately causing LLMs to deviate from contemporary human preferences and societal norms. Exis

En-Jui Chang, Ching-Yi Lai

We introduce a family of high-rate amplitude-damping (AD) Shor Codes, designed to effectively correct AD errors while maintaining immunity to collective coherent (CC) errors. The proposed $[[(w+1)(w+K), K]]$ AD codes can approximately correct up to $w$ AD errors, with flexible parameters $(w, K)$, and we provide a rigorous proof that these codes satisfy the

Benchang Dong, Zhili Chen, Xin Chen, Shiwen Wei

Binarized Neural Networks (BNN) offer efficient implementations for machine learning tasks and facilitate Privacy-Preserving Machine Learning (PPML) by simplifying operations with binary values. Nevertheless, challenges persist in terms of communication and accuracy in their application scenarios. In this work, we introduce CBNN, a three-party secure computa

Xiao Yang, Xuejiao Zhao, Zhiqi Shen

Anomaly detection aims to identify deviations from normal patterns within data. This task is particularly crucial in dynamic graphs, which are common in applications like social networks and cybersecurity, due to their evolving structures and complex relationships. Although recent deep learning-based methods have shown promising results in anomaly detection

Hanxian He, Campbell Wilson, Thanh Thi Nguyen, Janis Dalins

When it comes to classifying child sexual abuse images, managing similar inter-class correlations and diverse intra-class correlations poses a significant challenge. Vision transformer models, unlike conventional deep convolutional network models, leverage a self-attention mechanism to capture global interactions among contextual local elements. This allows

Shengkun Yang, Zhichang Guo, Jia Li, Fanghui Song

This paper focuses on solving the multiplicative gamma denoising problem via a variation model. Variation-based regularization models have been extensively employed in a variety of inverse problem tasks in image processing. However, sufficient geometric priors and efficient algorithms are still very difficult problems in the model design process. To overcome

Cuong Tran Van, Thanh V. T. Tran, Van Nguyen, Truong Son Hy

Emotion Recognition in Conversations (ERC) facilitates a deeper understanding of the emotions conveyed by speakers in each utterance within a conversation. Recently, Graph Neural Networks (GNNs) have demonstrated their strengths in capturing data relationships, particularly in contextual information modeling and multimodal fusion. However, existing methods o

Charles Luo

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their scalability raises a critical question: Have we reached the scaling ceiling? This paper addresses this pivotal question by developing a unified theoretical framework that integrates mathematical and statistical insights to explain the scaling dynamics of LLMs. We present: 1. Ce

Karthik S. Vedula, Annika Gupta, Akshay Swaminathan, Ivan Lopez

Large language models (LLMs) excel at clinical information extraction but their computational demands limit practical deployment. Knowledge distillation--the process of transferring knowledge from larger to smaller models--offers a potential solution. We evaluate the performance of distilled BERT models, which are approximately 1,000 times smaller than moder

Fathi Said Emhemed Shaninah, AbdulRahman M. A. Baraka, Mohd Halim Mohd Noor

Tabular data is a common format for storing information in rows and columns to represent data entries and their features. Although deep neural networks have become the main approach for modeling a wide range of domains including computer vision and NLP, many of them are not well-suited for tabular data. Recently, a few deep learning models have been proposed

Zehong Wang, Zheyuan Zhang, Tianyi Ma, Nitesh V Chawla

Foundation models are pretrained on large-scale corpora to learn generalizable patterns across domains and tasks -- such as contours, textures, and edges in images, or tokens and sentences in text. In contrast, discovering such generalities in graph-structured data, especially across heterogeneous graph tasks, remains an open challenge. To address this, we p

Pei-Cheng Tung, Ke-Jung Chen

Dwarf galaxies are thought of as the building blocks of large galaxies such as our Milky Way. This paper presents new high-resolution hydrodynamical simulations of dwarf galaxies and their intergalactic medium with the \texttt{GIZMO} code. Our simulations consider the key physical processes of galaxy evolution, such as gas cooling, chemistry, and stellar and

Yang-Hao Chan, Zhenglu Li, Steven G. Louie

We develop a first-principles approach to compute infrared (IR) vibrational absorption and Raman scattering spectra with excitonic effects included. Our method is based on a perturbative expansion of electron-phonon and electron-light couplings in the time-dependent adiabatic GW (TD-aGW) theory. We show that excitonic effects in the IR absorption spectrum ca

Stephen Langdon, David J. Needham

As a helicopter descends towards a bed of sand, a high velocity particle laden cloud can form around the helicopter body, a phenomenon known as "brownout", and a consequence of which can potentially be a significant deterioration in visibility for the helicopter pilot. Here we consider a recently developed physically based rational mathematical model for the

Xinying Deng, Yong Li, Xue Yang

Through certain appropriate constructions, we establish periodic solutions in distribution for some stochastic differential equations with infinite-dimensional Levy noise. Additionally, we obtain the corresponding periodic measures and periodic transition semigroup. Under suitable conditions, we also achieve a certain contractivity in the space of probabilit

Ulrich Horst, Wei Xu, Rouyi Zhang

We consider a microstructure foundation for rough volatility models driven by Poisson random measures. In our model the volatility is driven by self-exciting arrivals of market orders as well as self-exciting arrivals of limit orders and cancellations. The impact of market order on future order arrivals is captured by a Hawkes kernel with power law decay, an

Yuchen Yan, Yuzhong Chen, Huiyuan Chen, Xiaoting Li

Graph Neural Networks (GNNs) have exhibited remarkable efficacy in diverse graph learning tasks, particularly on static homophilic graphs. Recent attention has pivoted towards more intricate structures, encompassing (1) static heterophilic graphs encountering the edge heterophily issue in the spatial domain and (2) event-based continuous graphs in the tempor

Saurabh Agarwal, Anyong Mao, Aditya Akella, Shivaram Venkataraman

Large Language Models (LLMs) are increasingly being deployed in applications such as chatbots, code editors, and conversational agents. A key feature of LLMs is their ability to engage in multi-turn interactions with humans or external tools, enabling a wide range of tasks. Each new request in a multi-turn interaction depends on the intermediate state, speci

Jakob Baumsteiger, Lorenzo Celiberti, Patrick Rinke, Milica Todorović

The investigation of magnetic energy landscapes and the search for ground states of magnetic materials using ab initio methods like density functional theory (DFT) is a challenging task. Complex interactions, such as superexchange and spin-orbit coupling, make these calculations computationally expensive and often lead to non-trivial energy landscapes. Conse

Sho Ko, Nathan Zhang, Olivia Hsu, Ardavan Pedram

We propose DFModel, a modeling framework for mapping dataflow computation graphs onto large-scale systems. Mapping a workload to a system requires optimizing dataflow mappings at various levels, including the inter-chip (between chips) level and the intra-chip (within a chip) level. DFModel is, to the best of our knowledge, the first framework to perform the

Thanh Thi Nguyen, Campbell Wilson, Imad Khan, Janis Dalins

Forensic science plays a crucial role in legal investigations, and the use of advanced technologies, such as object detection based on machine learning methods, can enhance the efficiency and accuracy of forensic analysis. Human hands are unique and can leave distinct patterns, marks, or prints that can be utilized for forensic examinations. This paper compa

Jason Landsborough, Neil C. Rowe, Thuy D. Nguyen, Sunny Fugate

Deception is being increasingly explored as a cyberdefense strategy to protect operational systems. We are studying implementation of deception-in-depth strategies with initially three logical layers: network, host, and data. We draw ideas from military deception, network orchestration, software deception, file deception, fake honeypots, and moving-target de

LearnLM Team, Abhinit Modi, Aditya Srikanth Veerubhotla, Aliya Rysbek

Today's generative AI systems are tuned to present information by default, rather than engage users in service of learning as a human tutor would. To address the wide range of potential education use cases for these systems, we reframe the challenge of injecting pedagogical behavior as one of \textit{pedagogical instruction following}, where training and eva

Jiapeng Xu

Many studies have revealed that sentence comprehension relies more on semantic processing than on syntactic processing. However, previous studies have predominantly emphasized the preference for semantic processing, focusing on the semantic perspective. In contrast, this current study highlights the under-utilization of syntactic processing, from a syntactic

Uzoamaka Ezeakunne, Chrisantus Eze, Xiuwen Liu

Despite the progress made in deepfake detection research, recent studies have shown that biases in the training data for these detectors can result in varying levels of performance across different demographic groups, such as race and gender. These disparities can lead to certain groups being unfairly targeted or excluded. Traditional methods often rely on f

Mark A. Keppler, Sean P. O'Connor, Zachary A. Steelman, Xianglei Liu

Compressed streak imaging (CSI) is a computational imaging strategy that can acquire video at over 150 trillion frames per second. Despite this achievement, CSI faces challenges in detecting subtle intensity fluctuations in slow-moving, continuously illuminated objects. This limitation, largely attributable to high streak compression and motion blur, has cur

Dan Abramovich, Ming Hao Quek, Bernd Schober

Stack-theoretic blow-ups have proven to be efficient in resolving singularities over fields of characteristic zero. In this article, we move forward towards positive characteristic where new challenges arise. In particular, the dimension of the tangent space of the Artin stack created after a weighted blow-up may increase, which makes it hard to apply induct

Dejan Štepec, Maja Jerše, Snežana Đokić, Jera Jeruc

We present Patherea, a unified framework for point-based cell detection and classification that enables the development and fair evaluation of state-of-the-art methods. To support this, we introduce a large-scale dataset that replicates the clinical workflow for Ki-67 proliferation index estimation. Our method directly predicts cell locations and classes wit

Adriano Cherchiglia, Macello Jales, Guilherme Nogueira, Maressa P. Sampaio

Research in neutrino physics has been very active, both in experimental advances, with a new generation of detectors in operation and planning, and in theoretical discussions regarding the fundamental nature of the neutrino. This scientific dynamism has attracted many new students to the field. One of the first topics studied in neutrino physics by newcomers

Shogo Watanabe

This report presents a small language model (SLM) for Japanese clinical and medicine, named NCVC-slm-1. This 1B parameters model was trained using Japanese text classified to be of high-quality. Moreover, NCVC-slm-1 was augmented with respect to clinical and medicine content that includes the variety of diseases, drugs, and examinations. Using a carefully de

Reinhard M. Grassmann, Jessica Burgner-Kahrs

We present a framework based on Clarke coordinates for spatial displacement-actuated continuum robots with an arbitrary number of joints. This framework consists of three modular components, i.e., a planner, trajectory generator, and controller defined on the manifold. All components are computationally efficient, compact, and branchless, and an encoder can

Nitin Kaushal, Marcel Franz

We study the emergence of altermagnetism from repulsive interactions for electrons on the Lieb lattice as a model of quasi-2D oxychalcogenides with the so-called "anti-CuO$_{2}$" lattice structure. A comprehensive study of the Lieb lattice Hubbard model, using unrestricted Hartree-Fock and exact diagonalization techniques, establishes the presence of spin-${