May 2024 arXiv papers — page 49

Max Liu, Chan-Hung Yu, Wei-Hsu Lee, Cheng-Wei Hung

Programmatic reinforcement learning (PRL) has been explored for representing policies through programs as a means to achieve interpretability and generalization. Despite promising outcomes, current state-of-the-art PRL methods are hindered by sample inefficiency, necessitating tens of millions of program-environment interactions. To tackle this challenge, we

Xuefeng Gao, Lingfei Li, Xun Yu Zhou

We study continuous-time reinforcement learning (RL) for stochastic control in which system dynamics are governed by jump-diffusion processes. We formulate an entropy-regularized exploratory control problem with stochastic policies to capture the exploration--exploitation balance essential for RL. Unlike the pure diffusion case initially studied by Wang et a

Elena Cordero, Gianluca Giacchi, Edoardo Pucci

In this work, we extend Wigner's original framework to analyze linear operators by examining the relationship between their Wigner and Schwartz kernels. Our approach includes the introduction of (quasi-)algebras of Fourier integral operators (FIOs), which encompass FIOs of type I and II. The symbols of these operators reside in (weighted) modulation spaces,

Yan Chen, Liang Du, Lei Duan

Kernel methods are extensively employed for nonlinear data clustering, yet their effectiveness heavily relies on selecting suitable kernels and associated parameters, posing challenges in advance determination. In response, Multiple Kernel Clustering (MKC) has emerged as a solution, allowing the fusion of information from multiple base kernels for clustering

Mu Liang, Ang Li, Xiaoyan Hu, Christos Masouros

Constructive interference (CI) precoding, which converts the harmful multi-user interference into beneficial signals, is a promising and efficient interference management scheme in multi-antenna communication systems. However, CI-based symbol-level precoding (SLP) experiences high computational complexity as the number of symbol slots increases within a tran

Vikram Shahapur, Blessing Dixon, Urvishkumar Bharti

Till now, many path planning algorithms have been proposed in the literature. The objective of these algorithms is to find the quickest path between initial position to the end position in a certain environment. The complexity of these algorithms depends on the internal parameters such as motor speed or sensor range and on other external parameters, includin

Jiayi Yao, Hanchen Li, Yuhan Liu, Siddhant Ray

Large language models (LLMs) often incorporate multiple text chunks in their inputs to provide the necessary contexts. To speed up the prefill of the long LLM inputs, one can pre-compute the KV cache of a text and re-use the KV cache when the context is reused as the prefix of another LLM input. However, the reused text chunks are not always the input prefix

Taichi Uchida, Yoshihiro Kanamori, Yuki Endo

Achieving aesthetically pleasing photography necessitates attention to multiple factors, including composition and capture conditions, which pose challenges to novices. Prior research has explored the enhancement of photo aesthetics post-capture through 2D manipulation techniques; however, these approaches offer limited search space for aesthetics. We introd

Nimol Thuon, Wangrui Zhang

Several institutions are collaborating on the development of a new web-based Open Education Resources (OER) system designed exclusively for non-commercial educational purposes. This initiative is underpinned by meticulous research aimed at constructing an OER system that optimizes user experiences across diverse user profiles. A significant emphasis is place

Chaoran Cheng, Jiahan Li, Jian Peng, Ge Liu

We introduce Statistical Flow Matching (SFM), a novel and mathematically rigorous flow-matching framework on the manifold of parameterized probability measures inspired by the results from information geometry. We demonstrate the effectiveness of our method on the discrete generation problem by instantiating SFM on the manifold of categorical distributions w

Rohan Chandra, Haresh Karnan, Negar Mehr, Peter Stone

Social robot navigation in crowded public spaces such as university campuses, restaurants, grocery stores, and hospitals, is an increasingly important area of research. One of the core strategies for achieving this goal is to understand humans' intent--underlying psychological factors that govern their motion--by learning their reward functions, typically vi

Muhammad Zeeshan Gul, Muhammad Sharif, Imran Hashim

This study aims to explore the Garcia-Salcedo ghost dark energy and generalized ghost dark energy models in the context of $f(\mathrm{R},\mathrm{T^2})$ theory, where $\mathrm{R}$ is the Ricci scalar and $\mathrm{T^2}$ is the self-contraction of stress-energy tensor. We investigate the non-interacting case only corresponding to flat Friedmann-Robertson-Walker

Faizuddin Ahmed, Allan R. P. Moreira

In this work, we investigate the behavior of non-relativistic quantum particles immersed in a cosmic string space-time background. Our study involves the examination of these particles as they interact with a range of influences, including potential, magnetic, and quantum flux fields. We employ analytical methods to solve the associated wave equation, leadin

Yawen Zou, Chunzhi Gu, Jun Yu, Shangce Gao

Black-Box unsupervised domain adaptation (BBUDA) learns knowledge only with the prediction of target data from the source model without access to the source data and source model, which attempts to alleviate concerns about the privacy and security of data. However, incorrect pseudo-labels are prevalent in the prediction generated by the source model due to t

Zhihan Liu, Miao Lu, Shenao Zhang, Boyi Liu

Aligning generative models with human preference via RLHF typically suffers from overoptimization, where an imperfectly learned reward model can misguide the generative model to output undesired responses. We investigate this problem in a principled manner by identifying the source of the misalignment as a form of distributional shift and uncertainty in lear

Yuankai Luo, Hongkang Li, Qijiong Liu, Lei Shi

We present a novel end-to-end framework that generates highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations, termed node identifiers (node IDs), to tackle inference challenges on large-scale graphs. By employing vector quantization, we compress continuous node embeddings from multiple layers of a Graph Neur

Allen Nie, Ching-An Cheng, Andrey Kolobov, Adith Swaminathan

We study the potential of using large language models (LLMs) as an interactive optimizer for solving maximization problems in a text space using natural language and numerical feedback. Inspired by the classical optimization literature, we classify the natural language feedback into directional and non-directional, where the former is a generalization of the

Chenhao Zhang, Renhao Li, Minghuan Tan, Min Yang

Using large language models (LLMs) to assist psychological counseling is a significant but challenging task at present. Attempts have been made on improving empathetic conversations or acting as effective assistants in the treatment with LLMs. However, the existing datasets lack consulting knowledge, resulting in LLMs lacking professional consulting competen

Zijun Gao, Qi Wang, Taiyuan Mei, Xiaohan Cheng

The traditional SegNet architecture commonly encounters significant information loss during the sampling process, which detrimentally affects its accuracy in image semantic segmentation tasks. To counter this challenge, we introduce an innovative encoder-decoder network structure enhanced with residual connections. Our approach employs a multi-residual conne

Nina Bielinski, Rajas Chari, Julian May-Mann, Soyeun Kim

Manipulating solids using the time-periodic drive of a laser pulse is a promising route to generate new phases of matter. Whether such `Floquet-Bloch' manipulation can be achieved in topological magnetic systems with disorder has so far been unclear. In this work, we realize Floquet-Bloch manipulation of the Dirac surface-state mass of the topological antife

Abdussamad M, Rahul Verma, Nirbhay Kumar Behera, Sadhana Dash

Measurement of higher order cumulants of the distributions of conserved quantities, like net-charge, net-baryon and net-strangeness in heavy-ion collisions, is proposed as a sensitive tool to determine the freeze-out parameters and the nature of phase transitions at the LHC energies. Baseline measurements for heavy-ion collisions are essential to understand

Nilesh Suriyarachchi, Rohan Chandra, Arya Anantula, John S. Baras

Better fuel efficiency leads to better financial security as well as a cleaner environment. We propose a novel approach for improving fuel efficiency in unstructured and unregulated traffic environments. Existing intelligent transportation solutions for improving fuel efficiency, however, apply only to traffic intersections with sparse traffic or traffic whe

Michael Milgram, Roy Hughes

Here, we study both analytically and numerically, an integral $Z(\sigma,r)$ related to the mean value of a generalized moment of Riemann's zeta function. Analytically, we predict finite, but discontinuous values and verify the prediction numerically, employing a modified form of Ces\`aro summation. Further, it is proven and verified numerically that for cert

Meng Zhu, Xinlu Li, Fanxing Zheng, Jianting Dong

Noncollinear antiferromagnets (nc-AFMs) have attracted increasing research attention in spintronics due to their unique spin structures and fascinating charge and spin transport properties. By using first-principles calculations, we comprehensively investigate the charge and spin Hall effects in representative noncollinear antiferromagnet Mn3Pt. Our study re

Andrea Lucchini, Daniele Nemmi

The generating graph encodes how generating pairs are spread among the elements of a group. For more than ten years it has been conjectured that this graph is connected for every finite group. In this paper, we give evidence supporting this conjecture: we prove that it holds for all but a finite number of almost simple groups and give a reduction to groups w

FNU Shivam, Megan Leight, Mary Kate Kelly, Claire Davis

The study of Maya hieroglyphic writing unlocks the rich history of cultural and societal knowledge embedded within this ancient civilization's visual narrative. Artificial Intelligence (AI) offers a novel lens through which we can translate these inscriptions, with the potential to allow non-specialists access to reading these texts and to aid in the deciphe

Chao Zhang, Zhijie Fan, Barbara Capogrosso-Sansone, Youjin Deng

In recent years, twisted bilayer systems such as bilayer graphene have attracted a great deal of attention as the twist angle introduces a degree of freedom which can be used to non-trivially modify system properties. This idea has been picked up in the cold atom community, first with a theoretical proposal to simulate twisted bilayers in state-dependent opt

Min Hun Lee, Silvana Xin Yi Choo, Shamala D/O Thilarajah

With advanced AI/ML, there has been growing research on explainable AI (XAI) and studies on how humans interact with AI and XAI for effective human-AI collaborative decision-making. However, we still have a lack of understanding of how AI systems and XAI should be first presented to users without technical backgrounds. In this paper, we present the findings

Weike Deng, Sheng Long, Jiliang Jing

We extend our research on the energy flux and waveform characteristics of gravitational waves generated by merging nonspinning binary black holes through self-consistent effective one-body theory \cite{L2023} to include binary systems with slowly spinning black holes. Initially, we decompose the equation for the null tetrad component of the gravitationally p

Hao Wang, Jianwei Li, Zhengyu Li

AI-generated text detection plays an increasingly important role in various fields. In this study, we developed an efficient AI-generated text detection model based on the BERT algorithm, which provides new ideas and methods for solving related problems. In the data preprocessing stage, a series of steps were taken to process the text, including operations s

Ghazaleh Shirvani, Saeid Ghasemshirazi

In an era dominated by the Internet of Things, ensuring the longevity and sustainability of IoT devices has emerged as a pressing concern. This study explores the various complex difficulties which contributed to the early decommissioning of IoT devices and suggests methods to improve their lifespan management. By examining factors such as security vulnerabi

Zheng Wang, Shu Xian Teo, Jieer Ouyang, Yongjun Xu

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant memories from an external database. However, existing RAG methods typically organize all memories in a whole database, potentially limiting focus on crucial memories and introducing noise. In this paper, we introduce a multiple partition paradigm for RAG (called

Hao Tang, Keya Hu, Jin Peng Zhou, Sicheng Zhong

Iteratively improving and repairing source code with large language models (LLMs), known as refinement, has emerged as a popular way of generating programs that would be too complex to construct in one shot. Given a bank of test cases, together with a candidate program, an LLM can improve that program by being prompted with failed test cases. But it remains

Chau Pham, Bryan A. Plummer

Multi-Channel Imaging (MCI) contains an array of challenges for encoding useful feature representations not present in traditional images. For example, images from two different satellites may both contain RGB channels, but the remaining channels can be different for each imaging source. Thus, MCI models must support a variety of channel configurations at te

Yingyu Liang, Zhenmei Shi, Zhao Song, Yufa Zhou

Diffusion models have made rapid progress in generating high-quality samples across various domains. However, a theoretical understanding of the Lipschitz continuity and second momentum properties of the diffusion process is still lacking. In this paper, we bridge this gap by providing a detailed examination of these smoothness properties for the case where

W. H. Elsanhoury

Here, we report the kinematical parameters of inner-halo hot subdwarfs located within (d lower than or equal 15 kpc) at high Galactic latitudes (b^o greater than or equal 20). The study included three program stars for one of the extreme He-rich groups (eHe-1) with eccentricity (e=0.65) and the z-component of the angular momentum (J_z=4288.66 kpc km s-1), th

Hui-Fang Xue, Jia-Shu Niu

Harmonics are a ubiquitous feature across various pulsating stars. They are traditionally viewed as mere replicas of the independent primary pulsation modes and have thus been excluded from asteroseismological models. Recent research, however, has uncovered a significant discrepancy: in high-amplitude $\delta$ Scuti (HADS) stars, harmonics exhibit uncorrelat

Ziming Liu, Longjian Liu, Robert E. Heidel, Xiaopeng Zhao

This article uses machine learning (ML) and explainable artificial intelligence (XAI) techniques to investigate the relationship between nutritional status and mortality rates associated with Alzheimers disease (AD). The Third National Health and Nutrition Examination Survey (NHANES III) database is employed for analysis. The random forest model is selected

Huayuan Ye, Shenzhuo Zhang, Shiqi Jiang, Jing Liao

Image steganography can hide information in a host image and obtain a stego image that is perceptually indistinguishable from the original one. This technique has tremendous potential in scenarios like copyright protection, information retrospection, etc. Some previous studies have proposed to enhance the robustness of the methods against image disturbances

Tyler Morris, Ziming Liu, Longjian Liu, Xiaopeng Zhao

As the number of dementia patients rises, the need for accurate diagnostic procedures rises as well. Current methods, like using an MRI scan, rely on human input, which can be inaccurate. However, the decision logic behind machine learning algorithms and their outputs cannot be explained, as most operate in black-box models. Therefore, to increase the accura

Jiankun Wang, Sumyeong Ahn, Taykhoom Dalal, Xiaodan Zhang

Alzheimer's disease (AD) is the fifth-leading cause of death among Americans aged 65 and older. Screening and early detection of AD and related dementias (ADRD) are critical for timely intervention and for identifying clinical trial participants. The widespread adoption of electronic health records (EHRs) offers an important resource for developing ADRD scre

Pengcheng Jiang, Lang Cao, Cao Xiao, Parminder Bhatia

Knowledge Graph Embedding (KGE) techniques are crucial in learning compact representations of entities and relations within a knowledge graph, facilitating efficient reasoning and knowledge discovery. While existing methods typically focus either on training KGE models solely based on graph structure or fine-tuning pre-trained language models with classifica

Yang Cao, Yingyu Liang, Zhenmei Shi, Zhao Song

Tensor Attention, a multi-view attention that is able to capture high-order correlations among multiple modalities, can overcome the representational limitations of classical matrix attention. However, the $O(n^3)$ time complexity of tensor attention poses a significant obstacle to its utilization in transformers, where $n$ is the input sequence length. In t

Nora Ganter

We discuss some categorical aspects of the objects that appear in the construction of the Monster and other sporadic simple groups. We define the basic representation of the categorical torus $\mathcal T$ classified by an even symmetric bilinear form $I$ and of the semi-direct product of $\mathcal T$ with its canonical involution. We compute the centraliser

Lei Zhang, Zhiqian Chen, Chang-Tien Lu, Liang Zhao

Network interdiction problems are combinatorial optimization problems involving two players: one aims to solve an optimization problem on a network, while the other seeks to modify the network to thwart the first player's objectives. Such problems typically emerge in an attacker-defender context, encompassing areas such as military operations, disease spread

Leyang Zhang, Yaoyu Zhang, Tao Luo

This paper presents a comprehensive analysis of critical point sets in two-layer neural networks. To study such complex entities, we introduce the critical embedding operator and critical reduction operator as our tools. Given a critical point, we use these operators to uncover the whole underlying critical set representing the same output function, which ex

Yuta Nozaki, Kunihiro Wasa, Katsuhisa Yamanaka

A ladder lottery, known as ``Amidakuji'' in Japan, is a common way to decide an assignment at random. In this paper, we investigate reconfiguration and enumeration problems of cyclic ladder lotteries. First, when a permutation $\pi$ and an optimal displacement vector $\mathbf{x}$ are given, we investigate the reconfiguration and enumeration problems of the `

Hao Liang, Jingyu Lu, Manolis C. Tsakiris, Lihong Zhi

We study the unlabeled sensing problem that aims to solve a linear system of equations $A x =\pi(y) $ for an unknown permutation $\pi$. For a generic matrix $A$ and a generic vector $y$, we construct a system of polynomial equations whose unique solution satisfies $ A\xi^*=\pi(y)$. In particular, $\xi^*$ can be recovered by solving the rank-one moment matrix

Zechun Liu, Changsheng Zhao, Igor Fedorov, Bilge Soran

Post-training quantization (PTQ) techniques applied to weights, activations, and the KV cache greatly reduce memory usage, latency, and power consumption of Large Language Models (LLMs), but may lead to large quantization errors when outliers are present. Rotating activation or weight matrices helps remove outliers and benefits quantization. In this work, we

Runlin Lei, Yuwei Hu, Yuchen Ren, Zhewei Wei

Graph Neural Networks (GNNs) excel across various applications but remain vulnerable to adversarial attacks, particularly Graph Injection Attacks (GIAs), which inject malicious nodes into the original graph and pose realistic threats. Text-attributed graphs (TAGs), where nodes are associated with textual features, are crucial due to their prevalence in real-

Seyed Mohsen Moosavi Khansari, Fazlollah Kazemi Hasanvand

In this study, we explore the dynamics of quantum entanglement using the negativity criterion for the W_zeta quantum state. We investigate changes in negativity in terms of anisotropy parameters, gamma, the strength of the external magnetic field applied to the spin chain, eta, the triple interaction strength, alpha. We examine how these parameters affect th

Pedro A. Sánchez, Antonio Cerrato, Joan J. Cerdà, Carles Bona-Cases

Using nonequilibrium computer simulations, we study the response of ferromagnetic nanofilaments, consisting of stabilized onedimensional chains of ferromagnetic nanoparticles, under external rotating magnetic fields. In difference with their analogous microscale and stiff counterparts, which have been actively studied in recent years, nonequilibrium properti

Man Luo, Christopher J. Warren, Lu Cheng, Haidar M. Abdul-Muhsin

The integration of Large Language Models (LLMs) into the healthcare domain has the potential to significantly enhance patient care and support through the development of empathetic, patient-facing chatbots. This study investigates an intriguing question Can ChatGPT respond with a greater degree of empathy than those typically offered by physicians? To answer

J. E. Hirsch, F. Marsiglio

In their paper arXiv:2405.08189, Supercond. Sci. Technol. 37 (2024) 065010 [1], Bud'ko et al. present experimental results for trapped magnetic flux for a tiny sample of a type II superconductor. The paper aims to provide evidence in support of the interpretation that similar measurements performed in samples of hydrogen-rich materials under high pressure by

Neha Kalibhat, Priyatham Kattakinda, Sumit Nawathe, Arman Zarei

Vision transformers have established a precedent of patchifying images into uniformly-sized chunks before processing. We hypothesize that this design choice may limit models in learning comprehensive and compositional representations from visual data. This paper explores the notion of providing semantically-meaningful visual tokens to transformer encoders wi

Dinh Dũng

We studied linear weighted sampling algorithms and their optimality for approximate recovery of functions with mixed smoothness on $\mathbb{R}^d$ from a set of $n$ their sampled values. Functions to be recovered are in weighted Sobolev spaces $W^r_{p,w}(\mathbb{R}^d)$ of mixed smoothness, and the approximation error is measured by the norm of the weighted Le

Donghoon Jang, Shintarô Kuroki, Mikiya Masuda, Takashi Sato

A regular semisimple Hessenberg variety $\mathrm{Hess}(S,h)$ is a smooth subvariety of the full flag variety $\mathrm{Fl}(\mathbb{C}^n)$ associated with a regular semisimple matrix $S$ of order $n$ and a function $h$ from $\{1,2,\dots,n\}$ to itself satisfying a certain condition. We show that when $\mathrm{Hess}(S,h)$ is connected and not the entire space $

Jiapeng Li, Xiaodan Shao, Feng Chen, Shaohua Wan

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this paper, we design a novel networked ISAC framework to explore the collaboration among mul

Damien Martins Gomes, Yanlei Zhang, Eugene Belilovsky, Guy Wolf

First-order optimization methods are currently the mainstream in training deep neural networks (DNNs). Optimizers like Adam incorporate limited curvature information by employing the diagonal matrix preconditioning of the stochastic gradient during the training. Despite their widespread, second-order optimization algorithms exhibit superior convergence prope

Sven Weinzierl, Sandra Zilker, Sebastian Dunzer, Martin Matzner

Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications are used to analyse and improve processes efficiently. Three frequent examples of using ML are providing decision support through predictions, discovering accurate process models, and i

Haoting Zhang, Donglin Zhan, Yunduan Lin, Jinghai He

In healthcare applications, there is a growing need to develop machine learning models that use data from a single source, such as that from a wrist wearable device, to monitor physical activities, assess health risks, and provide immediate health recommendations or interventions. However, the limitation of using single-source data often compromises the mode

John C Vining, Howard A. Blair

Quantum walks have frequently envisioned the behavior of a quantum state traversing a classically defined, generally finite, graph structure. While this approach has already generated significant results, it imposes a strong assumption: all nodes where the walker is not positioned are quiescent. This paper will examine some mathematical structures that under

Jinlin Liu, Kai Yu, Mengyang Feng, Xiefan Guo

Recent advancements in human video synthesis have enabled the generation of high-quality videos through the application of stable diffusion models. However, existing methods predominantly concentrate on animating solely the human element (the foreground) guided by pose information, while leaving the background entirely static. Contrary to this, in authentic,

Austin Finlayson, Rui Wu, Chia-Cheng Lin, Brian Sylcott

This work-in-progress paper discusses the use of student-centered pedagogy to teach clinical oculomotor examination via Virtual Reality (VR). Traditional methods, such as PowerPoint slides and lab activities, are often insufficient for providing hands-on experience due to the high cost of clinical equipment. To address this, a VR-based application was develo

Samuel Lippl, Kim Stachenfeld

Compositional generalization (the ability to respond correctly to novel combinations of familiar components) is thought to be a cornerstone of intelligent behavior. Compositionally structured (e.g. disentangled) representations support this ability; however, the conditions under which they are sufficient for the emergence of compositional generalization rema

Shangding Gu, Bilgehan Sel, Yuhao Ding, Lu Wang

In numerous reinforcement learning (RL) problems involving safety-critical systems, a key challenge lies in balancing multiple objectives while simultaneously meeting all stringent safety constraints. To tackle this issue, we propose a primal-based framework that orchestrates policy optimization between multi-objective learning and constraint adherence. Our

M. Krishna

In this paper we study the local spectral statistics in the localised region of various random operator models, including the $d$-dimensional the Anderson model and random Schr\"odinger operators. It is already established, in the above models, that at an energy $E$, in the localised energy region of the spectrum, where the density of states $n(E) > 0$, the

Hung Le, Quan Tran, Dung Nguyen, Kien Do

How can Large Language Models (LLMs) be aligned with human intentions and values? A typical solution is to gather human preference on model outputs and finetune the LLMs accordingly while ensuring that updates do not deviate too far from a reference model. Recent approaches, such as direct preference optimization (DPO), have eliminated the need for unstable

Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang

To generate data from trained diffusion models, most inference algorithms, such as DDPM, DDIM, and other variants, rely on discretizing the reverse SDEs or their equivalent ODEs. In this paper, we view such approaches as decomposing the entire denoising diffusion process into several segments, each corresponding to a reverse transition kernel (RTK) sampling

Jiayu Chen, Tian Lan, Vaneet Aggarwal

Skills are effective temporal abstractions established for sequential decision making, which enable efficient hierarchical learning for long-horizon tasks and facilitate multi-task learning through their transferability. Despite extensive research, research gaps remain in multi-agent scenarios, particularly for automatically extracting subgroup coordination

Sarah C. Lotspeich, Ashley E. Mullan, Lucy D'Agostino McGowan, Staci A. Hepler

Healthy foods are essential for a healthy life, but accessing healthy food can be more challenging for some people than others. This disparity in food access may lead to disparities in well-being, potentially with disproportionate rates of diseases in communities that face more challenges in accessing healthy food (i.e., low-access communities). Identifying

Nikolai Kudasov, Renata Shakirova, Egor Shalagin, Karina Tyulebaeva

Handling bound identifiers correctly and efficiently is critical in implementations of compilers, proof assistants, and theorem provers. When choosing a representation for abstract syntax with binders, implementors face a trade-off between type safety with intrinsic scoping, efficiency, and generality. The "foil" by Maclaurin, Radul, and Paszke combines an e

Bangzheng Li, Ningshan Ma, Zifan Wang

We introduce a new on-policy algorithm called Rewarded Region Replay (R3), which significantly improves on PPO in solving environments with discrete action spaces. R3 improves sample efficiency by using a replay buffer which contains past successful trajectories with reward above a certain threshold, which are used to update a PPO agent with importance sampl

James Maier, Nishanth Mohankumar

This study explores the potential of open-source video conditional generation models as encoders for downstream tasks, focusing on instance segmentation using the BAIR Robot Pushing Dataset. The researchers propose using video prediction models as general visual encoders, leveraging their ability to capture critical spatial and temporal information which is

Yuchen Zhu, Tianrong Chen, Lingkai Kong, Evangelos A. Theodorou

The generative modeling of data on manifolds is an important task, for which diffusion models in flat spaces typically need nontrivial adaptations. This article demonstrates how a technique called `trivialization' can transfer the effectiveness of diffusion models in Euclidean spaces to Lie groups. In particular, an auxiliary momentum variable was algorithmi

Linsen Li, Pratyush Anand, Kaiming He, Dirk Englund

A central challenge in quantum information science and technology is achieving real-time estimation and feedforward control of quantum systems. This challenge is compounded by the inherent inhomogeneity of quantum resources, such as qubit properties and controls, and their intrinsically probabilistic nature. This leads to stochastic challenges in error detec

Youngjoo Yun, Yinqiu He

If the same data is used for both clustering and for testing a null hypothesis that is formulated in terms of the estimated clusters, then the traditional hypothesis testing framework often fails to control the Type I error. Gao et al. [2022] and Chen and Witten [2023] provide selective inference frameworks for testing if a pair of estimated clusters indeed

Timo Schneider, Pengcheng Xu, Torsten Hoefler

In the era of post-Moore computing, network offload emerges as a solution to two challenges: the imperative for low-latency communication and the push towards hardware specialisation. Various methods have been employed to offload protocol- and data-processing onto network interface cards (NICs), from firmware modification to running full Linux on NICs for ap

Dongmin Gang, Heesu Kang, Seongmin Kim

Using 3D-3D correspondence, we construct 3D dual bulk field theories for general Virasoro minimal models $M(P,Q)$. These theories correspond to Seifert fiber spaces $S^2 ((P,P-R),(Q,S),(3,1))$ with two integers $(R,S)$ satisfying $PS-QR =1$. In the unitary case, where $|P-Q|=1$, the bulk theory has a mass gap and flows to a unitary topological field theory (

Chuanhao Li, Runhan Yang, Tiankai Li, Milad Bafarassat

Large Language Models (LLMs) like GPT-4 have revolutionized natural language processing, showing remarkable linguistic proficiency and reasoning capabilities. However, their application in strategic multi-agent decision-making environments is hampered by significant limitations including poor mathematical reasoning, difficulty in following instructions, and

Yifeng Peng, Xinyi Li, Zhiding Liang, Ying Wang

Classical max pooling plays a crucial role in reducing data dimensionality among various well-known deep learning models, yet it often leads to the loss of vital information. We proposed a novel hybrid quantum downsampling module (HQD), which is a noise-resilient algorithm. By integrating a substantial number of quantum bits (qubits), our approach ensures th

Hsin-Po Wang, Chi-Wei Chin

In array-based DNA synthesis, multiple strands of DNA are synthesized in parallel to reduce the time cost from the sum of their lengths to the length their shortest common supersequences. To maximize the amount of information that can be synthesized into DNA within a finite amount of time, we study the number of unordered sets of $n$ strands of DNA that have

Hsin-Po Wang, Venkatesan Guruswami

Group testing (GT) is the art of identifying binary signals and the marketplace for exchanging new ideas for related fields such as unique-element counting, compressed sensing, traitor tracing, and geno-typing. A GT scheme can be nonadaptive or adaptive; the latter is preferred when latency is ess of an issue. To construct adaptive GT schemes, a popular stra

Hsin-Po Wang, Venkatesan Guruswami

Successive cancellation list (SCL) decoders of polar codes excel in practical performance but pose challenges for theoretical analysis. Existing works either limit their scope to erasure channels or address general channels without taking advantage of soft information. In this paper, we propose the "successive cancellation sampling" (SCS) decoder. SCS hires

Zhen Huang, Hristina Dokic

Software vulnerabilities are prevalent but fixing software vulnerabilities is not trivial. Studies have shown that a considerable prepatch window exists because it often takes weeks or months for software vendors to fix a vulnerability. Existing approaches aim to reduce the pre-patch window by generating and applying mitigation patches that prevent adversari

Sahiba Arora

The spectral theory of semigroup generators is a crucial tool for analysing the asymptotic properties of operator semigroups. Typically, Tauberian theorems, such as the ABLV theorem, demand extensive information about the spectrum to derive convergence results. However, the scenario is significantly simplified for positive semigroups on Banach lattices. This

Hsin-Po Wang, Ryan Gabrys, Venkatesan Guruswami

We modify Cheraghchi-Nakos [CN20] and Price-Scarlett's [PS20] fast binary splitting approach to nonadaptive group testing. We show that, to identify a uniformly random subset of $k$ infected persons among a population of $n$, it takes only $\ln(2 - 4\varepsilon) ^{-2} k \ln n$ tests and decoding complexity $O(\varepsilon^{-2} k \ln n)$, for any small $\varep

Ivo Ivanov, Carsten Markgraf

Lane detection is a fundamental task in autonomous driving. While the problem is typically formulated as the detection of continuous boundaries, we study the problem of detecting lane boundaries that are sparsely marked by 2D points with many false positives. This problem arises in the Formula Student Driverless (FSD) competition and is challenging due to it

Yifeng Peng, Xinyi Li, Zhiding Liang, Ying Wang

Open set anomaly detection (OSAD) is a crucial task that aims to identify abnormal patterns or behaviors in data sets, especially when the anomalies observed during training do not represent all possible classes of anomalies. The recent advances in quantum computing in handling complex data structures and improving machine learning models herald a paradigm s

Eduárd Illés, Dániel Jánosi, Tamás Kovács

Time-dependent potentials are common in galactic systems that undergo significant evolution, interactions, or encounters with other galaxies, or when there are dynamic processes like star formation and merging events. Recent studies show that an ensemble approach along with the so-called snapshot framework in dynamical system theory provide a powerful tool t

Yuriy Makhlin, Alexander B. Zorin

We analyze properties of bifurcation quantum detectors based on weakly nonlinear superconducting resonance circuits, in particular, with application to quantum readout. The developed quantitative description demonstrates strong influence of higher harmonics on their characteristics. While this effect is relevant for various circuits, including the convention

Ping Li, Wen-Ping Liao, Sheng-Bang Qian, Lin-Jia Li

We reported a cyclic variation of $O-C$ diagram with a semi-amplitude of 0.0033 days and a period of 1.05 years for the pulsating eclipsing binary HZ Dra. The cyclic variation can be explained by the light travel-time effect via the presence of a close-in third body orbiting around HZ Dra in an elliptical orbit with a maximum semi-major axis of 0.92 au. Base

Wanwan Zhang

This paper aims to investigate a multi-dimensional transport equation with nonlocal velocity and fractional dissipation.The balance between the nonlinearity and dissipation gives rise to three different cases, namely the subcritical, critical and supercritical ranges. We study those three cases and obtain a set of results containing local well-posedness, glo

Jianling Wang, Haokai Lu, Yifan Liu, He Ma

Traditional recommendation systems are subject to a strong feedback loop by learning from and reinforcing past user-item interactions, which in turn limits the discovery of novel user interests. To address this, we introduce a hybrid hierarchical framework combining Large Language Models (LLMs) and classic recommendation models for user interest exploration.

Jesus Noyola-Rodriguez, Georgy Omel'yanov

We consider a generalization of the mKdV model of shallow water out-flows. This generalization is a family of equations with nonlinear dispersion terms containing, in particular, KdV, mKdV, Benjamin-Bona-Mahony, Camassa-Holm, and Degasperis-Procesi equations. Nonlinear dispersion, generally speaking, implies instability of classical solutions and wave breaki

Hsin-Po Wang, Venkatesan Guruswami

Polar codes that approach capacity at a near-optimal speed, namely with scaling exponents close to $2$, have been shown possible for $q$-ary erasure channels (Pfister and Urbanke), the BEC (Fazeli, Hassani, Mondelli, and Vardy), all BMS channels (Guruswami, Riazanov, and Ye), and all DMCs (Wang and Duursma). There is, nevertheless, a subtlety separating the

Daniel Sanz-Alonso, Omar Al-Ghattas

This is a concise mathematical introduction to Monte Carlo methods, a rich family of algorithms with far-reaching applications in science and engineering. Monte Carlo methods are an exciting subject for mathematical statisticians and computational and applied mathematicians: the design and analysis of modern algorithms are rooted in a broad mathematical tool

Pratik Mukherjee, Burak M. Gonultas, O. Goktug Poyrazoglu, Volkan Isler

We address the problem of stable and robust control of vehicles with lateral error dynamics for the application of lane keeping. Lane departure is the primary reason for half of the fatalities in road accidents, making the development of stable, adaptive and robust controllers a necessity. Traditional linear feedback controllers achieve satisfactory tracking

Tingting Dan, Ziquan Wei, Won Hwa Kim, Guorong Wu

The human brain is a complex inter-wired system that emerges spontaneous functional fluctuations. In spite of tremendous success in the experimental neuroscience field, a system-level understanding of how brain anatomy supports various neural activities remains elusive. Capitalizing on the unprecedented amount of neuroimaging data, we present a physics-infor

Qiaosi Wang, Chidimma L. Anyi, Vedant Das Swain, Ashok K. Goel

Many hyper-personalized AI systems profile people's characteristics (e.g., personality traits) to provide personalized recommendations. These systems are increasingly used to facilitate interactions among people, such as providing teammate recommendations. Despite improved accuracy, such systems are not immune to errors when making inferences about people's