November 2024 arXiv papers — page 184

Atoosa Chegini, Hamid Kazemi, Iman Mirzadeh, Dong Yin

In Large Language Model (LLM) development, Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning models with human values and preferences. RLHF traditionally relies on the Kullback-Leibler (KL) divergence between the current policy and a frozen initial policy as a reference, which is added as a penalty in policy optimization algorithms li

Sangdaow Noppitak, Emmanuel Okafor, Olarik Surinta

Effective water resource management is crucial in agricultural regions like northeastern Thailand, where limited water retention in sandy soils poses significant challenges. In response to this issue, the Aerial Image Water Resource (AIWR) dataset was developed, comprising 800 aerial images focused on natural and artificial water bodies in this region. The d

Carlos E. Frasser

The following is an exposition of a course of algebra that Prof. Aleksandr Aleksandrovich Zykov (1922-2013) distributed among the participants of his seminar in graph theory not far away from Odessa, Ukraine, on September, 1991. It is a privilege for me to be able to reproduce, with some good additions, the English version of this remarkable course that he d

Fali Wang, Zhiwei Zhang, Xianren Zhang, Zongyu Wu

Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains. Despite their proficiency in various tasks, LLMs like PaLM 540B and Llama-3.1 405B face limitations due to large parameter sizes and computational demands, often requiring cloud API use which raises

Weihua Du, Qiushi Lyu, Jiaming Shan, Zhenting Qi

We introduce Constrained Human-AI Cooperation (CHAIC), an inclusive embodied social intelligence challenge designed to test social perception and cooperation in embodied agents. In CHAIC, the goal is for an embodied agent equipped with egocentric observations to assist a human who may be operating under physical constraints -- e.g., unable to reach high plac

Nathan E. Glatt-Holtz, Vincent R. Martinez, Hung D. Nguyen

We study a class of semi-linear differential Volterra equations with polynomial-type potentials that incorporates the effects of memory while being subjected to random perturbations via an additive Gaussian noise. We show that for a broad class of non-linear potentials, the system always admits invariant probability measures. However, the presence of memory

Rhucha Deshpande, Oleg Lunin

To construct higher-dimensional counterparts of the Kerr-Newman black holes, we consider Einstein's equations sourced by a vector field and a negative cosmological constant. In contrast to the four-dimensional case, the Maxwell's equations are modified by sources generated by topological Chern-Simons couplings, the situation already encountered in the minima

Quanyan Zhu, Tamer Başar

Socio-technical networks represent emerging cyber-physical infrastructures that are tightly interwoven with human networks. The coupling between human and technical networks presents significant challenges in managing, controlling, and securing these complex, interdependent systems. This paper investigates game-theoretic frameworks for the design and control

Nguyen Dung Chinh, Vinh N. T. Pham, Nguyen Duy Vy

Using the Green's function approach, we investigate the resonance energy transfer (RET) rate between two parallel, identical two-level atoms in the presence of three types of cylindrical system: a distributed Bragg reflector (DBR), a perfectly reflecting wall (PRW), and a two-layer silicon fiber. Our analysis, incorporating the cut-off frequency condition, r

Feiping Nie, Yitao Song, Wei Chang, Rong Wang

In the graph-based semi-supervised learning, the Green-function method is a classical method that works by computing the Green's function in the graph space. However, when applied to large graphs, especially those sparse ones, this method performs unstably and unsatisfactorily. We make a detailed analysis on it and propose a novel method from the perspective

Yangtao Deng, Xiang Shi, Zhuo Jiang, Xingjian Zhang

Large-scale distributed model training requires simultaneous training on up to thousands of machines. Faulty machine detection is critical when an unexpected fault occurs in a machine. From our experience, a training task can encounter two faults per day on average, possibly leading to a halt for hours. To address the drawbacks of the time-consuming and labo

Allen Z. Ren, Brian Ichter, Anirudha Majumdar

Large language models (LLMs) have exhibited remarkable reasoning and planning capabilities. Most prior work in this area has used LLMs to reason through steps from an initial to a goal state or criterion, thereby effectively reasoning in a forward direction. Nonetheless, many planning problems exhibit an inherent asymmetry such that planning backward from th

Shan Jiang, Chenguang Zhu, Sarfraz Khurshid

Software testing remains the most widely used methodology for validating quality of code. However, effectiveness of testing critically depends on the quality of test suites used. Test cases in a test suite consist of two fundamental parts: (1) input values for the code under test, and (2) correct checks for the outputs it produces. These checks are commonly

Yu Mao, Jerome Gilles

A novel approach is presented to recover an image degraded by atmospheric turbulence. Given a sequence of frames affected by turbulence, we construct a variational model to characterize the static image. The optimization problem is solved by Bregman Iteration and the operator splitting method. Our algorithm is simple, efficient, and can be easily generalized

Chen-Huan Wu

We investigates the dynamics of an open quantum system comprising a two-level electronic system coupled to local boson mode and a bosonic bath. The system is described by four distinct states, including the ground and excited electronic states, each with its corresponding zero- and one-boson vibrational levels. The dissipative dynamics arising from interacti

David J. Abers, George Hripcsak, Lena Mamykina, Melike Sirlanci

This article develops a novel data assimilation methodology, addressing challenges that are common in real-world settings, such as severe sparsity of observations, lack of reliable models, and non-stationarity of the system dynamics. These challenges often cause identifiability issues and can confound model parameter initialization, both of which can lead to

Le Tri Dat, Nguyen Dung Chinh, Vinh N. T. Pham, Vo Quoc Phong

Polariton lasing is a promising phenomenon with potential applications in next-generation lasers that operate without the need for population inversion. Applying a perpendicular magnetic field to a quantum well (QW) significantly alters the properties of exciton-polaritons. In this theoretical study, we investigate how the lasing threshold of QW exciton-pola

Zhenrui Yue, Huimin Zeng, Yang Zhang, Julian McAuley

While sequential recommendation achieves significant progress on capturing user-item transition patterns, transferring such large-scale recommender systems remains challenging due to the disjoint user and item groups across domains. In this paper, we propose a vector quantized meta learning for transferable sequential recommenders (MetaRec). Without requirin

Shang-Shun Zhang, Gábor B. Halász, Cristian D. Batista

Identifying experimental probes capable of diagnosing extreme quantum behavior is widely regarded as one of the foremost challenges in modern condensed matter physics. Here, we propose a novel approach for detecting chiral Kitaev spin liquid states through measurements of the local dynamical spin structure factor on the boundary using scanning tunneling micr

Amy Yang, Jingyi Yang, Aya Ibrahim, Xinfeng Xie

We present context parallelism for long-context large language model inference, which achieves near-linear scaling for long-context prefill latency with up to 128 H100 GPUs across 16 nodes. Particularly, our method achieves 1M context prefill with Llama3 405B model in 77s (93% parallelization efficiency, 63% FLOPS utilization) and 128K context prefill in 3.8

Maya Sankar

For any uniformity $r$ and residue $k$ modulo $r$, we give an exact characterization of the $r$-uniform hypergraphs that homomorphically avoid tight cycles of length $k$ modulo $r$, in terms of colorings of $(r-1)$-tuples of vertices. This generalizes the result that a graph avoids all odd closed walks if and only if it is bipartite, as well as a result of K

Duc Dang Trung Tran, Byeongkeun Kang, Yeejin Lee

Recently, transformer-based techniques incorporating superpoints have become prevalent in 3D instance segmentation. However, they often encounter an over-segmentation problem, especially noticeable with large objects. Additionally, unreliable mask predictions stemming from superpoint mask prediction further compound this issue. To address these challenges, w

Feiping Nie, Yitao Song, Jingjing Xue, Rong Wang

We propose the DPSM method, a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space. Unlike traditional density-based clustering methods, which necessitate calculating the distance between any two nodes, our proposed technique determines density through a propagation

Zilin Huang, Xiangyan Tang, Hongyu Li, Xinyi Cao

In the era of the Internet of Things (IoT) and data sharing, users frequently upload their personal information to enterprise databases to enjoy enhanced service experiences provided by various online services. However, the widespread presence of system vulnerabilities, remote network intrusions, and insider threats significantly increases the exposure of pr

Irina Aref'eva, Daniil Stepanenko, Igor Volovich

In the thermodynamics of black holes in asymptotically flat space, the third law of thermodynamics is violated, and entropy cannot be consistently modeled through conventional statistical mechanics. Notably, the third law of thermodynamics is violated for the Schwarzschild black hole, and its entropy can only be described using an unconventional model, such

Xueyan Niu, Cristina Savin, Eero P. Simoncelli

Prediction is a fundamental capability of all living organisms, and has been proposed as an objective for learning sensory representations. Recent work demonstrates that in primate visual systems, prediction is facilitated by neural representations that follow straighter temporal trajectories than their initial photoreceptor encoding, which allows for predic

Yanshi Sun, Zhiguo Ding, Yun Hou, George K. Karagiannidis

This paper aims to prove the significant superiority of hybrid non-orthogonal multiple access (NOMA) over orthog onal multiple access (OMA) in terms of energy efficiency. In particular, a novel hybrid NOMA scheme is proposed in which a user can transmit signals not only by using its own time slot but also by using the time slots of other users. The data rate

William Liang, Sam Wang, Hung-Ju Wang, Osbert Bastani

Recent work has demonstrated that a promising strategy for teaching robots a wide range of complex skills is by training them on a curriculum of progressively more challenging environments. However, developing an effective curriculum of environment distributions currently requires significant expertise, which must be repeated for every new domain. Our key in

Bin Guo, Shaun D. Hampton

Symmetric orbifold CFTs contain twist operators that can join and split copies of the CFT, leading to the creation of pairs from the vacuum. In this paper, we study the pair creation processes involving four twist-2 operators. In addition to the pair creation previously observed purely in the left or right moving sectors, we find a novel mixing between left

Byeong-Hoo Lee, Kang Yin

Robotic arms are increasingly being used in collaborative environments, requiring an accurate understanding of human intentions to ensure both effectiveness and safety. Electroencephalogram (EEG) signals, which measure brain activity, provide a direct means of communication between humans and robotic systems. However, the inherent variability and instability

Kebin Peng, John Quarles, Kevin Desai

In this paper, we propose a novel method for monocular depth estimation in dynamic scenes. We first explore the arbitrariness of object's movement trajectory in dynamic scenes theoretically. To overcome the arbitrariness, we use assume that points move along a straight line over short distances and then summarize it as a triangular constraint loss in two dim

Shaofei Zhao, Siming Huang, Kexuan Li, Weiyu Zhou

Lung adenocarcinoma (LUAD) is characterized by substantial genetic heterogeneity, posing challenges in identifying reliable biomarkers for improved diagnosis and treatment. Tumor Mutational Burden (TMB) has traditionally been regarded as a predictive biomarker, given its association with immune response and treatment efficacy. In this study, we treated TMB a

Yilan Shen, Boyang Li, Xi Zhang

In pursuit of enhancing the comprehensive efficiency of production systems, our study focused on the joint optimization problem of scheduling and machine maintenance in scenarios where product rework occurs. The primary challenge lies in the interdependence between product \underline{q}uality, machine \underline{r}eliability, and \underline{p}roduction sched

Azka Rodoshi Oishi, Md Jamil Ahsan, Azka Sejuti, B M Tazbiul Hassan Anik

Traumatic Brain Injuries (TBIs) resulting from Road Traffic Crashes (RTCs) can have fatal and disabling effects on patients. In this study, we evaluated the TBIs outcomes of patients involved in RTCs and identify key contributing factors affecting these outcomes. Data on 207 patients recorded by physicians at a tertiary hospital in Bangladesh was collected.

David Sabin-Miller, Daniel M. Abrams

The dynamics and spontaneous organization of coupled particles is a classic problem in modeling and applied mathematics. Here we examine the behavior of particles coupled by the Ricker potential, exhibiting finite local repulsion transitioning to distal attraction, leading to an energy-minimizing ``preferred distance''. When compressed by a background potent

Chuanchuan Wang, Ahmad Sufril Azlan Mohmamed, Mohd Halim Bin Mohd Noor, Xiao Yang

This paper presents the ARN-LSTM architecture, a novel multi-stream action recognition model designed to address the challenge of simultaneously capturing spatial motion and temporal dynamics in action sequences. Traditional methods often focus solely on spatial or temporal features, limiting their ability to comprehend complex human activities fully. Our pr

Shlomo Libo Feigin, Maximilian Fleissner, Debarghya Ghoshdastidar

Data augmentations play an important role in the recent success of self-supervised learning (SSL). While augmentations are commonly understood to encode invariances between different views into the learned representations, this interpretation overlooks the impact of the pretraining architecture and suggests that SSL would require diverse augmentations which

Cheng Zhang, Lan Wei, Ji Fan, Zening Liu

In this paper, a two-stage intelligent scheduler is proposed to minimize the packet-level delay jitter while guaranteeing delay bound. Firstly, Lyapunov technology is employed to transform the delay-violation constraint into a sequential slot-level queue stability problem. Secondly, a hierarchical scheme is proposed to solve the resource allocation between m

Alexandra Vassar, Jake Renzella, Emily Ross, Andrew Taylor

This paper investigates supervised fine-tuning of large language models (LLMs) to improve their pedagogical alignment in computing education, addressing concerns that LLMs may hinder learning outcomes. The project utilised a proprietary dataset of 2,500 high quality question/answer pairs from programming course forums, and explores two research questions: th

Chen Tang, Yi Ling, Qing-Quan Jiang, Guo-Ping Li

We investigate the perturbation of the scalar field as well as the electromagnetic field over a sort of regular black holes which are characterized by the sub-Planckian curvature and the Minkowskian core. Specifically, we compute the quasinormal modes(QNMs) by employing the pseudo-spectral method. The outburst of overtones is manifestly observed in the QNMs

Vu-Anh Le, Mehmet Dik

This paper presents a mathematics-informed approach to neural operator design, building upon the theoretical framework established in our prior work. By integrating rigorous mathematical analysis with practical design strategies, we aim to enhance the stability, convergence, generalization, and computational efficiency of neural operators. We revisit key the

Zhenli Xu, Yue Zhao, Qi Zhou

The random batch method is advantageous in accelerating force calculations in particle simulations, but it poses a challenge of removing the artificial heating effect in application to the Langevin dynamics. We develop an approach to solve this issue by estimating the force variance, resulting in a variance-reduced random batch Langevin dynamics. Theoretical

Mohammad Ful Hossain Seikh

Searches for ultra-high energy ($E_\nu \geq 10$ PeV) cosmogenic and astrophysical neutrinos (UHENs) have been conducted by several experiments over the last two decades. The Askaryan Radio Array (ARA), located near the geographical South Pole, was one of the first two experiments that used radio antennas sensitive to orthogonal polarizations for detection of

Dingrui Yang, Lingyi Li, Na Zhang, Hongyi Yu

We theoretically investigated the chiral phonons of honeycomb-type bilayer Wigner crystals recently discovered in van der Waals structures of layered transition metal dichalcogenides. These chiral phonons can emerge under the inversion symmetry breaking introduced by an effective mass imbalance between the two layers or a moir\'e potential in one layer, as w

Thai Vu Nguyen, Long Bao Le, Anderson Avila

In Federated Learning (FL), training is conducted on client devices, typically with limited computational resources and storage capacity. To address these constraints, we propose an automatic pruning scheme tailored for FL systems. Our solution improves computation efficiency on client devices, while minimizing communication costs. One of the challenges of t

Tanya Gatsak, Kumar Abhishek, Hanene Ben Yedder, Saeid Asgari Taghanaki

PET imaging is an invaluable tool in clinical settings as it captures the functional activity of both healthy anatomy and cancerous lesions. Developing automatic lesion segmentation methods for PET images is crucial since manual lesion segmentation is laborious and prone to inter- and intra-observer variability. We propose PET-Disentangler, a 3D disentanglem

Hyeonggeun Han, Sehwan Kim, Hyungjun Joo, Sangwoo Hong

Models trained with empirical risk minimization (ERM) are prone to be biased towards spurious correlations between target labels and bias attributes, which leads to poor performance on data groups lacking spurious correlations. It is particularly challenging to address this problem when access to bias labels is not permitted. To mitigate the effect of spurio

Yiming Sun, Fan Yu, Shaoxiang Chen, Yu Zhang

Visual object tracking aims to locate a targeted object in a video sequence based on an initial bounding box. Recently, Vision-Language~(VL) trackers have proposed to utilize additional natural language descriptions to enhance versatility in various applications. However, VL trackers are still inferior to State-of-The-Art (SoTA) visual trackers in terms of t

Emilio Tejeda, Jesús A. Toalá

The Bondi-Hoyle-Lyttleton (BHL) accretion model is widely used to describe how a compact object accretes material from a companion's stellar wind in binary systems. However, its standard implementation becomes inaccurate when the wind velocity ($v_\mathrm{w}$) is comparable to or less than the orbital velocity ($v_\mathrm{o}$), predicting non-physical accret

X. -H. Wang, G. Shu, H. Qian, X. Li

The room temperature continuous wave (CW) very-high-frequency (VHF) gun is one of the candidates for the electron gun of the high-repetition-rate free-electron lasers (FELs). The VHF gun operates with a cathode gradient of ~ 20 MV/m and an accelerating voltage of ~ 750 kV. The gun dark current emission leads to beam loss along the FEL machine, therefore is a

Pamela Freeman, Jo-Anne C. Brown

Radio astronomy observatories, such as the Dominion Radio Astrophysical Observatory in Penticton, British Columbia, try to limit radio frequency interference to observe incredibly faint astronomical signals. These protective measures include placing observatories in geographically remote locations, the implementation of radio-frequency-interference-free quie

Tevin Wang, Jingyuan He, Chenyan Xiong

Retrieval-augmented generation (RAG) combines knowledge from domain-specific sources into large language models to ground answer generation. Current RAG systems lack customizable visibility on the context documents and the model's attentiveness towards such documents. We propose RAGViz, a RAG diagnosis tool that visualizes the attentiveness of the generated

John Komp, Dananjay Srinivas, Maria Pacheco, Ashutosh Trivedi

An (artificial cardiac) pacemaker is an implantable electronic device that sends electrical impulses to the heart to regulate the heartbeat. As the number of pacemaker users continues to rise, so does the demand for features with additional sensors, adaptability, and improved battery performance. Reinforcement learning (RL) has recently been proposed as a pe

Siyuan Chen, Qingyi Si, Chenxu Yang, Yunzhi Liang

The advent of large language models (LLMs) has significantly propelled the advancement of Role-Playing Agents (RPAs). However, current Role-Playing Agents predominantly focus on mimicking a character's fundamental attributes while neglecting the replication of linguistic style, and they are incapable of effectively replicating characters when performing task

Kun Huang, Fang-Lue Zhang, Fangfang Zhang, Yu-Kun Lai

Geometric estimation is required for scene understanding and analysis in panoramic 360{\deg} images. Current methods usually predict a single feature, such as depth or surface normal. These methods can lack robustness, especially when dealing with intricate textures or complex object surfaces. We introduce a novel multi-task learning (MTL) network that simul

Xinyu Xu, Huazhen Liu, Feiming Wei, Huilin Xiong

Point cloud is often regarded as a discrete sampling of Riemannian manifold and plays a pivotal role in the 3D image interpretation. Particularly, rotation perturbation, an unexpected small change in rotation caused by various factors (like equipment offset, system instability, measurement errors and so on), can easily lead to the inferior results in point c

Dang Nguyen, Viet Dac Lai, Seunghyun Yoon, Ryan A. Rossi

Existing LLM agent systems typically select actions from a fixed and predefined set at every step. While this approach is effective in closed, narrowly scoped environments, it presents two major challenges for real-world, open-ended scenarios: (1) it significantly restricts the planning and acting capabilities of LLM agents, and (2) it requires substantial h

Lizuo Liu, Tongtong Li, Anne Gelb, Yoonsang Lee

We propose an entropy-stable conservative flux form neural network (CFN) that integrates classical numerical conservation laws into a data-driven framework using the entropy-stable, second-order, and non-oscillatory Kurganov-Tadmor (KT) scheme. The proposed entropy-stable CFN uses slope limiting as a denoising mechanism, ensuring accurate predictions in both

Xiaozhu Yu, Kai Yi, Yu Guang Wang, Yiqing Shen

Enzymes are biological catalysts that can accelerate chemical reactions compared to uncatalyzed reactions in aqueous environments. Their catalytic efficiency is quantified by the turnover number (kcat), a parameter in enzyme kinetics. Enhancing enzyme activity is important for optimizing slow chemical reactions, with far-reaching implications for both resear

Jin-Lei Yang, Jie Li

The neutrino oscillation experiments provide definitive evidence of new physics beyond the Standard Model (SM), and the neutrino mass-squared differences and flavor mixing have been precisely measured. This study examines the neutrino sector within the flavor-dependent $U(1)_F$ model, where the unique fermion sector can simultaneously address both the flavor

Lei Shi, Wei Liu, Xiang Li, Xin Zhang

This article investigates the pseudo transitions of the Blume-Capel model on two-dimensional finite-size lattices. By employing the Wang-Landau sampling method and microcanonical inflection point analysis, we identified the positions of phase transitions as well as higher-order phase transitions. Through Metropolis sampling and canonical ensemble analysis, w

Dohyun Kim, Pedro Sandoval-Segura

The construction of large datasets for deep learning has raised concerns regarding unauthorized use of online data, leading to increased interest in protecting data from third-parties who want to use it for training. The Convolution-based Unlearnable DAtaset (CUDA) method aims to make data unlearnable by applying class-wise blurs to every image in the datase

Roozbeh Hazrat, Elizabeth Pacheco

We prove what might have been expected: The Williams Conjecture in symbolic dynamics and Graded Morita Equivalence Conjecture for Leavitt/$C^*$-graph algebras hold for ``small graphs'', i.e., connected graphs with three vertices, no parallel edges, no sinks with no trivial hereditary and saturated subsets. Namely, two small graphs are shift equivalent if and

Sen Li, Ke Li, Yu Liu, Qifeng Liao

In this paper we present a conditional KRnet (cKRnet) based domain decomposed uncertainty quantification (CKR-DDUQ) approach to propagate uncertainties across different physical domains in models governed by partial differential equations (PDEs) with random inputs. This approach is based on the domain decomposed uncertainty quantification (DDUQ) method prese

Yanyi Zhang, Binglin Qiu, Qi Jia, Yu Liu

Most incremental learners excessively prioritize coarse classes of objects while neglecting various kinds of states (e.g. color and material) attached to the objects. As a result, they are limited in the ability to reason fine-grained compositionality of state-object pairs. To remedy this limitation, we propose a novel task called Compositional Incremental L

Jiarui Fang, Jinzhe Pan, Xibo Sun, Aoyu Li

Diffusion models are pivotal for generating high-quality images and videos. Inspired by the success of OpenAI's Sora, the backbone of diffusion models is evolving from U-Net to Transformer, known as Diffusion Transformers (DiTs). However, generating high-quality content necessitates longer sequence lengths, exponentially increasing the computation required f

Taoyu Wu, Yu Guang Wang, Yiqing Shen

Protein inverse folding aims to identify viable amino acid sequences that can fold into given protein structures, enabling the design of novel proteins with desired functions for applications in drug discovery, enzyme engineering, and biomaterial development. Diffusion probabilistic models have emerged as a promising approach in inverse folding, offering bot

Xiaoru He, Ling-Zheng Meng, Junpeng Hou, Xi-Wang Luo

With recent advances in simulating quantum phenomena in cold atoms, the higher-rank spin tensor Hall effect was discovered in larger spin systems with spin-tensor-momentum coupling, which is an extension of the celebrated spin Hall effects in larger spins. Previously, it has been proposed that a 2D electron gas with Rashba spin-orbit coupling can generate di

Michael Batista, Patrick Murphy, Oleg A. Igoshin, Misha Perepelitsa

In this paper, we consider 1D agent-based and kinetic models of aggregation with reversals. In particular, we fit a Gamma distribution to represent the run times in myxobacteria and analyze numerically the importance of non-exponential reversal times. We demonstrate that non-exponential reversal times aid aggregation and result in tighter aggregates. We comp

Madalena Caldeira, Plinio Moreno

The Next Best View problem is a computer vision problem widely studied in robotics. To solve it, several methodologies have been proposed over the years. Some, more recently, propose the use of deep learning models. Predictions obtained with the help of deep learning models naturally have some uncertainty associated with them. Despite this, the standard mode

Kun Shi, Pengshun Luo, Jinquan Liu, Hang Yin

Kelvin Probe Force Microscopy (KPFM) is widely used to measure the surface potential on samples, from which electrostatic patch force can be calculated. However, since the KPFM measurements represent a weighted average of local potentials on the sample, the accuracy of the evaluation critically depends on the precision and lateral resolution of the method. I

Jianhai Bao, Jiaqing Hao

In this paper, we are concerned with a modified Euler scheme for the SDE under consideration, where the drift is of super-linear growth and dissipative merely outside a closed ball. By adopting the synchronous coupling, along with the construction of an equivalent quasi-metric, the $L^2$-Wasserstein contraction of the modified Euler scheme is addressed provi

Ryan Branstetter, Samuel Chian, Joseph Cromp, William L He

In the months leading up to political elections in the United States, forecasts are widespread and take on multiple forms, including projections of what party will win the popular vote, state ratings, and predictions of vote margins at the state level. It can be challenging to evaluate how accuracy changes in the lead up to Election Day or to put probabilist

Jun Wang, Zhaoheng Guo, Erik Isele, Philip H. Bucksbaum

We present a comprehensive framework of modeling covariance in angular streaking experiments. Within the impulsive streaking regime, the displacement of electron momentum distribution (MD) provides a tight connection between the dressing-free MD and the dressed MD. Such connection establishes universal structures in the composition of streaking covariance th

J. A. López-Vázquez, Chin-Fei Lee, Hsien Shang, Sylvie Cabrit

We present the Atacama Large Millimeter/submillimeter Array Band 7 observations of the CO (J=3-2) line emission of the protostellar system HH 212 at $\sim$24 au spatial resolution and compare them to those of the SiO (J=8-7) and SO (J=8-7) line emission reported in the literature. We find that the CO line traces four distinct regions: (1) an outer outflow sh

Chaehwa Jeong, Juhyeok Lee, Hyesung Jo, KwangHo Lee

Pt-based electrocatalysts are the primary choice for fuel cells due to their superior oxygen reduction reaction (ORR) activity. To enhance ORR performance and durability, extensive studies have investigated transition metal alloying, doping, and shape control to optimize the three key governing factors for ORR: geometry, local chemistry, and strain of their

Matthew McDermott, Jason Rife

In this paper we reexamine the process through which a Neural Radiance Field (NeRF) can be trained to produce novel LiDAR views of a scene. Unlike image applications where camera pixels integrate light over time, LiDAR pulses arrive at specific times. As such, multiple LiDAR returns are possible for any given detector and the classification of these returns

Shuai Liu, Yuheng Zhang, Abdulkarim Hariri, Abdur-Raheem Al-Hallak

Silicon nitride (Si$_3$N$_4$) photonic integrated circuits (PICs) have emerged as a versatile platform for a wide range of applications, such as nonlinear optics, narrow-linewidth lasers, and quantum photonics. While thin-film Si$_3$N$_4$ processes have been extensively developed, many nonlinear and quantum optics applications require the use of thick Si$_3$

He Bai, Asa Ferguson, Leonard Wainstein, Jonathan Wells

We extend prior work comparing linear multilevel models (MLM) and fixed effect (FE) models to the generalized linear model (GLM) setting, where the coefficient on a treatment variable is of primary interest. This leads to three insights. (i) First, as in the linear setting, MLM can be thought of as a regularized form of FE (RegFE). This explains why group-le

H. Ogawa, Y. Takeuchi, H. Sekiya, K. Iyoki

This paper investigates the removal of radon from purified and ambient airs by Ag-zeolite. Ag-zeolite is known to have very high performance for airborne radon removal. The dependence of zeolite type and silver content on the performance of radon removal was evaluated. The performance of radon removal by single pass and radon emanation were also evaluated. I

Ioannis Anagnostides, Alkis Kalavasis, Tuomas Sandholm

A celebrated connection in the interface of online learning and game theory establishes that players minimizing swap regret converge to correlated equilibria (CE) -- a seminal game-theoretic solution concept. Despite the long history of this problem and the renewed interest it has received in recent years, a basic question remains open: how many iterations a

Ioannis Anagnostides, Alkis Kalavasis, Tuomas Sandholm

A celebrated result in the interface of online learning and game theory guarantees that the repeated interaction of no-regret players leads to a coarse correlated equilibrium (CCE) -- a natural game-theoretic solution concept. Despite the rich history of this foundational problem and the tremendous interest it has received in recent years, a basic question s

Haoyang Zheng, Guang Lin

Sparse Identification of Nonlinear Dynamical Systems (SINDy) is a powerful tool for the data-driven discovery of governing equations. However, it encounters challenges when modeling complex dynamical systems involving high-order derivatives or discontinuities, particularly in the presence of noise. These limitations restrict its applicability across various

Harini Narayanan, Sindhu Ghanta

Chronic wounds are a significant burden on individuals and the healthcare system, affecting millions of people and incurring high costs. Wound classification using deep learning techniques is a promising approach for faster diagnosis and treatment initiation. However, lack of high quality data to train the ML models is a major challenge to realize the potent

Mark Zhandry

QMA is the class of languages that can be decided by an efficient quantum verifier given a quantum witness, whereas QCMA is the class of such languages where the efficient quantum verifier only is given a classical witness. A challenging fundamental goal in quantum query complexity is to find a classical oracle separation for these classes. In this work, we

Maya Bechler-Speicher, Moshe Eliasof

Graph Neural Networks (GNNs) have gained significant popularity for learning representations of graph-structured data due to their expressive power and scalability. However, despite their success in domains such as social network analysis, recommendation systems, and bioinformatics, GNNs often face challenges related to stability, generalization, and robustn

Yudi Zhang, Pei Xiao, Lu Wang, Chaoyun Zhang

In-context learning (ICL) and Retrieval-Augmented Generation (RAG) have gained attention for their ability to enhance LLMs' reasoning by incorporating external knowledge but suffer from limited contextual window size, leading to insufficient information injection. To this end, we propose a novel framework, RuAG, to automatically distill large volumes of offl

Daniel J. W. Touw, Michel van de Velden

The classifier chain is a widely used method for analyzing multi-labeled data sets. In this study, we introduce a generalization of the classifier chain: the classifier chain network. The classifier chain network enables joint estimation of model parameters, and allows to account for the influence of earlier label predictions on subsequent classifiers in the

Jishu Das

Let $F$ be a multi-quadratic totally real number field. Let $σ_1,\dots, σ_r$ denote its distinct embeddings. Given $s \in F,$ we give an explicit formula for $\| σ(s)\|$ and $\sum_{i<j} σ_i(s)σ_j(s),$ where $\| σ(s)\|=\sqrt{\sum_{i=1}^r(σ_i(s))^2}.$ Let $\mathfrak{M}$ be a fractional ideal in $F$ and $\min\left( \mathfrak{M}\right):=\min\{\|σ(s)\| \, | \, s

Philipp Frey, Stephan Rachel

Hard-core bosons (HCB) in one dimension are predicted to show surprisingly interesting dynamics after a quantum quench. Far from equilibrium, quasi-condensation at finite momenta has been observed in numerical studies, while the equilibrium state at late times is expected to violate conventional thermodynamics. The integrability of the model supposedly const

Adel Ben Moussa, Jules Lamers, Didina Serban

In this note we announce some results extending our recent work with A. Toufik on the free-fermion point q=i of the Haldane-Shastry chain to the case with an even number N of sites. The resulting long-range version of the Heisenberg XX chain may be viewed as a model of fermions with extended gl(1|1) symmetry. Unlike for odd N, the conserved charges are nilpo

Gregory Berkolaiko, Yaiza Canzani, Graham Cox, Peter Kuchment

A spectral minimal partition of a manifold is a decomposition into disjoint open sets that minimizes a spectral energy functional. While it is known that bipartite minimal partitions correspond to nodal partitions of Courant-sharp Laplacian eigenfunctions, the non-bipartite case is much more challenging. In this paper, we unify the bipartite and non-bipartit

Lok Pati Tripathi, Aditi Tomar, Amiya K. Pani

A non-uniform implicit-explicit L1 mixed finite element method (IMEX-L1-MFEM) is investigated for a class of time-fractional partial integro-differential equations (PIDEs) with space-time dependent coefficients and non-self-adjoint elliptic part. The proposed fully discrete method combines an IMEX-L1 method on a graded mesh in the temporal variable with a mi

Alice Giampino, Antonio Canale, Bernardo Nipoti

Several approaches have been proposed in the literature for clustering multivariate ordinal data. These methods typically treat missing values as absent information, rather than recognizing them as valuable for profiling population characteristics. To address this gap, we introduce a Bayesian nonparametric model for co-clustering multivariate ordinal data th

Mizuki Sanatani, Yuuya Chiba, Naoto Shiraishi

We provide a rigorous proof of the absence of nontrivial local conserved quantities in all spin-1/2 chains with symmetric nearest-neighbor interaction, except for known integrable systems. This result shows that there are no further integrable system that awaits to be discovered. Our finding also implies that there is no intermediate systems with a finite nu

Mizuki Sanatani, Yuuya Chiba, Naoto Shiraishi

General spin-1/2 chains with symmetric nearest-neighbor interaction are studied. We rigorously prove that all spin models in this class, except for known integrable systems, are non-integrable in the sense that they possess no nontrivial local conserved quantities. This result confirms that there are no missing integrable systems, i.e., integrable systems in

Igor Filikhin, Roman Ya. Kezerashvili, Branislav Vlahovic

Using the folding procedure, we investigate the bound state of the $Ω$+$α$ system based on $Ω$-$N$ ($^{5}S_{2}$) HAL QCD potential. Previous theoretical analyses have indicated the existence of a deeply bound ground state, which is attributed to the strong $Ω$-nucleon interaction. By employing well-established parameterizations of nucleon density within the

Yaolei Shen, Antonio Franchi, Chiara Gabellieri

In this work, we present a model-based optimal boundary control design for an aerial robotic system composed of a quadrotor carrying a flexible cable. The whole system is modeled by partial differential equations (PDEs) combined with boundary conditions described by ordinary differential equations (ODEs). The proper orthogonal decomposition (POD) method is a

Theodora Bourni, Timothy Buttsworth, Ramiro Lafuente, Mat Langford

For each $n\ge 3$, we construct a &#39;pancake-like&#39;, $O(2)\times O(n-1)$-invariant ancient Ricci flow with positive curvature operator and bounded &#34;girth&#34;, and we determine its asymptotic limits backwards in time. This solution is new even in dimension three. The construction hinges on the Ricci flow invariance of certain conditions on the curva

Quadry Chance, Daniel Foreman-Mackey, Sarah Ballard, Andrew Casey

Data Release 3 (DR3) from the Gaia Mission includes radial velocity measurements of over 33 million targets. Among many scientific applications, the overlap of this stellar sample with targeted exoplanet transit survey stars presents an opportunity to understand planet occurrence in the context of stellar multiplicity on a large scale. Yet, any interpretatio