April 2024 arXiv papers — page 90

Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla

Link prediction (LP) is a fundamental task in graph representation learning, with numerous applications in diverse domains. However, the generalizability of LP models is often compromised due to the presence of noisy or spurious information in graphs and the inherent incompleteness of graph data. To address these challenges, we draw inspiration from the Info

Chengyang Yan, Donald G. Dansereau

The performance of perception tasks is heavily influenced by imaging systems. However, designing cameras with high task performance is costly, requiring extensive camera knowledge and experimentation with physical hardware. Additionally, cameras and perception tasks are mostly designed in isolation, whereas recent methods that jointly design cameras and task

Philippe Lecheminant, Keisuke Totsuka

The nature of the insulating phases of the SU($N$)-generalization of the one-dimensional Kondo lattice model is investigated by means of non-perturbative approaches. By extending the Lieb-Schultz-Mattis (LSM) argument to multi-component fermion systems with translation and global SU($N$) symmetries, we derive two indices which depend on the filling and the `

Zhiqiang Zhao, Ping Ren, Qian Yang

Excellent students are not only those who master more effective and efficient learning techniques to acquire and apply information. Even in the absence of correct learning, they are able to self-motivate, evaluate, and adjust their behavior. This study aims to explore the relationship between student self-management and academic achievement, with a focus on

Haley Broadus, Elena Pavelescu

We consider embeddings of maximal outerplanar graphs whose vertices all lie on a cycle $\mathcal{C}$ bounding a face. Each edge of the graph that is not in $\mathcal{C}$, a chord, is assigned a length equal to the length of the shortest path in $\mathcal{C}$ between its endpoints. We define the total chord length of a graph as the sum of lengths of all its c

Guangran Cheng, Chuheng Zhang, Wenzhe Cai, Li Zhao

While large language models (LLMs) are successful in completing various language processing tasks, they easily fail to interact with the physical world by generating control sequences properly. We find that the main reason is that LLMs are not grounded in the physical world. Existing LLM-based approaches circumvent this problem by relying on additional pre-d

Jorge Cayao

The emergence of Majorana bound states in finite length superconductor-semiconductor hybrid systems has been predicted to occur in the form of oscillatory energy levels with parity crossings around zero energy. Each zero-energy crossing is expected to produce a quantized zero-bias conductance peak but several studies have reported conductance peaks pinned at

Sanggeon Yun, Ryozo Masukawa, SungHeon Jeong, Mohsen Imani

Customizable image retrieval from large datasets remains a critical challenge, particularly when preserving spatial relationships within images. Traditional hashing methods, primarily based on deep learning, often fail to capture spatial information adequately and lack transparency. In this paper, we introduce NeuroHash, a novel neuro-symbolic framework leve

Hideitsu Hino, Keisuke Yano

This paper investigates the information geometrical structure of a determinantal point process (DPP). It demonstrates that a DPP is embedded in the exponential family of log-linear models. The extent of deviation from an exponential family is analyzed using the $\mathrm{e}$-embedding curvature tensor, which identifies partially flat parameters of a DPP. On t

Leena Mathur, Paul Pu Liang, Louis-Philippe Morency

Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal that involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents (human or artificial). Progress towards Social-AI has accelerated in the past decade across several computing comm

Eric Gaidos, Aleezah Ali, Adam L. Kraus, Jason F. Rowe

M dwarf stars are the most promising hosts for detection and characterization of small and potentially habitable planets, and provide leverage relative to solar-type stars to test models of planet formation and evolution. Using \emph{Gaia} astrometry, adaptive optics imaging, and calibrated gyrochronologic relations to estimate stellar properties and filter

Chong-Ye Chen, Mu-Jing Li, Zhe Yang, Da-Ming Jin

This study presents a comprehensive investigation of anisotropy in a holographic p-wave superconductor model, revealing novel insights into the behavior of quantum information measures in strongly coupled systems. Through rigorous semi-analytical methods, we uncover the existence of an isotropic point emerging at a critical temperature $T_{II}$, marking a si

Li Wang, Zhenbo Wang, Shu Chen

We propose a family of exactly solvable quasiperiodic lattice models with analytical complex mobility edges, which can incorporate mosaic modulations as a straightforward generalization. By sweeping a potential tuning parameter $\delta$, we demonstrate a kind of interesting butterfly-like spectra in complex energy plane, which depicts energy-dependent extend

Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla

Graph structured data, specifically text-attributed graphs (TAG), effectively represent relationships among varied entities. Such graphs are essential for semi-supervised node classification tasks. Graph Neural Networks (GNNs) have emerged as a powerful tool for handling this graph-structured data. Although gradient descent is commonly utilized for training

Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet

Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, without any weight updates. Newly expanded context windows allow us to investigate ICL with hundreds or thousands of examples -- the many-shot regime. Going from few-shot to many-shot, we observe significant performance g

Brendan P. Crill, Michael Werner, Rachel Akeson, Matthew Ashby

SPHEREx, the Spectro-Photometer for the History of the Universe, Epoch of Reionization, and ices Explorer, is a NASA MIDEX mission planned for launch in 2024. SPHEREx will carry out the first all-sky spectral survey at wavelengths between 0.75 micron and 5 micron with spectral resolving power ~40 between 0.75 and 3.8 micron and ~120 between 3.8 and 5 micron

Jiayang Li, Junjun Jiang, Pengwei Liang, Jiayi Ma

In this paper, we introduce MaeFuse, a novel autoencoder model designed for Infrared and Visible Image Fusion (IVIF). The existing approaches for image fusion often rely on training combined with downstream tasks to obtain highlevel visual information, which is effective in emphasizing target objects and delivering impressive results in visual quality and ta

Haotian Xu, Zhaorui Zhang, Sheng Di, Benben Liu

Federated learning has been identified as an efficient decentralized training paradigm for scaling the machine learning model training on a large number of devices while guaranteeing the data privacy of the trainers. FedAvg has become a foundational parameter update strategy for federated learning, which has been promising to eliminate the effect of the hete

Kang Wang, Zhishu Shen, Zhen Lei, Tiehua Zhang

Traffic signal control systems (TSCSs) are integral to intelligent traffic management, fostering efficient vehicle flow. Traditional approaches often simplify road networks into standard graphs, which results in a failure to consider the dynamic nature of traffic data at neighboring intersections, thereby neglecting higher-order interconnections necessary fo

Erkan Bayram, Shenyu Liu, Mohamed-Ali Belabbas, Tamer Başar

Given a training set in the form of a paired $(\mathcal{X},\mathcal{Y})$, we say that the control system $\dot x = f(x,u)$ has learned the paired set via the control $u^*$ if the system steers each point of $\mathcal{X}$ to its corresponding target in $\mathcal{Y}$. If the training set is expanded, most existing methods for finding a new control $u^*$ requir

Naomi Tsuji, Takaaki Tanaka, Samar Safi-Harb, Felix Aharonian

HESS J1641-463 is an unidentified gamma-ray source with a hard TeV gamma-ray spectrum, and thus it has been proposed to be a possible candidate for cosmic ray (CR) accelerators up to PeV energies (a PeVatron candidate). The source spatially coincides with the radio supernova remnant (SNR) G338.5+0.1, but has not yet been fully explored in the X-ray band. We

Sihong Lei, Shiqi Xia, Daohong Song, Jingjun Xu

Robust higher-order optical vortices are much in demand for applications in optical manipulation, optical communications, quantum entanglement and quantum computing. However, in numerous experimental settings, a controlled generation of optical vortices with arbitrary orbital angular momentum (OAM) remains a substantial challenge. Here, we present a concept

Assil Fadle, Mehdi Talbi, Nizar Touzi

We provide a general It\=o\,-Wentzell formula for a random field of maps on the Wasserstein space of probability measures, defined by continuous semimartingales, and evaluated along the flow of conditional distributions of another continuous semimartingale. Our method follows standard arguments of It\=o calculus, and thus bypasses the approximation by empiri

George Halal, S. E. Clark, Mehrnoosh Tahani

Using 3D dust maps and Planck polarized dust emission data, we investigate the influence of the 3D geometry of the nearby interstellar medium (ISM) on the statistics of the dust polarization on large ($80'$) scales. We test recent models that assume that the magnetic field probed by the polarized dust emission is preferentially tangential to the Local Bubble

Qing En, Yuhong Guo

Lung-infected area segmentation is crucial for assessing the severity of lung diseases. However, existing image-text multi-modal methods typically rely on labour-intensive annotations for model training, posing challenges regarding time and expertise. To address this issue, we propose a novel attribute knowledge-guided framework for unsupervised lung-infecte

Ru Wang, Xiaojun Ji, Hao Wang, Wenjian Liu

We present a reduction of Milestoning (ReM) algorithm to analyze the high-dimensional Milestoning kinetic network. The algorithm reduces the Milestoning network to low dimensions but preserves essential kinetic information, such as local residence time, exit time, and mean first passage time between any two states. This is achieved in three steps. First, nod

Asmaa Ahmad, Edward Valachovic

The outbreak of the SARS-CoV-2 virus, which led to an unprecedented global pandemic, has underscored the critical importance of understanding seasonal patterns. This knowledge is fundamental for decision-making in healthcare and public health domains. Investigating the presence, intensity, and precise nature of seasonal trends, as well as these temporal patt

Xiang-Ning Su, Hui Zhang, Ji-Lin Zhou

Many studies have analyzed planetary occurrence rates and their dependence on the host's properties to provide clues to planet formation, but few have focused on the mutual occurrence ratio of different kinds of planets. Such relations reveal whether and how one type of planet evolves into another, e.g. from a cold Jupiter to a warm or even hot Jupiter, and

H. N. Mhaskar, S. Kitimoon, Raghu G. Raj

Motivated by a number of applications in signal processing, we study the following question. Given samples of a multidimensional signal of the form $$ f(\boldsymbol\ell)=\sum_{k=1}^K a_k\exp(-i\langle \boldsymbol\ell, \mathbf{w}_k\rangle), \quad \mathbf{w}_1,\cdots,\mathbf{w}_k\in\mathbb{R}^q, \ \boldsymbol\ell\in \mathbb{Z}^q, \ |\boldsymbol\ell| <n, $$ det

Qi Han, Zhibo Tian, Chengwei Xia, Kun Zhan

Semi-supervised image classification, leveraging pseudo supervision and consistency regularization, has demonstrated remarkable success. However, the ongoing challenge lies in fully exploiting the potential of unlabeled data. To address this, we employ information entropy neural estimation to utilize the potential of unlabeled samples. Inspired by contrastiv

Chen-Hao Hao, Xin Su, Yong-Qiang Wang

In this paper, we study the spherically symmetric traversable wormholes with a scalar field supported by a phantom field in the anti-de Sitter (AdS) asymptotic spacetime. Despite coupling the scalar matter field, these wormholes remain massless and symmetric for reflection of the radial coordinate $r \rightarrow -r$. The solution possesses a finite Noether c

Zihao Huo, Zhihui Luo, Peng Zhang, Aiqin Yang

The recent transport measurement of La$_3$Ni$_2$O$_7$ uncover a "right-triangle" shape of the superconducting dome in the pressure-temperature (P-T) phase diagram. Motivated by this, we perform theoretical first-principles studies of La$_3$Ni$_2$O$_7$ with the pressure ranging from 0 to 100 GPa. Notably, we reveal a pressure dependence of the Ni-$d_{z^2}$ el

Edmond Tong, Anthony Opipari, Stanley Lewis, Zhen Zeng

In order for robots to interact with objects effectively, they must understand the form and function of each object they encounter. Essentially, robots need to understand which actions each object affords, and where those affordances can be acted on. Robots are ultimately expected to operate in unstructured human environments, where the set of objects and af

Zebing Mao, Sota Suzuki, Hiroyuki Nabae, Shoko Miyagawa

Fecal incontinence, arising from a myriad of pathogenic mechanisms, has attracted considerable global attention. Despite its significance, the replication of the defecatory system for studying fecal incontinence mechanisms remains limited largely due to social stigma and taboos. Inspired by the rectum's functionalities, we have developed a soft robotic syste

Nuo Chen, Kentaro Motohara, Lee Spitler, Kimihiko Nakajima

We investigate the galaxy properties of $\sim$400 low-mass ($<10^9\,M_{\odot}$) H$\alpha$ emitters (HAEs) at z $\sim$ 2.3 in the ZFOURGE survey. The selection of these HAEs is based on the excess in the observed $K_s$ broad-band flux compared to the stellar continuum estimated from the best-fit SED. These low-mass HAEs have elevated SFR(H$\alpha$) above the

Nicole Immorlica, Brendan Lucier, Markus Mobius, James Siderius

We introduce a model of online algorithms subject to strict constraints on data retention. An online learning algorithm encounters a stream of data points, one per round, generated by some stationary process. Crucially, each data point can request that it be removed from memory $m$ rounds after it arrives. To model the impact of removal, we do not allow the

Yoshimi Egawa, Michitaka Furuya

For a family $\mathcal{H}$ of graphs, a graph $G$ is said to be {\it $\mathcal{H}$-free} if $G$ contains no member of $\mathcal{H}$ as an induced subgraph. We let $\tilde{\mathcal{G}}_{3}(\mathcal{H})$ denote the family of connected $\mathcal{H}$-free graphs having minimum degree at least $3$. In this paper, we characterize the non-caterpillar trees $T$ havi

Qiang Li, Michal Yemini, Hoi-To Wai

This paper studies the convergence of clipped stochastic gradient descent (SGD) algorithms with decision-dependent data distribution. Our setting is motivated by privacy preserving optimization algorithms that interact with performative data where the prediction models can influence future outcomes. This challenging setting involves the non-smooth clipping o

Seungjin Yoon, Yu Sung Choi, Mark Tame, Jae Woong Yoon

We propose a quantum plasmonic sensor using Hong-Ou-Mandel (HOM) interferometry that measures the refractive index of an analyte, embedded in a plasmonic beam splitter composed of a dual-Kretschmann configuration, which serves as a frustrated total internal reflection beamsplitter. The sensing performance of the HOM interferometry, combined with single-photo

Yunier Bello-Cruz, J. G. Melo, L. F. Prudente, R. V. G. Serra

We present a proximal gradient method for solving convex multiobjective optimization problems, where each objective function is the sum of two convex functions, with one assumed to be continuously differentiable. The algorithm incorporates a backtracking line search procedure that requires solving only one proximal subproblem per iteration, and is exclusivel

Jinghai He, Haoyu Liu, Yuhang Wu, Zeyu Zheng

We provide a preliminary study on utilizing GPU (Graphics Processing Unit) to accelerate computation for three simulation optimization tasks with either first-order or second-order algorithms. Compared to the implementation using only CPU (Central Processing Unit), the GPU implementation benefits from computational advantages of parallel processing for large

Muhammad Z. Alam, Zeeshan Kaleem, Sousso Kelouwani

Vision sensors are versatile and can capture a wide range of visual cues, such as color, texture, shape, and depth. This versatility, along with the relatively inexpensive availability of machine vision cameras, played an important role in adopting vision-based environment perception systems in autonomous vehicles (AVs). However, vision-based perception syst

Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov

The industrial multi-generator Wave Energy Converters (WEC) must handle multiple simultaneous waves coming from different directions called spread waves. These complex devices in challenging circumstances need controllers with multiple objectives of energy capture efficiency, reduction of structural stress to limit maintenance, and proactive protection again

Andre del Carpio Gutierrez, Paul Denny, Andrew Luxton-Reilly

Parsons problems provide useful scaffolding for introductory programming students learning to write code. However, generating large numbers of high-quality Parsons problems that appeal to the diverse range of interests in a typical introductory course is a significant challenge for educators. Large language models (LLMs) may offer a solution, by allowing stu

Rihito Sakurai, Haruto Takahashi, Koichi Miyamoto

A long-standing issue in mathematical finance is the speed-up of option pricing, especially for multi-asset options. A recent study has proposed to use tensor train learning algorithms to speed up Fourier transform (FT)-based option pricing, utilizing the ability of tensor trains to compress high-dimensional tensors. Another usage of the tensor train is to c

Amit Kumar Singh Yadav, Kratika Bhagtani, Davide Salvi, Paolo Bestagini

Methods that can generate synthetic speech which is perceptually indistinguishable from speech recorded by a human speaker, are easily available. Several incidents report misuse of synthetic speech generated from these methods to commit fraud. To counter such misuse, many methods have been proposed to detect synthetic speech. Some of these detectors are more

Valdemar Švábenský, Jan Vykopal, Martin Horák, Martin Hofbauer

For undergraduate students of computing, learning to solve complex practical problems in a team is an essential skill for their future careers. This skill is needed in various fields, such as in cybersecurity and IT governance. Tabletop exercises are an innovative teaching method used in practice for training teams in incident response and evaluation of cont

Yunier Bello-Cruz, Max L. N. Gonçalves, Jefferson G. Melo, Cassandra Mohr

This paper presents and investigates an inexact proximal gradient method for solving composite convex optimization problems characterized by an objective function composed of a sum of a full-domain differentiable convex function and a non-differentiable convex function. We introduce an explicit line search applied specifically to the differentiable component

F. Crespo, M. Uribe, E. Martínez

We consider a completely integrable system of differential equations in arbitrary dimensions whose phase space contains an open set foliated by periodic orbits. This research analyzes the persistence and stability of the periodic orbits under a nonlinear periodic perturbation. For this purpose, we use the Melnikov method and Floquet theory to establish condi

Junbiao Pang, Zailin Dong, Jiaxin Deng, Mengyuan Zhu

Parsing Computer-Aided Design (CAD) drawings is a fundamental step for CAD revision, semantic-based management, and the generation of 3D prototypes in both the architecture and engineering industries. Labeling symbols from a CAD drawing is a challenging yet notorious task from a practical point of view. In this work, we propose to label and spot symbols from

Chaoxi Niu, Guansong Pang, Ling Chen

Real-life graph data often expands continually, rendering the learning of graph neural networks (GNNs) on static graph data impractical. Graph continual learning (GCL) tackles this problem by continually adapting GNNs to the expanded graph of the current task while maintaining the performance over the graph of previous tasks. Memory replay-based methods, whi

Mari Ohfuchi, Shintaro Sato

High-fidelity quantum state transfer and remote entanglement between superconducting fixed-frequency qubits have not yet been realized. In this study, we propose an alternative remote cross-resonance gate. Considering multiple modes of a superconducting coaxial cable connecting qubits, we must find conditions under which the cross-resonance gate operates wit

Arpiar Avetis Grigorian, Jason Dexter

Sagittarius A* (Sgr A*), the supermassive black hole at the center of the Milky Way, undergoes large-amplitude near-infrared (NIR) flares that can coincide with the continuous rotation of the NIR emission region. One promising explanation for this observed NIR behavior is a magnetic flux eruption, which occurs in three-dimensional General Relativistic Magnet

Changbin Li, Kangshuo Li, Yuzhe Ou, Lance M. Kaplan

Deep neural networks (DNNs) have been shown to perform well on exclusive, multi-class classification tasks. However, when different classes have similar visual features, it becomes challenging for human annotators to differentiate them. This scenario necessitates the use of composite class labels. In this paper, we propose a novel framework called Hyper-Evid

Eric Keto

We re-analyze the data of the BU-FCRAO $^{13}{\rm CO}$ Galactic Ring Survey (GRS) to understand the dynamics of the turbulent molecular interstellar medium. We define molecular clouds by their spatial half-power contours of $^{13}{\rm CO}$ integrated intensity, independent of a boundary based on thresholding or tiling. We find properties of hydrostatic equil

Nawfal Guefrachi, Jian Shi, Hakim Ghazzai, Ahmad Alsharoa

The integration of Light Detection and Ranging (LiDAR) and Internet of Things (IoT) technologies offers transformative opportunities for public health informatics in urban safety and pedestrian well-being. This paper proposes a novel framework utilizing these technologies for enhanced 3D object detection and activity classification in urban traffic scenarios

Jacqueline K. Faherty, Ben Burningham, Jonathan Gagné, Genaro Suárez

Beyond our solar system, aurorae have been inferred from radio observations of isolated brown dwarfs (e.g. Hallinan et al. 2006; Kao et al. 2023). Within our solar system, giant planets have auroral emission with signatures across the electromagnetic spectrum including infrared emission of H3+ and methane. Isolated brown dwarfs with auroral signatures in the

Wei Duan, Jie Lu, Junyu Xuan

Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless collaboration among agents, often represented by an underlying relation graph. Existing methods for learning this graph primarily focus on agent-pair relations, neglecting higher-order relationships. While several approaches attempt to extend cooperation modelling to encompass behavi

Jaehyung Kim, Jaehyun Nam, Sangwoo Mo, Jongjin Park

Large language models (LLMs) have made significant advancements in various natural language processing tasks, including question answering (QA) tasks. While incorporating new information with the retrieval of relevant passages is a promising way to improve QA with LLMs, the existing methods often require additional fine-tuning which becomes infeasible with r

Jan-Philipp Fränken, Kanishk Gandhi, Tori Qiu, Ayesha Khawaja

As AI systems like language models are increasingly integrated into decision-making processes affecting people's lives, it's critical to ensure that these systems have sound moral reasoning. To test whether they do, we need to develop systematic evaluations. We provide a framework that uses a language model to translate causal graphs that capture key aspects

Alessandro Zito, Jeffrey W. Miller

Non-negative matrix factorization (NMF) is widely used in many applications for dimensionality reduction. Inferring an appropriate number of factors for NMF is a challenging problem, and several approaches based on information criteria or sparsity-inducing priors have been proposed. However, inference in these models is often complicated and computationally

Yaoming Chu, Xiangbei Li, Jianming Cai

Entanglement, a hallmark of quantum mechanics, is a vital resource for quantum technologies. Generating highly entangled multipartite states is a key goal in current quantum experiments. We unveil a novel framework for understanding entanglement generation dynamics in Hamiltonian systems by quantum delocalization of an effective operator wavefunction on a co

Russell Schwab, Son Tu, Olga Turanova

In this article, we apply the viscosity solutions theory for integro-differential equations to the \emph{one-phase} Muskat equation (also known as the Hele-Shaw problem with gravity). We prove global well-posedness for the corresponding Hamilton-Jacobi-Bellmann equation with bounded, uniformly continuous initial data, in all dimensions.

P. B. Cushman, M. C. Fritts, A. D. Chambers, A. Roy

Background reduction in the SuperCDMS dark matter experiment depends on removing surface events within individual detectors by identifying the location of each incident particle interaction. Position reconstruction is achieved by combining pulse shape information over multiple phonon channels, a task well-suited to machine learning techniques. Data from an A

Omar Rinchi, Ahmad Alsharoa, Denise A. Baker

Breathing monitoring is crucial in healthcare for early detection of health issues, but traditional methods face challenges like invasiveness, privacy concerns, and limited applicability in daily settings. This paper introduces light detection and ranging (LiDAR) sensors as a remote, privacy-respecting alternative for monitoring breathing metrics, including

Min Sheng, Chongtao Guo, Lei Huang

Traditionally, communication, navigation, and remote sensing (CNR) satellites are separately performed, leading to resource waste, information isolation, and independent optimization for each functionality. Taking future automated driving as an example, it faces great challenges in providing high-reliable and low-latency lane-level positioning, decimeter-lev

Gari Lincoln Chua

Given a prime number $p$ and a positive integer $m$, we provide a family of diagonal hypersurfaces $\{ f_n \}_{n = 1}^{\infty}$ in $m$ variables, for which the denominator of $\text{ fpt } (f_{n})$ (in lowest terms) is always $p$ and whose $F$-pure thresholds stabilize after a certain $n$. We also provide another family of diagonal hypersurfaces $\{ g_n \}_{

Adam Hyland, Murtaza Ali

In education there exists a tension between two modes of learning: traditional lecture-based instruction and more tinkering-based creative learning. In this paper, we outline our efforts as two Ph.D. students (who are skilled in visualization but are not, importantly, professionally trained visualization experts) to implement creative learning activities in

Yeonguk Yu, Sungho Shin, Seunghyeok Back, Minhwan Ko

Test-time adaptation (TTA) aims to adapt a pre-trained model to a new test domain without access to source data after deployment. Existing approaches typically rely on self-training with pseudo-labels since ground-truth cannot be obtained from test data. Although the quality of pseudo labels is important for stable and accurate long-term adaptation, it has n

Adrit Rao, Andrea Fisher, Ken Chang, John Christopher Panagides

Data augmentations are widely used in training medical image deep learning models to increase the diversity and size of sparse datasets. However, commonly used augmentation techniques can result in loss of clinically relevant information from medical images, leading to incorrect predictions at inference time. We propose the Interactive Medical Image Learning

Gabriel Chuang, Oussama Hanguir, Clifford Stein

In the process of redistricting, one important metric is the number of competitive districts, that is, districts where both parties have a reasonable chance of winning a majority of votes. Competitive districts are important for achieving proportionality, responsiveness, and other desirable qualities; some states even directly list competitiveness in their l

Daniela Bortoletto, Aleksandra Dimitrievska, Maurice Garcia-Sciveres, Timon Heim

The ATLAS Inner Tracker (ITk) upgrade for the High-Luminosity LHC (HL-LHC) requires a radiation-tolerant pixel readout chip, which must withstand a total ionising dose (TID) of up to 1 Grad. The readout ASIC for the ITk upgrade has been designed by the RD53 collaboration using 65 nm CMOS technology. In order to characterise the radiation tolerance of the chi

M. E. Messinger, A. Porter

For a graph $G$, the vertices of the $k$-dominating graph, denoted $\mathcal{D}_k(G)$, correspond to the dominating sets of $G$ with cardinality at most $k$. Two vertices of $\mathcal{D}_k(G)$ are adjacent if and only if the corresponding dominating sets in $G$ can be obtained from one other by adding or removing a single vertex of $G$. Since $\mathcal{D}_k(

Yotam Dikstein, Max Hopkins

We prove optimal concentration of measure for lifted functions on high dimensional expanders (HDX). Let $X$ be a $k$-dimensional HDX. We show for any $i\leq k$ and $f:X(i)\to [0,1]$: \[\Pr_{s\in X(k)}\left[\left|\underset{{t\subseteq s}}{\mathbb{E}}[f(t)]-\mu\right|\geq\varepsilon\right]\leq exp\left(-\varepsilon^2\frac{k}{i}\right).\] Using this fact, we pr

Aytekin Çibik, Farjana Siddiqua, William Layton

In 1991, Ramshaw and Mesina proposed a novel synthesis of penalty methods and artificial compression methods. When the two were balanced they found the combination was 3-4 orders more accurate than either alone. This report begins the study of their interesting method applied to the Navier-Stokes equations. We perform stability analysis, semi-discrete error

Iulia Cristian, Barbara Niethammer, Juan J. L. Velázquez

We study an inhomogeneous coagulation equation that contains a transport term in the spatial variable modeling the sedimentation of clusters. We prove local existence of mass conserving solutions for a class of coagulation kernels for which in the space homogeneous case instantaneous gelation (i.e., instantaneous loss of mass) occurs. Our result holds true i

Yuze Liu, Xi Yu

Machine learning model development in chemistry and materials science often grapples with the challenge of small scale, unbalanced labelled datasets, a common limitation in scientific experiments. These dataset imbalances can precipitate overfit ting and diminish model generalization. Our study explores the efficacy of the farthest point sampling (FPS) strat

Veronica Pasquarella

This introductory work combines bottom-up and top-down approaches towards understanding the underlying categorical structure of possible unifying theories descending from string theory. Guided by well-established developments in the realm of categorical algebraic geometry, we explain why abelianisation could potentially lead to furthering the understanding o

Nhat Thanh Tran, Jack Xin

We study a fast local-global window-based attention method to accelerate Informer for long sequence time-series forecasting. While window attention being local is a considerable computational saving, it lacks the ability to capture global token information which is compensated by a subsequent Fourier transform block. Our method, named FWin, does not rely on

Sidiney B. Montanhano

Multi-agent scenarios, like Wigner&#39;s friend and Frauchiger-Renner scenarios, can show contradictory results when a non-classical formalism must deal with the knowledge between agents. Such paradoxes are described with multi-modal logic as violations of the structure in classical logic. Even if knowledge is treated in a relational way with the concept of

Christian Tomani, Kamalika Chaudhuri, Ivan Evtimov, Daniel Cremers

A major barrier towards the practical deployment of large language models (LLMs) is their lack of reliability. Three situations where this is particularly apparent are correctness, hallucinations when given unanswerable questions, and safety. In all three cases, models should ideally abstain from responding, much like humans, whose ability to understand unce

Farzam Ebrahimnejad, Ansh Nagda, Shayan Oveis Gharan

We study the complexity of approximating the permanent of a positive semidefinite matrix $A\in \mathbb{C}^{n\times n}$. 1. We design a new approximation algorithm for $\mathrm{per}(A)$ with approximation ratio $e^{(0.9999 + \gamma)n}$, exponentially improving upon the current best bound of $e^{(1+\gamma-o(1))n}$ [AGOS17,YP22]. Here, $\gamma \approx 0.577$ is

Xiumin Du, Jianhui Li

We obtain $L^p$ estimates of the maximal Schr\"odinger operator in $\mathbb R^n$ using polynomial partitioning, bilinear refined Strichartz estimates, and weighted restriction estimates.

Emilio Cantu-Cervini

Traditional Federated Learning (FL) methods typically train a single global model collaboratively without exchanging raw data. In contrast, Personalized Federated Learning (PFL) techniques aim to create multiple models that are better tailored to individual clients' data. We present a novel personalization approach based on stacked generalization where clien

Jingwei Liu, Fulvio Melia

Space-based missions studying the cosmic microwave background (CMB) have progressively refined the parameter space in conventional models of inflation shortly ($\sim 10^{-37}$ seconds) after the big bang. While most inflationary scenarios proposed thus far in the context of GR have since been ruled out, the basic idea of inflation may still be tenable, albei

Jingyang Zhao, Mingyu Xiao, Chao Xu

The Traveling Tournament Problem (TTP) is a well-known benchmark problem in the field of tournament timetabling, which asks us to design a double round-robin schedule such that each pair of teams plays one game in each other's home venue, minimizing the total distance traveled by all $n$ teams ($n$ is even). TTP-$k$ is the problem with one more constraint th

Albert Powers, Philip Angelos, Alexandria Bond, Emily Farina

The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify

Elismar R. Oliveira, Vilmar Trevisan

We study limit points of the spectral radii of $A_{\alpha}$-matrices of graphs. Adapting a method used by J. B. Shearer in 1989, we prove a density property of $A_{\alpha}$-limit points of caterpillars for $\alpha$ close to zero. Precisely, we show that for $\alpha \in [0, 1/2)$ there exists a positive number $\tau_2(\alpha)>2$ such that any value $\lambda>

Quan Shi, Michael Tang, Karthik Narasimhan, Shunyu Yao

Computing olympiads contain some of the most challenging problems for humans, requiring complex algorithmic reasoning, puzzle solving, in addition to generating efficient code. However, it has been understudied as a domain to evaluate language models (LMs). In this paper, we introduce the USACO benchmark with 307 problems from the USA Computing Olympiad, alo

Xiumin Du, Jianhui Li, Hong Wang, Ruixiang Zhang

We obtain some sharp $L^p$ weighted Fourier restriction estimates of the form $\|Ef\|_{L^p(B^{n+1}(0,R),Hdx)} \lessapprox R^{\beta}\|f\|_2$, where $E$ is the Fourier extension operator over the truncated paraboloid, and $H$ is a weight function on $\mathbb R^{n+1}$ which is $n$-dimensional up to scale $\sqrt R$.

Akira Kamatsuka, Koki Kazama, Takahiro Yoshida

This study presents alternating optimization (AO) algorithms for computing $\alpha$-mutual information ($\alpha$-MI) and $\alpha$-capacity based on variational characterizations of $\alpha$-MI using a reverse channel. Specifically, we derive several variational characterizations of Sibson, Arimoto, Augustin--Csisz{\' a}r, and Lapidoth--Pfister MI and introdu

Tom Savage, Ehecatl Antonio del Rio Chanona

Bayesian optimization has been successfully applied throughout Chemical Engineering for the optimization of functions that are expensive-to-evaluate, or where gradients are not easily obtainable. However, domain experts often possess valuable physical insights that are overlooked in fully automated decision-making approaches, necessitating the inclusion of h

MicroBooNE collaboration, P. Abratenko, O. Alterkait, D. Andrade Aldana

We report the first double-differential cross section measurement of neutral-current neutral pion (NC$\pi^0$) production in neutrino-argon scattering, as well as single-differential measurements of the same channel in terms of final states with and without protons. The kinematic variables of interest for these measurements are the $\pi^0$ momentum and the $\

Trym A. E. Lindell, Ola H. Ramstad, Ionna Sandvig, Axel Sandvig

A primary challenge in utilizing in-vitro biological neural networks for computations is finding good encoding and decoding schemes for inputting and decoding data to and from the networks. Furthermore, identifying the optimal parameter settings for a given combination of encoding and decoding schemes adds additional complexity to this challenge. In this stu

Jonathan S. Van Doren, M. Houssem Kasbaoui

We investigate the dynamics of an oscillatory boundary layer developing over a bed of collisional and freely evolving sediment grains. We perform Euler-Lagrange simulations at Reynolds numbers $\mathrm{Re}_\delta= 200$, 400, and 800, density ratio $\rho_p/\rho_f = 2.65$, Galileo number $\mathrm{Ga} = 51.9$, maximum Shields numbers from $5.60 \times 10^{-2}$

Chanwoo Bae, Guanhong Tao, Zhuo Zhang, Xiangyu Zhang

Cyber attacks cause over \$1 trillion loss every year. An important task for cyber security analysts is attack forensics. It entails understanding malware behaviors and attack origins. However, existing automated or manual malware analysis can only disclose a subset of behaviors due to inherent difficulties (e.g., malware cloaking and obfuscation). As such,

F. A. Gómez Albarracín

Undoubtedly, machine learning techniques are being increasingly applied to a wide range of situations in the field of condensed matter. Amongst these techniques, unsupervised techniques are especially attractive, since they imply the possibility of extracting information from the data without previous labeling. In this work, we resort to the technique known

Zhihong Deng, Jing Jiang, Guodong Long, Chengqi Zhang

In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maximizing returns. Recent works have proposed many different types of fairness notions, but how unfairness arises in RL problems remains unclear. In this paper, we address this gap in

Shufang Xu, Ming Mei, Jean-Christophe Nave, Wancheng Sheng

In this paper, we study the asymptotic stability of viscous shock waves for Burgers' equation with fast diffusion $u_t+f(u)_x=\mu (u^m)_{xx}$ on $\mathbb{R} \times (0, +\infty)$ when $0<m<1$. For the proposed constant states $u_->u_+=0$, the equation with fast diffusion $(u^m)_{xx}=m\left(\frac{u_x}{u^{1-m}}\right)_x$ processes a strong singularity at $u_+=0

Yusra Alkendi, Rana Azzam, Sajid Javed, Lakmal Seneviratne

Moving object segmentation is critical to interpret scene dynamics for robotic navigation systems in challenging environments. Neuromorphic vision sensors are tailored for motion perception due to their asynchronous nature, high temporal resolution, and reduced power consumption. However, their unconventional output requires novel perception paradigms to lev