May 2024 arXiv papers — page 96

Yassin Bahid, Olga Kutsenko, Nancy Rodriguez, David White

Ukraine's tug-of-war between Russia and the West has had significant and lasting consequences for the country. In 2013, Viktor Yanukovych, the Ukrainian president aligned with Russia, opted against signing an association agreement with the European Union. This agreement aimed to facilitate trade and travel between the EU and Ukraine. This decision sparked wi

Jingwen Fu, Zhizheng Zhang, Yan Lu, Nanning Zheng

Compositional Generalization (CG) embodies the ability to comprehend novel combinations of familiar concepts, representing a significant cognitive leap in human intellectual advancement. Despite its critical importance, the deep neural network (DNN) faces challenges in addressing the compositional generalization problem, prompting considerable research inter

Tingting Li, Gensheng Pei, Xinhao Cai, Huafeng Liu

Unsupervised semantic segmentation (USS) aims to achieve high-quality segmentation without manual pixel-level annotations. Existing USS models provide coarse category classification for regions, but the results often have blurry and imprecise edges. Recently, a robust framework called the segment anything model (SAM) has been proven to deliver precise bounda

Jiaxin Li, Hongmei Yao, Shao-Ming Fei, Zhaobing Fan

We provide a class of positive and trace-preserving maps based on symmetric measurements. From these positive maps we present separability criteria, entanglement witnesses, as well as the lower bounds of concurrence. We show by detailed examples that our separability criteria, entanglement witnesses and lower bounds can detect and estimate the quantum entang

Xin Liu, Yaran Chen, Dongbin Zhao

In visual Reinforcement Learning (RL), upstream representation learning largely determines the effect of downstream policy learning. Employing auxiliary tasks allows the agent to enhance visual representation in a targeted manner, thereby improving the sample efficiency and performance of downstream RL. Prior advanced auxiliary tasks all focus on how to extr

Hao He, Chao Li, Wolfgang Ganglberger, Kaileigh Gallagher

The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction

Tyler Presser, Agnimitra Dasgupta, Daniel Erwin, Assad Oberai

Generative modeling has drawn much attention in creative and scientific data generation tasks. Score-based Diffusion Models, a type of generative model that iteratively learns to denoise data, have shown state-of-the-art results on tasks such as image generation, multivariate time series forecasting, and robotic trajectory planning. Using score-based diffusi

Y. Yang, X. T. Chen, Z. L. Li, J. B. Pan

We investigate the ultrafast carrier and phonon dynamics in the ferrimagnetic semiconductor Mn$_3$Si$_2$Te$_6$ using time-resolved optical pump-probe spectroscopy. Our results reveal that the electron-phonon thermalization process with a subpicosecond timescale is prolonged by the hot-phonon bottleneck effect. We identify the subsequent relaxation processes

Antony T. H. Fung

The Cyclic Surgery Theorem and Moser's work on surgeries on torus knots imply that for any non-trivial knot in $S^3$, there are at most two integer surgeries that produce a lens space. This paper investigates how many positive integer surgeries on a given knot in $S^3$ can produce a manifold rational homology cobordant to a lens space. Tools include Gree

Jiayu Shang, Cheng Peng, Yongxin Ji, Jiaojiao Guan

Motivation: Protein embedding, which represents proteins as numerical vectors, is a crucial step in various learning-based protein annotation/classification problems, including gene ontology prediction, protein-protein interaction prediction, and protein structure prediction. However, existing protein embedding methods are often computationally expensive due

Qi-yue Yu, Shi-wen Lin, Shu Lin

For binary source transmission, this paper introduces the concept of element-pair (EP) and establishes that when the Cartesian product of $J$ distinct EPs satisfies the unique sum-pattern mapping (USPM) structural property, these $J$ EPs can form a uniquely-decodable EP (UD-EP) code. EPs are treated as virtual resources allocated to different users in finite

Lingxiao Lei, Weichen Wang, Guangyao Huang, Shun Hu

The synthetic Floquet lattice, generated by multiple strong drives with mutually incommensurate frequencies, provides a powerful platform for the quantum simulation of topological phenomena. In this study, we propose a 4-band tight-binding model of the Chern insulator with a Chern number C = $\pm$2 by coupling two layers of the half-BHZ lattice and subsequen

Yihao Zhao, Cuiyun Yuan, Ying Liang, Yang Li

The delineation of tumor target and organs-at-risk is critical in the radiotherapy treatment planning. Automatic segmentation can be used to reduce the physician workload and improve the consistency. However, the quality assurance of the automatic segmentation is still an unmet need in clinical practice. The patient data used in our study was a standardized

Yanxin Zheng, Wensheng Gan, Zefeng Chen, Zhenlian Qi

To address challenges in the digital economy's landscape of digital intelligence, large language models (LLMs) have been developed. Improvements in computational power and available resources have significantly advanced LLMs, allowing their integration into diverse domains for human life. Medical LLMs are essential application tools with potential across var

Mizuki Kato, Kyoko Namura, Shinya Kawai, Samir Kumar

This study investigates the behavior of microbubbles generated by the local heating of an ethanol/water mixture and the surrounding flow. The mixture is photothermally heated by focusing a continuous-wave laser on a FeSi$_2$ thin film. Although the liquid is not degassed, vapor-rich bubbles are stably generated in an ethanol concentration range of 1.5-50 wt%

Canh V. Pham

In this work, we consider the Submodular Maximization under Knapsack (SMK) constraint problem over the ground set of size $n$. The problem recently attracted a lot of attention due to its applications in various domains of combination optimization, artificial intelligence, and machine learning. We improve the approximation factor of the fastest deterministic

Jiahao Weng, Yan Xie

In this study, we constructed daily high-frequency sentiment data and used the VAR method to attempt to predict the next day's implied volatility surface. We utilized 630,000 text data entries from the East Money Stock Forum from 2014 to 2023 and employed deep learning methods such as BERT and LSTM to build daily market sentiment indicators. By applying FFT

Jinxin Xu, Haixin Wu, Yu Cheng, Liyang Wang

This paper addresses the optimization of scheduling for workers at a logistics depot using a combination of genetic algorithm and simulated annealing algorithm. The efficient scheduling of permanent and temporary workers is crucial for optimizing the efficiency of the logistics depot while minimizing labor usage. The study begins by establishing a 0-1 intege

Eric Shen

In $2023$, Defant and Li defined the Ungarian Markov chain $\mathbf{U}_L$ associated to a finite lattice $L$. This Markov chain has state space $L$, and from any state $x \in L$ transitions to the meet of $\{x\} \cup T$, where $T$ is a randomly selected subset of the elements of $L$ covered by $x$. For any lattice $L$, let $\mathcal{E}(L)$ be the expected nu

Zidu Yin, Zhen Zhang, Dong Gong, Stefano V. Albrecht

Reinforcement Learning (RL) algorithms often struggle with low training efficiency. A common approach to address this challenge is integrating model-based planning algorithms, such as Monte Carlo Tree Search (MCTS) or Value Iteration (VI), into the environmental model. However, VI requires iterating over a large tensor which updates the value of the precedin

Gaoming Chen, Kun Song, Xiang Xu, Wenhang Liu

Mutual localization serves as the foundation for collaborative perception and task assignment in multi-robot systems. Effectively utilizing limited onboard sensors for mutual localization between marker-less robots is a worthwhile goal. However, due to inadequate consideration of large scale variations of the observed robot and localization refinement, previ

Jun Zhou, Yaoshun Li, Hongchen Tan, Mingjie Wang

In recent years, point cloud normal estimation, as a classical and foundational algorithm, has garnered extensive attention in the field of 3D geometric processing. Despite the remarkable performance achieved by current Neural Network-based methods, their robustness is still influenced by the quality of training data and the models' performance. In this stud

Huawei Lin, Jikai Long, Zhaozhuo Xu, Weijie Zhao

Given a Large Language Model (LLM) generation, how can we identify which training data led to this generation? In this paper, we proposed RapidIn, a scalable framework adapting to LLMs for estimating the influence of each training data. The proposed framework consists of two stages: caching and retrieval. First, we compress the gradient vectors by over 200,0

Muxuan Liang, Yang Ning, Maureen A Smith, Ying-Qi Zhao

Penalized empirical risk minimization with a surrogate loss function is often used to learn a high-dimensional linear decision rule in classification problems. Although much of the literature focus on the generalization error, there is a lack of inference procedures for identifying the driving factors of the estimated decision rule, especially when the surro

Amit Raj, Kapil Ahuja, Yann Busnel

This paper explores the application of Artificial Intelligence (AI) techniques for generating the trajectories of fleets of Unmanned Aerial Vehicles (UAVs). The two main challenges addressed include accurately predicting the paths of UAVs and efficiently avoiding collisions between them. Firstly, the paper systematically applies a diverse set of activation f

Junichi Akita

In this paper, we describe the algorithm to measure the stroke of solenoid using the electric characteristics of the solenoid, without mechanical attachments. We also describe the experimental results of controlling the solenoid stroke at intermediate position.

Muxuan Liang, Yingqi Zhao, Daniel W. Lin, Matthew Cooperberg

Active surveillance (AS) using repeated biopsies to monitor disease progression has been a popular alternative to immediate surgical intervention in cancer care. However, a biopsy procedure is invasive and sometimes leads to severe side effects of infection and bleeding. To reduce the burden of repeated surveillance biopsies, biomarker-assistant decision rul

Po-Shen Hsin, Ryohei Kobayashi, Guanyu Zhu

We construct a family of infinitely many new candidate non-Abelian self-correcting topological quantum memories in $D\geq 5+1$ spacetime dimensions without particle excitations using local commuting non-Pauli stabilizer lattice models and field theories of $\mathbb{Z}_2^3$ higher-form gauge fields with nontrivial topological action. We call such non-Pauli st

Zhepeng Cen, Yihang Yao, Zuxin Liu, Ding Zhao

In the field of safe reinforcement learning (RL), finding a balance between satisfying safety constraints and optimizing reward performance presents a significant challenge. A key obstacle in this endeavor is the estimation of safety constraints, which is typically more difficult than estimating a reward metric due to the sparse nature of the constraint sign

Tsumoru Shintake

This paper discusses a simple, low-cost, highly efficient two-mirror projector with a simplified illumination system. The EUV source power can be reduced by 1/10 compared to the current six-mirror EUV projector system. The required EUV power is 20 watts for process speed of 100 wafers per hour. The proposed in-line projector achieves 0.2 NA (20 mm field) and

Sreejith Nair, Zhifei Yang, Kevin Storr, Bharat Jalan

Binary rutile oxides of 5d metals such as IrO2, stand out as a paradox due to limited experimental studies despite the rich predicted quantum phenomena. Here, we investigate the electrical transport properties of IrO2 by engineering epitaxial thin films grown via hybrid molecular beam epitaxy. Our findings reveal phonon-limited carrier transport and thicknes

Yifan Liu, Chenchen Kuai, Haoxuan Ma, Xishun Liao

Human travel trajectory mining is crucial for transportation systems, enhancing route optimization, traffic management, and the study of human travel patterns. Previous rule-based approaches without the integration of semantic information show a limitation in both efficiency and accuracy. Semantic information, such as activity types inferred from Points of I

Adway Patra, Alexander Barg

We consider regenerating codes in distributed storage systems where connections between the nodes are constrained by a graph. In this problem, the failed node downloads the information stored at a subset of vertices of the graph for the purpose of recovering the lost data. Compared to the standard setting, regenerating codes on graphs address two additional

Conggai Li, Wei Ni, Ming Ding, Youyang Qu

Many real-world interconnections among entities can be characterized as graphs. Collecting local graph information with balanced privacy and data utility has garnered notable interest recently. This paper delves into the problem of identifying and protecting critical information of entity connections for individual participants in a graph based on cohesive s

Easton Kelso, Ananta Soneji, Sazzadur Rahaman, Yan Soshitaishvili

The education technology (EdTech) landscape is expanding rapidly in higher education institutes (HEIs). This growth brings enormous complexity. Protecting the extensive data collected by these tools is crucial for HEIs as data breaches and misuses can have dire security and privacy consequences on the data subjects, particularly students, who are often compe

Minghao Guo, James M. Stone, Eliot Quataert, Chang-Goo Kim

We present three-dimensional magnetohydrodynamic (MHD) simulations of the fueling of supermassive black holes in elliptical galaxies from a turbulent cooling medium on galactic scales, taking M87* as a typical case. We find that the mass accretion rate is increased by a factor of $\sim 10$ compared with analogous hydrodynamic simulations. The scaling of $\do

Mingrui Wang, Ruoyue Tang, Xinrui Ren, Yanqing Cui

The properties of supercritical fluids are dictated by intermolecular interactions that involve two or more molecules. Such intermolecular interactions were described via intermolecular potentials in historical supercritical combustion modeling studies, but have been treated empirically and with no consideration of radical interactions or multi-body interact

Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu

Large Language Models (LLMs) fine-tuned via Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) significantly improve the alignment of human-AI values, further raising the upper bound of AI capabilities, particularly in reasoning-intensive, long-context Chain-of-Thought (CoT) tasks. However, existing fr

Peter Howard, Adam Larios, Quyuan Lin

We consider coarsening dynamics associated with a Burgers--Cahn--Hilliard system modeling a two-phase flow in one space dimension. Our emphasis is on the effect that coupling between the phase and fluid dynamics has on coarsening rates, and on the mechanisms driving this effect. We start with a detailed examination of coarsening dynamics for the uncoupled Ca

Shao-Yuan Lo, Vishal M. Patel

Deep networks are vulnerable to adversarial examples. Adversarial Training (AT) has been a standard foundation of modern adversarial defense approaches due to its remarkable effectiveness. However, AT is extremely time-consuming, refraining it from wide deployment in practical applications. In this paper, we aim at a non-AT defense: How to design a defense m

Xiang-kun Shao, Nan-jing Huang, Donal O'Regan

This paper provides the upper and lower bounds of blowup time and blowup rate as well as the exponential growth estimate of blowup solutions for a pseudo-parabolic equation with singular potential. These results complement the ones obtained in the previous literature.

Dean Allemang, Juan Sequeda

There is increasing evidence that question-answering (QA) systems with Large Language Models (LLMs), which employ a knowledge graph/semantic representation of an enterprise SQL database (i.e. Text-to-SPARQL), achieve higher accuracy compared to systems that answer questions directly on SQL databases (i.e. Text-to-SQL). Our previous benchmark research showed

Le Bin Ho

We investigate how squeezing techniques can improve the measurement precision in multiphase quantum metrology. While these methods are well-studied and effectively used in single-phase estimations, their usage in multiphase situations has yet to be examined. We fill this gap by investigating the mechanism of quantum enhancement in the multiphase scenarios. O

Taiyuan Mei, Yun Zi, Xiaohan Cheng, Zijun Gao

The internal structure and operation mechanism of large-scale language models are analyzed theoretically, especially how Transformer and its derivative architectures can restrict computing efficiency while capturing long-term dependencies. Further, we dig deep into the efficiency bottleneck of the training phase, and evaluate in detail the contribution of ad

Bingjia Yang, Yunsie Chung, Archer Y. Yang, Bo Yuan

In drug discovery, in vitro and in vivo experiments reveal biochemical activities related to the efficacy and toxicity of compounds. The experimental data accumulate into massive, ever-evolving, and sparse datasets. Quantitative Structure-Activity Relationship (QSAR) models, which predict biochemical activities using only the structural information of compou

Difang Huang, Ying Liang, Boyao Wu, Yanyi Ye

We identify the effectiveness of social distancing policies in reducing the transmission of the COVID-19 spread. We build a model that measures the relative frequency and geographic distribution of the virus growth rate and provides hypothetical infection distribution in the states that enacted the social distancing policies, where we control time-varying, o

Haesung Lee, Gerald Trutnau

Building on results developed in https://doi.org/10.48550/arXiv.2404.14902, where Itô-SDEs with possibly degenerate and discontinuous dispersion coefficient and measurable drift were analyzed with respect to a given (sub-)invariant measure, we develop here additional elliptic regularity results for PDEs and consider the same equations with some further regul

Shubham Kumar, Narendra N. Hegade, Alejandro Gomez Cadavid, Murilo Henrique de Oliveira

We introduce a hardware-specific, problem-dependent digital-analog quantum algorithm of a counterdiabatic quantum dynamics tailored for optimization problems. Specifically, we focus on trapped-ion architectures, taking advantage from global Mølmer-Sørensen gates as the analog interactions complemented by digital gates, both of which are available in the stat

Xiaoyue Sun, Junya Yagi

We define three families of quivers in which the braid relations of the symmetric group $S_n$ are realized by mutations and automorphisms. A sequence of eight braid moves on a reduced word for the longest element of $S_4$ yields three trivial cluster transformations with 8, 32 and 32 mutations. For each of these cluster transformations, a unitary operator re

Mustapha Raissouli, Mohamed Chergui, Lahcen Tarik

In this paper, we are interested in investigating a weighted variant of Hermite-Hadamard type inequalities involving convex functionals. The approach undertaken makes it possible to refine and reverse certain inequalities already known in the literature. It also allows us to provide new weighted convex functional means and establish some related properties w

Mustapha Raissouli, Lahcen Tarik, Mohamed Chergui

In this article, we focus on establishing a new variant of Hermite-Hadamard type inequalities for operator convex maps using an appropriate probability measure. To underline the usefulness of these inequalities, we investigate some refinements of some well-known operator inequalities, as well as the definition of new weighted operator means.

Bishal Thapaliya, Robyn Miller, Jiayu Chen, Yu-Ping Wang

Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest, or dynamic functional connectivity matrices with a sliding window approach. T

Diaraf Seck

Our aim is to do a come back on Schiffer's and Pompeiu's conjectures with shape optimization tools, maximum principles and Serrin's symmetry method. We propose a way to get affirmative answers in some cases. We propose also sufficient conditions thanks to Riemannian approach of infinite dimension that could be useful for numerical simulations of the shape of

Jason Yang

We present a simple proof that finding a rank-$R$ canonical polyadic decomposition of a 3-dimensional tensor over a finite field $\mathbb{F}$ is fixed-parameter tractable with respect to $R$ and $\mathbb{F}$. We also show a nontrivial upper bound on the time complexity of this problem.

Mustapha Raissouli, Lahcen Tarik, Mohamed Chergui

Recently, the so-called Hermite-Hadamard inequality for (operator) convex functions with one variable has known extensive several developments by virtue of its nice properties and various applications. The fundamental target of this paper is to investigate a weighted variant of Hermite-Hadamard inequality in multiple variables that extends the univariate cas

Siddharth H. Nair, Charlott Vallon, Francesco Borrelli

Multi-Objective Learning Model Predictive Control is a novel data-driven control scheme which improves a linear system's closed-loop performance with respect to several convex control objectives over iterations of a repeated task. At each task iteration, collected system data is used to construct terminal components of a Model Predictive Controller. The form

Nicholas Dufour, Arkanath Pathak, Pouya Samangouei, Nikki Hariri

The prevalence and harms of online misinformation is a perennial concern for internet platforms, institutions and society at large. Over time, information shared online has become more media-heavy and misinformation has readily adapted to these new modalities. The rise of generative AI-based tools, which provide widely-accessible methods for synthesizing rea

G. Welper

It is well understood that neural networks with carefully hand-picked weights provide powerful function approximation and that they can be successfully trained in over-parametrized regimes. Since over-parametrization ensures zero training error, these two theories are not immediately compatible. Recent work uses the smoothness that is required for approximat

Ujjal Das, Yehuda Pinchover

We give partial affirmative answers to Landis conjecture in all dimensions for two different types of linear, second order, elliptic operators in a domain $\Omega\subset \mathbb{R}^N$. In particular, we provide a sharp decay criterion that ensures when a solution of a nonnegative Schr\"odinger equation in $\mathbb{R}^N$ with a potential $V\leq 1$ is trivial.

Chang Yu, Xuan Li, Lei Lan, Yin Yang

PBD and its extension, XPBD, have been predominantly applied to compliant constrained elastodynamics, with their potential in finite strain (visco-) elastoplasticity remaining underexplored. XPBD is often perceived to stand in contrast to other meshless methods, such as the MPM. MPM is based on discretizing the weak form of governing partial differential equ

Hicham Arroussi, Huijie Liu, Cezhong Tong, Zicong Yang

Let $n$ be a positive integer and $\mathbf{g}=(g_0,g_1,\cdots,g_{n-1})$, with $g_k\in H(\mathbb{D})$ for $k=0,1,\cdots,n-1$. Let $I_{\mathbf{g}}^{(n)}$ be the generalized Volterra-type operators on $H(\mathbb{C})$, which is represented as $$ I_{\mathbf{g}}^{(n)}f=I^n\left(fg_0+f'g_1+\cdots+f^{(n-1)}g_{n-1}\right), $$ where $I$ denotes the integration operato

Anton Razzhigaev, Matvey Mikhalchuk, Elizaveta Goncharova, Nikolai Gerasimenko

This paper reveals a novel linear characteristic exclusive to transformer decoders, including models such as GPT, LLaMA, OPT, BLOOM and others. We analyze embedding transformations between sequential layers, uncovering a near-perfect linear relationship (Procrustes similarity score of 0.99). However, linearity decreases when the residual component is removed

Leonid Pekker, David Pekker

The projection method is the standard approach for numerically integrating the incompressible Navier-Stokes equation initial-boundary-value problem. Typical boundary conditions specify either the velocity or the gradient velocity on the boundary. Here, we consider the pressure-tangential-velocity boundary condition in which the tangential components of the v

Yinghao Huang, Leo Ho, Dafei Qin, Mingyi Shi

We address the problem of accurate capture and expressive modelling of interactive behaviors happening between two persons in daily scenarios. Different from previous works which either only consider one person or focus on conversational gestures, we propose to simultaneously model the activities of two persons, and target objective-driven, dynamic, and cohe

Junlee Kim, Jaebeom Park, Byungsik Hong, Juhee Hong

The primary purpose of studying quarkonium production in relativistic heavy-ion collisions is to understand the properties of the quark-gluon plasma. At various collision systems, measurements of quarkonium states of different binding energies, such as $\Upsilon(nS)$, can provide comprehensive information. A model study has been performed to investigate the

Wanru Guo

The exploration of network structures through the lens of graph theory has become a cornerstone in understanding complex systems across diverse fields. Identifying densely connected subgraphs within larger networks is crucial for uncovering functional modules in biological systems, cohesive groups within social networks, and critical paths in technological i

Sarath Sreedharan, Anagha Kulkarni, Subbarao Kambhampati

From its inception, AI has had a rather ambivalent relationship with humans -- swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human-AI interaction is that

I-Chi Chen, Nouhaila Innan, Suman Kumar Roy, Jason Saroni

Within the evolving domain of quantum computational chemistry, the Variational Quantum Eigensolver (VQE) has been developed to explore not only the ground state but also the excited states of molecules. In this study, we compare the performance of Variational Quantum Deflation (VQD) and Subspace-Search Variational Quantum Eigensolver (SSVQE) methods in deter

Colin D. Grab

While research of reinforcement learning applied to financial markets predominantly concentrates on finding optimal behaviours, it is worth to realize that the reinforcement learning returns $G_t$ and state value functions themselves are of interest and play a pivotal role in the evaluation of assets. Instead of focussing on the more complex task of finding

Ahnaf Mozib Samin, M. Firoz Ahmed, Md. Mushtaq Shahriyar Rafee

With the utilization of Transformer architecture, large Vision and Language (V&L) models have shown promising performance in even zero-shot settings. Several studies, however, indicate a lack of robustness of the models when dealing with complex linguistics and visual attributes. In this work, we introduce a novel V&L benchmark - ColorFoil, by creating color

Canlin Zhang, Xiuwen Liu

Deep neural networks trained in an end-to-end manner are proven to be efficient in a wide range of machine learning tasks. However, there is one drawback of end-to-end learning: The learned features and information are implicitly represented in neural network parameters, which cannot be used as regularities, concepts or knowledge to explicitly represent the

Yididiya Y. Nadew, Xuhui Fan, Christopher J. Quinn

Gaussian process factor analysis (GPFA) is a latent variable modeling technique commonly used to identify smooth, low-dimensional latent trajectories underlying high-dimensional neural recordings. Specifically, researchers model spiking rates as Gaussian observations, resulting in tractable inference. Recently, GPFA has been extended to model spike count dat

Ziang Guo, Zakhar Yagudin, Selamawit Asfaw, Artem Lykov

Camera, LiDAR and radar are common perception sensors for autonomous driving tasks. Robust prediction of 3D object detection is optimally based on the fusion of these sensors. To exploit their abilities wisely remains a challenge because each of these sensors has its own characteristics. In this paper, we propose FADet, a multi-sensor 3D detection network, w

Elynn Chen, Xi Chen, Wenbo Jing, Yichen Zhang

As tensors become widespread in modern data analysis, Tucker low-rank Principal Component Analysis (PCA) has become essential for dimensionality reduction and structural discovery in tensor datasets. Motivated by the common scenario where large-scale tensors are distributed across diverse geographic locations, this paper investigates tensor PCA within a dist

Peter Ashwin, Julian Newman, Raphael Römer

Nonlinear dynamical systems subjected to a combination of noise and time-varying forcing can exhibit sudden changes, critical transitions or tipping points where large or rapid dynamic effects arise from changes in a parameter that are small or slow. Noise-induced tipping can occur where extremes of the forcing causes the system to leave one attractor and tr

Alexander I. Lebedev

Spontaneous strain and spontaneous folding of thin nanoplatelets are known phenomena whose microscopic mechanisms are still debating. In this work, first-principles calculations are used to study the mechanical stresses that arise in Janus CdSe nanoplatelets and result in their spontaneous strain. Calculations reveal the existence of three microscopic mechan

Myoung-Woo Yoo, Virginia O. Lorenz, Axel Hoffmann, David G. Cahill

In information technology devices, current-driven state switching is crucial in various disciplines including spintronics, where the contribution of heating to the switching mechanism plays an inevitable role. Recently, current-driven antiferromagnetic order switching has attracted considerable attention due to its implications for next-generation spintronic

Christiaan G. A. Viviers, Lena Filatova, Maurice Termeer, Peter H. N. de With

Accurate 6-DoF pose estimation of surgical instruments during minimally invasive surgeries can substantially improve treatment strategies and eventual surgical outcome. Existing deep learning methods have achieved accurate results, but they require custom approaches for each object and laborious setup and training environments often stretching to extensive s

Alden Green, Elad Romanov

We study principal components regression (PCR) in an asymptotic high-dimensional regression setting, where the number of data points is proportional to the dimension. We derive exact limiting formulas for the estimation and prediction risks, which depend in a complicated manner on the eigenvalues of the population covariance, the alignment between the popula

Orfeas Menis-Mastromichalakis, Natasa Sofou, Giorgos Stamou

The massive digitization of artworks during the last decades created the need for categorization, analysis, and management of huge amounts of data related to abstract concepts, highlighting a challenging problem in the field of computer science. The rapid progress of artificial intelligence and neural networks has provided tools and technologies that seem wo

R. G. Cassia, R. R. Kerswell

Machine learning techniques are being used as an alternative to traditional numerical discretization methods for solving hyperbolic partial differential equations (PDEs) relevant to fluid flow. Whilst numerical methods are higher fidelity, they are computationally expensive. Machine learning methods on the other hand are lower fidelity but can provide signif

Ahmed Bou-Rabee, Ewain Gwynne

We prove that random walks on a family of tilings of d-dimensional Euclidean space, with a canonical choice of conductances, converge to Brownian motion modulo time parameterization. This class of tilings includes Delaunay triangulations (the dual of Voronoi tesselations) and sphere packings. Our regularity assumptions are deterministic and mild. For example

Nikhil Byrapuram, Adam Ge, Selena Ge, Tanya Khovanova

In 2013, Conway and Ryba wrote a fascinating paper called Fibonometry. The paper, as one might guess, is about the connection between Fibonacci numbers and trigonometry. We were fascinated by this paper and looked at how we could generalize it. We discovered that we weren't the first. In this paper, we describe our journey and summarize the results.

Jingwei Xu, Junyu Lai, Yunpeng Huang

The pretrain+fine-tune paradigm is foundational for deploying large language models (LLMs) across various downstream applications. Within this framework, Low-Rank Adaptation (LoRA) stands out for its parameter-efficient fine-tuning (PEFT), producing numerous reusable task-specific LoRA adapters. However, this approach requires explicit task intention selecti

Jinzhi Shen, Ke Ma

In response to the COVID-19 pandemic, the integration of interpretable machine learning techniques has garnered significant attention, offering transparent and understandable insights crucial for informed clinical decision making. This literature review delves into the applications of interpretable machine learning in predicting the prognosis of respiratory

Brian Wheatman, Xiaojun Dong, Zheqi Shen, Laxman Dhulipala

A fundamental building block in any graph algorithm is a graph container - a data structure used to represent the graph. Ideally, a graph container enables efficient access to the underlying graph, has low space usage, and supports updating the graph efficiently. In this paper, we conduct an extensive empirical evaluation of graph containers designed to supp

Themba Dube, Amartya Goswami

The aim of this paper is to investigate further properties of $z$-elements in multiplicative lattices. We utilize $z$-closure operators to extend several properties of $z$-ideals to $z$-elements and introduce various distinguished subclasses of $z$-elements, such as $z$-prime, $z$-semiprime, $z$-primary, $z$-irreducible, and $z$-strongly irreducible elements

Heinrich Peters, Moran Cerf, Sandra C. Matz

This study investigates the capacity of Large Language Models (LLMs) to infer the Big Five personality traits from free-form user interactions. The results demonstrate that a chatbot powered by GPT-4 can infer personality with moderate accuracy, outperforming previous approaches drawing inferences from static text content. The accuracy of inferences varied a

Sean Vaskov, Wilko Schwarting, Chris L. Baker

Reinforcement Learning (RL) for control has become increasingly popular due to its ability to learn rich feedback policies that take into account uncertainty and complex representations of the environment. When considering safety constraints, constrained optimization approaches, where agents are penalized for constraint violations, are commonly used. In such

Hadeel Saadany, Ashraf Tantawy, Constantin Orasan

With the advent of Neural Machine Translation (NMT) systems, the MT output has reached unprecedented accuracy levels which resulted in the ubiquity of MT tools on almost all online platforms with multilingual content. However, NMT systems, like other state-of-the-art AI generative systems, are prone to errors that are deemed machine hallucinations. The probl

Anway S. Pimpalkar, Deeplaxmi V. Niture

This study presents a proof of concept for a contactless elevator operation system aimed at minimizing human intervention while enhancing safety, intelligence, and efficiency. A microcontroller-based edge device executing tiny Machine Learning (tinyML) inferences is developed for elevator operation. Using person detection and keyword spotting algorithms, the

Louis Esser, Jennifer Li

We find sharp upper bounds on the order of the automorphism group of a hypersurface in complex projective space in every dimension and degree. In each case, we prove that the hypersurface realizing the upper bound is unique up to isomorphism and provide explicit generators for the automorphism group.

Amartya Goswami, Themba Dube

The objective of this paper is to extend certain properties observed in $d$-ideals of rings and $d$-elements of frames to Baer elements in multiplicative lattices introduced in D. D. Anderson, C. Jayaram, and P. A. Phiri, Baer lattices, \textit{Acta Sci. Math. (Szeged)}, 59 (1994), 61--74. Additionally, we present results concerning these elements that have

Joachim Asch, Olivier Bourget

We establish limiting absorption principles for contractions on a Hilbert space. Our sufficient conditions are based on positive commutator estimates. We discuss the dynamical implications of this principle to the corresponding discrete-time semigroup and provide several applications. Notably to Toeplitz operators and contractive quantum walks.

L. Delzescaux, D. Mouhanna, M. Tissier

We investigate the effects of quenched elastic disorder on the nature of the crumpling-to-flat transition of $D$-dimensional polymerized membranes using a two-loop computation near the upper critical dimension $D_c=4$. While the pure system undergoes fluctuation-induced first order transitions below $D_c$ and for an embedding dimension $d<d_{c,pure}\simeq 21

C. A. Downing, M. S. Ukhtary

A quantum system which can store energy, and from which one can extract useful work, is known as a quantum battery. Such a device raises interesting issues surrounding how quantum physics can provide certain advantages in the charging, energy storage or discharging of the quantum battery as compared to their classical equivalents. However, the pernicious eff

Adrian Vågberg, Martin Brunzell, Max Widarsson, Patrick Mutter

In this study, we demonstrated the direct use of an inherently narrowband backward wave in the mid-infrared for CO$_2$ and H$_2$O vapor spectroscopy in ambient air. This wave is generated in a backward wave optical parametric oscillator (BWOPO) pumped by a multi longitudinal mode laser at 1030 nm, eliminating the need for additional spectral narrowing or wav

Antonio Lages, Pedro Lopes

A quandle is an algebraic structure whose binary operation is idempotent, right-invertible and right self-distributive. Right-invertibility ensures right translations are permutations and right self-distributivity ensures further they are automorphisms. For finite connected quandles, all right translations have the same cycle structure, called the profile of

Tharun V. Puthanveettil, Abhijay Singh, Yashveer Jain, Vinay Bukka

The work introduces a bio-inspired leader-follower system based on an innovative mechanism proposed as software latching that aims to improve collaboration and coordination between a leader agent and the associated autonomous followers. The system utilizes software latching to establish real-time communication and synchronization between the leader and follo

Subhajit Sahu

Real-world graphs often evolve over time, making community or cluster detection a crucial task. In this technical report, we extend three dynamic approaches - Naive-dynamic (ND), Delta-screening (DS), and Dynamic Frontier (DF) - to our multicore implementation of the Leiden algorithm, known for its high-quality community detection. Our experiments, conducted