March 2024 arXiv papers — page 52

Mengyue Zha

We study differentially private (DP) estimation of a rank-$r$ matrix $M \in \mathbb{R}^{d_1\times d_2}$ under the trace regression model with Gaussian measurement matrices. Theoretically, the sensitivity of non-private spectral initialization is precisely characterized, and the differential-privacy-constrained minimax lower bound for estimating $M$ under the

Eranda Somathilake, Mamadou Diagne

This paper addresses the management of water flow in a rectangular open channel, considering the dynamic nature of both the channel's bathymetry and the suspended sediment particles caused by entrainment and deposition effects. The control-oriented model under study is a set of coupled nonlinear partial differential equations (PDEs) describing conservation o

Xiang-Dong Li, Guoping Liu

In this paper, we give a new derivation of the incompressible Navier-Stokes equations on a compact Riemannian manifold $M$ via the Bellman dynamic programming principle on the infinite dimensional group $SG={\rm SDiff}(M)$ of volume preserving diffeomorphisms. In particular, when the viscosity vanishes, we give a new derivation of the incompressible Euler eq

Mohammad Afzal Shadab, Anja Rutishauser, Cyril Grima, Marc Andre Hesse

Motivated by the refreezing of melt water in firn we revisit the one-dimensional percolation of liquid water and non-reactive gas in porous ice. We analyze the dynamics of infiltration in the absence of capillary forces and heat conduction to understand the coupling between advective heat and mass transport in firn. In this limit, we formulate a kinematic wa

Emily Nardoni, Matteo Sacchi, Orr Sela, Gabi Zafrir

Recently there has been an increasing interest in the study of generalized symmetries in dimensions higher than two. This has lead to the discovery of various manifestations of generalized symmetries, notably higher-group and non-invertible symmetries, in four dimensions. In this paper we shall examine what happens to this structure when the 4d theory is com

Yicheng Deng, Hideaki Hayashi, Hajime Nagahara

Facial expression spotting is a significant but challenging task in facial expression analysis. The accuracy of expression spotting is affected not only by irrelevant facial movements but also by the difficulty of perceiving subtle motions in micro-expressions. In this paper, we propose a Multi-Scale Spatio-Temporal Graph Convolutional Network (SpoT-GCN) for

Zhaoyuan Gu, Yuntian Zhao, Yipu Chen, Rongming Guo

This study introduces a robust planning framework that utilizes a model predictive control (MPC) approach, enhanced by incorporating signal temporal logic (STL) specifications. This marks the first-ever study to apply STL-guided trajectory optimization for bipedal locomotion, specifically designed to handle both translational and orientational perturbations.

Yinda Chen, Che Liu, Xiaoyu Liu, Rossella Arcucci

The burgeoning integration of 3D medical imaging into healthcare has led to a substantial increase in the workload of medical professionals. To assist clinicians in their diagnostic processes and alleviate their workload, the development of a robust system for retrieving similar case studies presents a viable solution. While the concept holds great promise,

Hiromichi Takagi

Subsequent to the previous paper [Tak5], we are concerned with the classification of complex prime $\mathbb{Q}$-Fano $3$-folds of anti-canonical codimension 4 which are produced, as weighted complete intersections of appropriate weighted projectivizations of certain affine varieties related with $\mathbb{P}^{2}\times\mathbb{P}^{2}$-fibrations. Such affine va

Shengyi Huang, Michael Noukhovitch, Arian Hosseini, Kashif Rasul

This work is the first to openly reproduce the Reinforcement Learning from Human Feedback (RLHF) scaling behaviors reported in OpenAI's seminal TL;DR summarization work. We create an RLHF pipeline from scratch, enumerate over 20 key implementation details, and share key insights during the reproduction. Our RLHF-trained Pythia models demonstrate significant

Dmitry A. Konovalov

The Mars Spectrometry 2: Gas Chromatography challenge was sponsored by NASA and run on the DrivenData competition platform in 2022. This report describes the solution which achieved the second-best score on the competition's test dataset. The solution utilized two-dimensional, image-like representations of the competition's chromatography data samples. A num

Anuj Karpatne, Xiaowei Jia, Vipin Kumar

This paper presents an overview of scientific modeling and discusses the complementary strengths and weaknesses of ML methods for scientific modeling in comparison to process-based models. It also provides an introduction to the current state of research in the emerging field of scientific knowledge-guided machine learning (KGML) that aims to use both scient

Guangdong Jing

We investigate the linear quadratic stochastic optimal control problems in infinite dimension without Markovian restriction for coefficients. The necessary and sufficient conditions for open-loop optimal controls are presented. We prove the Fr\'echet differentiable of the cost functional with respect to the control variable, and the Fr\'echet derivatives are

Qi Deng, Zhong-guo Zhou

We propose that the liquidity of an asset includes two components: liquidity jump and liquidity diffusion. We show that liquidity diffusion has a higher correlation with crypto wash trading than liquidity jump and demonstrate that treatment on wash trading significantly reduces the level of liquidity diffusion, but only marginally reduces that of liquidity j

Flavio Ponzina, Tajana Rosing

Hyperdimensional computing (HDC) is emerging as a promising AI approach that can effectively target TinyML applications thanks to its lightweight computing and memory requirements. Previous works on HDC showed that limiting the standard 10k dimensions of the hyperdimensional space to much lower values is possible, reducing even more HDC resource requirements

Pierre-Louis Curien, Guillaume Laplante-Anfossi

We define term rewriting systems on the vertices and faces of nestohedra, and show that the former are confluent and terminating. While the associated posets on vertices generalize Barnard--McConville's flip order for graph-associahedra, the preorders on faces generalize the facial weak order for permutahedra and the generalized Tamari order for associahedra

Xianghang Zhang, Tingzhang Shi, H. T. Quan

We study the non-equilibrium work in a pedagogical model of relativistic ideal gas. We obtain the exact work distribution and verify the Jarzynski's equality. In the non-relativistic limit, our results recover the non-relativistic results [arXiv:cond-mat/0502434]. We also find that, unlike the non-relativistic case, the work distribution no longer has zeros

Yue Deng, Zheng Chen, Changyang He, Zhicong Lu

Speaking out for women's daily needs on social media has become a crucial form of everyday feminism in China. Gender debate naturally intertwines with such feminist advocacy, where users in opposite stances discuss gender-related issues through intense discourse. The complexities of gender debate necessitate a systematic understanding of discursive strategie

Sven Hirsch, Yiyue Zhang

In 1981, Schoen-Yau and Witten showed that in General Relativity both the total energy $E$ and the total mass $m$ of an initial data set modeling an isolated gravitational system are non-negative. Moreover, if $E=0$, the initial data set must be contained in Minkowski space. In this paper, we show that if $m=0$, i.e. if $E$ equals the total momentum $|P|$, t

Junsouk Choi, Hee Cheol Chung, Irina Gaynanova, Yang Ni

Single-cell sequencing technologies have significantly advanced molecular and cellular biology, offering unprecedented insights into cellular heterogeneity by allowing for the measurement of gene expression at an individual cell level. However, the analysis of such data is challenged by the prevalence of low counts due to dropout events and the skewed nature

Kamaran Salh Rasul, Nawroz Abdul-razzak Tahir

Accessions are prospective sources of genetic variability, as well as valuable genetic resources to deal with present and future crop breeding difficulties. The assessment of population structure and genetic diversity of tomatoes (Solanum lycopersicum L.) that have been distributed in Iraqi Kurdistan region critical in breeding programs for the production of

Z. Li, L. Q. Lai

Geometric momentum is the appropriate momentum for a particle constrained to move on a curved surface, which depends on the extrinsic curvature and leads to observable effects, and curvature-induced quantum potentials appear for a nonrelativistic free particle on the surface. In the context of multi-component quantum states, the geometric momentum should be

Junhong Zhao, Wei Ying, Yaoqiang Pan, Zhenfeng Yi

Accurate collection of plant phenotyping is critical to optimising sustainable farming practices in precision agriculture. Traditional phenotyping in controlled laboratory environments, while valuable, falls short in understanding plant growth under real-world conditions. Emerging sensor and digital technologies offer a promising approach for direct phenotyp

Martin Larsson, Shukun Long

Given a general It\^o semimartingale, its Markovian projection is an It\^o process, with Markovian differential characteristics, that matches the one-dimensional marginal laws of the original process. We construct Markovian projections for It\^o semimartingales with jumps, whose flows of one-dimensional marginal laws are solutions to non-local Fokker--Planck

Melody Lee

We propose a modified MSA algorithm on quantum annealers with applications in areas of bioinformatics and genetic sequencing. To understand the human genome, researchers compare extensive sets of these genetic sequences -- or their protein counterparts -- to identify patterns. This comparison begins with the alignment of the set of (multiple) sequences. Howe

Wei Yang, Yu-Xuan Kang, Arshad Ali, Tao-Tao Sui

Scalar perturbations in the inflation can be amplified when the base inflation potential $V_b(\phi)$ incorporates a local bump $f(\phi)$ such as $V(\phi)=V_b(\phi)(1+f(\phi))$. This modification will lead to a peak in the curvature power spectrum, increasing a significant abundance of primordial black holes (PBHs). However, since there is no underlying physi

Tatyana Barron

In signal processing, a signal is a function. Conceptually, replacing a function by its graph, and extending this approach to a more abstract setting, we define a signal as a submanifold M of a Riemannian manifold (with corners) that satisfies additional conditions. In particular, it is a relative cobordism between two manifolds with boundaries. We define en

Timur Ibrayev, Amitangshu Mukherjee, Sai Aparna Aketi, Kaushik Roy

Deep neural network (DNN) based machine perception frameworks process the entire input in a one-shot manner to provide answers to both "what object is being observed" and "where it is located". In contrast, the "two-stream hypothesis" from neuroscience explains the neural processing in the human visual cortex as an active vision system that utilizes two sepa

Alexis A. Aguilar-Arevalo, Nicolas Avalos, Xavier Bertou, Carla Bonifazi

The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) aims to detect the coherent scattering (CE$\nu$NS) of reactor antineutrinos off silicon nuclei using thick fully-depleted high-resistivity silicon CCDs. Two Skipper-CCD sensors with sub-electron readout noise capability were installed at the experiment next to the Angra-2 reactor in 2021, making C

Mohammad Sajid Shahriar, Faisal Ahmed, Genshe Chen, Khanh D. Pham

This letter indicates the critical need for prioritized multi-tenant quality-of-service (QoS) management by emerging mobile edge systems, particularly for high-throughput beyond fifth-generation networks. Existing traffic engineering tools utilize complex functions baked into closed, proprietary infrastructures, largely limiting design flexibility, scalabili

Shreya Sharma, Dana Hughes, Katia Sycara

This paper describes CBGT-Net, a neural network model inspired by the cortico-basal ganglia-thalamic (CBGT) circuits found in mammalian brains. Unlike traditional neural network models, which either generate an output for each provided input, or an output after a fixed sequence of inputs, the CBGT-Net learns to produce an output after a sufficient criteria f

Jihye Lee, Fabio Ricci

We prove comparison results for the Isoperimetric profile function in the setting of manifolds with integral bounds on the Ricci curvature. We extend previous work of Ni and Wang and Bayle and Rosales under the usual pointwise bounds for the Ricci curvature.

Gioacchino Antonelli, Mattia Fogagnolo, Stefano Nardulli, Marco Pozzetta

This paper deals with quasi-local isoperimetric versions of the positive mass theorem on $3$-manifolds endowed with continuous complete metrics having nonnegative scalar curvature in a suitable weak sense. As a corollary, we derive existence results for isoperimetric sets in such low regularity setting. Our main tool is a new local version of the weak invers

Yijing Yang, Vasileios Magoulianitis, Jiaxin Yang, Jintang Xue

Automatic prostate segmentation is an important step in computer-aided diagnosis of prostate cancer and treatment planning. Existing methods of prostate segmentation are based on deep learning models which have a large size and lack of transparency which is essential for physicians. In this paper, a new data-driven 3D prostate segmentation method on MRI is p

Anton Gorodetski, Victor Kleptsyn

We consider discrete Schr\"odinger operators on $\ell^2(\mathbb{Z})$ with bounded random but not necessarily identically distributed values of the potential. We prove spectral localization (with exponentially decaying eigenfunctions) as well as dynamical localization for this model. An important ingredient of the proof is a non-stationary version of the para

Vasileios Magoulianitis, Jiaxin Yang, Yijing Yang, Jintang Xue

Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Al

A. C. Bagy, Z. Chbani, H. Riahi

Given a proper convex lower semicontinuous function defined on a Hilbert space and whose solution set is supposed nonempty. For attaining a global minimizer when this convex function is continuously differentiable, we approach it by a first-order continuous dynamical system with a time rescaling parameter and a Tikhonov regularization term. We show, along th

Jonas T. Hartwig, Dwight Anderson Williams

Given a map $\Xi\colon U(\mathfrak{g})\rightarrow A$ of associative algebras, with $U(\mathfrak{g})$ the universal enveloping algebra of a (complex) finite-dimensional reductive Lie algebra $\mathfrak{g}$, the restriction functor from $A$-modules to $U(\mathfrak{g})$-modules is intimately tied to the representation theory of an $A$-subquotient known as the r

Timothy Huber, Nathaniel Mayes, Jeffery Opoku, Dongxi Ye

In this work, Ramanujan type congruences modulo powers of primes $p \ge 5$ are derived for a general class of products that are modular forms of level $p$. These products are constructed in terms of Klein forms and subsume generating functions for $t$-core partitions known to satisfy Ramanujan type congruences for $p=5,7,11$. The vectors of exponents corresp

Shashwat Jain, Vikram Krishnamurthy, Muralidhar Rangaswamy, Bosung Kang

How to design a Markov Decision Process (MDP) based radar controller that makes small sacrifices in performance to mask its sensing plan from an adversary? The radar controller purposefully minimizes the Fisher information of its emissions so that an adversary cannot identify the controller's model parameters accurately. Unlike classical open loop statistica

Denis Benois, Kâzım Büyükboduk

Our objective in the present work is to develop a fairly complete arithmetic theory of critical $p$-adic $L$-functions on the eigencurve. To this end, we carry out the following tasks: a) We give an "étale" construction of Bellaïche's $p$-adic $L$-functions at a $θ$-critical point on the cuspidal eigencurve. b) We introduce the algebraic counterp

Shengwen Gan, Shaoming Guo, Larry Guth, Terence L. J. Harris

Let $γ:[0,1]\rightarrow \mathbb{S}^{2}$ be a non-degenerate curve in $\mathbb{R}^3$, that is to say, $\det\big(γ(θ),γ'(θ),γ"(θ)\big)\neq 0$. For each $θ\in[0,1]$, let $V_θ=γ(θ)^\perp$ and let $π_θ:\mathbb{R}^3\rightarrow V_θ$ be the orthogonal projections. We prove that if $A\subset \mathbb{R}^3$ is a Borel set, then for a.e. $θ\in [0,1]$ we have $\t

Zichen Wang, Ilia Kulikov, Tarig Mustafa, Jan Behrends

Magnetic resonance methods offer a unique chance for in-depth study of conductive organic material systems, not only accounts for number of charge carriers, but also allows manipulations of spin dynamics of particles. Here we present a study of continuous-wave electrically detected magnetic resonance on a range of organic conjugate polymers under transistor

Matheus Nunes Soares, Fábio Reis do Santos

An integral inequality is derived for compact submanifolds (with or without boundary) in the unit sphere. This result leads to a characterization of spheres.

Elena Kosygina, Atilla Yilmaz

We establish homogenization for nondegenerate viscous Hamilton-Jacobi equations in one space dimension when the diffusion coefficient $a(x,\omega) > 0$ and the Hamiltonian $H(p,x,\omega)$ are general stationary ergodic processes in $x$. Our result is valid under mild regularity assumptions on $a$ and $H$ plus standard coercivity and growth assumptions (in $p

Yang Jiao, Gloria Wong-Padoongpatt, Mei Yang

Analytic features in gambling study are performed based on the amount of data monitoring on user daily actions. While performing the detection of problem gambling, existing datasets provide relatively rich analytic features for building machine learning based model. However, considering the complexity and cost of collecting the analytic features in real appl

Daniel Faber, Adalat Jabrayilov, Petra Mutzel

In this paper, we suggest new SAT encodings of the partial-ordering based ILP model for the graph coloring problem (GCP) and the bandwidth coloring problem (BCP). The GCP asks for the minimum number of colors that can be assigned to the vertices of a given graph such that each two adjacent vertices get different colors. The BCP is a generalization, where eac

Benson Farb, Eduard Looijenga

This is a paper in smooth $4$-manifold topology, inspired by the N\'{e}ron-Lang Theorem in number theory. More precisely, we prove that a smooth version $\MW(\pi)$ of Mordell-Weil group of an elliptic fibration $\pi:M\to\Pb^1$ is finitely generated. We compute $\MW(\pi_d)$ explicitly for elliptic fibrations $\pi_d:M_d\to\Pb^1$, where $M_d$ is a simply-connec

Justin Lidard, Hang Pham, Ariel Bachman, Bryan Boateng

Tasks where robots must anticipate human intent, such as navigating around a cluttered home or sorting everyday items, are challenging because they exhibit a wide range of valid actions that lead to similar outcomes. Moreover, zero-shot cooperation between human-robot partners is an especially challenging problem because it requires the robot to infer and ad

Mohamed Camil Belhadjoudja, Miroslav Krstic, Emmanuel Witrant

Nonlinear convection, the source of turbulence in fluid flows, may hold the key to stabilizing turbulence by solving a specific cubic polynomial equation. We consider the incompressible Navier-Stokes equations in a two-dimensional channel. The tangential and normal velocities are assumed to be periodic in the streamwise direction. The pressure difference bet

Pradeep Dubey, Siddhartha Sahi, Guanyang Wang

We give examples of situations -- stochastic production, military tactics, corporate merger -- where it is beneficial to concentrate risk rather than to diversify it, that is, to put all eggs in one basket. Our examples admit a dual interpretation: as optimal strategies of a single player (the `principal') or, alternatively, as dominant strategies in a non-c

Nguyen Ha My Dang, Paul Bouteyre, Gaëlle Trippé-Allard, Céline Chevalier

Exciton-polaritons represent a promising platform that combines the strengths of both photonic and electronic systems for future optoelectronic devices. However, their application is currently limited to laboratory research due to the high cost and complexity of fabrication methods, which are not compatible with the mature CMOS technology developed for micro

Minzhou Pan, Zhenting Wang, Xin Dong, Vikash Sehwag

In this paper, we propose WaterMark Detection (WMD), the first invisible watermark detection method under a black-box and annotation-free setting. WMD is capable of detecting arbitrary watermarks within a given reference dataset using a clean non-watermarked dataset as a reference, without relying on specific decoding methods or prior knowledge of the waterm

Garry Goldstein

In this work we present a new basis set for electronic structures (Density Functional Theory (DFT)) calculations. This basis set extends Soler Williams Linearized Augmented Plane Wave (SLAPW) basis sets by allowing variable Muffin Tin (MT) sphere radii for the different angular momentum channels and for different magnitude wave vectors of the augmented plane

Robert Underwood, Jon C. Calhoun, Sheng Di, Franck Cappello

Learning and Artificial Intelligence (ML/AI) techniques have become increasingly prevalent in high performance computing (HPC). However, these methods depend on vast volumes of floating point data for training and validation which need methods to share the data on a wide area network (WAN) or to transfer it from edge devices to data centers. Data compression

Haz Sameen Shahgir, Khondker Salman Sayeed, Abhik Bhattacharjee, Wasi Uddin Ahmad

The advent of Vision Language Models (VLM) has allowed researchers to investigate the visual understanding of a neural network using natural language. Beyond object classification and detection, VLMs are capable of visual comprehension and common-sense reasoning. This naturally led to the question: How do VLMs respond when the image itself is inherently unre

Jiacheng Chen, Yuefan Wu, Jiaqi Tan, Hang Ma

This paper presents a vector HD-mapping algorithm that formulates the mapping as a tracking task and uses a history of memory latents to ensure consistent reconstructions over time. Our method, MapTracker, accumulates a sensor stream into memory buffers of two latent representations: 1) Raster latents in the bird's-eye-view (BEV) space and 2) Vector latents

Hai Yang, Feng Yuan, Hui Li, Yosuke Mizuno

The formation of jets in black hole accretion systems is a long-standing problem. It has been proposed that a jet can be formed by extracting the rotation energy of the black hole ("BZ-jet") or the accretion flow ("disk-jet"). While both models can produce collimated relativistic outflows, neither has successfully explained the observed jet morphology. By em

Majd Ghrear, Peter Sadowski, Sven Einar Vahsen

We present the first method to probabilistically predict 3D direction in a deep neural network model. The probabilistic predictions are modeled as a heteroscedastic von Mises-Fisher distribution on the sphere $\mathbb{S}^2$, giving a simple way to quantify aleatoric uncertainty. This approach generalizes the cosine distance loss which is a special case of ou

Kodai Sakurai

We briefly introduce H-COUP_3.0, which we developed for evaluating higher-order corrections to any Higgs boson decays in various extended Higgs sectors. Focusing on two Higgs doublet models (2HDMs), we then discuss how the non-decoupling effects of the additional Higgs bosons are significant in Higgs boson decays.

Viet Dung Nguyen, Reynold Bailey, Gabriel J. Diaz, Chengyi Ma

Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. Segmentation models trained using supervised machine learning can excel at this task, their effectiveness is determined by the degree of overlap between the narrow distributions of image properties defined by the target dataset and highly specific

Yanlin Zhou, Manshi Limbu, Gregory J. Stein, Xuan Wang

Team Coordination on Graphs with Risky Edges (TCGRE) is a recently emerged problem, in which a robot team collectively reduces graph traversal cost through support from one robot to another when the latter traverses a risky edge. Resembling the traditional Multi-Agent Path Finding (MAPF) problem, both classical and learning-based methods have been proposed t

Ali Rostami, Ri Li, Sina Kheirkhah

Three-dimensional (3D) clustering characteristics of large-stokes number sprays interacting with turbulent swirling co-flows are investigated experimentally. The Astigmatic Interferometric Particle Imaging (AIPI) technique is utilized for simultaneous measurement of the spray droplets position in 3D space and their corresponding diameter. The Stokes number e

Luchuan Song, Pinxin Liu, Guojun Yin, Chenliang Xu

The one-shot talking-head generation learns to synthesize a talking-head video with one source portrait image under the driving of same or different identity video. Usually these methods require plane-based pixel transformations via Jacobin matrices or facial image warps for novel poses generation. The constraints of using a single image source and pixel dis

Zhenglin Li, Yangchen Huang, Mengran Zhu, Jingyu Zhang

Change detection is a fundamental task in computer vision that processes a bi-temporal image pair to differentiate between semantically altered and unaltered regions. Large language models (LLMs) have been utilized in various domains for their exceptional feature extraction capabilities and have shown promise in numerous downstream applications. In this stud

Ethan Cotterill, Cristhian Garay López

Curve singularities are classical objects of study in algebraic geometry. The key player in their combinatorial structure is the {\it value semigroup}, or its compactification, the {\it value semiring}. One natural problem is to explicitly determine the value semirings of distinguished infinite classes of singularities, with a view to understanding their asy

Allen Z. Ren, Jaden Clark, Anushri Dixit, Masha Itkina

We consider the problem of Embodied Question Answering (EQA), which refers to settings where an embodied agent such as a robot needs to actively explore an environment to gather information until it is confident about the answer to a question. In this work, we leverage the strong semantic reasoning capabilities of large vision-language models (VLMs) to effic

Eren Unlu

In transformer architectures, position encoding primarily provides a sense of sequence for input tokens. While the original transformer paper's method has shown satisfactory results in general language processing tasks, there have been new proposals, such as Rotary Position Embedding (RoPE), for further improvement. This paper presents geotokens, input compo

Jeremy F. Alm, Ashlee Bostic, Claire Chenault, Kenyon Coleman

The question of characterizing the (finite) representable relation algebras in a ``nice" way is open. The class $\mathbf{RRA}$ is known to be not finitely axiomatizable in first-order logic. Nevertheless, it is conjectured that ``almost all'' finite relation algebras are representable. All finite relation algebras with three or fewer atoms are representable.

Bálint Csanády, Lajos Muzsai, Péter Vedres, Zoltán Nádasdy

Large Language Models (LLMs), such as GPT-4 and Llama 2, show remarkable proficiency in a wide range of natural language processing (NLP) tasks. Despite their effectiveness, the high costs associated with their use pose a challenge. We present LlamBERT, a hybrid approach that leverages LLMs to annotate a small subset of large, unlabeled databases and uses th

Md Kaykobad Reza, S M Maksudul Alam, Yiran Luo, Youzhe Liu

In this study, we propose a novel graph-based approach to model, analyze and comprehend user interactions within a social media platform based on post-comment relationship. We construct a user interaction graph from social media data and analyze it to gain insights into community dynamics, user behavior, and content preferences. Our investigation reveals tha

Jinkun Zhang, Yuezhou Liu, Edmund Yeh

Emerging edge computing paradigms enable heterogeneous devices to collaborate on complex computation applications. However, for congestible links and computing units, delay-optimal forwarding and offloading for service chain tasks (e.g., DNN with vertical split) in edge computing networks remains an open problem. In this paper, we formulate the service chain

Fnu Hairi, Zifan Zhang, Jia Liu

In actor-critic framework for fully decentralized multi-agent reinforcement learning (MARL), one of the key components is the MARL policy evaluation (PE) problem, where a set of $N$ agents work cooperatively to evaluate the value function of the global states for a given policy through communicating with their neighbors. In MARL-PE, a critical challenge is h

Gyungbae Park

This paper studies debiased machine learning when nuisance parameters appear in indicator functions. An important example is maximized average welfare gain under optimal treatment assignment rules. For asymptotically valid inference for a parameter of interest, the current literature on debiased machine learning relies on Gateaux differentiability of the fun

Florian Chen, Felix Weitkämper, Sagar Malhotra

We study the generalization behavior of Markov Logic Networks (MLNs) across relational structures of different sizes. Multiple works have noticed that MLNs learned on a given domain generalize poorly across domains of different sizes. This behavior emerges from a lack of internal consistency within an MLN when used across different domain sizes. In this pape

Samuel Ssekajja

We study lightlike hypersurfaces of an indefinite almost contact metric-manifold $\bar{M}$. We prove that there are only two types of such hypersurfaces, known as ascreen and inascreen, with respect to the position of the structure vector field of $\bar{M}$. We also show that the second class of hypersurfaces naturally admits an almost Hermitian structure.

Raphael Pestourie, Constant Bourdeloux, Fabrice Lemoult, Mathias Fink

Multiple-user multiple-input multiple-output applications have recently gained a lot of attention. Here, we show an efficient optimization formulation for the design of all the temporal and spatial degrees of freedom of an acoustic reconfigurable metasurface for the cocktail party problem. In the frequency domain, the closed-form least square solution matche

Ali Bagci

Dirac-Coulomb type differential equation and its solution relativistic exponential-type spinor orbitals are introduced. They provide a revised form for operator invariants, namely Dirac invariants, simplifying the treatment of the angular components in calculation of many-electron systems. The relativistic Coulomb energy is determined by employing a spectral

K. Matczak, A. Mućka, A. B. Romanowska

Dyadic rationals are rationals whose denominator is a power of $2$. We define dyadic $n$-dimensional convex sets as the intersections with $n$-dimensional dyadic space of an $n$-dimensional real convex set. Such a dyadic convex set is said to be a dyadic $n$-dimensional polytope if the real convex set is a polytope whose vertices lie in the dyadic space. Dya

You Xie, Hongyi Xu, Guoxian Song, Chao Wang

We propose X-Portrait, an innovative conditional diffusion model tailored for generating expressive and temporally coherent portrait animation. Specifically, given a single portrait as appearance reference, we aim to animate it with motion derived from a driving video, capturing both highly dynamic and subtle facial expressions along with wide-range head mov

A. A. Dontsov, D. N. Aristov, A. P. Dmitriev

We analyze the uniform conductivity of a one-dimensional degenerate fermion system placed in a random disorder potential so smooth that backward scattering can be neglected. We use the nonlinear Luttinger liquid model to consider effects of both interaction and the curvature of fermionic dispersion. The finite frequency conductivity, calculated in the lowest

Laura Pierson

The chromatic symmetric function $X_G$ is a sum of monomials corresponding to proper vertex colorings of a graph $G$. Crew, Pechenik, and Spirkl (2023) recently introduced a $K$-theoretic analogue $\overline{X}_G$ called the Kromatic symmetric function, where each vertex is instead assigned a nonempty set of colors such that adjacent vertices have nonoverlap

Abhijit Mazumdar, Rafal Wisniewski, Manuela L. Bujorianu

In this paper, we present an online reinforcement learning algorithm for constrained Markov decision processes with a safety constraint. Despite the necessary attention of the scientific community, considering stochastic stopping time, the problem of learning optimal policy without violating safety constraints during the learning phase is yet to be addressed

Jinkun Zhang, Edmund Yeh

Deploying data- and computation-intensive applications such as large-scale AI into heterogeneous dispersed computing networks can significantly enhance application performance by mitigating bottlenecks caused by limited network resources, including bandwidth, storage, and computing power. However, current resource allocation methods in dispersed computing do

C. Hoffman, J. Cheng, R. Morales, D. Ji

Alzheimer's disease (AD) is a complex neurodegenerative condition that manifests at multiple levels and involves a spectrum of abnormalities ranging from the cellular to cognitive. Here, we investigate the impact of AD-related tau-pathology on hippocampal circuits in mice engaged in spatial navigation, and study changes of neuronal firing and dynamics of ext

David Xiao

A Hedge Fund Index is very useful for tracking the performance of hedge fund investments, especially the timing of fund redemption. This paper presents a methodology for constructing a hedge fund index that is more like a quantitative fund of fund, rather than a weighted sum of a number of early replicable market indices, which are re-balanced periodically.

Premankur Banerjee, Jason Cherin, Jayati Upadhyay, Jason Kutch

The paper presents a system for simulating surfing in Virtual Reality (VR), emphasizing the recreation of aquatic motions and user-initiated propulsive forces using a 6-Degree of Freedom (DoF) motion platform. We present an algorithmic approach to accurately render surfboard kinematics and interactive paddling dynamics, validated through experimental evaluat

Shohei Imai, Naoto Tsuji

We present a systematic framework to construct model Hamiltonians that have unconventional superconducting pairing states as exact energy eigenstates, by incorporating multibody interactions (i.e., interactions among more than two particles). The multibody interactions are introduced in a form of the local density-density coupling in such a way that any pair

Yaacov Kopeliovich, Michael Pokojovy, Julia Bernatska

We consider a stock that follows a geometric Brownian motion (GBM) and a riskless asset continuously compounded at a constant rate. We assume that the stock can go bankrupt, i.e., lose all of its value, at some exogenous random time (independent of the stock price) modeled as the first arrival time of a homogeneous Poisson process. For this setup, we study M

Andrew Steane

We obtain the gravitational emission from particles scattering via the Yukawa interaction, presenting both classical and approximate quantum results. We also estimate the contribution from the tensor part of the internucleon interaction. This emission is the main source of a very-high frequency component to the stochastic background in the Solar System and i

Hermenegildo Borges de Oliveira

A mathematical model that governs turbulent flows through permeable media is considered in this work. The model under consideration is based on a double-averaging concept which in turn is described by the time-averaging technique characteristic of the turbulence k-epsilon model and by the volume-averaging methodology that is used to model unstable flows thro

Mengke Wu, Weizi Liu, Yanyun Wang, Mike Yao

Smart recommendation algorithms have revolutionized content delivery and improved efficiency across various domains. However, concerns about user agency arise from the algorithms' inherent opacity (information asymmetry) and one-way output (power asymmetry). This study introduces a dual-control mechanism aimed at enhancing user agency, empowering users to ma

Aryan Satpathy, Nilaksh Singh, Dhruva Rajwade, Somesh Kumar

Self-Supervised Learning (SSL) has shown great promise in learning representations from unlabeled data. The power of learning representations without the need for human annotations has made SSL a widely used technique in real-world problems. However, SSL methods have recently been shown to be vulnerable to backdoor attacks, where the learned model can be exp

Yi Wang, Chetan Arora, Xiao Liu, Thuong Hoang

Personas are commonly used in software projects to gain a better understanding of end-users' needs. However, there is a limited understanding of their usage and effectiveness in practice. This paper presents the results of a two-step investigation, comprising interviews with 26 software developers, UI/UX designers, business analysts and product managers and

Albin Larsson Forsberg, Alexandros Nikou, Aneta Vulgarakis Feljan, Jana Tumova

One of the main challenges in multi-agent reinforcement learning is scalability as the number of agents increases. This issue is further exacerbated if the problem considered is temporally dependent. State-of-the-art solutions today mainly follow centralized training with decentralized execution paradigm in order to handle the scalability concerns. In this p

Chengcheng Yang

This paper generalizes the Michell Truss problem and Gangbo's paper from 1-dimension to higher dimensions using geometric measure theory. Given an elastic surface $S$ made of $(k-1)$-beams under an equilibriated system $F$ of external forces, then we ask the following two questions: 1. What are the necessary and sufficient conditions for the existence of an

Susanne Pumpluen

Nonassociative differential extensions are generalizations of associative differential extensions, either of a purely inseparable field extension $K$ of exponent one of a field $F$, $F$ of characteristic $p$, or of a central division algebra over a purely inseparable field extension of $F$. Associative differential extensions are well known central simple al

François Pacaud, Sungho Shin

We investigate the potential of Graphics Processing Units (GPUs) to solve large-scale nonlinear programs with a dynamic structure. Using ExaModels, a GPU-accelerated automatic differentiation tool, and the interior-point solver MadNLP, we significantly reduce the time to solve dynamic nonlinear optimization problems. The sparse linear systems formulated in t

Jian Tang, Thomas Siyuan Ding, Hongyu Chen, Anyuan Gao

The convergence of topology and correlations represents a highly coveted realm in the pursuit of novel quantum states of matter. Introducing electron correlations to a quantum spin Hall (QSH) insulator can lead to the emergence of a fractional topological insulator and other exotic time-reversal-symmetric topological order, not possible in quantum Hall and C

Michele Cotrufo, Jonas Krakofsky, Sander A. Mann, Gerhard Böhm

Nonlinear intersubband polaritonic metasurfaces support one of the strongest known ultrafast nonlinear responses in the mid-infrared frequency range across all condensed matter systems. Beyond harmonic generation and frequency mixing, these nonlinearities can be leveraged for ultrafast optical switching and power limiting, based on tailored transitions from