February 2024 arXiv papers — page 18

Hai Nguyen, Tadashi Kozuno, Cristian C. Beltran-Hernandez, Masashi Hamaya

This study tackles the representative yet challenging contact-rich peg-in-hole task of robotic assembly, using a soft wrist that can operate more safely and tolerate lower-frequency control signals than a rigid one. Previous studies often use a fully observable formulation, requiring external setups or estimators for the peg-to-hole pose. In contrast, we use

Abhishek Kumar, Rafail Frantzeskakis, Edwin Barnes

We study the stroboscopic non-equilibrium quantum dynamics of periodically kicked Hamiltonians involving homogeneous central-spin interactions. The system exhibits a strong fragmentation of Hilbert space into four-dimensional Floquet-Krylov subspaces, which oscillate between two disjointed two-dimensional subspaces and thus break the discrete time-translatio

Lili Fan, Ruonan Liu, Heyang Li

In this paper, we construct exact solutions that character three-dimensional, nonlinear trapped lee waves propagation superimposed on longitudinal atmospheric currents in the $\beta$-plane approximation. The solutions obtained are presented in Lagrangian coordinates, and are Gerstner-like solutions. In the process, we also derive the dispersion relation and

He Zhao

In electron cooling, the space charge (SC) is an important effect, which will affect the e-beam velocity distribution and thus the cooling performance. In this paper, we analyse several important effects that due to the space charge field, such like transverse and longitudianl space charge force, drift velocity caused by SC and magnetic field, and longitudin

Benjamín Moraga

The action of a finite group $G$ on a compact Riemann surface $X$ naturally induces another action of $G$ on its Jacobian variety $\operatorname{J}(X)$. In many cases, each component of the group algebra decomposition of $\operatorname{J}(X)$ is isogenous to a Prym varieties of an intermediate covering of the Galois covering $\pi_G\colon X \to X/G$; in such

Nisha Pillai, Ganga Gireesan, Michael J. Rothrock, Bindu Nanduri

Understanding how multiple features are associated and contribute to a specific objective is as important as understanding how each feature contributes to a particular outcome. Interpretability of a single feature in a prediction may be handled in multiple ways; however, in a multi-objective prediction, it is difficult to obtain interpretability of a combina

Zhe Wang, Jianping Wu, Mengjun Zheng, Chenchen Geng

Many tools exist for extracting structural and physiochemical descriptors from linear peptides to predict their properties, but similar tools for hydrocarbon-stapled peptides are lacking.Here, we present StaPep, a Python-based toolkit designed for generating 2D/3D structures and calculating 21 distinct features for hydrocarbon-stapled peptides.The current ve

Xinyu Bian, Yuyi Mao, Jun Zhang

Grant-free random access (RA) has been recognized as a promising solution to support massive connectivity due to the removal of the uplink grant request procedures. While most endeavours assume perfect synchronization among users and the base station, this paper investigates asynchronous grant-free massive RA, and develop efficient algorithms for joint user

James Leng, Ashwin Sah, Mehtaab Sawhney

Let $r_k(N)$ denote the size of the largest subset of $[N] = \{1,\ldots,N\}$ with no $k$-term arithmetic progression. We show that for $k\ge 5$, there exists $c_k>0$ such that \[r_k(N)\ll N\exp(-(\log\log N)^{c_k}).\] Our proof is a consequence of recent quasipolynomial bounds on the inverse theorem for the Gowers $U^k$-norm as well as the density increment

James Leng, Ashwin Sah, Mehtaab Sawhney

We prove quasipolynomial bounds on the inverse theorem for the Gowers $U^{s+1}[N]$-norm. The proof is modeled after work of Green, Tao, and Ziegler and uses as a crucial input recent work of the first author regarding the equidistribution of nilsequences. In a companion paper, this result will be used to improve the bounds on Szemer\'{e}di's theorem.

Kenji Sugisaki, Tatsuya Nakano, Yuji Mochizuki

The fragment molecular orbital (FMO) scheme is one of the popular fragmentation-based methods and has the potential advantage of making the circuit flat in quantum chemical calculations on quantum computers. In this study, we used a GPU-accelerated quantum simulator (cuQuantum) to perform the electron correlation part of the FMO calculation as unitary couple

R. Bailey Bond, Pu Ren, Jerome F. Hajjar, Hao Sun

There is growing interest in using machine learning (ML) methods for structural metamodeling due to the substantial computational cost of traditional simulations. Purely data-driven strategies often face limitations in model robustness, interpretability, and dependency on extensive data. To address these challenges, this paper introduces a novel physics-info

Yijun Wang, Tao Wang, Luis C. Ho, Yuxing Zhong

The fundamental plane of black hole activity (BHFP) describes the correlation between radio luminosity ($L_R$), X-ray luminosity ($L_X$), and black hole mass. It reflects a disc-jet connection. However, dependence of BHFP on various physical properties of active galactic nuclei (AGNs) and host galaxies is unclear, especially for low-luminosity AGNs, which is

Ganga Gireesan, Nisha Pillai, Michael J Rothrock, Bindu Nanduri

Pathogen control is a critical aspect of modern poultry farming, providing important benefits for both public health and productivity. Effective poultry management measures to reduce pathogen levels in poultry flocks promote food safety by lowering risks of food-borne illnesses. They also support animal health and welfare by preventing infectious diseases th

Kai Fukami, Susumu Goto, Kunihiko Taira

Nonlinear machine learning for turbulent flows can exhibit robust performance even outside the range of training data. This is achieved when machine-learning models can accommodate scale-invariant characteristics of turbulent flow structures. This study presents a data-driven approach to reveal scale-invariant vortical structures across Reynolds numbers that

Tomoya Aono, Tatsuya Sakamoto, Toyoho Ishimura, Motomitsu Takahashi

The Japanese sardine (Sardinops melanostictus) is a small pelagic fish found in the Sea of Japan, the marginal sea of the western North Pacific. It is an important species for regional fisheries, but their transportation and migration patterns during early life stages remain unclear. In this study, we analyzed the stable oxygen isotope ratios of otoliths of

Xintong Xu, Matthias Kuehne, Harrison A. Walker, De-Liang Bao

Fluids under extreme confinement exhibit unique structures and intermolecular bonding, distinct from their bulk analogs, driving innovative applications at the water-energy nexus. Probing confined water experimentally at the length scale of intermolecular and surface forces has, however, remained a challenge. Here, we report direct molecular-level observatio

Daniel Melcer, Nathan Fulton, Sanjay Krishna Gouda, Haifeng Qian

Large Language Models are powerful tools for program synthesis and advanced auto-completion, but come with no guarantee that their output code is syntactically correct. This paper contributes an incremental parser that allows early rejection of syntactically incorrect code, as well as efficient detection of complete programs for fill-in-the-middle (FIM) task

Michael Potter, Murat Akcakaya, Marius Necsoiu, Gunar Schirner

Radar Automated Target Recognition (RATR) for Unmanned Aerial Vehicles (UAVs) involves transmitting Electromagnetic Waves (EMWs) and performing target type recognition on the received radar echo, crucial for defense and aerospace applications. Previous studies highlighted the advantages of multistatic radar configurations over monostatic ones in RATR. Howeve

Jason J. Yu, Tristan Aumentado-Armstrong, Fereshteh Forghani, Konstantinos G. Derpanis

This paper considers the problem of generative novel view synthesis (GNVS), generating novel, plausible views of a scene given a limited number of known views. Here, we propose a set-based generative model that can simultaneously generate multiple, self-consistent new views, conditioned on any number of views. Our approach is not limited to generating a sing

Zhe Fang, Koichi Hattori, Jin Hu

We address the linear-mode analysis performed near an equilibrium configuration in the fluid rest frame with a dynamical magnetic field perturbed on a constant configuration. We develop a simple and general algorithm for an analytic solution search that works on an order-by-order basis in the derivative expansion. This method can be applied to general sets o

Yi Zhang, Fei Yang, Shuang Peng, Fangyu Wang

Large language models (LLMs) have demonstrated state-of-the-art performance across various tasks. However, the latency of inference and the large GPU memory consumption of LLMs restrict their deployment performance. Recently, there have been some efficient attempts to quantize LLMs, yet inference with large batch size or long sequence still has the issue of

Minji Kim, Kevin O'Connor, Vladas Pipiras, Themistoklis Sapsis

In a multifidelity setting, data are available under the same conditions from two (or more) sources, e.g. computer codes, one being lower-fidelity but computationally cheaper, and the other higher-fidelity and more expensive. This work studies for which low-fidelity outputs, one should obtain high-fidelity outputs, if the goal is to estimate the probability

Yihao Ding, Lorenzo Vaiani, Caren Han, Jean Lee

This paper presents a groundbreaking multimodal, multi-task, multi-teacher joint-grained knowledge distillation model for visually-rich form document understanding. The model is designed to leverage insights from both fine-grained and coarse-grained levels by facilitating a nuanced correlation between token and entity representations, addressing the complexi

Lifeng Jin, Baolin Peng, Linfeng Song, Haitao Mi

The most common training pipeline for large language models includes pretraining, finetuning and aligning phases, with their respective resulting models, such as the pretrained model and the finetuned model. Finetuned and aligned models show improved abilities of instruction following and safe generation, however their abilities to stay factual about the wor

Lin Li, Zi Li, Xia Hua, Xin Tong

We present a detailed method for accumulating Ca$^{+}$ ions controllably in a linear Paul trap. The ions are generated by pulsed laser ablation and dynamically loaded into the ion trap by switching the trapping potential on and off. The loaded ions are precooled by buffer gas and then laser-cooled to form Coulomb crystals for verifying quantity. The number o

Wei-Can Yang, Chuan-Yin Xia, Yu Tian, Makoto Tsubota

In two-dimensional turbulence systems, the emergence of large-scale structures holds profound physical implications, particularly as it indicates the occurrence of inverse energy cascades, thereby garnering significant attention. In this paper, we report a novel vortex clusters formation in the background of near-extreme Reissner-Nordstr$\ddot{o}$m black hol

Mengran Zhu, Ye Zhang, Yulu Gong, Kaijuan Xing

In the realm of consumer lending, accurate credit default prediction stands as a critical element in risk mitigation and lending decision optimization. Extensive research has sought continuous improvement in existing models to enhance customer experiences and ensure the sound economic functioning of lending institutions. This study responds to the evolving l

Zeyang Liu, Lipeng Wan, Xinrui Yang, Zhuoran Chen

Effective exploration is crucial to discovering optimal strategies for multi-agent reinforcement learning (MARL) in complex coordination tasks. Existing methods mainly utilize intrinsic rewards to enable committed exploration or use role-based learning for decomposing joint action spaces instead of directly conducting a collective search in the entire action

Abhay Ashtekar, Simone Speziale

Null infinity arises as a boundary of the Penrose conformal completion of an asymptotically flat physical space-time. We first note that null infinity is a weakly isolated horizon (WIH), and then show that its familiar properties can be derived from the general WIH framework. This seems quite surprising because physics associated with black hole (and cosmolo

Zhewei Wu, Ruilong Yu, Qihe Liu, Shuying Cheng

Adversarial attacks in visual object tracking have significantly degraded the performance of advanced trackers by introducing imperceptible perturbations into images. However, there is still a lack of research on designing adversarial defense methods for object tracking. To address these issues, we propose an effective auxiliary pre-processing defense networ

Katherine Metcalf, Miguel Sarabia, Natalie Mackraz, Barry-John Theobald

Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that dynamics-aware reward functions improve the sample efficiency of PbRL by an order of magnitude. In our experiments we iterate between: (1) learning a dynamics-aware state-action re

Zhi-Biao Liang, Feng-Xiao Liu, Xian-Hui Zhong

In a nonrelativistic potential quark model framework, we carry out a calculation of the mass spectrum for the low-lying $1S$ all-heavy pentaquark state by adopting the explicitly correlated Gaussian method. The obtained states are compact and lie far above the lowest dissociation baryon-meson threshold. Moreover, using the obtained masses and wave functions

L. J. Toomey, G. Hobbs, D. C. Price, J. R. Dawson

Radio astronomy file formats are now required to store wide frequency bandwidths and multiple simultaneous receiver beams and must be able to account for versatile observing modes and numerous calibration strategies. The need to capture and archive high-time and high frequency-resolution data, along with the comprehensive metadata that fully describe the dat

Kanyifeechukwu J. Oguine, Roger D. Soberanis-Mukul, Nathan Drenkow, Mathias Unberath

Purpose: Accurate tool segmentation is essential in computer-aided procedures. However, this task conveys challenges due to artifacts' presence and the limited training data in medical scenarios. Methods that generalize to unseen data represent an interesting venue, where zero-shot segmentation presents an option to account for data limitation. Initial explo

Lei Wang, Wanyu Xu, Zhiqiang Hu, Yihuai Lan

This paper introduces a new in-context learning (ICL) mechanism called In-Image Learning (I$^2$L) that combines demonstration examples, visual cues, and chain-of-thought reasoning into an aggregated image to enhance the capabilities of Large Multimodal Models (e.g., GPT-4V) in multimodal reasoning tasks. Unlike previous approaches that rely on converting ima

Jun Huang, Jiawei Zhang, Qi Wang, Weihong Han

Large Language Models (LLMs) represent an advanced evolution of earlier, simpler language models. They boast enhanced abilities to handle complex language patterns and generate coherent text, images, audios, and videos. Furthermore, they can be fine-tuned for specific tasks. This versatility has led to the proliferation and extensive use of numerous commerci

Le Zhuo, Zewen Chi, Minghao Xu, Heyan Huang

We propose ProtLLM, a versatile cross-modal large language model (LLM) for both protein-centric and protein-language tasks. ProtLLM features a unique dynamic protein mounting mechanism, enabling it to handle complex inputs where the natural language text is interspersed with an arbitrary number of proteins. Besides, we propose the protein-as-word language mo

Koki Maeda, Shuhei Kurita, Taiki Miyanishi, Naoaki Okazaki

Given the accelerating progress of vision and language modeling, accurate evaluation of machine-generated image captions remains critical. In order to evaluate captions more closely to human preferences, metrics need to discriminate between captions of varying quality and content. However, conventional metrics fail short of comparing beyond superficial match

Jarah Evslin, Hui Liu

Classically, reflectionless kinks transmit all incident radiation. Recently, we have used an analyticity argument together with a solution of the Lippmann-Schwinger equation to write down the leading quantum correction to the reflection probability. The argument was fast but rather indirect. In the present paper, we calculate the reflection coefficient and p

Koshi Oishi, Yota Hashizume, Tomohiko Jimbo, Hirotaka Kaji

Transport systems on networks are crucial in various applications, but face a significant risk of being adversely affected by unforeseen circumstances such as disasters. The application of entropy-regularized optimal transport (OT) on graph structures has been investigated to enhance the robustness of transport on such networks. In this study, we propose an

Hira Saleem, Flora Salim, Cormac Purcell

Operational Numerical Weather Prediction (NWP) system relies on computationally expensive physics-based models. Recently, transformer models have shown remarkable potential in weather forecasting achieving state-of-the-art results. However, traditional transformers discretize spatio-temporal dimensions, limiting their ability to model continuous dynamical we

Sangwook Lee

Given a hypersurface singularity (not necessarily isolated) with a finite abelian group action, we develop a method to define an explicit product structure on the twisted Koszul algebra (whose invariant subalgebra is the orbifold Koszul algebra).

Kenji Fukaya, Yong-Geun Oh, Hiroshi Ohta, Kaoru Ono

This is a corrigendum of Lemma 9.1 of the paper [FOOO3] in the title. This lemma is not correct as pointed out by A. Daemi and a referee of the paper [DF]. The corrigendum does not affect the applications of this lemma in [FOOO3] and other papers and exactly the same proofs as therein apply if one replaces the statement of [FOOO3,Lemma 9.1] by Lemma 2 of the

Zeeshawn Kazi, Isaac M. Shelby, Ruhee Nirodi, Joseph Turnbull

DNA flexibility is a key determinant of biological function, from nucleosome positioning to transcriptional regulation, motivating a direct measurement of the bend-torque response of individual DNA molecules. In this work, DNA bending is detected using a nano-mechanical torque balance formed by tethering a ferromagnetic nanoparticle probe by an individual DN

Jinhong Li, Yiyang Geng, Qiuping Wang, Shujie Han

Zoned Namespace (ZNS) defines a new abstraction for host software to flexibly manage storage in flash-based SSDs as append-only zones. It also provides a Zone Append primitive to further boost the write performance of ZNS SSDs by exploiting intra-zone parallelism. However, making Zone Append effective for reliable and scalable storage, in the form of a RAID

Ruy Fabila-Monroy, Sergio Gerardo Gómez-Galicia, César Hernández-Cruz, Ana Laura Trujillo-Negrete

Let $G$ be a graph on $n$ vertices and $1 \le k \le n$ a fixed integer. The \textit{$k$-token graph} of $G$ is the graph $F_k(G)$ whose vertex set consists of all $k$-subsets of the vertex set of $G$, where two vertices $A$ and $B$ are adjacent in $F_k(G)$ whenever their symmetric difference $A\triangle B$ is an edge of $G$. In this paper we study the treewi

Alfonso Lagares de Toledo, Christopher E. Carr

Small low-cost instruments enable new and exciting mission opportunities yet their constrained volume and limited budgets make them especially susceptible to suffering anomalies during flight. Radiation effects as well as sensor or actuator failure can all pose a serious threat to the continued collection of scientific data as well as cause the partial or co

Reza Reihanisaransari, Chalapathi Charan Gajjela, Xinyu Wu, Ragib Ishrak

Ovarian cancer detection has traditionally relied on a multi-step process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hype

Zhou Yang, Zhaochun Ren, Yufeng Wang, Chao Chen

Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses. Psychological theories posit that comprehending emotional and cognitive states necessitates iteratively capturing and understanding associated words across dialogue utterances. However, existing approaches regard dialogue

Jaewon Yoo, Changbom Park, Cristiano G. Sabiu, Ankit Singh

One intriguing approach for studying the dynamical evolution of galaxy clusters is to compare the spatial distributions among various components, such as dark matter, member galaxies, gas, and intracluster light (ICL). Utilizing the recently introduced Weighted Overlap Coefficient (WOC) \citep{2022ApJS..261...28Y}, we analyze the spatial distributions of com

Toshiki Sato, Makoto Sawada, Keiichi Maeda, John P. Hughes

The progenitor of the W49B supernova remnant is still under debate. One of the candidates is a jet-driven core-collapse supernova. In such a highly asymmetric explosion, a strong $\alpha$-rich freezeout is expected in local high entropy regions, which should enrich elements synthesized by the capture of $\alpha$-particles such as $^{44}$Ti and $^{48}$Cr (dec

Yasin Sadeghi Bazargani, Majid Mirzaei, Navid Sobhi, Mirsaeed Abdollahi

Diabetes mellitus (DM) predisposes patients to vascular complications. Retinal images and vasculature reflect the body's micro- and macrovascular health. They can be used to diagnose DM complications, including diabetic retinopathy (DR), neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as forecast the risk of cardiovascular events

Leticia Barchini, Peter E. Trapa

Fix an integral semisimple element $\lambda$ in the Lie algebra $\mathfrak{g}$ of a complex reductive algebraic group $G$. Let $L$ denote the centralizer of $\lambda$ in $G$ and let $\mathfrak{g}(-1)$ denote the $-1$ eigenspace of $\mathrm{ad}(\lambda)$ in $\mathfrak{g}$. Under a natural hypothesis (which is always satisfied for classical subgroups of $\math

Shohei Kohatsu

This paper is concerned with the Keller--Segel system with flux limitation, \begin{align} \tag{$\ast$} \begin{cases} u_t=\Delta u - \nabla \cdot (uf(|\nabla v|^{2})\nabla v), \\ v_t=\Delta v - v + u \end{cases} \end{align} in bounded $n$-dimensional domains with homogeneous Neumann boundary conditions, where $f$ generalizes the prototype obtained on letting

Giuseppe Attanasio, Beatrice Savoldi, Dennis Fucci, Dirk Hovy

Current automatic speech recognition (ASR) models are designed to be used across many languages and tasks without substantial changes. However, this broad language coverage hides performance gaps within languages, for example, across genders. Our study systematically evaluates the performance of two widely used multilingual ASR models on three datasets, enco

Minzhi Li, Weiyan Shi, Caleb Ziems, Diyi Yang

As Natural Language Processing (NLP) systems become increasingly integrated into human social life, these technologies will need to increasingly rely on social intelligence. Although there are many valuable datasets that benchmark isolated dimensions of social intelligence, there does not yet exist any body of work to join these threads into a cohesive subfi

Toshihiro Koga

Let $\{q_n\}_{n=0}^\infty\subset [0,1]$ satisfy $q_0=0$, $\sum_{n=0}^\infty q_n=1$, and $\gcd\{n\geq 1\mid q_n\neq 0\}=1$. We consider the following process: Let $x$ be a real number. We first set $x=0$. Then $x$ is increased by $i$ with probability $q_i~(i=0,1,2,\cdots)$ every time. For $n\geq 0$, let $p_n$ be the probability such that $x=n$ occurs, so we h

Leticia Barchini, Peter E. Trapa

Motivated by relating the representation theory of the split real and $p$-adic forms of a connected reductive algebraic group $G$, we describe a subset of $2^r$ orbits on the complex flag variety for a certain symmetric subgroup. (Here $r$ is the semisimple rank of $G$.) This set of orbits has the property that, while the closure of individual orbits are gen

Ishak Ayad, Nicolas Larue, Maï K. Nguyen

Inverse problems span across diverse fields. In medical contexts, computed tomography (CT) plays a crucial role in reconstructing a patient's internal structure, presenting challenges due to artifacts caused by inherently ill-posed inverse problems. Previous research advanced image quality via post-processing and deep unrolling algorithms but faces challenge

Alfonso Ballon-Bayona, Adão S. da Silva Junior

We present a simple holographic QCD model that provides a unified description of vector mesons and nucleons in a confining background based on Einstein-dilaton gravity. For the confining background we consider analytical solutions of the Einstein-dilaton equations where the dilaton is a quadratic function of the radial coordinate far from the boundary. We bu

Marcel Rieger

In particle physics, workflow management systems are primarily used as tailored solutions in dedicated areas such as Monte Carlo production. However, physicists performing data analyses are usually required to steer their individual, complex workflows manually, frequently involving job submission in several stages and interaction with distributed storage sys

Paulo Firmino, Laurentiu Leustean

In this paper we obtain, by using proof mining methods, quantitative results on the asymptotic regularity of the viscosity approximation method (VAM) with error terms for m-accretive operators in Banach spaces. For concrete instances of the parameter sequences, linear rates are computed by applying a lemma due to Sabach and Shtern.

Guangji Bai, Yijiang Li, Chen Ling, Kibaek Kim

The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands. Pruning has emerged as a pivotal compression strategy, introducing sparsity to enhance both memory and computational efficiency. Yet, traditional global pruning is impractical for LLMs due to sca

Sayak Ghosh, Matthias S. Ikeda, Anzumaan R. Chakraborty, Thanapat Worasaran

The iron-based high-$T_c$ superconductors exhibit rich phase diagrams with intertwined phases, including magnetism, nematicity and superconductivity. The superconducting $T_c$ in many of these materials is maximized in the regime of strong nematic fluctuations, making the role of nematicity in influencing the superconductivity a topic of intense research. He

Xi Fang, Weijie Xu, Fiona Anting Tan, Jiani Zhang

Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding. Each task presents unique challenges and opportunities. However, there is currently a lack of comprehensive revie

Benjamin Zanger, Olivier Zahm, Tiangang Cui, Martin Schreiber

Transport-based density estimation methods are receiving growing interest because of their ability to efficiently generate samples from the approximated density. We further invertigate the sequential transport maps framework proposed from arXiv:2106.04170 arXiv:2303.02554, which builds on a sequence of composed Knothe-Rosenblatt (KR) maps. Each of those maps

Wen Cao, Ehsan Miandji, Jonas Unger

This paper considers a compressive multi-spectral light field camera model that utilizes a one-hot spectralcoded mask and a microlens array to capture spatial, angular, and spectral information using a single monochrome sensor. We propose a model that employs compressed sensing techniques to reconstruct the complete multi-spectral light field from undersampl

Alexandra B. Artusio-Glimpse, David A. Long, Sean M. Bresler, Nikunjkumar Prajapati

We show that the use of an optical frequency comb probe leads to dramatically improved bandwidth (as high as 12+/-1 MHz) for the detection of modulated radio frequencies in Rydberg atom-based electrometry.

Vikranth Lokeshwar Dhandapani, Shashi Jain

In this paper, we present an artificial neural network framework for portfolio compression of a large portfolio of European options with varying maturities (target portfolio) by a significantly smaller portfolio of European options with shorter or same maturity (compressed portfolio), which also represents a self-replicating static hedge portfolio of the tar

Wenyuan Zhao, Yu-Shin Huang, Ruida Zhou, Chao Tian

We study the problem of weakly private information retrieval (PIR) when there is heterogeneity in servers' trustworthiness under the maximal leakage (Max-L) metric and mutual information (MI) metric. A user wishes to retrieve a desired message from N non-colluding servers efficiently, such that the identity of the desired message is not leaked in a significa

Yunjia Yang, Runze Li, Yufei Zhang, Haixin Chen

The transonic buffet is a detrimental phenomenon occurs on supercritical airfoils and limits aircraft's operating envelope. Traditional methods for predicting buffet onset rely on multiple computational fluid dynamics simulations to assess a series of airfoil flowfields and then apply criteria to them, which is slow and hinders optimization efforts. This art

Ruisi Zhang, Farinaz Koushanfar

This paper introduces EmMark,a novel watermarking framework for protecting the intellectual property (IP) of embedded large language models deployed on resource-constrained edge devices. To address the IP theft risks posed by malicious end-users, EmMark enables proprietors to authenticate ownership by querying the watermarked model weights and matching the i

Mina J. Kian, Mingyu Zong, Katrin Fischer, Abhyuday Singh

Cognitive behavioral therapy (CBT) is a widely used therapeutic method for guiding individuals toward restructuring their thinking patterns as a means of addressing anxiety, depression, and other challenges. We developed a large language model (LLM)-powered prompt-engineered socially assistive robot (SAR) that guides participants through interactive CBT at-h

Theodor Amariucai, Alex Warstadt

In contrast to children, language models (LMs) exhibit considerably inferior data efficiency when acquiring language. In this submission to the BabyLM Challenge (Warstadt et al., 2023), we test the hypothesis that this data efficiency gap is partly caused by a lack of multimodal input and grounding in the learning environment of typical language models. Alth

Arun Ram

This paper uses Lusztig varieties to give central elements of the Iwahori-Hecke algebra corresponding to unipotent conjugacy classes in the finite Chevalley group $GL_n(\mathbb{F}_q)$. We explain how these central elements are related to Macdonald polynomials and how this provides a framework for generalizing integral form and modified Macdonald polynomials

Chu-Cheng Lin, Xinyi Wang, Jonathan H. Clark, Han Lu

Adapting pretrained large language models (LLMs) to various downstream tasks in tens or hundreds of human languages is computationally expensive. Parameter-efficient fine-tuning (PEFT) significantly reduces the adaptation cost, by tuning only a small amount of parameters. However, common PEFT methods LoRA (Hu et al., 2022) suffer from suboptimal performance

Zhaofeng Tian, William He, Boyang Tian, Ren Zhong

Indoor autonomous driving testbeds have emerged to complement expensive outdoor testbeds and virtual simulations, offering scalable and cost-effective solutions for research in navigation, traffic optimization, and swarm intelligence. However, they often lack the robust sensing and computing infrastructure for advanced research. Addressing these limitations,

Deepeka Garg, Benjamin Patrick Evans, Leo Ardon, Annapoorani Lakshmi Narayanan

Mortgages account for the largest portion of household debt in the United States, totaling around \$12 trillion nationwide. In times of financial hardship, alleviating mortgage burdens is essential for supporting affected households. The mortgage servicing industry plays a vital role in offering this assistance, yet there has been limited research modelling

Jérémie Klinger, Grant M. Rotskoff

Physical systems driven away from equilibrium by an external controller dissipate heat to the environment; the excess entropy production in the thermal reservoir can be interpreted as a "cost" to transform the system in a finite time. The connection between measure theoretic optimal transport and dissipative nonequilibrium dynamics provides a language for qu

Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka, Joshua B. Tenenbaum

People often give instructions whose meaning is ambiguous without further context, expecting that their actions or goals will disambiguate their intentions. How can we build assistive agents that follow such instructions in a flexible, context-sensitive manner? This paper introduces cooperative language-guided inverse plan search (CLIPS), a Bayesian agent ar

Deeksha Adil, Thatchaphol Saranurak

We present a dynamic algorithm for maintaining $(1+\epsilon)$-approximate maximum eigenvector and eigenvalue of a positive semi-definite matrix $A$ undergoing \emph{decreasing} updates, i.e., updates which may only decrease eigenvalues. Given a vector $v$ updating $A\gets A-vv^{\top}$, our algorithm takes $\tilde{O}(\mathrm{nnz}(v))$ amortized update time, i

Kunmo Koo, Nikhil S. Chellam, Sangyoon Shim, Chad A. Mirkin

Environmental transmission electron microscopy (E-TEM) enables direct observation of nanoscale chemical processes crucial for catalysis and materials design. However, the high-energy electron probe can dramatically alter reaction pathways through radiolysis - the dissociation of molecules under electron beam irradiation. While extensively studied in liquid-c

Thomas Hader, Daniela Kaufmann, Ahmed Irfan, Stéphane Graham-Lengrand

This system description introduces an enhancement to the Yices2 SMT solver, enabling it to reason over non-linear polynomial systems over finite fields. Our reasoning approach fits into the model-constructing satisfiability (MCSat) framework and is based on zero decomposition techniques, which find finite basis explanations for theory conflicts over finite f

Cheng Zhen, Nischal Aryal, Arash Termehchy, Alireza Aghasi

Real-world data is often incomplete and contains missing values. To train accurate models over real-world datasets, users need to spend a substantial amount of time and resources imputing and finding proper values for missing data items. In this paper, we demonstrate that it is possible to learn accurate models directly from data with missing values for cert

Sławomir Garcarz, Avik Pal, Pim Praat

This paper focuses on reproducing and extending the results of the paper: "Modeling Personalized Item Frequency Information for Next-basket Recommendation" which introduced the TIFU-KNN model and proposed to utilize Personalized Item Frequency (PIF) for Next Basket Recommendation (NBR). We utilized publicly available grocery shopping datasets used in the ori

Niloufar Dadkhah, Walter R. L. Lambrecht

A study is presented of the electronic band structure and optical absorption spectrum of monolayer WSe$_2$ using an all-electron quasiparticle self-consistent $GW$ approach, QS$G\hat W$, in which the screened Coulomb interaction $\hat W$ is calculated including ladder diagrams representing electron-hole interaction. The Bethe-Salpeter Equation is used to cal

Alessio Baldazzi, Nicolò Leone, Matteo Sanna, Stefano Azzini

Among supervised learning models, Support Vector Machine stands out as one of the most robust and efficient models for classifying data clusters. At the core of this method, a kernel function is employed to calculate the distance between different elements of the dataset, allowing for their classification. Since every kernel function can be expressed as a sc

Colin Aitken

In 1974, Gugenheim and May showed that the cohomology $\text{Ext}_A(R,R)$ of a connected augmented algebra over a field $R$ is generated by elements with $s = 1$ under matric Massey products. In particular, this applies to the $E_2$ page of the $H\mathbb{F}_p$-based Adams spectral sequence. By studying a novel sequence of deformations of a presentably symmet

Mahsa Ashouri, Nicholas C. Henderson

Prediction methods for time-to-event outcomes often utilize survival models that rely on strong assumptions about noninformative censoring or on how individual-level covariates and survival functions are related. When the main interest is in predicting individual-level restricted mean survival times (RMST), reliance on such assumptions can lead to poor predi

Young Shin Kim, Hyun-Gyoon Kim

In this study, we discuss a machine learning technique to price exotic options with two underlying assets based on a non-Gaussian Levy process model. We introduce a new multivariate Levy process model named the generalized normal tempered stable (gNTS) process, which is defined by time-changed multivariate Brownian motion. Since the gNTS process does not pro

Amin Sarihi, Ahmad Patooghy, Abdel-Hameed A. Badawy, Peter Jamieson

This work focuses on advancing security research in the hardware design space by formally defining the realistic problem of Hardware Trojan (HT) detection. The goal is to model HT detection more closely to the real world, i.e., describing the problem as "The Seeker's Dilemma" (an extension of Hide&Seek on a graph), where a detecting agent is unaware of wheth

Hollan Haule, Ian Piper, Patricia Jones, Tsz-Yan Milly Lo

In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping. In contrast to previous research focused on electronic health records (EHR), here we propose an ML approach for phenotyping using routinely collected physiological time series data. Our new algorithm integ

Roy Xie, Chengxuan Huang, Junlin Wang, Bhuwan Dhingra

Large language models (LLMs) have significantly transformed the educational landscape. As current plagiarism detection tools struggle to keep pace with LLMs' rapid advancements, the educational community faces the challenge of assessing students' true problem-solving abilities in the presence of LLMs. In this work, we explore a new paradigm for ensuring fair

Larissa N. A. Martins, Flávio B. Gonçalves, Thais P. Galletti

Safe and reliable disclosure of information from confidential data is a challenging statistical problem. A common approach considers the generation of synthetic data, to be disclosed instead of the original data. Efficient approaches ought to deal with the trade-off between reliability and confidentiality of the released data. Ultimately, the aim is to be ab

Roy Xie, Orevaoghene Ahia, Yulia Tsvetkov, Antonios Anastasopoulos

Identifying linguistic differences between dialects of a language often requires expert knowledge and meticulous human analysis. This is largely due to the complexity and nuance involved in studying various dialects. We present a novel approach to extract distinguishing lexical features of dialects by utilizing interpretable dialect classifiers, even in the

Boyang Chen, Claire E. Heaney, Christopher C. Pain

Numerical discretisations of partial differential equations (PDEs) can be written as discrete convolutions, which, themselves, are a key tool in AI libraries and used in convolutional neural networks (CNNs). We therefore propose to implement numerical discretisations as convolutional layers of a neural network, where the weights or filters are determined ana

Altifani Rizky Hayyu, Stanisław Baran, Andrzej Szytuła

Magnetocaloric performance of the RE$_{5}$Pd$_2$In$_4$ (RE = Tb-Tm) rare earth compounds has been investigated using measurements of magnetization in the function of temperature and applied magnetic field. The maximum magnetic entropy change ($-\Delta S_{M}^{max}$) at magnetic flux density change ($\Delta \mu_0 H$) of 0-9~T has been determined to be 3.3~J$\c

Hong-Ye Hu, Andi Gu, Swarnadeep Majumder, Hang Ren

Extracting information efficiently from quantum systems is a major component of quantum information processing tasks. Randomized measurements, or classical shadows, enable predicting many properties of arbitrary quantum states using few measurements. While random single-qubit measurements are experimentally friendly and suitable for learning low-weight Pauli

Ashkan Taghipour, Morteza Ghahremani, Mohammed Bennamoun, Aref Miri Rekavandi

While latent diffusion models (LDMs) excel at creating imaginative images, they often lack precision in semantic fidelity and spatial control over where objects are generated. To address these deficiencies, we introduce the Box-it-to-Bind-it (B2B) module - a novel, training-free approach for improving spatial control and semantic accuracy in text-to-image (T