May 2024 arXiv papers — page 62

Mingqing Wang, Zhiwei Nie, Yonghong He, Athanasios V. Vasilakos

Protein function prediction is currently achieved by encoding its sequence or structure, where the sequence-to-function transcendence and high-quality structural data scarcity lead to obvious performance bottlenecks. Protein domains are "building blocks" of proteins that are functionally independent, and their combinations determine the diverse biological fu

Leyuan Wang, Liuyu Xiang, Yunlong Wang, Huijia Wu

Deep neural networks suffer from catastrophic forgetting when continually learning new concepts. In this paper, we analyze this problem from a data imbalance point of view. We argue that the imbalance between old task and new task data contributes to forgetting of the old tasks. Moreover, the increasing imbalance ratio during incremental learning further agg

Ze-Yuan Tang, Jun-Jie Feng, Jun-Hui Fan

A warm corona has been widely proposed to explain the soft X-ray excess (SE) above the 2--10 keV power law extrapolation in AGNs. In actual spectral fittings, the warm coronal seed photon temperature ($T_{\rm s}$) is usually assumed to be far away from the soft X-ray, but $kT_{\rm s}$ can reach close to 0.1 keV in standard accretion disc model. In this study

Leyuan Wang, Liuyu Xiang, Yujie Wei, Yunlong Wang

Online Lifelong Learning (OLL) addresses the challenge of learning from continuous and non-stationary data streams. Existing online lifelong learning methods based on image classification models often require preset conditions such as the total number of classes or maximum memory capacity, which hinders the realization of real never-ending learning and rende

Jihui Nie, Dehui Du, Jiangnan Zhao

Intelligent Cyber-Physical Systems (ICPS) represent a specialized form of Cyber-Physical System (CPS) that incorporates intelligent components, notably Convolutional Neural Networks (CNNs) and Deep Reinforcement Learning (DRL), to undertake multifaceted tasks encompassing perception, decision-making, and control. The utilization of DRL for decision-making fa

Meiling Li, Hongrun Ren, Haixu Xiong, Zhenxing Qian

Generation models have shown promising performance in various tasks, making trading around machine learning models possible. In this paper, we aim at a novel prompt trading scenario, prompt bundle trading (PBT) system, and propose an online pricing mechanism. Based on the combinatorial multi-armed bandit (CMAB) and three-stage hierarchical Stackelburg (HS) g

Wei Huang, Pengfei Wu, Tianhe Xu, Hao Zhang

Seafloor anchor nodes, which form a geodetic network, are designed to provide surface and underwater users with positioning, navigation and timing (PNT) services. Due to the non-uniform distribution of underwater sound speed, accurate positioning of underwater anchor nodes is a challenge work. Traditional anchor node positioning typically uses cross or circu

Saaketh Koundinya Gundavarapu, Shreya Agarwal, Arushi Arora, Chandana Thimmalapura Jagadeeshaiah

Machine unlearning, a novel area within artificial intelligence, focuses on addressing the challenge of selectively forgetting or reducing undesirable knowledge or behaviors in machine learning models, particularly in the context of large language models (LLMs). This paper introduces a methodology to align LLMs, such as Open Pre-trained Transformer Language

Lizhi Bai, Chunqi Tian, Jun Yang, Siyu Zhang

Neural implicit representations have recently demonstrated considerable potential in the field of visual simultaneous localization and mapping (SLAM). This is due to their inherent advantages, including low storage overhead and representation continuity. However, these methods necessitate the size of the scene as input, which is impractical for unknown scene

Puning Zhao, Rongfei Fan, Huiwen Wu, Qingming Li

Label differential privacy (DP) is a framework that protects the privacy of labels in training datasets, while the feature vectors are public. Existing approaches protect the privacy of labels by flipping them randomly, and then train a model to make the output approximate the privatized label. However, as the number of classes $K$ increases, stronger random

Weisheng Niu, Jinping Zhuge

This paper is concerned with the elliptic equation $-\text{div} (A_\varepsilon \nabla u_\varepsilon) = \text{div} f$ in a bounded $C^1$ domain, where $A_\varepsilon$ takes a form of $A_\varepsilon(x) = A(x/\varepsilon_1, x/\varepsilon_2,\cdots, x/\varepsilon_n)$, with $A(y_1,y_2,\cdots,y_n)$ being 1-periodic in each $y_i$. We prove the uniform Calder\'{o}n-Z

Xishun Liao, Qinhua Jiang, Brian Yueshuai He, Yifan Liu

Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations. Existing deep learning models tend to overlook the semantic interdependencies among activities and households and rely on restricted GPS data, while activity-based models depend on rigid assumptions and extensive data,

Habitamu Y. Walelign, Xinxin Cai, Bikun Li, Edwin Barnes

Fault-tolerant quantum computation requires low physical-qubit gate errors. Many approaches exist to reduce gate errors, including both hardware- and control-optimization strategies. Dynamically corrected gates are designed to cancel specific errors and offer the potential for high-fidelity gates, but they have yet to be implemented in singlet-triplet spin q

Jing Wang, Yuanqiu Huang, Zhangdong Ouyang

The generalized $k$-connectivity of a graph $G$, denoted by $\kappa_k(G)$, is the minimum number of internally edge disjoint $S$-trees for any $S\subseteq V(G)$ and $|S|=k$. The generalized $k$-connectivity is a natural extension of the classical connectivity and plays a key role in applications related to the modern interconnection networks. The godan graph

Allison Palmiero, Kevin Liu, Julie Colnot, Nitish Chopra

Background & Purpose: FLASH or ultra-high dose rate (UHDR) radiation therapy (RT) has gained attention in recent years for its ability to spare normal tissues relative to conventional dose rate (CDR) RT in various preclinical trials. However, clinical implementation of this promising treatment option has been limited because of the lack of availability of ac

Rinku Jacob, R. Misra, K P Harikrishnan, G Ambika

We present Link Density (LD) computed from the Recurrence Network (RN) of a time series data as an effective measure that can detect dynamical transitions in a system. We illustrate its use using time series from the standard Rossler system in the period doubling transitions and the transition to chaos. Moreover, we find that the standard deviation of LD can

Cheng Li, Damien Teney, Linyi Yang, Qingsong Wen

Cultural bias is pervasive in many large language models (LLMs), largely due to the deficiency of data representative of different cultures. Typically, cultural datasets and benchmarks are constructed either by extracting subsets of existing datasets or by aggregating from platforms such as Wikipedia and social media. However, these approaches are highly dep

Bo Zhang, Yuchen Han, Hong-Liang Wu, Hao Wu

High-accuracy microwave sensing is widely demanded in various fields, ranging from cosmology to microwave quantum technology. Quantum receivers based on inorganic solid-state spin systems are promising candidates for such purpose because of the stability and compatibility, but their best sensitivity is currently limited to a few pT/$\sqrt{\rm{Hz}}$. Here, by

Cong Lu, Shengran Hu, Jeff Clune

Go-Explore is a powerful family of algorithms designed to solve hard-exploration problems built on the principle of archiving discovered states, and iteratively returning to and exploring from the most promising states. This approach has led to superhuman performance across a wide variety of challenging problems including Atari games and robotic control, but

Patrícia Gonçalves, Kohei Hayashi, Makiko Sasada

We consider partial exclusion processes~(PEPs) on the one-dimensional square lattice, that is, a system of interacting particles where each particle random walks according to a jump rate satisfying an exclusion rule that allows up to a certain number of particles can exist on each site. Particularly, we assume that the jump rate is given as a product of two

Ruihan Wu, Pengrun Huang, Kamalika Chaudhuri

Membership Inference Attacks have emerged as a dominant method for empirically measuring privacy leakage from machine learning models. Here, privacy is measured by the {\em{advantage}} or gap between a score or a function computed on the training and the test data. A major barrier to the practical deployment of these attacks is that they do not scale to larg

Konstantin Batygin, David Nesvorný

The formation of the Inner Oort Cloud (IOC) - a vast halo of icy bodies residing far beyond Neptune's orbit - is an expected outcome of the solar system's primordial evolution within a stellar cluster. Recent models have shown that the process of early planetesimal capture within the trans-Neptunian region may have been sufficiently high for the cumulative m

Kanji Morikawa, Yutaka Ohira, Takumi Ohmura

Particle acceleration and magnetic field amplification in relativistic shocks propagating in inhomogeneous media are investigated by three-dimensional magnetohydrodynamical (MHD) simulations and test-particle simulations. The MHD simulations show that the interaction between the relativistic shock and dense clumps amplifies the downstream magnetic field to t

Pratyusha Musunuru, Yuchao Li, Jamison Weber, Dimitri Bertsekas

In this work, we consider data association problems involving multi-object tracking (MOT). In particular, we address the challenges arising from object occlusions. We propose a framework called approximate dynamic programming track (ADPTrack), which applies dynamic programming principles to improve an existing method called the base heuristic. Given a set of

Shicheng Liu, Xiangyun Meng, Qilong Zhai

In this paper, we propose a weak Galerkin finite element method (WG) for solving singularly perturbed convection-diffusion problems on a Bakhvalov-type mesh in 2D. Our method is flexible and allows the use of discontinuous approximation functions on the meshe. An error estimate is devised in a suitable norm and the optimal convergence order is obtained. Fina

Xianglin Yang, Jin Song Dong

Monitoring the inner state of deep neural networks is essential for auditing the learning process and enabling timely interventions. While conventional metrics like validation loss offer a surface-level view of performance, the evolution of a model's hidden representations provides a deeper, complementary window into its internal dynamics. However, the liter

Akshit Achara, Sanand Sasidharan, Gagan N

Clinical text is rich in information, with mentions of treatment, medication and anatomy among many other clinical terms. Multiple terms can refer to the same core concepts which can be referred as a clinical entity. Ontologies like the Unified Medical Language System (UMLS) are developed and maintained to store millions of clinical entities including the de

Zhi Cheng, Suraj Kashyap, Brendan Smoker, Giorgio Burella

The present work aims to study the cavitating turbulent flow of a full-scale marine propeller and explore the physical mechanism underpinning the underwater radiated noise. We employ the standard dynamic large eddy simulation for the turbulent wake flow and the Schnerr-Sauer cavitation model, while the Ffowcs-Williams-Hawkings acoustic analogy is considered

Chinedu Ekuma

The thickness of 2D materials not only plays a crucial role in determining the performance of nanoelectronic and optoelectronic devices but also introduces complexities in predicting volume-dependent properties such as energy storage capacity, due to the intrinsic vacuum within these materials. Although a plethora of experimental techniques, including but no

Yueyue Liu, Hongyu Zhang, Yuantian Miao, Van-Hoang Le

Large Language Models (LLMs) have garnered considerable attention owing to their remarkable capabilities, leading to an increasing number of companies offering LLMs as services. Different LLMs achieve different performance at different costs. A challenge for users lies in choosing the LLMs that best fit their needs, balancing cost and performance. In this pa

Ganzhao Yuan

We consider a class of structured, nonconvex, nonsmooth optimization problems under orthogonality constraints, where the objectives combine a smooth function, a nonsmooth concave function, and a nonsmooth weakly convex function. This class of problems finds diverse applications in statistical learning and data science. Existing methods for addressing these p

Li Chen, Alexandra Holzinger, Ansgar Jüngel

A central limit theorem is shown for moderately interacting particles in the whole space. The interaction potential approximates singular attractive or repulsive potentials of sub-Coulomb type. It is proved that the fluctuations become asymptotically Gaussians in the limit of infinitely many particles. The methodology is inspired by the classical work of Oel

Nohemi Sofia Leon Contreras, Marina D'Amato, Francesco Ciompi, Clement Grisi

Training neural networks with high-quality pixel-level annotation in histopathology whole-slide images (WSI) is an expensive process due to gigapixel resolution of WSIs. However, recent advances in self-supervised learning have shown that highly descriptive image representations can be learned without the need for annotations. We investigate the application

Thi-Hai-Yen Vu, Daniel Bennett, Gayani Nadeera Pallewella, Johnathon Maniatis

Moire superlattices formed in van der Waals heterostructures due to twisting, lattice mismatch and strain present an opportunity for creating novel metamaterials with unique properties not present in the individual layers themselves. Ferroelectricity for example, arises due to broken inversion symmetry in twisted and strained bilayers of 2D semiconductors wi

Yuanhao Cai, Zihao Xiao, Yixun Liang, Minghan Qin

High dynamic range (HDR) novel view synthesis (NVS) aims to create photorealistic images from novel viewpoints using HDR imaging techniques. The rendered HDR images capture a wider range of brightness levels containing more details of the scene than normal low dynamic range (LDR) images. Existing HDR NVS methods are mainly based on NeRF. They suffer from lon

Jingzhe Shi, Qinwei Ma, Huan Ma, Lei Li

Scaling law that rewards large datasets, complex models and enhanced data granularity has been observed in various fields of deep learning. Yet, studies on time series forecasting have cast doubt on scaling behaviors of deep learning methods for time series forecasting: while more training data improves performance, more capable models do not always outperfo

Eamon Duede, William Dolan, André Bauer, Ian Foster

This study empirically investigates claims of the increasing ubiquity of artificial intelligence (AI) within roughly 80 million research publications across 20 diverse scientific fields, by examining the change in scholarly engagement with AI from 1985 through 2022. We observe exponential growth, with AI-engaged publications increasing approximately thirteen

Mrigank Pawagi, Viraj Kumar

To broaden participation, competitive programming contests may include beginner-level problems that do not require knowledge of advanced Computer Science concepts (e.g., algorithms and data structures). However, since most participants have easy access to AI code-generation tools, these problems often become trivial to solve. For beginner-friendly programmin

Nicholas J Dobbins

Background: Biomedical entity normalization is critical to biomedical research because the richness of free-text clinical data, such as progress notes, can often be fully leveraged only after translating words and phrases into structured and coded representations suitable for analysis. Large Language Models (LLMs), in turn, have shown great potential and hig

Yanyan Zhang, Mingkun Li, Pan Zhang, Yueqing Du

Mid-infrared frequency comb spectroscopy enables measurement of molecular at megahertz spectral resolution, sub-hertz frequency accuracy and microsecond acquisition speed. However, the widespread adoption of this technique has been hindered by the complexity and alignment sensitivity of mid-infrared frequency comb sources. Leveraging the underexplored mid-in

Martin Haugh, Raghav Singal

The dishonest casino is a well-known hidden Markov model (HMM) often used in education to introduce HMMs and graphical models. A sequence of die rolls is observed with the casino switching between a fair and a loaded die. Instead of recovering the latent regime through filtering, smoothing, or the Viterbi algorithm, we ask a counterfactual question: how much

Huidong Liang, Xingchen Wan, Xiaowen Dong

We address the problem of optimizing over functions defined on node subsets in a graph. The optimization of such functions is often a non-trivial task given their combinatorial, black-box and expensive-to-evaluate nature. Although various algorithms have been introduced in the literature, most are either task-specific or computationally inefficient and only

Xuanyu Zhang, Jiarui Meng, Runyi Li, Zhipei Xu

3D Gaussian Splatting (3DGS) has already become the emerging research focus in the fields of 3D scene reconstruction and novel view synthesis. Given that training a 3DGS requires a significant amount of time and computational cost, it is crucial to protect the copyright, integrity, and privacy of such 3D assets. Steganography, as a crucial technique for encr

Justin Hom, Jennifer Patience, Karen Knierman, Molly N. Simon

Research-based active learning approaches are critical for the teaching and learning of undergraduate STEM majors. Course-based undergraduate research experiences (CUREs) are becoming more commonplace in traditional, in-person academic environments, but have only just started to be utilized in online education. Online education has been shown to create acces

Moses Charikar, Chirag Pabbaraju, Kirankumar Shiragur

Recent advances in large language models have shown capabilities that are extraordinary and near-superhuman. These models operate with such complexity that reliably evaluating and aligning them proves challenging for humans. This leads to the natural question: can guidance from weak models (like humans) adequately direct the capabilities of strong models? In

Shang Liu, Zhongze Cai, Guanting Chen, Xiaocheng Li

Predicting simple function classes has been widely used as a testbed for developing theory and understanding of the trained Transformer's in-context learning (ICL) ability. In this paper, we revisit the training of Transformers on linear regression tasks, and different from all the existing literature, we consider a bi-objective prediction task of predicting

Yuyue Zhao, Jiancan Wu, Xiang Wang, Wei Tang

Conventional recommender systems (RSs) face challenges in precisely capturing users' fine-grained preferences. Large language models (LLMs) have shown capabilities in commonsense reasoning and leveraging external tools that may help address these challenges. However, existing LLM-based RSs suffer from hallucinations, misalignment between the semantic space o

Eugenio Frias-Miranda, Hong-Anh Nguyen, Jeremy Hampton, Trenner Jones

The integration of technology into exercise regimens has emerged as a strategy to enhance normal human capabilities and return human motor function after injury or illness by enhancing motor learning and retention. Much research has focused on how active devices, whether confined to a lab or made into a wearable format, can apply forces at set times and cond

Eugene Gorsky, Tonie Scroggin

We construct an explicit isomorphism between an open subset in the open positroid variety $\Pi_{k,n}^{\circ}$ in the Grassmannian $\mathrm{Gr}(k,n)$ and the product of two open positroid varieties $\Pi_{k,n-a+1}^{\circ}\times \Pi_{k,a+k-1}^{\circ}$. In the respective cluster structures, this isomorphism is given by freezing a certain subset of cluster variab

Alexander S. Kechris, Michael Wolman

Standard results in descriptive set theory provide sufficient conditions for a Borel set $P \subseteq \mathbb{N}^\mathbb{N} \times \mathbb{N}^\mathbb{N}$ to admit a Borel uniformization, namely, when $P$ has "small" sections or "large" sections. We consider an invariant analogue of these results: Given a Borel equivalence relation $E$ and an $E$-invariant Bo

Abbas Ghaddar, David Alfonso-Hermelo, Philippe Langlais, Mehdi Rezagholizadeh

In this work, we dive deep into one of the popular knowledge-grounded dialogue benchmarks that focus on faithfulness, FaithDial. We show that a significant portion of the FaithDial data contains annotation artifacts, which may bias models towards completely ignoring the conversation history. We therefore introduce CHARP, a diagnostic test set, designed for a

Daniel Mourad

We show that, in general, there is no degree corresponding to the composition of two problems in the computable reducibility lattice.

Lenka Mihoković, Mustapha Raïssouli

We present analysis of some new means recently introduced by M. Raïssouli and A. Rezgui. We establish comparison relations and results on $(K,N)$-sub/super-stabilizability where $K$ and $N$ belong to the class of power means, denoted by $B_p$, and $M$ is one of the classical or recently studied new means. Assuming that means $K$, $M$ and $N$ have asymptotic

S. Popa, S. Schaller, A. Fielicke, J. Lim

Molecules containing a lanthanide atom have sets of electronic states arising from excitation of an inner-shell electron. These states have received little attention, but are thought to play an important role in laser cooling of such molecules and may be a useful resource for testing fundamental physics. We study a series of inner-shell excited states in YbF

Lucian Smith, Herbert M Sauro

Antimony is a high-level, human-readable text-based language designed for defining and sharing models in the systems biology community. It enables scientists to describe biochemical networks and systems using a simple and intuitive syntax. It allows users to easily create, modify, and distribute reproducible computational models. By allowing the concise repr

Chanyong Park

Applying the holographic method, we investigate correlation functions of boundary and defect conformal field theories. To describe boundary conformal field theory, we consider an end of the world brane in an asymptotic AdS space which behaves as a boundary in the dual conformal field theory. In this holographic setup, we calculate correlation functions invol

Yuetian Luo, Rina Foygel Barber

Algorithmic stability is a central notion in learning theory that quantifies the sensitivity of an algorithm to small changes in the training data. If a learning algorithm satisfies certain stability properties, this leads to many important downstream implications, such as generalization, robustness, and reliable predictive inference. Verifying that stabilit

Yanfei Zhou, Matteo Sesia

This paper introduces a conformal inference method to evaluate uncertainty in classification by generating prediction sets with valid coverage conditional on adaptively chosen features. These features are carefully selected to reflect potential model limitations or biases. This can be useful to find a practical compromise between efficiency -- by providing i

Ludvig Hult, Dave Zachariah, Petre Stoica

Inventory control is subject to service-level requirements, in which sufficient stock levels must be maintained despite an unknown demand. We propose a data-driven order policy that certifies any prescribed service level under minimal assumptions on the unknown demand process. The policy achieves this using any online learning method along with integral acti

Chatchai Noytaptim, Xiao Zhong

Answering a question asked by Hsia and Tucker in their paper on the finiteness of greatest common divisors of iterates of polynomials, we prove that if $f, g \in \mathbb{C}(X)$ are compositionally independent rational functions and $c \in \mathbb{C}(X)$, then there are at most finitely many $\lambda\in\mathbb{C}$ with the property that there is an $n$ such t

Nick Collins

A white noise signal can access any possible configuration of values, though statistically over many samples tends to a uniform spectral distribution, and is highly unlikely to produce intelligible sound. But how unlikely? The probability that white noise generates a music-like signal over different durations is analyzed, based on some necessary features obs

Jessica Sutter, Karin Sandstrom, Jérémy Chastenet, Adam K. Leroy

Polycyclic aromatic hydrocarbons (PAHs) are a ubiquitous component of the interstellar medium (ISM) in z~0 massive, star-forming galaxies and play key roles in ISM energy balance, chemistry, and shielding. Wide field of view, high resolution mid-infrared (MIR) images from JWST provides the ability to map the fraction of dust in the form of PAHs and the prope

Junwoo Jang, Maani Ghaffari

This study addresses the challenge of integrating social norms into robot navigation, which is essential for ensuring that robots operate safely and efficiently in human-centric environments. Social norms, often unspoken and implicitly understood among people, are difficult to explicitly define and implement in robotic systems. To overcome this, we derive th

E. Fourney, J. W. Burdick, E. D. Rimon

This paper considers three related mobile robot multi-target sensory coverage and inspection planning problems in 2-D environments. In the first problem, a mobile robot must find the shortest path to observe multiple targets with a limited range sensor in an obstacle free environment. In the second problem, the mobile robot must efficiently observe multiple

Seyed Shahabaldin Tohidi, Tobias K. S. Ritschel, Georgios Tsaousoglou, Uffe Høgsbro Thygesen

Demand-side management provides a great potential for improving the efficiency and reliability of energy systems. This requires a mechanism to connect the market level and the demand side. The flexibility function is a novel approach that bridges the gap between the markets and the dynamics of physical assets at the lower levels of the energy systems and act

Guoyao Shen, Mengyu Li, Stephan Anderson, Chad W. Farris

Recent advancements in deep learning have enabled the development of generalizable models that achieve state-of-the-art performance across various imaging tasks. Vision Transformer (ViT)-based architectures, in particular, have demonstrated strong feature extraction capabilities when pre-trained on large-scale datasets. In this work, we introduce the Magneti

Suyoung Kim, Jiyeon Hwang, Ho-Young Jung

Recently, deep end-to-end learning has been studied for intent classification in Spoken Language Understanding (SLU). However, end-to-end models require a large amount of speech data with intent labels, and highly optimized models are generally sensitive to the inconsistency between the training and evaluation conditions. Therefore, a natural language unders

Keoikantse Mogonediwa

Music genre classification is an area that utilizes machine learning models and techniques for the processing of audio signals, in which applications range from content recommendation systems to music recommendation systems. In this research I explore various machine learning algorithms for the purpose of music genre classification, using features extracted

Daniel Bochen Tan, Wan-Hsuan Lin, Jason Cong

Dynamically field-programmable qubit arrays based on neutral atoms feature high fidelity and highly parallel gates for quantum computing. However, it is challenging for compilers to fully leverage the novel flexibility offered by such hardware while respecting its various constraints. In this study, we break down the compilation for this architecture into th

Fabian Spaeh, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis

This study introduces a novel approach for learning mixtures of Markov chains, a critical process applicable to various fields, including healthcare and the analysis of web users. Existing research has identified a clear divide in methodologies for learning mixtures of discrete and continuous-time Markov chains, while the latter presents additional complexit

Hui Li, Hongyu Wang, Zhijin Chen, Bohan Sun

Singing voice conversion is to convert the source singing voice into the target singing voice except for the content. Currently, flow-based models can complete the task of voice conversion, but they struggle to effectively extract latent variables in the more rhythmically rich and emotionally expressive task of singing voice conversion, while also facing iss

Evelyn Yee, Alice Li, Chenyu Tang, Yeon Ho Jung

Large language models (LLMs) often improve their performance in downstream tasks when they generate Chain of Thought reasoning text before producing an answer. We investigate how LLMs recover from errors in Chain of Thought. Through analysis of error recovery behaviors, we find evidence for unfaithfulness in Chain of Thought, which occurs when models arrive

Layla Hamad Elnil Mugbil Sorkatti

We develop a structure theory for nilpotent symplectic alternating algebras. We then give a classification of all nilpotent symplectic alternating algebras of dimension up to 10 over any field. The study reveals a new subclasses of powerful groups that we call powerfully nilpotent groups and powerfully soluble groups.

Dengwang Tang, Rahul Jain, Ashutosh Nayyar, Pierluigi Nuzzo

In this paper, we introduce the constrained best mixed arm identification (CBMAI) problem with a fixed budget. This is a pure exploration problem in a stochastic finite armed bandit model. Each arm is associated with a reward and multiple types of costs from unknown distributions. Unlike the unconstrained best arm identification problem, the optimal solution

Daniel Aronoff

In a Proof-of-Work blockchain such as Bitcoin mining hashrate is increasing in the block reward. An increase in hashrate reduces network vulnerability to attack (a reduction in security cost) while increasing carbon emissions and electricity cost (an increase in externalities cost). This implies a tradeoff in total cost at different levels of hashrate and th

Gonzalo Navarro

While operations {\em rank} and {\em select} on static bitvectors can be supported in constant time, lower bounds show that supporting updates raises the cost per operation to $\Theta(\log n/ \log\log n)$ on bitvectors holding $n$ bits. This is a shame in scenarios where updates are possible but uncommon. We develop a representation of bitvectors that we cal

Balázs Kacskovics, Dániel Barta

We used two numerical models, namely the \texttt{CBwaves} and \texttt{SEOBNRE} algorithms, based on the post-Newtonian and effective-one-body approaches for binary black holes evolving on eccentric orbits. We performed 20.000 new simulations for non-spinning and 240.000 simulations for aligned-spin configurations on a common grid of parameter values over the

Xi Cao, Abdullah Irfan, Michael Mollenhauer, Kaushik Singirikonda

Nonreciprocal microwave routing plays a crucial role for measuring quantum circuits, and allows for realizing cascaded quantum systems for generating and stabilizing entanglement between non-interacting qubits. The most commonly used tools for implementing directionality are ferrite-based circulators. These devices are versatile, but suffer from excess loss,

Christodoulos Kechris, Jerome Thevenot, Tomas Teijeiro, Vincent A. Stadelmann

Acoustical knee health assessment has long promised an alternative to clinically available medical imaging tools, but this modality has yet to be adopted in medical practice. The field is currently led by machine learning models processing acoustical features, which have presented promising diagnostic performances. However, these methods overlook the intrica

Ainesh Bakshi, Pravesh Kothari, Goutham Rajendran, Madhur Tulsiani

A set of high dimensional points $X=\{x_1, x_2,\ldots, x_n\} \subset R^d$ in isotropic position is said to be $\delta$-anti concentrated if for every direction $v$, the fraction of points in $X$ satisfying $|\langle x_i,v \rangle |\leq \delta$ is at most $O(\delta)$. Motivated by applications to list-decodable learning and clustering, recent works have consi

Austin C. Kozlowski, Hyunku Kwon, James A. Evans

By training deep neural networks on massive archives of digitized text, large language models (LLMs) learn the complex linguistic patterns that constitute historic and contemporary discourses. We argue that LLMs can serve as a valuable tool for sociological inquiry by enabling accurate simulation of respondents from specific social and cultural contexts. App

Maxime Burchi, Radu Timofte

The DreamerV3 agent recently demonstrated state-of-the-art performance in diverse domains, learning powerful world models in latent space using a pixel reconstruction loss. However, while the reconstruction loss is essential to Dreamer's performance, it also necessitates modeling unnecessary information. Consequently, Dreamer sometimes fails to perceive cruc

Han Song, Zhongche Qu, Zhi Zhang, Zihan Ye

As the current initialization method in the state-of-the-art Stereo Visual-Inertial SLAM framework, ORB-SLAM3 has limitations. Its success depends on the performance of the pure stereo SLAM system and is based on the underlying assumption that pure visual SLAM can accurately estimate the camera trajectory, which is essential for inertial parameter estimation

Bao Hoang, Yijiang Pang, Siqi Liang, Liang Zhan

Independent and identically distributed (i.i.d.) data is essential to many data analysis and modeling techniques. In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical diversity, determined by the decentralized nature of medical data. However, data from various sites are easily bia

Juan Soto, Ramón Carmenaty, Miguel Lastra, Juan M. Fernández-Luna

Customer segmentation is a fundamental process to develop effective marketing strategies, personalize customer experience and boost their retention and loyalty. This problem has been widely addressed in the scientific literature, yet no definitive solution for every case is available. A specific case study characterized by several individualizing features is

Tom Goertzen

This work presents a construction method for interlocking assemblies based on planar crystallographic symmetries. Planar crystallographic groups, also known as wallpaper groups, correspond to tessellations of the plane with a tile, called a fundamental domain, such that the action of the group can be used to tessellate the plane with the given tile. The main

Madison Threadgill, Andreas Gerstlauer

In the era of deep learning (DL), convolutional neural networks (CNNs), and large language models (LLMs), machine learning (ML) models are becoming increasingly complex, demanding significant computational resources for both inference and training stages. To address this challenge, distributed learning has emerged as a crucial approach, employing paralleliza

Yuxuan Wang, Guoqiang Lan, Jun Song

Medium and high entropy ceramics, with their distinctive disordered structures, exhibit ultra-low thermal conductivity and high temperature stability. These properties make them strong contenders for next generation thermal barrier coating (TBC) materials. However, predicting their thermal conductivity has been challenging, primarily due to their unique phon

Yuxuan Lu, Shengwei Xu, Yichi Zhang, Yuqing Kong

Peer prediction mechanisms motivate high-quality feedback with provable guarantees. However, current methods only apply to rather simple reports, like multiple-choice or scalar numbers. We aim to broaden these techniques to the larger domain of text-based reports, drawing on the recent developments in large language models. This vastly increases the applicab

Cédric Dion

Let $p>3$ be a prime number and $K$ be an imaginary quadratic field where $p$ splits. Let $K_\infty$ be the $\mathbf{Z}_p^2$-extension of $K$ and let $K_n$ be a finite subextension of $K_\infty/K$. Let $E$ be an elliptic curve with good ordinary reduction at $p$. Under some hypotheses, we show that the Mazur-Tate element attached to $E$ over $K_n$ by S. Hara

Victor H. Jorge-Pérez, Paulo D. C. Martins, J. A. Lima

The main purpose of this paper is to provide formulas for the Hilbert-Kunz multiplicity of fiber product rings and Nagata idealizations. We give explicit formulas for the Hilbert-Kunz multiplicity of a fiber product $R \times_T S$, where $R$, $S$, and $T$ are Noetherian local rings sharing the same characteristic and residue field. We compute the Hilbert-Kun

Elliot Paquette, Courtney Paquette, Lechao Xiao, Jeffrey Pennington

We consider the solvable neural scaling model with three parameters: data complexity, target complexity, and model-parameter-count. We use this neural scaling model to derive new predictions about the compute-limited, infinite-data scaling law regime. To train the neural scaling model, we run one-pass stochastic gradient descent on a mean-squared loss. We de

Daniel Neira O., Pablo A. Estévez, Francisco Förster

In this work, we propose a deep learning-based classification model of astronomical objects using alerts reported by the Zwicky Transient Facility (ZTF) survey. The model takes as inputs sequences of stamp images and metadata contained in each alert, as well as features from the All-WISE catalog. The proposed model, called temporal stamp classifier, is able

V. Fernández, R. Amorín, V. Firpo, C. Morisset

The upcoming generation of telescopes, instruments, and surveys is poised to usher in an unprecedented "Big Data" era in the field of astronomy. Within this context, even seemingly modest tasks such as spectral line analyses could become increasingly challenging for astronomers. In this paper, we announce the release of ${\rm L{\small I}M{\small E}}$. This p

Chuheng Wu, S. Farokh Atashzar, Mohammad M. Ghassemi, Tuka Alhanai

Surface Electromyography (sEMG) is a non-invasive signal that is used in the recognition of hand movement patterns, the diagnosis of diseases, and the robust control of prostheses. Despite the remarkable success of recent end-to-end Deep Learning approaches, they are still limited by the need for large amounts of labeled data. To alleviate the requirement fo

Boshi Wang, Xiang Yue, Yu Su, Huan Sun

We study whether transformers can learn to implicitly reason over parametric knowledge, a skill that even the most capable language models struggle with. Focusing on two representative reasoning types, composition and comparison, we consistently find that transformers can learn implicit reasoning, but only through grokking, i.e., extended training far beyond

Maxime Bouthors, Josep Crego, François Yvon

Retrieval-augmented machine translation leverages examples from a translation memory by retrieving similar instances. These examples are used to condition the predictions of a neural decoder. We aim to improve the upstream retrieval step and consider a fixed downstream edit-based model: the multi-Levenshtein Transformer. The task consists of finding a set of

Eric B. Jones, Cody James Winkleblack, Colin Campbell, Caleb Rotello

We present a hardware-reconfigurable ansatz on $N_q$-qubits for the variational preparation of many-body states of the Anderson impurity model (AIM) with $N_{\text{imp}}+N_{\text{bath}}=N_q/2$ sites, which conserves total charge and spin z-component within each variational search subspace. The many-body ground state of the AIM is determined as the minimum ov

Prateek Verma, Elizabeth Adeogun, Elizabeth S. Greene, Sami Dridi

An imbalance between bodily heat production and heat dissipation leads to heat stress in organisms. In addition to diminished animal well-being, heat stress is detrimental to the poultry industry as poultry entails fast growth and high yield, resulting in greater metabolic activity and higher body heat production. When stressed, cells overexpress heat shock

Gauri Kambhatla, Matthew Lease, Ashwin Rajadesingan

To promote constructive discussion of controversial topics online, we propose automatic reframing of disagreeing responses to signal receptiveness to a preceding comment. Drawing on research from psychology, communications, and linguistics, we identify six strategies for reframing. We automatically reframe replies to comments according to each strategy, usin