May 2024 arXiv papers — page 63

Michael Neumann, Thorben Kuchel, Philipp Diebold, Eva-Maria Schön

Context: In agile transformations, there are many challenges such as alignment between agile practices and the organizational goals and strategies or issues with shifts in how work is organized and executed. One very important challenge but less considered and treated in research are cultural challenges associated with an agile mindset. Although research sho

Keertana Chidambaram, Karthik Vinay Seetharaman, Vasilis Syrgkanis

Reinforcement Learning from Human Feedback (RLHF) has become central to aligning large language models with human values, typically by first learning a reward model from preference data which is then used to update the model with reinforcement learning. Recent alternatives such as Direct Preference Optimization (DPO) simplify this pipeline by directly optimi

Long Le, Marcel Hussing, Eric Eaton

This work studies the intersection of continual and federated learning, in which independent agents face unique tasks in their environments and incrementally develop and share knowledge. We introduce a mathematical framework capturing the essential aspects of distributed continual learning, including agent model and statistical heterogeneity, continual distr

Fangjun Li, David C. Hogg, Anthony G. Cohn

Spatial reasoning plays a vital role in both human cognition and machine intelligence, prompting new research into language models' (LMs) capabilities in this regard. However, existing benchmarks reveal shortcomings in evaluating qualitative spatial reasoning (QSR). These benchmarks typically present oversimplified scenarios or unclear natural language descr

Samuel Barton, Adelle Coster, Diane Donovan, James Lefevre

This paper introduces a novel hypergraph classification algorithm. The use of hypergraphs in this framework has been widely studied. In previous work, hypergraph models are typically constructed using distance or attribute based methods. That is, hyperedges are generated by connecting a set of samples which are within a certain distance or have a common attr

Chun-Fu Chen, Bill Moriarty, Shaohan Hu, Sean Moran

The recent rapid advancements in both sensing and machine learning technologies have given rise to the universal collection and utilization of people's biometrics, such as fingerprints, voices, retina/facial scans, or gait/motion/gestures data, enabling a wide range of applications including authentication, health monitoring, or much more sophisticated analy

Kimball A. Milton, Nima Pourtolami, Gerard Kennedy

In an earlier paper, we explored how quantum vacuum torque can arise: a body or nanoparticle that is out of thermal equilibrium with its environment experiences a spontaneous torque. But this requires that the body be composed of nonreciprocal material, which seems to necessitate the presence of an external influence, such as a magnetic field. Then the elect

Keerthi Nakkalil, Marco Bomben

Radiation damage significantly impacts the performance of silicon tracking detectors in Large Hadron Collider (LHC) experiments such as ATLAS and CMS, with signal reduction being the most critical effect; adjusting sensor bias voltage and detection thresholds can help mitigate these effects, generating simulated data that accurately mirror the performance ev

T. Konstantin Rusch, Nathan Kirk, Michael M. Bronstein, Christiane Lemieux

Discrepancy is a well-known measure for the irregularity of the distribution of a point set. Point sets with small discrepancy are called low-discrepancy and are known to efficiently fill the space in a uniform manner. Low-discrepancy points play a central role in many problems in science and engineering, including numerical integration, computer vision, mac

Peter Dankelmann, Sonwabile Mafunda, Sufiyan Mallu

Let $G$ be a finite, simple connected graph. The average distance of a vertex $v$ of $G$ is the arithmetic mean of the distances from $v$ to all other vertices of $G$. The remoteness $\rho(G)$ of $G$ is the maximum of the average distances of the vertices of $G$. In this paper, we give sharp upper bounds on the remoteness of a graph of given order, connectiv

Martianus Frederic Ezerman, Markus Grassl, San Ling, Ferruh Özbudak

Quasi-twisted codes are used here as the classical ingredients in the so-called Construction X for quantum error-control codes. The construction utilizes nearly self-orthogonal codes to design quantum stabilizer codes. We expand the choices of the inner product to also cover the symplectic and trace-symplectic inner products, in addition to the original Herm

Yutao Feng, Yintong Shang, Xiang Feng, Lei Lan

We present ElastoGen, a knowledge-driven AI model that generates physically accurate 4D elastodynamics. Unlike deep models that learn from video- or image-based observations, ElastoGen leverages the principles of physics and learns from established mathematical and optimization procedures. The core idea of ElastoGen is converting the differential equation, c

Abele Mălan, Jérémie Decouchant, Thiago Guzella, Lydia Chen

Effective large-scale process optimization in manufacturing industries requires close cooperation between different human expert parties who encode their knowledge of related domains as Bayesian network models. For instance, Bayesian networks for domains such as lithography equipment, processes, and auxiliary tools must be conjointly used to effectively iden

Matteo Bettini, Ryan Kortvelesy, Amanda Prorok

The study of behavioral diversity in Multi-Agent Reinforcement Learning (MARL) is a nascent yet promising field. In this context, the present work deals with the question of how to control the diversity of a multi-agent system. With no existing approaches to control diversity to a set value, current solutions focus on blindly promoting it via intrinsic rewar

Sze Ming Lee, Yunxiao Chen, Tony Sit

High-dimensional multivariate longitudinal data, which arise when many outcome variables are measured repeatedly over time, are becoming increasingly common in social, behavioral and health sciences. We propose a latent variable model for drawing statistical inferences on covariate effects and predicting future outcomes based on high-dimensional multivariate

Xianzhi Du, Tom Gunter, Xiang Kong, Mark Lee

Mixture-of-Experts (MoE) enjoys performance gain by increasing model capacity while keeping computation cost constant. When comparing MoE to dense models, prior work typically adopt the following setting: 1) use FLOPs or activated parameters as a measure of model complexity; 2) train all models to the same number of tokens. We argue that this setting favors

Muhammad Raees, Inge Meijerink, Ioanna Lykourentzou, Vassilis-Javed Khan

AI systems are increasingly being adopted across various domains and application areas. With this surge, there is a growing research focus and societal concern for actively involving humans in developing, operating, and adopting these systems. Despite this concern, most existing literature on AI and Human-Computer Interaction (HCI) primarily focuses on expla

Kihyuk Hong, Woojin Chae, Yufan Zhang, Dabeen Lee

We study the problem of infinite-horizon average-reward reinforcement learning with linear Markov decision processes (MDPs). The associated Bellman operator of the problem not being a contraction makes the algorithm design challenging. Previous approaches either suffer from computational inefficiency or require strong assumptions on dynamics, such as ergodic

Bilas Paul, Shantanu Chakraborty, Ganga Sharma

The persistent underrepresentation of women and other gender minorities in physical science fields has been an ongoing concern. This study investigates gender dynamics in introductory physics laboratory courses, focusing on whether students of different gender identities exhibit equal inclination and confidence in conducting lab experiments, and whether they

A. M. Escobar Ruiz, L. Jiménez-Lara, J. Llibre, Marco A. Zurita

We study the classical planar two-center problem of a particle $m$ subjected to harmonic-like interactions with two fixed centers. For convenient values of the dimensionless parameter of this problem we use the averaging theory for showing analytically the existence of periodic orbits bifurcating from two of the three equilibrium points of the Hamiltonian sy

Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar, David Moens

This paper presents an innovative approach, called credal wrapper, to formulating a credal set representation of model averaging for Bayesian neural networks (BNNs) and deep ensembles (DEs), capable of improving uncertainty estimation in classification tasks. Given a finite collection of single predictive distributions derived from BNNs or DEs, the proposed

Dening Lu, Jun Zhou, Kyle, Gao

Recently, LiDAR point cloud processing and analysis have made great progress due to the development of 3D Transformers. However, existing 3D Transformer methods usually are computationally expensive and inefficient due to their huge and redundant attention maps. They also tend to be slow due to requiring time-consuming point cloud sampling and grouping proce

Sebastian M. Cioabă, Vishal Gupta, Celso Marques

In this paper, we study a question of Hong from 1993 related to the minimum spectral radii of the adjacency matrices of connected graphs of given order and size. Hong asked if it is true that among all connected graphs of given number of vertices $n$ and number of edges $e$, the graphs having minimum spectral radius (the minimizer graphs) must be almost regu

Kenji Eric Sadanari, Kazuyuki Omukai, Kazuyuki Sugimura, Tomoaki Matsumoto

Recent cosmological hydrodynamic simulations have suggested that the first stars in the universe often form as binary or multiple systems. However, previous studies typically overlooked the potential influence of magnetic fields during this process, assuming them to be weak and minimally impactful. Emerging theoretical investigations, however, propose an alt

Kenneth L. Baker, Allison H. Moore, Danielle O'Donnol, Scott Taylor

A totally oriented Klein graph is a trivalent spatial graph in the 3-sphere with a 3-coloring of its edges and an orientation on each bicolored link. A totally oriented Klein foam is a 3-colored 2-complex in the 4-ball whose boundary is a Klein foam and whose bicolored surfaces are oriented. We extend Gille-Robert's signature for 3-Hamiltonian Klein graphs t

Mehmet Ali Özarslan, İlkay Onbaşı Elidemir

Recently, \"Ozarslan and Elidemir (2023) introduced a methodology for constructing two-variable biorthogonal polynomial families with the help of one-variable biorthogonal and orthogonal polynomial families. The primary objective of the paper is to introduce novel class of two-variable biorthogonal polynomials namley bivariate Jacobi Konhauser polynomials. W

Ayyub Alzahem, Wadii Boulila, Maha Driss, Anis Koubaa

Addressing uncertainty in Deep Learning (DL) is essential, as it enables the development of models that can make reliable predictions and informed decisions in complex, real-world environments where data may be incomplete or ambiguous. This paper introduces a novel algorithm leveraging Dempster-Shafer Theory (DST) to integrate multiple pre-trained models to

Arnav Goel, Medha Hira, Anubha Gupta

The field of prosody transfer in speech synthesis systems is rapidly advancing. This research is focused on evaluating learning methods for adapting pre-trained monolingual text-to-speech (TTS) models to multilingual conditions, i.e., Supervised Fine-Tuning (SFT) and Transfer Learning (TL). This comparison utilizes three distinct metrics: Mean Opinion Score

Likun Cao, Ziwen Chen, James Evans

Startups face a classic dilemma in innovation strategy: should they pursue cumulative, low-risk improvements or disruptive, high-risk breakthroughs? The Henderson and Clark framework suggests that architectural innovation, which reconfigures existing economic modules in novel ways, tends to be disruptive and risky for established organizations, but the succe

Dening Lu, Jun Zhou, Kyle Gao, Linlin Xu

3D Transformers have achieved great success in point cloud understanding and representation. However, there is still considerable scope for further development in effective and efficient Transformers for large-scale LiDAR point cloud scene segmentation. This paper proposes a novel 3D Transformer framework, named 3D Learnable Supertoken Transformer (3DLST). T

James G. McHugh, Xue Li, Isaac Soltero, Vladimir I. Fal'ko

Semiconducting transition metal dichalcogenides (MX$_2$) occur in 2H and rhombohedral (3R) polytypes, respectively distinguished by anti-parallel and parallel orientation of consecutive monolayer lattices. In its bulk form, 3R-MX$_2$ is ferroelectric, hosting an out-of-plane electric polarisation, the direction of which is dictated by stacking. Here, we pred

Divya Jyoti Bajpai, Manjesh Kumar Hanawal

Pre-trained Language Models (PLMs), like BERT, with self-supervision objectives exhibit remarkable performance and generalization across various tasks. However, they suffer in inference latency due to their large size. To address this issue, side branches are attached at intermediate layers, enabling early inference of samples without requiring them to pass

Medha Hira, Arnav Goel, Anubha Gupta

This paper presents CrossVoice, a novel cascade-based Speech-to-Speech Translation (S2ST) system employing advanced ASR, MT, and TTS technologies with cross-lingual prosody preservation through transfer learning. We conducted comprehensive experiments comparing CrossVoice with direct-S2ST systems, showing improved BLEU scores on tasks such as Fisher Es-En, V

Maoyu Zhang, Biao Cai, Dong Li, Xiaoyue Niu

Network data enriched with textual information, referred to as text networks, arise in a wide range of applications, including email communications, scientific collaborations, and legal contracts. In such settings, both the structure of interactions (i.e., who connects with whom) and their content (i.e., what is communicated) are useful for understanding net

Yaxin Hu, Laura Stegner, Yasmine Kotturi, Caroline Zhang

In this paper, we explore the design and use of conversational telepresence robots to help homebound older adults interact with the external world. An initial needfinding study (N=8) using video vignettes revealed older adults' experiential needs for robot-mediated remote experiences such as exploration, reminiscence and social participation. We then designe

Marcelo O. Magnasco

Changing a circuit dynamically, without actually changing the hardware itself, is called reconfiguration, and is of great importance due to its manifold technological applications. Circuit reconfiguration appears to be a feature of the cerebral cortex, and hence understanding the neuroarchitectural and dynamical features underlying self-reconfiguration may p

Albert Bonnefous, Yann Brenier, Roya Mohayaee

Mathematicians have been proposing for sometimes that Monge-Amp\`ere equation, a nonlinear generalization of the Poisson equation, where trace of the Hessian is replaced by its determinant, provides an alternative non-relativistic description of gravity. Monge-Amp\`ere equation is affine invariant, has rich geometric properties, connects to optimal transport

Siyu Ren, Junhui Hou, Weiyao Lin, Wenping Wang

We present NeCGS, the first neural compression paradigm, which can compress a geometry set encompassing thousands of detailed and diverse 3D mesh models by up to 900 times with high accuracy and preservation of detailed geometric structures. Specifically, we first propose TSDF-Def, a new implicit representation that is capable of \textbf{accurately} represen

Manav Prabhakar, Jwalandhar Girnar, Arpan Kusari

While much research has recently focused on generating physics-based adversarial samples, a critical yet often overlooked category originates from physical failures within on-board cameras-components essential to the perception systems of autonomous vehicles. Camera failures, whether due to external stresses causing hardware breakdown or internal component f

Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash

This technical report introduces Aya 23, a family of multilingual language models. Aya 23 builds on the recent release of the Aya model (\"Ust\"un et al., 2024), focusing on pairing a highly performant pre-trained model with the recently released Aya collection (Singh et al., 2024). The result is a powerful multilingual large language model serving 23 langua

Quan Nguyen, Anindya Sarkar, Roman Garnett

Active search formalizes a specialized active learning setting where the goal is to collect members of a rare, valuable class. The state-of-the-art algorithm approximates the optimal Bayesian policy in a budget-aware manner, and has been shown to achieve impressive empirical performance in previous work. However, even this approximate policy has a superlinea

Ziyu Yan

This thesis investigates the dynamics of multimarket contact and airline mergers on collusive pricing of airlines. In align with Bernheim and Whinston (1990) and Athey et.al.(2004), it detects collusive pricing via pairwise price difference and price rigidity. The piece of work extends previous work by incorporating additional controls such as distinction be

I. I. Pavlov, A. D. Chaikovskaia, D. V. Karlovets

We explore the process of orbital angular momentum (OAM) transfer from a twisted light beam to an electron in atomic ionization within the first Born approximation. The characteristics of the ejected electron are studied regardless of the detection scheme. We find that the outgoing electron possesses a definite projection of OAM when a single atom is located

Osmar N. Souza

The work presents a study on the quantum theory of periodic graphs applied to mono- and bilayer hexagonal materials. Different parameters associated with the atoms present at the vertices of these materials were analyzed, verifying the existence of gaps in the spectral bands and expressing the width of these openings according to the parameters. The study wa

Revanth Gangi Reddy, Omar Attia, Yunyao Li, Heng Ji

Ranking is a fundamental and popular problem in search. However, existing ranking algorithms usually restrict the granularity of ranking to full passages or require a specific dense index for each desired level of granularity. Such lack of flexibility in granularity negatively affects many applications that can benefit from more granular ranking, such as sen

M. M. Phillips, C. Ashall, Peter J. Brown, L. Galbany

Understanding the nature of the luminous 1991T-like supernovae is of great importance to supernova cosmology as they are likely to have been more common in the early universe. In this paper we explore the observational properties of 1991T-like supernovae to study their relationship to other luminous, slow-declining Type~Ia supernovae (SNe Ia). From the spect

Alon Friedman, Kevin Hawley, Paul Rosen, Md Dilshadur Rahman

Peer review is a popular feedback mechanism in higher education that actively engages students and provides researchers with a means to assess student engagement. However, there is little empirical support for the durability of peer review, particularly when using data predictive modeling to analyze student comments. This study uses Na\"ive Bayes modeling to

Ali Edalati, Alireza Ghaffari, Mahsa Ghazvini Nejad, Lu Hou

Deployment of Large Language Models (LLMs) has major computational costs, due to their rapidly expanding size. Compression of LLMs reduces the memory footprint, latency, and energy required for their inference. Post-training Quantization (PTQ) techniques have been developed to compress LLMs while avoiding expensive re-training. Most PTQ approaches formulate

Eloy Peña-Asensio, Pau Grèbol-Tomàs, Josep M. Trigo-Rodríguez, Pablo Ramírez-Moreta

On 18 May 2024, a superbolide traversed the western part of the Iberian Peninsula, culminating its flight over the Atlantic Ocean and generating significant media attention. This event was caused by a weak carbonaceous meteoroid of 1 m, entering the atmosphere at 40.4 km$\,$s$^{-1}$ with an average slope of 8.5$^\circ$. The luminous phase started at 133 km a

Leia Stirling, Joseph Montgomery, Mark Draelos, Christoforos Mavrogiannis

The University of Michigan Robotics program focuses on the study of embodied intelligence that must sense, reason, act, and work with people to improve quality of life and productivity equitably across society. ROB 204, part of the core curriculum towards the undergraduate degree in Robotics, introduces students to topics that enable conceptually designing a

David Theidel, Viviane Cotte, René Sondenheimer, Viktoriia Shiriaeva

High-harmonic generation is a light up-conversion process occurring in a strong laser field, leading to coherent bursts of extreme ultrashort broadband radiation [1]. As a new perspective, we propose that ultrafast strong-field electronic or photonic processes such as high-harmonic generation can potentially generate non-classical states of light well before

Lauren A. Sgro, Paul A. Dalba, Thomas M. Esposito, Franck Marchis

NASA's Transiting Exoplanet Survey Satellite (TESS) has identified over 7,000 candidate exoplanets via the transit method, with gas giants among the most readily detected due to their large radii. Even so, long intervals between TESS observations for much of the sky lead to candidates for which only a single transit is detected in one TESS sector, leaving th

Zander W. Blasingame, Chen Liu

The optimization of the latents and parameters of diffusion models with respect to some differentiable metric defined on the output of the model is a challenging and complex problem. The sampling for diffusion models is done by solving either the probability flow ODE or diffusion SDE wherein a neural network approximates the score function allowing a numeric

Xufeng Zhao, Cornelius Weber, Stefan Wermter

Language-conditioned robotic skills make it possible to apply the high-level reasoning of Large Language Models (LLMs) to low-level robotic control. A remaining challenge is to acquire a diverse set of fundamental skills. Existing approaches either manually decompose a complex task into atomic robotic actions in a top-down fashion, or bootstrap as many combi

Md Yousuf Harun, Kyungbok Lee, Jhair Gallardo, Giri Krishnan

Embeddings produced by pre-trained deep neural networks (DNNs) are widely used; however, their efficacy for downstream tasks can vary widely. We study the factors influencing transferability and out-of-distribution (OOD) generalization of pre-trained DNN embeddings through the lens of the tunnel effect hypothesis, which is closely related to intermediate neu

Paul Szypryt, Douglas A. Bennett, Ian Fogarty Florang, Joseph W. Fowler

Single-photon detectors based on the superconducting transition-edge sensor are used in a number of visible to near-infrared applications, particularly for photon-number-resolving measurements in quantum information science. To be practical for large-scale spectroscopic imaging or photonic quantum computing applications, the size of visible to near-infrared

Maria F. Gamal'

A criterion on the similarity of a (bounded, linear) operator $T$ on a (complex, separable) Hilbert space $\mathcal H$ in terms of shift-type invariant subspaces of $T$ to a contraction of class $C_{\cdot 0}$ with finite unequal defects is given. Namely, $T$ is similar to such a contraction if and only if the minimal quantity of (closed) invariant subspaces

Ramesh Adhikari, Costas Busch, Miroslav Popovic

Sharding is a promising technique for addressing the scalability issues of blockchain, and this technique is especially important for IoT, edge, or mobile computing. It divides the $n$ participating nodes into $s$ disjoint groups called shards, where each shard processes transactions in parallel. We examine batch scheduling problems on the shard graph $G_s$,

M. Avrigeanu, E. Šimečková, J. Mrázek, C. Costache

The activities of the EUROfusion consortium on the development of high quality nuclear data for fusion applications include evaluations of deuteron induced reactions and related data libraries for needs of the DEMO fusion power plant and IFMIF-DONES neutron--source nuclear analyses. Molybdenum is one of the major constituents of the reference stainless steel

Antoine Gonon, Léon Zheng, Pascal Carrivain, Quoc-Tung Le

Kronecker-sparse (KS) matrices -- whose supports are Kronecker products of identity and all-ones blocks -- underpin the structure of Butterfly and Monarch matrices and offer the promise of more efficient models. However, existing GPU kernels for KS matrix multiplication suffer from high data movement costs, with up to 50% of time spent on memory-bound tensor

Collin Zhang, John X. Morris, Vitaly Shmatikov

We consider the problem of language model inversion: given outputs of a language model, we seek to extract the prompt that generated these outputs. We develop a new black-box method, output2prompt, that learns to extract prompts without access to the model's logits and without adversarial or jailbreaking queries. In contrast to previous work, output2prompt o

Gordon Baym, Jen-Chieh Peng, C. J. Pethick

We ask the question of how angular momentum is conserved in electroweak interaction processes. To introduce the problem with a minimum of mathematics, we first raise the same issue in elastic scattering of a circularly polarized photon by an atom, where the scattered photon has a different spin direction than the original photon, and note its presence in sca

Yuki Takezawa, Han Bao, Ryoma Sato, Kenta Niwa

Gradient descent and its variants are de facto standard algorithms for training machine learning models. As gradient descent is sensitive to its hyperparameters, we need to tune the hyperparameters carefully using a grid search. However, the method is time-consuming, particularly when multiple hyperparameters exist. Therefore, recent studies have analyzed pa

Wei Liu, Shouvick Mondal, Tse-Hsun Chen

Database-backed applications rely on the database access code to interact with the underlying database management systems (DBMSs). Although many prior studies aim at database access issues like SQL anti-patterns or SQL code smells, there is a lack of study of database access bugs during the maintenance of database-backed applications. In this paper, we empir

William Fleshman, Benjamin Van Durme

We introduce RE-Adapt, an approach to fine-tuning large language models on new domains without degrading any pre-existing instruction-tuning. We reverse engineer an adapter which isolates what an instruction-tuned model has learned beyond its corresponding pretrained base model. Importantly, this requires no additional data or training. We can then fine-tune

Antoine Gonon, Nicolas Brisebarre, Elisa Riccietti, Rémi Gribonval

Robustness with respect to weight perturbations underpins guarantees for generalization, pruning and quantization. Existing guarantees rely on Lipschitz bounds in parameter space, cover only plain feed-forward MLPs, and break under the ubiquitous neuron-wise rescaling symmetry of ReLU networks. We prove a new Lipschitz inequality expressed through the $\ell^

Tony G. Chen, Julia Di, Stephanie Newdick, Mathieu Lapotre

ReachBot is a robot concept for the planetary exploration of caves and lava tubes, which are often inaccessible with traditional robot locomotion methods. It uses extendable booms as appendages, with grippers mounted at the end, to grasp irregular rock surfaces and traverse these difficult terrains. We have built a partial ReachBot prototype consisting of a

Aaron Potechin, Hing Yin Tsang

In this paper, we consider induced subgraphs of the Hamming graph $H(n,3)$. We show that if $U \subseteq \mathbb{Z}_3^n$ and $U$ induces a subgraph of $H(n,3)$ with maximum degree at most $1$ then 1. If $U$ is disjoint from a maximum size independent set of $H(n,3)$ then $|U| \leq 3^{n-1}+1$. Moreover, all such $U$ with size $3^{n-1}+1$ are isomorphic to eac

Chase J Sakitis, Daniel B Rowe

In fMRI, capturing brain activation during a task is dependent on how quickly k-space arrays are obtained. Acquiring full k-space arrays, which are reconstructed into images using the inverse Fourier transform (IFT), that make up volume images can take a considerable amount of scan time. Under-sampling k-space reduces the acquisition time, but results in ali

Cecilia Ferrando, Daniel Sheldon

Sufficient statistic perturbation (SSP) is a widely used method for differentially private linear regression. SSP adopts a data-independent approach where privacy noise from a simple distribution is added to sufficient statistics. However, sufficient statistics can often be expressed as linear queries and better approximated by data-dependent mechanisms. In

Alaa Altassan, Murat Alan

Let $ k \geq 2 $ be an integer. The $ k- $generalized Fibonacci sequence is a sequence defined by the recurrence relation $ F_{n}^{(k)}=F_{n-1}^{(k)} + \cdots + F_{n-k}^{(k)}$ for all $ n \geq 2$ with the initial values $ F_{i}^{(k)}=0 $ for $ i=2-k, \ldots, 0 $ and $ F_{1}^{(k)}=1.$ In 2020, Banks and Luca, among other things, determined all Fibonacci numbe

Chafiq Benhida, Raúl E. Curto, George R. Exner

In the study of the geometrically regular weighted shifts (GRWS) -- see [5] -- signed power representing measures (which we call Berger-type charges) played an important role. Motivated by their utility in that context, we establish a general theory for Berger-type charges. We give the first result of which we are aware showing that k-hyponormality alone (as

Yingying Fang, Zihao Jin, Xiaodan Xing, Simon Walsh

In medical imaging, particularly in early disease detection and prognosis tasks, discerning the rationale behind an AI model's predictions is crucial for evaluating the reliability of its decisions. Conventional explanation methods face challenges in identifying discernible decisive features in medical image classifications, where discriminative features are

Jorge L. C. Domingos, F. Q. Potiguar, C. L. N. Oliveira, W. P. Ferreira

We investigate the dynamics of two-dimensional assemblies of rod-shaped magnetic colloids under the influence of an external rotating magnetic field. Using Molecular Dynamics, we simulate the formation of patterns that emerge based on the synchronization degree between the magnetic rods and the rotating field. We then explore the structural and dynamic chara

Enrico Le Donne, Nicola Paddeu, Alessandro Socionovo

We begin by characterizing metabelian distributions in terms of principal bundle structures. Then, we prove that in sub-Riemannian manifolds with metabelian distributions of rank $r$, the projection of strictly singular trajectories to some $r$-dimensional manifold must remain within an analytic variety. As a consequence, for rank-2 metabelian distributions,

Mariam Al-Hawaj

In this project, we develop a new connection between the dynamics of quadratic polynomials on the complex plane and the dynamics of homeomorphisms of surfaces. In particular, given a quadratic polynomial, we investigate whether one can construct an extension of it which is a generalized pseudo-Anosov homeomorphism. Generalized pseudo-Anosov means it preserve

Blake Harris, Viswanath Nagarajan

We show that the greedy algorithm for adaptive-submodular cover has approximation ratio at least 1.3*(1+ln Q). Moreover, the instance demonstrating this gap has Q=1. So, it invalidates a prior result in the paper ``Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization'' by Golovin-Krause, that claimed a (1+ln Q)^2 approximatio

Gustavo H. Rodrigues, Bruno Aristimunha, Sylvain Chevallier, Raphael Y. de Camargo

Automated classification of electroencephalogram (EEG) signals is complex due to their high dimensionality, non-stationarity, low signal-to-noise ratio, and variability between subjects. Deep neural networks (DNNs) have shown promising results for EEG classification, but the above challenges hinder their performance. Euclidean Alignment (EA) and Data Augment

Eman Abu Ishgair, Marcela S. Melara, Santiago Torres-Arias

The software supply chain comprises a highly complex set of operations, processes, tools, institutions and human factors involved in creating a piece of software. A number of high-profile attacks that exploit a weakness in this complex ecosystem have spurred research in identifying classes of supply chain attacks. Yet, practitioners often lack the necessary

Li Ji-An, Corey Y. Zhou, Marcus K. Benna, Marcelo G. Mattar

Understanding connections between artificial and biological intelligent systems can reveal fundamental principles of general intelligence. While many artificial intelligence models have a neuroscience counterpart, such connections are largely missing in Transformer models and the self-attention mechanism. Here, we examine the relationship between interacting

Christoffer Fink, Olov Schelén, Ulf Bodin

Distributed ledger technology such as blockchain is considered essential for supporting large numbers of micro-transactions in the Machine Economy, which is envisioned to involve billions of connected heterogeneous and decentralized cyber-physical systems. This stresses the need for performance and scalability of distributed ledger technologies. Sharding div

Yuwen Gu

The Tweedie generalized linear models are commonly applied in the insurance industry to analyze semicontinuous claim data. For better prediction of the aggregated claim size, the mean and dispersion of the Tweedie model are often estimated together using the double generalized linear models. In some actuarial applications, it is common to observe an excessiv

Lauri Oksanen, Tianyu Yang, Yang Yang

We develop a linearized boundary control method for the inverse boundary value problem of determining a density in the acoustic wave equation. The objective is to reconstruct an unknown perturbation in a known background density from the linearized Neumann-to-Dirichlet map. A key ingredient in the derivation is a linearized Blagovescenskii's identity with a

Maria Tsampazi, Salvatore D'Oro, Michele Polese, Leonardo Bonati

The highly heterogeneous ecosystem of NextG wireless communication systems calls for novel networking paradigms where functionalities and operations can be dynamically and optimally reconfigured in real time to adapt to changing traffic conditions and satisfy stringent and diverse QoS demands. Open RAN technologies, and specifically those being standardized

N. G. Karaçaylı, P. Martini, D. H. Weinberg, S. Ferraro

The squeezed cross-bispectrum \bispeconed\ between the gravitational lensing in the Cosmic Microwave Background and the 1D \lya\ forest power spectrum can constrain bias parameters and break degeneracies between $\sigma_8$ and other cosmological parameters. We detect \bispeconed\ with $4.8\sigma$ significance at an effective redshift $z_\mathrm{eff}=2.4$ usi

Arindam Dutta, Anirban Pathak

Unconditional security in quantum key distribution (QKD) relies on authenticating the identities of users involved in key distribution. While classical identity authentication schemes were initially utilized in QKD implementations, concerns regarding their vulnerability have prompted the exploration of quantum identity authentication (QIA) protocols. In this

Amin Ahmadi Kasani, Hedieh Sajedi

Estimating the Bone Age of children is very important for diagnosing growth defects, and related diseases, and estimating the final height that children reach after maturity. For this reason, it is widely used in different countries. Traditional methods for estimating bone age are performed by comparing atlas images and radiographic images of the left hand,

Rachith Aiyappa, Xin Wang, Munjung Kim, Ozgur Can Seckin

Link prediction -- a task of distinguishing actual hidden edges from random unconnected node pairs -- is one of the quintessential tasks in graph machine learning. Despite being widely accepted as a universal benchmark and a downstream task for representation learning, the validity of the link prediction benchmark itself has been rarely questioned. Here, we

E. Mediavilla, J. Jiménez-Vicente

Gravitational microlensing is a robust tool to detect and directly measure the abundance and mass of any kind of compact objects, either in our galaxy or in the extragalatic domain. On basis to generic, broadly applicable arguments, it is concluded that the observed microlensing magnifications are too small and the microlensing events less frequent than the

Zheng Sun, Ting Li, Yijun Hou, Hui Tian

The solar eruption that occurred on 2023 November 28 (SOL2023-11-28) triggered an intense geomagnetic storm on Earth on 2023 December 1. The associated Earth's auroras manifested at the most southern latitudes in the northern hemisphere observed in the past two decades. In order to explore the profound geoeffectiveness of this event, we conducted a comprehen

Jiecheng Lu, Yan Sun, Shihao Yang

Recent Transformer-based large language models (LLMs) demonstrate in-context learning ability to perform various functions based solely on the provided context, without updating model parameters. To fully utilize the in-context capabilities in time series forecasting (TSF) problems, unlike previous Transformer-based or LLM-based time series forecasting metho

Yizhuo Chen, Chun-Fu Chen, Hsiang Hsu, Shaohan Hu

The growing richness of large-scale datasets has been crucial in driving the rapid advancement and wide adoption of machine learning technologies. The massive collection and usage of data, however, pose an increasing risk for people's private and sensitive information due to either inadvertent mishandling or malicious exploitation. Besides legislative soluti

Songyin Wu, Deepak Vembar, Anton Sochenov, Selvakumar Panneer

Real-time rendering has been embracing ever-demanding effects, such as ray tracing. However, rendering such effects in high resolution and high frame rate remains challenging. Frame extrapolation methods, which don't introduce additional latency as opposed to frame interpolation methods such as DLSS 3 and FSR 3, boost the frame rate by generating future fram

Junyao Li, Ming-Yang Zhuang, Yue Shen, Marta Volonteri

Kpc-scale dual and offset Active Galactic Nuclei (AGNs) are signposts of accreting supermassive black holes (SMBHs) triggered during late-stage galaxy mergers, offering crucial insights into the coevolution of SMBHs and galaxies. However, robustly confirmed systems at high redshift (e.g., $z>1$) are scarce and biased towards the most luminous and unobscured

Weiyu Li, Jiarui Liu, Hongyu Yan, Rui Chen

We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in an interactive manner. Despite the significant advancements in 3D generation, existing methods still struggle with lengt

Jiacong Sun, Pouya Houshmand, Marian Verhelst

In-Memory Computing (IMC) has emerged as a promising paradigm for energy-efficient, throughput-efficient and area-efficient machine learning at the edge. However, the differences in hardware architectures, array dimensions, and fabrication technologies among published IMC realizations have made it difficult to grasp their relative strengths. Moreover, previo

Mario Döbler, Robert A. Marsden, Tobias Raichle, Bin Yang

In deep learning, maintaining model robustness against distribution shifts is critical. This work explores a broad range of possibilities to adapt vision-language foundation models at test-time, with a particular emphasis on CLIP and its variants. The study systematically examines prompt-based techniques and existing test-time adaptation methods, aiming to i

Avidan Shah, Danny Tran, Yuhan Tang

Curriculum learning has been growing in the domain of reinforcement learning as a method of improving training efficiency for various tasks. It involves modifying the difficulty (lessons) of the environment as the agent learns, in order to encourage more optimal agent behavior and higher reward states. However, most curriculum learning methods currently invo

Konpal Shaukat Ali, Martin Haenggi, Arafat Al-Dweik, Marwa Chafii

In wireless networks assisted by intelligent reflecting surfaces (IRSs), jointly modeling the signal received over the direct and indirect (reflected) paths is a difficult problem. In this work, we show that the network geometry (locations of serving base station, IRS, and user) can be captured using the so-called triangle parameter $\Delta$. We introduce a

Erik Rye, Dave Levin

Wi-Fi-based Positioning Systems (WPSes) are used by modern mobile devices to learn their position using nearby Wi-Fi access points as landmarks. In this work, we show that Apple's WPS can be abused to create a privacy threat on a global scale. We present an attack that allows an unprivileged attacker to amass a worldwide snapshot of Wi-Fi BSSID geolocations