March 2024 arXiv papers — page 79

Steven W. Stahler

As a stellar group forms within its parent molecular cloud, new members first appear in the deep interior. These overcrowded stars continually diffuse outward to the cloud boundary, and even beyond. Observations have so far documented only the interior drift. Those stars that actually leave the cloud form an expanding envelope that I call the "stellar mantle

Hanqi Zhou, Robert Bamler, Charley M. Wu, Álvaro Tejero-Cantero

Intelligent tutoring systems optimize the selection and timing of learning materials to enhance understanding and long-term retention. This requires estimates of both the learner's progress (''knowledge tracing''; KT), and the prerequisite structure of the learning domain (''knowledge mapping''). While recent deep learning models achieve high KT accuracy, th

Frank Shih, Faming Liang

Reinforcement learning (RL) tackles sequential decision-making problems by creating agents that interacts with their environment. However, existing algorithms often view these problem as static, focusing on point estimates for model parameters to maximize expected rewards, neglecting the stochastic dynamics of agent-environment interactions and the critical

Rui Luo, Mark Zolotas, Drake Moore, Taskin Padir

Shared control can ease and enhance a human operator's ability to teleoperate robots, particularly for intricate tasks demanding fine control over multiple degrees of freedom. However, the arbitration process dictating how much autonomous assistance to administer in shared control can confuse novice operators and impede their understanding of the robot's beh

Isak Samsten, Zed Lee

Shapelets are discriminative subsequences, originally embedded in shapelet-based decision trees but have since been extended to shapelet-based transformations. We propose Castor, a simple, efficient, and accurate time series classification algorithm that utilizes shapelets to transform time series. The transformation organizes shapelets into groups with vary

Mahindra Rautela, Alan Williams, Alexander Scheinker

Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to limited non-destructive measurements, computationally demanding simulations, and inherent uncertainties in the system. We propose a two-step unsupervised deep learning framework named

Valentina Di Marco, Andrew Zic, Ryan M. Shannon, Eric Thrane

A number of pulsar timing arrays have recently reported preliminary evidence for the existence of a nanohertz frequency gravitational-wave background. These analyses rely on detailed noise analyses, which are inherently complex due to the many astrophysical and instrumental factors that contribute to the pulsar noise budget. We investigate whether realistic

Walter A. Strauss, Masahiro Suzuki

We consider a plasma that is created by a high voltage difference $\lambda$, which is known as a Townsend discharge. We consider it to be confined to the region $\Omega$ between two concentric spheres, two concentric cylinders, or more generally between two star-shaped surfaces. We first prove that if the plasma is initially relatively dilute, then either it

Zack While, R. Jordan Crouser, Ali Sarvghad

Despite the explosive growth of the aging population worldwide, older adults have been largely overlooked by visualization research. This paper is a critical reflection on the underrepresentation of older adults in visualization research. We discuss why investigating visualization at the intersection of aging matters, why older adults may have been omitted f

Dipayan Chakraborty, Florent Foucaud, Michael A. Henning, Tuomo Lehtilä

An identifying code of a closed-twin-free graph $G$ is a dominating set $S$ of vertices of $G$ such that any two vertices in $G$ have a distinct intersection between their closed neighborhoods and $S$. It was conjectured that there exists an absolute constant $c$ such that for every connected graph $G$ of order $n$ and maximum degree $\Delta$, the graph $G$

Jing Zhang, Irving Fang, Juexiao Zhang, Hao Wu

Lithic Use-Wear Analysis (LUWA) using microscopic images is an underexplored vision-for-science research area. It seeks to distinguish the worked material, which is critical for understanding archaeological artifacts, material interactions, tool functionalities, and dental records. However, this challenging task goes beyond the well-studied image classificat

Jagatpreet Singh Nir, Dennis Giaya, Hanumant Singh

Deep learning techniques have significantly advanced in providing accurate visual odometry solutions by leveraging large datasets. However, generating uncertainty estimates for these methods remains a challenge. Traditional sensor fusion approaches in a Bayesian framework are well-established, but deep learning techniques with millions of parameters lack eff

Mohamed A. Allayla, Serkan Ayvaz

Online social media platforms have recently become integral to our society and daily routines. Every day, users worldwide spend a couple of hours on such platforms, expressing their sentiments and emotional state and contacting each other. Analyzing such huge amounts of data from these platforms can provide a clear insight into public sentiments and help det

Taiqi He, Kwanghee Choi, Lindia Tjuatja, Nathaniel R. Robinson

Thousands of the world's languages are in danger of extinction--a tremendous threat to cultural identities and human language diversity. Interlinear Glossed Text (IGT) is a form of linguistic annotation that can support documentation and resource creation for these languages' communities. IGT typically consists of (1) transcriptions, (2) morphological segmen

J. Schultheiß, L. Puntigam, M. Winkler, S. Krohns

We explore the impact of a magnetic field on the ferroelectric domain pattern in polycrystalline hexagonal ErMnO3 at cryogenic temperatures. Utilizing piezoelectric force microscopy measurements at 1.65 K, we observe modifications of the topologically protected ferroelectric domain structure induced by the magnetic field. These alterations likely result from

Yulong Shisu, Susano Mingwin, Yongshuai Wanwag, Zengqiang Chenso

The accurate analysis of medical images is vital for diagnosing and predicting medical conditions. Traditional approaches relying on radiologists and clinicians suffer from inconsistencies and missed diagnoses. Computer-aided diagnosis systems can assist in achieving early, accurate, and efficient diagnoses. This paper presents an improved Evolutionary Algor

Jonathan Guiang, Slava Krutelyov, Manos Vourliotis, Yanxi Gu

In this work, we present a study on ways that tracking algorithms can be improved with machine learning (ML). We base this study on the line segment tracking (LST) algorithm that we have designed to be naturally parallelized and vectorized in order to efficiently run on modern processors. LST has been developed specifically for the CMS Experiment at the LHC,

Will Grilliette

By applying simplification operations to categories of multigraphs, several natural graph operations are shown to demonstrate categorical issues. The replacement of an undirected edge with a directed cycle for digraphs admits both a left and a right adjoint, while the analogous operation for quivers only admits a left adjoint. The clique-replacement graph, i

Yongshuo Zong, Ondrej Bohdal, Timothy Hospedales

Large language models (LLMs) famously exhibit emergent in-context learning (ICL) -- the ability to rapidly adapt to new tasks using few-shot examples provided as a prompt, without updating the model's weights. Built on top of LLMs, vision large language models (VLLMs) have advanced significantly in areas such as recognition, reasoning, and grounding. However

Hanzhou Liu, Binghan Li, Chengkai Liu, Mi Lu

Recent deblurring networks have effectively restored clear images from the blurred ones. However, they often struggle with generalization to unknown domains. Moreover, these models typically focus on distortion metrics such as PSNR and SSIM, neglecting the critical aspect of metrics aligned with human perception. To address these limitations, we propose Debl

Zsuzsanna Lipták, Francesco Masillo, Gonzalo Navarro

We consider the problem of maintaining a collection of strings while efficiently supporting splits and concatenations on them, as well as comparing two substrings, and computing the longest common prefix between two suffixes. This problem can be solved in optimal time $\mathcal{O}(\log N)$ whp for the updates and $\mathcal{O}(1)$ worst-case time for the quer

Songbo Wang

We present two methods to obtain $O(1/N^2)$ local propagation of chaos bounds for $N$ diffusive particles in $W^{-1,\infty}$ mean field interaction. This extends the recent finding of Lacker [Probab. Math. Phys., 4(2):377-432, 2023] to the case of singular interactions. The first method is based on a hierarchy of relative entropies and Fisher informations, a

Sunny Kumar Singh, Manu Kurian, Vinod Chandra

This study aims to develop second-order relativistic viscous magnetohydrodynamics (MHD) derived from kinetic theory within an extended relaxation time approximation (momentum/energy dependent) for the collision kernel. The investigation involves a detailed examination of shear stress tensor evolution equations and associated transport coefficients. The Boltz

Isidora Teofilovic, Ali Cem, David Sanchez-Jacome, Daniel Perez-Lopez

Photonic integrated circuits play an important role in the field of optical computing, promising faster and more energy-efficient operations compared to their digital counterparts. This advantage stems from the inherent suitability of optical signals to carry out matrix multiplication. However, even deterministic phenomena such as thermal crosstalk make prec

Akos Borsanyi

In this paper I prove a conjecture which gives a lower bound for the largest absolute value of the coefficients of the n-th cyclotomic polynomial for some n. Moreover this estimate is essentially sharp.

V. Nikolic, G. Velev, R. Bruce, T. Tope

Fermi National Accelerator Laboratory (Fermilab) is currently constructing a new High Field Vertical Magnet Test Facility (HFVMTF) designed for testing High Temperature Superconducting (HTS) cables under high magnetic fields. This facility is expected to offer capabilities similar to those of EDIPO at PSI and FRESCA2 at CERN. The background magnetic field of

Kaisa Matomäki, Joni Teräväinen

In this note we investigate connections between zero density estimates for the Riemann zeta function and large value estimates for Dirichlet polynomials. It is well known that estimates of the latter type imply estimates of the former type. Our goal is to show that there is an implication to the other direction as well, i.e. zero density estimates for the Ri

Martina Monti, Ana Quedo

A Generalized Hyperelliptic Variety (GHV) is the quotient of an abelian variety by a free action of a finite group which does not contain any translation. These varieties are natural generalizations of bi-elliptic surfaces. In this paper we prove the Kawamata-Morrison Cone Conjecture for these manifolds using the analogous results established by Prendergast-

Sujoy K. Modak

The observable universe is fundamentally inhomogeneous and anisotropic. Quantum description of the generation of these inhomogeneities and anisotropies is ill-understood and unsatisfactory. After providing a brief account of the standard approach of the generation of the classical density perturbations starting from the quantum fluctuations of inflaton field

Kyusung Hwang

Quantum spin liquids are exotic many-body states featured with long-range entanglement and fractional anyon quasiparticles. Quantum phase transitions of spin liquids are particularly interesting problems related with novel phenomena of anyon condensation and anyon confinement. Here we study a quantum dimer model which implements a transition between a $\math

Zetai Cen, Clifford Lam

We propose tensor time series imputation when the missing pattern in the tensor data can be general, as long as any two data positions along a tensor fibre are both observed for enough time points. The method is based on a tensor time series factor model with Tucker decomposition of the common component. One distinguished feature of the tensor time series fa

Joseph Pollard, Gareth P. Alexander

Integer winding disclinations are unstable in a nematic and are removed by an `escape into the third dimension', resulting in a non-singular texture. This process is frustrated in a cholesteric material due to the requirement of maintaining a uniform handedness and instead results in the formation of strings of point defects, as well as complex three-dimensi

Alexander Dunn

We prove a power saving upper bound for the sum of Fourier coefficients $\rho_f(\cdot)$ of a fixed cubic metaplectic cusp form $f$ over primes. Our result is the cubic analogue of a celebrated 1990 Theorem of Duke and Iwaniec, and the cuspidal analogue of a Theorem due to the author and Radziwill for the bias in cubic Gauss sums. The proof has two main input

Philipp Kopper, David Rügamer, Raphael Sonabend, Bernd Bischl

Scoring rules are an established way of comparing predictive performances across model classes. In the context of survival analysis, they require adaptation in order to accommodate censoring. This work investigates using scoring rules for model training rather than evaluation. Doing so, we establish a general framework for training survival models that is mo

Michael I Ganzburg, Miquel Saucedo, Sergey Tikhonov

In this paper, we study the form of the constant $C$ in the Bernstein--Nikolskii inequalities $\|f^{(s)}\|_q \lesssim C(s, p, q)\left\|f\right\|_p,\,0<p<q \leq\infty$, for trigonometric polynomials and entire functions of exponential type. We obtain the optimal behavior of the constant with respect to the smoothness parameter $s$.

Yuexi Du, Regina J. Hooley, John Lewin, Nicha C. Dvornek

Digital Breast Tomosynthesis (DBT) is a widely used medical imaging modality for breast cancer screening and diagnosis, offering higher spatial resolution and greater detail through its 3D-like breast volume imaging capability. However, the increased data volume also introduces pronounced data imbalance challenges, where only a small fraction of the volume c

Pedro Leandro La Rotta, Jingxi Xu, Ava Chen, Lauren Winterbottom

We propose MetaEMG, a meta-learning approach for fast adaptation in intent inferral on a robotic hand orthosis for stroke. One key challenge in machine learning for assistive and rehabilitative robotics with disabled-bodied subjects is the difficulty of collecting labeled training data. Muscle tone and spasticity often vary significantly among stroke subject

Jack Jeffries, David Lieberman

Bernstein's inequality is a central result in the theory of $D$-modules on smooth varieties. While Bernstein's inequality fails for rings of differential operators on general singularities, recent work of \`{A}lvarez Montaner, Hern\'andez, Jeffries, N\'u\~nez-Betancourt, Teixeira, and Witt establishes Bernstein's inequality for invariants of finite groups in

Sergio L. Cacciatori, Henri Epstein, Ugo Moschella

We discuss general one and two-loops banana diagrams with arbitrary masses on the de Sitter spacetime by using direct methods of dS quantum field theory in the dimensional regularization approach. In the one-loop case we also compute the effective potential for an $O(N)$ model in $d=4$ dimension as an explicit function of the cosmological constant $\Lambda$,

Podshara Chanrungmaneekul, Kejia Ren, Joshua T. Grace, Aaron M. Dollar

Calibrating robots into their workspaces is crucial for manipulation tasks. Existing calibration techniques often rely on sensors external to the robot (cameras, laser scanners, etc.) or specialized tools. This reliance complicates the calibration process and increases the costs and time requirements. Furthermore, the associated setup and measurement procedu

Dag Normann, Sam Sanders

Many theorems of mathematics have the form that for a certain problem, e.g. a differential equation or polynomial (in)equality, there exists a solution. The sequential version then states that for a sequence of problems, there is a sequence of solutions. The original and sequential theorem can often be proved via the same (or similar) proof and often have th

Sergio L. Cacciatori, Henri Epstein, Ugo Moschella

We discuss general one and two-loop banana diagrams and one-loop diagrams with external lines with arbitrary masses on the anti de Sitter spacetime by using methods of AdS quantum field theory in the dimensional regularization approach. The banana diagrams explicitly computed in this paper are indeed the necessary ingredients for the evaluation of the two-lo

Jerome H. Friedman

The output of a machine learning algorithm can usually be represented by one or more multivariate functions of its input variables. Knowing the global properties of such functions can help in understanding the system that produced the data as well as interpreting and explaining corresponding model predictions. A method is presented for representing a general

J. Wang, C. G. Petra, J. L. Peterson

Bayesian optimization is a powerful optimization tool for problems where native first-order derivatives are unavailable. Recently, constrained Bayesian optimization (CBO) has been applied to many engineering applications where constraints are essential. However, several obstacles remain with current CBO algorithms that could prevent a wider adoption. We prop

Peitong Duan, Jeremy Warner, Yang Li, Bjoern Hartmann

Feedback on user interface (UI) mockups is crucial in design. However, human feedback is not always readily available. We explore the potential of using large language models for automatic feedback. Specifically, we focus on applying GPT-4 to automate heuristic evaluation, which currently entails a human expert assessing a UI's compliance with a set of desig

Christopher Chambers, Alan Miller, Ruodu Wang, Qinyu Wu

Max-stability is the property that taking a maximum between two inputs results in a maximum between two outputs. We study max-stability with respect to first-order stochastic dominance, the most fundamental notion of stochastic dominance in decision theory. Under two additional standard axioms of nondegeneracy and lower semicontinuity, we establish a represe

Jack Whaley-Baldwin, Miguel Martinez-Canales, Chris J. Pickard

We investigate the pressure-temperature ($p$-$T$) phase diagram of elemental lithium (Li) up to multiterapascal (TPa) pressures using ab-initio random structure search (AIRSS) and density functional theory (DFT). At zero temperature, beyond the high-pressure $Fd\bar{3}m$ diamond structure predicted in previous studies, we find eleven solid-state phase transi

Nandana Menon, Amrita Basak

Multi-fidelity (MF) modeling is a powerful statistical approach that can intelligently blend data from varied fidelity sources. This approach finds a compelling application in predicting melt pool geometry for laser-directed energy deposition (L-DED). One major challenge in using MF surrogates to merge a hierarchy of melt pool models is the variability in in

Jurdana Masuma Iqrah, Wei Wang, Hongjie Xie, Sushil Prasad

The observation of the advancing and retreating pattern of polar sea ice cover stands as a vital indicator of global warming. This research aims to develop a robust, effective, and scalable system for classifying polar sea ice as thick/snow-covered, young/thin, or open water using Sentinel-2 (S2) images. Since the S2 satellite is actively capturing high-reso

Yongtao Wu, Fanghui Liu, Carl-Johann Simon-Gabriel, Grigorios G Chrysos

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robust architectures, especially when adversarial training is considered. In this work, we aim to address the

José Gustavo Coelho

Let $p$ be a prime number, $m$ be an even positive integer, and $\mathbb{F}_q$ be a finite field with $q = p^m$ elements. In this paper, we compute the number of solutions with all coordinates in $\mathbb{F}_q^*$ for diagonal equations of the form $$a_1 x_1^{d} + \dots + a_s x_s^{d} = b, \quad a_i \in \mathbb{F}_q^*, \, b \in \mathbb{F}_q,$$ when the coeffic

Hayden Webb, Podshara Chanrungmaneekul, Shenli Yuan, Kaiyu Hang

In-hand manipulation is a crucial ability for reorienting and repositioning objects within grasps. The main challenges in this are not only the complexity of the computational models, but also the risks of grasp instability caused by active finger motions, such as rolling, sliding, breaking, and remaking contacts. This paper presents the development of the R

Yiyang Jia

We construct a family of near-CFT$_1$ models with a conserved U(1) charge, whose basic degrees of freedom are canonical bosons. The Sachdev-Ye-Kitaev (SYK) model -- the first microscopic model that realizes the near-CFT$_1$ dynamics -- is based on random $p$-local interactions among fermions. However, a bosonic near-CFT$_1$ model has remained elusive in the

Michele Resta, Davide Bacciu

Modern Neural Machine Translation systems exhibit strong performance in several different languages and are constantly improving. Their ability to learn continuously is, however, still severely limited by the catastrophic forgetting issue. In this work, we leverage a key property of encoder-decoder Transformers, i.e. their generative ability, to propose a no

Aljoša Ošep, Tim Meinhardt, Francesco Ferroni, Neehar Peri

We propose the SAL (Segment Anything in Lidar) method consisting of a text-promptable zero-shot model for segmenting and classifying any object in Lidar, and a pseudo-labeling engine that facilitates model training without manual supervision. While the established paradigm for Lidar Panoptic Segmentation (LPS) relies on manual supervision for a handful of ob

Yuchao Li, Dimitri Bertsekas

In this paper we consider a transformer with an $n$-gram structure, such as the one underlying ChatGPT. The transformer provides next word probabilities, which can be used to generate word sequences. We consider methods for computing word sequences that are highly likely, based on these probabilities. Computing the optimal (i.e., most likely) word sequence s

Jiang Hu, Quanzheng Li

Recent advancements in large-scale pretrained models have significantly improved performance across a variety of tasks in natural language processing and computer vision. However, the extensive number of parameters in these models necessitates substantial memory and computational resources for full training. To adapt these models for downstream tasks or spec

Wei Yu, Qing-Cui Bu, Shuang-Nan Zhang, He-Xin Liu

We present the results obtained from an X-ray timing study of the new black hole candidate (BHC) Swift J1727.8-1613. The work is based on Hard X-ray Modulation Telescope (Insight-HXMT) observations carried out during the 2023 outburst. Prominent type-C low-frequency Quasi-periodic Oscillations (LFQPOs) are detected throughout the observations. With the subst

H. M. Fausey, S. Vejlgaard, A. J. van der Horst, K. E. Heintz

The Epoch of Reionization (EoR) is a key period of cosmological history in which the intergalactic medium (IGM) underwent a major phase change from being neutral to almost completely ionized. Gamma-ray bursts (GRBs) are luminous and unique probes of their environments that can be used to study the timeline for the progression of the EoR. Here we present a de

Soroush Ghandi, Benjamin Quost, Cassio de Campos

This work addresses integrating probabilistic propositional logic constraints into the distribution encoded by a probabilistic circuit (PC). PCs are a class of tractable models that allow efficient computations (such as conditional and marginal probabilities) while achieving state-of-the-art performance in some domains. The proposed approach takes both a PC

Kevin Murphy, Joao C. V. Soares, Justin K. Yim, Dustin Nottage

Soldiers in the field often need to cross negative obstacles, such as rivers or canyons, to reach goals or safety. Military gap crossing involves on-site temporary bridges construction. However, this procedure is conducted with dangerous, time and labor intensive operations, and specialized machinery. We envision a scalable robotic solution inspired by advan

Jennifer Scott, Miroslav Tůma

Incomplete factorizations have long been popular general-purpose algebraic preconditioners for solving large sparse linear systems of equations. Guaranteeing the factorization is breakdown free while computing a high quality preconditioner is challenging. A resurgence of interest in using low precision arithmetic makes the search for robustness more importan

Francisco Guzman-Cajica, Francisco S. Guzman

In this paper we numerically solve the time dependent Schr\"odinger equation for scenarios using wave packets. These examples include the free wave packet, which we use to show the difference between group and phase velocities, the packet in a harmonic oscillator potential with non-trivial initial conditions in one and two dimensions, which is compared with

Leonardo G. J. M. Voltarelli, Arthur A. B. Pessa, Luciano Zunino, Rafael S. Zola

Permutation entropy and its associated frameworks are remarkable examples of physics-inspired techniques adept at processing complex and extensive datasets. Despite substantial progress in developing and applying these tools, their use has been predominantly limited to structured datasets such as time series or images. Here, we introduce the k-nearest neighb

Florian Lehner, Christian Lindorfer, Christoforos Panagiotis

We prove that on any transitive graph $G$ with infinitely many ends, a self-avoiding walk of length $n$ is ballistic with extremely high probability, in the sense that there exist constants $c,t>0$ such that $\mathbb{P}_n(d_G(w_0,w_n)\geq cn)\geq 1-e^{-tn}$ for every $n\geq 1$. Furthermore, we show that the number of self-avoiding walks of length $n$ grows a

Evan O'Dorney

A \emph{numerical semigroup} is a subset $\Lambda$ of the nonnegative integers that is closed under addition, contains $0$, and omits only finitely many nonnegative integers (called the \emph{gaps} of $\Lambda$). The collection of all numerical semigroups may be visually represented by a tree of element removals, in which the children of a semigroup $\Lambda

Ruiping Yang, Bing Wei, Claude Rozé, Saïd Idlahcen

A Statistic Vectorial Complex Ray Model (SVCRM) is proposed for the scattering of a plane wave by a non-spherical dielectric particle in three dimensions. This method counts the complex amplitudes of all rays arriving in a tiny box in the observation direction. It avoids the two-dimensional interpolation necessary in the Vectorial Complex Ray Model (VCRM) fo

Jiwoo Song, Daning Huang

Modal analysis has become an essential tool to understand the coherent structure of complex flows. The classical modal analysis methods, such as dynamic mode decomposition (DMD) and spectral proper orthogonal decomposition (SPOD), rely on a sufficient amount of data that is regularly sampled in time. However, often one needs to deal with sparse temporally ir

Kuan Fang Ren, Qingwei Duan, Claude Rozé, Minglin Yang

Accurate and efficient prediction of three-dimensional (3D) fields in wave interactions with large, complex-shaped objects is essential for applications in electromagnetic computation, computer graphics, optical metrology, and freeform optics. However, existing methods face significant challenges: numerical techniques are computationally intensive and imprac

Nikita Kornilov, Petr Mokrov, Alexander Gasnikov, Alexander Korotin

Over the several recent years, there has been a boom in development of Flow Matching (FM) methods for generative modeling. One intriguing property pursued by the community is the ability to learn flows with straight trajectories which realize the Optimal Transport (OT) displacements. Straightness is crucial for the fast integration (inference) of the learned

Kimberly Ayers, Ami Radunskaya

The full family of discrete logistic maps has been widely studied both as a canonical example of the period-doubling route to chaos, and as a model of natural processes. In this paper we present a study of the stochastic process described by iterations of the logistic map with a random parameter. In addition to bringing together previously known results, we

Gabriel Araujo, Marcos Kalinowski, Markus Endler, Fabio Calefato

Context: Machine Learning Operations (MLOps) has emerged as a set of practices that combines development, testing, and operations to deploy and maintain machine learning applications. Objective: In this paper, we assess the benefits and limitations of using the MLOps principles in online supervised learning. Method: We conducted two focus group sessions on t

Aneesh Rangnekar, Nishant Nadkarni, Jue Jiang, Harini Veeraraghavan

Medical image foundation models have shown the ability to segment organs and tumors with minimal fine-tuning. These models are typically evaluated on task-specific in-distribution (ID) datasets. However, reliable performance on ID datasets does not guarantee robust generalization on out-of-distribution (OOD) datasets. Importantly, once deployed for clinical

Bo-Ru Lu, Nikita Haduong, Chien-Yu Lin, Hao Cheng

Transformer-based NLP models are powerful but have high computational costs that limit deployment. Finetuned encoder-decoder models are popular in specialized domains and can outperform larger more generalized decoder-only models, such as GPT-4. We introduce a new configuration for encoder-decoder models that improves efficiency on structured output and deco

Yishu Wei, Yu Deng, Cong Sun, Mingquan Lin

Objectives: Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space.

Eric I. Rosenthal, Souvik Biswas, Giovanni Scuri, Hope Lee

The negatively charged tin-vacancy center in diamond (SnV$^-$) is an emerging platform for building the next generation of long-distance quantum networks. This is due to the SnV$^-$'s favorable optical and spin properties including bright emission, insensitivity to electronic noise, and long spin coherence times at temperatures above 1 Kelvin. Here, we demon

Tadeja Veršič, Marina Rejkuba, Magda Arnaboldi, Ortwin Gerhard

Within the $\Lambda$CDM cosmology, dark matter haloes are expected to deviate from spherical symmetry. Constraining the halo shapes at large galactocentric distances is challenging due to the low density of luminous tracers. The well-studied early-type galaxy NGC 5128 (Centaurus A - CenA), has a large number of radial velocities for globular clusters (GCs) a

Ehsan Lari, Reza Arablouei, Vinay Chakravarthi Gogineni, Stefan Werner

Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicious clients tamper with their local models to manipulate the global model. In this work, we investigate the resilience of the partial-sharing online FL (PSO-Fed) algorithm against s

M Manvith Prabhu, Haricharana Srinivasa, Anand Kumar M

This paper summarizes Team SCaLAR's work on SemEval-2024 Task 5: Legal Argument Reasoning in Civil Procedure. To address this Binary Classification task, which was daunting due to the complexity of the Legal Texts involved, we propose a simple yet novel similarity and distance-based unsupervised approach to generate labels. Further, we explore the Multi-leve

Divyansh Singhvi, Diganta Misra, Andrej Erkelens, Raghav Jain

Language is an intricately structured system, and a key goal of NLP interpretability is to provide methodological insights for understanding how language models represent this structure internally. In this paper, we use Shapley Taylor interaction indices (STII) in order to examine how language and speech models internally relate and structure their inputs. P

Raphael Norman-Tenazas, David Kleinberg, Erik C. Johnson, Daniel P. Lathrop

It has been shown that unclocked, recurrent networks of Boolean gates in FPGAs can be used for low-SWaP reservoir computing. In such systems, topology and node functionality of the network are randomly initialized. To create a network that solves a task, weights are applied to output nodes and learning is achieved by adjusting those weights with conventional

Rajendra Beekie, Shan Chen, Hao Jia

We study the dynamics of the two dimensional Navier-Stokes equations linearized around a shear flow on a (non-square) torus which possesses exactly two non-degenerate critical points. We obtain linear inviscid damping and vorticity depletion estimates for the linearized flow that are uniform with respect to the viscosity, and enhanced dissipation type decay

Victor Manuel Larios, José Guadalupe Robledo, Leopoldo Gómez, R. Rincón

As part of the activities of the IEEE-GDL CCD working group of physical infrastructure, this whitepaper is intented to be an initial guide to understand the layers, taxonomy of services and best practices for the development of smart buildings. Open standards are claimed in order to increase interoperability between layers and services. Moreover, two buildin

Daniele Iannotti, Alioscia Hamma

Geometrical methods in quantum information are very promising for both providing technical tools and intuition into difficult control or optimization problems. Moreover, they are of fundamental importance in connecting pure geometrical theories, like GR, to quantum mechanics, like in the AdS/CFT correspondence. In this paper, we first make a survey of the mo

Zheng Lin, Guanqiao Qu, Wei Wei, Xianhao Chen

The increasing complexity of deep neural networks poses significant barriers to democratizing them to resource-limited edge devices. To address this challenge, split federated learning (SFL) has emerged as a promising solution by of floading the primary training workload to a server via model partitioning while enabling parallel training among edge devices.

Richard Dengler

This work examines a field theory for RNA-like molecules in a good solvent. The field theory is based on a lattice model for single- and double-strand RNA with a periodic base sequence, and otherwise contains all known relevant details (polymer types, polymer lengths and interactions). As for the somewhat less explicit $O\left(n\right)$-symmetric model there

R. Casadio, I. Kuntz, R. da Rocha

The effective action for quantum gravity coupled to matter contains corrections arising from the functional measure. We analyse the effect of such corrections for anisotropic self-gravitating compact objects described by means of the gravitational decoupling method applied to isotropic solutions of the Einstein field equations. In particular, we consider the

Patrick Lawton, Ashkaan K. Fahimipour, Kurt E. Anderson

Decisions to disperse from a habitat stand out among organismal behaviors as pivotal drivers of ecosystem dynamics across scales. Encounters with other species are an important component of adaptive decision-making in dispersal, resulting in widespread behaviors like tracking resources or avoiding consumers in space. Despite this, metacommunity models often

Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric

Offline reinforcement learning algorithms have proven effective on datasets highly connected to the target downstream task. Yet, leveraging a novel testbed (MOOD) in which trajectories come from heterogeneous sources, we show that existing methods struggle with diverse data: their performance considerably deteriorates as data collected for related but differ

Brayden Casella, Philip M. Anderson, Michael Kleber, Richard P. Mann

We demonstrate the existence of a non-terminating game of Beggar-My-Neighbor, discovered by lead author Brayden Casella. We detail the method for constructing this game and identify a cyclical structure of 62 tricks that is reached by 30 distinct starting hands. We further present a short history of the search for this solution since the problem was posed, a

Dani de Boe, Gen Ye, Fabrizio Renzi, Inês S. Albuquerque

A set of conditions that any effective field theory needs to satisfy in order to allow for the existence of a viable UV completion has recently gained attention in the cosmological context under the name of $\textit{positivity bounds}$. In this paper we revisit the derivation of such bounds for Horndeski gravity and translate them into a complete set of viab

Abbad Moussa, Houcine Aounallah, Sebastián Valladares, Clara Rojas

In this work, we investigate the relativistic quantum motions of spin--zero scalar bosons via the Duffin--Kemmer--Petiau (DKP) equation with a position--dependent mass (PDM) system in the background of the topological defect space--time produced by a cosmic string. We determine the radial wave equation and obtain the exact analytical solutions of the wave eq

Thomas Laurent

This paper introduces the task of "train ego-path detection", a refined approach to railway track detection designed for intelligent onboard vision systems. Whereas existing research lacks precision and often considers all tracks within the visual field uniformly, our proposed task specifically aims to identify the train's immediate path, or "ego-path", with

Anthony Goeckner, Yueyuan Sui, Nicolas Martinet, Xinliang Li

Existing multi-agent coordination techniques are often fragile and vulnerable to anomalies such as agent attrition and communication disturbances, which are quite common in the real-world deployment of systems like field robotics. To better prepare these systems for the real world, we present a graph neural network (GNN)-based multi-agent reinforcement learn

Kevin Hughes, Arie Israel, Azita Mayeli

Let $F$, $S$ be bounded measurable sets in $\mathbb{R}^d$. Let $P_F : L^2(\mathbb{R}^d) \rightarrow L^2(\mathbb{R}^d) $ be the orthogonal projection on the subspace of functions with compact support on $F$, and let $B_S : L^2(\mathbb{R}^d) \rightarrow L^2(\mathbb{R}^d)$ be the orthogonal projection on the subspace of functions with Fourier transforms having

Samuel Coward, Michael Beukman, Jakob Foerster

We present JaxUED, an open-source library providing minimal dependency implementations of modern Unsupervised Environment Design (UED) algorithms in Jax. JaxUED leverages hardware acceleration to obtain on the order of 100x speedups compared to prior, CPU-based implementations. Inspired by CleanRL, we provide fast, clear, understandable, and easily modifiabl

Yuzhu Sun, Mien Van, Stephen McIlvanna, Nguyen Minh Nhat

The evolution and growing automation of collaborative robots introduce more complexity and unpredictability to systems, highlighting the crucial need for robot's adaptability and flexibility to address the increasing complexities of their environment. In typical industrial production scenarios, robots are often required to be re-programmed when facing a more

Max Zeuner, Matthias Hutzler

We present a formalization of quasi-compact and quasi-separated schemes (qcqs-schemes) in the Cubical Agda proof assistant. We follow Grothendieck's functor of points approach, which defines schemes, the quintessential notion of modern algebraic geometry, as certain well-behaved functors from commutative rings to sets. This approach is often regarded as conc

Su-Ping Jin, Hua-Yu Jiang

We present an improved calculation of the $D_{(s)}^*D_{(s)}V$ and $B_{(s)}^*B_{(s)}V$ coupling constants, where $V$ denotes $\rho$, $K^\ast$, $\omega$, and $\phi$ meson. These couplings govern the QCD long-distance dynamics in interactions between heavy pseudoscalar/vector mesons and light vector mesons. Our analysis is conducted within the framework of QCD

Francesco Paissan, Mirco Ravanelli, Cem Subakan

Despite the impressive performance of deep learning models across diverse tasks, their complexity poses challenges for interpretation. This challenge is particularly evident for audio signals, where conveying interpretations becomes inherently difficult. To address this issue, we introduce Listenable Maps for Audio Classifiers (L-MAC), a posthoc interpretati