December 2024 arXiv papers — page 86

Mark J Everitt, Siyuan Ji

There are a number of emerging quantum technologies that have the potential to be disruptive in application areas such as computation, communication and sensing. In such a rapidly emerging field, there is a need for: transparency and accountability pertaining to devices, their performance, and limitations; the ability to assess new entities for integration i

K. S. Kuzmin, N. M. Levashko, M. I. Krivoruchenko

An extended vector meson dominance model is developed to describe electromagnetic nucleon form factors. The model includes families of the $\rho$- and $\omega$-mesons with the associated radial excitations. The free parameters of the model are determined using a global statistical analysis of experimental data on the electromagnetic nucleon form factors in s

Sansrit Paudel, Subek Acharya, Piriyankan Kirupaharan, Bishal KC

Dyslexia is a neurological learning disability that primarily disrupts one's ability to read, write, and spell, affecting an estimated 15-20% of the global population. This high prevalence underscores the importance of developing effective interventions. This study presents a systematic literature review conducted between 2015 and 2024 to evaluate current tr

Pierre Auclair-Desrotour, Gwenaël Boué, Baptiste Loire

Tides are the main driving force behind the long-term evolution of planetary systems. The associated energy dissipation and momentum exchanges are commonly described by Love numbers, which relate the exciting potential to the tidally perturbed potential. These transfer functions are generally assumed to depend solely on tidal frequency and body rheology, fol

Chao Ma, Wenbo Gong, Meyer Scetbon, Edward Meeds

Adaptive optimizers such as Adam (Kingma & Ba, 2015) have been central to the success of large language models. However, they often require to maintain optimizer states throughout training, which can result in memory requirements several times greater than the model footprint. This overhead imposes constraints on scalability and computational efficiency. Sto

Junnan Liu, Hongwei Liu, Linchen Xiao, Ziyi Wang

The rapid advancement of large language models (LLMs) has shown remarkable progress in complex reasoning tasks. However, a significant disparity exists between benchmark performances and real-world applications. We attribute this gap primarily to current evaluation protocols and metrics, which inadequately capture the full spectrum of LLM capabilities, espec

Anton Alekseev, Alina Tillabaeva, Gulnara Dzh. Kabaeva, Sergey I. Nikolenko

The Kyrgyz language, as a low-resource language, requires significant effort to create high-quality syntactic corpora. This study proposes an approach to simplify the development process of a syntactic corpus for Kyrgyz. We present a tool for transferring syntactic annotations from Turkish to Kyrgyz based on a treebank translation method. The effectiveness o

Tom McClelland

Could an AI have conscious experiences? Any answer to this question should conform to Evidentialism - that is, it should be based not on intuition, dogma or speculation but on solid scientific evidence. I argue that such evidence is hard to come by and that the only justifiable stance on the prospects of artificial consciousness is agnosticism. In the curren

Duncan K. Adams, Aditya Parikh, Oren Slone, Rouven Essig

Stellar streams from disrupted globular clusters are dynamically cold structures that are sensitive to perturbations from dark matter subhalos, allowing them in principle to trace the dark matter substructure in the Milky Way. We model, within the context of $\Lambda$CDM, the likelihood of dark matter subhalos to produce a significant feature in a GD-1-like

Maxime Herda, Ariane Trescases, Antoine Zurek

In this paper we design, analyze and simulate a finite volume scheme for a cross-diffusion system which models chemotaxis with local sensing. This system has the same Lyapunov function (or entropy) as the celebrated minimal Keller-Segel system, but unlike the latter, its solutions are known to exist globally in 2D. The long-time behavior of solutions is only

Ainaz Jamshidi, Muhammad Arif, Sabir Ali Kalhoro, Alexander Gelbukh

The generation of high-quality medical time series data is essential for advancing healthcare diagnostics and safeguarding patient privacy. Specifically, synthesizing realistic phonocardiogram (PCG) signals offers significant potential as a cost-effective and efficient tool for cardiac disease pre-screening. Despite its potential, the synthesis of PCG signal

Z. Awan, J. Shabeer, U. Saleem, S. Mehmood

Atomic resolution STEM images often suffer from noise due to low electron doses and instrument imperfections, hence it is challenging to obtain critical structural details required for material analysis. To address the problem, we propose a Physics-Informed Neural Network (PINN) framework for denoising STEM images. Our method integrates spectral fidelity, to

Gordon Lim, Stefan Larson, Kevin Leach

Tobacco3482 is a widely used document classification benchmark dataset. However, our manual inspection of the entire dataset uncovers widespread ontological issues, especially large amounts of annotation label problems in the dataset. We establish data label guidelines and find that 11.7% of the dataset is improperly annotated and should either have an unkno

Isabella L. Giovannelli, Steven M. Anlage

The scattering matrix $S$ linearly relates the vector of incoming waves to outgoing wave excitations, and contains an enormous amount of information about the scattering system and its connections to the scattering channels. Time delay is one way to extract information from $S$, and the transmission time delay $\tau_T$ is a complex (even for Hermitian system

Lorenzo Barca, Gunnar Bali, Sara Collins

We determine the nucleon-sigma terms from lattice QCD. We find that the dominant excited state contamination in the nucleon three-point function with a scalar current is due to the transition between the nucleon and a S-wave scattering state of a nucleon and a scalar (sigma) meson. In this proof-of-concept study, we analyse a single $N_f=3$ ensemble with the

Maximilian Fischer, Peter Neher, Peter Schüffler, Sebastian Ziegler

Digital pathology offers a groundbreaking opportunity to transform clinical practice in histopathological image analysis, yet faces a significant hurdle: the substantial file sizes of pathological Whole Slide Images (WSI). While current digital pathology solutions rely on lossy JPEG compression to address this issue, lossy compression can introduce color and

Sergey Masaev, Georgiy Dorrer, Andrey Minkin, Aleksey Bogdanov

Application of Universal Competencies in Russian educational institutions is very important. Based on them, educational standards are invented. However, there is no universal assessment of the application of the Universal Competencies in practice. The main idea of the research is a general assessment of the application of universal competencies. For this, th

Cameron Calcluth, Oliver Hahn, Juani Bermejo-Vega, Alessandro Ferraro

Classically simulating circuits with bosonic codes is challenging due to the prohibitive cost of simulating quantum systems with many, possibly infinite, energy levels. We propose an algorithm to simulate circuits with encoded Gottesman-Kitaev-Preskill (GKP) states, specifically for odd-dimensional encoded qudits. Our approach is tailored to be especially ef

Jackson Gold, Maria Cuellar

The assumption of fingerprint uniqueness is foundational in forensic science and central to criminal identification practices. However, empirical evidence supporting this assumption is limited, and recent findings from artificial intelligence challenge its validity. This paper uses a probabilistic approach to examine whether fingerprint patterns remain uniqu

Jiate Li, Meng Pang, Binghui Wang

Link prediction in dynamic graphs (LPDG) has been widely applied to real-world applications such as website recommendation, traffic flow prediction, organizational studies, etc. These models are usually kept local and secure, with only the interactive interface restrictively available to the public. Thus, the problem of the black-box evasion attack on the LP

Ramtin Ehsani, Rezvaneh Rezapour, Preetha Chatterjee

Studies have shown that toxic behavior can cause contributors to leave, and hinder newcomers' (especially from underrepresented communities) participation in Open Source Software (OSS) projects. Thus, detection of toxic language plays a crucial role in OSS collaboration and inclusivity. Off-the-shelf toxicity detectors are ineffective when applied to OSS com

Purnima Narayan, Nathan K. Johnson-McDaniel, Anuradha Gupta

Gravitational wave (GW) observations of binary black hole (BBH) coalescences provide a unique opportunity to test general relativity (GR) in the strong-field regime. To ensure the reliability of these tests, it is essential to identify and address potential sources of error, particularly those arising from missing physics in the waveform models used in GW da

Peizhi Du, Rouven Essig, Bernard J. Rauscher, Hailin Xu

Direct-detection searches for dark matter are insensitive to dark matter particles that have large interactions with ordinary matter, which are stopped in the atmosphere or the Earth's crust before reaching terrestrial detectors. We use ``dark'' calibration images taken with the HgCdTe detectors in the Near-Infrared Spectrograph (NIRSpec) on the James Webb S

Alain Blondel, Christophe Grojean, Patrick Janot, Guy Wilkinson

``All future $e^+e^-$ Higgs factories have similar reach for the precise measurement of the Higgs boson properties.'': this popular statement has often led to the impression that all $\rm e^+e^-$ options are scientifically equivalent when it comes to choosing the future post-LHC collider at CERN. More recently, the concept of sustainability has been added in

Ruibin Xu, B. N. J. Persson

We present experimental wear data for polymethyl methacrylate (PMMA) sliding on tile, sandpaper, and polished steel surfaces, as well as for soda-lime, borosilicate, and quartz glass sliding on sandpaper. The results are compared with a recently developed theory \cite{ToBe} of sliding wear based on crack propagation (fatigue), originally formulated for elast

Michael Novitsky, Moran Barenboim, Vadim Indelman

Online planning under uncertainty remains a critical challenge in robotics and autonomous systems. While tree search techniques are commonly employed to construct partial future trajectories within computational constraints, most existing methods discard information from previous planning sessions considering continuous spaces. This study presents a novel, c

Yuval Peled, Niv Peleg

We study the maximum dimension $d=d(n,p)$ for which an Erd\H{o}s-R\'enyi $G(n,p)$ random graph is $d$-rigid. Our main results reveal two different regimes of rigidity in $G(n,p)$ separated at $p_c=C_*\log n/n,~C_*=2/(1-\log 2)$ -- the point where the graph's minimum degree exceeds half its average degree. We show that if $p < (1-\varepsilon)p_c $, then $d(n,

Xiao Zhou, Luoyi Sun, Dexuan He, Wenbin Guan

Vision-language foundation models have shown great promise in computational pathology but remain primarily data-driven, lacking explicit integration of medical knowledge. We introduce KEEP (KnowledgE-Enhanced Pathology), a foundation model that systematically incorporates disease knowledge into pretraining for cancer diagnosis. KEEP leverages a comprehensive

Sergey Masaev, Georgiy Dorrer, Vladimir Cyganov

A research shows the phase space of the system. The phase space of such a system is determined by the development structure of four subsystems with different objective functions. The control loop of such a system is formed. Using the control loop, optimal control is generated. The dynamic control region is calculated on the basis of a matrix determining the

M. Menichelli, S. Aziz, A. Bashiri, M. Bizzarri

Radiation damage tests in hydrogenated amorphous silicon (a-Si:H) flexible flux and dose-measuring devices have been performed with a 3 MeV proton beam, to evaluate combined displacement and total ionizing dose damage. The tested devices had two different configurations and thicknesses. The first device was a 2 um thick n-i-p diode having a 5 mm x 5 mm area.

Eliezer Batista, Felipe Lopes Castro, Mykola Khrypchenko

In this work, we introduce the notion of a partial action of a group on a strict monoidal category. We propose, in the context of Monoidal categories, new constructions analogous to those existing for partial group actions over an algebra such as the globalization, the subalgebra of partial invariants, and the partial smash product.

Hsin-Hsiung Huang, Feng Yu, Kang Li, Teng Zhang

We propose a novel Fr\'echet sufficient dimension reduction (SDR) method based on kernel distance covariance, tailored for metric space-valued responses such as count data, probability densities, and other complex structures. The method leverages a kernel-based transformation to map metric space-valued responses into a feature space, enabling efficient dimen

Yash Bhusare, Yogesh Maan, Ajay Kumar

The discovery of Persistent Radio Sources (PRSs) associated with three repeating fast radio bursts (FRBs) has provided insight into the local environments of these FRBs. Here, we present deep radio observations of the fields surrounding three highly active repeating FRBs namely, FRB 20220912A, FRB 20240114A, and FRB 20240619D using the upgraded Giant Metrewa

Saheed Popoola

Internship and industry-affiliated capstone projects are popular ways to expose students to real world experiences and bridge the gap between academic training and industry requirements. However, these two approaches often require active industry collaboration, and many students struggle to find industry placements. Open-source contributions are a crucial al

Shuqin Zhu, Shahram Ghandeharizadeh

We present flight patterns for a collision-free passage of swarms of drones through one or more openings. The narrow openings provide drones with access to an infrastructure component such as charging stations to charge their depleted batteries and hangars for storage. The flight patterns are a staging area (queues) that match the rate at which an infrastruc

Ali Feizmohammadi, Yi-Hsuan Lin

We prove an entanglement principle for fractional Laplace operators on $\mathbb R^n$ for $n\geq 2$ as follows; if different fractional powers of the Laplace operator acting on several distinct functions on $\mathbb R^n$, which vanish on some nonempty open set $O$, are known to be linearly dependent on $O$, then all the functions must be globally zero. This r

Safa Ben Atitallah, Maha Driss, Wadii Boulila, Anis Koubaa

With the rapid rise of the Internet of Things (IoT), ensuring the security of IoT devices has become essential. One of the primary challenges in this field is that new types of attacks often have significantly fewer samples than more common attacks, leading to unbalanced datasets. Existing research on detecting intrusions in these unbalanced labeled datasets

Mojgan Aghakhanloo, Nathan Smith, Jennifer E. Andrews, Alexei V. Filippenko

We present spectra of the supernova (SN) impostor AT 2016blu spanning over a decade. This transient exhibits quasiperiodic outbursts with a $\sim$113 d period, likely triggered by periastron encounters in an eccentric binary system where the primary star is a luminous blue variable (LBV). The overall spectrum remains fairly consistent during quiescence and e

Monnie McGee, Bivin Sadler

A college education historically has been seen as method of moving upward with regards to income brackets and social status. Indeed, many colleges recognize this connection and seek to enroll talented low income students. While these students might have their education, books, room, and board paid; there are other items that they might be expected to use tha

Sanchita Ghosh, Soumyoraj Mallick, Tanushree Roy

Monitoring of internal short circuit (ISC) in Lithium-ion battery packs is imperative to safe operations, optimal performance, and extension of pack life. Since ISC in one of the modules inside a battery pack can eventually lead to thermal runaway, it is crucial to detect its early onset. However, the inaccuracy and aging variability of battery models and th

Rafael Dewes, Rayna Dimitrova

Quantitative requirements play an important role in the context of multi-agent systems, where there is often a trade-off between the tasks of individual agents and the constraints that the agents must jointly adhere to. We study multi-agent systems whose requirements are formally specified in the quantitative temporal logic LTL[$\mathcal{F}$] as a combinatio

Adam Tropper

We study the celestial CFT dual to theories with bulk supersymmetry. The boundary theory realizes supersymmetry in the spirit of the Green-Schwarz superstring: there is manifest 4d super-Poincar\'e symmetry, but no 2d superconformal symmetry. Nevertheless, we can extend the celestial sphere itself to a supermanifold -- the celestial supersphere. This provide

Vadim J. Jabotinski, Alexander N. Vlasov, Simon J. Cooke

Theory and modeling of new discovered effects of the oscillating e-beam boundary formed in a magnetic focusing channel on the stability, gain, and power of VE amplifiers are presented. The RF structure and e-beam circuit parameters are computed for different beam envelope radii and oscillation amplitudes and then the self-excitation thresholds are obtained f

Takumi Goto, Justin Vasselli, Taro Watanabe

Various evaluation metrics have been proposed for Grammatical Error Correction (GEC), but many, particularly reference-free metrics, lack explainability. This lack of explainability hinders researchers from analyzing the strengths and weaknesses of GEC models and limits the ability to provide detailed feedback for users. To address this issue, we propose att

Vinicius M. Placco, David Herrera, Brian M. Merino, Paul Hirst

The Gemini High-resolution Optical SpecTrograph (GHOST) at Gemini South started regular queue operations in early 2024, bringing a long-sought open-access capability to the astronomy community. This research note briefly describes an effort to provide easy-to-access reduced spectra for GHOST programs from all Gemini partner countries and encourage prompt dat

Sam Olesker-Taylor, Thomas Sauerwald, John Sylvester

Random walks on expanders play a crucial role in Markov Chain Monte Carlo algorithms, derandomization, graph theory, and distributed computing. A desirable property is that they are rapidly mixing, which is equivalent to having a spectral gap $\gamma$ (asymptotically) bounded away from $0$. Our work has two main strands. First, we establish a dichotomy for t

Kenji Terao

We study isolated points on the modular curves $X_{H}$, for $H$ a subgroup of $\operatorname{GL}_{2}(\mathbb{Z}/n \mathbb{Z})$ for some $n \geq 1$. In particular, we prove a single-sink theorem for such isolated points, which traces the existence of all such isolated points with the same $j$-invariant back to an isolated point on a single curve. Building on

Daniele Nello, Alessandro Silva

We propose powering a quantum clock with the non-thermal resources offered by the stationary state of an integrable quantum spin chain, driven out of equilibrium by a quench in a parameter of our choice. Analyzing the bias conditions of the clock, we establish a direct connection with the negativity of the steady-state response function. Using experimentally

Ambedkar Dukkipati, Ranga Shaarad Ayyagari, Bodhisattwa Dasgupta, Parag Dutta

Learning agents that excel at sequential decision-making tasks must continuously resolve the problem of exploration and exploitation for optimal learning. However, such interactions with the environment online might be prohibitively expensive and may involve some constraints, such as a limited budget for agent-environment interactions and restricted explorat

Mustafa Yücel, Francisco S. Cuesta, Karim Achouri

Metasurfaces are efficient and versatile electromagnetic structures that have already enabled the implementation of a wide range of microwave and photonic wave shaping applications. Despite the extensive research into metasurfaces, a rigorous and comprehensive understanding of their angular dispersion remains vastly under-explored. Here, we use the generaliz

Hubie Chen, Markus Schneider

We study the problem of statically optimizing select-project-join-union (SPJU) plans where unary key constraints are allowed. A natural measure of a plan, which we call the output degree and which has been studied previously, is the minimum degree of a polynomial bounding the plan's output relation, as a function of the input database's maximum relation size

Tiannan Wang, Meiling Tao, Ruoyu Fang, Huilin Wang

In this work, we introduce the task of life-long personalization of large language models. While recent mainstream efforts in the LLM community mainly focus on scaling data and compute for improved capabilities of LLMs, we argue that it is also very important to enable LLM systems, or language agents, to continuously adapt to the diverse and ever-changing pr

Jianlyu Chen, Nan Wang, Chaofan Li, Bo Wang

Evaluation plays a crucial role in the advancement of information retrieval (IR) models. However, current benchmarks, which are based on predefined domains and human-labeled data, face limitations in addressing evaluation needs for emerging domains both cost-effectively and efficiently. To address this challenge, we propose the Automated Heterogeneous Inform

Jeonggyu Huh, Jaegi Jeon

We present a Pontryagin-Guided Direct Policy Optimization (PG-DPO) framework for Merton's portfolio problem, unifying modern neural-network-based policy parameterization with the adjoint viewpoint from Pontryagin's maximum principle (PMP). Instead of approximating the value function (as done in deep BSDE methods), we track a policy-fixed BSDE for the adjoint

M. A. Miller, J. W. Hughes, T. Eich, G. R. Tynan

The separatrix operational space (SepOS) model [Eich \& Manz, \emph{Nuclear Fusion} (2021)] is shown to predict the L-H transition, the L-mode density limit, and the ideal MHD ballooning limit in terms of separatrix parameters for a wide range of Alcator C-Mod plasmas. The model is tested using Thomson scattering measurements across a wide range of operating

Axel Durbet, Paul-Marie Grollemund, Pascal Lafourcade, Kevin Thiry-Atighehchi

Biometric systems are widely used for identity verification and identification, including authentication (i.e., one-to-one matching to verify a claimed identity) and identification (i.e., one-to-many matching to find a subject in a database). The matching process relies on measuring similarities or dissimilarities between a fresh biometric template and enrol

Roberto Mondini, Neema Kotonya, Robert L. Logan, Elizabeth M Olson

Online reporting platforms have enabled citizens around the world to collectively share their opinions and report in real time on events impacting their local communities. Systematically organizing (e.g., categorizing by attributes) and geotagging large amounts of crowdsourced information is crucial to ensuring that accurate and meaningful insights can be dr

Junda Wang, Minghui Hu, Ning Li, Abdulaziz Al-Ali

Online learning of deep neural networks suffers from challenges such as hysteretic non-incremental updating, increasing memory usage, past retrospective retraining, and catastrophic forgetting. To alleviate these drawbacks and achieve progressive immediate decision-making, we propose a novel Incremental Online Learning (IOL) process of Randomized Neural Netw

Shi-Mei Ma, Hong Bian, Jun-Ying Liu, Jean Yeh

Based on a determinantal formula for the higher derivative of a quotient of two functions, we first present the determinantal expressions of Eulerian polynomials and Andre polynomials. In particular, we discover that the Euler number (number of alternating permutations) can be expressed as a lower Hessenberg determinant. We then investigate the determinantal

Kevin McKee

Tasks in which rewards depend upon past information not available in the current observation set can only be solved by agents that are equipped with short-term memory. Usual choices for memory modules include trainable recurrent hidden layers, often with gated memory. Reservoir computing presents an alternative, in which a recurrent layer is not trained, but

Thekla Hamm, Fabian Klute, Irene Parada

Computing the crossing number of a graph is one of the most classical problems in computational geometry. Both it and numerous variations of the problem have been studied, and overcoming their frequent computational difficulty is an active area of research. Particularly recently, there has been increased effort to show and understand the parameterized tracta

Jon Saad-Falcon, Rajan Vivek, William Berrios, Nandita Shankar Naik

As language models become integral to critical workflows, assessing their behavior remains a fundamental challenge -- human evaluation is costly and noisy, while automated metrics provide only coarse, difficult-to-interpret signals. We introduce natural language unit tests, a paradigm that decomposes response quality into explicit, testable criteria, along w

K. El Bourakadi, G. Otalora

The Cosmic Dark Ages mark a pivotal era of the universe's evolution, transitioning from a neutral, opaque medium to the emergence of the first stars and galaxies that initiated cosmic reionization. This study examines the thermodynamics of the intergalactic medium (IGM), molecular hydrogen cooling, and gravitational collapse that led to structure formation.

Greique A. Valk, Sandro B. Rembold

We investigate the kinematical and dynamical properties of cluster galaxy populations classified according to their dominant source of gas ionization, namely: star-forming (SF) galaxies, optical active galactic nuclei (AGN), mixed SF plus AGN ionization (transition objects, T), and quiescent (Q) galaxies. We stack 8892 member galaxies from 336 relaxed galaxy

Tomoya Shiota, Kenji Ishihara, Tuan Minh Do, Toshio Mori

Machine learning interatomic potentials (MLIPs) are changing atomistic simulations in the field of chemistry and materials science. However, constructing a single universal MLIP that can accurately model molecular and crystalline systems remains challenging. A central obstacle is the integration of diverse datasets generated under different computational con

M. M. Ocampo, M. M. Miller Bertolami, L. G. Althaus, F. C Wachlin

During the various steps of stellar evolution are formed convectives zones that alter the chemical stratification in stars. Usually, in astrophysics is used the Mixing Length Theory (MLT) for modeling the convective movement and, in general, it is used with the Schwarzschild instability criterion, which neglects the impact of chemical composition gradients i

Xinxin Zhang, S. Hassan HosseinNia

Reset control enhances the performance of high-precision mechatronics systems. This paper introduces a generalized reset feedback control structure that integrates a single reset-state reset controller, a shaping filter for tuning reset actions, and linear compensators arranged in series and parallel configurations with the reset controller. This structure o

T. M. Dunster, A. Gil, D. Ruiz-Antolín, J. Segura

A numerical algorithm (implemented in Matlab) for computing the zeros of the parabolic cylinder function $U(a,z)$ in domains of the complex plane is presented. The algorithm uses accurate approximations to the first zero plus a highly efficient method based on a fourth-order fixed point method with the parabolic cylinder functions computed by Taylor series a

Jing Gao, Ulf-G. Meißner, Yue-Long Shen, Dong-Hao Li

We construct light-cone sum rules (LCSR) for the $B\to K^*$ form factors in the large recoil region using vacuum-to-$B$-meson correlation functions, and systematically calculate subleading-power corrections to these form factors at tree level, including next-to-leading power contributions from the hard-collinear propagator, the subleading effective current $

Sergey Masaev, Andrey Minkin, Dmitriy Edimichev

Analysis of the scientific literature showed that a lot of work is devoted to assessing the effectiveness of fire safety management in an enterprise. It is worth noting that today there is no universal method for the integrated assessment of fire safety management, taking into account the interconnectedness of all enterprise subsystems and the influence of e

Fabian Huch

Interactive theorem provers are complex systems that require sophisticated platform efforts - and hence systems programming environments - to manage effectively. The Isabelle platform exemplifies this with its Isabelle/Scala systems programming environment, which has proven to be very successful. In contrast, much of the project infrastructure has relied on

Boris Houska, Matthias A. Müller, Mario E. Villanueva

In this article, we survey the primary research on polyhedral computing methods for constrained linear control systems. Our focus is on the modeling power of convex optimization, featured to design set-based robust and optimal controllers. In detail, we review the state-of-the-art techniques for computing geometric structures such as robust control invariant

Rumeysa Bodur, Binod Bhattarai, Tae-Kyun Kim

Text-guided image editing finds applications in various creative and practical fields. While recent studies in image generation have advanced the field, they often struggle with the dual challenges of coherent image transformation and context preservation. In response, our work introduces prompt augmentation, a method amplifying a single input prompt into se

Sai Teja Erukude, Akhil Joshi, Lior Shamir

CNNs have become one of the most commonly used computational tool in the past two decades. One of the primary downsides of CNNs is that they work as a ``black box", where the user cannot necessarily know how the image data are analyzed, and therefore needs to rely on empirical evaluation to test the efficacy of a trained CNN. This can lead to hidden biases t

S. K. Jeena, Projjwal Banerjee

Among very metal-poor (VMP) stars, $\alpha$-poor VMP ($\alpha$PVMP) stars that have sub-solar values of ${\rm [X/Fe]}$ for Mg and other $\alpha$ elements are rare and are thought to have been formed from gas polluted by Type 1a supernova (SN 1a). However, recent analyses indicate that pure core-collapse supernova (CCSN) ejecta can also be a likely source. We

Jonathan Biteau

Cosmic rays begin to reveal their secrets at energies above 5 EeV. Beyond this characteristic energy, known as the spectral "ankle", the arrival-direction data from the Pierre Auger Observatory show anisotropy on large angular scales of increasing amplitude with energy. This discovery provides observational evidence that cosmic rays beyond the ankle originat

Zhiyuan Zhou, Heye Huang, Boqi Li, Shiyue Zhao

Recent advancements in autonomous vehicles (AVs) use Large Language Models (LLMs) to perform well in normal driving scenarios. However, ensuring safety in dynamic, high-risk environments and managing safety-critical long-tail events remain significant challenges. To address these issues, we propose SafeDrive, a knowledge- and data-driven risk-sensitive decis

Philippe Goulet Coulombe, Maximilian Goebel, Karin Klieber

Machine learning predictions are typically interpreted as the sum of contributions of predictors. Yet, each out-of-sample prediction can also be expressed as a linear combination of in-sample values of the predicted variable, with weights corresponding to pairwise proximity scores between current and past economic events. While this dual route leads nowhere

Lorenzo Veronese, Andrea Moglia, Luca Mainardi, Pietro Cerveri

AI-based neural decoding reconstructs visual perception by leveraging generative models to map brain activity, measured through functional MRI (fMRI), into latent hierarchical representations. Traditionally, ridge linear models transform fMRI into a latent space, which is then decoded using latent diffusion models (LDM) via a pre-trained variational autoenco

Lenny Jones

A series of recent articles has shown that there exist only three monogenic cyclic quartic trinomials in ${\mathbb Z}[x]$, and they are all of the form $x^4+bx^2+d$. In this article, we conduct an analogous investigation for cubic trinomials in ${\mathbb Z}[x]$. Two irreducible cyclic cubic trinomials are said to be equivalent if their splitting fields are e

Anthony Zhou, Amir Barati Farimani

Neural surrogates for partial differential equations (PDEs) have become popular due to their potential to quickly simulate physics. With a few exceptions, neural surrogates generally treat the forward evolution of time-dependent PDEs as a black box by directly predicting the next state. While this is a natural and easy framework for applying neural surrogate

Dimitrios G. Konstantinides, Charalampos D. Passalidis

We study a multidimensional renewal risk model, with common counting process and cadlag returns. Considering that the claim vectors have common distribution from some multivariate distribution class with heavy tail, are mutually weakly dependent, and each one has arbitrarily dependent components, we obtain uniformly asymptotic estimations for the probability

Tomasz Adamowicz, María J. González, Marcin Gryszówka

We study the class of functions on Lipschitz-graph domains satisfying a differential-oscillation condition and show that such functions are $\epsilon$-approximable. As a consequence we obtain the quantitative Fatou theorem in the spirit of works e.g. by Garnett and Bortz-Hofmann. Such a class contains harmonic functions, as well as non-harmonic ones, for exa

Mohammad Mahdi Abootorabi, Ehsaneddin Asgari

This study introduces CLASP (Contrastive Language-Speech Pretraining), a multilingual, multimodal representation tailored for audio-text information retrieval. CLASP leverages the synergy between spoken content and textual data. During training, we utilize our newly introduced speech-text dataset, which encompasses 15 diverse categories ranging from fiction

Stanislas Ducotterd, Sebastian Neumayer, Michael Unser

We aim at the solution of inverse problems in imaging, by combining a penalized sparse representation of image patches with an unconstrained smooth one. This allows for a straightforward interpretation of the reconstruction. We formulate the optimization as a bilevel problem. The inner problem deploys classical algorithms while the outer problem optimizes th

Marion Bassi, Esteban-Alonso Rodrıguez-Mena, Boris Brun, Simon Zihlmann

Hole spins in silicon or germanium quantum dots have emerged as a compelling solid-state platform for scalable quantum processors. Besides relying on well-established manufacturing technologies, hole-spin qubits feature fast, electric-field-mediated control stemming from their intrinsically large spin-orbit coupling [1, 2]. This key feature is accompanied by

Ivan Gudoshnikov

We investigate the existence and non-existence of a function-valued strain solution in various models of elastoplasticity from the perspective of the constraint-based ``dual'' formulations. We describe abstract frameworks for linear elasticity, elasticity-perfect plasticity and elasticity-hardening plasticity in terms of adjoint linear operators and convert

Chengkai Zhu, Shuyu He, Yu-Ao Chen, Lei Zhang

Identifying unknown Hamiltonians from their quantum dynamics is a pivotal challenge in quantum technologies. In this paper, we introduce Hamiltonian recognition, a framework that bridges quantum hypothesis testing and quantum metrology, aiming to identify the Hamiltonian governing quantum dynamics from a known set of Hamiltonians. To identify $H$ for an unkn

Sauli Lindberg

Let $r,s \in [2,\infty]$ and consider the Navier-Stokes equations on $\mathbb{R}^3$. We study the following two questions for suitable $s$-homogeneous Banach spaces $X \subset \mathcal{S}'$: does every $u_0 \in L^2_\sigma$ have a weak solution that belongs to $L^r(0,\infty;X)$, and are the $L^r(0,\infty;X)$ norms of the solutions bounded uniformly in viscosi

Vipula Rawte, Deja N Scott, Gaurav Kumar, Aishneet Juneja

Accurate information is crucial for democracy as it empowers voters to make informed decisions about their representatives and keeping them accountable. In the US, state election commissions (SECs), often required by law, are the primary providers of Frequently Asked Questions (FAQs) to voters, and secondary sources like non-profits such as League of Women V

Ethan O. Nadler, Rui An, Daneng Yang, Hai-Bo Yu

We present eight cosmological dark matter (DM)--only zoom-in simulations of a Milky Way--like system that include suppression of the linear matter power spectrum $P(k)$, and/or velocity-dependent DM self-interactions, as the third installment of the COZMIC suite. We consider a model featuring a massive dark photon that mediates DM self-interactions and decay

Si-Yue Yu, Dewang Xu, Boris S. Kalita, Sijia Li

We investigate the deconvolved color profiles of 223 disk galaxies at redshifts of $z=1$-3 observed by the James Webb Space Telescope (JWST) as part of the Cosmic Evolution Early Release Science survey (CEERS). The filters were selected to approximate the rest-frame $B-Y$ color, which is used to identify U-shaped color profiles -- those becoming progressivel

Naveenkumar G Venkataswamy, Yu Liu, Surendra Singh, Soumyabrata Dey

Iris recognition is widely acknowledged for its exceptional accuracy in biometric authentication, traditionally relying on near-infrared (NIR) imaging. Recently, visible spectrum (VIS) imaging via accessible smartphone cameras has been explored for biometric capture. However, a thorough study of iris recognition using smartphone-captured 'High-Quality' VIS i

J. Devin

One of the main purposes in $\gamma$-ray astronomy is linked to the origin of Galactic cosmic rays. Unlike cosmic rays, $\gamma$ rays can be used to probe their production sites in the Galaxy and to find which type of astrophysical sources is able to accelerated particles up to PeV energies. Twenty years of observations with current Imaging Atmospheric Chere

Anni Tang, Tianyu He, Junliang Guo, Xinle Cheng

Encoding video content into compact latent tokens has become a fundamental step in video generation and understanding, driven by the need to address the inherent redundancy in pixel-level representations. Consequently, there is a growing demand for high-performance, open-source video tokenizers as video-centric research gains prominence. We introduce VidTok,

Devika Khurana, Sascha Desmettre, Evelyn Buckwar

The first-passage time (FPT) is a fundamental concept in stochastic processes, representing the time it takes for a process to reach a specified threshold for the first time. Often, considering a time-dependent threshold is essential for accurately modeling stochastic processes, as it provides a more accurate and adaptable framework. In this paper, we extend

Haoshen Wang, Zhentao Liu, Kaicong Sun, Xiaodong Wang

The generation of medical images presents significant challenges due to their high-resolution and three-dimensional nature. Existing methods often yield suboptimal performance in generating high-quality 3D medical images, and there is currently no universal generative framework for medical imaging. In this paper, we introduce a 3D Medical Latent Diffusion (3

Jianing He, Qi Zhang, Hongyun Zhang, Xuanjing Huang

Early exiting is an effective paradigm for improving the inference efficiency of pre-trained language models (PLMs) by dynamically adjusting the number of executed layers for each sample. However, in most existing works, easy and hard samples are treated equally by each classifier during training, which neglects the test-time early exiting behavior, leading

Brégier Romain, Baradel Fabien, Lucas Thomas, Galaaoui Salma

Multi-person human mesh recovery (HMR) consists in detecting all individuals in a given input image, and predicting the body shape, pose, and 3D location for each detected person. The dominant approaches to this task rely on neural networks trained to output a single prediction for each detected individual. In contrast, we propose CondiMen, a method that out

Ilan Doron-Arad

We study neural network training (NNT): optimizing a neural network's parameters to minimize the training loss over a given dataset. NNT has been studied extensively under theoretic lenses, mainly on two-layer networks with linear or ReLU activation functions where the parameters can take any real value (here referred to as continuous NNT (C-NNT)). However,