May 2024 arXiv papers — page 93

Saadia Gabriel, Isha Puri, Xuhai Xu, Matteo Malgaroli

Large language models (LLMs) are already being piloted for clinical use in hospital systems like NYU Langone, Dana-Farber and the NHS. A proposed deployment use case is psychotherapy, where a LLM-powered chatbot can treat a patient undergoing a mental health crisis. Deployment of LLMs for mental health response could hypothetically broaden access to psychoth

Michel Brion

Given a finite group scheme $G$ over a field and a $G$-variety $X$, we obtain a criterion for $X$ to be $G$-normal in the sense of \cite{Br24}. When $G$ is diagonalizable, we describe the local structure of $G$-normal varieties in codimension $1$ and their dualizing sheaf. As an application, we obtain a version of the Hurwitz formula for $G$-normal varieties

Hendrik Roch, Syed Afrid Jahan, Chun Shen

In nuclear and particle physics, reconciling sophisticated simulations with experimental data is vital for understanding complex systems like the Quark Gluon Plasma (QGP) generated in heavy-ion collisions. However, computational demands pose challenges, motivating using Gaussian Process emulators for efficient parameter extraction via Bayesian calibration. W

Neena Aloysius, Geetha M, Prema Nedungadi

Conventional Deep Learning frameworks for continuous sign language recognition (CSLR) are comprised of a single or multi-modal feature extractor, a sequence-learning module, and a decoder for outputting the glosses. The sequence learning module is a crucial part wherein transformers have demonstrated their efficacy in the sequence-to-sequence tasks. Analyzin

Niklas Müller, Soufiane el Kabil, Gerrit Vosse, Lina Hansen

Recent advancements in electron microscopy have introduced innovative techniques enabling the inelastic interaction of fast electrons with tightly confined and intense light fields. These techniques, commonly summarized under the term photon-induced nearfield electron microscopy now offer unprecedented capabilities for a precise mapping of the characteristic

Roy Maor Lotan, Inbal Talgam-Cohen, Yaniv Romano

Auctions are key for maximizing sellers' revenue and ensuring truthful bidding among buyers. Recently, an approach known as differentiable economics based on machine learning (ML) has shown promise in learning powerful auction mechanisms for multiple items and participants. However, this approach has no guarantee of strategy-proofness at test time. Strategy-

Kai-Jia Sun, Dai-Neng Liu, Yun-Peng Zheng, Jin-Hui Chen

Understanding the properties of hypernuclei is crucial for constraining the nature of hyperon-nucleon ($Y\text{-}N$) interactions, which plays a key role in determining the inner structure of compact stars. The lightest hypernuclei and antihypernuclei are the hypertriton ($^3_\Lambda\text{H}$), which consists of a pair of nucleons and a $\Lambda$ hyperon, an

R. Álvarez-Garrote, C. Brizzolari, A. Canto, E. Calvo

The Photon Detection System (PDS) of the first DUNE far detector (FD1) is composed of 6000 photon detection units, named X-ARAPUCA. The detection of the prompt light pulse generated by the particle energy release in liquid argon (LAr) will complement and boost the DUNE Liquid Argon Time Projection Chamber (LArTPC). It will improve the non-beam events tagging

Péter L. Erdős, István Miklós, Lajos Soukup

The notion of $P$-stability of an infinite set of degree sequences plays influential role in approximating the permanents, rapidly sampling the realizations of graphic degree sequences, or even studying and improving network privacy. While there exist several known sufficient conditions for $P$-stability, we don't know any useful necessary condition for it.

Ya Deng, Botong Wang

If $X$ is a closed $2n$-dimensional aspherical manifold, i.e., the universal cover of $X$ is contractible, then the Chern-Hopf-Thurston conjecture predicts that $(-1)^n\chi(X)\geq 0$. We prove this conjecture when $X$ is a complex projective manifold whose fundamental group admits an almost faithful linear representation over any field. In fact, we prove a m

Krishna Kaipa, Puspendu Pradhan

The problem studied in this work is to determine the higher weight spectra of the Projective Reed-Muller codes associated to the Veronese $3$-fold $\mathcal V$ in $PG(9,q)$, which is the image of the quadratic Veronese embedding of $PG(3,q)$ in $PG(9,q)$. We reduce the problem to the following combinatorial problem in finite geometry: For each subset $S$ of

Raffaele Sommese, Gautam Akiwate, Antonia Affinito, Moritz Müller

Malicious actors exploit the DNS namespace to launch spam campaigns, phishing attacks, malware, and other harmful activities. Combating these threats requires visibility into domain existence, ownership and nameservice activity that the DNS protocol does not itself provide. To facilitate visibility and security-related study of the expanding gTLD namespace,

Luca Giovenzana, Alan Thompson

Tyurin degenerations of K3 surfaces are degenerations whose central fibre consists of a pair of rational surfaces glued along a smooth elliptic curve. We study the lattice theory of such Tyurin degenerations, establishing a notion of lattice polarisation that is compatible with existing definitions for the general fibre and the rational surfaces comprising t

Coenraad Mouton

Understanding generalization in deep neural networks is an active area of research. A promising avenue of exploration has been that of margin measurements: the shortest distance to the decision boundary for a given sample or that sample's representation internal to the network. Margin-based complexity measures have been shown to be correlated with the genera

M. C. Vergara, D. R. G. Schleicher, A. Escala, B. Reinoso

This paper explores the theoretical relation between star clusters and black holes within, focusing on the potential role of nuclear star clusters (NSCs), globular clusters (GCs), and ultra compact dwarf galaxies (UCDs) as environments that allow for black hole formation via stellar collisions. This study aims to identify the optimal conditions for stellar c

Anthony Mallama, Richard E. Cole, Jay Respler, Scott Harrington

Observations of Starlink V2 Mini satellites during orbit-raising suggest that SpaceX applies brightness mitigation when they reach a height of 357 km. The mean apparent magnitudes for objects below that height threshold is 2.68 while the mean for those above is 6.46. When magnitudes are adjusted to a uniform distance of 1000 km the means are 4.58 and 7.52, r

Rukun Qiao, Hiroshi Kawasaki, Hongbin Zha

We introduce a novel depth estimation technique for multi-frame structured light setups using neural implicit representations of 3D space. Our approach employs a neural signed distance field (SDF), trained through self-supervised differentiable rendering. Unlike passive vision, where joint estimation of radiance and geometry fields is necessary, we capitaliz

Zohar Komargodski, Siwei Zhong

We study junctions between confining strings. We show that the effective theory of such junctions is very predictive with only one new parameter, the junction's mass, controlling the first couple of terms in the expansion in the system size. By open-closed duality these considerations about the baryon junction map to interaction vertices of closed strings. T

Glennys R. Farrar

We propose that ultrahigh energy cosmic rays are produced in binary neutron star mergers. This scenario can account for the heretofore inexplicable narrow rigidity range of UHECRs, because the jets of BNS mergers are generated by a gravitationally-driven dynamo and thus are nearly identical due to the narrow range of BNS masses. Observed UHECRs with energies

Weilian Zhou, Sei-Ichiro Kamata, Haipeng Wang, Man-Sing Wong

Hyperspectral image (HSI) classification is pivotal in the remote sensing (RS) field, particularly with the advancement of deep learning techniques. Sequential models, adapted from the natural language processing (NLP) field such as Recurrent Neural Networks (RNNs) and Transformers, have been tailored to this task, offering a unique viewpoint. However, sever

Yao Ouyang, Yonghui Sun, Hua-Peng Zhang

The characterizations when two natural upper bounds of the set of copulas with a given diagonal section are copulas have been well studied in the literature. Given a curvilinear section, however, there is only a partial result concerning the characterization when a natural upper bound of the set of copulas is a copula. In this paper, we completely solve the

TianFang Zhang, Mamoru Doi, Mitsuru Kokubo, Shigeyuki Sako

We studied the optical variability of 241 BL Lacs and 83 flat-spectrum radio quasars (FSRQ) from the 4LAC catalog using data from the Tomo-e Gozen Northern Sky Transient Survey, with $\sim$ 50 epochs per blazar on average. We excluded blazars whose optical variability may be underestimated due to the influence of their host galaxy, based on their optical lum

Hai Zhang, Boyuan Zheng, Tianying Ji, Jinhang Liu

Offline meta reinforcement learning (OMRL) has emerged as a promising approach for interaction avoidance and strong generalization performance by leveraging pre-collected data and meta-learning techniques. Previous context-based approaches predominantly rely on the intuition that alternating optimization between the context encoder and the policy can lead to

Yuchen He, Jinghua Wang, Jingsong He, Ye Li

Rogue waves (RWs) can form on the ocean surface due to quasi-four wave resonant interaction or superposition principle. Both mechanisms have been acutely studied. The first of the two is known as the nonlinear focusing mechanism and leads to an increased probability of rogue waves when wave conditions are favourable, i.e., when unidirectionality and high nar

Nicola Bastianello, Luca Schenato, Ruggero Carli

Multi-agent systems are increasingly widespread in a range of application domains, with optimization and learning underpinning many of the tasks that arise in this context. Different approaches have been proposed to enable the cooperative solution of these optimization and learning problems, including first- and second-order methods, and dual (or Lagrangian)

Vittorio Nespeca, Tina Comes, Frances Brazier

Collective intelligence of diverse groups is key for tackling many of today's grand challenges such as fostering resilience and climate adaptation. Information exchange across such diverse groups is crucial for collective intelligence, especially in volatile environments. To facilitate inter-group information exchange, Informational Boundary Spanners (IBSs)

Iveta Bečková, Štefan Pócoš, Giulia Belgiovine, Marco Matarese

The addressee estimation (understanding to whom somebody is talking) is a fundamental task for human activity recognition in multi-party conversation scenarios. Specifically, in the field of human-robot interaction, it becomes even more crucial to enable social robots to participate in such interactive contexts. However, it is usually implemented as a binary

Gavril Farkas, Rahul Pandharipande, Alessio Sammartano

We construct irrational irreducible components of the Hilbert scheme of points of affine n-dimensional space, for n at least 12. We start with irrational components of the Hilbert scheme of curves in P^3 and use methods developed by Jelisiejew to relate these to irreducible components of the Hilbert schemes of points of A^n. The result solves Problem XX of [

Lino Reggiani, Eleonora Alfinito

The internal total-energy of the black-body is a physical quantity of paramount importance in the development of modern physics. Accordingly, together with a brief historical development, we report and comment last breaking news (2018-2024) concerning the definition and properties of this quantity. The first comment concerns with the inclusion of the Casimir

Jiawei Xu, Bruno Clerckx

Rate-Splitting Multiple Access (RSMA) has emerged as a potent and reliable multiple access and interference management technique in wireless communications. While downlink Multiple-Input Multiple-Ouput (MIMO) RSMA has been widely investigated, uplink MIMO RSMA has not been fully explored. In this paper, we investigate the performance of uplink RSMA in short-

Marius Bozga, Joseph Sifakis

Developing safe autonomous driving systems is a major scientific and technical challenge. Existing AI-based end-to-end solutions do not offer the necessary safety guarantees, while traditional systems engineering approaches are defeated by the complexity of the problem. We study a method for building compositionally safe autonomous driving systems, based on

Paweł Raźny, Nikolay Sheshko

In this article we study covering spaces of symplectic toric orbifolds and symplectic toric orbifold bundles. In particular, we show that all symplectic toric orbifold coverings are quotients of some symplectic toric orbifold by a finite subgroup of a torus. We then give a general description of the labeled polytope of a toric orbifold bundle in terms of the

Xinyang Li, Jiaxin Wang, Yixin Xuan, Gongxin Yao

We propose GGAvatar, a novel 3D avatar representation designed to robustly model dynamic head avatars with complex identities and deformations. GGAvatar employs a coarse-to-fine structure, featuring two core modules: Neutral Gaussian Initialization Module and Geometry Morph Adjuster. Neutral Gaussian Initialization Module pairs Gaussian primitives with defor

Margherita Grespan, Hareesh Thuruthipilly, Agnieszka Pollo, Michelle Lochner

We apply a state-of-the-art transformer algorithm to 221 deg$^2$ of the Kilo Degree Survey (KiDS) to search for new strong gravitational lenses (SGL). We test four transformer encoders trained on simulated data from the Strong Lens Finding Challenge on KiDS survey data. The best performing model is fine-tuned on real images of SGL candidates identified in pr

Tony J. Puthenpurakal

Let $A$ be a Noetherian ring of dimension $d$ and let $\mathcal{D}^b(A)$ be the bounded derived category of $A$. Let $\mathcal{D}_i^b(A)$ denote the thick subcategory of $\mathcal{D}^b(A)$ consisting of complexes $\mathbf{X}_\bullet$ with $\dim H^n(\mathbf{X}_\bullet) \leq i$ for all $n$. Set $\mathcal{D}_{-1}^b(A) = 0$. Consider the Verdier quotients $\math

Mirko Pittaluga, Yuen San Lo, Adam Brzosko, Robert I. Woodward

Quantum communications harness quantum phenomena like superposition and entanglement to enhance information transfer between remote nodes. Coherent quantum communications, essential for phase-based quantum internet architecture, require optical coherence among nodes and typically involve single-photon interference. Challenges like preserving optical coherenc

Jiajia Shen, Yibin Fu, Alberto Pirrera, Rainer M. J. Groh

The study of growth-induced surface wrinkling in constrained bilayers comprising a thin film attached to a thick substrate is a canonical model for understanding pattern formation in many biological systems. While the bilayer model has received much prior attention, the nonlinear behaviour for arrangements with similar film and substrate properties, or subst

Ilaria Battiston, Lotte Felius, Sam Ansmink, Laurens Kuiper

We provide an evaluation of an analytical workload in a confidential computing environment, combining DuckDB with two technologies: modular columnar encryption in Parquet files (data at rest) and the newest version of the Intel SGX Trusted Execution Environment (TEE), providing a hardware enclave where data in flight can be (more) securely decrypted and proc

Ugo Dal Lago, Davide Davoli, Bruce M. Kapron

Separation logic is a substructural logic which has proved to have numerous and fruitful applications to the verification of programs working on dynamic data structures. Recently, Barthe, Hsu and Liao have proposed a new way of giving semantics to separation logic formulas in which separating conjunction is interpreted in terms of probabilistic independence.

Ana Nikolikj, Ana Kostovska, Gjorgjina Cenikj, Carola Doerr

This study examines the generalization ability of algorithm performance prediction models across various benchmark suites. Comparing the statistical similarity between the problem collections with the accuracy of performance prediction models that are based on exploratory landscape analysis features, we observe that there is a positive correlation between th

William Kuszmaul, Alek Westover

This paper introduces the \emph{serial-parallel decision problem}. Consider an online scheduler that receives a series of tasks, where each task has both a parallel and a serial implementation. The parallel implementation has the advantage that it can make progress concurrently on multiple processors, but the disadvantage that it is (potentially) work-ineffi

Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu

Text-Centric Visual Question Answering (TEC-VQA) in its proper format not only facilitates human-machine interaction in text-centric visual environments but also serves as a de facto gold proxy to evaluate AI models in the domain of text-centric scene understanding. Nonetheless, most existing TEC-VQA benchmarks have focused on high-resource languages like En

Ahmet Kazan, Mustafa Altın

In this paper, for a soliton surface $\Omega=\Omega(u,v)$ associated with the Betchov-Da Rios equation, we obtain the derivative formulas of an extended Darboux frame field of a unit speed curve $u$-parameter curve $\Omega=\Omega(u,v)$ for all $v$. Also, we get the geometric invariants $k$ and $h$ of the soliton surface $\Omega=\Omega(u,v)$ and we obtain the

Charles M. Elliott, Thomas Sales

We study two fully discrete evolving surface finite element schemes for the Cahn-Hilliard equation on an evolving surface, given a smooth potential with polynomial growth. In particular we establish optimal order error bounds for a (fully implicit) backward Euler time-discretisation, and an implicit-explicit time-discretisation, with isoparametric surface fi

Silvia García-Méndez, Francisco de Arriba-Pérez, María del Carmen Somoza-López

Transformer architectures contribute to managing long-term dependencies for Natural Language Processing, representing one of the most recent changes in the field. These architectures are the basis of the innovative, cutting-edge Large Language Models (LLMs) that have produced a huge buzz in several fields and industrial sectors, among the ones education stan

Qianmei Liu, Yufei Kuang, Jie Wang

Deep reinforcement learning (DRL) algorithms can suffer from modeling errors between the simulation and the real world. Many studies use adversarial learning to generate perturbation during training process to model the discrepancy and improve the robustness of DRL. However, most of these approaches use a fixed parameter to control the intensity of the adver

Thorsten Heidersdorf, Jonas Nehme, Catharina Stroppel

We develop the theory of projective endofunctors for modules of Khovanov algebras $K$ of type B. In particular we compute the composition factors and the graded layers of the image of a simple module under such a projective functor. We then study variants of such functors for a subquotient $e\tilde{K}e$. Via a comparison of two graded lifts of the Brauer alg

Sergey S. Avdeev, Aleksandr S. Baburin, Evgeniy V. Sergeev, Alexei B. Kramarenko

Photonic integrated circuits (PICs) play a crucial role in almost every aspect of modern life, such as data storage, telecommunications, medical diagnostics, green energy, autonomous driving, agriculture, and high-performance computing. To fully harness their benefits, an efficient coupling mechanism is required to successfully launch light into waveguides f

J. Voss, I. A. Ado, M. Titov

We propose that a high-quality flat interface or boundary can serve as a long-range skew scatterer for charged quasiparticles in a metal. When an electric current flows parallel to the interface, the balance between clockwise and counterclockwise reflections is disrupted, leading to a net orbital magnetization. This magnetization is maximized at the interfac

Yaxin Liu, Yan Zhou, Ziming Li, Jinchuan Zhang

As an important multimodal sentiment analysis task, Joint Multimodal Aspect-Sentiment Analysis (JMASA), aiming to jointly extract aspect terms and their associated sentiment polarities from the given text-image pairs, has gained increasing concerns. Existing works encounter two limitations: (1) multi-level modality noise, i.e., instance- and feature-level no

Antonio Parziale, Moises Diaz, Miguel A. Ferrer, Angelo Marcelli

Building upon findings in computational model of handwriting learning and execution, we introduce the concept of stability to explain the difference between the actual movements performed during multiple execution of the subject's signature, and conjecture that the most stable parts of the signature should play a paramount role in evaluating the similarity b

Alexandre Cafaro, Amaury Leroy, Guillaume Beldjoudi, Pauline Maury

We introduce a novel unsupervised approach to reconstructing a 3D volume from only two planar projections that exploits a previous\-ly-captured 3D volume of the patient. Such volume is readily available in many important medical procedures and previous methods already used such a volume. Earlier methods that work by deforming this volume to match the project

Zhichao Sun, Yuliang Gu, Yepeng Liu, Zerui Zhang

Anomaly detection in chest X-rays is a critical task. Most methods mainly model the distribution of normal images, and then regard significant deviation from normal distribution as anomaly. Recently, CLIP-based methods, pre-trained on a large number of medical images, have shown impressive performance on zero/few-shot downstream tasks. In this paper, we aim

Chuanghong Weng, Ehsan Nekouei

This paper presents a stochastic sampling framework for privacy-aware data sharing, where a sensor observes a process correlated with private information. A sampler determines whether to retain or discard sensor observations, balancing the tradeoff between data utility and privacy. Retained samples are shared with an adversary who may attempt to infer the pr

Anshul Prajapati, Punit Sharma

In this paper, we compute the structured eigenvalue backward error of a Rosenbrock system matrix $S(z)=\left[\begin{array}{cc} A-zI & B \\ C & P(z) \end{array}\right]$ for a given scalar $\lambda\in \mathbb C$. We have developed simplified formulas for the structured eigenvalue backward error of the Rosenbrock system matrix, considering both full and partial

Ansis Rosmanis

In this note, I generalize the techniques of my recent work (arXiv:2309.14944) and show that, even if just a single known qubit of query registers is affected by the depolarizing noise of rate p, quantum search among n elements cannot be done any faster than in O(np) queries. This holds both when the affected qubit is one of the log(n) index qubits and when

Antonio Amariti, Fabio Mantegazza

In this paper we study 3d $\mathcal{N}=2$ $SU(N)$ confining gauge theories with a matter field in the rank-two index symmetric representation. The models found here are obtained from the application of the duplication formula for hyperbolic gamma functions from \emph{parent} confining models, with antisymmetric fields and (anti)-fundamental matter by \emph{f

Zheng Li, Lijia Si, Caili Guo, Yang Yang

Text-based Person Retrieval (TPR) aims to retrieve person images that match the description given a text query. The performance improvement of the TPR model relies on high-quality data for supervised training. However, it is difficult to construct a large-scale, high-quality TPR dataset due to expensive annotation and privacy protection. Recently, Large Lang

Fan Liu, Rui Wang, Jie Yang, Wei-Zhong Zhao

We construct the generalized $\beta$ and $(q,t)$-deformed partition functions through $W$ representations, where the expansions are respectively with respect to the generalized Jack and Macdonald polynomials labeled by $N$-tuple of Young diagrams. We find that there are the profound interrelations between our deformed partition functions and the $4d$ and $5d

Jae-Hwan Choi, Beom-Seok Han, Daehan Park

This article investigates the existence, uniqueness, and regularity of solutions to nonlinear stochastic reaction-diffusion-advection equations (SRDAEs) with spatially homogeneous colored noises and infinitesimal generators of subordinate Brownian motions in mixed norm $L_q(L_p)$-spaces. We introduce a new condition (strongly reinforced Dalang's condition) o

S. Akansha

Graph Neural Networks (GNNs) have emerged as potent tools for predicting outcomes in graph-structured data. Despite their efficacy, a significant drawback of GNNs lies in their limited ability to provide robust uncertainty estimates, posing challenges to their reliability in contexts where errors carry significant consequences. Moreover, GNNs typically excel

Tatiana V. Afanasieva, Pavel V. Platov, Anastasia I. Medvedeva

One of the new trends in the development of recommendation algorithms is the dissemination of their capabilities to support the population in managing their health. This article focuses on the problem of improving the effectiveness of cardiovascular diseases (CVD) prevention, since CVD is the leading cause of death worldwide. To address this issue, a knowled

Ziyin Zhang, Zhaokun Jiang, Lizhen Xu, Hongkun Hao

We present GSM-MC, a multiple-choice (MC) dataset constructed by collecting answers and incorrect predictions on GSM8K from 60 open-source models. Through extensive experiments, we show that LLMs' performance on the MC version of this popular benchmark is strongly correlated with their performance on the original version and is quite robust to distractor cho

Abbi Abdel-Rehim, Hector Zenil, Oghenejokpeme Orhobor, Marie Fisher

Large language models LLMs have transformed AI and achieved breakthrough performance on a wide range of tasks In science the most interesting application of LLMs is for hypothesis formation A feature of LLMs which results from their probabilistic structure is that the output text is not necessarily a valid inference from the training text These are termed ha

Michael Dorner, Andreas Bauer, Florian Angermeir

Software is at the core of most scientific discoveries today. Therefore, the quality of research results highly depends on the quality of the research software. Rigorous testing, as we know it from software engineering in the industry, could ensure the quality of the research software but it also requires a substantial effort that is often not rewarded in ac

Ana Nikolikj, Ana Kostovska, Diederick Vermetten, Carola Doerr

This study explores the influence of modules on the performance of modular optimization frameworks for continuous single-objective black-box optimization. There is an extensive variety of modules to choose from when designing algorithm variants, however, there is a rather limited understanding of how each module individually influences the algorithm performa

Cesar Diaz-Londono, Stavros Orfanoudakis, Pedro P. Vergara, Peter Palensky

Integrating electric vehicles (EVs) into the power grid can revolutionize energy management strategies, offering both challenges and opportunities for creating a more sustainable and resilient grid. In this context, model predictive control (MPC) emerges as a powerful tool for addressing the complexities of Grid-to-vehicle (G2V) and vehicle-to-grid (V2G) ena

Zvonimir Bujanović, Luka Grubišić, Daniel Kressner, Hei Yin Lam

Various iterative eigenvalue solvers have been developed to compute parts of the spectrum for a large sparse matrix, including the power method, Krylov subspace methods, contour integral methods, and preconditioned solvers such as the so called LOBPCG method. All of these solvers rely on random matrices to determine, e.g., starting vectors that have, with hi

Paolo Gidoni, Marco Morandotti, Marta Zoppello

Controllability results of four models of two-link microscale swimmers that are able to change the length of their links are obtained. The problems are formulated in the framework of Geometric Control Theory, within which the notions of fiber, total, and gait controllability are presented, together with sufficient conditions for the latter two. The dynamics

Fernando Mateo, Joan Vila-Francés, Emilio Soria-Olivas, Marcelino Martínez-Sober Juan Gómez-Sanchis

Predictive maintenance in manufacturing industry applications is a challenging research field. Packaging machines are widely used in a large number of logistic companies' warehouses and must be working uninterruptedly. Traditionally, preventive maintenance strategies have been carried out to improve the performance of these machines. However, this kind of po

Vikash Kumar, A. Swaminathan

In this work, the explicit expressions of coefficients involved in quasi Christoffel polynomials of order one and quasi-Geronimus polynomials of order one are determined for Jacobi polynomials. These coefficients are responsible for establishing the orthogonality of quasi-spectral polynomials of Jacobi polynomials. Additionally, the orthogonality of quasi-Ch

Eduard Barbu, Marharyta Domnich, Raul Vicente, Nikos Sakkas

This study presents insights gathered from surveys and discussions with specialists in three domains, aiming to find essential elements for a universal explanation framework that could be applied to these and other similar use cases. The insights are incorporated into a software tool that utilizes GP algorithms, known for their interpretability. The applicat

Aliasghar Sarizadeh

This paper refined and introduced some notations (namely attractors, physical attractors, proper attractors, topologically exact and topologically mixing) within the context of relations. We establish necessary and sufficient conditions, including that the phase space of a topologically exact system is an attractor for its inverse, and vice versa, and that a

Kai Cheng, Ting Wang, Xiao Du, Shuyi Du

Explicit Congestion Notification (ECN)-based congestion control schemes have been widely adopted in high-speed data center networks (DCNs), where the ECN marking threshold plays a determinant role in guaranteeing a packet lossless DCN. However, existing approaches either employ static settings with immutable thresholds that cannot be dynamically self-adjuste

J. Nathan Kutz, Maryam Reza, Farbod Faraji, Aaron Knoll

Reduced order models are becoming increasingly important for rendering complex and multiscale spatio-temporal dynamics computationally tractable. The computational efficiency of such surrogate models is especially important for design, exhaustive exploration and physical understanding. Plasma simulations, in particular those applied to the study of ${\bf E}\

Fabrizio Iacone, Luca Rossini, Andrea Viselli

We consider forecast comparison in the presence of instability when this affects only a short period of time. We demonstrate that global tests do not perform well in this case, as they were not designed to capture very short-lived instabilities, and their power vanishes altogether when the magnitude of the shock is very large. We then discuss and propose app

Pujian Mao, Baijun Zeng

In this note, we transform the linear order (at order $G$) metric from a system of pointlike bodies source in the post-Minkowskian expansion to the Bondi coordinates. We show that the Bondi 4-momentum and angular momentum coincide with the relativistic definitions of 4-momentum and angular momentum for the system of pointlike bodies. The angular momentum com

Cailean Osborne, Jennifer Ding, Hannah Rose Kirk

Open model developers have emerged as key actors in the political economy of artificial intelligence (AI), but we still have a limited understanding of collaborative practices in the open AI ecosystem. This paper responds to this gap with a three-part quantitative analysis of development activity on the Hugging Face (HF) Hub, a popular platform for building,

Ricardo La Rosa, Corey Hulse, Bangdi Liu

While there have been extensive studies in code generation by large language models (LLM), where benchmarks like HumanEval have been surpassed with an impressive 96.3% success rate, these benchmarks predominantly judge a model's performance on basic function-level code generation and lack the critical thinking and concept of scope required of real-world scen

Eran Rosenbluth, Jan Tönshoff, Martin Ritzert, Berke Kisin

Graph Transformers (GTs) such as SAN and GPS are graph processing models that combine Message-Passing GNNs (MPGNNs) with global Self-Attention. They were shown to be universal function approximators, with two reservations: 1. The initial node features must be augmented with certain positional encodings. 2. The approximation is non-uniform: Graphs of differen

Tabea M. G. Pakull, Hendrik Damm, Ahmad Idrissi-Yaghir, Henning Schäfer

This paper details the efforts of the WisPerMed team in the BioLaySumm2024 Shared Task on automatic lay summarization in the biomedical domain, aimed at making scientific publications accessible to non-specialists. Large language models (LLMs), specifically the BioMistral and Llama3 models, were fine-tuned and employed to create lay summaries from complex sc

James Freeburn, Jeff Cooke, Anais Möller, Dougal Dobie

The relativistic outflows that produce Long GRBs (LGRBs) can be described by a structured jet model where prompt $\gamma$-ray emission is restricted to a narrow region in the jet's core. Viewing the jet off-axis from the core, a population of afterglows without an associated GRB detection can be predicted. In this work, we conduct an archival search for thes

J. L. Ampuero, A. Anadón, H. Damas, J. Ghanbaja

In this study, we investigate the spin-charge current conversion characteristics of chemically disordered ferromagnetic single FePt thin films by spin-pumping ferromagnetic resonance experiments performed on both a resonance cavity and on patterned devices. We clearly observe a self-induced signal in a single FePt layer. The sign of a single FePt spin pumpin

Yagub Aliyev

In the paper the maximum and the minimum of the ratio of the difference of the arithmetic mean and the geometric mean, and the difference of the power mean and the geometric mean of $n$ variables, are studied. A new optimization argument was used which reduces $n$ variable optimization problem to a single variable. All possible cases of the choice of the pow

Stefan Mitrovic, Tanja Stojadinovic

We introduce two classes of graphs - suns and dumbbells, both with few variations and explore their chromatic symmetric function and its $e$-positivity. We also give many connections of these two classes with other classes of connected graphs.

S. D. Korolkov, V. V. Izmodenov

The interaction between stellar winds and the partially ionized local interstellar medium (LISM) is quite common in astrophysics. However, the main difficulty in describing the neutral components lies in the fact that the mean free path of an interstellar atom, l, can be comparable to the characteristic size of an astrosphere, L (i.e., the Knudsen number, wh

Divya Setia, Shushma Rani, Tanusree Khandai

In this paper, we consider the tensor product of local Weyl modules for $\mathfrak{sl}_{n+1}[t]$ whose highest weights are multiples of the first and $n^{th}$ fundamental weights. We determine the graded character of these tensor product modules in terms of the graded character of local Weyl modules and prove that these modules admit a filtration whose succe

Alessio Cela, Ajith Urundolil Kumaran, Xiaohan Yan

We compute the rational Chow ring of the moduli stack of planar nodal curves of fixed degree and express it in terms of tautological classes. Along the way, we extend Vial's results on Chow groups of Brauer-Severi varieties to $G$-equivariant settings.

Yunus Güral, Mehmet Gürcan

Investigation of the reliability of technical systems is one of the application areas of stochastic processes. The reliability of a technical system is based on two main elements. The first is the connection type of the system, and the second is the distribution of the working times of the components consisting of the system. In this study, system signatures

Dawid Wiśniewski, Zofia Rostek, Artur Nowakowski

People use language for various purposes. Apart from sharing information, individuals may use it to express emotions or to show respect for another person. In this paper, we focus on the formality level of machine-generated translations and present FAME-MT -- a dataset consisting of 11.2 million translations between 15 European source languages and 8 Europea

Fons Hartendorp, Tom Seinen, Erik van Mulligen, Suzan Verberne

Biomedical entity linking, a main component in automatic information extraction from health-related texts, plays a pivotal role in connecting textual entities (such as diseases, drugs and body parts mentioned by patients) to their corresponding concepts in a structured biomedical knowledge base. The task remains challenging despite recent developments in nat

Hengwei Luan, You Wu, Jingyi Kang, Liufei Huang

Spinodal decomposition in multicomponent alloys has attracted increasing attention due to its beneficial effect on their mechanical and functional properties and potential applications. Both based on the Cahn-Hillard equation, the reference element method (REM) and the projection matrix method (PMM) are the two main methods to predict the occurrence of spino

Yao Cheng, Xuesong Wang, Martin Stynes

A singularly perturbed reaction-diffusion problem posed on the unit square in $\mathbb{R}^2$ is solved numerically by a local discontinuous Galerkin (LDG) finite element method. Typical solutions of this class of 2D problems exhibit boundary layers along the sides of the domain; these layers generally cause difficulties for numerical methods. Our LDG method

Alessandro Cucinotta

We show that the heat flow provides good approximation properties for the area functional on proper $\RCD(K,\infty)$ spaces, implying that in this setting the area formula for functions of bounded variation holds and that the area functional coincides with its relaxation. We then obtain partial regularity and uniqueness results for functions whose hypographs

Kamil Guttmann, Mikołaj Pokrywka, Adrian Charkiewicz, Artur Nowakowski

This paper explores Minimum Bayes Risk (MBR) decoding for self-improvement in machine translation (MT), particularly for domain adaptation and low-resource languages. We implement the self-improvement process by fine-tuning the model on its MBR-decoded forward translations. By employing COMET as the MBR utility metric, we aim to achieve the reranking of tran

Wenjia Xu, Yaxuan Yao, Jiaqi Cao, Zhiwei Wei

The application of unmanned aerial vehicles (UAV) has been widely extended recently. It is crucial to ensure accurate latitude and longitude coordinates for UAVs, especially when the global navigation satellite systems (GNSS) are disrupted and unreliable. Existing visual localization methods achieve autonomous visual localization without error accumulation b

Yuanyan Su, Teng Li, Wei Hong, Zhi Ning Chen

In this paper, a compact wideband dual-polarized Luneburg lens antenna (LLA) with reduced anisotropy and improved impedance matching is proposed in Ka band with a wide 2D beamscanning capability. Based on transformation optics, the spherical Luneburg lens is compressed into a cylindrical one, while the merits of high gain, broad band, wide scanning, and free

Anubhab Sahoo, Tejendra Dixit, K. V. Anil Kumar, K. Lakshmi Ganapathi

Herein, $\mathrm{MoS_{2}}$ quantum dot (QDs) with controlled optical, structural, and electronic properties are synthesized using the femtosecond pulsed laser ablation in liquid (fs-PLAL) technique by varying pulse-width, ablation power, and ablation time to harness the potential for next-generation optoelectronics and quantum technology. Furthermore, this w

Jinhyung Park

The purpose of this note is twofold. First, we give a quick proof of Ballico-Chiantini's theorem stating that a Fano or Calabi-Yau variety of dimension at least 4 in codimension two is a complete intersection. Second, we improve Barth-Van de Ven's result asserting that if the degree of a smooth projective variety of dimension $n$ is less than approximately $

Zhendong Yu, Haiping Huang

Generative diffusion models apply the concept of Langevin dynamics in physics to machine leaning, attracting a lot of interests from engineering, statistics and physics, but a complete picture about inherent mechanisms is still lacking. In this paper, we provide a transparent physics analysis of diffusion models, formulating the fluctuation theorem, entropy