May 2024 arXiv papers — page 104

Grzegorz Greczynski

The correct referencing of the binding energy (BE) scale is essential for the accuracy of chemical analysis by x-ray photoelectron spectroscopy. The C 1s C C/C-H peak from adventitious carbon (AdC), most commonly used for that purpose, was previously shown to shift by several eVs following changes in the sample work function, thus indicating that AdC aligns

Bharath Balaji, S. Raghavan

This study, conducted in 2017, explores the use of Machine learning algorithms to predict Characteristics of Transmission Lines such as Impedance or resonance frequency using design parameters of Transmission Lines. Using formulas and equations that define the characteristics of Transmission lines, training data was generated. We trained different models for

D. Subhalingam, Keshav Kolluru, Mausam, Saurabh Singal

In the e-commerce domain, the accurate extraction of attribute-value pairs (e.g., Brand: Apple) from product titles and user search queries is crucial for enhancing search and recommendation systems. A major challenge with neural models for this task is the lack of high-quality training data, as the annotations for attribute-value pairs in the available data

Shuxin Guo, Qiang Liu

It is a challenge to estimate fund performance by compounded returns. Arguably, it is incorrect to use yearly returns directly for compounding, with reported annualized return of above 60% for Medallion for the 31 years up to 2018. We propose an estimation based on fund sizes and trading profits and obtain a compounded return of 31.8% before fees. Alternativ

Marco Spanghero, Panos Papadimitratos

Global Navigation Satellite Systems (GNSS) provide global positioning and timing. Multiple receivers with known reference positions (stations) can assist mobile receivers (rovers) in obtaining GNSS corrections and achieve centimeter-level accuracy on consumer devices. However, GNSS spoofing and jamming, nowadays achievable with off-the-shelf devices, are ser

Thomas Y. Hou

We numerically investigate the nearly self-similar blowup of the generalized axisymmetric Navier--Stokes equations. First, we rigorously derive the axisymmetric Navier--Stokes equations with swirl in both odd and even dimensions, marking the first such derivation for dimensions greater than three. Building on this, we generalize the equations to arbitrary po

Xin Li

This paper proposes a unified framework in which consciousness emerges as a cycle-consistent, affectively anchored inference process, recursively structured by the interaction of emotion and cognition. Drawing from information theory, optimal transport, and the Bayesian brain hypothesis, we formalize emotion as a low-dimensional structural prior and cognitio

Ross Dempsey, Bendeguz Offertaler, Silviu S. Pufu, Yifan Wang

We study properties of point-like impurities preserving flavor symmetry and supersymmetry in four-dimensional ${\cal N} = 2$ field theories. At large distances, such impurities are described by half-BPS superconformal line defects. By working in the $\text{AdS}_2\times \text{S}^2$ conformal frame, we develop a novel and simpler way of deriving the superconfo

Dan Braun, Jordan Taylor, Nicholas Goldowsky-Dill, Lee Sharkey

Identifying the features learned by neural networks is a core challenge in mechanistic interpretability. Sparse autoencoders (SAEs), which learn a sparse, overcomplete dictionary that reconstructs a network's internal activations, have been used to identify these features. However, SAEs may learn more about the structure of the datatset than the computationa

Jay N. Paranjape, Shameema Sikder, S. Swaroop Vedula, Vishal M. Patel

In recent years, various large foundation models have been proposed for image segmentation. There models are often trained on large amounts of data corresponding to general computer vision tasks. Hence, these models do not perform well on medical data. There have been some attempts in the literature to perform parameter-efficient finetuning of such foundatio

Bernd Finkbeiner, Hadar Frenkel, Niklas Metzger, Julian Siber

We present an automata-based algorithm to synthesize omega-regular causes for omega-regular effects on executions of a reactive system, such as counterexamples uncovered by a model checker. Our theory is a generalization of temporal causality, which has recently been proposed as a framework for drawing causal relationships between trace properties on a given

Riccardo Ravasio, Kabir Husain, Constantine G. Evans, Rob Phillips

Life uses non-equilibrium mechanisms to create ordered structures not attainable at equilibrium; the resulting order is assumed to provide functional benefits that outweigh costs of time and energy needed by these mechanisms. Here, we show that models of DNA replication and self-assembly, when expanded to include known stalling effects, can evolve error corr

Mehdi Nemati, Sima Soltani Renani

Let ${\Bbb G}$ be a locally compact quantum group and ${\mathcal T}(L^2({\Bbb G}))$ be the Banach algebra of trace class operators on $L^2({\Bbb G})$ with the convolution induced by the right fundamental unitary of ${\Bbb G}$. We study the space of harmonic operators $\widetilde{\mathcal H}_\omega$ in ${\mathcal B}(L^2({\Bbb G}))$ associated to a contractive

C. Ferreyra, M. Badillo, M. J. Sánchez, M. Acuautla

Memristors are considered key building blocks for the development of neuromorphic computing hardware. For ferroelectric memristors with a capacitor-like structure, the polarization direction modulates the height of the Schottky barriers -- present at ferroelectric/metal interfaces -- that control the device resistance. Here, we unveil the coexistence of mult

Kalina V. Nedkova, Marc Rafelski, Harry I. Teplitz, Vihang Mehta

We use the Ultraviolet Imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey fields (UVCANDELS) to measure half-light radii in the rest-frame far-UV for $\sim$16,000 disk-like galaxies over $0.5\leq z \leq 3$. We compare these results to rest-frame optical sizes that we measure in a self-consistent way and find that the stellar mass-s

Aaron M. Graham, Stylianos D. Asimonis

This paper presents the design and analysis of a superdirective rectenna optimized for enhanced RF-to-DC efficiency and realised gain, featuring direct impedance matching between the antenna and the rectifier. Employing passively loaded, low profile strip dipoles on a low-loss substrate, the rectenna achieves a realised gain of 6.9 dBi and an RF-to-DC effici

Praveen Kumar, Priyadarshni, Rajiv Misra

Unmanned Aerial Vehicles (UAVs) have gained popularity in data harvesting (DH) and coverage path planning (CPP) to survey a given area efficiently and collect data from aerial perspectives, while data harvesting aims to gather information from various Internet of Things (IoT) sensor devices, coverage path planning guarantees that every location within the de

Marco Spanghero, Panos Papadimitratos

Global Navigation Satellite Systems (GNSS) provide precise location, while Real Time Kinematics (RTK) allow mobile receivers (termed rovers), leveraging fixed stations, to correct errors in their Position Navigation and Timing (PNT) solution. This allows compensating for multi-path effects, ionospheric errors, and observation biases, enabling consumer receiv

Martin Gorbahn, Ulserik Moldanazarova, Kai Henryk Sieja, Emmanuel Stamou

Using the measured and projected invisible mass spectrum of the $K^+\to\pi^+\nu\bar\nu$ mode, we determine the current and future constraints within the model-independent framework of the weak effective theory at dimension-six. We work in two different operator bases depending whether neutrinos are Majorana or Dirac fermions. This makes it possible to transp

Richmond Y. Wong, Jason Caleb Valdez, Ashten Alexander, Ariel Chiang

We use a design workbook of speculative scenarios as a values elicitation activity with 14 participants. The workbook depicts use case scenarios with smart home camera technologies that involve surveillance and uneven power relations. The scenarios were initially designed by the researchers to explore scenarios of privacy and surveillance within three social

Jakub Jędrak, Gonzalo Angulo

When encountering complex fluorescence decays that deviate from exponentiality, a very appealing and powerful approach is to use lifetime (or equivalent rate constant) distributions. These are related by Laplace transform to multi-exponential functions, stretched exponentials, Becquerel's law, and others. In the case of bimolecular quenching, time-independen

Yuhang Lin, Heike Hofmann

We propose a reproducible pipeline for extracting representative signals from 2D topographic scans of the tips of cut wires. The process fully addresses many potential problems in the quality of wire cuts, including edge effects, extreme values, trends, missing values, angles, and warping. The resulting signals can be further used in source determination, wh

Moritz Mock, Thomas Forrer, Barbara Russo

Developers use different means to document the security concerns of their code. Because of all of these opportunities, they may forget where the information is stored, or others may not be aware of it, and leave it unmaintained for so long that it becomes obsolete, if not useless. In this work, we analyzed different sources of code documentation from four la

Dodd Gray, Gavin N. West, Rajeev J. Ram

Inverse design of optical components based on adjoint sensitivity analysis has the potential to address the most challenging photonic engineering problems. However existing inverse design tools based on finite-difference-time-domain (FDTD) models are poorly suited for optimizing waveguide modes for adiabatic transformation or perturbative coupling, which lie

Jura Rensberg, Angela Barreda, Kevin Wolf, Andreas Undisz

We investigate hyper-doping, a promising approach to introduce a high concentration of impurities into silicon beyond its solid solubility limit, for its potential applications in near-infrared plasmonics. We systematically explore the incorporation of dopants into silicon using ion implantation and pulsed laser melting annealing processes. Reflectance spect

Pontus Laurell, Allen Scheie, Elbio Dagotto, D. Alan Tennant

The detection and certification of entanglement and quantum correlations in materials is of fundamental and far-reaching importance, and has seen significant recent progress. It impacts both our understanding of the basic science of quantum many-body phenomena as well as the identification of systems suitable for novel technologies. Frameworks suitable to co

András Némethi, Willem Veys

We fix a complex analytic normal singularity germ $(X,o)$ of dimension $\geq 2$ and a (not necessarily irreducible) reduced Weil divisor $(S,o)\subset (X,o)$. The embedded resolution of the pair determines a multi-index filtration of the local ring $\mathcal{O}_{X,o}$, which measures the embedded geometry of the pair. Furthermore, from the (induced) resoluti

Theodore Broeren, Kristopher Klein

When studying turbulence, it is often desirable to be able to estimate the local spatial gradient of a vector quantity using in situ measurements from a small number of irregularly spaced points. While previous studies have focused on the accuracy of these methods as the number of measurement points varies, we focus on the accuracy of gradient estimations as

Sören Laue, Tomislav Prusina

Unconstrained optimization problems are typically solved using iterative methods, which often depend on line search techniques to determine optimal step lengths in each iteration. This paper introduces a novel line search approach. Traditional line search methods, aimed at determining optimal step lengths, often discard valuable data from the search process

Boldizsár Poór, Razin A. Shaikh, Quanlong Wang

The ZX-calculus is a graphical language for reasoning about quantum computing and quantum information theory. As a complete graphical language, it incorporates a set of axioms rich enough to derive any equation of the underlying formalism. While completeness of the ZX-calculus has been established for qubits and the Clifford fragment of prime-dimensional qud

Zheyu Lin, Ning Jiang, Tinggui Wang, Xu Kong

The unluckiest star orbits a supermassive black hole elliptically. Every time it reaches the pericenter, it shallowly enters the tidal radius and gets partially tidal disrupted, producing a series of flares. Confirmation of a repeated partial tidal disruption event (pTDE) requires not only evidence to rule out other types of transients, but also proof that o

Aliaume Lopez

We are interested in characterizing which classes of finite graphs are well-quasi-ordered by the induced subgraph relation. To that end, we devise an algorithm to decide whether a class of finite graphs well-quasi-ordered by the induced subgraph relation when the vertices are labelled using a finite set. In this process, we answer positively to a conjecture

Dimitrios P. Panagoulias, Persephone Papatheodosiou, Anastasios P. Palamidas, Mattheos Sanoudos

Large Language Models (LLMs) constitute a breakthrough state-of-the-art Artificial Intelligence (AI) technology which is rapidly evolving and promises to aid in medical diagnosis either by assisting doctors or by simulating a doctor's workflow in more advanced and complex implementations. In this technical paper, we outline Cognitive Network Evaluation Toolk

Marcos M. Vasconcelos, Yifei Zhang

Many modern distributed systems consist of devices that generate more data than what can be transmitted via a communication link in near real time with high-fidelity. We consider the scheduling problem in which a device has access to multiple data sources, but at any moment, only one of them is revealed in real-time to a remote receiver. Even when the source

James Caddy, Christoph Treude

Communities on GitHub often use issue labels as a way of triaging issues by assigning them priority ratings based on how urgently they should be addressed. The labels used are determined by the repository contributors and not standardised by GitHub. This makes it difficult for priority-related reasoning across repositories for both researchers and contributo

Mingxiang Fu, Yu Song, Jiameng Lv, Liang Cao

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe. However, effectively analyzing this vast amount of data poses a significant challenge. Astronomers are turning to deep learning techniques to address this, but the methods are limited by their specific training sets, leading to

I-Hsuan Kao, Junyu Tang, Gabriel Calderon Ortiz, Menglin Zhu

Electrical readout of magnetic states is a key to realize novel spintronics devices for efficient computing and data storage. Unidirectional magnetoresistance (UMR) in bilayer systems, consisting of a spin source material and a magnetic layer, refers to a change in the longitudinal resistance upon the reversal of magnetization, which typically originates fro

Winston Heap, Anurag Sahay

We prove a sharp upper bound for the fourth moment of the Hurwitz zeta function $\zeta(s,\alpha)$ on the critical line when the shift parameter $\alpha$ is irrational and of irrationality exponent strictly less than 3. As a consequence, we determine the order of magnitude of the $2k$th moment for all $0 \leqslant k \leqslant 2$ in this case. In contrast to t

Anuj Dawar, Ioannis Eleftheriadis

We revisit the work studying homomorphism preservation for first-order logic in sparse classes of structures initiated in [Atserias et al., JACM 2006] and [Dawar, JCSS 2010]. These established that first-order logic has the homomorphism preservation property in any sparse class that is monotone and addable. It turns out that the assumption of addability is n

Elena Yu. Egorova, Alena S. Kazmina, Ilya A. Simakov, Ilya N. Moskalenko

Building a scalable universal high-performance quantum processor is a formidable challenge. In particular, the problem of realizing fast high-perfomance two-qubit gates of high-fidelity remains needful. Here we propose a building block for a scalable quantum processor consisting of two transmons and a tunable three-mode coupler allowing for a ZZ interaction

Fei Wang, Jun Cheng

Most existing methods often rely on complex models to predict scene depth with high accuracy, resulting in slow inference that is not conducive to deployment. To better balance precision and speed, we first designed SmallDepth based on sparsity. Second, to enhance the feature representation ability of SmallDepth during training under the condition of equal c

José-Ignacio Antón, Juan Ponce, Rafael Muñoz de Bustillo

This study addresses the impact of illicit drug use on the labour market outcomes of men in Mexico. We leverage statistical information from three waves of a comparable national survey and make use of Lewbel's heteroskedasticity-based instrumental variable strategy to deal with the endogeneity of drug consumption. Our results suggests that drug consumption h

Hongyi Yang, Fangyuan Chang, Dian Zhu, Muroi Fumie

This systematic review assessed the current state and future prospects of artificial intelligence (AI) in schizophrenia rehabilitation management. We reviewed 61 studies on AI-related data types, feature engineering methods, algorithmic models, and evaluation metrics published from 2012-2024. The review categorizes AI applications into the following key appl

Leon Carus

The analysis of $b\to s\ell^+\ell^-$ flavor-changing neutral current decays is a powerful test of the Standard Model (SM). Due to the strong suppression of these modes in the SM, potential New Physics contributions can have a significant impact on the measured physics observables. The angular analysis of $B^{0} \to K^{*0} \mu^{+} \mu^{-}$ decays gives access

M. Gatti, G. Campailla, N. Jeffrey, L. Whiteway

We present a simulation-based cosmological analysis using a combination of Gaussian and non-Gaussian statistics of the weak lensing mass (convergence) maps from the first three years (Y3) of the Dark Energy Survey (DES). We implement: 1) second and third moments; 2) wavelet phase harmonics; 3) the scattering transform. Our analysis is fully based on simulati

Sanjeev Pratap Singh, Naveed Afzal

The rising complexity of cyber threats calls for a comprehensive reassessment of current security frameworks in business environments. This research focuses on Stealth Data Exfiltration, a significant cyber threat characterized by covert infiltration, extended undetectability, and unauthorized dissemination of confidential data. Our findings reveal that conv

Shiqi Huang, Tingfa Xu, Ziyi Shen, Shaheer Ullah Saeed

The goal of image registration is to establish spatial correspondence between two or more images, traditionally through dense displacement fields (DDFs) or parametric transformations (e.g., rigid, affine, and splines). Rethinking the existing paradigms of achieving alignment via spatial transformations, we uncover an alternative but more intuitive correspond

Gabriel Soares Rocha, Lorenzo Gavassino, Nicki Mullins

Using the information current, we develop a Lorentz-covariant framework for modeling equilibrium fluctuations in relativistic kinetic theory in the grand-canonical ensemble. The resulting stochastic theory is proven to be causal and covariantly stable, and its predictions do not depend on the choice of spacetime foliation used to define the grand-canonical p

Ziyou Guo, Yan Sun, Tieru Wu

Time series (TS) forecasting has been an unprecedentedly popular problem in recent years, with ubiquitous applications in both scientific and business fields. Various approaches have been introduced to time series analysis, including both statistical approaches and deep neural networks. Although neural network approaches have illustrated stronger ability of

Jin Guang, Yaosheng Xu, J. G. Dai

This paper examines a continuous-time routing system with general interarrival and service time distributions, operating under the join-the-shortest-queue and power-of-two-choices policies. Under a weaker set of assumptions than those commonly found in the literature, we prove that the scaled steady-state queue length at each station converges weakly to an i

Charis Stamouli, Lars Lindemann, George J. Pappas

In this paper, we focus on the problem of shrinking-horizon Model Predictive Control (MPC) in uncertain dynamic environments. We consider controlling a deterministic autonomous system that interacts with uncontrollable stochastic agents during its mission. Employing tools from conformal prediction, existing works derive high-confidence prediction regions for

Jun Hu, Xiaoming Lang, Feng Zhang, Yinian Mao

While Global Navigation Satellite System (GNSS) is often used to provide global positioning if available, its intermittency and/or inaccuracy calls for fusion with other sensors. In this paper, we develop a novel GNSS-Visual-Inertial Navigation System (GVINS) that fuses visual, inertial, and raw GNSS measurements within the square-root inverse sliding window

Vladimir Yu. Fedorov, Jean-Philippe Colombier

We use ab-initio electromagnetic simulations to investigate light absorption by rough surfaces in the context of the formation of laser-induced periodic surface structures. Our approach involves modeling a realistic rough surface using a statistical description of its continuous height distribution via a corresponding correlation function. We study the influ

Joseph M. Colosimo, Derek B. Fox, Abraham D. Falcone, David M. Palmer

We report the results of an extensive set of simulations exploring the sensitivity of the BlackCAT CubeSat to long-duration gamma-ray bursts (GRBs). BlackCAT is a NASA APRA-funded CubeSat mission for the detection and real-time sub-arcminute localization of high-redshift ($z\gtrsim 3.5$) GRBs. Thanks to their luminous and long-lived afterglow emissions, GRBs

Spyridon Bakas, Siddhesh P. Thakur, Shahriar Faghani, Mana Moassefi

Glioblastoma is the most common primary adult brain tumor, with a grim prognosis - median survival of 12-18 months following treatment, and 4 months otherwise. Glioblastoma is widely infiltrative in the cerebral hemispheres and well-defined by heterogeneous molecular and micro-environmental histopathologic profiles, which pose a major obstacle in treatment.

Yixing Huang, Zahra Khodabakhshi, Ahmed Gomaa, Manuel Schmidt

Objectives: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data. Materials and methods: A total of s

Adrien Sauvaget

Volumes of moduli spaces of hyperbolic cone surfaces were previously defined and computed when the angles of the cone singularities are at most 2pi. We propose a general definition of these volumes without restriction on the angles. This construction is based on flat geometry as our proposed volume is a limit of Masur-Veech volumes of moduli spaces of multi-

P. Sarveswarasarma, T. Sathulakjan, V. J. V. Godfrey, Thanuja D. Ambegoda

This paper presents a novel approach to the digital signing of electronic documents through the use of a camera-based interaction system, single-finger tracking for sign recognition, and multi commands executing hand gestures. The proposed solution, referred to as "Air Signature," involves writing the signature in front of the camera, rather than relying on

Georges Habib, Ken Richardson, Robert Wolak

A Riemannian metric on a closed manifold is said to be geometrically formal if the wedge product of any two harmonic forms is harmonic; equivalently, the interior product of any two harmonic forms is harmonic. Given a Riemannian foliation on a closed manifold, we say that a bundle-like metric is transversely geometrically formal if the interior product of an

M. Burak Dogruel, Edward Taylor, Michelle Cluver, Matthew Colless

Empirical correlations connecting starlight to galaxy dynamics (e.g., the fundamental plane (FP) of elliptical/quiescent galaxies and the Tully--Fisher relation of spiral/star-forming galaxies) provide cosmology-independent distance estimation and are central to local Universe cosmology. In this work, we introduce the mass hyperplane (MH), which is the stell

Vyacheslav Mozolenko, Marina Fadeeva, Lev Shchur

The development of new algorithms for simulations in physics is as important as the development of new analytical methods. In this paper, we present a comparison of the recently developed microcanonical population annealing (MCPA) algorithm with the rather mature Wang-Landau algorithm. The comparison is performed on two cases of the Potts model that exhibit

Michail Tarasiou, Stylianos Moschoglou, Jiankang Deng, Stefanos Zafeiriou

Recent advancements in text-to-image generation using diffusion models have significantly improved the quality of generated images and expanded the ability to depict a wide range of objects. However, ensuring that these models adhere closely to the text prompts remains a considerable challenge. This issue is particularly pronounced when trying to generate ph

M. Billò, M. Frau, F. Galvagno, A. Lerda

We revisit the analysis of the integrated 2-point functions of local operators with a $\frac{1}{2}$-BPS Wilson line in $\mathcal{N}=4$ SYM. After including suitable parity-odd terms in the parametrization of the defect correlators, we are able to solve the superconformal Ward identities in terms of an unconstrained function of the cross-ratios. Exploiting th

Rickard Stureborg, Sanxing Chen, Ruoyu Xie, Aayushi Patel

One way to personalize chatbot interactions is by establishing common ground with the intended reader. A domain where establishing mutual understanding could be particularly impactful is vaccine concerns and misinformation. Vaccine interventions are forms of messaging which aim to answer concerns expressed about vaccination. Tailoring responses in this domai

Zhaopei Huang, Jinming Zhao, Qin Jin

Understanding the process of emotion generation is crucial for analyzing the causes behind emotions. Causal Emotion Entailment (CEE), an emotion-understanding task, aims to identify the causal utterances in a conversation that stimulate the emotions expressed in a target utterance. However, current works in CEE mainly focus on modeling semantic and emotional

Stan Meijer, Alberto Bertipaglia, Barys Shyrokau

This paper presents a novel approach to automated drifting with a standard passenger vehicle, which involves a Nonlinear Model Predictive Control to stabilise and maintain the vehicle at high sideslip angle conditions. The proposed controller architecture is split into three components. The first part consists of the offline computed equilibrium maps, which

Iolo Jones

We introduce diffusion geometry as a new framework for geometric and topological data analysis. Diffusion geometry uses the Bakry-Emery $\Gamma$-calculus of Markov diffusion operators to define objects from Riemannian geometry on a wide range of probability spaces. We construct statistical estimators for these objects from a sample of data, and so introduce

Raja Abhishek Appana, Faissal El Idrissi, Prashanth Ramesh, Marcello Canova

Understanding battery degradation in electric vehicles (EVs) under real-world conditions remains a critical yet under-explored area of research. Central to this investigation is the challenge of estimating the specific degradation modes in aged cells with no available information on usage history, bypassing the conventional yet invasive method of tear-down t

Changping Wang, Peng Wang

Tang-Zhang, Choe-Hoppe, showed independently that one can produce minimal submanifolds in spheres via Clifford type minimal product of minimal submanifolds. In this note, we show that the minimal product is immersed by its first eigenfunctions (of its Laplacian) if and only if the two beginning minimal submanifolds are immersed by their first eigenfunctions.

Luca Rettenberger, Markus Reischl, Mark Schutera

The assessment of bias within Large Language Models (LLMs) has emerged as a critical concern in the contemporary discourse surrounding Artificial Intelligence (AI) in the context of their potential impact on societal dynamics. Recognizing and considering political bias within LLM applications is especially important when closing in on the tipping point towar

Rajat Kumar Goyal, M. Chandrasekhar

The significant advancements in perovskite solar cell (PSC) research and development have reignited optimism for the practicality of solar energy. A deeper comprehension of the fundamental mechanisms is essential for enhancing the current solar technology. Impedance spectroscopy (IS) characterization, in conjunction with other analytical methods, stands out

Bojan Mohar

For a graph $G$, and a nonnegative integer $g$, let $a_g(G)$ be the number of $2$-cell embeddings of $G$ in an orientable surface of genus $g$ (counted up to the combinatorial homeomorphism equivalence). In 1989, Gross, Robbins, and Tucker [Genus distributions for bouquets of circles, J. Combin. Theory Ser. B 47 (1989), 292-306] proposed a conjecture that th

Lorenzo Sani, Alex Iacob, Zeyu Cao, Bill Marino

Generative pre-trained large language models (LLMs) have demonstrated impressive performance over a wide range of tasks, thanks to the unprecedented amount of data they have been trained on. As established scaling laws indicate, LLMs' future performance improvement depends on the amount of computing and data sources they can leverage for pre-training. Federa

Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier

The Shapley value (SV) is a prevalent approach of allocating credit to machine learning (ML) entities to understand black box ML models. Enriching such interpretations with higher-order interactions is inevitable for complex systems, where the Shapley Interaction Index (SII) is a direct axiomatic extension of the SV. While it is well-known that the SV yields

Hong Yuan, Minda Ma

The transportation sector is the third-largest global energy consumer and emitter, making it a focal point in the transition toward the net-zero future. To accelerate the decarbonization of passenger cars, this work is the first to propose a bottom-up charging demand model to estimate the operational electricity use and associated carbon emissions of best-se

Rodrigo C. V. Coelho, Hanqing Zhao, Guilherme N. C. Amaral, Ivan I. Smalyukh

Topology establishes a unifying framework for a diverse range of scientific areas including particle physics, cosmology, and condensed matter physics. One of the most fascinating manifestations of topology in the context of condensed matter is the topological Hall effect, and its relative: the Skyrmion Hall effect. Skyrmions are stable vortex-like spin confi

Moritz Mock, Jorge Melegati, Barbara Russo

Test Driven Development (TDD) is one of the major practices of Extreme Programming for which incremental testing and refactoring trigger the code development. TDD has limited adoption in the industry, as it requires more code to be developed and experienced developers. Generative AI (GenAI) may reduce the extra effort imposed by TDD. In this work, we introdu

David A. Jordan

We determine the $\sigma$-derivations of quantum tori and quantum affine spaces for a toric automorphism $\sigma$. By standard results, every toric automorphism $\sigma$ of a quantum affine space $\mathcal{A}$ and every $\sigma$-derivation of $\mathcal{A}$ extend uniquely to the corresponding quantum torus $\mathcal{T}$. We shall see that, for a toric automo

Alberto Bertipaglia, Davide Tavernini, Umberto Montanaro, Mohsen Alirezaei

This paper presents an original approach to vehicle obstacle avoidance. It involves the development of a nonlinear Model Predictive Contouring Control, which uses torque vectoring to stabilise and drive the vehicle in evasive manoeuvres at the limit of handling. The proposed algorithm combines motion planning, path tracking and vehicle stability objectives,

Jone Ugarte-Valdivielso, Jose I. Aizpurua, Manex Barrenetxea-Iñarra

Transformers are one of the key assets in AC distribution grids and renewable power integration. During transformer energization inrush currents appear, which lead to transformer degradation and can cause grid instability events. These inrush currents are a consequence of the transformer's magnetic core saturation during its connection to the grid. Transform

Reghukrishnan Gangadharan, Victor Roy

We obtain a formal integral solution to the 3+1 D Boltzmann Equation in relaxation time approximation. The gradient series obtained from this integral solution contains exponentially decaying non-hydrodynamic terms. It is shown that this gradient expansion can have a finite radius of convergence under certain assumptions of analyticity. We then argue that, i

Jin L. C. Guo, Jan-Philipp Steghöfer, Andreas Vogelsang, Jane Cleland-Huang

Traceability, the ability to trace relevant software artifacts to support reasoning about the quality of the software and its development process, plays a crucial role in requirements and software engineering, particularly for safety-critical systems. In this chapter, we provide a comprehensive overview of the representative tasks in requirement traceability

Laura Antoni-Micollier, Maxime Arnal, Romain Gautier, Camille Janvier

Gravity measurements provide valuable information on the mass distribution below the earth surface relevant to various areas of geosciences such as hydrology, geodesy, geophysics, volcanology, and natural resources management. During the past decades, the needs for sensitivity, robustness, compactness, and transportability of instruments measuring the gravit

Hang Chen, Peng Wang

In this paper, we prove that a closed minimal hypersurface in $\SSS$ with $\lambda_1<n$ has Morse index at least $n+4$, providing a partial answer to a conjecture of Perdomo. As a corollary, we re-obtain a partial proof of the famous Urbano Theorem for minimal tori in $\mathbb{S}^3$: a minimal torus in $\mathbb{S}^3$ has Morse index at least $5$, with equali

Ana Paula Wives, Isabelli Seiler de Medeiros Mendes, Sofia Turatti dos Santos, Diego Bonatto

D-xylose is the second most abundant monosaccharide found in lignocellulose and is of biotechnological importance for producing second-generation ethanol and other high-value chemical compounds. D-xylose conversion to ethanol is promoted by microbial fermentation, mainly by bacteria, yeasts, or filamentous fungi. Considering yeasts, species belonging to the

Phillip Sloan, Philip Clatworthy, Edwin Simpson, Majid Mirmehdi

Increasing demands on medical imaging departments are taking a toll on the radiologist's ability to deliver timely and accurate reports. Recent technological advances in artificial intelligence have demonstrated great potential for automatic radiology report generation (ARRG), sparking an explosion of research. This survey paper conducts a methodological rev

S. de Regt, S. Gandhi, I. A. G. Snellen, Y. Zhang

It has been proposed that the distinct formation and evolution of exoplanets and brown dwarfs may affect the chemical and isotopic content of their atmospheres. Recent work has indeed shown differences in the $^{12}$C/$^{13}$C isotope ratio, provisionally attributed to the top-down formation of brown dwarfs and the core accretion pathway of super-Jupiters. T

Xabier Marcano

There is a strong experimental program searching for massive sterile neutrinos. Here we focus on the heavy regime above the GeV scale, where their existence can be probed in either high-energy colliders or in high-precision facilities. We first review the current experimental status at colliders, showing that the LHC already improves LEP results for mixings

Pablo Giuliani, Kyle Godbey, Vojtech Kejzlar, Witold Nazarewicz

One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework. In many cases, however, the models used in the mixing process are similar. In addition to contaminating the model space, the existence of such similar, or even redundant, models during

A. A. Kamashev, N. N. Garif'yanov, A. A. Validov, V. Kataev

We report the superconducting properties of the Co/Pb/Co heterostructures with thin insulating interlayers. The main specific feature of these structures is the intentional oxidation of both superconductor/ferromagnet (S/F) interfaces. We study variation of the critical temperature of our systems due to switching between parallel and antiparallel configurati

Hiromasa Tajima, Yasusada Nambu

We investigate the entropy dynamics of de Sitter spacetime during the inflationary phase. The cosmological horizon in de Sitter spacetime, which limits the causally accessible region for an observer, exhibits thermal properties similar to a black hole event horizon. According to holographic principles, the entropy within a causally connected region is bounde

Hanci Chi

This paper derives a sufficient condition for the existence of cohomogeneity one Einstein metrics on double disk bundles of two summands type. The condition is an inequality that involves geometric data from the principal orbits.

Jinhan Liu, Qiyu Chen, Junjie Xu, Junjie Li

Search and recommendation (S&R) are the two most important scenarios in e-commerce. The majority of users typically interact with products in S&R scenarios, indicating the need and potential for joint modeling. Traditional multi-scenario models use shared parameters to learn the similarity of multiple tasks, and task-specific parameters to learn the divergen

Donald Yau

A conjecture of May states that there is an up-to-adjunction strictification of symmetric bimonoidal functors between bipermutative categories. The main result of this paper proves a weaker form of May's conjecture that starts with multiplicatively strong symmetric bimonoidal functors. As the main application, for May's multiplicative infinite loop space mac

Sébastien Quetin, Andrew Heschl, Mauricio Murillo, Rohit Murali

Purpose: To present a high-performing, robust, and flexible deep learning pipeline for automatic segmentation of 30 organs-at-risk (OARs) in head and neck (H&N) cancer patients, using MRI, CT, or both. Method: We trained a segmentation pipeline on paired CT and MRI-T1 scans from 296 patients. We combined data from the H&N OARs CT and MR segmentation (HaN-Seg

Tao Wu, Shuqiu Ge, Jie Qin, Gangshan Wu

Spatio-temporal action detection (STAD) is an important fine-grained video understanding task. Current methods require box and label supervision for all action classes in advance. However, in real-world applications, it is very likely to come across new action classes not seen in training because the action category space is large and hard to enumerate. Also

Josef F. Dorfmeister, Peng Wang

In the past decades, the authors made some systematic research on global and local properties of Willmore surfaces in terms of the DPW method. In this note we give a survey, mainly including the basic framework of the DPW method for the global geometry of Willmore surfaces via the conformal Gauss map, applications on constructions of Willmore $2$-spheres, ch

Hongxi Wang, Haoxiang Luo, Wei Zhang, Hua Chen

Thanks to recent explosive developments of data-driven learning methodologies, reinforcement learning (RL) emerges as a promising solution to address the legged locomotion problem in robotics. In this paper, we propose CTS, a novel Concurrent Teacher-Student reinforcement learning architecture for legged locomotion over uneven terrains. Different from conven

Nuria Bautista-Puig, Enrique Orduna-Malea, Philippe Mongeon

Citizen Science (CS) is related to public engagement in scientific research. The tasks in which the citizens can be involved are diverse and can range from data collection and tagging images to participation in the planning and research design. However, little is known about the involvement degree of the citizens to CS projects, and the contribution of those

Chin-Hung Chen, Wen-Hung Huang, Boris Karanov, Alex Young

Recently, new types of interference in electric vehicles (EVs), such as converters switching and/or battery chargers, have been found to degrade the performance of wireless digital transmission systems. Measurements show that such an interference is characterized by impulsive behavior and is widely varying in time. This paper uses recorded data from our EV t