February 2024 arXiv papers — page 69

Rong Luo, Edita Máčajová, Martin Škoviera, Cun-Quan Zhang

We prove that a signed graph admits a nowhere-zero $8$-flow provided that it is flow-admissible and the underlying graph admits a nowhere-zero $4$-flow. When combined with the 4-color theorem, this implies that every flow-admissible bridgeless planar signed graph admits a nowhere-zero $8$-flow. Our result improves and generalizes previous results of Li et al

M. Castilla, Juan Carlos Bravo, M. Ordoñez, Juan Carlos Montaño

In this paper, a generalization of the concept of electrical power for periodic current and voltage waveforms based on a new generalized complex geometric algebra (GCGA) is proposed. This powerful tool permits, in n-sinusoidal/nonlinear situations, representing and calculating the voltage, current, and apparent power in a single-port electrical network in te

Sayantan Adak, Daivik Agrawal, Animesh Mukherjee, Somak Aditya

We investigate the knowledge of object affordances in pre-trained language models (LMs) and pre-trained Vision-Language models (VLMs). A growing body of literature shows that PTLMs fail inconsistently and non-intuitively, demonstrating a lack of reasoning and grounding. To take a first step toward quantifying the effect of grounding (or lack thereof), we cur

Nadine Probol, Margot Mieskes

There has been a range of studies of how autism is displayed in voice, speech, and language. We analyse studies from the biomedical, as well as the psychological domain, but also from the NLP domain in order to find linguistic, prosodic and acoustic cues that could indicate autism. Our survey looks at all three domains. We define autism and which comorbiditi

Mohammad Hadi Hedayatzadeh, Ali Partofard

We prove a deformation theorem for prismatic higher $(G,\mu)$-displays over quasi-syntomic rings. As an application, we extend the classification of $p$-divisible groups via prismatic Dieudonn\'e modules to a class of rings, properly containing quasi-syntomic rings. Finally, we relate the stack of prismatic higher $(G,\mu)$-displays to integral local Shimura

Domna G. Kotsifaki, Viet Giang Truong, Mirco Dindo, Frank Cichos

Investigating the dynamics of single biomolecules is essential for unlocking new frontiers in biophysics and medicine. Here, we present a transformative approach using metamaterial optical tweezers to trap and study individual urease molecules - an enzyme that catalyzes urea hydrolysis and serves as a key biomarker for pathogenic infections. By generating th

Sumit Tambe, Kentaro Kato, Zahir Hussain

This paper reports on the efficacy of the G\"ortler number in scaling the laminar-turbulent boundary-layer transition on rotating cones facing axial inflow. Depending on the half-cone angle $\psi$ and axial flow strength, the competing centrifugal and crossflow instabilities dominate the transition. Traditionally, the flow is evaluated by using two parameter

Yuwen Yang, Yuxiang Lu, Suizhi Huang, Shalayiding Sirejiding

The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model training on multi-task learning datasets. However, a comprehensive evaluation method, integrating the unique features of both FL and MTL, is currently absent in the field. This paper fil

Zhiyuan Li, Hong Liu, Denny Zhou, Tengyu Ma

Instructing the model to generate a sequence of intermediate steps, a.k.a., a chain of thought (CoT), is a highly effective method to improve the accuracy of large language models (LLMs) on arithmetics and symbolic reasoning tasks. However, the mechanism behind CoT remains unclear. This work provides a theoretical understanding of the power of CoT for decode

Hsiao-Ru Pan, Bernhard Schölkopf

Learning from off-policy data is essential for sample-efficient reinforcement learning. In the present work, we build on the insight that the advantage function can be understood as the causal effect of an action on the return, and show that this allows us to decompose the return of a trajectory into parts caused by the agent's actions (skill) and parts outs

Giorgia Greco, Giuseppe Antonio Elia, Yves Kayser, Burkhard Beckhoff

This work aims to study the insertion of AlCl_4^- anion in the crystalline structure of oriented pyrolytic graphite (PG) at the point of view of the anion itself. The electronic and atomic structures of the anion at different intercalation stages are studied. In particular double-edge (bicolor) X-ray absorption spectroscopy at the Al and Cl K-edges is carrie

Yanan Zhao, Yuelong Li, Haichuan Zhang, Vishal Monga

In recent years, algorithm unrolling has emerged as a powerful technique for designing interpretable neural networks based on iterative algorithms. Imaging inverse problems have particularly benefited from unrolling-based deep network design since many traditional model-based approaches rely on iterative optimization. Despite exciting progress, typical unrol

Matthias Schuster, Volker Schulz

Models of physical phenomena that use nonlocal operators are better suited for some applications than their classical counterparts that employ partial differential operators. However, the numerical solution of these nonlocal problems can be quite expensive. Therefore, Local-to-Nonlocal couplings have emerged that combine partial differential operators with n

Marco Bresciani, Manuel Friedrich, Carlos Mora-Corral

We investigate the existence of minimizers of variational models with Eulerian-Lagrangian formulations. We consider energy functionals depending on the deformation of a body, defined on its reference configuration, and an Eulerian map defined on the unknown deformed configuration in the actual space. Our existence theory moves beyond the purely elastic setti

Dehai Min, Nan Hu, Rihui Jin, Nuo Lin

Augmenting Large Language Models (LLMs) for Question Answering (QA) with domain specific data has attracted wide attention. However, domain data often exists in a hybrid format, including text and semi-structured tables, posing challenges for the seamless integration of information. Table-to-Text Generation is a promising solution by facilitating the transfo

Taira Tsuchiya, Shinji Ito

In this work, we explore online convex optimization (OCO) and introduce a new condition and analysis that provides fast rates by exploiting the curvature of feasible sets. In online linear optimization, it is known that if the average gradient of loss functions exceeds a certain threshold, the curvature of feasible sets can be exploited by the follow-the-lea

Pir Sami Ullah Shah, Naveed Ahmad, Mirza Omer Beg

Applying DevOps practices to machine learning system is termed as MLOps and machine learning systems evolve on new data unlike traditional systems on requirements. The objective of MLOps is to establish a connection between different open-source tools to construct a pipeline that can automatically perform steps to construct a dataset, train the machine learn

Winnie Kirui, Elzanie Bothma, Marius Smuts, Anke Steyn

We propose new goodness-of-fit tests for the Poisson distribution. The testing procedure entails fitting a weighted Poisson distribution, which has the Poisson as a special case, to observed data. Based on sample data, we calculate an empirical weight function which is compared to its theoretical counterpart under the Poisson assumption. Weighted Lp distance

Shahar Katz, Yonatan Belinkov, Mor Geva, Lior Wolf

Understanding how Transformer-based Language Models (LMs) learn and recall information is a key goal of the deep learning community. Recent interpretability methods project weights and hidden states obtained from the forward pass to the models' vocabularies, helping to uncover how information flows within LMs. In this work, we extend this methodology to LMs'

Martiño Rivera-Dourado, Marcos Gestal, Alejandro Pazos, Jose Vázquez-Naya

FIDO2 authentication is starting to be applied in numerous web authentication services, aiming to replace passwords and their known vulnerabilities. However, this new authentication method has not been integrated yet with network authentication systems. In this paper, we introduce FIDO2CAP: FIDO2 Captive-portal Authentication Protocol. Our proposal describes

Chi Zhang, Linzhang Wang, Manuel Rigger

Datalog is a popular and widely-used declarative logic programming language. Datalog engines apply many cross-rule optimizations; bugs in them can cause incorrect results. To detect such optimization bugs, we propose an automated testing approach called Incremental Rule Evaluation (IRE), which synergistically tackles the test oracle and test case generation

Wen Wu, Bo Li, Chao Zhang, Chung-Cheng Chiu

The subjective perception of emotion leads to inconsistent labels from human annotators. Typically, utterances lacking majority-agreed labels are excluded when training an emotion classifier, which cause problems when encountering ambiguous emotional expressions during testing. This paper investigates three methods to handle ambiguous emotion. First, we show

Anneliese Riess, Kristian Schwethelm, Johannes Kaiser, Tamara T. Mueller

The gold standard for privacy in machine learning, Differential Privacy (DP), is often interpreted through its guarantees against membership inference. However, translating DP budgets into quantitative protection against the more damaging threat of data reconstruction remains a challenging open problem. Existing theoretical analyses of reconstruction risk ar

G. G. L. Nashed, Shin'ichi Nojiri

In this paper, we propose a model including four scalar fields coupled with general gravity theories, which is a generalization of the two-scalar model proposed in Phys. Rev. D \textbf{103} (2021) no.4, 044055, where it has been shown that any given spherically symmetric static/time-dependent spacetime can be realized by using the two-scalar model. We show t

Florent Cogen, Emily Little, Virginie Dussartre, Quentin Bustarret

The ATLAS model simulates the various stages of the electricity market chain in Europe, including the formulation of offers by different market actors, the coupling of European markets, strategic optimization of production portfolios and, finally, real-time system balancing processes. ATLAS was designed to simulate the various electricity markets and process

Jun Wan, Zuo-Ru Zhang

In study the generalized Jacobsthal and Jaco-Lucas polynomials, Sun introduced the interesting numerical triangle Jaco-Lucas sequence $\{JL_{n,k}\}_{n\geq k\geq0}$. In this paper, we proved this sequence is log-concave with the only mode by computer algebra.

Matthias Kunik, Adrian Kolb, Siegfried Müller, Ferdinand Thein

The ultra-relativistic Euler equations for an ideal gas are described in terms of the pressure, the spatial part of the dimensionless four-velocity and the particle density. Radially symmetric solutions of these equations are studied in two and three space dimensions. Of particular interest in the solutions are the formation of shock waves and a pressure blo

Ricardo Fitas

This paper presents an algorithm based on Particle Swarm Optimization (PSO), adapted for multi-objective optimization problems: the Elitist PSO (MO-ETPSO). The proposed algorithm integrates core strategies from the well-established NSGA-II approach, such as the Crowding Distance Algorithm, while leveraging the advantages of Swarm Intelligence in terms of ind

Benzion Shklyar

The problem of partial null controllability for linear autonomous evolution equations, which are controlled by a one-dimensional control, is under consideration. The partial null-controllability conditions for coupled abstract evolution systems have been obtained using the moment problem approach.

Ziyad Oulhaj, Mathieu Carrière, Bertrand Michel

Unsupervised data representation and visualization using tools from topology is an active and growing field of Topological Data Analysis (TDA) and data science. Its most prominent line of work is based on the so-called Mapper graph, which is a combinatorial graph whose topological structures (connected components, branches, loops) are in correspondence with

Finn J. P. Allison, Ozgur Selsil, Stewart G. Haslinger, Alexander B. Movchan

Classical Faraday's law on electromagnetic induction states that a change of magnetic field through a coil wire induces a current in the wire. A mechanical analogue of the Lorentz force, induced by a magnetic field on an electric charge, is the gyroscopic force. Here, we demonstrate a mechanical analogy with a chiral elastic waveguide subjected to gyroscopic

Jie Yan, Jing Liu, Yi-Zi Ning, Zhong-Yuan Zhang

In federated clustering, multiple data-holding clients collaboratively group data without exchanging raw data. This field has seen notable advancements through its marriage with contrastive learning, exemplified by Cluster-Contrastive Federated Clustering (CCFC). However, CCFC suffers from heterogeneous data across clients, leading to poor and unrobust perfo

Tongxu Luo, Jiahe Lei, Fangyu Lei, Weihao Liu

Fine-tuning is often necessary to enhance the adaptability of Large Language Models (LLM) to downstream tasks. Nonetheless, the process of updating billions of parameters demands significant computational resources and training time, which poses a substantial obstacle to the widespread application of large-scale models in various scenarios. To address this i

Enrique C. Gabrick, Eduardo L. Brugnago, Silvio L. T. de Souza, Kelly C. Iarosz

We study three different strategies of vaccination in a SEIRS (Susceptible--Exposed--Infected--Recovered--Susceptible) seasonal forced model, which are: ($i$) continuous vaccination; ($ii$) periodic short time localized vaccination and ($iii$) periodic pulsed width campaign. Considering the first strategy, we obtain an expression for the basic reproduction n

James Bell, Thomas Drury, Tobias Mütze, Christian Bressen Pipper

Estimands using the treatment policy strategy for addressing intercurrent events are common in Phase III clinical trials. One estimation approach for this strategy is retrieved dropout whereby observed data following an intercurrent event are used to multiply impute missing data. However, such methods have had issues with variance inflation and model fitting

Sami Kivistö, Frederick Gent, Merja Tornikoski, Joni Tammi

Context. Aalto University Mets\"ahovi Radio Observatory has collected solar intensity maps for over 45 years. Most data coverage is on the 37 GHz frequency band, tracking emissions primarily at the chromosphere and coronal transition region. The data spans four sunspot cycles or two solar magnetic cycles. Aims. We present solar maps, including recently resto

Emily Little, Florent Cogen, Quentin Bustarret, Virginie Dussartre

The ATLAS model simulates the various stages of the electricity market chain in Europe, including the formulation of offers by different market actors, the coupling of European markets, strategic optimization of production portfolios and, finally, real-time system balancing processes. ATLAS was designed to simulate the various electricity markets and process

Zhengbao Jiang, Zhiqing Sun, Weijia Shi, Pedro Rodriguez

In order for large language model (LLM)-based assistants to effectively adapt to evolving information needs, it must be possible to update their factual knowledge through continued training on new data. The standard recipe for doing so involves continued pre-training on new documents followed by instruction-tuning on question-answer (QA) pairs. However, we f

Li Mi, Syrielle Montariol, Javiera Castillo-Navarro, Xianjie Dai

Asking questions about visual environments is a crucial way for intelligent agents to understand rich multi-faceted scenes, raising the importance of Visual Question Generation (VQG) systems. Apart from being grounded to the image, existing VQG systems can use textual constraints, such as expected answers or knowledge triplets, to generate focused questions.

Tianyu Zheng, Ge Zhang, Xingwei Qu, Ming Kuang

Drawing upon the intuition that aligning different modalities to the same semantic embedding space would allow models to understand states and actions more easily, we propose a new perspective to the offline reinforcement learning (RL) challenge. More concretely, we transform it into a supervised learning task by integrating multimodal and pre-trained langua

Wenjun Hou, Yi Cheng, Kaishuai Xu, Yan Hu

Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated reports. In this paper, we emphasize another crucial quality that it should possess, i.e., inter-report consistency, which refers to the capability of generating consistent reports for semantically equivalent radiographs. T

Sankarshanaa Sagaram, Krish Didwania, Laven Srivastava, Aditya Kasliwal

The increasing adoption of solar energy necessitates advanced methodologies for monitoring and maintenance to ensure optimal performance of solar panel installations. A critical component in this context is the accurate segmentation of solar panels from aerial or satellite imagery, which is essential for identifying operational issues and assessing efficienc

Gyeongman Kim, Doohyuk Jang, Eunho Yang

Recent advancements in large language models (LLMs) have raised concerns about inference costs, increasing the need for research into model compression. While knowledge distillation (KD) is a prominent method for this, research on KD for generative language models like LLMs is relatively sparse, and the approach of distilling student-friendly knowledge, whic

Goran L. Pilbratt, Pedro Garcia-Lario, Arvind N. Parmar

After an introduction to the ESA Herschel Space Observatory including a mission overview, science objectives, results and productivity we examine the process and outcomes of the announcements of observing opportunities (AOs). For Herschel, in common with other ESA observatories, there were no rules, quotas, or guidelines for the allocation of observing time

Fajri Koto, Haonan Li, Sara Shatnawi, Jad Doughman

The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models. While state-of-the-art models are partially trained on large Arabic texts, evaluating their performance in Arabic remains challenging due to the limited availability of relevant datasets. To bridge this

Young-Pil Choi, Dong-ha Kim, Dowan Koo, Eitan Tadmor

We investigate the critical threshold phenomena in a large class of one dimensional pressureless Euler--Poisson (EP) equations, with non-vanishing background states. First, we establish local-in-time well-posedness in proper regularity spaces, which are adapted for a certain \textit{neutrality condition} to hold. The neutrality condition is shown to be neces

Juan Carlos Escanciano, Ricardo Parra

The use of machine learning methods for predictive purposes has increased dramatically over the past two decades, but uncertainty quantification for predictive comparisons remains elusive. This paper addresses this gap by extending the classic inference theory for predictive ability in time series to modern machine learners, such as the Lasso or Deep Learnin

John C. Zarnecki, Arvind N. Parmar

We have collected data pertaining to the Principal Investigators (PIs), and co-PIs (where appropriate) for all ESA-led Science Directorate missions since the first such launch, namely of COS-B in 1975. For a total of 28 missions (including 4 in preparation awaiting launch), 437 individuals have been recorded along with their institution, location, academic a

Fatemeh Sadat Khademi, Maryam Bahreini

In this article, tip-enhanced Raman spectroscopy (TERS) is investigated as a precise method for analysis of biological samples. Using Finite Difference Time Domain (FDTD) simulation, it has been tried to design the required structures for analysis of these samples. At first, by comparing different TERS structures and considering the material, dimensions and

Erik Kuulkers, Celia Sanchez-Fernandez, Arvind N. Parmar

We examine the outcomes of the regular announcements of observing opportunities for ESA's gamma-ray observatory INTEGRAL issued between 2000 and 2021. We investigate how success rates vary with the lead proposer's gender, academic age and the country where the proposer's institute is located. The more than 20 years operational lifetime enable the evolution o

An Liu, Zonghan Yang, Zhenhe Zhang, Qingyuan Hu

While Large language models (LLMs) have demonstrated considerable capabilities across various natural language tasks, they often fall short of the performance achieved by domain-specific state-of-the-art models. One potential approach to enhance domain-specific capabilities of LLMs involves fine-tuning them using corresponding datasets. However, this method

Cristian Tirelli, Juan Sapriza, Rubén Rodríguez Álvarez, Lorenzo Ferretti

Coarse-Grain Reconfigurable Arrays (CGRAs) represent emerging low-power architectures designed to accelerate Compute-Intensive Loops (CILs). The effectiveness of CGRAs in providing acceleration relies on the quality of mapping: how efficiently the CIL is compiled onto the platform. State of the Art (SoA) compilation techniques utilize modulo scheduling to mi

Hardik Kothari, Maria Giuseppina Chiara Nestola, Marco Favino, Rolf Krause

Due to its optimal complexity, the multigrid (MG) method is one of the most popular approaches for solving large-scale linear systems arising from the discretization of partial differential equations. However, the parallel implementation of standard MG methods, which are inherently multiplicative, suffers from increasing communication complexity. In such cas

Dipan Dey, Manoj Gupta

We present an $f$-fault tolerant distance oracle for an undirected weighted graph where each edge has an integral weight from $[1 \dots W]$. Given a set $F$ of $f$ edges, as well as a source node $s$ and a destination node $t$, our oracle returns the \emph{shortest path} from $s$ to $t$ avoiding $F$ in $O((cf \log (nW))^{O(f^2)})$ time, where $c > 1$ is a co

Ioannis P. A. Papadopoulos, Sheehan Olver

We develop a sparse hierarchical $hp$-finite element method ($hp$-FEM) for the Helmholtz equation with variable coefficients posed on a two-dimensional disk or annulus. The mesh is an inner disk cell (omitted if on an annulus domain) and concentric annuli cells. The discretization preserves the Fourier mode decoupling of rotationally invariant operators, suc

Joshua Hoffer, Gernot Eichmann, Christian S. Fischer

We discuss the spectrum and the internal composition of ground and excited four-quark states in the charm and bottom energy region. To this end we extend previous calculations within the framework of the relativistic four-body Faddeev-Yakubovsky equation to include quantum numbers with $J^{P C} = 0^{++} , 0^{-+} , 1^{--} , 1^{+-}$ and $1^{+ +}$ and study the

Gregory G. Howes

A specific set of dimensionless plasma and turbulence parameters is introduced to characterize the nature of turbulence and its dissipation in weakly collisional space and astrophysical plasmas. Key considerations are discussed for the development of predictive models of the turbulent plasma heating that characterize the partitioning of dissipated turbulent

Fabian Schaipp, Guillaume Garrigos, Umut Simsekli, Robert Gower

There are several applications of stochastic optimization where one can benefit from a robust estimate of the gradient. For example, domains such as distributed learning with corrupted nodes, the presence of large outliers in the training data, learning under privacy constraints, or even heavy-tailed noise due to the dynamics of the algorithm itself. Here we

You-Qi Lu, Yu-Yu Zhang

Quantum tricriticality, a unique form of high-order criticality, is expected to exhibit fascinating features including unconventional critical exponents and universal scaling laws. However, a quantum tricritical point (QTCP) is much harder to access, and the corresponding phenomena at tricriticality have rarely been investigated. In this study, we explore a

Bernd Konrad, Maxim Efremov

To advance precise inertial navigation, we present a compact quantum sensor which is based on novel quantum phenomenon of the angular Bloch oscillations and measures solely the angular acceleration of slow external rotation. We investigate the dynamics of ultra-cold atoms confined in a toroidal trap with a ring-lattice along the azimuth angle, realized with

Anushree Pandey, Sovik Roy, Md. Manirul Ali, Biplab Ghosh

Mixed spin-1/2 states violating Bell-CHSH inequality is useful for teleportation. There exist states which do not violate Bell-inequality but is still useful as teleportation channels. Maximally entangled mixed states of Munro class and Ishizaka-Hiroshima class are such types which although satisfy Bell-CHSH inequality, yet can perform better as teleportatio

Qian Chen, Rui Wen, Shi Yin, Wei-jie Fu

Fluctuations of conserved charges, such as the net-baryon number fluctuations, are influenced by different dynamical evolution processes. In this paper, we investigate the influence of hadronic rescatterings on different orders of cumulants of the net-baryon number distribution. At the start of hadronic rescatterings, we introduce net-baryon number distribut

Liyan Xu, Zhenlin Su, Mo Yu, Jin Xu

Factual inconsistencies pose a significant hurdle for the faithful summarization by generative models. While a major direction to enhance inconsistency detection is to derive stronger Natural Language Inference (NLI) models, we propose an orthogonal aspect that underscores the importance of incorporating task-specific taxonomy into the inference. To this end

Tong Xie, Yixuan Hu, Renjie Wei, Meng Li

Stochastic computing (SC) has emerged as a promising computing paradigm for neural acceleration. However, how to accelerate the state-of-the-art Vision Transformer (ViT) with SC remains unclear. Unlike convolutional neural networks, ViTs introduce notable compatibility and efficiency challenges because of their nonlinear functions, e.g., softmax and Gaussian

Branislav Pecher, Ivan Srba, Maria Bielikova

When solving NLP tasks with limited labelled data, researchers typically either use a general large language model without further update, or use a small number of labelled samples to tune a specialised smaller model. In this work, we answer an important question -- how many labelled samples are required for the specialised small models to outperform general

Guido De Marchi, Arvind N. Parmar

We examine over 68,000 refereed publications based on data from 25 missions in the ESA Science Programme and 11 additional missions in which ESA is involved as a junior partner. The publications cover the fields of astronomy, planetary science, and heliophysics and are spread over almost 50 years, spanning the period between the year a mission was launched a

Branislav Pecher, Ivan Srba, Maria Bielikova

While learning with limited labelled data can improve performance when the labels are lacking, it is also sensitive to the effects of uncontrolled randomness introduced by so-called randomness factors (e.g., varying order of data). We propose a method to systematically investigate the effects of randomness factors while taking the interactions between them i

Zong-Lin Gao, Sang NguyenQuang, Wen-Hsiao Peng, Xiem HoangVan

Learned hierarchical B-frame coding aims to leverage bi-directional reference frames for better coding efficiency. However, the domain shift between training and test scenarios due to dataset limitations poses a challenge. This issue arises from training the codec with small groups of pictures (GOP) but testing it on large GOPs. Specifically, the motion esti

Xiao Qin, Yu-Yu Zhang

Quantum fluctuations of a quantum Rabi triangle are studied using an analytical approach beyond the mean-field theory. By applying an artificial magnetic field among three cavities, time-reversal symmetry breaking is manifested through a directional transfer dynamics of photons. In contrast to previous studies, we focus on the scaling exponents of the fluctu

Alicia Guo, Pat Pataranutaporn, Pattie Maes

AI-based virtual assistants are increasingly used to support daily ideation tasks. The values or bias present in these agents can influence output in hidden ways. They may also affect how people perceive the ideas produced with these AI agents and lead to implications for the design of AI-based tools. We explored the effects of AI agents with different value

Jiayi Lin, Hande Dong, Yutao Xie, Lei Zhang

The scaling law is becoming a fundamental law in many machine learning areas. That is, test error falls off with the power law when increasing training data, model size, and computing resource. However, whether this law is suitable for the task of code understanding is not well studied, and most current language models for code understanding are about 100M p

Franco Galante, Giovanni Neglia, Emilio Leonardi

In numerous settings, agents lack sufficient data to directly learn a model. Collaborating with other agents may help, but it introduces a bias-variance trade-off, when local data distributions differ. A key challenge is for each agent to identify clients with similar distributions while learning the model, a problem that remains largely unresolved. This stu

Julien Bensmail, Foivos Fioravantes, Fionn Mc Inerney, Nicolas Nisse

Given a graph $G$ and $k \in \mathbb{N}$, we introduce the following game played in $G$. Each round, Alice colours an uncoloured vertex of $G$ red, and then Bob colours one blue (if any remain). Once every vertex is coloured, Alice wins if there is a connected red component of order at least $k$, and otherwise, Bob wins. This is a Maker-Breaker version of th

Mohsen Azarmi, Mahdi Rezaei, He Wang

Accurate pedestrian intention prediction (PIP) by Autonomous Vehicles (AVs) is one of the current research challenges in this field. In this article, we introduce PIP-Net, a novel framework designed to predict pedestrian crossing intentions by AVs in real-world urban scenarios. We offer two variants of PIP-Net designed for different camera mounts and setups.

Khadija Khatun, Chen Shen, Lei Shi, Jun Tanimoto

Destructive agents, who opt out of the game and indiscriminately harm others, paradoxically foster cooperation, representing an intriguing variant of the voluntary participation strategy. Yet, their impact on cooperation remains inadequately understood, particularly in the context of pairwise social dilemma games and in comparison to their counterparts, cons

Son Nguyen Van, Hoai Nguyen Xuan

The Poisson process, especially the nonhomogeneous Poisson process (NHPP), is an essentially important counting process with numerous real-world applications. Up to date, almost all works in the literature have been on the estimation of NHPPs with infinite data using non-data driven binning methods. In this paper, we formulate the problem of estimation of NH

Si Luo, Yinan Fang, Yingdan Wang, Stefano Chesi

We discuss a general formalism to optimize quasi-adiabatic state-transfer protocols, where high fidelity is achieved by maintaining the system in a dark subspace protected from the dominant dissipative channels. We cast the residual fidelity loss, induced by a combination of dissipation and non-adiabatic transitions, in the form of a classical action where t

Jinu Lee, Wonseok Hwang

To improve the performance and explainability of LLM-based natural language reasoning, structured reasoning can be applied to generate explicitly structured proofs. Among different methods for structured reasoning, we specifically focus on backward chaining, where the proof goal is recursively decomposed to subgoals by searching and applying rules. We argue

Stephan G. Heinemann, Mathew J. Owens, Manuela Temmer, James A. Turtle

Coronal holes are recognized as the primary sources of heliospheric open magnetic flux (OMF). However, a noticeable gap exists between in-situ measured OMF and that derived from remote sensing observations of the Sun. In this study, we investigate the OMF evolution and its connection to solar structures throughout 2014, with special emphasis on the period fr

Dag McGeorge, Jon Arne Glomsrud

A growing number of safety-critical industries agree that building confidence in complex systems can be achieved through evidence and structured argumentation framed in assurance cases. Nevertheless, according to practical industry experience, assurance cases can easily become too rigorous and difficult to develop and maintain when applied to complex systems

Zhi Yang Tho, Francis K. C. Hui, Tao Zou

We propose a joint mean and correlation regression model for multivariate discrete and (semi-)continuous response data, that simultaneously regresses the mean of each response against a set of covariates, and the correlations between responses against a set of similarity/distance measures. A set of joint estimating equations are formulated to construct an es

Ning Zhang

In this paper, combining the covolume, we study the Minkowski theory for the non-compact convex set with an asymptotic boundary condition. In particular, the mixed covolume of two non-compact convex sets is introduced and its geometric interpretation is obtained by the Hadamard variational formula. The Brunn-Minkowski and Minkowski inequalities for covolume

Marco Naguib, Xavier Tannier, Aurélie Névéol

Large language models (LLMs) have become the preferred solution for many natural language processing tasks. In low-resource environments such as specialized domains, their few-shot capabilities are expected to deliver high performance. Named Entity Recognition (NER) is a critical task in information extraction that is not covered in recent LLM benchmarks. Th

Abdulla Alfalasi, Esrat Khan, Mohamed Alhashmi, Raed Aldweik

A transformative approach to mental health therapy lies at the crossroads of cultural heritage and advanced technology. This paper introduces an innovative method that fuses machine learning techniques with traditional Emirati motifs, focusing on the United Arab Emirates (UAE). We utilize the Stable Diffusion XL (SDXL) model, enhanced with Low-Rank Adaptatio

Christian Schuessler, Wenxuan Zhang, Johanna Bräunig, Marcel Hoffmann

In the fast-paced field of human-computer interaction (HCI) and virtual reality (VR), automatic gesture recognition has become increasingly essential. This is particularly true for the recognition of hand signs, providing an intuitive way to effortlessly navigate and control VR and HCI applications. Considering increased privacy requirements, radar sensors e

Simon Becker, Izak Oltman, Martin Vogel

We consider small random perturbations of the standard high-symmetry tunneling potentials in the Bistritzer-MacDonald Hamiltonian describing twisted bilayer graphene. Using methods developed by Sj\"ostrand for studying the spectral asymptotics of non-selfadjoint pseudo-differential operators, we prove that for sufficiently small twisting angles the Hamiltoni

Peter Mlkvik, Maximilian E. Merkel, Nicola A. Spaldin, Claude Ederer

We present a combined density-functional theory and single-site dynamical mean-field theory (DMFT) study of vanadium dioxide (VO$_2$) using an unconventional set of bond-centered orbitals as the basis of the correlated subspace. VO$_2$ is a prototypical material undergoing a metal-insulator transition (MIT), hosting both intriguing physical phenomena and the

Guoqing Zhang, Yang Li

We introduce a novel approach for the reconstruction of tubular shapes from skeletal representations. Our method processes all skeletal points as a whole, eliminating the need for splitting input structure into multiple segments. We represent the tubular shape as a truncated signed distance function (TSDF) in a voxel hashing manner, in which the signed dista

Guillermo B. Morales

Advancing our knowledge of how the brain processes information remains a key challenge in neuroscience. This thesis combines three different approaches to the study of the dynamics of neural networks and their encoding representations: a computational approach, that builds upon basic biological features of neurons and their networks to construct effective mo

Jiang-Yu Lu, Wu-Yu Chen, Lei Li, Tao Huang

Symmetry breaking generally induce exotic physical properties, particularly for low-dimensional materials. Herein we demonstrate that symmetry breaking induces a giant Stark effect in 2D Janus materials using group IV-V monolayers with a four-atom-layer structure as a model system, which are constructed by Ge and As element substitution of symmetrical SnSb m

Nikolaos Giakoumidis, Christos-Nikolaos Anagnostopoulos

Nowadays, the use of advanced sensors, such as terrestrial 3D laser scanners, mobile LiDARs and Unmanned Aerial Vehicles (UAV) photogrammetric imaging, has become the prevalent practice for 3D Reality Modeling and digitization of large-scale monuments of Cultural Heritage (CH). In practice, this process is heavily related to the expertise of the surveying te

Naihong Hu, Hengyi Wang

This paper is devoted to investigating the centre of two-parameter quantum groups $U_{r,s}(\mathfrak{g})$ via establishing the Harish-Chandra homomorphism. Based on the Rosso form and the representation theory of weight modules, we prove that when rank $\mathfrak{g}$ is even, the Harish-Chandra homomorphism is an isomorphism, and in particular, the centre of

Simon Boeder, Fabian Gigengack, Benjamin Risse

Semantic occupancy has recently gained significant traction as a prominent 3D scene representation. However, most existing methods rely on large and costly datasets with fine-grained 3D voxel labels for training, which limits their practicality and scalability, increasing the need for self-monitored learning in this domain. In this work, we present a novel a

Kaiwen Wei, Jingyuan Zhang, Hongzhi Zhang, Fuzheng Zhang

Large Language Models (LLMs) exhibit remarkable generative capabilities, enabling the generation of valuable information. Despite these advancements, previous research found that LLMs sometimes struggle with adhering to specific constraints (e.g., in specific place or at specific time), at times even overlooking them, which leads to responses that are either

Yufu Liu, Xianjun Wang, Yunlin Li, Haoran Zhang

Although several efforts have been taken to enhance the photonic spin Hall shift in deep-subwavelength region, according to effective medium theory, the fundamental confliction between near-zero reflection coefficient and near-zero incident angle still hinders the further application. Here, we reveal a fundamental breakdown of effective medium theory due to

Kimji N. Pellano, Inga Strümke, Espen Alexander F. Ihlen

The advancement of deep learning in human activity recognition (HAR) using 3D skeleton data is critical for applications in healthcare, security, sports, and human-computer interaction. This paper tackles a well-known gap in the field, which is the lack of testing in the applicability and reliability of XAI evaluation metrics in the skeleton-based HAR domain

Jinlong Pang, Jialu Wang, Zhaowei Zhu, Yuanshun Yao

The pursuit of fairness in machine learning (ML), ensuring that the models do not exhibit biases toward protected demographic groups, typically results in a compromise scenario. This compromise can be explained by a Pareto frontier where given certain resources (e.g., data), reducing the fairness violations often comes at the cost of lowering the model accur

Bochao Zou, Zizheng Guo, Jiansheng Chen, Junbao Zhuo

Remote photoplethysmography (rPPG) is a non-contact method for detecting physiological signals based on facial videos, holding high potential in various applications. Due to the periodicity nature of rPPG signals, the long-range dependency capturing capacity of the transformer was assumed to be advantageous for such signals. However, existing methods have no

Simone Biondini, Nora Brambilla, Gramos Qerimi, Antonio Vairo

For non-relativistic thermal dark matter, close-to-threshold effects largely dominate the evolution of the number density for most of the times after thermal freeze-out, and hence affect the cosmological relic density. A precise evaluation of the relevant interaction rates in a thermal medium representing the early universe includes accounting for the relati

Guan-Ting Lin, Cheng-Han Chiang, Hung-yi Lee

In spoken dialogue, even if two current turns are the same sentence, their responses might still differ when they are spoken in different styles. The spoken styles, containing paralinguistic and prosodic information, mark the most significant difference between text and speech modality. When using text-only LLMs to model spoken dialogue, text-only LLMs canno