December 2023 arXiv papers — page 112

Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton

We present the first optimal rates for infinite-dimensional vector-valued ridge regression on a continuous scale of norms that interpolate between $L_2$ and the hypothesis space, which we consider as a vector-valued reproducing kernel Hilbert space. These rates allow to treat the misspecified case in which the true regression function is not contained in the

Xao-Dan Zhao, Lei Wang

We consider the Painleve asymptotics for a solution of integrable coupled Hirota equationwith a 3*3 Lax pair whose initial data decay rapidly at infinity. Using Riemann-Hilbert techniques and Deift-Zhou nonlinear steepest descent arguments, in a transition zone defined by /x/t-1/(12a)/t^2/3<=C, where C>0 is a constant, it turns out that the leading-order ter

Rodrigo Guadarrama, Eduard Vorobyov, Christian Rab, Manuel Güdel

The effect of accretion bursts on massive young stellar objects (MYSOs) represents a new research field in the study of young stars and their environment. We aim to investigate the impact of an accretion burst on massive disks with different types of envelopes and to study the effects of an accretion burst on the temperature structure and the chemistry of th

Bin Shen

In this manuscript, we study bounded positive solutions to the Finslerian Allen-Cahn equation. The Allen-Cahn equation is widely applied and connected to many mathematical branches. We find the Finslerian Allen-Cahn equation is also an Euler-Lagrange equation to a Liapunov entropy functional. We prove the global gradient estimates of its positive solutions o

F. J. Lobillo, José Manuel Muñoz

Linear complementary pairs (LCPs) of codes have been studied since they were introduced in the context of discussing mitigation measures against possible hardware attacks to integrated circuits. In this situation, the security parameters for LCPs of codes are defined as the (Hamming) distance and the dual distance of the codes in the pair. We study the prope

Glen Hopkins, Kristjan Kalm

Here we search for the best automated classification approach for a set of complex legal documents. Our classification task is not trivial: our aim is to classify ca 30,000 public courthouse records from 12 states and 267 counties at two different levels using nine sub-categories. Specifically, we investigated whether a fine-tuned large language model (LLM)

Daniel Weber

Robots are becoming increasingly popular in a wide range of environments due to their exceptional work capacity, precision, efficiency, and scalability. This development has been further encouraged by advances in Artificial Intelligence, particularly Machine Learning. By employing sophisticated neural networks, robots are given the ability to detect and inte

Keiya Ishiguro, Satsuki Nishimura, Hajime Otsuka

We study the well-known type IIA intersecting D-brane models on the $T^6/(\mathbb{Z}_2 \times \mathbb{Z}'_2)$ orientifold via a machine-learning approach. We apply several autoencoder models with and without positional encoding to the D6-brane configurations satisfying certain concrete models described in arXiv:hep-th/0510170 and attempt to extract some feat

Ri Cheng, Ruian He, Xuhao Jiang, Shili Zhou

Existing recurrent optical flow estimation networks are computationally expensive since they use a fixed large number of iterations to update the flow field for each sample. An efficient network should skip iterations when the flow improvement is limited. In this paper, we develop a Context-Aware Iteration Policy Network for efficient optical flow estimation

Dariush Jahani, Mohammadreza Alikhani, Yaser Abdi

100 % absorption in a two-dimensional electron gas (2DEG) with Dirac spectrum is demonstrated to be obtained by controlling the interference of multiple incident radiations, referred to as coherent perfect absorption (CPA). However, when a 2DEG such as graphene is exposed to a magnetostatic bias, it resonantly could absorb electromagnetic radiation by transi

Manon Flageat, Bryan Lim, Antoine Cully

Many applications in Reinforcement Learning (RL) usually have noise or stochasticity present in the environment. Beyond their impact on learning, these uncertainties lead the exact same policy to perform differently, i.e. yield different return, from one roll-out to another. Common evaluation procedures in RL summarise the consequent return distributions usi

Mingjian Zhu, Hanting Chen, Mouxiao Huang, Wei Li

The misuse of AI imagery can have harmful societal effects, prompting the creation of detectors to combat issues like the spread of fake news. Existing methods can effectively detect images generated by seen generators, but it is challenging to detect those generated by unseen generators. They do not concentrate on amplifying the output discrepancy when dete

Fabio Vieira Roger Leenders Joris Mulder

Large relational-event history data stemming from large networks are becoming increasingly available due to recent technological developments (e.g. digital communication, online databases, etc). This opens many new doors to learning about complex interaction behavior between actors in temporal social networks. The relational event model has become the gold s

Wojciech Brylinski, Marek Gazdzicki, Francesco Giacosa, Mark Gorenstein

Recently, the NA61/SHINE collaboration at the CERN SPS reported evidence of isospin-symmetry violation in high-energy nuclear collisions [Nature Commun. 16, 2849 (2025)]. The effect was observed in the relative yields of charged and neutral kaons and cannot be explained by known sources of isospin symmetry breaking. In this work, we extend the theoretical an

Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu

Causal inference from longitudinal observational data is a challenging problem due to the difficulty in correctly identifying the time-dependent confounders, especially in the presence of latent time-dependent confounders. Instrumental variable (IV) is a powerful tool for addressing the latent confounders issue, but the traditional IV technique cannot deal w

Jan Sobotka, Petr Šimánek, Daniel Vašata

Optimization is an integral part of modern deep learning. Recently, the concept of learned optimizers has emerged as a way to accelerate this optimization process by replacing traditional, hand-crafted algorithms with meta-learned functions. Despite the initial promising results of these methods, issues with stability and generalization still remain, limitin

Anirban Chatterjee

We explore the interaction between dark matter and curvature-driven dark energy within viable $f(R)$ gravity models, employing the phase-space analysis approach of linear stability theory. By incorporating an interacting term, denoted as $\mathcal{Q}=\alpha H \tilde{\rho}_{\rm m}\left(\frac{\kappa^2 }{3H^2}\rho_{\rm curv} + 1 \right)$, into the continuity eq

Mintu Nandi, Sudip Chattopadhyay, Somshubhro Bandyopadhyay, Suman K Banik

Signal propagation in biochemical networks is characterized by the inherent randomness in gene expression and fluctuations of the environmental components, commonly known as intrinsic and extrinsic noise, respectively. We present a theoretical framework for noise propagation in a generic two-step cascade (S$\rightarrow$X$\rightarrow$Y) regarding intrinsic an

David Ziemkiewicz, Gerard Czajkowski, Sylwia Zielińska-Raczyńska

Combining the microscopic calculation of superlattice minibands and the macroscopic real density matrix approach one can obtain electric susceptibilities of the superlattice system irradiated by an electromagnetical wave. It is shown how to compute the dispersion relation, excitonic resonances positions and susceptibility of Cu$_2$O/MgO based superlattice (S

Mirco Ciallella, Stephane Clain, Elena Gaburro, Mario Ricchiuto

In this paper we present a novel approach for the design of high order general boundary conditions when approximating solutions of the Euler equations on domains with curved boundaries, using meshes which may not be boundary conformal. When dealing with curved boundaries and/or unfitted discretizations, the consistency of boundary conditions is a well-known

Ayush Singh, Aayush J Rana, Akash Kumar, Shruti Vyas

In this work, we focus on label efficient learning for video action detection. We develop a novel semi-supervised active learning approach which utilizes both labeled as well as unlabeled data along with informative sample selection for action detection. Video action detection requires spatio-temporal localization along with classification, which poses sever

Yuxuan Song, Jingjing Gong, Minkai Xu, Ziyao Cao

The generation of 3D molecules requires simultaneously deciding the categorical features~(atom types) and continuous features~(atom coordinates). Deep generative models, especially Diffusion Models (DMs), have demonstrated effectiveness in generating feature-rich geometries. However, existing DMs typically suffer from unstable probability dynamics with ineff

Roland Albers, Henrik Andrews, Gabriele Boccacci, Vasco D. C Pires

Induced magnetospheres form around planetary bodies with atmospheres through the interaction of the solar wind with their ionosphere. Induced magnetospheres are highly dependent on the solar wind conditions and have only been studied with single spacecraft missions in the past. This gap in knowledge could be addressed by a multi-spacecraft plasma mission, op

Anran Qi, Takeo Igarashi

We address the problem of modifying a given well-designed 2D sewing pattern to accommodate garment edits in the 3D space. Existing methods usually adjust the sewing pattern by applying uniform flattening to the 3D garment. The problems are twofold: first, it ignores local scaling of the 2D sewing pattern such as shrinking ribs of cuffs; second, it does not r

Shaopeng Zhai, Jie Wang, Tianyi Zhang, Fuxian Huang

Building embodied agents on integrating Large Language Models (LLMs) and Reinforcement Learning (RL) have revolutionized human-AI interaction: researchers can now leverage language instructions to plan decision-making for open-ended tasks. However, existing research faces challenges in meeting the requirement of open-endedness. They typically either train LL

D. E. Morosan, J. Pomoell, C. Palmroos, N. Dresing

One of the most prominent sources for energetic particles in our solar system are huge eruptions of magnetised plasma from the Sun called coronal mass ejections (CMEs), which usually drive shocks that accelerate charged particles up to relativistic energies. In particular, energetic electron beams can generate radio bursts through the plasma emission mechani

I-Jieh Liu, Ci-Siang Lin, Fu-En Yang, Yu-Chiang Frank Wang

Federated Learning (FL) is an emerging paradigm that enables multiple users to collaboratively train a robust model in a privacy-preserving manner without sharing their private data. Most existing approaches of FL only consider traditional single-label image classification, ignoring the impact when transferring the task to multi-label image classification. N

Angela Pistoia, Tonia Ricciardi

We construct a new family of sign-changing solutions for a two-dimensional Lane-Emden problem with large exponent whose shape resembles a tower with alternating sign of bubbles solving different singular Liouville equations on the whole plane.

Patrik Vacek, David Hurych, Karel Zimmermann, Patrick Perez

Learning without supervision how to predict 3D scene flows from point clouds is essential to many perception systems. We propose a novel learning framework for this task which improves the necessary regularization. Relying on the assumption that scene elements are mostly rigid, current smoothness losses are built on the definition of "rigid clusters" in the

Mario Piris

This chapter provides a comprehensive review of fundamental concepts related to approximate natural orbital functionals (NOFs), emphasizing their significance in quantum chemistry and physics. Focusing on fermions, the discussion excludes considerations of finite temperature and systems with a variable number of particles. The theoretical foundation for appr

Timothy W. H. Yiu, Harish K. Vedantham, Joseph R. Callingham, Maximilian N. Günther

Radio observations of stars trace the plasma conditions and magnetic field properties of stellar magnetospheres and coronae. Depending on the plasma conditions at the emitter site, radio emission in the metre- and decimetre-wave bands is generated via different mechanisms such as gyrosynchrotron, electron cyclotron maser instability, and plasma radiation pro

Santiago Iglesias Álvarez, Enrique Díez Alonso, María Luisa Sánchez, Javier Rodríguez Rodríguez

The transit method is one of the most relevant exoplanet detection techniques, which consists of detecting periodic eclipses in the light curves of stars. This is not always easy due to the presence of noise in the light curves, which is induced, for example, by the response of a telescope to stellar flux. For this reason, we aimed to develop an artificial n

Eliran Abutbul, Yohay Kaplan, Naama Krasne, Oren Somekh

With yearly revenue exceeding one billion USD, Yahoo Gemini native advertising marketplace serves more than two billion impressions daily to hundreds of millions of unique users. One of the fastest growing segments of Gemini native is dynamic-product-ads (DPA), where major advertisers, such as Amazon and Walmart, provide catalogs with millions of products fo

Onur Dizdar, Stephen Wang

In this letter, we design a downlink multi-user communication framework based on Rate-Splitting Multiple Access (RSMA) for semantic-aware networks. First, we formulate an optimization problem to obtain the optimal user scheduling, precoding, and power allocation schemes jointly. We consider the metric Age of Incorrect Information (AoII) in the objective func

Yuwei Han, Yuni Lai, Yulin Zhu, Kai Zhou

Graph Neural Networks (GNNs) have become widely used in the field of graph mining. However, these networks are vulnerable to structural perturbations. While many research efforts have focused on analyzing vulnerability through poisoning attacks, we have identified an inefficiency in current attack losses. These losses steer the attack strategy towards modify

Kieron Ivy Turk, Alice Hutchings

Personal item tracking devices are popular for locating lost items such as keys, wallets, and suitcases. Originally created to help users find personal items quickly, these devices are now being abused by stalkers and domestic abusers to track their victims' location over time. Some device manufacturers created `anti-stalking features' in response, and later

Alex Grant, Colm O'Dwyer

The influence of thickness gradient and structural order on the spectral response of opal photonic crystals (PhCs) grown by evaporation-induced self-assembly (EISA) are presented. SEM imaging and angle resolved optical transmission spectroscopy are used to investigate the evolution of the PBG along a thickness gradient for opals grown from five different col

Eduard Eiben, Robert Ganian, Thekla Hamm, Viktoriia Korchemna

Synchronous dynamic systems are well-established models that have been used to capture a range of phenomena in networks, including opinion diffusion, spread of disease and product adoption. We study the three most notable problems in synchronous dynamic systems: whether the system will transition to a target configuration from a starting configuration, wheth

Jiří Lipovský, Tomáš Macháček

The spectral determinant is usually defined using the spectral zeta function that is meromorphically continued to zero. In this definition, the complex logarithms of the eigenvalues appear. Hence the notion of the spectral determinant depends on the way how one chooses the branch cut in the definition of the logarithm. We give results for the non-self-adjoin

Ioana Ghenciu, Roxana Popescu

Suppose $X$ and $Y$ are Banach spaces, $K$ is a compact Hausdorff space, $\Sigma$ is the $\sigma$-algebra of Borel subsets of $K$, $C(K,X)$ is the Banach space of all continuous $X$-valued functions (with the supremum norm), and $T:C(K,X)\to Y$ is a strongly bounded operator with representing measure $m:\Sigma \to L(X,Y)$. We show that if $\hat{T}: B(K, X) \

Dmitri Sokolovski

Feynman famously recommended accepting the basic principles of quantum mechanics without trying to guess the machinery behind the law. One of the corollaries of the Uncertainty Principle is that the knowledge of probability amplitudes does not allow one to make meaningful statements about the past of an unobserved quantum system. A particular type of reasoni

Ferenc Fejes, Ferenc Orosi, Balázs Varga, János Farkas

Deterministic communication means reliable packet forwarding with close to zero packet loss and bounded latency. Packet loss or delay above a threshold caused by, e.g., equipment failure or malfunction could be catastrophic for applications that require deterministic communication. To meet loss related targets, per-packet service protection has been introduc

Yang Zhou, Wenzhang Wang, Guiguo Ge, Jinting Li

A dynamic gravimeter with an atomic interferometer (AI) can perform absolute gravity measurements with high precision. AI-based dynamic gravity measurement is a type of joint measurement that uses AI sensors and a classical accelerometer. The coupling of the two sensors may degrade the measurement precision. In this study, we analyzed the cross-coupling effe

Qi Shi

Two different forms of responsibility, counterfactual and seeing-to-it, have been extensively discussed in the philosophy and AI in the context of a single agent or multiple agents acting simultaneously. Although the generalisation of counterfactual responsibility to a setting where multiple agents act in some order is relatively straightforward, the same ca

Diego Vidaurre, Laura Masaracchia, Nick Y. Larsen, Lenno R. P. T Ruijters

We propose the Gaussian-Linear Hidden Markov model (GLHMM), a generalisation of different types of HMMs commonly used in neuroscience. In short, the GLHMM is a general framework where linear regression is used to flexibly parameterise the Gaussian state distribution, thereby accommodating a wide range of uses -- including unsupervised, encoding and decoding

Sanjeev Kumar, Radha Raman Gautam

We investigate the properties of neutrino mass matrices that incorporate texture zeros and generalized CP symmetries associated with tribimaximal mixing. By combining these approaches, we derive predictive neutrino mass matrices and explore their implications for mass hierarchies, mixing angles, and CP-violating phases. We find that the three angles defining

Sindu B. S., Jan Hamaekers

Epoxy polymers are used in wide range of applications. The properties and performance of epoxy polymers depend upon various factors like the type of constituents and their proportions used and other process parameters. The conventional way of developing epoxy polymers is usually labor-intensive and may not be fully efficient, which has resulted in epoxy poly

Yogesh M Joshi

Thixotropy is characterized by an increase in viscosity when a material is subjected to no flow (quiescent) or weak flow conditions and a decrease in viscosity when it is subjected to strong flow conditions. The characteristic timescale associated with the thixotropic phenomenon, particularly how the viscosity increases with time, has been termed the thixotr

Alfonso Maiellaro, Hervé Aubin, Andrej Mesaros, Pascal Simon

Antiferromagnetic spin-1 chains host the celebrated symmetry protected topological Haldane phase, whose spin-1/2 edge states were evidenced in bulk by, e.g., Electron Spin Resonance (ESR). Recent success in assembling effective spin-1 antiferromagnetic chains from nanographene and porphyrin molecules opens the possibility of local, site-by-site, characteriza

Lifan Luo, Boyang Zhang, Zhijie Peng, Yik Kin Cheung

As automation technologies advance, the need for compact and multi-modal sensors in robotic applications is growing. To address this demand, we introduce CompdVision, a novel sensor that employs a compound-eye imaging system to combine near-field 3D visual and tactile sensing within a compact form factor. CompdVision utilizes two types of vision units to add

Rohan Deb, Yikun Ban, Shiliang Zuo, Jingrui He

Recent works have shown a reduction from contextual bandits to online regression under a realizability assumption [Foster and Rakhlin, 2020, Foster and Krishnamurthy, 2021]. In this work, we investigate the use of neural networks for such online regression and associated Neural Contextual Bandits (NeuCBs). Using existing results for wide networks, one can re

Eduard Eiben, Robert Ganian, Iyad Kanj

In Coordinated Motion Planning (CMP), we are given a rectangular-grid on which $k$ robots occupy $k$ distinct starting gridpoints and need to reach $k$ distinct destination gridpoints. In each time step, any robot may move to a neighboring gridpoint or stay in its current gridpoint, provided that it does not collide with other robots. The goal is to compute

Chengting Yu, Fengzhao Zhang, Hanzhi Ma, Aili Wang

Traditional end-to-end (E2E) training of deep networks necessitates storing intermediate activations for back-propagation, resulting in a large memory footprint on GPUs and restricted model parallelization. As an alternative, greedy local learning partitions the network into gradient-isolated modules and trains supervisely based on local preliminary losses,

Irinel Caprini

The difference between fixed-order (FO) and contour-improved (CI) formulations of QCD perturbation theory limits the precision of the strong coupling determined from the hadronic decay of the $\tau$ lepton. Recently, several attempts to understand the mathematical origin of the difference and to solve it by subtracting the dominant infrared renormalon diverg

Khaled Eldowa, Andrea Paudice

In this paper, we provide novel tail bounds on the optimization error of Stochastic Mirror Descent for convex and Lipschitz objectives. Our analysis extends the existing tail bounds from the classical light-tailed Sub-Gaussian noise case to heavier-tailed noise regimes. We study the optimization error of the last iterate as well as the average of the iterate

Yang Trista Cao, Anna Sotnikova, Jieyu Zhao, Linda X. Zou

Multilingual large language models have gained prominence for their proficiency in processing and generating text across languages. Like their monolingual counterparts, multilingual models are likely to pick up on stereotypes and other social biases present in their training data. In this paper, we study a phenomenon we term stereotype leakage, which refers

Konstantinos Dogeas, Thomas Erlebach, Frank Kammer, Johannes Meintrup

Temporal graphs are graphs where the edge set can change in each time step, and the vertex set stays the same. Exploration of temporal graphs whose snapshot in each time step is a connected graph, called connected temporal graphs, has been widely studied. We extend the concept of graph automorphisms from static graphs to temporal graphs and show that symmetr

Clément Pierquin, Bastien Zimmermann, Matthieu Boussard

Artificial intelligence and data access are already mainstream. One of the main challenges when designing an artificial intelligence or disclosing content from a database is preserving the privacy of individuals who participate in the process. Differential privacy for synthetic data generation has received much attention due to the ability of preserving priv

Alexander Braverman, David Kazhdan

Let $G$ be a split reductive group over a finite field $k$. In this note we study the space $V$ of finitely supported functions on the set of isomorphism classes $G$-bundles on the projective line ${\mathbb P}^1$ endowed with a trivialization at $0$ and $\infty$. We show that $V$ is naturally isomorphic to the regular bimodule over the Hecke algebra $A$ of t

Matthieu Terris, Chao Tang, Adrian Jackson, Yves Wiaux

Plug-and-Play (PnP) algorithms are appealing alternatives to proximal algorithms when solving inverse imaging problems. By learning a Deep Neural Network (DNN) denoiser behaving as a proximal operator, one waives the computational complexity of optimisation algorithms induced by sophisticated image priors, and the sub-optimality of handcrafted priors compare

Ivan Fung, Lahiru Samarakoon, Samuel J. Broughton

Due to the scarcity of publicly available diarization data, the model performance can be improved by training a single model with data from different domains. In this work, we propose to incorporate domain information to train a single end-to-end diarization model for multiple domains. First, we employ domain adaptive training with parameter-efficient adapte

Kamber R. Schwarz, Thomas Henning, Valentin Christiaens, Danny Gasman

SY Cha is a T Tauri star surrounded by a protoplanetary disk with a large cavity seen in the millimeter continuum but has the spectral energy distribution (SED) of a full disk. Here we report the first results from JWST-MIRI Medium Resolution Spectrometer (MRS) observations taken as part of the MIRI mid-INfrared Disk Survey (MINDS) GTO Program. The much impr

Brendan Alinquant, Robert Osburn

In this note, we prove the last remaining case of the original 15 two-term supercongruence conjectures for sporadic sequences. The proof utilizes a new representation for this sequence (due to Gorodetsky) as the constant term of powers of a Laurent polynomial.

Abdelrahman Eldesokey, Peter Wonka

We propose a zero-shot approach for generating consistent videos of animated characters based on Text-to-Image (T2I) diffusion models. Existing Text-to-Video (T2V) methods are expensive to train and require large-scale video datasets to produce diverse characters and motions. At the same time, their zero-shot alternatives fail to produce temporally consisten

Xiaochuan Li, Baoyu Fan, Runze Zhang, Liang Jin

The emergence of ChatGPT has once again sparked research in generative artificial intelligence (GAI). While people have been amazed by the generated results, they have also noticed the reasoning potential reflected in the generated textual content. However, this current ability for causal reasoning is primarily limited to the domain of language generation, s

Boxuan Yang, Bibek Bhujel, Daniel G. Chica, Evan J. Telford

The magnetic proximity effect can induce a spin dependent exchange shift in the band structure of graphene. This produces a magnetization and a spin polarization of the electron/hole carriers in this material, paving the way for its use as an active component in spintronics devices. The electrostatic control of this spin polarization in graphene has however

Yimo Deng, Huangxun Chen

To prevent Text-to-Image (T2I) models from generating unethical images, people deploy safety filters to block inappropriate drawing prompts. Previous works have employed token replacement to search adversarial prompts that attempt to bypass these filters, but they have become ineffective as nonsensical tokens fail semantic logic checks. In this paper, we app

Alexander Edthofer, Iris Feldhammer, Thomas Fenzl, Andreas Körner

Sleep stage classification is a widely discussed topic, due to its importance in the diagnosis of sleep disorders, e.g. insomnia. Analysis of the brain activity during sleep is necessary to gain further insight into the processing that occurs in our brains. We want to use permutation entropy as a model for this analysis. Therefore, the signal processing in t

Jing Xu

Although transformer is preferred in natural language processing, some studies has only been applied to the field of medical imaging in recent years. For its long-term dependency, the transformer is expected to contribute to unconventional convolution neural net conquer their inherent spatial induction bias. The lately suggested transformer-based segmentatio

Yong Xiao, Yue-Ying Liu

The inclusion of higher derivative terms in the gravitational action brings about corrections to the original forms of black hole solutions and thermodynamics. A simple and valuable approach has emerged over two decades ago, which states that the first order corrections to black hole thermodynamics, caused by higher derivative terms, can be achieved without

Ming Lu, Zhihao Duan, Fengqing Zhu, Zhan Ma

Recently, probabilistic predictive coding that directly models the conditional distribution of latent features across successive frames for temporal redundancy removal has yielded promising results. Existing methods using a single-scale Variational AutoEncoder (VAE) must devise complex networks for conditional probability estimation in latent space, neglecti

Kaipeng Zheng, Weiran Huang, Lichao Sun

Few-shot learning has been studied to adapt models to tasks with very few samples. It holds profound significance, particularly in clinical tasks, due to the high annotation cost of medical images. Several works have explored few-shot learning on medical images, yet they still require a large number of medical images for pre-training models to gain domain-sp

Abdullah Shafqat, Oliver Weeger, Bai-Xiang Xu

In this work, an efficient and robust isogeometric three-dimensional solid-beam finite element is developed for large deformations and finite rotations with merely displacements as degrees of freedom. The finite strain theory and hyperelastic constitutive models are considered and B-Spline and NURBS are employed for the finite element discretization. Similar

Sabine Käfer, Niklas Niemeyer, Johannes Tölle, Johannes Neugebauer

We present an implementation of Triplet Excitation-Energy Transfer (TEET) couplings based on subsystem-based Time-Dependent Density-Functional Theory (sTDDFT). TEET couplings are systematically investigated by comparing "exact" and approximate variants of sTDDFT. We demonstrate that, while sTDDFT utilizing explicit approximate Non-Additive Kinetic Energy (NA

Matthieu Elineau, Renaud Loison, Stéphane Méric, Raphaël Gillard

This letter proposes the design and measurement of a periodic metasurface that achieves anomalous reflection with reduced RCS in a given parasitic direction. A previous study proposed a semi-analytical model to predict the RCS behaviour of such a metasurface. However, this first study did not include any experimental exploration to verify the theoretical res

Matteo Bortoletto, Lei Shi, Andreas Bulling

We propose the Intuitive Reasoning Network (IRENE) - a novel neural model for intuitive psychological reasoning about agents' goals, preferences, and actions that can generalise previous experiences to new situations. IRENE combines a graph neural network for learning agent and world state representations with a transformer to encode the task context. When e

Leila Methnani, Virginia Dignum, Andreas Theodorou

Understanding when and why to apply any given eXplainable Artificial Intelligence (XAI) technique is not a straightforward task. There is no single approach that is best suited for a given context. This paper aims to address the challenge of selecting the most appropriate explainer given the context in which an explanation is required. For AI explainability

Alain Blaustein

During the past century, biologists and mathematicians investigated two mechanisms underlying bacteria motion: the run phase during which bacteria move in straight lines and the tumble phase in which they change their orientation. When surrounded by a chemical attractant, experiments show that bacteria increase their run time as moving up concentration gradi

Patrick Bernard

We prove that Ma{\~n}{\'e} generic convex Hamiltonians have only non-degenerate periodic orbits on a given energy level. This result was stated, but not proved, in the literature.

Jérémie Dudouet, Diego Gruyer

Over the past few decades, a vast amount of information on the structure of atomic nuclei has been collected, compiled, and evaluated. Accurate and reliable data are essential for the understanding of the behavior of atomic nuclei. Accessing and utilizing these data, spread among different databases, has remained challenging for many researchers due to the c

Patrick Bernard

The purpose of this paper is to provide a short and self-contained account on Siegel's Theorem, as improved by Bruno, which states that a holomorphic map f of C which fixes 0 can be locally linearized, under certain conditions on the multiplier.

Krishna Kaipa, Nupur Patanker, Puspendu Pradhan

We study the problem of classifying the lines of the projective $3$-space $PG(3,q)$ over a finite field $GF(q)$ into orbits of the group $G=PGL(2,q)$ of linear symmetries of the twisted cubic $C$. A generic line neither intersects $C$ nor lies in any of its osculating planes. While the non-generic lines have been classified into $G$-orbits in literature, it

Esteban Christiann, Eric Sanlaville, Jason Schoeters

A temporal graph can be represented by a graph with an edge labelling, such that an edge is present in the network if and only if the edge is assigned the corresponding time label. A journey is a labelled path in a temporal graph such that labels on successive edges of the path are increasing, and if all vertices admit journeys to all other vertices, the tem

Fan Ma, Xiaojie Jin, Heng Wang, Yuchen Xian

Recent advances in large video-language models have displayed promising outcomes in video comprehension. Current approaches straightforwardly convert video into language tokens and employ large language models for multi-modal tasks. However, this method often leads to the generation of irrelevant content, commonly known as "hallucination", as the length of t

Ying-Ming Xie, Yizhou Liu, Naoto Nagaosa

The skyrmion crystal (SkX) and helix (HL) phases, present in typical chiral magnets, can each be considered as forms of density waves but with distinct topologies. The SkX exhibits gyrodynamics analogous to electrons under a magnetic field, while the HL state resembles topological trivial spin density waves. However, unlike the charge density waves, the theo

Stefano Scali, Chukwudubem Umeano, Oleksandr Kyriienko

We develop a quantum topological data analysis (QTDA) protocol based on the estimation of the density of states (DOS) of the combinatorial Laplacian. Computing topological features of graphs and simplicial complexes is crucial for analyzing datasets and building explainable AI solutions. This task becomes computationally hard for simplicial complexes with ov

Mytraya Gattu, G. J. Sreejith, J. K. Jain

The fractional quantum Hall states are non-Fermi liquids of electrons, in that their ground states and low energy excitations are described not in terms of electrons but in terms of composite fermions which are bound states of electrons and $2p$ quantized vortices. An electron or a hole at filling factor $\nu=n/(2pn+1)$, where $p,n$ are integers, is a comple

P. Laskos-Patkos, P. S. Koliogiannis, Ch. C. Moustakidis

The recent analysis on the central compact object in the HESS J1731-347 remnant suggests interestingly small values for its mass and radius. Such an observation favors soft nuclear models that may be challenged by the observation of massive compact stars. In contrast, the recent PREX-II experiment, concerning the neutron skin thickness of $^{208}$Pb, points

Naufal Shidqi, Chaeyoon Jeong, Sungwon Park, Elke Zeller

Climate downscaling is a crucial technique within climate research, serving to project low-resolution (LR) climate data to higher resolutions (HR). Previous research has demonstrated the effectiveness of deep learning for downscaling tasks. However, most deep learning models for climate downscaling may not perform optimally for high scaling factors (i.e., 4x

Marlom Ramalho, Jouni Suhonen

Recent years have witnessed an expanding interest in experimental studies of $\beta$ electrons (electrons emitted in $\beta^-$ decay transitions) and their energy distributions, the so-called $\beta$-electron spectra. These experiments are interested mainly in $\beta$ transitions with electron spectra sensitive to the effective value of the weak axial coupli

Maxime Würsch, Andrei Kucharavy, Dimitri Percia David, Alain Mermoud

The cybersecurity landscape evolves rapidly and poses threats to organizations. To enhance resilience, one needs to track the latest developments and trends in the domain. It has been demonstrated that standard bibliometrics approaches show their limits in such a fast-evolving domain. For this purpose, we use large language models (LLMs) to extract relevant

Evgeny Bespalov

We consider the problem of existence of perfect $2$-colorings in the Doob graphs $D(m,n)$ and $4$-ary Hamming graphs $H(n,4)$. We characterize all parameters for which multifold $1$-perfect code in $D(m,n)$ exists. Also, we prove that for any pair $(b,c)$ that satisfy standard conditions (Lloyd's and sphere-packing conditions) there is perfect $(b,c)$-colori

A. Lega, D. Novel, T. Facchinelli, C. Sgro'

Gas Electron Multiplier (GEM) detectors are crucial for enabling high-resolution X-ray polarisation of astrophysical sources when coupled to custom pixel readout ASIC in Gas Pixel Detectors (GPD), as in the Imaging X-ray Polarimetry Explorer (IXPE), the Polarlight cubesat pathfinder and the PFA telescope onboard the future large enhanced X-ray Timing and Pol

Junli Jiang, Pavel Naumov

In many real-world situations, there is often not enough information to know that a certain strategy will succeed in achieving the goal, but there is a good reason to believe that it will. The paper introduces the term ``doxastic'' for such strategies. The main technical contribution is a sound and complete logical system that describes the interplay between

Eriks Klotins, Michael Unterkalmsteiner, Panagiota Chatzipetrou, Tony Gorschek

Context: Software start-ups are emerging as suppliers of innovation and software-intensive products. However, traditional software engineering practices are not evaluated in the context, nor adopted to goals and challenges of start-ups. As a result, there is insufficient support for software engineering in the start-up context. Objective: We aim to collect d

Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun

Large language models (LLMs) are increasingly used for complex tasks that require multiple generation calls, advanced prompting techniques, control flow, and structured inputs/outputs. However, efficient systems are lacking for programming and executing these applications. We introduce SGLang, a system for efficient execution of complex language model progra

Andac Demir, Francis Prael, Bulent Kiziltan

In this study, we present a novel computational method for generating molecular fingerprints using multiparameter persistent homology (MPPH). This technique holds considerable significance for drug discovery and materials science, where precise molecular property prediction is vital. By integrating SE(3)-invariance with Vietoris-Rips persistent homology, we

Eduard Eiben, Robert Ganian, Iyad Kanj, Sebastian Ordyniak

Hypersphere classification is a classical and foundational method that can provide easy-to-process explanations for the classification of real-valued and binary data. However, obtaining an (ideally concise) explanation via hypersphere classification is much more difficult when dealing with binary data than real-valued data. In this paper, we perform the firs

Jiawei Sun, Bin Zhao, Dong Wang, Zhigang Wang

Quantitative phase imaging (QPI) through multi-core fibers (MCFs) has been an emerging in vivo label-free endoscopic imaging modality with minimal invasiveness. However, the computational demands of conventional iterative phase retrieval algorithms have limited their real-time imaging potential. We demonstrate a learning-based MCF phase imaging method, that

Madalina Olteanu, Fabrice Rossi, Florian Yger

The impact of outliers and anomalies on model estimation and data processing is of paramount importance, as evidenced by the extensive body of research spanning various fields over several decades: thousands of research papers have been published on the subject. As a consequence, numerous reviews, surveys, and textbooks have sought to summarize the existing