April 2024 arXiv papers — page 179

Swetha Ganesh, Washim Uddin Mondal, Vaneet Aggarwal

We present two Policy Gradient-based algorithms with general parametrization in the context of infinite-horizon average reward Markov Decision Process (MDP). The first one employs Implicit Gradient Transport for variance reduction, ensuring an expected regret of the order $\tilde{\mathcal{O}}(T^{2/3})$. The second approach, rooted in Hessian-based techniques

N. Parmiggiani, A. Bulgarelli, A. Macaluso, A. Ursi

AGILE is an Italian Space Agency (ASI) space mission launched in 2007 to study X-ray and gamma-ray phenomena in the energy range from $\sim$20 keV to $\sim$10 GeV. The AGILE AntiCoincidence System (ACS) detects hard-X photons in the 50 - 200 keV energy range and continuously stores each panel's count rates in the telemetry. We developed a new Deep Learning (

Yifan Wu, Mengjin Dong, Rohit Jena, Chen Qin

Deformable image registration (DIR) is crucial in medical image analysis, enabling the exploration of biological dynamics such as organ motions and longitudinal changes in imaging. Leveraging Neural Ordinary Differential Equations (ODE) for registration, this extension work discusses how this framework can aid in the characterization of sequential biological

Brian J. McMahon, Kenton R. Brown, Creston D. Herold, Brian C. Sawyer

We implement individual addressing and readout of ions in a rigidly rotating planar crystal in a compact, permanent magnet Penning trap. The crystal of $^{40}$Ca$^+$ is trapped and stabilized without defects via a rotating triangular potential. The trapped ion fluorescence is detected in the rotating frame for parallel readout. The qubit is encoded in the me

Oleksandr Zheliuk, Yuliia Kreminska, Qundong Fu, Davide Pizzirani

Two-dimensional electron systems (2DES) are promising for investigating correlated quantum phenomena. In particular, 2D oxides provide a platform that can host various quantum phases such as quantized Hall effect, superconductivity, or magnetism. The realization of such quantum phases in 2D oxides heavily relies on dedicated heterostructure growths. Here we

Eric Modesitt, Haicheng Yin, Williams Huang Wang, Brian Lu

The task of Electroencephalogram (EEG) analysis is paramount to the development of Brain-Computer Interfaces (BCIs). However, to reach the goal of developing robust, useful BCIs depends heavily on the speed and the accuracy at which BCIs can understand neural dynamics. In response to that goal, this paper details the integration of pre-trained Vision Transfo

Sara Rosenthal, Avirup Sil, Radu Florian, Salim Roukos

Retrieval Augmented Generation (RAG) has become a popular application for large language models. It is preferable that successful RAG systems provide accurate answers that are supported by being grounded in a passage without any hallucinations. While considerable work is required for building a full RAG pipeline, being able to benchmark performance is also n

Xuting Yang, Pritha Mukherjee, Minjeong Kim, Hongyan Mei

We demonstrate atomic magnetometry using a metasurface polarizing beamsplitter fabricated on a silicon-on-sapphire (SOS) platform. The metasurface splits a beam that is near-resonant with the rubidium atoms (795 nm) into orthogonal linear polarizations, enabling measurement of magnetically sensitive circular birefringence in a rubidium vapor through balanced

Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein

Controllability plays a crucial role in video generation, as it allows users to create and edit content more precisely. Existing models, however, lack control of camera pose that serves as a cinematic language to express deeper narrative nuances. To alleviate this issue, we introduce CameraCtrl, enabling accurate camera pose control for video diffusion model

D. Ciangottini, A. Forti, L. Heinrich, N. Skidmore

This white paper presents the current status of the R&D for Analysis Facilities (AFs) and attempts to summarize the views on the future direction of these facilities. These views have been collected through the High Energy Physics (HEP) Software Foundation's (HSF) Analysis Facilities forum, established in March 2022, the Analysis Ecosystems II workshop, that

Oleksandr Gamayun, Oleg Lychkovskiy

A mobile impurity particle immersed in a quantum fluid forms a polaron - a quasiparticle consisting of the impurity and a local disturbance of the fluid around it. We ask what happens to a one-dimensional polaron after a kick, i.e. an abrupt application of a force that instantly delivers a finite impulse to the impurity. In the framework of an integrable mod

Rob Geada, David Towers, Matthew Forshaw, Amir Atapour-Abarghouei

The boundless possibility of neural networks which can be used to solve a problem -- each with different performance -- leads to a situation where a Deep Learning expert is required to identify the best neural network. This goes against the hope of removing the need for experts. Neural Architecture Search (NAS) offers a solution to this by automatically iden

Alexandros Haliassos, Andreas Zinonos, Rodrigo Mira, Stavros Petridis

Self-supervision has recently shown great promise for learning visual and auditory speech representations from unlabelled data. In this work, we propose BRAVEn, an extension to the recent RAVEn method, which learns speech representations entirely from raw audio-visual data. Our modifications to RAVEn enable BRAVEn to achieve state-of-the-art results among se

Daniel B. Seaton, Amir Caspi, Nathalia Alzate, Sarah J. Davis

We present results of a dual eclipse expedition to observe the solar corona from two sites during the annular solar eclipse of 2023 October 14, using a novel coronagraph designed to be accessible for amateurs and students to build and deploy. The coronagraph "CATEcor" builds on the standardized eclipse observing equipment developed for the Citizen CATE 2024

Slava G. Turyshev, Sheng-wey Chiow, Nan Yu

Tetrahedral configurations of spacecraft on unperturbed heliocentric orbits allow for highly precise observations of small spatial changes in the gravitational field, especially those affecting the gravity gradient tensor (GGT). The resulting high sensitivity may be used to search for new physics that could manifest itself via deviations from general relativ

Bhaskar Shukla, Pranaya Pratik Das, David Dudal, Subhash Mahapatra

We study the relationship between the standard or extended thermodynamic phase structure of various AdS black holes and the Lyapunov exponents associated with the null and time-like geodesics. We consider dyonic, Bardeen, Gauss-Bonnet, and Lorentz-symmetry breaking massive gravity black holes and calculate the Lyapunov exponents of massless and massive parti

Alexander Sakhnovich

Using factorisation and Arov-Krein inequality results, we derive important inequalities (in terms of $S$-nodes) in interpolation problems.

Rakheon Kim, Jingfei Zhang

While covariance matrices have been widely studied in many scientific fields, relatively limited progress has been made on estimating conditional covariances that permits a large covariance matrix to vary with high-dimensional subject-level covariates. In this paper, we present a new sparse covariance regression framework that models the covariance matrix as

Dawei Li, William Hogan, Jingbo Shang

Recent works in relation extraction (RE) have achieved promising benchmark accuracy; however, our adversarial attack experiments show that these works excessively rely on entities, making their generalization capability questionable. To address this issue, we propose an adversarial training method specifically designed for RE. Our approach introduces both se

Alexander Bernal, J. Alberto Casas, Jesus M. Moreno

We evaluate the maximal Clauser-Horne-Shimony-Holt (CHSH) violation for a generic (typically mixed) qubit-qudit state, obtaining easily computable expressions in arbitrary qudit dimension. This represents the optimal (2-2-2) Bell nonlocality for this kind of systems. The work generalizes the well-known Horodeckis result for a qubit-qubit setup. We also give

Rabindra Basnet, Dinesh Upreti, Taksh Patel, Santosh Karki Chhetri

Tuning magnetic properties in layered magnets is an important route to realize novel phenomenon related to two-dimensional (2D) magnetism. Recently, tuning antiferromagnetic (AFM) properties through substitution and intercalation techniques have been widely studied in MPX3 compounds. Interesting phenomena, such as diverse AFM structures and even the signatur

Denis Antipov, Benjamin Doerr, Alexandra Ivanova

Experience shows that typical evolutionary algorithms can cope well with stochastic disturbances such as noisy function evaluations. In this first mathematical runtime analysis of the $(1+\lambda)$ and $(1,\lambda)$ evolutionary algorithms in the presence of prior bit-wise noise, we show that both algorithms can tolerate constant noise probabilities without

Bhavya Pardasani, K. Decker French

Star-forming galaxies can reach quiescence via rapid transition through merger-triggered starbursts that consequently affect both their kinematics and star formation rates. In this work, we analyze the spatially resolved kinematics of 89 post-starburst galaxies (PSBs) with data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey and p

Suyash Vardhan Mathur, Akshett Rai Jindal, Hardik Mittal, Manish Shrivastava

Conversation is the most natural form of human communication, where each utterance can range over a variety of possible emotions. While significant work has been done towards the detection of emotions in text, relatively little work has been done towards finding the cause of the said emotions, especially in multimodal settings. SemEval 2024 introduces the ta

Aakash A. Sahai

Nanosciences largely rely on plasmons which are quasiparticles constituted by collective oscillations of quantum electron gas composed of conduction band electrons that occupy discrete quantum states. Our work has introduced non-perturbative plasmons with oscillation amplitudes that approach the extreme limit set by breakdown in characteristic coherence. In

Luca Esposito, Lorenzo Lamberti, Giovanni Pisante

We establish regularity results for almost-minimizers of a class of variational problems involving both bulk and interface energies. The bulk energy is of Dirichlet type. The surface energy exhibits anisotropic behaviour and is defined by means of an ellipsoidal density that is H\"older continuous with respect to the position variable.

Sergey Ostapchenko, Günter Sigl

Motivated by the excess of the muon content of cosmic ray induced extensive air showers (EAS), relative to EAS modeling, observed by the Pierre Auger Observatory, and by the tension between Auger data and air shower simulations on the maximal muon production depth $X^{\mu}_{\max}$, we investigate the possibility to modify the corresponding EAS simulation res

Jiyuan Zhong, Hu Ke, Ming Yan

Fundus image segmentation on unseen domains is challenging, especially for the over-parameterized deep models trained on the small medical datasets. To address this challenge, we propose a method named Adaptive Feature-fusion Neural Network (AFNN) for glaucoma segmentation on unseen domains, which mainly consists of three modules: domain adaptor, feature-fus

Yulin Zhang, Yifei Li, Wenjun Ying

We proposed a structure-preserving stabilized parametric finite element method (SPFEM) for the evolution of closed curves under anisotropic surface diffusion with an arbitrary surface energy $\hat{\gamma}(\theta)$. By introducing a non-negative stabilizing function $k(\theta)$ depending on $\hat{\gamma}(\theta)$, we obtained a novel stabilized conservative w

Chen Yang, Yangfan He, Aaron Xuxiang Tian, Dong Chen

In this paper, we introduce a novel approach for autonomous driving trajectory generation by harnessing the complementary strengths of diffusion probabilistic models (a.k.a., diffusion models) and transformers. Our proposed framework, termed the "World-Centric Diffusion Transformer"(WcDT), optimizes the entire trajectory generation process, from feature extr

Grace Guo, Dustin Arendt, Alex Endert

Explainable AI (XAI) tools represent a turn to more human-centered and human-in-the-loop AI approaches that emphasize user needs and perspectives in machine learning model development workflows. However, while the majority of ML resources available today are developed for Python computational environments such as JupyterLab and Jupyter Notebook, the same has

Alberto Bressan, Marco Mazzola, Khai T. Nguyen

The first part of the paper studies a class of optimal control problems in Bolza form, where the dynamics is linear w.r.t.~the control function. A necessary condition is derived, for the optimality of a trajectory which starts at a conjugate point. The second part is concerned with a classical problem in the Calculus of Variations, with free terminal point.

Ryan A DeCrescent, Zixuan Wang, Joseph T Bush, Poolad Imany

Genuine quantum-mechanical effects are readily observable in modern optomechanical systems comprising bosonic ("classical") optical resonators. Here we describe unique features and advantages of optical two-level systems, or qubits, for optomechanics. The qubit state can be coherently controlled using both phonons and resonant or detuned photons. We experime

Lifan Yuan, Ganqu Cui, Hanbin Wang, Ning Ding

We introduce Eurus, a suite of large language models (LLMs) optimized for reasoning. Finetuned from Mistral-7B and CodeLlama-70B, Eurus models achieve state-of-the-art results among open-source models on a diverse set of benchmarks covering mathematics, code generation, and logical reasoning problems. Notably, Eurus-70B beats GPT-3.5 Turbo in reasoning throu

Yufei Duan, Florian Achermann, Jaeyoung Lim, Roland Siegwart

Fixed-wing small uncrewed aerial vehicles (sUAVs) possess the capability to remain airborne for extended durations and traverse vast distances. However, their operation is susceptible to wind conditions, particularly in regions of complex terrain where high wind speeds may push the aircraft beyond its operational limitations, potentially raising safety conce

Herry Pribawanto Suryawan, José Luís da Silva

In this paper, we investigate the Green measure for a class of non-Gaussian processes in $\mathbb{R}^{d}$. These measures are associated with the family of generalized grey Brownian motions $B_{\beta,\alpha}$, $0<\beta\le1$, $0<\alpha\le2$. This family includes both fractional Brownian motion, Brownian motion, and other non-Gaussian processes. We show that t

L. Castaldini, N. Parmiggiani, A. Bulgarelli, L. Baroncelli

The ASTRI Mini-Array project, led by the Italian National Institute for Astrophysics, aims to construct and operate nine Imaging Atmospheric Cherenkov Telescopes for high-energy gamma-ray source study and stellar intensity interferometry. Located at the Teide Astronomical Observatory in Tenerife, the project's software is essential for remote operation, emph

Julia Allen, Kamil Augsten, Giovanni Benato, Neven Blaskovic Kraljevic

This document presents the outcomes of a comprehensive survey conducted among early career researchers (ECRs) in academic particle physics. Running from September 24, 2022, to March 3, 2023, the survey gathered responses from 759 ECRs employed in 39 countries. The study aimed to gain insights into the career prospects and experiences of ECRs while also delvi

Ilmari Kangasniemi, Jani Onninen

Sobolev mappings exhibiting only pointwise quasiregularity-type bounds have arisen in various applications, leading to a recently developed theory of quasiregular values. In this article, we show that by using rescaling, one obtains a direct bridge between this theory and the classical theory of quasiregular maps. More precisely, we prove that a non-constant

Jinbae Im, JeongYeon Nam, Nokyung Park, Hyungmin Lee

Scene Graph Generation (SGG) is a challenging task of detecting objects and predicting relationships between objects. After DETR was developed, one-stage SGG models based on a one-stage object detector have been actively studied. However, complex modeling is used to predict the relationship between objects, and the inherent relationship between object querie

Lily Blaiset, Bruno Bresson, Ludovic Olanier, Élisabeth Guazzelli

We present a simple route to obtain large quantities of suspensions of non-Brownian particles with stimuli-responsive surface properties to study the relation between their flow and interparticle interactions. We perform an alkaline hydrolysis reaction on poly(methyl methacrylate) (PMMA) particles to obtain poly(sodium methacrylate) (PMAA-Na) particles. We c

Pierre C. Bellec, Takuya Koriyama

This paper studies the asymptotics of resampling without replacement in the proportional regime where dimension $p$ and sample size $n$ are of the same order. For a given dataset $(X,y)\in \mathbb{R}^{n\times p}\times \mathbb{R}^n$ and fixed subsample ratio $q\in(0,1)$, the practitioner samples independently of $(X,y)$ iid subsets $I_1,...,I_M$ of $\{1,...,n

Alexander Levin

We generalize the Gr\"obner basis method for free D-modules to the case of several term orderings induced by a partition of the set of basic variables. Using this generalized Gr\"obner basis technique we prove the existence and give a method of computation of a dimension polynomial in several variables associated with a finitely generated D-module. We show t

Twm Stone, Jaz Stoddart

The concept of a 'Ballmer Peak' was first proposed in 2007, postulating that there exists a very specific blood alcohol content which confers superhuman programming ability. More generally, there is a commonly held belief among software engineers that coding is easier and more productive after a few drinks. Using the industry standard for assessment of codin

Katy L. Chubb, Séverine Robert, Clara Sousa-Silva, Sergei N. Yurchenko

The goal of this white paper is to provide a snapshot of the data availability and data needs primarily for the Ariel space mission, but also for related atmospheric studies of exoplanets and brown dwarfs. It covers the following data-related topics: molecular and atomic line lists, line profiles, computed cross-sections and opacities, collision-induced abso

Zhuo Chen, Chengyue Jiang, Kewei Tu

In the age of neural natural language processing, there are plenty of works trying to derive interpretations of neural models. Intuitively, when gold rationales exist during training, one can additionally train the model to match its interpretation with the rationales. However, this intuitive idea has not been fully explored. In this paper, we propose a fram

Krzysztof Jankowski, Bartlomiej Sobieski, Mateusz Kwiatkowski, Jakub Szulc

Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications. The Segment Anything Model is one of the first and most well-known foundation models for computer vision segmentation tasks. This work presents a multi-faceted red-teaming analysis that tests

Mario Bessa, Helder Vilarinho

Our main goal is to understand the stability of second order linear homogeneous differential equations $\ddot x(t)+\alpha(t)\dot x(t)+\beta(t)x(t)=0$ for $C^0$-generic values of the variable parameters $\alpha(t)$ and $\beta(t)$. For that we embed the problem into the framework of the general theory of continuous-time linear cocycles induced by the random OD

Junlan Chen, Ziyuan Pu, Nan Zheng, Xiao Wen

Crash data is often greatly imbalanced, with the majority of crashes being non-fatal crashes, and only a small number being fatal crashes due to their rarity. Such data imbalance issue poses a challenge for crash severity modeling since it struggles to fit and interpret fatal crash outcomes with very limited samples. Usually, such data imbalance issues are a

Hui Xiao, Yuting Hong, Li Dong, Diqun Yan

Semi-supervised semantic segmentation relieves the reliance on large-scale labeled data by leveraging unlabeled data. Recent semi-supervised semantic segmentation approaches mainly resort to pseudo-labeling methods to exploit unlabeled data. However, unreliable pseudo-labeling can undermine the semi-supervision processes. In this paper, we propose an algorit

Jan A. Krzywda, Weikun Liu, Evert van Nieuwenburg, Oswin Krause

We present QDarts, an efficient simulator for realistic charge stability diagrams of quantum dot array (QDA) devices in equilibrium states. It allows for pinpointing the location of concrete charge states and their transitions in a high-dimensional voltage space (via arbitrary two-dimensional cuts through it), and includes effects of finite tunnel coupling,

Kai Li, Guo Chen, Runxuan Yang, Xiaolin Hu

Existing CNN-based speech separation models face local receptive field limitations and cannot effectively capture long time dependencies. Although LSTM and Transformer-based speech separation models can avoid this problem, their high complexity makes them face the challenge of computational resources and inference efficiency when dealing with long audio. To

Laszlo B. Kish, Andrea Antal

A new type of non-invasive deep-brain stimulation is conceived and demonstrated by computer simulations. The process is based on spatio-temporal Fourier synthesis using multiple electrode pairs with sinusoidal current drive to limit skin sensations and concentrate the stimulus power to a small spatial volume and into large rare spikes in the times domain, wh

Alberto Blanco-Justicia, Najeeb Jebreel, Benet Manzanares, David Sánchez

The objective of digital forgetting is, given a model with undesirable knowledge or behavior, obtain a new model where the detected issues are no longer present. The motivations for forgetting include privacy protection, copyright protection, elimination of biases and discrimination, and prevention of harmful content generation. Effective digital forgetting

A. Semena, A. Vikhlinin, I. Mereminskiy, A. Lutovinov

We describe an X-ray source detection method entirely based on the maximum likelihood analysis, in application to observations with the ART-XC telescope onboard the Spectrum Roentgen Gamma observatory. The method optimally combines the data taken at different conditions, a situation commonly found in scanning surveys or mosaic observations with a telescope w

Tianle Li, Ge Zhang, Quy Duc Do, Xiang Yue

Large Language Models (LLMs) have made significant strides in handling long sequences. Some models like Gemini could even to be capable of dealing with millions of tokens. However, their performance evaluation has largely been confined to metrics like perplexity and synthetic tasks, which may not fully capture their true abilities in more challenging, real-w

Junchen Fu, Xuri Ge, Xin Xin, Alexandros Karatzoglou

Multimodal foundation models are transformative in sequential recommender systems, leveraging powerful representation learning capabilities. While Parameter-efficient Fine-tuning (PEFT) is commonly used to adapt foundation models for recommendation tasks, most research prioritizes parameter efficiency, often overlooking critical factors like GPU memory effic

Jackson Burns, William Green

Quantitative Structure Property Relationship studies aim to define a mapping between molecular structure and arbitrary quantities of interest. This was historically accomplished via the development of descriptors which requires significant domain expertise and struggles to generalize. Thus the field has morphed into Molecular Property Prediction and been giv

Yairon Cid-Ruiz, Jack Jeffries

We prove a differential version of the Artin-Rees lemma with the use of Noetherian differential operators. As a consequence, we obtain several uniformity results for nonreduced rings.

Xin Yin, Chao Ni, Shaohua Wang

This paper proposes a pipeline for quantitatively evaluating interactive Large Language Models (LLMs) using publicly available datasets. We carry out an extensive technical evaluation of LLMs using Big-Vul covering four different common software vulnerability tasks. This evaluation assesses the multi-tasking capabilities of LLMs based on this dataset. We fin

Zhonglun Cao

We first introduce the notion of Hamiltonian structure for a partial difference equation. Then we construct some infinite quivers, and realize the discrete KdV equation, the Hirota-Miwa equation and its various reductions as the mutation relations of the corresponding cluster algebras. Finally, we show that the log-canonical Poisson structures associated to

Namrata Shivagunde, Vladislav Lialin, Sherin Muckatira, Anna Rumshisky

The ability of large language models (LLMs) to $``$learn in context$"$ based on the provided prompt has led to an explosive growth in their use, culminating in the proliferation of AI assistants such as ChatGPT, Claude, and Bard. These AI assistants are known to be robust to minor prompt modifications, mostly due to alignment techniques that use human feedba

Enmin Zhu, Jerome Yen

This paper explores the intersection of Natural Language Processing (NLP) and financial analysis, focusing on the impact of sentiment analysis in stock price prediction. We employ BERTopic, an advanced NLP technique, to analyze the sentiment of topics derived from stock market comments. Our methodology integrates this sentiment analysis with various deep lea

Matthew Jagielski, Om Thakkar, Lun Wang

Speech models are often trained on sensitive data in order to improve model performance, leading to potential privacy leakage. Our work considers noise masking attacks, introduced by Amid et al. 2022, which attack automatic speech recognition (ASR) models by requesting a transcript of an utterance which is partially replaced with noise. They show that when a

Sicheng Li, Hao Li, Yiyi Liao, Lu Yu

The emergence of Neural Radiance Fields (NeRF) has greatly impacted 3D scene modeling and novel-view synthesis. As a kind of visual media for 3D scene representation, compression with high rate-distortion performance is an eternal target. Motivated by advances in neural compression and neural field representation, we propose NeRFCodec, an end-to-end NeRF com

M. J. Keith, S. Johnston, A. Karastergiou, P. Weltevrede

We report here on the timing of 597 pulsars over the last four years with the MeerKAT telescope. We provide Times-of-Arrival, pulsar ephemeris files and per-epoch measurements of the flux density, dispersion measure (DM) and rotation measure (RM) for each pulsar. In addition we use a Gaussian process to model the timing residuals to measure the spin frequenc

Qiu Shi Wang

We classify superpotentials for the Hamiltonian system corresponding to the cohomogeneity one gradient Ricci soliton equations. Aside from recovering known examples of superpotentials for steady solitons, we find a new superpotential on a specific case of the B\'erard Bergery-Calabi ansatz. The latter is used to obtain an explicit formula for a steady comple

A. Bershadskii

It is shown, using results of numerical simulations, and geophysical and solar observations, that the transition from deterministic chaos to hard turbulence in the magnetic field generated by the small-scale MHD dynamos occurs through a randomization process. This randomization process has been described using the notion of distributed chaos and the main par

Yue Tan, Xuejie Liu, Xiaoyun Chen, Youchang Yang

Inspired by that Belle\uppercase\expandafter{\romannumeral2} Collaboration recently reported $T_{cc}$, which can be interpreted as a molecular $DD^{*}$, we investigated the trihadron system of $T_{cc}$ partner with $IJ^{P}$=$01^{-}$ in the framework of a chiral quark model. It's widely accepted that the main component of $X(3872)$ contains the molecular $\ba

Alexandra Bazarova, Maria Kovaleva, Ilya Kuleshov, Evgenia Romanenkova

In today's world, banks use artificial intelligence to optimize diverse business processes, aiming to improve customer experience. Most of the customer-related tasks can be categorized into two groups: 1) local ones, which focus on a client's current state, such as transaction forecasting, and 2) global ones, which consider the general customer behaviour, e.

Christoph Glasmacher, Michael Schuldes, Sleiman El Masri, Lutz Eckstein

Scenario-based testing of automated driving functions has become a promising method to reduce time and cost compared to real-world testing. In scenario-based testing automated functions are evaluated in a set of pre-defined scenarios. These scenarios provide information about vehicle behaviors, environmental conditions, or road characteristics using paramete

Juan M. Borrero, Adur Pastor Yabar, Basilio Ruiz Cobo

Inferences of the magnetic field in the solar atmosphere by means of spectropolarimetric inversions (i.e., Stokes inversion codes) yield magnetic fields that are non-solenoidal($\nabla\cdot{\bf B} \ne 0$). Because of this, results obtained by such methods are sometimes put into question. We aim to develop and implement a new technique that can retrieve magne

Julian Holland, Cristiana Lungu, Rudolf Weber, Max Emperle

Ultrafast lasers, with pulse durations below a few picoseconds, are of significant interest to the industry, offering a cutting-edge approach to enhancing manufacturing processes and enabling the fabrication of intricate components with unparalleled accuracy. When processing metals at irradiances exceeding the evaporation threshold of about 10^10 W/cm2 these

Daryna Dementieva, Valeriia Khylenko, Georg Groh

Despite the extensive amount of labeled datasets in the NLP text classification field, the persistent imbalance in data availability across various languages remains evident. To support further fair development of NLP models, exploring the possibilities of effective knowledge transfer to new languages is crucial. Ukrainian, in particular, stands as a languag

Torben Wiedemann

We define and study root graded groups, that is, groups graded by finite root systems. This notion generalises several existing concepts in the literature, including in particular Jacques Tits' notion of RGD-systems. The most prominent examples of root graded groups are Chevalley groups over commutative associative rings. Our main result is that every root g

Vinkle Srivastav, Keqi Chen, Nicolas Padoy

We present a new self-supervised approach, SelfPose3d, for estimating 3d poses of multiple persons from multiple camera views. Unlike current state-of-the-art fully-supervised methods, our approach does not require any 2d or 3d ground-truth poses and uses only the multi-view input images from a calibrated camera setup and 2d pseudo poses generated from an of

Lena Strobl, Dana Angluin, David Chiang, Jonathan Rawski

We study the sequence-to-sequence mapping capacity of transformers by relating them to finite transducers, and find that they can express surprisingly large classes of transductions. We do so using variants of RASP, a programming language designed to help people "think like transformers," as an intermediate representation. We extend the existing Boolean vari

J. Xu, J. L. Han

The magnetic fields in our Milky Way can be revealed by the distribution of Faraday rotation measures (RMs) of radio sources behind the Galaxy and of radio pulsars inside the Galaxy. Based on the antisymmetry of the Faraday sky in the inner Galaxy to the Galactic coordinates, the magnetic field toroids above and below the Galactic plane with reversed field d

Daryna Dementieva, Nikolay Babakov, Alexander Panchenko

Text detoxification is a textual style transfer (TST) task where a text is paraphrased from a toxic surface form, e.g. featuring rude words, to the neutral register. Recently, text detoxification methods found their applications in various task such as detoxification of Large Language Models (LLMs) (Leong et al., 2023; He et al., 2024; Tang et al., 2023) and

F. J. S. Nascimento

In this article it is proved that an analytical planar vector field with a non-degenerate center at $(0,0)$ is analytically conjugate, in a neighborhood of $(0,0)$, to a Hamiltonian vector field of the form $y\frac{\partial}{\partial x}-V'(x)\frac{\partial}{\partial y}$, where $V$ is an analytic function defined in a neighborhood of the origin such that $V(0

Ritali Ghosh, Alankar Dutta, Prateek Sharma

We explore the survival of a galaxy's circumgalactic medium (CGM) as it experiences ram pressure stripping (RPS) moving through the intracluster medium (ICM). For a satellite galaxy, the CGM is often assumed to be entirely stripped/evaporated, an assumption that may not always be justified. We carry out 3D-hydrodynamic simulations of the interstellar and cir

Katarina Domijan

An empirical study was carried out to compare different implementations of ensemble models aimed at improving prediction in spectroscopic data. A wide range of candidate models were fitted to benchmark datasets from regression and classification settings. A statistical analysis using linear mixed model was carried out on prediction performance criteria resul

Divij Jain, Saatvik Kher, Lena Liang, Yufeng Wu

We propose a machine learning pipeline for forensic shoeprint pattern matching that improves on the accuracy and generalisability of existing methods. We extract 2D coordinates from shoeprint scans using edge detection and align the two shoeprints with iterative closest point (ICP). We then extract similarity metrics to quantify how well the two prints match

Katarzyna Siudzińska

We introduce a generalization of symmetric measurements to collections of unequinumerous positive, operator-valued measures (POVMs). For informationally complete sets, we propose construction methods from orthonormal Hermitian operator bases. The correspondence between operator bases and measurements can be as high as four-to-four, with a one-to-one correspo

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Six $C$-even states, denoted as $X$, with quantum numbers $J^{PC}=0^{-+}$, $1^{\pm+}$, or $2^{\pm+}$, are searched for via the $e^+e^-\to\gamma D_{s}^{\pm}D_{s}^{*\mp}$ process using $(1667.39\pm8.84)~\mathrm{pb}^{-1}$ of $e^+e^-$ collision data collected with the BESIII detector operating at the BEPCII storage ring at center-of-mass energy of $\sqrt{s}=(468

Zhiwei Liu, Yunfan Bai, Shiqi Zheng, Anping Huang

Relativistic heavy-ion collisions give rise to the formation of both deconfined QCD matter and a strong magnetic field. The spin of heavy quarks is influenced by interactions with the external magnetic field as well as by random scatterings with thermal light partons. The presence of QCD matter comprising charged quarks can extend the lifetime and strength o

Manuel López-Ibáñez, Diederick Vermetten, Johann Dreo, Carola Doerr

A widely accepted way to assess the performance of iterative black-box optimizers is to analyze their empirical cumulative distribution function (ECDF) of pre-defined quality targets achieved not later than a given runtime. In this work, we consider an alternative approach, based on the empirical attainment function (EAF) and we show that the target-based EC

C. Terry

This paper studies bounds in a strong form of regularity for $3$-uniform hypergraphs which was developed by Frankl, Gowers, Kohayakawa, Nagle, R\"{o}dl, Skokan, and Schacht. Regular decompositions of this type involve two structural components: a partition on the vertex set and a partition on the pairs of vertices. The regularity of such decompositions are m

Enmin Zhu, Jerome Yen

This study presents an innovative approach to portfolio optimization by integrating Transformer models with Generative Adversarial Networks (GANs) within the Black-Litterman (BL) framework. Capitalizing on Transformers' ability to discern long-range dependencies and GANs' proficiency in generating accurate predictive models, our method enhances the generatio

Lian-Hui Yu, Xiao-Yu Li, Geng Chen, Qin-Sheng Zhu

Leveraging quantum properties to enhance complex learning tasks has been proven feasible, with excellent recent achievements in the field of unsupervised learning. However, current quantum schemes neglect adaptive adjustments for unsupervised task scenarios. This work proposes a novel quantum unsupervised similarity learning method, QUSL. Firstly, QUSL uses

Artem Popov, Alexander Studenikin

Neutrino propagation in the Galactic and extragalactic magnetic fields is considered. We extend an approach developed in \cite{Popov:2019nkr} to describe neutrino flavour and spin oscillations using wave packets. The evolution equations for the neutrino wave packets in a uniform and non-uniform magnetic fields are derived. The analytical expressions for neut

Natalie Fardian-Melamed, Artiom Skripka, Changhwan Lee, Benedikt Ursprung

Mechanical force is an essential feature for many physical and biological processes.1-12 Remote measurement of mechanical signals with high sensitivity and spatial resolution is needed for diverse applications, including robotics,13 biophysics,14-20 energy storage,21-24 and medicine.25-27 Nanoscale luminescent force sensors excel at measuring piconewton forc

Zhuang Ge, Shiwei Bai, Tommi Eronen, Ari Jokinen

The absolute mass of $^{84}$Sr was determined using the phase-imaging ion-cyclotron-resonance technique with the JYFLTRAP double Penning trap mass spectrometer. A more precise value for the mass of $^{84}$Sr is essential for providing potential indications of physics beyond the Standard Model through high-precision isotope shift measurements of Sr atomic tra

C. Terry

We consider here the strong regularity for $3$-uniform hypergraphs developed by Frankl, Gowers, Kohayakawa, Nagle, R\"{o}dl, Skokan, and Schacht. This type of regular decomposition comes with two components, a partition of the vertices, and a partition of the pairs of vertices. The data of a regular decomposition also includes two parameters measuring quasir

Aren Karapetyan, Efe C. Balta, Anastasios Tsiamis, Andrea Iannelli

Continuous-time adaptive controllers for systems with a matched uncertainty often comprise an online parameter estimator and a corresponding parameterized controller to cancel the uncertainty. However, such methods are often impossible to implement directly, as they depend on an unobserved estimation error. We consider the equivalent discrete-time setting wi

Zhuo Chen, Xinyu Wang, Yong Jiang, Pengjun Xie

In the era of large language models, applying techniques such as Retrieval Augmented Generation can better address Open-Domain Question-Answering problems. Due to constraints including model sizes and computing resources, the length of context is often limited, and it becomes challenging to empower the model to cover overlong contexts while answering questio

David Conlon, Jacob Fox, Benjamin Gunby, Xiaoyu He

A fundamental problem in Ramsey theory is to determine the growth rate in terms of $n$ of the Ramsey number $r(H, K_n^{(3)})$ of a fixed $3$-uniform hypergraph $H$ versus the complete $3$-uniform hypergraph with $n$ vertices. We study this problem, proving two main results. First, we show that for a broad class of $H$, including links of odd cycles and tight

Peleg Bar-Lev

We define a structure of an algebra on the Lagrangian Floer cohomology of a Lagrangian submanifold over the quantum cohomology of the ambient symplectic manifold. The structure is analogous to the one defined by Biran-Cornea, but is constructed in the differential forms model. In the spirit of Ganatra and Hugtenburg, we define another such algebra structure

Gregory J. Galloway, Abraão Mendes

The aim of this paper is to extend some basic results about marginally outer trapped surfaces to the context of surfaces having general null expansion. Motivated in part by recent work of Chai-Wan, we introduce the notion of $\mathfrak{g}$-stability for a general closed hypersurface $\Sigma$ in an ambient initial data set and prove that, under natural energy

Kun Chu, Xufeng Zhao, Cornelius Weber, Mengdi Li

Although there has been rapid progress in endowing robots with the ability to solve complex manipulation tasks, generating control policies for bimanual robots to solve tasks involving two hands is still challenging because of the difficulties in effective temporal and spatial coordination. With emergent abilities in terms of step-by-step reasoning and in-co