April 2024 arXiv papers — page 6

Tianyi Bai, Jean-François Delmas, Yueyun Hu

We are interested in the branching capacity of the range of a random walk in $\mathbb Z^d$.Schapira [28] has recently obtained precise asymptotics in the case $d\ge 6$ and has demonstrated a transition at dimension $d=6$. We study the case $d=5$ and prove that the renormalized branching capacity converges in law to the Brownian snake capacity of the range of

Hao Wu, Peng-Hui Chen, Fei Niu, Zu-Xing Yang

To assess the potential of cold-fusion for synthesizing superheavy nuclei (SHN) with proton numbers 104-113, we systematically calculated 145 naturally occurring projectile-target combinations within the DNS model. Reactions predominantly show maximum cross-sections in the 1n to 2n channels, peaking near the Coulomb barrier with a sum of barrier and Q-value

Kaiqiao Han, Yi Yang, Zijie Huang, Xuan Kan

Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes. However, widely used brain signals such as Blood Oxygen Level Dependent (BOLD) time series generated from functional Magnetic Resonance Imaging (fMRI) often manifest three challenges

Robert Okuła, Piotr Mironowicz

We present how the mechanisms of quantum Darwinism allow for the leakage of information in the standard BB84 quantum key distribution protocol, a paradigmatic prepare and measure quantum cryptography scenario. We work within the decoherence theory framework and employ the model of measurements provided by quantum Darwinism. We investigate how much of the inf

Jie Hu, Yawen Huang, Yilin Lu, Guoyang Xie

Anomaly synthesis is one of the effective methods to augment abnormal samples for training. However, current anomaly synthesis methods predominantly rely on texture information as input, which limits the fidelity of synthesized abnormal samples. Because texture information is insufficient to correctly depict the pattern of anomalies, especially for logical a

John Southworth

V454 Aur is an eclipsing binary system containing two solar-type stars on an orbit of relatively long period (P = 27.02 d) and large eccentricity (e = 0.381). Eclipses were detected using data from the Hipparcos satellite, and a high-quality double-lined spectroscopic orbit has been presented by Griffin (2001). The NASA Transiting Exoplanet Survey Satellite

David Ifeoluwa Adelani, A. Seza Doğruöz, Iyanuoluwa Shode, Anuoluwapo Aremu

Nigeria is a multilingual country with 500+ languages. Naija is a Nigerian Pidgin spoken by approximately 120M speakers and it is a mixed language (e.g., English, Portuguese, Yoruba, Hausa and Igbo). Although it has mainly been a spoken language until recently, there are some online platforms (e.g., Wikipedia), publishing in written Naija as well. West Afric

Yuzhe Gu, Enmao Diao

Neural speech codecs aim to compress input signals into minimal bits while maintaining content quality in a low-latency manner. However, existing neural codecs often trade model complexity for reconstruction performance. These codecs primarily use convolutional blocks for feature transformation, which are not inherently suited for capturing the local redunda

E. E. Krasovskii, R. O. Kuzian

Attosecond dynamics of electron transmission through atomically-thin crystalline films is studied with an {\em ab initio} scattering theory. The temporal character of the electron propagation through graphene multilayers is traced to the band structure of bulk graphite: In the forbidden gaps the wave packet transit time $\tau_\mathrm{T}$ saturates with thick

Boris Kruglikov, Eivind Schneider

Scalar relative invariants play an important role in the theory of group actions on a manifold as their zero sets are invariant hypersurfaces. Relative invariants are central in many applications, where they often are treated locally since an invariant hypersurface may not be a locus of a single function. Our aim is to establish a global theory of relative i

Guobin Shen, Dongcheng Zhao, Xiang He, Linghao Feng

Decoding non-invasive brain recordings is pivotal for advancing our understanding of human cognition but faces challenges due to individual differences and complex neural signal representations. Traditional methods often require customized models and extensive trials, lacking interpretability in visual reconstruction tasks. Our framework integrates 3D brain

Olivier Letoffe, Xuanxiang Huang, Joao Marques-Silva

SHAP scores represent the proposed use of the well-known Shapley values in eXplainable Artificial Intelligence (XAI). Recent work has shown that the exact computation of SHAP scores can produce unsatisfactory results. Concretely, for some ML models, SHAP scores will mislead with respect to relative feature influence. To address these limitations, recently pr

Yuhang Yang, Xin Ren, Qingqing Wang, Zhiyu Lu

We reconstruct the cosmological background evolution under the scenario of dynamical dark energy through the Gaussian process approach, using the latest Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillations (BAO) combined with other observations. Our results reveal that the reconstructed dark-energy equation-of-state (EoS) parameter $w(z)$

Daniel A. Bustamante Lopez, Wanzheng Hu, Dominik M. Juraschek

Nonlinear frequency conversion by optical rectification, as well as difference- and sum-frequency generation are fundamental processes for producing electromagnetic radiation at different frequencies. Here, we demonstrate that coherently excited infrared-active phonons can be used as transducers for generating nonlinear electric polarizations and magnetizati

Yuping Shi, Katharina Landfester, Stephen M. Morris

Replicating the microstructure and near-unity excitation energy transfer efficiency in natural light-harvesting complexes (LHCs) remains a major challenge for synthetic energy-harvesting devices. Biological photosynthesis can spontaneously regulate the active ensembles of involved energy absorbing and funnelling chlorophyll-containing proteins in response to

Zainab Alwaisi, Simone Soderi, Rocco De Nicola

With the rapid development of Internet of Things (IoT) technology, intelligent systems are increasingly integrating into everyday life and people's homes. However, the proliferation of these technologies raises concerns about the security of smart home devices. These devices often face resource constraints and may connect to unreliable networks, posing risks

D. Panas, S. Seth, V. Belle

Two major areas of interest in the era of Large Language Models regard questions of what do LLMs know, and if and how they may be able to reason (or rather, approximately reason). Since to date these lines of work progressed largely in parallel (with notable exceptions), we are interested in investigating the intersection: probing for reasoning about the imp

Mohammad Javad Ahmadi, Rafael F. Schaefer, H. Vincent Poor

This work considers the problem of integrated sensing and communications (ISAC) with a massive number of unsourced and uncoordinated users. In the proposed model, known as the unsourced ISAC system (UNISAC), all active communication and sensing users simultaneously share a short frame to transmit their signals, without requiring scheduling with the base stat

Aleksei Dorkin, Kairit Sirts

We present an information retrieval based reverse dictionary system using modern pre-trained language models and approximate nearest neighbors search algorithms. The proposed approach is applied to an existing Estonian language lexicon resource, S\~onaveeb (word web), with the purpose of enhancing and enriching it by introducing cross-lingual reverse diction

Chenyu Jiang, Ye Tian, Zhen Jia, Shuai Zheng

The Mixture-of-Expert (MoE) technique plays a crucial role in expanding the size of DNN model parameters. However, it faces the challenge of extended all-to-all communication latency during the training process. Existing methods attempt to mitigate this issue by overlapping all-to-all with expert computation. Yet, these methods frequently fall short of achie

Giuseppe Bisicchia, Jose Garcia-Alonso, Juan M. Murillo, Antonio Brogi

Victor Hugo's timeless observation, "Nothing is more powerful than an idea whose time has come", resonates today as Quantum Computing, once only a dream of a physicist, stands at the threshold of reality with the potential to revolutionise the world. To comprehend the surge of attention it commands today, one must delve into the motivations that birthed and

Chanran Kim, Jeongin Lee, Shichang Joung, Bongmo Kim

In the field of personalized image generation, the ability to create images preserving concepts has significantly improved. Creating an image that naturally integrates multiple concepts in a cohesive and visually appealing composition can indeed be challenging. This paper introduces "InstantFamily," an approach that employs a novel masked cross-attention mec

Elena Rufeil Fiori, Christian Maes

We define the heat capacity for steady periodically driven systems and as an example we compute it for dissipative two-level systems where the energy gap is time-modulated. There, as a function of ambient temperature, the Schottky peak remains the dominant feature. Yet, in contrast with equilibrium, the quasistatic thermal response of a nonequilibrium system

Noman Hanif Barbhuiya, Pritam K. Mohanty, Saikat Mondal, Aminul Hussian

Crystallization through vapor deposition is ubiquitous, and is inevitably influenced by impurities, which often impact the local structure. Interestingly, the effect of immobilizing some of the depositing particles themselves, which would still preserve local structural symmetry, remains largely unexplored. Herein, we perform colloidal vapor deposition on a

F. Balduini, A. Molinari, L. Rocchino, V. Hasse

The chiral anomaly, a hallmark of chiral spin-1/2 Weyl fermions, is an imbalance between left- and right-moving particles that underpins both high and low energy phenomena, including particle decay and negative longitudinal magnetoresistance in Weyl semimetals. The discovery that chiral crystals can host higher-spin generalizations of Weyl quasiparticles wit

Shan Wu, Kai-Qiang Qian, Rui-Hong Yue, Ming Zhang

In this work, we discuss the generalized Einstein-Maxwell-Dilaton gravity theory with a nonminimal coupling between the Maxwell field and scalar field. Considering different geometric properties of black hole horizon structure, the charged dilaton Lifshitz black hole solutions are presented in 4-dimensional spacetimes. Later, utilizing the Wald Formalism, we

Mithinti Srikanth, G. Ramakrishna

Public transport administrators rely on efficient algorithms for various problems that arise in public transport networks. In particular, our study focused on designing linear-time algorithms for two fundamental path problems: the earliest arrival time (\textsc{eat}) and the fastest path duration (\textsc{fpd}) on public transportation data. We conduct a com

Silvino Reyes Farina, Armin Schikorra

We prove a priori estimates for wave systems of the type \[ \partial_{tt} u - \Delta u = \Omega \cdot du + F(u) \quad \text{in $\mathbb{R}^d \times \mathbb{R}$} \] where $d \geq 4$ and $\Omega$ is a suitable antisymmetric potential. We show that the assumptions on $\Omega$ are applicable to wave- and half-wave maps, the latter by means of the Krieger-Sire re

Marco Arazzi, Stefanos Koffas, Antonino Nocera, Stjepan Picek

Federated Transfer Learning (FTL) is the most general variation of Federated Learning. According to this distributed paradigm, a feature learning pre-step is commonly carried out by only one party, typically the server, on publicly shared data. After that, the Federated Learning phase takes place to train a classifier collaboratively using the learned featur

Lorenzo Pes, Rick Luiken, Federico Corradi, Charlotte Frenkel

While the human brain efficiently adapts to new tasks from a continuous stream of information, neural network models struggle to learn from sequential information without catastrophically forgetting previously learned tasks. This limitation presents a significant hurdle in deploying edge devices in real-world scenarios where information is presented in an in

Zainab Alwaisi, Simone Soderi, Rocco De Nicola

The Internet of Things (IoT) has garnered significant interest in both research and industry due to its profound impact on human life. The rapid expansion of IoT technology has ushered in smart healthcare, smart devices, smart cities, and smart grids. However, the security of IoT devices, particularly in healthcare, has become a major concern, with recent at

Wen Yin, Jian Lou, Pan Zhou, Yulai Xie

Backdoor attacks have been well-studied in visible light object detection (VLOD) in recent years. However, VLOD can not effectively work in dark and temperature-sensitive scenarios. Instead, thermal infrared object detection (TIOD) is the most accessible and practical in such environments. In this paper, our team is the first to investigate the security vuln

Vicent Gimeno i Garcia, Vicente Palmer

Inspired by the equatorial concentration of measure phenomenon in the sphere, a result which is deduced from the general, (and intrinsic), concentration of measure in $\mathbb{S}^n(1)$, we describe in this paper an equatorial concentration of measure satisfied by the closed, (compact without boundary), isometric and minimal immersions $x:\Sigma^m \rightarrow

Zuxun Xiong, Xinwei Shen, Hongbin Sun

To enhance the reliability of Integrated Energy Systems (IESs) and address the research gap in reliability-based planning methods, this paper proposes a two-stage robust planning model specifically for park-level IESs. The proposed planning model considers uncertainties like load demand fluctuations and equipment contingencies, and provides a reliable scheme

Hiromasa Watanabe

It is generally known for $\mathrm{U}(N)$ gauge theory at finite temperature that phase transitions are manifested by taking the large-$N$ limit. Since the large-$N$ theory undergoes two thermodynamic phase transitions, a nontrivial intermediate phase can be realized in addition to the phases classified as the conventional confined and deconfined phases. In

Kohta Hatakeyama, Matsuo Sato, Gota Tanaka

We discretize Chern-Simons couplings in gauge invariant way. We obtain (p+q)-forms representing Chern-Simons couplings on (p + q)-simplexes from wedge products of p- and q-forms on p- and q-simplexes, respectively, where p- and q-simplexes form (p+q)-simplexes by having a common vertex. We show that the Chern-Simons couplings on simplicial complexes reduce t

Tianci Gao

We propose a novel framework for enhancing robotic adaptability and learning efficiency, which integrates unsupervised trajectory segmentation with adaptive probabilistic movement primitives (ProMPs). By employing a cutting-edge deep learning architecture that combines autoencoders and Recurrent Neural Networks (RNNs), our approach autonomously pinpoints cri

Kota Takeuchi, Tomohiro Inagaki

Gauge theory compactified on an orbifold is defined by gauge symmetry, matter contents, and boundary conditions. There are equivalence classes (ECs), each of which consists of physically equivalent boundary conditions. We propose the powerful necessary conditions, trace conservation laws (TCLs), which achieve a sufficient classification of ECs in U(N) and SU

Subhrangsu Sarkar, Roxana Capu, Yurii G. Pashkevich, Jonas Knobel

Heterostructures from complex oxides allow one to combine various electronic and magnetic orders as to induce new quantum states. A prominent example is the coupling between superconducting and magnetic orders in multilayers from high-Tc cuprates and manganites. A key role is played here by the interfacial CuO2 layer whose distinct properties remain to be fu

Mathieu Rita, Florian Strub, Rahma Chaabouni, Paul Michel

While Reinforcement Learning (RL) has been proven essential for tuning large language models (LLMs), it can lead to reward over-optimization (ROO). Existing approaches address ROO by adding KL regularization, requiring computationally expensive hyperparameter tuning. Additionally, KL regularization focuses solely on regularizing the language policy, neglecti

Brendan Reid

Stabilizer circuits play an important role in quantum error correction protocols, and will be vital for ensuring fault tolerance in future quantum hardware. While stabilizer circuits are defined on the Clifford generating set, {H, S, CX}, not all of these gates are native to quantum hardware. As such they must be compiled into the native gateset, with the ke

Alexandre Anahory Simoes, Leonardo Colombo, Fátima Silva Leite

In this paper we compare two numerical methods to integrate Riemannian cubic polynomials on the Stiefel manifold $\textbf{St}_{n,k}$. The first one is the adjusted de Casteljau algorithm, and the second one is a symplectic integrator constructed through discretization maps. In particular, we choose the cases of $n=3$ together with $k=1$ and $k=2$. The first

Alberto Bassanoni, Alessandro Vezzani, Raffaella Burioni

We study rare events in the extreme value statistics of stochastic symmetric jump processes with power tails in the distributions of the jumps, using the big-jump principle. The principle states that in the presence of stochastic processes with power tails statistics, if at a certain time a physical quantity takes on a value much larger than its typical valu

Anthony Scemama, Andreas Savin

In this article, we explore the construction of Hamiltonians with long-range interactions and their corrections using the short-range behavior of the wave function. A key aspect of our investigation is the examination of the one-particle potential, kept constant in our previous work, and the effects of its optimization on the adiabatic connection. Our method

CLAS Collaboration, L. Clark, B. McKinnon, D. G. Ireland

Background: Measurements of the polarization observables {\Sigma}, P, T, Ox, Oz for the reaction {\gamma}p {\rightarrow) KS0 {\Sigma}+ using a linearly polarized photon beam of energy 1.1 to 2.1 GeV are reported. Purpose: The measured data provide information on a channel that has not been studied extensively, but is required for a full coupled-channel analy

Lei Zhuang, Jingdong Zhao, Yuntao Li, Zichun Xu

Sampling-based motion planning (SBMP) algorithms are renowned for their robust global search capabilities. However, the inherent randomness in their sampling mechanisms often result in inconsistent path quality and limited search efficiency. In response to these challenges, this work proposes a novel deep learning-based motion planning framework, named Trans

Zihan Li, Pasin Manurangsi, Jonathan Scarlett, Warut Suksompong

We study the complexity of a fundamental algorithm for fairly allocating indivisible items, the round-robin algorithm. For $n$ agents and $m$ items, we show that the algorithm can be implemented in time $O(nm\log(m/n))$ in the worst case. If the agents' preferences are uniformly random, we establish an improved (expected) running time of $O(nm + m\log m)$. O

Sheng Jin, Ruijie Yao, Lumin Xu, Wentao Liu

Instance perception tasks (object detection, instance segmentation, pose estimation, counting) play a key role in industrial applications of visual models. As supervised learning methods suffer from high labeling cost, few-shot learning methods which effectively learn from a limited number of labeled examples are desired. Existing few-shot learning methods p

F. Strocchi

We exploit the non-perturbative result that the $\theta$ angle which defines the vacuum structure is not a $c$-number free parameter, as suggested by the instanton semi-classical approximation, but instead one of the points of the spectrum of the central operator $\tilde{\theta}$ which describes the gauge group topology. Hence, the value of such an angle sho

María Emilia Caballero, Loïc Chaumont, Víctor Rivero

A L\'evy processes resurrected in the positive half-line is a Markov process obtained by removing successively all jumps that make it negative. A natural question, given this construction, is whether the resulting process is absorbed at 0 or not. We first describe the law of the resurrected process in terms of that of the initial L\'evy process. Then in many

Shengjie Ma, Yanlin Weng, Tianjia Shao, Kun Zhou

We introduce 3D Gaussian blendshapes for modeling photorealistic head avatars. Taking a monocular video as input, we learn a base head model of neutral expression, along with a group of expression blendshapes, each of which corresponds to a basis expression in classical parametric face models. Both the neutral model and expression blendshapes are represented

Céline Hocquette, Johannes Langer, Andrew Cropper, Ute Schmid

The goal of inductive program synthesis is for a machine to automatically generate a program from user-supplied examples. A key underlying assumption is that humans can provide sufficient examples to teach a concept to a machine. To evaluate the validity of this assumption, we conduct a study where human participants provide examples for six programming conc

Gaurav N. Gadbail, P. K. Sahoo

In this work, we consider three different $f(Q)$ models, such as power-law, exponential, and logarithmic, to study which model better mimics $\Lambda$CDM evolution theoretically. Henceforth, we determine solutions to the $f(Q)$ gravity field equations in the isotropic and homogeneous universe. Since all the models contain two model parameters, we reduce the

Pazit Haim-Kislev, Richard Hind, Yaron Ostrover

In this paper we present some quantitative results concerning symplectic barriers. In particular, we answer a question raised by Sackel, Song, Varolgunes, and Zhu regarding the symplectic size of the $2n$-dimensional Euclidean ball with a codimension-two linear subspace removed.

Shaul Zemel

Many interesting families of polynomials are indexed by permutations or related objects, and are defined by applying divided difference operators, modified by polynomials, on some initial base case. The fact that these constructions produce well-defined polynomials is based on the applied modified divided difference operators satisfying the braid relations.

Weiquan Huang, Yifei Shen, Yifan Yang

State space models and Mamba-based models have been increasingly applied across various domains, achieving state-of-the-art performance. This technical report introduces the first attempt to train a transferable Mamba model utilizing contrastive language-image pretraining (CLIP). We have trained Mamba models of varying sizes and undertaken comprehensive eval

Hua Chen, Hong-Ge Chen, Jin-Ning Li

Let $U$ be a connected open subset of $\mathbb{R}^n$, and let $X=(X_1,X_{2},\ldots,X_m)$ be a system of H\"{o}rmander vector fields defined on $U$. This paper addresses sharp embedding results and geometric inequalities in the generalized Sobolev space $\mathcal{W}_{X,0}^{k,p}(\Omega)$, where $\Omega\subset\subset U$ is a general open bounded subset of $U$.

Wentao Ding, Jianze Li, Shuzhong Zhang

In this paper, we study the optimization problem on a compact matrix manifold. While existing feasible algorithms can be broadly categorized into retraction-based and projection-based methods, compared to the more comprehensive and in-depth algorithmic and convergence research framework for retraction-based line-search (RetrLS) algorithms using only tangent

Gianmarco Accordi, Davide Gadioli, Giorgio Seguini, Andrea R. Beccari

Virtual screening is a technique used in drug discovery to select the most promising molecules to test in a lab. To perform virtual screening, we need a large set of molecules as input, and storing these molecules can become an issue. In fact, extreme-scale high-throughput virtual screening applications require a big dataset of input molecules and produce an

Chrysa Avdellidou, Marco Delbo, David Nesvorny, Kevin J. Walsh

In our recent work, we combined dynamical simulations, meteoritic data and thermal models as well as asteroid observations to argue that the current parent body of the EL meteorites was implanted into the asteroid belt not earlier than 60 Myr after the beginning of the Solar System and that the most likely capture mechanism was the giant planet orbital insta

Amogh Kinikar, Thorsten G. Englmann, Marco Di Giovannantonio, Nicolò Bassi

Atomically precise graphene nanoribbons (GNRs) have a wide range of electronic properties that depend sensitively on their chemical structure. Several types of GNRs have been synthesized on metal surfaces through selective surface-catalyzed reactions. The resulting GNRs are adsorbed on the metal surface, which may lead to hybridization between the GNR orbita

Oriel A. Humes, Audrey C. Martin, Cristina A. Thomas, Joshua P. Emery

Primitive asteroids with low albedos and red slopes in the visible and near infrared (VNIR) are found in both the Main Belt and the Jupiter Trojan clouds. In order to determine whether the VNIR spectral similarities of primitive Main Belt asteroids and Jupiter Trojans are reflective of a true compositional similarity, we compare the mid-infrared silicate emi

Kekun Gao, Yuejun Yan, Yixuan Liu, Endong Liu

As a critical component of modern infrastructure, data centers account for a huge amount of power consumption and greenhouse gas emission. This paper studies the electricity purchase strategy for a data center to lower its energy cost while integrating local renewable generation under uncertainty. To facilitate efficient and scalable decision-making, we prop

Salahuddin Abdul Rahman, Özkan Karabacak, Rafal Wisniewski

Drawing inspiration from the Lyapunov control technique for quantum systems, feedback-based quantum algorithms have been proposed for calculating the ground states of Hamiltonians. In this work, we consider extending these algorithms to tackle calculating excited states. Inspired by the weighted subspace-search variational quantum eigensolver algorithm, we p

Ahrong Jeong, Mitsuki Yoshimura, Hyeonjun Kong, Zhiping Bian

Thermal switches, which electrically turn heat flow on and off, have attracted attention as thermal management devices. Electrochemical reduction/oxidation switches the thermal conductivity (\k{appa}\) of active metal oxide films. The performance of the previously proposed electrochemical thermal switches is low; on/off \k{appa}\-ratio is mostly less than 5

Zhanwei Zhang, Minghao Chen, Shuai Xiao, Liang Peng

Recent self-training techniques have shown notable improvements in unsupervised domain adaptation for 3D object detection (3D UDA). These techniques typically select pseudo labels, i.e., 3D boxes, to supervise models for the target domain. However, this selection process inevitably introduces unreliable 3D boxes, in which 3D points cannot be definitively ass

Zhendong Liu, Haifeng Xia, Tong Guo, Libo Sun

Human action video recognition has recently attracted more attention in applications such as video security and sports posture correction. Popular solutions, including graph convolutional networks (GCNs) that model the human skeleton as a spatiotemporal graph, have proven very effective. GCNs-based methods with stacked blocks usually utilize top-layer semant

Xiaoxuan Han, Songlin Yang, Wei Wang, Yang Li

Advanced text-to-image diffusion models raise safety concerns regarding identity privacy violation, copyright infringement, and Not Safe For Work content generation. Towards this, unlearning methods have been developed to erase these involved concepts from diffusion models. However, these unlearning methods only shift the text-to-image mapping and preserve t

Hyungkyu Ham, Jeongmin Hong, Geonwoo Park, Yunseon Shin

Emerging Compute Express Link (CXL) enables cost-efficient memory expansion beyond the local DRAM of processors. While its CXL$.$mem protocol provides minimal latency overhead through an optimized protocol stack, frequent CXL memory accesses can result in significant slowdowns for memory-bound applications whether they are latency-sensitive or bandwidth-inte

C. Nikolis, M. Gronke

Galactic outflows have a multiphase nature making them challenging to model analytically. Many previous studies have tried to produce models that come closer to reality. In this work, we continue these efforts and describe the interaction of the hot wind fluid with multiple cold cloud populations, with their number density determined by different probability

Zhigang Sun, Zixu Wang, Lavdim Halilaj, Juergen Luettin

Trajectory prediction in autonomous driving relies on accurate representation of all relevant contexts of the driving scene, including traffic participants, road topology, traffic signs, as well as their semantic relations to each other. Despite increased attention to this issue, most approaches in trajectory prediction do not consider all of these factors s

Jean-Bernard Lasserre

Given a determinate (multivariate) probability measure $\mu$, we characterize Gaussian mixtures $\nu\_\phi$ which minimize the Wasserstein distance $W\_2(\mu,\nu\_\phi)$ to $\mu$ when the mixing probability measure $\phi$ on the parameters $(m,\Sigma)$ of the Gaussians is supported on a compact set $S$.(i) We first show that such mixtures are optimal solutio

Wen-Cheng Yue, Zixiong Yuan, Peiyuan Huang, Yizhe Sun

Ferrotoroidicity, the fourth form of primary ferroic order, breaks both space and time inversion symmetry. So far, direct observation of ferrotoroidicity in natural materials remains elusive, which impedes the exploration of ferrotoroidic phase transitions. Here, we overcome the limitations of natural materials using an artificial nanomagnet system that can

Jun-Muk Hwang

For a symplectic vector space $V$, a projective subvariety $Z \subset {\bf P} V$ is a Legendrian variety if its affine cone $\widehat{Z} \subset V$ is Lagrangian. In addition to the classical examples of subadjoint varieties associated to simple Lie algebras, many examples of nonsingular Legendrian varieties have been discovered which have positive-dimension

Katie Wassal, Carolyn Ashurst, Jiri Hron, Miri Zilka

There has been a surge in the number and type of AI tools being tested and deployed within both national and local government in the UK, including within the social care sector. Given the many ongoing and planned future developments, the time is ripe to review and reflect on the state of AI in social care. We do so by conducting semi-structured interviews wi

Mohammad Bokaei, Jesper Jensen, Simon Doclo, Jan Østergaard

Ensuring intelligible speech communication for hearing assistive devices in low-latency scenarios presents significant challenges in terms of speech enhancement, coding and transmission. In this paper, we propose novel solutions for low-latency joint speech transmission and enhancement, leveraging deep neural networks (DNNs). Our approach integrates two stat

R. Bouet, J. Busto, V. Cecchini, P. Charpentier

As part of the R2D2 (Rare Decays with Radial Detector) R&D, the use of a gas detector with a spherical or cylindrical cathode, equipped with a single anode and operating at high pressure, was studied for the search of rare phenomena such as neutrinoless double-beta decay. The presented measurements were obtained with a cylindrical detector, covering gas pres

Arthur Vesperini, Matteo Cini, Roberto Franzosi

Entanglement and quantum correlations between atoms are not usually considered key ingredients of the superradiant phase transition. Here we consider the Tavis-Cummings model, a solvable system of two-levels atoms, coupled with a single-mode quantized electromagnetic field. This system undergoes a superradiant phase transition, even in a finite-size framewor

Joseph Janssen, Ardalan Tootchi, Ali A. Ameli

Spatial patterns of water table depth (WTD) play a crucial role in shaping ecological resilience, hydrological connectivity, and human-centric systems. Generally, a large-scale (e.g., continental or global) continuous map of static WTD can be simulated using either physically-based (PB) or machine learning-based (ML) models. We construct three fine-resolutio

Tomáš Jakl, Dan Marsden, Nihil Shah

We present a categorical theory of the composition methods in finite model theory -- a key technique enabling modular reasoning about complex structures by building them out of simpler components. The crucial results required by the composition methods are Feferman--Vaught--Mostowski (FVM) type theorems, which characterize how logical equivalence behaves und

German Sviridov, Zheng Tao Shen, Jorge Cardoso

Virtual Private Cloud (VPC) is the main network abstraction technology used in public cloud systems. VPCs are composed of a set of network services that permit the definition of complex network reachability properties among internal and external cloud entities such as tenants' VMs or some generic internet nodes. Although hiding the underlying complexity thro

Giorgos Giannopoulos, Maria Psalla, Loukas Kavouras, Dimitris Sacharidis

In this paper we examine algorithmic fairness from the perspective of law aiming to identify best practices and strategies for the specification and adoption of fairness definitions and algorithms in real-world systems and use cases. We start by providing a brief introduction of current anti-discrimination law in the European Union and the United States and

Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis, Aneta Vulgarakis Feljan

Reward machines inform reinforcement learning agents about the reward structure of the environment and often drastically speed up the learning process. However, reward machines only accept Boolean features such as robot-reached-gold. Consequently, many inherently numeric tasks cannot profit from the guidance offered by reward machines. To address this gap, w

Katikela Sreeharsha Kishore, Rahimanuddin Shaik

The growing prominence of large language models (LLMs) necessitates the exploration of their capabilities beyond English. This research investigates the Telugu language proficiency of ChatGPT and Gemini, two leading LLMs. Through a designed set of 20 questions encompassing greetings, grammar, vocabulary, common phrases, task completion, and situational reaso

Chaozheng Wang, Zongjie Li, Cuiyun Gao, Wenxuan Wang

Code generation aims to synthesize code and fulfill functional requirements based on natural language (NL) specifications, which can greatly improve development efficiency. In the era of large language models (LLMs), large code models (LCMs) have been recently proposed to generate source code. LCMs can generate highly feasible solutions for programming probl

Lisa Balsollier, Frédéric Lavancier

A birth-death-move process with mutations is a Markov model for a system of marked particles in interaction, that move over time, with births and deaths. In addition the mark of each particle may also change, which constitutes a mutation. Assuming a parametric form for this model, we derive its likelihood expression and prove its local asymptotic normality.

Simon Hettler, Mohammad Furqan, Andres Sotelo, Raul Arenal

We explore the possibility to perform an in-situ transmission electron microscopy (TEM) thermoelectric characterization of materials. A differential heating element on a custom in-situ TEM microchip allows to generate a temperature gradient across the studied materials, which are simultaneously measured electrically. A thermovoltage was induced in all studie

Henrik S. Røising, Andreas Kreisel, Brian M. Andersen

The thermal quasiparticles in a clean type-II superconductor with line nodes give rise to a quadratic low-temperature change of the penetration depth, $\Delta \lambda \sim T^2$, as first shown by Kosztin and Leggett [I. Kosztin and A. J. Leggett, Phys. Rev. Lett. 79, 135 (1997)]. Here, we generalize this result to multiple nodes and compare it to numerically

Siyi Xun, Qiaoyu Li, Xiaohong Liu, Guangtao Zhai

Computed Tomography (CT) is a frequently utilized imaging technology that is employed in the clinical diagnosis of many disorders. However, clinical diagnosis, data storage, and management are posed huge challenges by a huge volume of non-homogeneous CT data in terms of imaging quality. As a result, the quality assessment of CT images is a crucial problem th

Yunhao Zhang, Shaonan Wang, Xinyi Dong, Jiajun Yu

Neural language models, particularly large-scale ones, have been consistently proven to be most effective in predicting brain neural activity across a range of studies. However, previous research overlooked the comparison of these models with psychologically plausible ones. Moreover, evaluations were reliant on limited, single-modality, and English cognitive

Andreas Triantafyllopoulos, Björn W. Schuller

Imbuing machines with the ability to talk has been a longtime pursuit of artificial intelligence (AI) research. From the very beginning, the community has not only aimed to synthesise high-fidelity speech that accurately conveys the semantic meaning of an utterance, but also to colour it with inflections that cover the same range of affective expressions tha

Masaru Hamano, Shunya Hashimoto, Shuji Machihara

We discuss the global existence of solutions to a system of stochastic Schr\"odinger equations with multiplicative noise. Our setting of the quadratic nonlinear terms in dimension 4 is $L^2$-critical. We treat the solutions under the ground state. We estimate the time derivative of the quantity of energy by using the cancellation of the cubic terms in the sp

Tamara C. P. Florijn, Pinar Yolum, Tim Baarslag

Automated negotiation is a well-known mechanism for autonomous agents to reach agreements. To realize beneficial agreements quickly, it is key to employ a good bidding strategy. When a negotiating agent has a good back-up plan, i.e., a high reservation value, failing to reach an agreement is not necessarily disadvantageous. Thus, the agent can adopt a risk-s

Hao-Cheng Lo, Jung-Mei Chu, Jieh Hsiang, Chun-Chieh Cho

In patent prosecution, image-based retrieval systems for identifying similarities between current patent images and prior art are pivotal to ensure the novelty and non-obviousness of patent applications. Despite their growing popularity in recent years, existing attempts, while effective at recognizing images within the same patent, fail to deliver practical

Kirill Milintsevich, Gaël Dias, Kairit Sirts

This paper explores the impact of incorporating sentiment, emotion, and domain-specific lexicons into a transformer-based model for depression symptom estimation. Lexicon information is added by marking the words in the input transcripts of patient-therapist conversations as well as in social media posts. Overall results show that the introduction of externa

Jingchen Peng, Boxiang Ren, Lu Yang, Chenghui Peng

The integration of artificial intelligence (AI) and mobile networks is regarded as one of the most important scenarios for 6G. In 6G, a major objective is to realize the efficient transmission of task-relevant data. Then a key problem arises, how to design collaborative AI models for the device side and the network side, so that the transmitted data between

Yongchun Zhu, Jingwu Chen, Ling Chen, Yitan Li

User preferences follow a dynamic pattern over a day, e.g., at 8 am, a user might prefer to read news, while at 8 pm, they might prefer to watch movies. Time modeling aims to enable recommendation systems to perceive time changes to capture users' dynamic preferences over time, which is an important and challenging problem in recommendation systems. Especial

Niklas Braun, Markus Steimle, Martin Törngren, Markus Maurer

As simulation is increasingly used in scenario-based approaches to test Automated Driving Systems, the credibility of simulation results is a major concern. Arguably, credibility depends on the validity of the simulation setup and simulation models. When selecting appropriate simulation models, a trade-off must be made between validity, often connected to th

Hans A. Weidenmüller

For both reactions we use an approach similar to that of compound-nucleus reaction theory. For neutron-induced fission, we describe the compound system generated by absorption of the neutron and the nuclear system near the scission point as two statistically independent systems governed by random-matrix theory. The systems are connected either by a barrier p

Lucas Grativol Ribeiro, Lubin Gauthier, Mathieu Leonardon, Jérémy Morlier

This paper tackles the challenges of implementing few-shot learning on embedded systems, specifically FPGA SoCs, a vital approach for adapting to diverse classification tasks, especially when the costs of data acquisition or labeling prove to be prohibitively high. Our contributions encompass the development of an end-to-end open-source pipeline for a few-sh