March 2024 arXiv papers — page 133

Sanidhya Gupta, Ankur Raina

As large-scale quantum computers become a reality, they will likely exist as centralized cloud resources accessible to a broad user base. Securely delegating private quantum computations to untrusted servers is therefore a foundational challenge. This requires rigorous guarantees of privacy (blindness), correctness (completeness), and integrity against malic

Haiko Lietz

Social Network Analysis is a way of studying agents embedded in contexts. In about 1998, physicists discovered social networks as representations of complex systems. Small-world and scale-free networks are the paradigmatic models of this Network Science. Relying on various models and mechanisms of socio-cultural processes, an identity model is developed and

Mingjie Li, Masahiro Suzuki

The main concern of this paper is to mathematically investigate the formation of a plasma sheath near the surface of nonplanar walls. We study the existence and asymptotic stability of stationary solutions for the nonisentropic Euler-Poisson equations in a domain of which boundary is drawn by a graph, by employing a space weighted energy method. Moreover, th

J. J. Cabrera, A. Santo, A. Gil, C. Viegas

This paper presents MinkUNeXt, an effective and efficient architecture for place-recognition from point clouds entirely based on the new 3D MinkNeXt Block, a residual block composed of 3D sparse convolutions that follows the philosophy established by recent Transformers but purely using simple 3D convolutions. Feature extraction is performed at different sca

Youngmin Chung, Ji Hun Ha, Kyeong Chan Im, Joo Sang Lee

Recent advancements in Spatial Transcriptomics (ST) technology have facilitated detailed gene expression analysis within tissue contexts. However, the high costs and methodological limitations of ST necessitate a more robust predictive model. In response, this paper introduces TRIPLEX, a novel deep learning framework designed to predict spatial gene expressi

Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low

Neural architecture search (NAS) has become a key component of AutoML and a standard tool to automate the design of deep neural networks. Recently, training-free NAS as an emerging paradigm has successfully reduced the search costs of standard training-based NAS by estimating the true architecture performance with only training-free metrics. Nevertheless, th

Si Li, Peng Yang

In this paper, we study topological quantum mechanical models on symplectic orbifolds. The correlation map gives an explicit orbifold version of quantum HKR map. The exact semi-classical approximation in this model leads to a geometric and quantum field theoretic interpretation of the orbifold algebraic index.

Honghao Chen, Xiangxiang Chu, Yongjian Ren, Xin Zhao

Recently, some large kernel convnets strike back with appealing performance and efficiency. However, given the square complexity of convolution, scaling up kernels can bring about an enormous amount of parameters and the proliferated parameters can induce severe optimization problem. Due to these issues, current CNNs compromise to scale up to 51x51 in the fo

Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Sarah Lockfisch

Data reconstruction attacks on machine learning models pose a substantial threat to privacy, potentially leaking sensitive information. Although defending against such attacks using differential privacy (DP) provides theoretical guarantees, determining appropriate DP parameters remains challenging. Current formal guarantees on the success of data reconstruct

Rui Zhao, Jun Zhao

In today's digital landscape, the Web has become increasingly centralized, raising concerns about user privacy violations. Decentralized Web architectures, such as Solid, offer a promising solution by empowering users with better control over their data in their personal `Pods'. However, a significant challenge remains: users must navigate numerous applicati

Saksham Checker, Nikhil Churamani, Hatice Gunes

As social robots become increasingly integrated into daily life, ensuring their behaviours align with social norms is crucial. For their widespread open-world application, it is important to explore Federated Learning (FL) settings where individual robots can learn about their unique environments while also learning from each others' experiences. In this pap

Yunze Wei, Tianshuo Hu, Cong Liang, Yong Cui

The past few years have witnessed the flourishing of large-scale deep neural network models with ever-growing parameter numbers. Training such large-scale models typically requires massive memory and computing resources, necessitating distributed training. As GPU performance has rapidly evolved in recent years, computation time has shrunk, making communicati

Matías E. di Mauro, Benoît Braïda, Ion Errea, Trinidad Novoa

We present an efficient criterion for probing the critical temperature of hydrogen based superconductors. We start by expanding the applicability of 3D descriptors of electron localization to superconducting states within the framework of superconducting DFT. We first apply this descriptor to a model system, the hydrogen chain, which allows to prove two main

Nicolas Brantut

Strain hardening is a key feature observed in many rocks deformed in the so-called ``semi-brittle'' regime, where both crystal plastic and brittle deformation mechanisms operate. Dislocation storage has long been recognised as a major process leading to strain hardening. Here, we suggest that tensile microcracks may be viewed as dislocation sinks, by offerin

Jiashi Yang

A set of nonlinear equations for the macroscopic theory of elastic semiconductors is derived which generalizes the seminal work of Tiersten by including the inertia of the mobile charge carriers. The equations obtained can describe carrier plasma waves and their interactions with elastic waves. Another generalization in this paper is that the internal energy

Linmei Hu, Hongyu He, Duokang Wang, Ziwang Zhao

Personality detection aims to detect one's personality traits underlying in social media posts. One challenge of this task is the scarcity of ground-truth personality traits which are collected from self-report questionnaires. Most existing methods learn post features directly by fine-tuning the pre-trained language models under the supervision of limited pe

Surajit Basak, Aksel Kobiałka, Małgorzata Sternik, Jan Łażewski

We present the pressure dependence of the electronic and dynamical properties of six different CoGe phases: orthorhombic Cmmm, hexagonal P6/mmm and P$\bar{6}$2m, monoclinic C2/m, cubic P2$_{1}$3, and orthorhombic Pnma. Using first-principles DFT calculations and the direct force-constants method, we study the dynamical stability of individual phases under ex

Lior Arbel, Ishwarya Ananthabhotla, Zamir Ben-Hur, David Lou Alon

High fidelity spatial audio often performs better when produced using a personalized head-related transfer function (HRTF). However, the direct acquisition of HRTFs is cumbersome and requires specialized equipment. Thus, many personalization methods estimate HRTF features from easily obtained anthropometric features of the pinna, head, and torso. The first H

Shiqi Jiang, Ning Li, Chen Shi, Liping Guo

The Aesthetics Assessment of Children's Paintings (AACP) is an important branch of the image aesthetics assessment (IAA), playing a significant role in children's education. This task presents unique challenges, such as limited available data and the requirement for evaluation metrics from multiple perspectives. However, previous approaches have relied on tr

Vincent Lahoche, Dine Ousmane Samary

In this paper, we study the equilibrium states of a $N\times N$ stochastic complex random matrix $M$, whose entries evolve in time accordingly with a Langevin equation including both Gaussian white noises and a linear disorder, materialized by the Wigner random matrices. In large $N$-limit, the disorders behave as effective kinetics, and we examine a coarse-

Yijin Huang, Pujin Cheng, Roger Tam, Xiaoying Tang

Parameter-efficient transfer learning (PETL) is proposed as a cost-effective way to transfer pre-trained models to downstream tasks, avoiding the high cost of updating entire large-scale pre-trained models (LPMs). In this work, we present Fine-grained Prompt Tuning (FPT), a novel PETL method for medical image classification. FPT significantly reduces memory

Jose Garre Rubio

Gauging introduces gauge fields in order to localize an existing global symmetry, resulting in a dual global symmetry on the gauge fields that can be gauged again. By iterating the gauging process on spin chains with Abelian group symmetries and arranging the gauge fields in a 2D lattice, the local symmetries become the stabilizer of the $XZZX$-code for any

Wang Mian, Qian Zhang, Shuai Jing, Xiang-Guo Li

Recent research has revealed that the Cr family perovskite ReCrO$_3$ exhibits intriguing magnetic coupling interactions within Cr pairs, which may not follow the Goodenough-Kanamori (GK) rules due to the t-e hybridization between Cr$^\mathrm{III}$ ions. We investigate the complex magnetism involving both t-e hybridization and Re-$f$ orbitals in the molecular

Masoud Shokrnezhad, Hao Yu, Tarik Taleb, Richard Li

In the context of advancing 6G, a substantial paradigm shift is anticipated, highlighting comprehensive everything-to-everything interactions characterized by numerous connections and stringent adherence to Quality of Service/Experience (QoS/E) prerequisites. The imminent challenge stems from resource scarcity, prompting a deliberate transition to Computing-

Veronica Centorrino, Alexander Davydov, Anand Gokhale, Giovanni Russo

We analyze the convergence behavior of \emph{globally weakly} and \emph{locally strongly contracting} dynamics. Such dynamics naturally arise in the context of convex optimization problems with a unique minimizer. We show that convergence to the equilibrium is \emph{linear-exponential}, in the sense that the distance between each solution and the equilibrium

Shuxian Bi, Wenjie Wang, Hang Pan, Fuli Feng

Recommender systems mainly tailor personalized recommendations according to user interests learned from user feedback. However, such recommender systems passively cater to user interests and even reinforce existing interests in the feedback loop, leading to problems like filter bubbles and opinion polarization. To counteract this, proactive recommendation ac

Jing Zhao

Due to the influence of imaging equipment and complex imaging environments, most images in daily life have features of intensity inhomogeneity and noise. Therefore, many scholars have designed many image segmentation algorithms to address these issues. Among them, the active contour model is one of the most effective image segmentation algorithms.This paper

Ümit Mert Çağlar, Baris Yilmaz, Melek Türkmen, Erdem Akagündüz

Contemporary deep learning models have demonstrated promising results across various applications within seismology and earthquake engineering. These models rely primarily on utilizing ground motion records for tasks such as earthquake event classification, localization, earthquake early warning systems, and structural health monitoring. However, the extent

Simone Scardapane, Alessandro Baiocchi, Alessio Devoto, Valerio Marsocci

This article summarizes principles and ideas from the emerging area of applying \textit{conditional computation} methods to the design of neural networks. In particular, we focus on neural networks that can dynamically activate or de-activate parts of their computational graph conditionally on their input. Examples include the dynamic selection of, e.g., inp

Siyi Lu, Bo Hu, Xuemei Yang, Yang Li

Birefringence phase-matching based \c{hi}(2) ZnGeP2 (ZGP) waveguide platform has been recently reported for excellent mid-infrared laser generation. Here, a detailed theoretical characterization of mode transmission taking waveguide anisotropy and substrate material absorption into account in a micrometer ZGP waveguide platform (ZGP-on-SiO2) is conducted. Be

Francois Meyer, Jan Buys

Most data-to-text datasets are for English, so the difficulties of modelling data-to-text for low-resource languages are largely unexplored. In this paper we tackle data-to-text for isiXhosa, which is low-resource and agglutinative. We introduce Triples-to-isiXhosa (T2X), a new dataset based on a subset of WebNLG, which presents a new linguistic context that

Weiwei Gu, Senquan Wang

Blood Glucose (BG) control involves keeping an individual's BG within a healthy range through extracorporeal insulin injections is an important task for people with type 1 diabetes. However,traditional patient self-management is cumbersome and risky. Recent research has been devoted to exploring individualized and automated BG control approaches, among which

Luca Leuzzi, Tommaso Rizzo

We consider second-order phase transitions in which the order parameter is a replicated overlap matrix. We focus on a tricritical point that occurs in a variety of mean-field models and that, more generically, describes higher order liquid-liquid or liquid-glass transitions. We show that the static replicated theory implies slowing down with a logarithmic de

Mingze Wang, Lili Su, Cilin Yan, Sheng Xu

The intelligent interpretation of buildings plays a significant role in urban planning and management, macroeconomic analysis, population dynamics, etc. Remote sensing image building interpretation primarily encompasses building extraction and change detection. However, current methodologies often treat these two tasks as separate entities, thereby failing t

Ri-Zhao Qiu, Yafei Hu, Yuchen Song, Ge Yang

An open problem in mobile manipulation is how to represent objects and scenes in a unified manner so that robots can use both for navigation and manipulation. The latter requires capturing intricate geometry while understanding fine-grained semantics, whereas the former involves capturing the complexity inherent at an expansive physical scale. In this work,

Maqsood Hussain Shah, Yue Ding, Shaoshu Zhu, Yingqi Gu

With the rising concern over transportation emissions and pollution on a global scale, shared electric mobility services like E-cars, E-bikes, and E-scooters have emerged as promising solutions to mitigate these pressing challenges. However, existing shared E-mobility services exhibit critical design deficiencies, including insufficient service integration,

Miguel Perez, Selin Aydin, Horst Lichter

Jupyter Notebook is an interactive development environment commonly used for rapid experimentation of machine learning (ML) solutions. Describing the ML activities performed along code cells improves the readability and understanding of Notebooks. Manual annotation of code cells is time-consuming and error-prone. Therefore, tools have been developed that cla

Lijun Chang

The concept of $k$-defective clique, a relaxation of clique by allowing up-to $k$ missing edges, has been receiving increasing interests recently. Although the problem of finding the maximum $k$-defective clique is NP-hard, several practical algorithms have been recently proposed in the literature, with kDC being the state of the art. kDC not only runs the f

Fengyun Wang, Qianru Sun, Dong Zhang, Jinhui Tang

Semantic scene completion (SSC) aims to predict complete 3D voxel occupancy and semantics from a single-view RGB-D image, and recent SSC methods commonly adopt multi-modal inputs. However, our investigation reveals two limitations: ineffective feature learning from single modalities and overfitting to limited datasets. To address these issues, this paper pro

Huijie Tang, Federico Berto, Jinkyoo Park

Multi-Agent Reinforcement Learning (MARL) based Multi-Agent Path Finding (MAPF) has recently gained attention due to its efficiency and scalability. Several MARL-MAPF methods choose to use communication to enrich the information one agent can perceive. However, existing works still struggle in structured environments with high obstacle density and a high num

Shiri Alouf-Heffetz, Tanmay Inamdar, Pallavi Jain, Yash More

In liquid democracy, agents can either vote directly or delegate their vote to a different agent of their choice. This results in a power structure in which certain agents possess more voting weight than others. As a result, it opens up certain possibilities of vote manipulation, including control and bribery, that do not exist in standard voting scenarios o

Jiuding Yang, Hui Liu, Weidong Guo, Zhuwei Rao

Ensuring factual consistency between the summary and the original document is paramount in summarization tasks. Consequently, considerable effort has been dedicated to detecting inconsistencies. With the advent of Large Language Models (LLMs), recent studies have begun to leverage their advanced language understanding capabilities for inconsistency detection

Tian Yu, Shaolei Zhang, Yang Feng

Although Large Language Models (LLMs) have demonstrated impressive text generation capabilities, they are easily misled by untruthful contexts provided by users or knowledge augmentation tools, leading to hallucinations. To alleviate LLMs from being misled by untruthful context and take advantage of knowledge augmentation, we propose Truth-Aware Context Sele

Weiyi Wang, Jonathan Barés, Mathieu Renouf, Emilien Azéma

The flow behavior of granular matter is significantly influenced by the shape of constituent particles. This effect is particularly pronounced for very concave particles, which exhibit unique flow characteristics such as higher porosity and sharper phase transitions between jamming and unjamming states. Despite the richness and ubiquitousness of these system

Fernando Miguelez, Josu Doncel, Maria Dolores Ugarte

Industrial processes generate a massive amount of monitoring data that can be exploited to uncover hidden time losses in the system. This can be used to enhance the accuracy of maintenance policies and increase the effectiveness of the equipment. In this work, we propose a method for one-step probabilistic multivariate forecasting of time variables involved

Degaga Wolde Feyisa, Haylemicheal Berihun, Amanuel Zewdu, Mahsa Najimoghadam

Industrial projects rely heavily on lengthy, complex specification documents, making tedious manual extraction of structured information a major bottleneck. This paper introduces an innovative approach to automate this process, leveraging the capabilities of two cutting-edge AI models: Donut, a model that extracts information directly from scanned documents

Romeo Valentin

Although an ever-growing number of applications employ deep learning based systems for prediction, decision-making, or state estimation, almost no certification processes have been established that would allow such systems to be deployed in safety-critical applications. In this work we consider real-world problems arising in aviation and other safety-critica

Bin Wang, Xueqing Wen, Yaoxiong Wen

We prove the Strominger--Yau--Zaslow and topological mirror symmetries for parabolic Hitchin systems of types B and C. In contrast to type A, a geometric reinterpretation of Springer duality is necessary. Furthermore, unlike Hitchin's construction in the non-parabolic case, the map between generic fibers in type B and C needs more analysis due to the change

Kejie Bao, Huan Wang, Jiaxuan Guo, Yadong Jiang

The interplay between non-trivial topology and strong electron interaction can generate a variety of exotic quantum matter. Here we theoretically propose that monolayer transition metal trihalides MoF$_3$ and W$X_3$ ($X$= Cl, Br, I) have isolated nearly flat band near the Fermi level with higher Chern number $\mathcal{C}=+3$ and $\mathcal{C}=-2$, respectivel

L. Herrera, A. Di prisco, J. Ospino

A semi--numerical approach proposed many years ago for describing gravitational collapse in the post--quasi--static approximation, is modified in order to avoid the numerical integration of the basic differential equations the approach is based upon. For doing that we have to impose some restrictions on the fluid distribution. More specifically, we shall ass

Fabio Ancona, Mohamed Bentaibi, Francesco Rossi

We prove that a first-order cooperative system of interacting agents converges to consensus if the so-called Persistence Excitation condition holds. This condition requires that the interaction function between any pair of agents satisfies an integral lower bound. The interpretation is that the interaction needs to ensure a minimal amount of service.

Byeonghwi Kim, Minhyuk Seo, Jonghyun Choi

In learning an embodied agent executing daily tasks via language directives, the literature largely assumes that the agent learns all training data at the beginning. We argue that such a learning scenario is less realistic since a robotic agent is supposed to learn the world continuously as it explores and perceives it. To take a step towards a more realisti

Jungho Lee, Dogyoon Lee, Minhyeok Lee, Donghyung Kim

Neural radiance fields (NeRF) has attracted considerable attention for their exceptional ability in synthesizing novel views with high fidelity. However, the presence of motion blur, resulting from slight camera movements during extended shutter exposures, poses a significant challenge, potentially compromising the quality of the reconstructed 3D scenes. To

Markus Szymik

The Cremona groups are the groups of all birational transformations of rational varieties, or, in other words, the groups of all automorphisms of rational function fields. These groups are traditionally studied one dimension at a time. Instead, we will consider the whole sequence as the dimension increases. This shift in perspective raises questions about &#

Markus Szymik

We introduce a new invariant of fields that refines their real spectrum and is related to their absolute Galois group: the Artin-Schreier quandle. For formally real number fields, it is freely generated in its variety by a Cantor space of indeterminates. For Laurent series fields, we compute it in terms of the Artin-Schreier quandle of the coefficient field.

Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh, Michele Boggia

NLP in the age of monolithic large language models is approaching its limits in terms of size and information that can be handled. The trend goes to modularization, a necessary step into the direction of designing smaller sub-networks and components with specialized functionality. In this paper, we present the MAMMOTH toolkit: a framework designed for traini

Souvik Paul, Sunandan Gangopadhyay, Ashis Saha

In this work, we extend the study in \href{https://link.springer.com/article/10.1007/JHEP11(2022)013}{JHEP11(2022)013} incorporating the AdS/CFT duality to establish a relationship between the local temperatures (Tolman temperatures) of a large (AdS) spherical and a (AdS) planar Schwarzschild black hole near the AdS boundary considering Gauss-Bonnet curvatur

Quoc-Vinh Lai-Dang

This survey explores the adaptation of visual transformer models in Autonomous Driving, a transition inspired by their success in Natural Language Processing. Surpassing traditional Recurrent Neural Networks in tasks like sequential image processing and outperforming Convolutional Neural Networks in global context capture, as evidenced in complex scene recog

Humam Kourani, Alessandro Berti, Daniel Schuster, Wil M. P. van der Aalst

In the realm of Business Process Management (BPM), process modeling plays a crucial role in translating complex process dynamics into comprehensible visual representations, facilitating the understanding, analysis, improvement, and automation of organizational processes. Traditional process modeling methods often require extensive expertise and can be time-c

Dionysios Diamantopoulos, Roman Pletka, Slavisa Sarafijanovic, A. L. Narasimha Reddy

Ransomware, a fearsome and rapidly evolving cybersecurity threat, continues to inflict severe consequences on individuals and organizations worldwide. Traditional detection methods, reliant on static signatures and application behavioral patterns, are challenged by the dynamic nature of these threats. This paper introduces three primary contributions to addr

E. Calvo Alamillo, M. T. Crespo Vázquez, P. F. Rato Mendes, R. Álvarez Garrote

This paper describes a procedure for the validation of alpha-particle sources (exempt unsealed sources) to be used in experimental setups with liquefied gases at cryogenic temperatures (down to -196 C) and high vacuum. These setups are of interest for the development and characterization of neutrino and dark matter detectors based on liquid argon, among othe

Janez Žerovnik

The structure of minimal weight rainbow domination functions of cubic graphs are studied. Based on general observations for cubic graphs, generalized Petersen graphs $P(ck,k)$ are characterized whose 4- and 5-rainbow domination numbers equal the general lower bounds. As $t$-rainbow domination of cubic graphs for $t \ge 6$ is trivial, characterizations of suc

Igor Loutsenko, Oksana Yermolayeva

We review applications of factorization methods to the problem of finding stationary point vortex patterns in two-dimensional fluid mechanics. Then we present a new class of patterns related to periodic analogs of Schrodinger operators from the ``even" bi-spectral family. We also show that patterns related to soliton solutions of the KdV hierarchy constitute

Julian Suk, Baris Imre, Jelmer M. Wolterink

Many anatomical structures can be described by surface or volume meshes. Machine learning is a promising tool to extract information from these 3D models. However, high-fidelity meshes often contain hundreds of thousands of vertices, which creates unique challenges in building deep neural network architectures. Furthermore, patient-specific meshes may not be

JunDa Cheng, Wei Yin, Kaixuan Wang, Xiaozhi Chen

Multi-view depth estimation has achieved impressive performance over various benchmarks. However, almost all current multi-view systems rely on given ideal camera poses, which are unavailable in many real-world scenarios, such as autonomous driving. In this work, we propose a new robustness benchmark to evaluate the depth estimation system under various nois

Ravi Kashyap

We develop several innovations to bring the best practices of traditional investment funds to the blockchain landscape. Specifically, we illustrate how: 1) fund prices can be updated regularly like mutual funds; 2) performance fees can be charged like hedge funds; 3) mutually hedged blockchain investment funds can operate with investor protection schemes, su

Takao Komatsu, Neha Gupta, Manoj Upreti

We give an explicit formula for the $p$-Frobenius number of triples associated with Diophantine equations $x^2+y^2=z^r$, that is, the largest positive integer that can only be represented in $p$ ways by combining the three integers of the solutions of Diophantine equations $x^2+y^2=z^r$. When $r=2$, the Frobenius number has already been given.

Christopher J. Howland, Guru Sreevanshu Yerragolam, Roberto Verzicco, Detlef Lohse

Turbulent shear flows driven by a combination of a pressure gradient and buoyancy forcing are investigated using direct numerical simulations. Specifically, we consider the setup of a differentially heated vertical channel subject to a Poiseuille-like horizontal pressure gradient. We explore the response of the system to its three control parameters: the Gra

Matteo Sodano, Federico Magistri, Lucas Nunes, Jens Behley

Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles. Vision systems that operate in real-world environments must be able to understand their surroundings and need the ability to deal with novel situations. This paper tackles open-world semantic segmentation, i.e., the variant of interpreting image data in which object

Amin Mekacher, Max Falkenberg, Andrea Baronchelli

Koo is a microblogging platform based in India launched in 2020 with the explicit aim of catering to non-Western communities in their vernacular languages. With a near-complete dataset totalling over 71M posts and 399M user interactions, we show how Koo has attracted users from several countries including India, Nigeria and Brazil, but with variable levels o

Suganthi Pazhanivel Koushika, Anbalagan Krishnaveni, Sellaperumal Pazhanivelan, Alagirisamy Bharani

The loss of soil organic carbon (SOC) poses a severe danger to agricultural sustainability around the World. This review examines various farming practices and their impact on soil organic carbon storage. After a careful review of the literature, most of the research indicated that different farming practices, such as organic farming, cover crops, conservati

Prabir Barooah

Many loads have flexibility in demand that can be used to provide ancillary services to power grids. A large body of literature exists on designing algorithms to coordinate actions of many loads to provide such a service. The topic of characterizing the flexibility of one or a collection of loads - to determine what kinds of demand deviation from the baselin

Mingxuan Du, Jia Liu, Xiao-Ping Wang, Tianhao Wu

We study a millicharged co-interacting dark matter scenario, where the primary dark matter constituent is the dark photon $A'$ and the secondary component is the fermion $\chi$. In this model, $\chi$ interacts with $A'$ via a $U(1)'$ interaction while being millicharged with respect to normal photons. Our investigation focuses on the oscillation of $A'$ dark

Umar Bashir, Detlef Klimm, Michael Rusing, Matthias Bickermann

The phase diagram of the lithium niobate and lithium tantalate solid solutions was investigated using experimental data from differential thermal analysis (DTA) and crystal growth. We used XRF analysis to determine the elemental composition of crystals. Based on the Neumann-Kopp rule, essential data of end members lithium niobate (LN) and lithium tantalate (

Yingjie Zhao, Hongbo Zhou, Zian Zhang, Zhenxing Bo

Predicting the strength of materials requires considering various length and time scales, striking a balance between accuracy and efficiency. Peierls stress measures material strength by evaluating dislocation resistance to plastic flow, reliant on elastic lattice responses and crystal slip energy landscape. Computational challenges due to the non-local and

Jean-Baptiste Morée, Ryotaro Arita

We explore the possibility to control the superconducting (SC) transition temperature at optimal hole doping $T_{c}^{\rm opt}$ in cuprates by tuning the chemical formula (CF). $T_{c}^{\rm opt}$ can be theoretically predicted from the parameters of the \textit{ab initio} low-energy effective Hamiltonian (LEH) with one antibonding (AB) Cu$3d_{x^2-y^2}$/O$2p_{\

Jakob Greilhuber, Philipp Schepper, Philip Wellnitz

For the vertex selection problem $(\sigma,\rho)$-DomSet one is given two fixed sets $\sigma$ and $\rho$ of integers and the task is to decide whether we can select vertices of the input graph such that, for every selected vertex, the number of selected neighbors is in $\sigma$ and, for every unselected vertex, the number of selected neighbors is in $\rho$ [T

Luise Koch, Raji Ghawi, Jürgen Pfeffer, Janina Isabel Steinert

Technology-facilitated gender-based violence has become a global threat to women's political representation and democracy. Understanding how online hate affects its targets is thus paramount. We analyse 10 million tweets directed at female candidates in the Brazilian election in 2022 and examine their reactions to online misogyny. Using a self-trained machin

Xin-Li Sheng, Yan-Qing Zhao, Si-Wen Li, Francesco Becattini

We develop a general framework for studying the spin alignment $\rho_{00}$ for flavorless vector mesons by using the gauge/gravity duality. Focusing on the dilepton production through vector meson decay, we derive the relation between production rates at each spin channel and meson's spectral function, which can be evaluated by holographic models for a stron

Lei Du, Yanhong Bao

This paper studies the formal deformations of differential algebra morphisms. As a consequence, we develop a cohomology theory of differential algebra morphisms to interpret the lower degree cohomology groups as formal deformations. Then, we prove the Cohomology Comparison Theorem of differential algebra morphisms, i.e., the cohomology of a morphism of diffe

Qi Wang

In this paper, we consider a specialist predator-prey patchy model over the closed stream network. We study the dynamics and the asymptotic profiles of positive steady states according to the mortality rate of the specialist predators, advection and diffusion rates. We verify that the specialist predators can successfully invade as long as the mortality rate

P. L. Garrido, S. Goldstein, D. A. Huse, J. L. Lebowitz

We investigate the time evolution of the Boltzmann entropy of a dilute gas of N particles, N>>1, as it undergoes a free expansion doubling its volume. The microstate of the system, a point in the 4N dimensional phase space, changes in time via Hamiltonian dynamics. Its entropy, at any time $t$, is given by the logarithm of the phase space volume of all the m

Xuhua Ren, Hengcan Shi, Jin Li

Scene text recognition is an important and challenging task in computer vision. However, most prior works focus on recognizing pre-defined words, while there are various out-of-vocabulary (OOV) words in real-world applications. In this paper, we propose a novel open-vocabulary text recognition framework, Pseudo-OCR, to recognize OOV words. The key challenge

Dijia Cai, Zenghui Shi, Haiyang Fu, Huan Liu

The ionosphere is a vitally dynamic charged particle region in the Earth's upper atmosphere, playing a crucial role in applications such as radio communication and satellite navigation. The Slant Total Electron Contents (STEC) is an important parameter for characterizing wave propagation, representing the integrated electron density along the ray of radio si

Rei Barjami, Antonio Miele, Luca Mottola

We explore how to improve the energy performance of battery-less Internet of Things (IoT) devices at the cost of a reduction in the quality of the output. Battery-less IoT devices are extremely resource-constrained energy-harvesting devices. Due to erratic energy patterns from the ambient, their executions become intermittent; periods of active computation a

L. Papa, P. Russo, I. Amerini

Ground-truth RGBD data are fundamental for a wide range of computer vision applications; however, those labeled samples are difficult to collect and time-consuming to produce. A common solution to overcome this lack of data is to employ graphic engines to produce synthetic proxies; however, those data do not often reflect real-world images, resulting in poor

Helmut Abels, Harald Garcke, Jonas Haselböck

We prove existence of weak solutions to a diffuse interface model describing the flow of a fluid through a deformable porous medium consisting of two phases. The system non-linearly couples Biot's equations for poroelasticity, including phase-field dependent material properties, with the Cahn-Hilliard equation to model the evolution of the solid, and is furt

Anastasios Arsenos, Dimitrios Kollias, Evangelos Petrongonas, Christos Skliros

In the context of single domain generalisation, the objective is for models that have been exclusively trained on data from a single domain to demonstrate strong performance when confronted with various unfamiliar domains. In this paper, we introduce a novel model referred to as Contrastive Uncertainty Domain Generalisation Network (CUDGNet). The key idea is

Chengzhi Shen, Martin J. Menten, Hrvoje Bogunović, Ursula Schmidt-Erfurth

Analyzing temporal developments is crucial for the accurate prognosis of many medical conditions. Temporal changes that occur over short time scales are key to assessing the health of physiological functions, such as the cardiac cycle. Moreover, tracking longer term developments that occur over months or years in evolving processes, such as age-related macul

Gábor Bíró, Leonid Serkin, Guy Paić, Gergely Gábor Barnaföldi

The transverse momentum spectra and their multiplicity dependence serve as key tools for extracting parameters to be compared with theoretical models. Over the past decade, the scientific community has extensively studied the possibility of a system analogous to quark-gluon plasma, predicted in heavy nuclei collisions, also existing in collisions involving l

Yuan Lian

In this paper, we mainly consider on the entropy of the extended map conditional to the natural extension of a dynamical system for an Abelian group action and we calculate the entropy is zero.

Oliver Kim, Mohan Sridharan

Landmarks are facts or actions that appear in all valid solutions of a planning problem. They have been used successfully to calculate heuristics that guide the search for a plan. We investigate an extension to this concept by defining a novel "relevance score" that helps identify facts or actions that appear in most but not all plans to achieve any given go

Lorenzo Casarin, Christian Kennedy, Gabriele Tartaglino-Mazzucchelli

We compute the conformal anomalies for 6d (2,0) conformal supergravity by direct calculation in component fields. The main novel results consist of the type-B anomaly coefficients for the gravitino and the 3-form, as well as their explicit quadratic action on some specific backgrounds. We also comment on the graviton contribution, whose Lagrangian is essenti

Byung-Kwan Lee, Beomchan Park, Chae Won Kim, Yong Man Ro

The rise of large language models (LLMs) and instruction tuning has led to the current trend of instruction-tuned large language and vision models (LLVMs). This trend involves either meticulously curating numerous instruction tuning datasets tailored to specific objectives or enlarging LLVMs to manage vast amounts of vision language (VL) data. However, curre

Philip W. Livermore, Leyuan Wu, Longwei Chen, Sjoerd A. L. de Ridder

Magnetic sounding using data collected from the Juno mission can be used to provide constraints on Jupiter's interior. However, inwards continuation of reconstructions assuming zero electrical conductivity and a representation in spherical harmonics are limited by the enhancement of noise at small scales. Here we describe new reconstructions of Jupiter's int

Zhou Yang, Zhensu Sun, Terry Zhuo Yue, Premkumar Devanbu

Large language models for code (LLM4Code), which demonstrate strong performance (e.g., high accuracy) in processing source code, have significantly transformed software engineering. Many studies separately investigate the non-functional properties of LM4Code, but there is no systematic review of how these properties are evaluated and enhanced. This paper fil

Javier Serna, Giovanni Pinzón, Jesús Hernández, Ezequiel Manzo-Martínez

We developed a grid of stellar rotation models for low-mass and solar-type Classical T Tauri stars (CTTS) ($0.3M_{\odot}<M_{\ast}<1.2M_{\odot}$). These models incorporate the star-disk interaction and magnetospheric ejections to investigate the evolution of the stellar rotation rate as a function of the mass of the star $M_{\ast}$, the magnetic field ($B_{\a

Linwei Huai, Hongyu Li, Yulei Han, Yang Luo

Kagome superconductors AV$_3$Sb$_5$ (A = K, Rb and Cs) have attracted much recent attention due to the coexistence of multiple exotic orders. Among them, the charge density wave (CDW) order has been shown to host various unconventional behaviors. Here, we investigate the CDW order by a combination of both bulk and surface doping methods. While element substi

Shuchang Yan

Hybrid electric vehicles (HEVs) are becoming increasingly popular because they can better combine the working characteristics of internal combustion engines and electric motors. However, the minimum fuel consumption of an HEV for a battery electrical balance case under a specific assembly condition and a specific speed curve still needs to be clarified in ac

Shun Takizawa

This paper deals with Schr\"{o}dinger equations with potentials which are time-dependent non-smooth and at most quadratic growth. In the case where potentials are smooth with respect to spatial variables, fundamental solutions have explicit formulas in short time by D. Fujiwara. On the otherhand in the case where ones are non-smooth, we cannot expect that fu