April 2024 arXiv papers — page 50

Sean MacAvaney, Nicola Tonellotto

The PLAID (Performance-optimized Late Interaction Driver) algorithm for ColBERTv2 uses clustered term representations to retrieve and progressively prune documents for final (exact) document scoring. In this paper, we reproduce and fill in missing gaps from the original work. By studying the parameters PLAID introduces, we find that its Pareto frontier is fo

Benjamin McMillan

Given two smooth manifolds with tangent subbundle distributions, an embedding is Pfaffian if its differential sends the distribution on the source into the distribution on the target. In this paper, we consider the question of existence of Pfaffian embeddings in the specific case where the source is a (2,3,5)-manifold, the target is the 7-dimensional space o

Jason J. Bramburger, Dan J. Hill, David J. B. Lloyd

Localized patterns are coherent structures embedded in a quiescent state and occur in both discrete and continuous media across a wide range of applications. While it is well-understood how domain covering patterns (for example stripes and hexagons) emerge from a pattern-forming/Turing instability, analyzing the emergence of their localized counterparts rema

Kerstin Kläser, Błażej Banaszewski, Samuel Maddrell-Mander, Callum McLean

In biological tasks, data is rarely plentiful as it is generated from hard-to-gather measurements. Therefore, pre-training foundation models on large quantities of available data and then transfer to low-data downstream tasks is a promising direction. However, how to design effective foundation models for molecular learning remains an open question, with exi

Yingquan Wang, Pingping Zhang, Dong Wang, Huchuan Lu

Object Re-Identification (Re-ID) aims to identify and retrieve specific objects from images captured at different places and times. Recently, object Re-ID has achieved great success with the advances of Vision Transformers (ViT). However, the effects of the global-local relation have not been fully explored in Transformers for object Re-ID. In this work, we

Yuxuan Chen, Ce Wang, Yuan Hui, Mark Spivack

Physics--informed neural networks (PINN) have shown their potential in solving both direct and inverse problems of partial differential equations. In this paper, we introduce a PINN-based deep learning approach to reconstruct one-dimensional rough surfaces from field data illuminated by an electromagnetic incident wave. In the proposed algorithm, the rough s

Jens Ernstberger, Chengru Zhang, Luca Ciprian, Philipp Jovanovic

We introduce Zero-Knowledge Location Privacy (ZKLP), enabling users to prove to third parties that they are within a specified geographical region while not disclosing their exact location. ZKLP supports varying levels of granularity, allowing for customization depending on the use case. To realize ZKLP, we introduce the first set of Zero-Knowledge Proof (ZK

Sean N. Raymond

Understanding the origin and long-term evolution of the Solar System is a fundamental goal of planetary science and astrophysics. This chapter describes our current understanding of the key processes that shaped our planetary system, informed by empirical data such as meteorite measurements, observations of planet-forming disks around other stars, and exopla

K. G. Kislyakova, M. Güdel, D. Koutroumpa, J. A. Carter

Stellar winds of cool main sequence stars are very difficult to constrain observationally. One way to measure stellar mass loss rates is to detect soft X-ray emission from stellar astrospheres produced by charge exchange between heavy ions of the stellar wind and cold neutrals of the interstellar medium (ISM) surrounding the stars. Here we report detections

Junsong Zhang, Zisong Chen, Chunyu Lin, Lang Nie

Panoramic distortion poses a significant challenge in 360 depth estimation, particularly pronounced at the north and south poles. Existing methods either adopt a bi-projection fusion strategy to remove distortions or model long-range dependencies to capture global structures, which can result in either unclear structure or insufficient local perception. In t

Ivan Iliash, Simeon Allmendinger, Felix Meissen, Niklas Kühl

Generative AI, in general, and synthetic visual data generation, in specific, hold much promise for benefiting surgical training by providing photorealism to simulation environments. Current training methods primarily rely on reading materials and observing live surgeries, which can be time-consuming and impractical. In this work, we take a significant step

Yikun Zhang, Geyan Ye, Chaohao Yuan, Bo Han

Molecule-and-text cross-modal representation learning has emerged as a promising direction for enhancing the quality of molecular representation, thereby improving performance in various scientific fields. However, most approaches employ a global alignment approach to learn the knowledge from different modalities that may fail to capture fine-grained informa

Zhaofeng Lin, Yanqi Qiu, Kai Wang

We establish a law of large numbers for a certain class of vector-valued linear statistics for the Bergman determinantal point process on the unit disk. Our result seems to be the first LLN for vector-valued linear statistics in the setting of determinantal point processes. As an application, we prove that, for almost all configurations $X$ with respect to w

Muhammad Asif Auyb, Muhammad Tayyab Zamir, Imran Khan, Hannia Naseem

This paper focuses on a very important societal challenge of water quality analysis. Being one of the key factors in the economic and social development of society, the provision of water and ensuring its quality has always remained one of the top priorities of public authorities. To ensure the quality of water, different methods for monitoring and assessing

Julien Schanz

Recently, the work on quantum automorphism groups of graphs has seen renewed progress, which we expand in this paper. Quantum symmetry is a richer notion of symmetry than the classical symmetries of a graph. In general, it is non-trivial to decide whether a given graph does have quantum symmetries or not. For vertex-transitive graphs, the quantum symmetries

Niklas Wagner, Felix Mätzler, Samed R. Vossberg, Helen Schneider

Understanding emotions and expressions is a task of interest across multiple disciplines, especially for improving user experiences. Contrary to the common perception, it has been shown that emotions are not discrete entities but instead exist along a continuum. People understand discrete emotions differently due to a variety of factors, including cultural b

Tanbin Chowdhury, B. Rahman Rano, Ishtiaque M. Syed, S. H. Naqib

Rare-earth tritellurides (RTe3) are popular for their charge density wave (CDW) phase, magnetotransport properties and pressure induced superconducting state among other features. In this literature, Density functional theory has been exploited to study various properties of NdTe3. The calculated elastic and thermomechanical parameters, which were hitherto u

Rashid Barket, Matthew England, Jürgen Gerhard

Computer Algebra Systems (e.g. Maple) are used in research, education, and industrial settings. One of their key functionalities is symbolic integration, where there are many sub-algorithms to choose from that can affect the form of the output integral, and the runtime. Choosing the right sub-algorithm for a given problem is challenging: we hypothesise that

Riccardo Michielan, Clara Stegehuis, Matthias Walter

Geometric scale-free random graphs are popular models for networks that exhibit as heavy-tailed degree distributions, small-worldness and high clustering. In these models, vertices have weights that cause the heavy-tailed degrees and are embedded in a metric space so that close-by groups of vertices tend to cluster. The interplay between the vertex weights a

Ayan Sahoo, Aitijhya Saha, Debraj Rakshit

In this work, we investigate the Stark localization near the Aubry-Andr\'{e} (AA) critical point. We perform careful studies for reporting system-dependent parameters, such as localization length, inverse participation ratio (IPR), and energy gap between the ground and first excited state, for characterizing the localization-delocalization transition. We sho

Rita T. Sousa, Heiko Paulheim

Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the analysis of diverse data types, namely gene expression data. While gene expression data can provide valuable insights, challenges arise from the fact that the sample sizes in expre

Mengyuan Cui, Chunxia Li, Yuqin Yao

The symmetric (2+1)-dimensional Lotka-Volterra equation with self-consistent sources is constructed and solved by employing the source generation procedure, whose solutions are expressed in terms of pfaffians. As special cases of the pfaffian solutions, different types of explicit solutions are obtained, including dromions, soliton solutions and breather sol

Sassan Mokhtar, Eugenio Chisari, Nick Heppert, Abhinav Valada

Precisely grasping and reconstructing articulated objects is key to enabling general robotic manipulation. In this paper, we propose CenterArt, a novel approach for simultaneous 3D shape reconstruction and 6-DoF grasp estimation of articulated objects. CenterArt takes RGB-D images of the scene as input and first predicts the shape and joint codes through an

Deheng Zhang, Clara Fernandez-Labrador, Christopher Schroers

Creating artistic 3D scenes can be time-consuming and requires specialized knowledge. To address this, recent works such as ARF, use a radiance field-based approach with style constraints to generate 3D scenes that resemble a style image provided by the user. However, these methods lack fine-grained control over the resulting scenes. In this paper, we introd

Xu Han, Yuan Tang, Zhaoxuan Wang, Xianzhi Li

Existing Transformer-based models for point cloud analysis suffer from quadratic complexity, leading to compromised point cloud resolution and information loss. In contrast, the newly proposed Mamba model, based on state space models (SSM), outperforms Transformer in multiple areas with only linear complexity. However, the straightforward adoption of Mamba d

Yuchong Zhang, Yong Ma, Danica Kragic

The emergence of large vision models (LVMs) is following in the footsteps of the recent prosperity of Large Language Models (LLMs) in following years. However, there's a noticeable gap in structured research applying LVMs to human-robot interaction (HRI), despite extensive evidence supporting the efficacy of vision models in enhancing interactions between hu

Julia Gygax, Friedemann Zenke

Training spiking neural networks to approximate universal functions is essential for studying information processing in the brain and for neuromorphic computing. Yet the binary nature of spikes poses a challenge for direct gradient-based training. Surrogate gradients have been empirically successful in circumventing this problem, but their theoretical founda

Qihuang Zhong, Kang Wang, Ziyang Xu, Juhua Liu

Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks. However, CoT still falls short in dealing with complex math word problems, as it usually suffers from three pitfalls: semantic misunderstanding errors, calculation errors, and step-missing errors. Prior studies involve addressing the

Guohua Zhang, Aijing Sun, Ling Liu, Yi Fang

To address the challenge of constructing short girth-8 quasi-cyclic (QC) low-density parity-check (LDPC) codes, a novel construction framework based on vertical symmetry (VS) is proposed. Basic properties of the VS structure are presented. With the aid of these properties, existing explicit constructions for column weights from three to five which can be tra

Xiaoshuang Chen, Gengrui Zhang, Yao Wang, Yulin Wu

Modern large-scale recommender systems are built upon computation-intensive infrastructure and usually suffer from a huge difference in traffic between peak and off-peak periods. In peak periods, it is challenging to perform real-time computation for each request due to the limited budget of computational resources. The recommendation with a cache is a solut

Van-Phuc Bui, Daniel Abode, Pedro M. de Sant Ana, Karthik Muthineni

The paper examines a scenario wherein sensors are deployed within an Industrial Networked Control System, aiming to construct a digital twin (DT) model for a remotely operated Autonomous Guided Vehicle (AGV). The DT model, situated on a cloud platform, estimates and predicts the system's state, subsequently formulating the optimal scheduling strategy for exe

Marianna Felici, Jennifer Segale, Paul Withers, Christina O. Lee

We use 62 electron density profiles collected by the Radio Occultation Science Experiment (ROSE), on MAVEN, when Mars was hit by CIRs and ICMEs close to aphelion (April 2021) and during two dust storms (June-July 2022) to examine the response of the Martian ionosphere to solar events and to solar events hitting during dust storms. We do so through three prox

Valdemar Melicher

We argue that the current POW based consensus algorithm of the Bitcoin network suffers from a fundamental economic discrepancy between the real world transaction (txn) costs incurred by miners and the wealth that is being transacted. Put simply, whether one transacts 1 satoshi or 1 bitcoin, the same amount of electricity is needed when including this txn int

Suraj Kumar, Jeremy Lim, Nicholas Rivera, Wesley Wong

Strongly correlated electron systems are a cornerstone of modern physics, being responsible for groundbreaking phenomena from superconducting magnets to quantum computing. In most cases, correlations in electrons arise exclusively due to Coulomb interactions. In this work, we reveal that free electrons interacting simultaneously with a light field can become

Ye Zhang, Yifeng Wang, Zijie Fang, Hao Bian

Weakly supervised segmentation methods have gained significant attention due to their ability to reduce the reliance on costly pixel-level annotations during model training. However, the current weakly supervised nuclei segmentation approaches typically follow a two-stage pseudo-label generation and network training process. The performance of the nuclei seg

Muhammad Ahmad, Salvatore Distifano, Adil Mehmood Khan, Manuel Mazzara

Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets

Andres Tello, Huy Truong, Alexander Lazovik, Victoria Degeler

Currently, the number of common benchmark datasets that researchers can use straight away for assessing data-driven deep learning approaches is very limited. Most studies provide data as configuration files. It is still up to each practitioner to follow a particular data generation method and run computationally intensive simulations to obtain usable data fo

Ahmed Al-Tahmeesschi, Jukka Talvitie, Miguel López-Benítez, Hamed Ahmadi

Millimeter-wave (mmWave) is a key enabler for next-generation transportation systems. However, in an urban city scenario, mmWave is highly susceptible to blockages and shadowing. Therefore, base station (BS) placement is a crucial task in the infrastructure design where coverage requirements need to be met while simultaneously supporting localisation. This w

José Correa, Mathieu Mari, Andrew Xia

When launching new products, firms face uncertainty about market reception. Online reviews provide valuable information not only to consumers but also to firms, allowing firms to adjust the product characteristics, including its selling price. In this paper, we consider a pricing model with online reviews in which the quality of the product is uncertain, and

Esam Ghaleb, Ilya Burenko, Marlou Rasenberg, Wim Pouw

Gestures are inherent to human interaction and often complement speech in face-to-face communication, forming a multimodal communication system. An important task in gesture analysis is detecting a gesture's beginning and end. Research on automatic gesture detection has primarily focused on visual and kinematic information to detect a limited set of isolated

Ziqi Xie, Weidong Zhao, Xianhui Liu, Jian Zhao

Deep learning-based image stitching pipelines are typically divided into three cascading stages: registration, fusion, and rectangling. Each stage requires its own network training and is tightly coupled to the others, leading to error propagation and posing significant challenges to parameter tuning and system stability. This paper proposes the Simple and R

Wensheng Pan, Timin Gao, Yan Zhang, Runze Hu

Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Currently, leveraging semantic information to enhance IQA is a crucial research direction. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretrainin

Yanhua Lu, Tianshuai Shang, Pengxiang Du, Jian Li

A convolutional neural network (CNN) is employed to investigate nuclear mass. By introducing the masses of neighboring nuclei and the paring effects at the input layer of the network, local features of the target nucleus are extracted to predict its mass. Then, through learning the differences between the experimental nuclear masses and the predicted nuclear

Evgueni Doubtsov, Dmitry V. Rutsky

Let $\varphi_j$, $j=1,2, \dots, N$, be holomorphic self-maps of the unit disk $\mathbb{D}$ of $\mathbb{C}$. We prove that the compactness of a linear combination of the composition operators $C_{\varphi_j}: f\mapsto f\circ\varphi_j$ on the Hardy space $H^p(\mathbb{D})$ does not depend on $p$ for $0<p<\infty$. This answers a conjecture of Choe et al. about th

Sen Liu, Yiwei Guo, Xie Chen, Kai Yu

While acoustic expressiveness has long been studied in expressive text-to-speech (ETTS), the inherent expressiveness in text lacks sufficient attention, especially for ETTS of artistic works. In this paper, we introduce StoryTTS, a highly ETTS dataset that contains rich expressiveness both in acoustic and textual perspective, from the recording of a Mandarin

Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Manuel Mazzara, Salvatore Distifano

The traditional Transformer model encounters challenges with variable-length input sequences, particularly in Hyperspectral Image Classification (HSIC), leading to efficiency and scalability concerns. To overcome this, we propose a pyramid-based hierarchical transformer (PyFormer). This innovative approach organizes input data hierarchically into segments, e

Muhammad Ahmad, Manuel Mazzara, Salvatore Distifano

Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art (SOTA) models. When training, validation, and test sets overlap or share data, it introduces a bias that inflates performance metrics and prevents accurate assessment of a model's true ability to generalize to new examples. This paper presents an innovative disjoint sampli

Julien Delaunay, Luis Galárraga, Christine Largouët

Although counterfactual explanations are a popular approach to explain ML black-box classifiers, they are less widespread in NLP. Most methods find those explanations by iteratively perturbing the target document until it is classified differently by the black box. We identify two main families of counterfactual explanation methods in the literature, namely,

Thanh Toan Nguyen, Quoc Viet Hung Nguyen, Thanh Tam Nguyen, Thanh Trung Huynh

Recommender systems have become an integral part of online services to help users locate specific information in a sea of data. However, existing studies show that some recommender systems are vulnerable to poisoning attacks, particularly those that involve learning schemes. A poisoning attack is where an adversary injects carefully crafted data into the pro

Zhe Zhao, Pengkun Wang, Xu Wang, Haibin Wen

Pre-training GNNs to extract transferable knowledge and apply it to downstream tasks has become the de facto standard of graph representation learning. Recent works focused on designing self-supervised pre-training tasks to extract useful and universal transferable knowledge from large-scale unlabeled data. However, they have to face an inevitable question:

Marcin Briański, Robert Hickingbotham, David R. Wood

The defective chromatic number of a graph class $\mathcal{G}$ is the minimum integer $k$ such that for some integer $d$, every graph in $\mathcal{G}$ is $k$-colourable such that each monochromatic component has maximum degree at most $d$. Similarly, the clustered chromatic number of a graph class $\mathcal{G}$ is the minimum integer $k$ such that for some in

Guillaume Grelier, Jaime San Martín

For any $p\in[1,\infty)$, we prove that the set of simple functions taking at most $k$ different values is proximinal in B\"ochner spaces $L^p(X)$ whenever $X$ is a dual Banach space with $w^*$-sequentially compact unit ball. With additional properties on $X$ and its norm, we show these sets are approximatively $w^*$-compact for $p\in(1,\infty)$ and even app

Tony J. Puthenpurakal

Let $(A,\mathfrak{m})$ be a hypersurface local ring of dimension $d \geq 1$ and let $I$ be an $\mathfrak{m}$-primary ideal. We show that there is a non-negative integer $r_I$ (depending only on $I$) such that if $M$ is any non-free maximal Cohen-Macaulay $A$-module the function $n \rightarrow \ell(Tor^A_1(M, A/I^{n+1}))$ (which is of polynomial type) has deg

Haozhe Wang, Yuxuan Yang, Mei Lu

For an oriented graph $D$, the $inversion$ of $X \subseteq V(D)$ in $D$ is the digraph obtained from $D$ by reversing the direction of all arcs with both ends in $X$. The inversion number of $D$, denoted by $inv(D)$, is the minimum number of inversions needed to transform $D$ into an acyclic digraph. In this paper, we first show that $inv (\overrightarrow{C_

Fabian Bleitner, Camilla Nobili

We consider the two-dimensional Rayeigh-B\'enard convection problem between Navier-slip fixed-temperature boundary conditions and present a new upper bound for the Nusselt number. The result, based on a localization principle for the Nusselt number and an interpolation bound, exploits the regularity of the flow. On one hand our method yields a shorter proof

Edmir Xhoxhi, Vincent Albert Wolff

According to the World Health Organization, the involvement of Vulnerable Road Users (VRUs) in traffic accidents remains a significant concern, with VRUs accounting for over half of traffic fatalities. The increase of automation and connectivity levels of vehicles has still an uncertain impact on VRU safety. By deploying the Collective Perception Service (CP

Kaikai Deng, Dong Zhao, Wenxin Zheng, Yue Ling

Millimeter wave radar is gaining traction recently as a promising modality for enabling pervasive and privacy-preserving gesture recognition. However, the lack of rich and fine-grained radar datasets hinders progress in developing generalized deep learning models for gesture recognition across various user postures (e.g., standing, sitting), positions, and s

Dayananda Herurkar, Sebastian Palacio, Ahmed Anwar, Joern Hees

Anomaly detection in real-world scenarios poses challenges due to dynamic and often unknown anomaly distributions, requiring robust methods that operate under an open-world assumption. This challenge is exacerbated in practical settings, where models are employed by private organizations, precluding data sharing due to privacy and competitive concerns. Despi

S. K. Behera, A. Swain, P. C. Ramamurthy

We investigate the structural and electronic reconstruction associated with the commensurate charge-density-wave (CCDW) phase in bulk and monolayer 1T-TaS2 using density functional theory (DFT) and Wannier-based tight-binding modeling. Structural relaxation of a sqrt(13) x sqrt(13) supercell leads spontaneously to the formation of the Star-of-David (SoD) dis

Kenichi Konishi, Roberto Menta

Quantum states of a spin $\tfrac{1}{2}$ (a qubit) are parametrized by the space ${\mathbf {CP}}^1 \sim S^2$, the Bloch sphere. A spin $j$ for a generic $j$ (a $2j+1$-state system) is represented instead by a point of a larger space, ${\mathbf {CP}}^{2j}$. Here we study the state of a single angular momentum/spin in the limit, $j \to \infty$. The special clas

Jeonggyu Hwang

Photovoltaic materials are recognized for their potential as sustainable energy sources that enable the conversion between light and electrical energy. However, solar cells have been unable to surpass the theoretical limit of 32%, known as the Shockley-Queisser limit, and face challenges in effectively utilizing the broad spectrum of sunlight. To address thi

Jacek Wasik, Joseph Sutcliffe, Renaud Podor, Jarrod Lewis

Although the principal physical behaviour of a material is inherently connected to its fundamental crystal structure, the behaviours observed in the real-world are often driven by the microstructure, which for many polycrystalline materials, equates to the size and shape of the constituent crystal grains. Here we highlight a cutting edge synthesis route to t

Qianjun Lyu

This paper studies the optimal refund mechanism when an uninformed buyer can privately acquire information about his valuation of a product over time. We consider a class of refund mechanisms based on stochastic return policies: if the buyer requests a return, the seller will issue a (partial) refund while allowing the buyer to keep the product with some pro

Mohammed Djahafi, Nabila Salmi

Today, more and more embedded devices are being connected through a network, generally Internet, offering users different services. This concept refers to Internet of Things (IoT), bringing information and control capabilities in many fields like medicine, smart homes, home security, etc. Main drawbacks of IoT environment are its dependency on Internet conne

Edmir Xhoxhi, Vincent Albert Wolff, Yao Li, Florian Alexander Schiegg

Ensuring the safety of Vulnerable Road Users (VRUs) is a critical concern in transportation, demanding significant attention from researchers and engineers. Recent advancements in Vehicle-to-Everything (V2X) technology offer promising solutions to enhance VRU safety. Notably, VRUs often travel in groups, exhibiting similar movement patterns that facilitate t

Daneshvar Amrollahi, Hossein Hojjat, Philipp Rümmer

The input language for today's CHC solvers are commonly the standard SMT-LIB format, borrowed from SMT solvers, and the Prolog format that stems from Constraint-Logic Programming (CLP). This paper presents a new front-end of the Eldarica CHC solver that allows inputs in the Prolog language. We give a formal translation of a subset of Prolog into the SMT-LIB

Emanuele De Angelis, Hari Govind V K

CHC-COMP 2023 is the sixth edition of the Competition of Solvers for Constrained Horn Clauses. The competition was run in April 2023 and the results were presented at the 10th Workshop on Horn Clauses for Verification and Synthesis held in Paris, France, on April 23, 2023. This edition featured seven solvers (six competing and one hors concours) and six trac

Niccolò Veltri, Cheng-Syuan Wan

This work concerns the proof theory of (left) skew monoidal categories and their variants (e.g. closed monoidal, symmetric monoidal), continuing the line of work initiated in recent years by Uustalu et al. Skew monoidal categories are a weak version of Mac Lane's monoidal categories, where the structural laws are not required to be invertible, they are merel

Alberto Momigliano, Martina Sassella

We report on yet another formalization of the Church-Rosser property in lambda-calculi, carried out with the proof environment Beluga. After the well-known proofs of confluence for beta-reduction in the untyped settings, with and without Takahashi's complete developments method, we concentrate on eta-reduction and obtain the result for beta-eta modularly. We

Thaynara Arielly de Lima, Andréia Borges Avelar, André Luiz Galdino, Mauricio Ayala-Rincón

This paper discusses the extension of the Prototype Verification System (PVS) sub-theory for rings, part of the PVS algebra theory, with theorems related to the division algorithm for Euclidean rings and Unique Factorization Domains that are general structures where an analog of the Fundamental Theorem of Arithmetic holds. First, we formalize the general abs

Laura P. Gamboa Guzman, Kristin Y. Rozier

The foundations of formal models for epistemic and doxastic logics often rely on certain logical aspects of modal logics such as S4 and S4.2 and their semantics; however, the corresponding mathematical results are often stated in papers or books without including a detailed proof, or a reference to it, that allows the reader to convince themselves about them

Jingfeng Shao, Zhichang Guo, Zhongxiang Zhou

A doubly degenerate parabolic equation in non-divergent form with variable growth is investigated in this paper. In suitable spaces, we prove the existence of weak solutions of the equation for cases $1\leq m < 2$ and $m\geq 2$ in different ways. And we establish the non-expansion of support of the solution for the problem.

M. W. Aslam, A. A. Zafar, M. N. Aslam, A. A Bhatti

New research has highlighted a shortfall in the Standard Model (SM) because it predicts neutrinos to have zero mass. However, recent experiments on neutrino oscillation have revealed that the majority of neutrino parameters indeed indicate their significant mass. In response, scientists are increasingly incorporating discrete symmetries alongside continuous

Alaa Elobaid, Nathan Ramoly, Lara Younes, Symeon Papadopoulos

Biometric Verification (BV) systems often exhibit accuracy disparities across different demographic groups, leading to biases in BV applications. Assessing and quantifying these biases is essential for ensuring the fairness of BV systems. However, existing bias evaluation metrics in BV have limitations, such as focusing exclusively on match or non-match erro

A. Moustaj, J. P. J. Krebbekx, C. Morais Smith

By implementing a charge pumping scheme for one-dimensional aperiodic chains, we confirm the existence of topological phases in these systems whenever their finite-size realizations admit inversion symmetry. These phases are usually characterized by an anomalous edge response as a result of the bulk-boundary correspondence. We show that these signatures are

Pierluigi Francesco De Paola, Alessandro Borri, Fabrizio Dabbene, Karim Keshavjee

Despite the well-acknowledged benefits of physical activity for type 2 diabetes (T2D) prevention, the literature surprisingly lacks validated models able to predict the long-term benefits of exercise on T2D progression and support personalized risk prediction and prevention. To bridge this gap, we developed a novel mathematical model that formalizes the link

Kostiantyn Omelianchuk, Andrii Liubonko, Oleksandr Skurzhanskyi, Artem Chernodub

In this paper, we carry out experimental research on Grammatical Error Correction, delving into the nuances of single-model systems, comparing the efficiency of ensembling and ranking methods, and exploring the application of large language models to GEC as single-model systems, as parts of ensembles, and as ranking methods. We set new state-of-the-art perfo

Theo Lepage, Reda Dehak

Self-Supervised Learning (SSL) frameworks became the standard for learning robust class representations by benefiting from large unlabeled datasets. For Speaker Verification (SV), most SSL systems rely on contrastive-based loss functions. We explore different ways to improve the performance of these techniques by revisiting the NT-Xent contrastive loss. Our

Debasish Borah, Satyabrata Mahapatra, Partha Kumar Paul, Narendra Sahu

We study the possibility of generating dark matter (DM) and baryon asymmetry of the Universe (BAU) simultaneously in an asymmetric DM framework, which also alleviates the small-scale structure issues of cold DM. While the thermal relic of such self-interacting DM remains under-abundant due to efficient annihilation into light mediators, a nonzero asymmetry i

Anastasiia Kultaeva, Timur Biktagirov, Andreas Sperlich, Patrick Dörflinger

Metal-Organic frameworks (MOFs) are promising candidates for advanced photocatalytically active materials. These porous crystalline compounds have large active surface areas and structural tunability and are thus highly competitive with oxides, the well-established material class for photocatalysis. However, due to their complex organic and coordination chem

Chunyan Li, Yaroslav V. Kartashov

We propose a simple dissipative system with purely cubic defocusing nonlinearity and nonuniform linear gain that can support stable localized dissipative vortex solitons with high topological charges without the utilization of competing nonlinearities and nonlinear gain or losses. Localization of such solitons is achieved due to an intriguing mechanism when

Carlos Cano-Genoves, Emilio Insfrán, Silvia Abrahão

Goal-oriented analysis tools are used to assess goal models and assist analysts in decision-making. We introduce the VeGAn-Tool, which prioritizes goals according to their qualitative importance for the stakeholders and propagates this information in the goal model according to the different types of relationships. The FTOPSIS technique is used to calculate

Alessandro Trenta, Davide Bacciu, Andrea Cossu, Pietro Ferrero

We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology produces actual numerical solutions instead of bounds on them. We extend the original BootSTOP algorithm by adding multiple constraints derived from domain-specific knowledge, even in integral form, to improve the accuracy of the solutio

Hoang Chuong Nguyen, Tianyu Wang, Jose M. Alvarez, Miaomiao Liu

This paper focuses on self-supervised monocular depth estimation in dynamic scenes trained on monocular videos. Existing methods jointly estimate pixel-wise depth and motion, relying mainly on an image reconstruction loss. Dynamic regions1 remain a critical challenge for these methods due to the inherent ambiguity in depth and motion estimation, resulting in

Siqi Ping, Yuzhu Mao, Yang Liu, Xiao-Ping Zhang

Although large-scale pre-trained models hold great potential for adapting to downstream tasks through fine-tuning, the performance of such fine-tuned models is often limited by the difficulty of collecting sufficient high-quality, task-specific data. Federated Learning (FL) offers a promising solution by enabling fine-tuning across large-scale clients with a

Abdulhady Abas Abdullah, Hadi Veisi, Tarik Rashid

End-to-end transformer-based models epitomize the cutting-edge in Automatic Speech Recognition (ASR) systems. Despite their substantial benefits, these models demand extensive training data to perform optimally, presenting a significant challenge for low-resource languages such as Central Kurdish. Addressing this issue requires innovative methods and techniq

Xiaokang Zhou

The CKM angle $\gamma$ is important for testing the unitarity of the CKM matrix and searching for new physics. $\gamma$ can be extracted by the interference between $b\to u$ and $b\to c$ in the B factory such as LHCb and Belle-II. Determining $\gamma$ also needs strong parameter information from the charm factory, such as the BESIII experiment. With quantum-

Ross Greer, Mathias Viborg Andersen, Andreas Møgelmose, Mohan Trivedi

Driver activity classification is crucial for ensuring road safety, with applications ranging from driver assistance systems to autonomous vehicle control transitions. In this paper, we present a novel approach leveraging generalizable representations from vision-language models for driver activity classification. Our method employs a Semantic Representation

Philipp Schmoll, Harald O. Jeschke, Yasir Iqbal

Spangolite (Cu$_6$Al(SO$_4$)(OH)$_{12}$Cl$\cdot$3H$_2$O) is a hydroxy-hydrated copper sulfate mineral with a one-seventh depleted triangular lattice of Cu$^{2+}$ ions in each layer. Experimental measurements revealed a non-magnetic ground state at $T \sim 8\, \text{K}$ with magnetic properties dominated by dimerization. We propose a spatially anisotropic Hei

Alessandro Giuliani, Vieri Mastropietro, Slava Rychkov, Giuseppe Scola

We consider the Renormalization Group (RG) fixed-point theory associated with a fermionic $\psi^4_d$ model in $d=1,2,3$ with fractional kinetic term, whose scaling dimension is fixed so that the quartic interaction is weakly relevant in the RG sense. The model is defined in terms of a Grassmann functional integral with interaction $V^*$, solving a fixed-poin

Kevin Wilkinghoff, Alessia Cornaggia-Urrigshardt

In multi-sample keyword spotting, each keyword class is represented by multiple spoken instances, called samples. A na\"ive approach to detect keywords in a target sequence consists of querying all samples of all classes using sub-sequence dynamic time warping. However, the resulting processing time increases linearly with respect to the number of samples be

Ranim Khojah, Mazen Mohamad, Philipp Leitner, Francisco Gomes de Oliveira Neto

Large Language Models (LLMs) are frequently discussed in academia and the general public as support tools for virtually any use case that relies on the production of text, including software engineering. Currently there is much debate, but little empirical evidence, regarding the practical usefulness of LLM-based tools such as ChatGPT for engineers in indust

Vimalesh Kumar Vimal, Jorge Cayao

Majorana bound states emerge in topological superconductors as zero-energy edge states exhibiting spatial nonlocality. Despite the enormous advances, the detection of Majorana bound states is still challenging mainly because topologically trivial Andreev bound states produce similar signatures. In this work we consider a topological superconductor with Major

Dan Crawford, Roland Wiesendanger, Stephan Rachel

Initializing the ground state of a quantum bit (qubit) based on Majorana zero modes is one of the most pressing issues for future topological quantum computers. We explore a protocol for initializing such topological qubits based on magnet-superconductor hybrid networks by coupling magnetic chains to a single molecule magnet. The parity of the Majorana state

Eduardo Garnacho-Velasco, Pedro J. de A. Bicudo, J. Emilio F. T. Ribeiro, Felipe J. Llanes-Estrada

We present a detailed study of the Bardeen-Cooper-Schrieffer (BCS) gap equation ``replicae'' or excited vacuum states, orthogonal to the ground-state one, in the chiral-quark sector of the Hamiltonian Coulomb-gauge model of chromodynamics. Analyzing the number of negative eigenmodes of the energy density's Hessian we believe that we have identified all of th

Chen Zhang, Zhuorui Liu, Dawei Song

With the increasingly giant scales of (causal) large language models (LLMs), the inference efficiency comes as one of the core concerns along the improved performance. In contrast to the memory footprint, the latency bottleneck seems to be of greater importance as there can be billions of requests to a LLM (e.g., GPT-4) per day. The bottleneck is mainly due

Daniel Adler, David Wei, Melissa Will, Kritsana Srakaew

The relaxation behaviour of isolated quantum systems taken out of equilibrium is among the most intriguing questions in many-body physics. Quantum systems out of equilibrium typically relax to thermal equilibrium states by scrambling local information and building up entanglement entropy. However, kinetic constraints in the Hamiltonian can lead to a breakdow

Zichao Shu, Lijun Li, Rui Wang, Zetao Chen

A common prerequisite for evaluating a visual(-inertial) odometry (VO/VIO) algorithm is to align the timestamps and the reference frame of its estimated trajectory with a reference ground-truth derived from a system of superior precision, such as a motion capture system. The trajectory-based alignment, typically modeled as a classic hand-eye calibration, sig

Hong-lin Liao, Xuping Wang

We propose a unified theoretical framework to examine the energy dissipation properties at all stages of explicit exponential Runge-Kutta (EERK) methods for gradient flow problems. The main part of the novel framework is to construct the differential form of EERK method by using the difference coefficients of method and the so-called discrete orthogonal conv

Markos Diomataris, Nikos Athanasiou, Omid Taheri, Xi Wang

Synthesizing natural human motions that enable a 3D human avatar to walk and reach for arbitrary goals in 3D space remains an unsolved problem with many applications. Existing methods (data-driven or using reinforcement learning) are limited in terms of generalization and motion naturalness. A primary obstacle is the scarcity of training data that combines l