October 2023 arXiv papers — page 19

Peihao Li

Web3 and AI have been among the most discussed fields over the recent years, with substantial hype surrounding each field's potential to transform the world as we know it. However, as the hype settles, it's evident that neither AI nor Web3 can address all challenges independently. Consequently, the intersection of AI and Web3 is gaining increased attention,

Bruce Sagan

Recently, Merca and Schmidt proved a number of identities relating partitions of an integer with two classic number-theoretic functions, namely the M\"obius function and Euler's totient function. Their demonstrations were mainly algebraic. We give bijective proofs of some of these results. Our main tools are the concept of a rooted partition and an operation

Andrew Giuliani

Many stellarator coil design problems are plagued by multiple minima, where the locally optimal coil sets can sometimes vary substantially in performance. As a result, solving a coil design problem a single time with a local optimization algorithm is usually insufficient and better optima likely do exist. To address this problem, we propose a global optimiza

Andrea Colledan, Ugo Dal Lago

Circuit description languages are a class of quantum programming languages in which programs are classical and produce a description of a quantum computation, in the form of a quantum circuit. Since these programs can leverage all the expressive power of high-level classical languages, circuit description languages have been successfully used to describe com

Eddy B. de Leon

We discuss a class of solutions to the Ernst equation in terms of theta functions with characteristics. We show that it is necessary to take into account a phase factor, which arises from a shift by a lattice vector, and impose conditions on the characteristics of the theta functions in order for the presented function to solve the Ernst equation for all the

Jinze Zhao

Explainability is critical for deep learning applications in healthcare which are mandated to provide interpretations to both patients and doctors according to legal regulations and responsibilities. Explainable AI methods, such as feature importance using integrated gradients, model approximation using LIME, or neuron activation and layer conductance to pro

Krijn Doekemeijer, Nick Tehrany, Balakrishnan Chandrasekaran, Matias Bjørling

The recent emergence of NVMe flash devices with Zoned Namespace support, ZNS SSDs, represents a significant new advancement in flash storage. ZNS SSDs introduce a new storage abstraction of append-only zones with a set of new I/O (i.e., append) and management (zone state machine transition) commands. With the new abstraction and commands, ZNS SSDs offer more

Tobias Löw, Sylvain Calinon

In this work, we are presenting an extension of the cooperative dual-task space (CDTS) in conformal geometric algebra. The CDTS was first defined using dual quaternion algebra and is a well established framework for the simplified definition of tasks using two manipulators. By integrating conformal geometric algebra, we aim to further enhance the geometric e

Debottam Mandal, Sanjay Sarkar, Kamal Das, Amit Agarwal

Nonlinear transport phenomena offer an exciting probe into the band geometry and symmetry properties of a system. While most studies on nonlinear transport have looked at second-order nonreciprocal responses in noncentrosymmetric systems, the reciprocal third-order effects dominant in centrosymmetric systems remain largely uncharted. Here, we uncover two sig

Unai Fischer-Abaigar, Christoph Kern, Noam Barda, Frauke Kreuter

AI-driven decision-making systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. While these systems offer great potential benefits to institutional decision-making processes, such as improved efficiency and reliability, these systems face t

Tobias Löw, Philip Abbet, Sylvain Calinon

Geometry is a fundamental part of robotics and there have been various frameworks of representation over the years. Recently, geometric algebra has gained attention for its property of unifying many of those previous ideas into one algebra. While there are already efficient open-source implementations of geometric algebra available, none of them is targeted

Shikhar Murty, Pratyusha Sharma, Jacob Andreas, Christopher D. Manning

Recursion is a prominent feature of human language, and fundamentally challenging for self-attention due to the lack of an explicit recursive-state tracking mechanism. Consequently, Transformer language models poorly capture long-tail recursive structure and exhibit sample-inefficient syntactic generalization. This work introduces Pushdown Layers, a new self

Ruihao Gu

In this paper, we focus on the rigidity of $C^{2+}$-smooth codimension-one stable foliations of Anosov diffeomorphisms. Specifically, we show that if the regularity of these foliations is slightly bigger than $2$, then they will have the same smoothness as the diffeomorphisms.

Jingcheng Yuan, Mini Das

X-ray phase contrast imaging holds great promise for improving the visibility of light-element materials such as soft tissues and tumors. Single-mask differential phase contrastnimaging method stands out as a simple and effective approach to yield differential phase contrast. In this work, we introduce a novel model for a single-mask phase imaging system bas

Khashayar F. Kohan, Susan J. Gaskin

The present study aims to understand the process of turbulent entrainment into a jet, as affected by background turbulence, using scalar statistics. Planar-laser-induced fluorescence was employed to capture the orthogonal cross sections of the jet at a fixed downstream station with varying background turbulence intensities and length scales. The conditional

Valentin Gutev

In 1951, Ernest Michael wrote a definitive seminal article on hyperspaces raising a general question that became known as the hyperspace selection problem. The present paper contains some aspects of this problem, along with several open questions. Most of these considerations and questions are related to the usual map extension problem, but now in the settin

Changjiang Gao, Shujian Huang, Jixing Li, Jiajun Chen

Recent large language models (LLMs) have revealed strong abilities to understand natural language. Since most of them share the same basic structure, i.e. the transformer block, possible contributors to their success in the training process are scaling and instruction tuning. However, how these factors affect the models' language perception is unclear. This

Mark Wetzlinger, Matthias Althoff

Backward reachability analysis computes the set of states that reach a target set under the competing influence of control input and disturbances. Depending on their interplay, the backward reachable set either represents all states that can be steered into the target set or all states that cannot avoid entering it -- the corresponding solutions can be used

H. Lohani, P. Mishra, R. Goyal, V. P. S. Awana

We present a comparative study of our valence band photoemission results on Nb2Pd1.2Se5 and Nb2Pd0.95S5 superconductors which is supported by our DFT based electronic structure calculations. We observe that the VB spectra of both the compounds are qualitatively similar, except slight difference in the binding energy position of all features between the two c

Valerio Francesco Puglisi, Oliver Giudice, Sebastiano Battiato

Deep Audio Analyzer is an open source speech framework that aims to simplify the research and the development process of neural speech processing pipelines, allowing users to conceive, compare and share results in a fast and reproducible way. This paper describes the core architecture designed to support several tasks of common interest in the audio forensic

Katie Z Luo, Zhenzhen Liu, Xiangyu Chen, Yurong You

Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Although very successful for Large Language Models (LLMs), these advancements have not had a comparable impact in research for autonomous vehicles -- where alignment with human expect

Xinyu Huang, Haojun Yang, Shisheng Hu, Xuemin Shen

Digital twin (DT) is revolutionizing the emerging video streaming services through tailored network management. By integrating diverse advanced communication technologies, DTs are promised to construct a holistic virtualized network for better network management performance. To this end, we develop a DT-driven network architecture for video streaming (DTN4VS

Dongwei Shi, Xiu Yang

Nonlinearity presents a significant challenge in problems involving dynamical systems, prompting the exploration of various linearization techniques, including the well-known Carleman Linearization. In this paper, we introduce the Koopman Spectral Linearization method tailored for nonlinear autonomous dynamical systems. This innovative linearization approach

Christos Tsanikidis, Javad Ghaderi

Scheduling packets with end-to-end deadline constraints in multihop networks is an important problem that has been notoriously difficult to tackle. Recently, there has been progress on this problem in the worst-case traffic setting, with the objective of maximizing the number of packets delivered within their deadlines. Specifically, the proposed algorithms

Harun Kir

The purpose of this paper is to list the refined Humbert invariants for a given automorphism group of a curve $C/K$ of genus 2 over an algebraically closed field $K$ with characteristic $0$. This invariant is an algebraic generalization of the (usual) \textit{Humbert invariant}. It is a positive definite quadratic form associated to the curve $C$, and it enc

Neta Shaul, Juan Perez, Ricky T. Q. Chen, Ali Thabet

Diffusion or flow-based models are powerful generative paradigms that are notoriously hard to sample as samples are defined as solutions to high-dimensional Ordinary or Stochastic Differential Equations (ODEs/SDEs) which require a large Number of Function Evaluations (NFE) to approximate well. Existing methods to alleviate the costly sampling process include

Meagan Sundstrom, Logan Kageorge

Students' beliefs about the extent to which meaningful others, including their peers, recognize them as a strong science student are correlated with their persistence in science courses and careers. Yet, prior work has found a gender bias in peer recognition, in which student nominations of strong peers disproportionately favor men over women, in some instru

Nicolas Lanchier, Max Mercer

In the Deffuant model, individuals are located on the vertices of a graph, and are characterized by their opinion, a number in $[-1, 1]$. The dynamics depends on two parameters: a confidence threshold $\theta < 2$ and a convergent parameter $\mu_- \leq 1/2$. Neighbors on the graph interact at rate one, which results in no changes if the neighbors disagree by

Y. Kawazura

This Letter presents a magnetohydrodynamic model that describes the small-amplitude fluctuations with wavelengths comparable to ion inertial length in the presence of a relativistically strong mean magnetic field. The set of derived equations is virtually identical to the non-relativistic Hall reduced magnetohydrodynamics (Schekochihin et al. 2019), differin

Atsuhisa Ota, Misao Sasaki, Yi Wang

The radiation-dominated universe is a key ingredient of the standard Big Bang cosmology. Radiation comprises numerous quantum elementary particles, and the macroscopic behavior of radiation is described by taking the quantum thermal average of its constituents. While the interactions between individual particles and gravitational waves are often neglected in

Yuanze Li, Haolin Wang, Shihao Yuan, Ming Liu

Due to the training configuration, traditional industrial anomaly detection (IAD) methods have to train a specific model for each deployment scenario, which is insufficient to meet the requirements of modern design and manufacturing. On the contrary, large multimodal models~(LMMs) have shown eminent generalization ability on various vision tasks, and their p

Zhou Ni, Morteza Hashemi

Federated learning (FL) offers a decentralized training approach for machine learning models, prioritizing data privacy. However, the inherent heterogeneity in FL networks, arising from variations in data distribution, size, and device capabilities, poses challenges in user federation. Recognizing this, Personalized Federated Learning (PFL) emphasizes tailor

Gregory Dexter, Petros Drineas, David P. Woodruff, Taisuke Yasuda

Sketching algorithms have recently proven to be a powerful approach both for designing low-space streaming algorithms as well as fast polynomial time approximation schemes (PTAS). In this work, we develop new techniques to extend the applicability of sketching-based approaches to the sparse dictionary learning and the Euclidean $k$-means clustering problems.

Ismael Balafrej, Fabien Alibart, Jean Rouat

Recurrent spiking neural networks (RSNNs) are notoriously difficult to train because of the vanishing gradient problem that is enhanced by the binary nature of the spikes. In this paper, we review the ability of the current state-of-the-art RSNNs to solve long-term memory tasks, and show that they have strong constraints both in performance, and for their im

Edoardo Vecchi, Davide Bassetti, Fabio Graziato, Lukas Pospisil

Small data learning problems are characterized by a significant discrepancy between the limited amount of response variable observations and the large feature space dimension. In this setting, the common learning tools struggle to identify the features important for the classification task from those that bear no relevant information, and cannot derive an ap

Gabriel Franco, Giovanni Comarela, Mark Crovella

Learning from Label Proportions (LLP) is an established machine learning problem with numerous real-world applications. In this setting, data items are grouped into bags, and the goal is to learn individual item labels, knowing only the features of the data and the proportions of labels in each bag. Although LLP is a well-established problem, it has several

Vinod Raman, Unique Subedi, Ananth Raman, Ambuj Tewari

In online binary classification under \emph{apple tasting} feedback, the learner only observes the true label if it predicts ``1". First studied by \cite{helmbold2000apple}, we revisit this classical partial-feedback setting and study online learnability from a combinatorial perspective. We show that the Littlestone dimension continues to provide a tight qua

Brendan Healy, Patrick McNamee, Zahra Nili Ahmadabadi

This study addresses the application of deep learning techniques in joint sound signal classification and localization networks. Current state-of-the-art sound source localization deep learning networks lack feature aggregation within their architecture. Feature aggregation enhances model performance by enabling the consolidation of information from differen

Andreas Ziegler, Thomas Gossard, Karl Vetter, Jonas Tebbe

In recent years, robotic table tennis has become a popular research challenge for perception and robot control. Here, we present an improved table tennis robot system with high accuracy vision detection and fast robot reaction. Based on previous work, our system contains a KUKA robot arm with 6 DOF, with four frame-based cameras and two additional event-base

Zhiling Yan, Kai Zhang, Rong Zhou, Lifang He

In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i.e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task. Our experiments thoroughly assess GPT-4V's proficiency in answering questions paired with images using both pathology and radiology datasets from 11 modalities (e.g. Microsc

Shuhuai Ren, Sishuo Chen, Shicheng Li, Xu Sun

Large-scale video-language pre-training has made remarkable strides in advancing video-language understanding tasks. However, the heavy computational burden of video encoding remains a formidable efficiency bottleneck, particularly for long-form videos. These videos contain massive visual tokens due to their inherent 3D properties and spatiotemporal redundan

Sijin Chen, Zhize Li, Yuejie Chi

We consider the problem of finding second-order stationary points of heterogeneous federated learning (FL). Previous works in FL mostly focus on first-order convergence guarantees, which do not rule out the scenario of unstable saddle points. Meanwhile, it is a key bottleneck of FL to achieve communication efficiency without compensating the learning accurac

Xing-Yu Ren, Shang-Qiang Ning, Yang Qi, Qing-Rui Wang

In the past decade, there has been a systematic investigation of symmetry-protected topological (SPT) phases in interacting fermion systems. Specifically, by utilizing the concept of equivalence classes of finite-depth fermionic symmetric local unitary (FSLU) transformations and the fluctuating decorated symmetry domain wall picture, a large class of fixed-p

Pervaiz Iqbal Khan, Muhammad Nabeel Asim, Andreas Dengel, Sheraz Ahmed

An ever-increasing amount of social media content requires advanced AI-based computer programs capable of extracting useful information. Specifically, the extraction of health-related content from social media is useful for the development of diverse types of applications including disease spread, mortality rate prediction, and finding the impact of diverse

Pengyue Jia, Yiding Liu, Xiangyu Zhao, Xiaopeng Li

Query expansion, pivotal in search engines, enhances the representation of user information needs with additional terms. While existing methods expand queries using retrieved or generated contextual documents, each approach has notable limitations. Retrieval-based methods often fail to accurately capture search intent, particularly with brief or ambiguous qu

Sakher Khalil Alqaaidi, Elika Bozorgi, Afsaneh Shams, Krzysztof Kochut

Named Entity Recognition (NER) and Relation Classification (RC) are important steps in extracting information from unstructured text and formatting it into a machine-readable format. We present a survey of recent deep learning models that address named entity recognition and relation classification, with focus on few-shot learning performance. Our survey is

Amin Mansouri, Jason Hartford, Yan Zhang, Yoshua Bengio

Causal representation learning has showed a variety of settings in which we can disentangle latent variables with identifiability guarantees (up to some reasonable equivalence class). Common to all of these approaches is the assumption that (1) the latent variables are represented as $d$-dimensional vectors, and (2) that the observations are the output of so

Jungtaek Kim, Mingxuan Li, Oliver Hinder, Paul W. Leu

Nanophotonic structures have versatile applications including solar cells, anti-reflective coatings, electromagnetic interference shielding, optical filters, and light emitting diodes. To design and understand these nanophotonic structures, electrodynamic simulations are essential. These simulations enable us to model electromagnetic fields over time and cal

Shruti Dutta, Shashwat Mookherjee

This paper conducts an intricate analysis of musical emotions and trends using Spotify music data, encompassing audio features and valence scores extracted through the Spotipi API. Employing regression modeling, temporal analysis, mood transitions, and genre investigation, the study uncovers patterns within music-emotion relationships. Regression models line

Hong-Yan Zhang, Zhi-Qiang Feng, Si-Yu Feng, Yu Zhou

The Hurst exponent is a significant metric for characterizing time sequences with long-term memory property and it arises in many fields. The available methods for estimating the Hurst exponent can be categorized into time-domain and spectrum-domain methods. Although there are various estimation methods for the Hurst exponent, there are still some disadvanta

Thomas Hardjono, Alex Pentland

Data cooperatives with fiduciary obligations to members provide a useful source of truthful information regarding a given member whose personal data is managed by the cooperative. Since one of the main propositions the cooperative model is to protect the data privacy of members, we explore the notion of blinded attestations in which the identity of the subje

Alexander Kamenshchik, Federica Muscolino

We make an attempt to describe Carroll particles with a non-vanishing value of energy (i.e. the Carroll particles which always stay in rest) in the framework of two time physics, developed in the series of papers by I. Bars and his co-authors. In the spacetime with one additional time dimension and one additional space dimension one can localize the symmetry

Jose Miguel Sanz-Alcaine, Eduardo Sebastian, Francisco Jose Perez-Cebolla, Asier Arruti

The optimal design of power converters requires accurate knowledge of the dissipation elements of its system to achieve the desired performance and security requirements. Calorimetric methods have surpassed classical electrical methods for the estimation of semiconductor power losses but have mechanical limitations and resort to analytical electrothermal equ

Jianfeng Hou, Caihong Yang, Qinghou Zeng

Given two graphs $H$ and $F$, the maximum possible number of copies of $H$ in an $F$-free graph on $n$ vertices is denoted by $\mathrm{ex}(n, H, F)$. Let $(\ell+1) \cdot F$ denote $\ell+1$ vertex disjoint copies of $F$. In this paper, we determine the exact value of $\mathrm{ex}(n, C_3, (\ell+1)\cdot C_{2k+1})$ and its extremal graph, which generalizes some

Xiang Gao, Andrew D. Hanlon, Swagato Mukherjee, Peter Petreczky

We present a lattice QCD calculation of the transversity isovector- and isoscalar-quark parton distribution functions (PDFs) of the proton utilizing a perturbative matching at next-to-leading-order (NLO) accuracy. Additionally, we determine the isovector and isoscalar tensor charges for the proton. In both calculations, the disconnected contributions to the

Shengcai Liu, Caishun Chen, Xinghua Qu, Ke Tang

Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with the aid of domain expertise to achieve satisfactory performance. In this work, we present the first study on large language models (LLMs) as evolutionary combinatorial optimizers.

Petr Jizba, Gaetano Lambiase

By employing Tsallis' extensive but non-additive $\delta$-entropy, we formulate the first two laws of thermodynamics for gravitating systems. By invoking Carath\'{e}odory's principle, we pay particular attention to the integrating factor for the heat one-form. We show that the latter factorizes into the product of thermal and entropic parts, where the entrop

Neetu Sigger, Tuan Thanh Nguyen, Gianluca Tozzi, Quoc-Tuan Vien

Hyperspectral images (HSI) have become popular for analysing remotely sensed images in multiple domain like agriculture, medical. However, existing models struggle with complex relationships and characteristics of spectral-spatial data due to the multi-band nature and data redundancy of hyperspectral data. To address this limitation, we propose a new network

Xuan Zhang, Philipp Reiter, Olga Shishkina, Robert E. Ecke

We investigate states of rapidly rotating Rayleigh-B\'enard convection in a cylindrical cell over a range of Rayleigh number $3\times10^5\leq Ra \leq 5\times10^{9}$ and Ekman number $10^{-6} \leq Ek \leq 10^{-4}$ for Prandtl number $Pr = 0.8$ and aspect ratios $1/5 \leq \Gamma \leq 5$ using direct numerical simulations. We characterize, for perfectly insulat

Chao Shi, Zhongtian Yang, Xurong Chen, Wenbao Jia

We propose a method to calculate the $q\bar{q}$ light front wave functions (LFWFs) of photon at low-virtuality, i.e., the light front amplitude of $\gamma^*\rightarrow q\bar{q}$ at low $Q^2$, based on a light front projection approach. We exemplify this method using a contact interaction model within Dyson-Schwinger equations formalism and obtain the nonpert

Shulei Wang

Recent advances in self-supervised learning have highlighted the efficacy of data augmentation in learning data representation from unlabeled data. Training a linear model atop these enhanced representations can yield an adept classifier. Despite the remarkable empirical performance, the underlying mechanisms that enable data augmentation to unravel nonlinea

Artem Kalmykov, Brian Li

In this paper, we study intertwining operators between subregular Whittaker modules of $\gl_N$ generalizing, on the one hand, the classical exchange construction of dynamical quantum groups, on the other hand, earlier results for principal W-algebras. We explicitly construct them using the generators of W-algebras introduced by Brundan-Kleshchev. We interpre

Nikolaos Evangelou, Dimitrios G. Giovanis, George A. Kevrekidis, Grigorios A. Pavliotis

Deriving closed-form, analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In this paper, we propose a data-driven framework that pinpoints phase transitions for an ABM- the Desai-Zwanzig mod

Han Liu, Xingshuo Huang, Xiaotong Zhang, Qimai Li

Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction. Gradient estimation is a critical step in black-box adversarial attacks, as it will directly affect the query efficiency. Recent works have attempted to utilize gradient priors

Júlio C. Fabris, Mahamadou Hamani Daouda, Hermano Velten

Unimodular gravity (UG) is considered, under many aspects, equivalent to General Relativity (GR), even if the theory is invariant under a more restricted diffeomorphic class of transformations. We discuss the conditions for the equivalence between the two formulations by applying the UG to the static and spherically symmetric configurations being the energy-

Fanchao Liao, Jaap Vleugel, Gustav Bösehans, Dilum Dissanayake

Electric mobility hubs (eHUBS) are locations where multiple shared electric modes including electric cars and e-bikes are available. To assess their potential to reduce private car use, it is important to investigate to what extent people would switch to eHUBS modes after their introduction. Moreover, people may adapt their behaviour differently depending on

Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie

Invariant graph representation learning aims to learn the invariance among data from different environments for out-of-distribution generalization on graphs. As the graph environment partitions are usually expensive to obtain, augmenting the environment information has become the de facto approach. However, the usefulness of the augmented environment informa

Mustafa Jarrar, Ahmet Birim, Mohammed Khalilia, Mustafa Erden

This paper presents the ArBanking77, a large Arabic dataset for intent detection in the banking domain. Our dataset was arabized and localized from the original English Banking77 dataset, which consists of 13,083 queries to ArBanking77 dataset with 31,404 queries in both Modern Standard Arabic (MSA) and Palestinian dialect, with each query classified into on

Yusuke Kawamoto, Egor Shelukhin

Spectral invariants are quantitative measurements in symplectic topology coming from Floer homology theory. We study their dependence on the choice of coefficients in the context of Hamiltonian Floer homology. We discover phenomena in this setting which hold for $\mathbb{Z}$-coefficients and fail for all field coefficients. For example, we prove that the spe

Lu Liu

The Stochastic Series Expansion (SSE) quantum Monte Carlo method with directed loops is very efficient for spin and boson systems. The Heisenberg mode l and its generalizations, such as the $JQ_2$ model, are extensively simulated via this method. When introducing magnetic field in these models, the SSE method always combines the field with the diagonal part

Zhou Rui, Zhi-Tian Zou

Motivated by recent advances in experimental measurements of heavy baryon decays, the charmonium decays of single $b$-baryon are investigated systematically in the framework of perturbative QCD approach. We calculate the decay branching ratios and helicity amplitudes as well as many pertinent decay asymmetry parameters that characterize the angular decay dis

Jean Bertoin, Bastien Mallein

Reinforced Galton-Watson processes have been introduced in arxiv:2306.02476 as population models with non-overlapping generations, such that reproduction events along genealogical lines can be repeated at random. We investigate here some of their sample path properties such as asymptotic growth rates and survival, for which the effects of reinforcement on th

Mustafa Jarrar, Sanad Malaysha, Tymaa Hammouda, Mohammed Khalilia

SALMA, the first Arabic sense-annotated corpus, consists of ~34K tokens, which are all sense-annotated. The corpus is annotated using two different sense inventories simultaneously (Modern and Ghani). SALMA novelty lies in how tokens and senses are associated. Instead of linking a token to only one intended sense, SALMA links a token to multiple senses and p

Itai Arad, Raz Firanko, Rahul Jain

We show an area law in the mutual information for the maximally-mixed state $\Omega$ in the ground space of general Hamiltonians, which is independent of the underlying ground space degeneracy. Our result assumes the existence of a `good' approximation to the ground state projector (a good AGSP), a crucial ingredient in previous area-law proofs. Such approxi

Maedeh Taheri, Nicholas Sesing, Tina, T. Salguero

We report on the field-effect modulation of the charge-density-wave quantum condensate in the top-gated heterostructure devices implemented with quasi-one-dimensional NbS$_3$ nanowire channels and quasi-two-dimensional h-BN gate dielectric layers. The charge-density-wave phases and collective current in quasi-1D NbS$_3$ nanowires were verified via temperatur

László Székelyhidi

In an earlier paper we solved a long-standing problem which goes back to Laurent Schwartz's work on mean-periodic functions. Namely, we completely characterised those locally compact Abelian groups having spectral synthesis. The method is based on the localisation concept. In this paper we show that localisation can be used to prove another basic result in s

Kiarash Banihashem, MohammadTaghi Hajiaghayi, Suho Shin, Max Springer

We present an oracle-efficient relaxation for the adversarial contextual bandits problem, where the contexts are sequentially drawn i.i.d from a known distribution and the cost sequence is chosen by an online adversary. Our algorithm has a regret bound of $O(T^{\frac{2}{3}}(K\log(|\Pi|))^{\frac{1}{3}})$ and makes at most $O(K)$ calls per round to an offline

Alon Shoshan, Nadav Bhonker, Emanuel Ben Baruch, Ori Nizan

Training fingerprint recognition models using synthetic data has recently gained increased attention in the biometric community as it alleviates the dependency on sensitive personal data. Existing approaches for fingerprint generation are limited in their ability to generate diverse impressions of the same finger, a key property for providing effective data

Marcos Escobar-Anel, Yevhen Havrylenko, Rudi Zagst

Hedge fund managers with the first-loss scheme charge a management fee, a performance fee and guarantee to cover a certain amount of investors' potential losses. We study how parties can choose a mutually preferred first-loss scheme in a hedge fund with the manager's first-loss deposit and investors' assets segregated. For that, we solve the manager's non-co

Jingliang Duan, Jie Li, Xuyang Chen, Kai Zhao

In recent times, significant advancements have been made in delving into the optimization landscape of policy gradient methods for achieving optimal control in linear time-invariant (LTI) systems. Compared with state-feedback control, output-feedback control is more prevalent since the underlying state of the system may not be fully observed in many practica

Marcos Dajczer, Theodoros Vlachos

We investigate the compact submanifolds in Riemannian space forms of nonnegative sectional curvature that satisfy a lower bound on the Ricci curvature, that bound depending solely on the length of the mean curvature vector of the immersion. While generalizing the results, we give a positive answer to a conjecture by H. Xu and J. Gu in (2013, Geom. Funct. Ana

László Székelyhidi

In this paper we solve a long-standing problem which goes back to Laurent Schwartz's work on mean periodic functions. Namely, we completely characterise those locally compact Abelian groups having spectral synthesis. So far a characterisation theorem was available for discrete Abelian groups only. Here we use a kind of localisation concept for the ideals of

Nan He, Hanyu Lai, Chenyang Zhao, Zirui Cheng

Large Language Models (LLMs) exhibit impressive reasoning and data augmentation capabilities in various NLP tasks. However, what about small models? In this work, we propose TeacherLM-7.1B, capable of annotating relevant fundamentals, chain of thought, and common mistakes for most NLP samples, which makes annotation more than just an answer, thus allowing ot

Md Taimur Ahad, Yousuf Rayhan Emon

Identity documentation for refugees is a complex process and crucial for host nations. A secured identity management system ensures both security and the efficient provision of services for the host nation and the donor organizations. Realizing the benefits, a handful of studies enriched the blockchain-based security identification for refugees. The research

Shilong Yu, Yuefeng Yang, Yushuang Fan, Xuanlong Ma

For a digraph $\Gamma$, a subset $C$ of $V(\Gamma)$ is a perfect code if $C$ is a dominating set such that every vertex of $\Gamma$ is dominated by exactly one vertex in $C$. In this paper, we classify strongly connected 2-valent Cayley digraphs on abelian groups admitting a perfect code, and determine completely all perfect codes of such digraphs.

H. Lohani, P. Mishra, R. Goyal, V. P. S. Awana

We present a comparative study of the valence band electronic structure of Pd based ternary chalcogenide superconductors Nb2Pd0.95S5, Ta2Pd0.97S6 and Ta2Pd0.97Te6 using experimental photoemission spectroscopy and density functional based theoretical calculations. We observe a qualitatively similarity between valence band (VB) spectra of Nb2Pd0.95S5 and Ta2Pd

Deepak Patel, Praveen C. Srivastava, V. K. B. Kota, R. Sahu

Large-scale shell-model calculations have been performed for the study of two neutrino double-beta ($2\nu\beta\beta$) decay in $^{82}$Se, $^{94}$Zr, $^{108}$Cd, $^{124}$Sn, $^{128}$Te, $^{130}$Te, $^{136}$Xe, and $^{150}$Nd. We have employed JUN45 interaction to calculate the nuclear matrix element (NME) for $2\nu\beta\beta$ decay in $^{82}$Se. In the case o

Junshuo Liu, Rujing Xiong, Jialong Lu, Tiebin Mi

The conventional beam management procedure mandates that the user equipment (UE) periodically measure the received signal reference power (RSRP) and transmit these measurements to the base station (BS). The challenge lies in balancing the number of beams used: it should be large enough to identify high-RSRP beams but small enough to minimize reporting overhe

Harold Ship, Evgeny Shindin, Chen Wang, Diana Arroyo

This paper explores resource allocation in serverless cloud computing platforms and proposes an optimization approach for autoscaling systems. Serverless computing relieves users from resource management tasks, enabling focus on application functions. However, dynamic resource allocation and function replication based on changing loads remain crucial. Typica

Youming Zhao

We present an alternating direction method of multipliers (ADMM) for a generic overlapping group lasso problem, where the groups can be overlapping in an arbitrary way. Meanwhile, we prove the lower bounds and upper bounds for both the $\ell_1$ sparse group lasso problem and the $\ell_0$ sparse group lasso problem. Also, we propose the algorithms for computi

Zeshuai Deng, Zhuokun Chen, Shuaicheng Niu, Thomas H. Li

Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images. Conventional SR methods typically gather the paired training data by synthesizing LR images from HR images using a predetermined degradation model, e.g., Bicubic down-sampling. However, the realistic degradation type of tes

Sayak Bhowmik, Saikat Banerjee, Arijit Saha

We theoretically investigate a two-dimensional decorated honeycomb lattice framework to realize a second-order topological magnon insulator (SOTMI) phase featuring distinct corner-localized modes. Our study emphasizes the pivotal role of spin-magnon mapping in characterizing bosonic topological properties, which exhibit differences from their fermionic count

Namrata Pattanayak, Arun Kumar, A. K Nigam, Vladimir Pomjakushin

We consistently observe a unique pattern in remanence in a number of canted-antiferromagnets (AFM) and piezomagnets. A part of the remanence is $\textit{quasi-static}$ in nature and vanishes above a critical magnetic field. Present work is devoted to exploring this $\textit{quasi-static}$ remanence ($\mu$) in a series of isostructural canted-AFMs and piezoma

Luyi Qiu

Coordination development across various subsystems, particularly economic, social, cultural, and human resources subsystems, is a key aspect of urban sustainability that has a direct impact on the quality of urbanization. Hangzhou Metropolitan Circle composing Hangzhou, Huzhou, Jiaxing, Shaoxing, was the first metropolitan circle approved by National Develop

Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas

Designing reward functions for efficiently guiding reinforcement learning (RL) agents toward specific behaviors is a complex task. This is challenging since it requires the identification of reward structures that are not sparse and that avoid inadvertently inducing undesirable behaviors. Naively modifying the reward structure to offer denser and more freque

Andreas Göbel, Leslie Ann Goldberg, Marc Roth

The Weisfeiler-Leman (WL) dimension of a graph parameter $f$ is the minimum $k$ such that, if $G_1$ and $G_2$ are indistinguishable by the $k$-dimensional WL-algorithm then $f(G_1)=f(G_2)$. The WL-dimension of $f$ is $\infty$ if no such $k$ exists. We study the WL-dimension of graph parameters characterised by the number of answers from a fixed conjunctive q

Mohamad H. Alizade, Aref Einizade, Jhony H. Giraldo

Within the context of Graph Signal Processing (GSP), Graph Learning (GL) is concerned with the inference of the graph's underlying structure from nodal observations. However, real-world data often contains diverse information, necessitating the simultaneous clustering and learning of multiple graphs. In practical applications, valuable node-specific covariat

Bryan Robertson, Jeremy Schofield, Raymond Kapral

We present a derivation from first principles of the coupled equations of motion of an active self-diffusiophoretic Janus motor and the hydrodynamic densities of its fluid environment that are nonlinearly displaced from equilibrium. The derivation makes use of time-dependent projection operator techniques defined in terms of slowly varying coarse-grained mic

Yiwen Li, Zhengyan Zhu, Yongze Ye, Wenshan Hong

Specific heat can explore low-energy quasiparticle excitations of superconductors, so it is a powerful tool for bulk measurement on the superconducting gap structure and pairing symmetry. Here, we report an in-depth investigation on the specific heat of the multiband superconductors KCa$_2$(Fe$_{1-x}$Ni$_x$)$_4$As$_4$F$_2$ ($x$ = 0, 0.05, 0.13) single crysta

Wendi Zhao, Defang Duan, Decheng An, Qiwen Jiang

The compressed hydrogen-rich compounds have received extensive attention as promising candidates for room temperature superconductivity, however, the high pressure required to stabilize such materials hinders their wide practical application. In order to search for potential superconducting hydrides that are stable at low pressures, we have investigated the