March 2024 arXiv papers — page 53

Sunny Karim, Matthew D. Webb, Nichole Austin, Erin Strumpf

Difference-in-differences (DID) is commonly used to estimate treatment effects but is infeasible in settings where data are unpoolable due to privacy concerns or legal restrictions on data sharing, particularly across jurisdictions. In this study, we identify and relax the assumption of data poolability in DID estimation. We propose an innovative approach to

Gabriele Gullà

We are interested in investigating some definitions and assumptions stated in [4], in particular the notions of measurability and atomicity that the two authors used in order to give a representation for multiplicative linear functionals (m.l.f. so on) over the cartesian product of algebras. We want to briefly analyze the result under other strong proper axi

Sofía Perón Santana, Martín Domíguez, Omar Osenda

Designing a good transfer channel for arbitrary quantum states in spin chains implies optimizing a cost function, usually the averaged fidelity of transmission. The fidelity of transmission measures how much the transferred state resembles the state prepared at the beginning of the transfer protocol. When averaged over all the possible initial states, the fi

Can Bogoclu, Robert Vosshall, Kevin Cremanns, Dirk Roos

Probabilistic world models increase data efficiency of model-based reinforcement learning (MBRL) by guiding the policy with their epistemic uncertainty to improve exploration and acquire new samples. Moreover, the uncertainty-aware learning procedures in probabilistic approaches lead to robust policies that are less sensitive to noisy observations compared t

Reza Rastegar, Alex Roitershtein, Vadim Roytershteyn, Vijay Seetharam

We introduce and study a mathematical model of an art collector. In our model, the collector is a rational agent whose actions in the art market are driven by two competing long-term objectives, namely sustainable financial health and maintaining the collection. Mathematically, our model is a two-dimensional random linear dynamical system with transformation

Johannes Huebschmann

We discuss variants of Berikashvili's functor that arise in differential homological algebra, from simplicial bundles, from ordinary topological bundles, and in more general categorical settings. We prove that, under suitable circumstances, the value of Berikasvili's functor parametrizes isomorphism classes of bundles in various contexts.

Songlin Xu, Xinyu Zhang, Lianhui Qin

Student simulation in online education is important to address dynamic learning behaviors of students with diverse backgrounds. Existing simulation models based on deep learning usually need massive training data, lacking prior knowledge in educational contexts. Large language models (LLMs) may contain such prior knowledge since they are pre-trained from a l

Yushan Huang, Josh Millar, Yuxuan Long, Yuchen Zhao

The personalization of machine learning (ML) models to address data drift is a significant challenge in the context of Internet of Things (IoT) applications. Presently, most approaches focus on fine-tuning either the full base model or its last few layers to adapt to new data, while often neglecting energy costs. However, various types of data drift exist, a

Gabriele Gullà

We prove, via transfinite recursion, the existence, inside any linearly ordered set of appropriate regular cardinality $\lambda$, of a particular kind of well-ordered subsets characterized by the property of $\lambda$-fullness. Let $H$ be a set of regular cardinals: by using our results about well-ordered $\lambda$-full sets we show that if $\inf H$ is a wea

Neal Dalal, Marios Galanis, Charles Gammie, Samuel E. Gralla

Intensity interferometry is a technique developed many decades ago, that has recently enjoyed a renaissance thanks in part to advances in photodetector technology. We investigate the potential for long-baseline optical intensity interferometry to observe bright, active galactic nuclei (AGN) associated with rapidly accreting supermassive black holes. We argue

Jeongmin Lee, Taesoo Kwon, Hyunju Shin, Yoonsang Lee

We present an approach using deep reinforcement learning (DRL) to directly generate motion matching queries for long-term tasks, particularly targeting the reaching of specific locations. By integrating motion matching and DRL, our method demonstrates the rapid learning of policies for target location tasks within minutes on a standard desktop, employing a s

Jiayu Huo, Ruiqiang Xiao, Haotian Zheng, Yang Liu

Recently, automated medical image segmentation methods based on deep learning have achieved great success. However, they heavily rely on large annotated datasets, which are costly and time-consuming to acquire. Few-shot learning aims to overcome the need for annotated data by using a small labeled dataset, known as a support set, to guide predicting labels f

Gabriele Gullà

This brief survey comes from the slides of a seminar I gave to philosophy of mathematics students. I will present some different characterizations of Woodin cardinals, including the one obtained by Ernest Schimmerling in [6]. I will try to give to this paper the most self-contained possible structure, also by showing explicitly just the proofs usually leaved

Johannes Huebschmann

This is an overview of the idea of a crossed module. For a group, the triple that consists of the group, its group of automorphisms, and the canonical homomorphism from the group to its group of automorphisms constitutes a crossed module. Crossed modules arise from the identities among the relations of the presentation of a group, from the extension problem

Gabriele Gullà

Following a seminar the present author gave to an Automata Theory course to computer science students, it will be presented, in a very synthetic and mostly selfcontained way, the principal properties of context free languages (CFL), with particular attention given to the Pumping Lemma (PL), and of grammars which generate them(CFG). We refer to Chomsky and Sc

Adriana Salerno, Ursula Whitcher, Chenglong Yu

We use arithmetic and Hodge-theoretic techniques to study pencils of Calabi-Yau varieties realized as highly symmetric hypersurfaces in Grassmannians and their quotients, demonstrating that their geometric properties are distinct from the classical mirrors of Calabi-Yau Grassmannian hypersurfaces.

Michael MacIsaac, Salil Bavdekar, Douglas Spearot, Ghatu Subhash

A linear regression-based machine learned interatomic potential (MLIP) was developed for the silicon-carbon system. The MLIP was predominantly trained on structures discovered through a genetic algorithm, encompassing the entire silicon-carbon composition space, and uses as its foundation the Ultra-Fast Force Fields (UF3) formulation. To improve MLIP perform

KVS Chaithanya, Jan Rozman, Andrej Košmrlj, Rastko Sknepnek

Tissue homeostasis, the biological process of maintaining a steady state in tissue via control of cell proliferation, death, and metabolic function, is essential for the development, growth, maintenance, and proper function of living organisms. Disruptions to this process can lead to serious diseases and even death. In this study, we use the vertex model for

Muhammad Shoaib Farooq, Ayesha Tariq

Deep learning has become an extremely powerful tool for complex tasks such as image classification and segmentation. The medical industry often lacks high-quality, balanced datasets, which can be a challenge for deep learning algorithms that need sufficiently large amounts of data to train and increase their performance. This is especially important in the c

Charles Batty, Alexander Gomilko, Yuri Tomilov

Using a recently developed $\mathcal H$-calculus we propose a unified approach to the study of rational approximations of holomorphic semigroups on Banach spaces. We provide unified and simple proofs to a number of basic results on semigroup approximations and substantially improve some of them. We show that many of our estimates are essentially optimal, thu

Ugo Moschella

We review the role of the spectral condition as a characteristic feature unifying Minkowski, de Sitter and anti de Sitter Quantum Field Theory. In this context, we highlight the role of an important class of plane waves which are either de Sitter or anti de Sitter covariant and are compatible with the relevant analyticity domains linked to the spectral condi

Panagiotis Tselekidis

Inspired by a classical theorem of topological dimension theory, we prove that every geodesic metric space of asymptotic dimension $n$ containing a bi-infinite geodesic can be coarsely separated by a subset $S$ of asymptotic dimension equal to or smaller than $n-1$.\\ We define asymptotic Cantor manifolds, and we prove that every finitely generated group con

Jiangbei Yue, Baiyi Li, Julien Pettré, Armin Seyfried

We investigate a new task in human motion prediction, which is predicting motions under unexpected physical perturbation potentially involving multiple people. Compared with existing research, this task involves predicting less controlled, unpremeditated and pure reactive motions in response to external impact and how such motions can propagate through peopl

Matthew Huynh

We generalize a construction of Barthel-Brasselet-Fieseler-Gabber-Kaup in the setting of complex varieties to the setting of finite type, complex algebraic stacks. Given two such stacks $\mathcal{X},\mathcal{Y}$ with affine stabilizers, and a morphism between them, we construct a morphism from the pullback of the intersection complex of $\mathcal{Y}$ to the

Jorge Pinochet

One of the great mysteries of contemporary science is dark matter, an exotic substance of unknown nature that, in theory, makes up about 27\% of the total mass-energy density of the universe, and which does not appear to emit, absorb, or reflect any kind of light, meaning that it is invisible and can only be detected through its gravitational effects on obje

Petros Siasos

In this article, we generalize the set of manifolds over which the $L^p$-spectrum of the Laplacian on $k$-forms depends on $p$. We will consider the case of manifolds that are warped products at infinity and certain quotients of Hyperbolic space. In the case of warped products at infinity we prove that the $L^p$-spectrum of the Laplacian on $k$-forms contain

Xiang Huang, Shenghong Ju

Designing polymers with high intrinsic thermal conductivity (TC) is critically important for the thermal management of organic electronics and photonics. However, this is a challenging task owing to the diversity of the chemical space and the barriers to advanced synthetic experiments/characterization techniques for polymers. In this Tutorial, the fundamenta

Lukas Vöge, Vincent Gurgul, Stefan Lessmann

This paper introduces a novel approach for efficiently distilling LLMs into smaller, application-specific models, significantly reducing operational costs and manual labor. Addressing the challenge of deploying computationally intensive LLMs in specific applications or edge devices, this technique utilizes LLMs' reasoning capabilities to generate labels and

Isabelle Lorge, Li Zhang, Xiaowen Dong, Janet B. Pierrehumbert

The rise of social media platforms has led to an increase in polarised online discussions, especially on political and socio-cultural topics such as elections and climate change. We propose a simple and novel unsupervised method to predict whether the authors of two posts agree or disagree, leveraging user stances about named entities obtained from their pos

Arastoo Bozorgi, Mahya Soleimani Jadidi, Jonathan Anderson

Strong confidentiality, integrity, user control, reliability and performance are critical requirements in privacy-sensitive applications. Such applications would benefit from a data storage and sharing infrastructure that provides these properties even in decentralized topologies with untrusted storage backends, but users today are forced to choose between s

Mohammad Bajelani, Klaske van Heusden

In response to safety concerns associated with learning-based algorithms, safety filters have been proposed as a modular technique. Generally, these filters heavily rely on the system's model, which is contradictory if they are intended to enhance a data-driven or end-to-end learning solution. This paper extends our previous work, a purely Data-Driven Safety

Phong Nguyen-Thuan Do, Son Quoc Tran, Phu Gia Hoang, Kiet Van Nguyen

The success of Natural Language Understanding (NLU) benchmarks in various languages, such as GLUE for English, CLUE for Chinese, KLUE for Korean, and IndoNLU for Indonesian, has facilitated the evaluation of new NLU models across a wide range of tasks. To establish a standardized set of benchmarks for Vietnamese NLU, we introduce the first Vietnamese Languag

Lorenz Vaitl, Ludwig Winkler, Lorenz Richter, Pan Kessel

Recent work shows that path gradient estimators for normalizing flows have lower variance compared to standard estimators for variational inference, resulting in improved training. However, they are often prohibitively more expensive from a computational point of view and cannot be applied to maximum likelihood training in a scalable manner, which severely h

Jacky J. Chong, Laurent Lafleche, Chiara Saffirio

In this paper, we rewrite the time-dependent Bogoliubov$\unicode{x2013}$de Gennes equation in an appropriate semiclassical form and establish its semiclassical limit to a two-particle kinetic transport equation with an effective mean-field background potential satisfying the one-particle Vlasov equation. Moreover, for some semiclassical regimes, we obtain a

Gyubok Lee, Woosog Chay, Seonhee Cho, Edward Choi

Text-to-SQL enables users to interact with databases using natural language, simplifying the retrieval and synthesis of information. Despite the remarkable success of large language models (LLMs) in translating natural language questions into SQL queries, widespread deployment remains limited due to two primary challenges. First, the effective use of text-to

Jia-Wei Liao, Winston Wang, Tzu-Sian Wang, Li-Xuan Peng

QR codes, prevalent in daily applications, lack visual appeal due to their conventional black-and-white design. Integrating aesthetics while maintaining scannability poses a challenge. In this paper, we introduce a novel diffusion-model-based aesthetic QR code generation pipeline, utilizing pre-trained ControlNet and guided iterative refinement via a novel c

Eloy Serrano-Seco, Eduardo Moya-Lasheras, Edgar Ramirez-Laboreo

Electromechanical switching devices such as relays, solenoid valves, and contactors offer several technical and economic advantages that make them widely used in industry. However, uncontrolled operations result in undesirable impact-related phenomena at the end of the stroke. As a solution, different soft-landing controls have been proposed. Among them, fee

Omar Melikechi, Jeffrey W. Miller

Stability selection is a popular method for improving feature selection algorithms. One of its key attributes is that it provides theoretical upper bounds on the expected number of false positives, E(FP), enabling false positive control in practice. However, stability selection often selects few features because existing bounds on E(FP) are relatively loose.

Long He, Geng Sun, Zemin Sun, Pengfei Wang

Given flexible mobility, rapid deployment, and low cost, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) shows great potential to compensate for the lack of terrestrial edge computing coverage. However, limited battery capacity, computing and spectrum resources also pose serious challenges for UAV-enabled MEC, which shorten the service time

Zhicheng Du, Zhaotian Xie, Huazhang Ying, Likun Zhang

This study explores the ability of Image Captioning (IC) models to decode masked visual content sourced from diverse datasets. Our findings reveal the IC model's capability to generate captions from masked images, closely resembling the original content. Notably, even in the presence of masks, the model adeptly crafts descriptive textual information that goe

Zhicheng Du, Zhaotian Xie, Yan Tong, Peiwu Qin

This study constructs the LanguAge Model with Prompt EngineeRing (LAMPER) framework, designed to systematically evaluate the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts and their integration in zero-shot time series (TS) classification. We deploy LAMPER in experimental assessments using 128 univariate TS datasets sourc

Arash Ghaani Farashahi, Gregory S. Chirikjian

This paper presents a systematic study for the general theory of non-Abelian Fourier series of integrable functions on the homogeneous space $\mathbf{\Gamma}\backslash SE(d)$, where $SE(d)$ is the special Euclidean group in dimension $d$, and $\mathbf{\mathbf{\Gamma}}$ is a discrete and co-compact subgroup of $SE(d)$. Suppose that $\mu$ is the finite $SE(d)$

Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates

Malware detection is an interesting and valuable domain to work in because it has significant real-world impact and unique machine-learning challenges. We investigate existing long-range techniques and benchmarks and find that they're not very suitable in this problem area. In this paper, we introduce Holographic Global Convolutional Networks (HGConv) that u

Fernando Barreiro Megino, Kaushik De, Johannes Elmsheuser, Alexei Klimentov

The ATLAS experiment at CERN relies on a worldwide distributed computing Grid infrastructure to support its physics program at the Large Hadron Collider. ATLAS has integrated cloud computing resources to complement its Grid infrastructure and conducted an R&D program on Google Cloud Platform. These initiatives leverage key features of commercial cloud provid

Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv

Move structures have been studied in English for Specific Purposes (ESP) and English for Academic Purposes (EAP) for decades. However, there are few move annotation corpora for Research Article (RA) abstracts. In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts. The primary o

Ferdinando Cicalese, Francesca Ugazio

We study the complexity of constructing an optimal parsing $\varphi$ of a string ${\bf s} = s_1 \dots s_n$ under the constraint that given a position $p$ in the original text, and the LZ76-like (Lempel Ziv 76) encoding of $T$ based on $\varphi$, it is possible to identify/decompress the character $s_p$ by performing at most $c$ accesses to the LZ encoding, f

Xiangyu Chen, Fan Yang, Chen Wang

Path planning, which aims to find a collision-free path between two locations, is critical for numerous applications ranging from mobile robots to self-driving vehicles. Traditional search-based methods like A* search guarantee path optimality but are often computationally expensive when handling large-scale maps. While learning-based methods alleviate this

Manish Yadav, Sudeshna Sinha, Merten Stender

The quest to understand structure-function relationships in networks across scientific disciplines has intensified. However, the optimal network architecture remains elusive, particularly for complex information processing. Therefore, we investigate how optimal and specific network structures form to efficiently solve distinct tasks using a novel framework o

Slava Pimenov

We study the cohomology of Lie superalgebra of vector fields on affine super-spaces $\mathbb{A}^{m,n}$ with trivial coefficients. In this paper we extend the methodology developed in the previous paper (arXiv:2210.16585) to perform the calculation for arbitrary dimensions $(m, n)$ with $m \geq n$. This combined with previously known results for other dimensi

Adnan Malik, Aimen Rauf, Kazuharu Bamba, Wenbin Lin

The cosmological dynamics in the early universe are investigated to explore the possibility of the sign reversal of the Hubble parameter as a key feature of non-singular bouncing cosmological solutions in higher-order torsion gravity. The self-consistent multiple cosmological regimes are studied, such as the accelerated expansion, ultra-relativistic, radiati

Zhentao He, Chao Ji

In this paper, we consider the existence and multiplicity of solutions for the logarithmic Schr\"{o}dinger equation on lattice graphs $\mathbb{Z}^N$ $$ -\Delta u+V(x) u=u \log u^2, \quad x \in \mathbb{Z}^N, $$ When the potential $V$ is coercive, we obtain infinitely many solutions by adapting some arguments of the Fountain theorem. In the cases of periodic p

Geng Sun, Yixian Wang, Zemin Sun, Qingqing Wu

Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computing-intensive and latency-sensitive demands of users poses a significant challenge due to the limited resources of UAVs. To address this challenge, we present a multi-objective o

Mehmet Ali Ozarslan, İlkay Onbasi Elidemir

In the present paper, we introduce a method to construct two variable biorthogonal polynomial families with the help of one variable biorthogonal and orthogonal polynomial families. By using this new technique, we define 2D Hermite Konhauser polynomials and we investigate several properties of them such as biorthogonality property, operational formula and in

D. Bazeia, G. S. Santiago

This work deals with models described by three real scalar fields in one spatial dimension. We study the case where two of the three fields engender kinematical modifications, which respond as geometrical constrictions, that can be used to change the center and/or the tails of the kinklike configurations. An important advantage of our procedure is the constr

Yihang Zhao, Neil Vetter, Kaveh Aryan

This paper explores the integration of Large Language Models (LLMs) such as GPT-3.5 and GPT-4 into the ontology refinement process, specifically focusing on the OntoClean methodology. OntoClean, critical for assessing the metaphysical quality of ontologies, involves a two-step process of assigning meta-properties to classes and verifying a set of constraints

Juan Yang, Jeff Morgan, Bao Quoc Tang

The global existence and boundedness of solutions to quasi-linear reaction-diffusion systems are investigated. The system arises from compartmental models describing the spread of infectious diseases proposed in [Viguerie et al, Appl. Math. Lett. (2021); Viguerie et al, Comput. Mech. (2020)], where the diffusion rate is assumed to depend on the total populat

Manfred Marvin Marchione, Fabio Baione

This paper introduces an innovative method for constructing copula models capable of describing arbitrary non-monotone dependence structures. The proposed method enables the creation of such copulas in parametric form, thus allowing the resulting models to adapt to diverse and intricate real-world data patterns. We apply this novel methodology to analyze the

Gilbert Kerr, Gilberto Gonzalez-Parra

In this article, a new modified Laplace-Fourier method is developed in order to obtain the solutions of linear neutral delay differential equations. The proposed method provides a more accurate solution than the one provided by the pure Laplace method and the original Laplace-Fourier method. We develop and show the crucial modifications of the Laplace-Fourie

Yihang Zhao, Albert Meroño-Peñuela, Elena Simperl

This research investigates User Experience (UX) issues in dataset search, targeting Google Dataset Search and data.europa.eu. It focuses on 6 areas within UX: Initial Interaction, Search Process, Dataset Exploration, Filtering and Sorting, Dataset Actions, and Assistance and Feedback. The evaluation method combines 'The Pandemic Puzzle' user task, think-alou

Giorgio Arcadi, David Cabo-Almeida, Maíra Dutra, Pradipta Ghosh

Weakly Interacting Massive Particles (WIMPs) continue to be considered some of the best-motivated Dark Matter (DM) candidates. No conclusive signal, despite an extensive search program that combines, often in a complementary way, direct, indirect, and collider probes, has been however detected so far. This situation might change in the near future with the a

Bosiljka Tadic, Alexander Shapoval, Mikhail Shnirman

Recognising changes in collective dynamics in complex systems is essential for predicting potential events and their development. Possessing intrinsic attractors with laws associated with scale invariance, self-organised critical dynamics represent a suitable example for quantitatively studying changes in collective behaviour. We consider two prototypal mode

Daniele A. Di Pietro, Zhaonan Dong, Guido Kanschat, Pierre Matalon

We prove the uniform convergence of the geometric multigrid V-cycle for hybrid high-order (HHO) and other discontinuous skeletal methods. Our results generalize previously established results for HDG methods, and our multigrid method uses standard smoothers and local solvers that are bounded, convergent, and consistent. We use a weak version of elliptic regu

Hassan Sartaj, Asmar Muqeet, Muhammad Zohaib Iqbal, Muhammad Uzair Khan

Unmanned aerial systems (UAS) rely on various avionics systems that are safety-critical and mission-critical. A major requirement of international safety standards is to perform rigorous system-level testing of avionics software systems. The current industrial practice is to manually create test scenarios, manually/automatically execute these scenarios using

Xingyu Zhu, Shuo Wang, Jinda Lu, Yanbin Hao

Few-shot learning (FSL) based on manifold regularization aims to improve the recognition capacity of novel objects with limited training samples by mixing two samples from different categories with a blending factor. However, this mixing operation weakens the feature representation due to the linear interpolation and the overlooking of the importance of spec

Tuğrulcan Elmas, Mathis Randl, Youssef Attia

Follow back accounts inflate their follower counts by engaging in reciprocal followings. Such accounts manipulate the public and the algorithms by appearing more popular than they really are. Despite their potential harm, no studies have analyzed such accounts at scale. In this study, we present the first large-scale analysis of follow back accounts. We form

Arash Badie-Modiri, Chiara Boldrini, Lorenzo Valerio, János Kertész

Fully decentralised federated learning enables collaborative training of individual machine learning models on a distributed network of communicating devices while keeping the training data localised on each node. This approach avoids central coordination, enhances data privacy and eliminates the risk of a single point of failure. Our research highlights tha

Mohammad Bajelani, Mehran Attar, Walter Lucia, Klaske van Heusden

Nowadays, many control systems are networked and embed communication and computation capabilities. Such control architectures are prone to cyber attacks on the cyberinfrastructure. Consequently, there is an impellent need to develop solutions to preserve the plant's safety against potential attacks. To ensure safety, this paper introduces a modular safety fi

Kesheng Wang, Kunhui Xu, Xiaoyu Chen, Chunlei He

Automatic measurements of tear meniscus height (TMH) have been achieved by using deep learning techniques; however, annotation is significantly influenced by subjective factors and is both time-consuming and labor-intensive. In this paper, we introduce an automatic TMH measurement technique based on edge detection-assisted annotation within a deep learning f

Feng Lin, Dong Jae Kim, Tse-Husn, Chen

Software process models are essential to facilitate collaboration and communication among software teams to solve complex development tasks. Inspired by these software engineering practices, we present FlowGen - a code generation framework that emulates software process models based on multiple Large Language Model (LLM) agents. We emulate three process mode

Weibo Mao, Masahiro Nozaki, Kotaro Tamaoka, Mao Tian Tan

We explore non-equilibrium processes in two-dimensional conformal field theories (2d CFTs) due to the growth of operators induced by inhomogeneous and homogeneous Hamiltonians by investigating the time dependence of the partition function, energy density, and entanglement entropy. The non-equilibrium processes considered in this paper are constructed out of

Sabrine Arfaoui, Hicham Banouh, Anouar Ben Mabrouk

The uncertainty principle constitutes one of the famous physical concepts which continues to attract researchers from different related fields since its discovery due to its utility in many applications. Among the classical (Fourier-based) forms, modified variants such as wavelet-based and logarithmic versions have been investigated by researchers. In the pr

Sara Asadi Toularoud, Hanif Hadipour, Hamid Rahimpour Soleimani

Layered materials that exhibit magnetic ordering in their pristine form are very rare. Several standard approaches, such as adsorption of atoms, introduction of point defects, and edge engineering, have been developed to induce magnetism in two-dimensional materials. In this way, we investigate the electronic and magnetic properties of monolayer TiS$_2$ dope

Kodai Shimosato, Norimichi Ukita

This paper proposes a mask optimization method for improving the quality of object removal using image inpainting. While many inpainting methods are trained with a set of random masks, a target for inpainting may be an object, such as a person, in many realistic scenarios. This domain gap between masks in training and inference images increases the difficult

Aamal Hussain, Dan Leonte, Francesco Belardinelli, Georgios Piliouras

Multi-agent learning algorithms have been shown to display complex, unstable behaviours in a wide array of games. In fact, previous works indicate that convergent behaviours are less likely to occur as the total number of agents increases. This seemingly prohibits convergence to stable strategies, such as Nash Equilibria, in games with many players. To make

Adnan Malik, Tayyaba Naz, Aimen Rauf, M. Farasat Shamir

The basic aim of this manuscript is to investigate the cosmological solutions in the context of the modified $f(R, T)$ theory of gravity, where $R$ is the Ricci scalar and $T$ is the trace of the energy-momentum tensor. For our current work, we consider the Friedmann-Robertson-Walker space-time for finding the solutions of field equations. We investigate the

Vikram Dharodi, Evdokiya Kostadinova

A numerical study of the homo-interactions between two falling droplets and between two rising bubbles in a strongly coupled dusty plasma medium is presented in this article. This strongly coupled dusty plasma is considered as a viscoelastic fluid using the generalized hydrodynamic fluid model formalism. Two factors that affect homo-interactions are taken in

M. Prasow-Émond, J. Hlavacek-Larrondo, K. Fogarty, É. Artigau

Although the study of X-ray binaries has led to major breakthroughs in high-energy astrophysics, their circumbinary environment at scales of $\sim$100--10,000 astronomical units has not been thoroughly investigated. In this paper, we undertake a novel and exploratory study by employing direct and high-contrast imaging techniques on a sample of X-ray binaries

Neeraj Kumar, Chitra Venugopal

An almost complete intersection ideal can be seen as a $d$-sequence ideal with the minimal number of generators being one more than its height. In this paper, we give exact formulas for the regularity of powers of graded almost complete intersection ideals satisfying certain conditions. We also present the minimal bigraded free resolutions of Rees algebras a

Raphaël Menu, Jorge Yago Malo, Vladan Vuletić, Maria Luisa Chiofalo

Trapped ions in a periodic potential are a paradigm of a frustrated Wigner crystal. The dynamics is captured by a long-range Frenkel-Kontorova model. The classical ground state can be mapped to the one of an antiferromagnetic spin chain with long-range interactions in a magnetic field, whose strength is determined by the mismatch between chain's and substrat

Vincent Moulton, Andreas Spillner, Kristina Wicke

Phylogenetic diversity indices are commonly used to rank the elements in a collection of species or populations for conservation purposes. The derivation of these indices is typically based on some quantitative description of the evolutionary history of the species in question, which is often given in terms of a phylogenetic tree. Both rooted and unrooted ph

Palki Gakkhar, Ravi Kumar, D. Angom, B. K. Mani

We have implemented an all-particle multireference Fock-space relativistic coupled-cluster theory to probe $6s^2{\;^1}S_{0} - 6s6p{\;^3P^o_{0}}$ clock transition in an even isotope of Pb$^{2+}$. We have computed, excitation energy for several low lying states, E1 and M1 transition amplitudes, and the lifetime of the clock state. Moreover, we have also calcul

Franz Vilsmeier, Christian Riedel, Christian H. Back

Spin-wave (SW) propagation close to the hybridization-induced transmission stop band is investigated within a trapezoid-shaped 200\,nm thick yttrium iron garnet (YIG) film using time-resolved magneto-optic Kerr effect (TR-MOKE) microscopy and broadband spin wave spectroscopy, supported by micromagnetic simulations. The gradual reduction of the effective fiel

Lijie Xu, Chulin Xie, Yiran Guo, Gustavo Alonso

Current federated learning (FL) approaches view decentralized training data as a single table, divided among participants either horizontally (by rows) or vertically (by columns). However, these approaches are inadequate for handling distributed relational tables across databases. This scenario requires intricate SQL operations like joins and unions to obtai

T. Le, J. -L. Domenech, N. H. Ngo, H. Tran

The $\beta$-correction to the Hartmann-Tran (HT) profile, recently introduced to model the spectral shape of a molecular transition strongly affected by the Dicke narrowing effect (Konefal et al., JQSRT 242 (2020) 106784), the HT profile (Ngo et al., JQSRT 129 (2013) 89), and the speed dependent (complex) hard collision model (SDcHC, in which the velocity ch

Mathav Raj J, Kushala VM, Harikrishna Warrier, Yogesh Gupta

There is a compelling necessity from enterprises for fine tuning LLMs (Large Language Models) o get them trained on proprietary domain knowledge. The challenge is to imbibe the LLMs with domain specific knowledge using the most optimial resource and cost and in the best possible time. Many enterprises rely on RAG (Retrieval Augmented Generation) which does n

Mingliang Liang, Martha Larson

We introduce Gaussian masking for Language-Image Pre-Training (GLIP) a novel, straightforward, and effective technique for masking image patches during pre-training of a vision-language model. GLIP builds on Fast Language-Image Pre-Training (FLIP), which randomly masks image patches while training a CLIP model. GLIP replaces random masking with centered mask

Lanfeng Zhong, Zongyao Huang, Yang Liu, Wenjun Liao

Classification of pathological images is the basis for automatic cancer diagnosis. Despite that deep learning methods have achieved remarkable performance, they heavily rely on labeled data, demanding extensive human annotation efforts. In this study, we present a novel human annotation-free method by leveraging pre-trained Vision-Language Models (VLMs). Wit

Hancheng Ye, Chong Yu, Peng Ye, Renqiu Xia

Recent Vision Transformer Compression (VTC) works mainly follow a two-stage scheme, where the importance score of each model unit is first evaluated or preset in each submodule, followed by the sparsity score evaluation according to the target sparsity constraint. Such a separate evaluation process induces the gap between importance and sparsity score distri

Yiwen Chen, Yuyao Ye, Ziyi Chen, Chuheng Zhang

Robotics learning highly relies on human expertise and efforts, such as demonstrations, design of reward functions in reinforcement learning, performance evaluation using human feedback, etc. However, reliance on human assistance can lead to expensive learning costs and make skill learning difficult to scale. In this work, we introduce the Large Language Mod

Amirhossein Rezaei, Mohammad Parsa Akrami

In this document, we examine exact and efficient numerical approaches to the MIT Bag Model, a theoretical framework used to describe the properties of bound quarks in Hadrons. We present the exact and Boundary Value Problem (BVP) numerical approaches. Both methods are effective in calculating the eigen-functions and energy levels. Notably, the precision of t

Hiroshi Mori, Norimichi Ukita

A Recurrent Neural Network (RNN) for Video Super Resolution (VSR) is generally trained with randomly clipped and cropped short videos extracted from original training videos due to various challenges in learning RNNs. However, since this RNN is optimized to super-resolve short videos, VSR of long videos is degraded due to the domain gap. Our preliminary expe

Shaoyu Sun, Chunyang Wang, Xuelian Liu, Chunhao Shi

3D single object tracking within LIDAR point clouds is a pivotal task in computer vision, with profound implications for autonomous driving and robotics. However, existing methods, which depend solely on appearance matching via Siamese networks or utilize motion information from successive frames, encounter significant challenges. Issues such as similar obje

Koray Düztaş

We evaluate the possibility of the formation of naked singularities in the interaction of Kerr black holes with test massless spin-2 fields. We analyse the scattering problem for both extremal and nearly extremal black holes by incorporating the explicit form of the absorption probabilities. We show that extremal black holes become non-extremal after the int

Takumi Oshima, Tomotoshi Nishino

Thermodynamic properties of the ferromagnetic Ising model on the hierarchical pentagon lattice is studied by means of the tensor network methods. The lattice consists of pentagons, where 3 or 4 of them meet at each vertex. Correlation functions on the surface of the system up to n = 10 layers are evaluated by means of the time evolving block decimation (TEBD

Zemin Sun, Geng Sun, Qingqing Wu, Long He

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services in close proximity to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between

Shubhransh Singhvi, Charchit Gupta, Avital Boruchovsky, Yuval Goldberg

Owing to its immense storage density and durability, DNA has emerged as a promising storage medium. However, due to technological constraints, data can only be written onto many short DNA molecules called data blocks that are stored in an unordered way. To handle the unordered nature of DNA data storage systems, a unique address is typically prepended to eac

Navid Hashemi, Bardh Hoxha, Danil Prokhorov, Georgios Fainekos

This paper introduces a model-based approach for training feedback controllers for an autonomous agent operating in a highly nonlinear (albeit deterministic) environment. We desire the trained policy to ensure that the agent satisfies specific task objectives and safety constraints, both expressed in Discrete-Time Signal Temporal Logic (DT-STL). One advantag

Jin Hu

We systematically derive the quantum generalized nonlinear Langevin equation using Morozov's projection operator method. This approach extends the linear Mori-Zwanzig projection operator technique, allowing for the inclusion of nonlinear interactions among macroscopic modes. Additionally, we obtain the quantum generalized Fokker-Planck equation within the He

Jiwan Jung

DNN inference, known for its significant energy consumption and the resulting high carbon footprint, can be made more sustainable by adapting model size and accuracy to the varying carbon intensity throughout the day. Our heuristic algorithm uses larger, high-accuracy models during low-intensity periods and smaller, lower-accuracy ones during high-intensity

Valerio Di Giulio, Evelijn Akerboom, Albert Polman, F. Javier García de Abajo

Free electrons are unique tools to probe and manipulate nanoscale optical fields with emerging applications in ultrafast spectromicroscopy and quantum metrology. However, advances in this field are hindered by the small probability associated with the excitation of single optical modes by individual free electrons. Here, we theoretically investigate the scal