April 2024 arXiv papers — page 107

Yixin Zhang, Xiaojue Zhu, Jeffery A. Wood, Detlef Lohse

Understanding the stability mechanism of surface micro/nanobubbles adhered to gas-evolving electrodes is essential for improving the efficiency of water electrolysis, which is known to be hindered by the bubble coverage on electrodes. Using molecular simulations, the diffusion-controlled evolution of single electrolytic nanobubbles on wettability-patterned n

Tin-Yau Tsang

In this paper we would have a brief overview of several proposals of quasilocal mass which are based on Hamiltonian formulation. We also show the positivity of the Wang-Yau energy under a more general condition. We then further study the quasilocal mass and DEC fill-ins defined by the author in terms of completeness and shields.

Muhammad Saad Saeed, Shah Nawaz, Muhammad Salman Tahir, Rohan Kumar Das

The advancements of technology have led to the use of multimodal systems in various real-world applications. Among them, the audio-visual systems are one of the widely used multimodal systems. In the recent years, associating face and voice of a person has gained attention due to presence of unique correlation between them. The Face-voice Association in Mult

Maria Ley-Flores, Archit Chabbi, Riccardo Alessandri, Sam Marsden

There is considerable interest in designing new polymeric materials with built-in mechanisms for recycling. In this work, we present a systematic exploration of several polyethylene-based polymers that contain strategically incorporated cleavable bonds that are susceptible to chemical degradation. We consider ten distinct telechelic functionalities across va

Lokesh Yadav, Srimanta Pakhira

The conversion of waste carbon dioxide (CO2) gas into valuable products and fuels through an electrocatalytic CO2 reduction reaction (CO2RR) is a promising approach. The sluggish kinetics of the CO2RR require the development of novel strategies for electrocatalyst design. Two-dimensional (2D) materials emerge as promising candidates for CO2RR due to their di

Jerry Huang, Prasanna Parthasarathi, Mehdi Rezagholizadeh, Sarath Chandar

Large language models (LLMs) show an innate skill for solving language based tasks. But insights have suggested an inability to adjust for information or task-solving skills becoming outdated, as their knowledge, stored directly within their parameters, remains static in time. Tool use helps by offloading work to systems that the LLM can access through an in

Souvik Das, Lifeng Jin, Linfeng Song, Haitao Mi

Large language models (LLMs) exhibit impressive natural language capabilities but suffer from hallucination -- generating content ungrounded in the realities of training data. Recent work has focused on decoding techniques to improve factuality during inference by leveraging LLMs' hierarchical representation of factual knowledge, manipulating the predicted d

Xujun Liu, Xin Zhang, Yanting Zhang

For a sequence $S=(s_1, \ldots, s_k)$ of non-decreasing integers, a packing $S$-coloring of a graph $G$ is a partition of its vertex set $V(G)$ into $V_1, \ldots, V_k$ such that for every pair of distinct vertices $u,v \in V_i$, where $1 \le i \le k$, the distance between $u$ and $v$ is at least $s_i+1$. The packing chromatic number, $\chi_p(G)$, of a graph

Tian Jin, Wanzin Yazar, Zifei Xu, Sayeh Sharify

Large language models (LLMs) can solve challenging tasks. However, their inference computation on modern GPUs is highly inefficient due to the increasing number of tokens they must attend to as they generate new ones. To address this inefficiency, we capitalize on LLMs' problem-solving capabilities to optimize their own inference-time efficiency. We demonstr

Erwin Miña-Díaz, Aron Wennman

We study the asymptotic behavior of the Bergman orthogonal polynomials $(p_n)_{n=0}^{\infty}$ for a class of bounded simply connected domains $D$. The class is defined by the requirement that conformal maps $\varphi$ of $D$ onto the unit disk extend analytically across the boundary $L$ of $D$, and that $\varphi'$ has a finite number of zeros $z_1,\ldots, z_q

Louis Steinmeister, Markus Pauly

"If you ask ten experts, you will get ten different opinions." This common proverb illustrates the common association of expert forecasts with personal bias and lack of consistency. On the other hand, digitization promises consistency and explainability through data-driven forecasts employing machine learning (ML) and statistical models. In the following, we

Xia Chen, Jian Song

In this note, we establish the bounds \[ c\varepsilon^{\frac23}\le P\bigg\{\int_0^1\!\!\int_0^1\delta_0(B_s-\tilde{B}_r)dsdr\le \varepsilon \bigg\} \le C \varepsilon^{\frac23},\] for the mutual intersection local time of two independent 1-dimensional Brownian motions $B$ and $\tilde B$.

Alireza Ataei, Douglas Lundholm, Dinh-Thi Nguyen

This work considers two related families of nonlinear and nonlocal problems in the plane $\mathbb{R}^2$. The first main result derives the general integrable solution to a generalized Liouville equation using the Wronskian of two coprime complex polynomials. The second main result concerns an application to a generalized Ladyzhenskaya-Gagliardo-Nirenberg int

Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Muhammad Shafique

Recent trends have shown that autonomous agents, such as Autonomous Ground Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots, effectively improve human productivity in solving diverse tasks. However, since these agents are typically powered by portable batteries, they require extremely low power/energy consumption to operate in a long lifes

Baiqiang Huang, Shifeng Nian, Li-Heng Cai

Since the invention of polymer networks in the 19th century (e.g., crosslinked natural rubber by Goodyear), it has been a dogma that stiffer networks are less stretchable, a trade-off inherent to the molecular nature of polymer network strands. Here, we report a universal strategy for decoupling the stiffness and extensibility of single-network elastomers. I

Carlos Carrasco-Farre

Large Language Models (LLMs) are already as persuasive as humans. However, we know very little about how they do it. This paper investigates the persuasion strategies of LLMs, comparing them with human-generated arguments. Using a dataset of 1,251 participants in an experiment, we analyze the persuasion strategies of LLM-generated and human-generated argumen

Lalit Kumar, Sivaji Ganesh Sista, Konijeti Sreenadh

This paper analyses a Kirchhoff type quasilinear space-time fractional integro-differential equation with memory $(\mathcal{K}^{s}_{\alpha})$. Various a priori bounds are derived in different norms on the solution of the considered equation. Utilizing these a priori bounds, existence and uniqueness of the weak solution to the proposed model are proved. Furth

A. J. Rasmusson, Ilyoung Jung, Frank G. Schroer, Antonis Kyprianidis

We experimentally study the heating of trapped atomic ions during measurement of their internal qubit states. During measurement, ions are projected into one of two basis states and discriminated by their state-dependent fluorescence. We observe that ions in the fluorescing state rapidly scatter photons and heat at a rate of \mbox{$\dot{\bar{n}}\gtrsim 2\tim

Diana-Nicoleta Grigore, Mariana-Iuliana Georgescu, Jon Alvarez Justo, Tor Johansen

Few-shot knowledge distillation recently emerged as a viable approach to harness the knowledge of large-scale pre-trained models, using limited data and computational resources. In this paper, we propose a novel few-shot feature distillation approach for vision transformers. Our approach is based on two key steps. Leveraging the fact that vision transformers

Gabriel Sauter, Antonia Papapostolou, Audrey Pollien, Sergius Boschmann

This study delves into the enhancement of two-photon absorption (2PA) properties in diazaacene-bithiophene derivatives through a synergistic approach combining theoretical analysis and experimental validation. By investigating the structural modifications and their impact on 2PA cross sections, we identify key factors that significantly influence the 2PA eff

Ali Mohamed Ali, Chao Shen, Hashim A. Hashim

The need for fully autonomous mobile robots has surged over the past decade, with the imperative of ensuring safe navigation in a dynamic setting emerging as a primary challenge impeding advancements in this domain. In this paper, a Safety Critical Model Predictive Control based on Dynamic Feedback Linearization tailored to the application of differential dr

Zhiyu Zhao, Qirui Mi, Ning Yang, Xue Yan

We investigate multi-agent imitation learning (IL) within the framework of mean field games (MFGs), considering the presence of time-varying correlated signals. Existing MFG IL algorithms assume demonstrations are sampled from Mean Field Nash Equilibria (MFNE), limiting their adaptability to real-world scenarios. For example, in the traffic network equilibri

Xuejian Li, John R. Singler, Xiaoming He

We propose and analyze an inexact gradient method based on incremental proper orthogonal decomposition (iPOD) to address the data storage difficulty in time-dependent PDE-constrained optimization, particularly for a data assimilation problem as a detailed demonstration for the key ideas. The proposed method is proved robust by rigorous analysis. We first der

Jianyuan Ni, Hao Tang, Syed Tousiful Haque, Yan Yan

The combination of increased life expectancy and falling birth rates is resulting in an aging population. Wearable Sensor-based Human Activity Recognition (WSHAR) emerges as a promising assistive technology to support the daily lives of older individuals, unlocking vast potential for human-centric applications. However, recent surveys in WSHAR have been limi

Yufu Wang, Mingwei Zhu, Jiaqiang Yuan, Guanghui Wang

Cloud computing (cloud computing) is a kind of distributed computing, referring to the network "cloud" will be a huge data calculation and processing program into countless small programs, and then, through the system composed of multiple servers to process and analyze these small programs to get the results and return to the user. This report explores the i

Miguel A. Porras

Advances in the generation and the application of spatiotemporal optical vortices (STOV) are proceeding fast, but fundamental aspects of their nature remain obscure. Phys. Rev. A 107, L031501 (2023) (PRA) and Prog. Electromagn. Res. 177, 95 (2023) (PIER) provide contradictory results on the transverse orbital angular momentum (OAM) carried by STOVs. We show

Ali Mohamed Ali, Hashim A. Hashim, Chao Shen

In this work, we propose a cascaded scheme of linear Model prediction Control (MPC) based on Control Barrier Functions (CBF) with Dynamic Feedback Linearization (DFL) for Vertical Take-off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs). CBF is a tool that allows enforcement of forward invariance of a set using Lyapunov-like functions to ensure safety. Th

Giuseppe Orlando, Tommaso Benacchio, Luca Bonaventura

We present a quantitative assessment of the impact of high-order mappings on the simulation of flows over complex orography. Curved boundaries were not used in early numerical methods, whereas they are employed to an increasing extent in state of the art computational fluid dynamics codes, in combination with high-order methods, such as the Finite Element Me

Yuan-Zheng Lei, Yaobang Gong, Xianfeng Terry Yang

Traffic flow modeling relies heavily on fundamental diagrams. However, deterministic fundamental diagrams, such as single or multi-regime models, cannot capture the uncertainty pattern that underlies traffic flow. To address this limitation, a sparse non-parametric regression model is proposed in this paper to formulate the stochastic fundamental diagram. Un

Abhishek Tyagi, Reiley Jeyapaul, Chuteng Zhu, Paul Whatmough

As Neural Processing Units (NPU) or accelerators are increasingly deployed in a variety of applications including safety critical applications such as autonomous vehicle, and medical imaging, it is critical to understand the fault-tolerance nature of the NPUs. We present a reliability study of Arm's Ethos-U55, an important industrial-scale NPU being utilised

Yuvaraju Chinnam, Bosubabu Sambana

On social media platforms, the act of predicting reposting is seen as a challenging issue related to Short Message Services (SMS). This study examines the issue of predicting picture reposting in SMS and forecasts users' behavior in sharing photographs on Twitter. Several research vary. The paper introduces a network called Image Retweet Modeling (IRM) that

Zhanhao Zhang, Jan Lorenz Svensen, Morten Wahlgreen Kaysfeld, Anders Hilmar Damm Christensen

In this study, we introduce numerical methods for discretizing continuous-time linear-quadratic optimal control problems (LQ-OCPs). The discretization of continuous-time LQ-OCPs is formulated into differential equation systems, and we can obtain the discrete equivalent by solving these systems. We present the ordinary differential equation (ODE), matrix expo

Roberto Civino, Valerio Fedele

In a XOR-based alternating block cipher the plaintext is masked by a sequence of layers each performing distinct actions: a highly nonlinear permutation, a linear transformation, and the bitwise key addition. When assessing resistance against classical differential attacks (where differences are computed with respect to XOR), the cryptanalysts must only take

Liang Chang, Quinn T. Kolt, Zhenghan Wang, Qing Zhang

We investigate non-semisimple modular categories with an eye towards a structure theory, low-rank classification, and applications to low dimensional topology and topological physics. We aim to extend the well-understood theory of semisimple modular categories to the non-semisimple case by using representations of factorizable ribbon Hopf algebras as a case

Zhiqing Hong, Rongjie Huang, Xize Cheng, Yongqi Wang

A song is a combination of singing voice and accompaniment. However, existing works focus on singing voice synthesis and music generation independently. Little attention was paid to explore song synthesis. In this work, we propose a novel task called text-to-song synthesis which incorporating both vocals and accompaniments generation. We develop Melodist, a

Lukas Broers, Ludwig Mathey

We propose a formalism that captures the algebraic structure of many-body two-level quantum systems, and directly motivates an efficient numerical method. This formalism is based on the binary representation of the enumeration-indices of the elements of the corresponding Lie algebra. The action of arbitrarily large elements of that algebra reduces to a few b

Merim Dzaferagic, Bruno Missi Xavier, Diarmuid Collins, Vince D'Onofrio

O-RAN introduces intelligent and flexible network control in all parts of the network. The use of controllers with open interfaces allow us to gather real time network measurements and make intelligent/informed decision. The work in this paper focuses on developing a use-case for open and reconfigurable networks to investigate the possibility to predict hand

Tuan Anh Dao, Murtazo Nazarov

We present a novel high-order nodal artificial viscosity approach designed for solving Magnetohydrodynamics (MHD) equations. Unlike conventional methods, our approach eliminates the need for ad hoc parameters. The viscosity is mesh-dependent, yet explicit definition of the mesh size is unnecessary. Our method employs a multimesh strategy: the viscosity coeff

Alan Barnes

Recently Harada has proposed a gravitational theory which is of third order in the derivatives of the metric tensor. This has attracted some attention particularly as it predicts a late-time transition from cosmological decelaration to accelerated expansion without assuming the presence of dark energy or a non-zero cosmological constant. This theory has been

David Roodman

The julia package integrates the Julia programming language into Stata. Users can transfer data between Stata and Julia, issue Julia commands to analyze and plot, and pass results back to Stata. Julia's econometric ecosystem is not as mature as Stata's or R's or Python's. But Julia is an excellent environment for developing high-performance numerical applica

Wiktor Mucha, Martin Kampel

Action recognition is essential for egocentric video understanding, allowing automatic and continuous monitoring of Activities of Daily Living (ADLs) without user effort. Existing literature focuses on 3D hand pose input, which requires computationally intensive depth estimation networks or wearing an uncomfortable depth sensor. In contrast, there has been i

Kuratov Andrey

This research focuses on an innovative task of extracting equations from incomplete data, moving away from traditional methods used for complete solutions. The study addresses the challenge of extracting equations from data, particularly in the study of brain activity using electrophysiological data, which is often limited by insufficient information. The st

Jiamin Hu, Lu-Xing Yang, Xiaofan Yang, Kaifan Huang

Product co-creation based on company-sponsored online community has come to be a paradigm of developing new products collaboratively with customers. In such a product co-creation campaign, the sponsoring company needs to interact intensively with active community members about the design scheme of the product. We call the collection of the rates of the compa

Cecile Durot, Debarghya Mukherjee

Shuffled regression and unlinked regression represent intriguing challenges that have garnered considerable attention in many fields, including but not limited to ecological regression, multi-target tracking problems, image denoising, etc. However, a notable gap exists in the existing literature, particularly in vanishing noise, i.e., how the rate of estimat

Luca Buoncompagni, Fulvio Mastrogiovanni

The paper tackles the issue of mapping logic axioms formalised in the Ontology Web Language (OWL) within the Object-Oriented Programming (OOP) paradigm. The issues of mapping OWL axioms hierarchies and OOP objects hierarchies are due to OWL-based reasoning algorithms, which might change an OWL hierarchy at runtime; instead, OOP hierarchies are usually define

Qian Xiao, Dan Liu, Kevin Credit

Building Energy Rating (BER) stands as a pivotal metric, enabling building owners, policymakers, and urban planners to understand the energy-saving potential through improving building energy efficiency. As such, enhancing buildings' BER levels is expected to directly contribute to the reduction of carbon emissions and promote climate improvement. Nonetheles

Tristan Cazenave

Monte Carlo Tree Search and Monte Carlo Search have good results for many combinatorial problems. In this paper we propose to use Monte Carlo Search to design mathematical expressions that are used as exploration terms for Monte Carlo Tree Search algorithms. The optimized Monte Carlo Tree Search algorithms are PUCT and SHUSS. We automatically design the PUCT

Simona Corciulo, Mario Alessandro Bochicchio

Synesthesia, conceived as a neuropsychological condition, may prove valuable in studying the interaction between humans and machines by analyzing the co-occurrence of sensory or cognitive responses triggered by a stimulus. In our approach, synesthesia is elevated beyond a mere perceptual-cognitive anomaly, offering insights into the reciprocal interaction be

Pranay Lohia, Laurent Boue, Sharath Rangappa, Vijay Agneeswaran

Azure Core workload insights have time-series data with different metric units. Faults or Anomalies are observed in these time-series data owing to faults observed with respect to metric name, resources region, dimensions, and its dimension value associated with the data. For Azure Core, an important task is to highlight faults or anomalies to the user on a

Yafei Mao, Xuelu Li, Brandon Smith, Jinjin Li

Virtual try-on and product personalization have become increasingly important in modern online shopping, highlighting the need for accurate body measurement estimation. Although previous research has advanced in estimating 3D body shapes from RGB images, the task is inherently ambiguous as the observed scale of human subjects in the images depends on two unk

Yingxia Xi, Bo Gong, Jiguang Sun

Scattering resonances have important applications in many areas of science and engineering. They are the replacement of discrete spectral data for problems on non-compact domains. In this paper, we consider the computation of scattering resonances defined on the exterior to a compact sound hard obstacle. The resonances are the eigenvalues of a holomorphic Fr

Dror K. Markus, Effi Levi, Tamir Sheafer, Shaul R. Shenhav

Media Storms, dramatic outbursts of attention to a story, are central components of media dynamics and the attention landscape. Despite their significance, there has been little systematic and empirical research on this concept due to issues of measurement and operationalization. We introduce an iterative human-in-the-loop method to identify media storms in

Kevin Credit, Qian Xiao, Jack Lehane, Juan Vazquez

This paper describes the technical and conceptual development of the LuminLab platform, an online tool that integrates a purpose-fit human-centric AI chatbot and predictive energy model into a streamlined front-end that can rapidly produce and discuss building retrofit plans in natural language. The platform provides users with the ability to engage with a r

Khen Cohen, Haim Suchowski, Yaron Oz

Realizing quantum information processors is challenged by errors and noise across all platforms. While composite segmentation schemes have been proposed in many systems, their application to photonic quantum gates in dual-rail configurations has only recently been demonstrated. However, prior research has been limited to a small number of segments, full nois

Pedro Gonzalez-Fernandez

This paper proposes a unified theoretical model to identify and test a comprehensive set of probabilistic updating biases within a single framework. The model achieves separate identification by focusing on the updating of belief distributions, rather than point beliefs alone. Estimating the model in a laboratory experiment reveals significant individual het

Tuan Bui, Oanh Tran, Phuong Nguyen, Bao Ho

In today's rapidly evolving landscape of Artificial Intelligence, large language models (LLMs) have emerged as a vibrant research topic. LLMs find applications in various fields and contribute significantly. Despite their powerful language capabilities, similar to pre-trained language models (PLMs), LLMs still face challenges in remembering events, incorpora

Étienne Fouvry, Igor E. Shparlinski, Ping Xi

We obtain new bounds on some trilinear and quadrilinear character sums, which are non-trivial starting from very short ranges of the variables. An application to an apparently new problem on oscillations of characters on differences between Farey fractions is given. Other applications include a modular analogue of a multiplicative hybrid problem of Iwaniec a

Min Deng, Ming Xue, Jinghan Pang, Hui Luo

We investigate the ground-state phase diagram of a binary mixture of Bose-Einstein condensates (BECs) with competing interspecies $s$- and $p$-wave interactions. Exploiting a pseudopotential model for the $l=1$ partial wave, we derive an extended Gross-Pitaevskii (GP) equation for the BEC mixture that incorporates both $s$- and $p$-wave interactions. Based o

Zihan Cao, Xiao Wu, Liang-Jian Deng, Yu Zhong

In image fusion tasks, images from different sources possess distinct characteristics. This has driven the development of numerous methods to explore better ways of fusing them while preserving their respective characteristics.Mamba, as a state space model, has emerged in the field of natural language processing. Recently, many studies have attempted to exte

Jieyi Tan, Yansheng Li, Sergey A. Bartalev, Shinkarenko Stanislav

Remote sensing semantic segmentation (RSS) is an essential technology in earth observation missions. Due to concerns over geographic information security, data privacy, storage bottleneck and industry competition, high-quality annotated remote sensing images are often isolated and distributed across institutions. The issue of remote sensing data islands pose

Stefano Baroni

Transport theory describes the response of a macroscopic current to a thermodynamic force, thus producing entropy and apparently violating time-reversal symmetry. In this note I report a pedagogical derivation of the Green-Kubo formula for transport coefficients that highlights the intrinsically dynamical nature of this formula and showcases the relation bet

Kyle Shih-Huang Lo, Jörg Peters, Eric Spellman

Accurate completion and denoising of roof height maps are crucial to reconstructing high-quality 3D buildings. Repairing sparse points can enhance low-cost sensor use and reduce UAV flight overlap. RoofDiffusion is a new end-to-end self-supervised diffusion technique for robustly completing, in particular difficult, roof height maps. RoofDiffusion leverages

Jose M. Rojas Chaves, Subarna Tripathi

We propose a graph-based representation learning framework for video summarization. First, we convert an input video to a graph where nodes correspond to each of the video frames. Then, we impose sparsity on the graph by connecting only those pairs of nodes that are within a specified temporal distance. We then formulate the video summarization task as a bin

Sahar Diskin, Michael Krivelevich

We present a short and self-contained proof of a classical result due to Bollob\'as (1990): in the random hypercube process, with high probability the hitting time of connectedness equals the hitting time of having minimum degree at least one.

Wen Sun

We study the condensation phenomenon for the invariant measures of the mean-field model of reversible coagulation-fragmentation processes conditioned to a supercritical density of particles. It is shown that when the parameters of the associated balance equation satisfy a subexponential tail condition, there is one single giant particle that corresponds to t

Yuvaraju Chinnam, Bosubabu Sambana

In a nutshell, "the cloud" refers to a collection of interconnected computing resources made possible by an extensive, real-time communication network like the internet. Because of its potential to reduce processing costs, the emerging paradigm of cloud computing has recently attracted a large number of academics. The exponential expansion of cloud computing

Haosong Peng, Yufeng Zhan, DiHua Zhai, Xiaopu Zhang

As an emerging computing paradigm, edge computing offers computing resources closer to the data sources, helping to improve the service quality of many real-time applications. A crucial problem is designing a rational pricing mechanism to maximize the revenue of the edge computing service provider (ECSP). However, prior works have considerable limitations: c

Alexander Mielke, Mark A. Peletier, Johannes Zimmer

We reconsider the fundamental problem of coarse-graining infinite-dimensional Hamiltonian dynamics to obtain a macroscopic system which includes dissipative mechanisms. In particular, we study the thermodynamical implications concerning Hamiltonians, energy, and entropy and the induced geometric structures such as Poisson and Onsager brackets (symplectic and

Vishnu Joshi

The world around us is transforming as the field of the Internet of Things is taking over the world faster than we thought. Everyone in the tech industry is building wonderful things with the help of IoT. Smartwatches, smart coffee machines, smart television, smart homes are some of the examples. Building IoT sensor modules with sensors that connect to the i

Kouki Taniyama

Let $\alpha$ be a map from the set of all knot types ${\mathcal K}$ to a set $X$. Let $\beta$ be a map from ${\mathcal K}$ to a set $Y$. We define the relation between $\alpha$ and $\beta$ to be the image of a map $(\alpha,\beta)$ from ${\mathcal K}$ to $X\times Y$ sending an element $K$ of ${\mathcal K}$ to $(\alpha(K),\beta(K))$. We determine the relations

Finn Vehlhaber, Mauro Salazar

This paper presents a Model Predictive Control (MPC) scheme for flight scheduling and energy management of electric aviation networks, where electric aircraft transport passengers between electrified airports equipped with sustainable energy sources and battery storage, with the goal of minimizing grid dependency. Specifically, we first model the aircraft fl

Lexington Whalen, Homayoun Valafar

Every research project necessitates data, often requiring sharing and collaborative review within a team. However, there is a dearth of good open-source data sharing and reviewing services. Existing file-sharing services generally mandate paid subscriptions for increased storage or additional members, diverting research funds from addressing the core researc

Alex Saad-Falcon, Mark Bolding, James Dee, Ryan S. Westafer

The cystic fibrosis transmembrane conductance regulator (CFTR) is an important membrane protein in vertebrates. The function of CFTR is to transport chloride ions across the cell membrane, which is known to require adenosine triphosphate (ATP). Whereas most conventional wisdom suggests that ATP interacts with CFTR purely through random collisions via diffusi

Simon Eisenmann, Daniel Hein, Steffen Udluft, Thomas A. Runkler

This paper presents the first algorithm for model-based offline quantum reinforcement learning and demonstrates its functionality on the cart-pole benchmark. The model and the policy to be optimized are each implemented as variational quantum circuits. The model is trained by gradient descent to fit a pre-recorded data set. The policy is optimized with a gra

Xiang Li, Jia-Cheng Huang, Guang-Ze Zhang, Hao-En Li

The advent of Neural-network Quantum States (NQS) has significantly advanced wave function ansatz research, sparking a resurgence in orbital space variational Monte Carlo (VMC) exploration. This work introduces three algorithmic enhancements to reduce computational demands of VMC optimization using NQS: an adaptive learning rate algorithm, constrained optimi

Emanuel Malvetti

We study a single Markovian qubit governed by a Lindblad master equation and subject to fast unitary control. Using reduced control systems and optimal control theory we determine (i) controls for cooling and heating such systems in a time-optimal way as well as (ii) the set of stabilizable states in the Bloch ball. No restrictions on the Lindblad equation a

Jiayi Xia, Yen Chin Ong

Both classical and quantum arguments suggest that if Barrow entropy is correct, its index $\delta$ must be energy dependent, which would affect the very early universe. Based on thermodynamic stability that sufficiently large black holes should not fragment, we argue that Barrow entropy correction must be small except possibly at the Planckian regime. Furthe

Vasudha Venkatesan, Daniel Panangian, Mario Fuentes Reyes, Ksenia Bittner

In the field of remote sensing, the scarcity of stereo-matched and particularly lack of accurate ground truth data often hinders the training of deep neural networks. The use of synthetically generated images as an alternative, alleviates this problem but suffers from the problem of domain generalization. Unifying the capabilities of image-to-image translati

Xu Feng, Wenjian Yu, Yuyang Xie, Jie Tang

Aiming to provide a faster and convenient truncated SVD algorithm for large sparse matrices from real applications (i.e. for computing a few of largest singular values and the corresponding singular vectors), a dynamically shifted power iteration technique is applied to improve the accuracy of the randomized SVD method. This results in a dynamic shifts based

Sujata Goala, Mridu Prabal Goswami, Surajit Borkotokey

The distribution of efficient individuals in the economy and the efforts that they will put in if they are hired, there are two important concerns for a technologically advanced firm. wants to open a new branch. The firm does not have information about the exact level of efficiency of an individual when she is hired. We call this situation incomplete informa

Andrew Steckley, Noah Steckley

This study explores the cryptic Voynich Manuscript, by looking for subtle signs of scribal intent hidden in overlooked features of the "Voynichese" script. The findings indicate that distributions of tokens within paragraphs vary significantly based on positions defined not only by elements intrinsic to the script such as paragraph and line boundaries but al

Quang Minh Dinh, Minh Khoi Ho, Anh Quan Dang, Hung Phong Tran

Traffic video description and analysis have received much attention recently due to the growing demand for efficient and reliable urban surveillance systems. Most existing methods only focus on locating traffic event segments, which severely lack descriptive details related to the behaviour and context of all the subjects of interest in the events. In this p

Morgan H. Lynch

In this manuscript we examine the Unruh-thermalized CERN-NA63 radiation reaction data set from the point of view of a diphoton Rindler bath. Under the assumption that these Hawking-Unruh diphoton pairs are microscopic trans-Planckian black holes, we find the resultant heat capacity describes the measured energy spectrum and is thus a dual description of the

Michael Raju, Baptiste Jayet, Stefan Andersson-Engels

We developed a 2D Finite-Difference Time-Domain (FDTD) method for modeling a space-time modulated guidestar targeting wavefront shaping applications in disordered media. Space-time modulation in general (a particular example being the acousto-optic effect) is used here as a guidestar for the transverse confinement of light around the tagged region surrounded

Filomena Pacella, David Ruiz, Pieralberto Sicbaldi

Given a bounded regular domain $\omega \subset \mathbb{R}^{N-1}$ and the half-cylinder $\Sigma = \omega \times (0,+\infty)$, we consider the relative overdetermined torsion problem in $\Sigma$, i.e. \[\left\{ \begin{array}{ll} \Delta {u}+1=0 &\mbox{in $\Omega$},\newline \partial_\eta u = 0 &\mbox{on $\widetilde \Gamma_\Omega$},\newline u=0 &\mbox{on $\Gamma_

Fei Xue, Ignas Budvytis, Daniel Olmeda Reino, Roberto Cipolla

Visual relocalization is a key technique to autonomous driving, robotics, and virtual/augmented reality. After decades of explorations, absolute pose regression (APR), scene coordinate regression (SCR), and hierarchical methods (HMs) have become the most popular frameworks. However, in spite of high efficiency, APRs and SCRs have limited accuracy especially

C. Thornton, S. Karimi, S. Glenn, W. D. Brown

For the first time, we demonstrate the application of an inverse Compton scattering X-ray Source, driven by a laser-plasma accelerator, to image an additively manufactured component. X-rays with a mean energy of 380 keV were produced and used to image an additively manufactured part made of an Inconel (Nickel 718) alloy. Because inverse Compton scattering dr

Chih-Ling Chang, Fu-Jen Tsai, Zi-Ling Huang, Lin Gu

Image dehazing faces challenges when dealing with hazy images in real-world scenarios. A huge domain gap between synthetic and real-world haze images degrades dehazing performance in practical settings. However, collecting real-world image datasets for training dehazing models is challenging since both hazy and clean pairs must be captured under the same con

Aryan Esmailpour, Sara Saeedi Madani, Dariush Kiani

Let $G$ be a graph, and let $\lambda(G)$ denote the smallest eigenvalue of $G$. First, we provide an upper bound for $\lambda(G)$ based on induced bipartite subgraphs of $G$. Consequently, we extract two other upper bounds, one relying on the average degrees of induced bipartite subgraphs and a more explicit one in terms of the chromatic number and the indep

Haosong Peng, Yufeng Zhan, Peng Li, Yuanqing Xia

Cloud-edge collaborative computing paradigm is a promising solution to high-resolution video analytics systems. The key lies in reducing redundant data and managing fluctuating inference workloads effectively. Previous work has focused on extracting regions of interest (RoIs) from videos and transmitting them to the cloud for processing. However, a naive Inf

Lei-Hong Zhang, Ya-Nan Zhang, Linyi Yang, Yifu Wu

In the least-squares fitting framework, the Vandermonde with Arnoldi (V+A) method presented in [Brubeck, Nakatsukasa, and Trefethen, {SIAM Review}, 63 (2021), pp. 405-415] is an effective approach to compute a polynomial that approximates an underlying univariate function $f$. Extensions of V+A include its multivariate version and the univariate confluent V+

Tanveer Khan, Mindaugas Budzys, Antonis Michalas

The popularity of Machine Learning (ML) makes the privacy of sensitive data more imperative than ever. Collaborative learning techniques like Split Learning (SL) aim to protect client data while enhancing ML processes. Though promising, SL has been proved to be vulnerable to a plethora of attacks, thus raising concerns about its effectiveness on data privacy

Elliot Kolker-Hicks, Di Zhang, Dong Dai

High Performance Computing (HPC) systems are used across a wide range of disciplines for both large and complex computations. HPC systems often receive many thousands of computational tasks at a time, colloquially referred to as jobs. These jobs must then be scheduled as optimally as possible so they can be completed within a reasonable timeframe. HPC schedu

Jin Yang, Ping Wei, Huan Li, Ziyang Ren

Video moment retrieval and highlight detection are two highly valuable tasks in video understanding, but until recently they have been jointly studied. Although existing studies have made impressive advancement recently, they predominantly follow the data-driven bottom-up paradigm. Such paradigm overlooks task-specific and inter-task effects, resulting in po

Yasuhiro Tada

We discuss the charge polarization in a generalized Rice-Mele model at arbitrary particle filling per site as a model of charge ordered systems in one dimension. The model possesses neither the conventional bond-centered inversion symmetry nor the one site translation symmetry alone, but has combinations of these symmetries. We show that the charge polarizat

Maxim Goncharov, Pavel Kolesnikov, Yunhe Sheng, Rong Tang

In this paper, first we revisit the formal integration of Lie algebras, which give rise to braces in some special cases. Then we establish the formal integration theory for complete Rota-Baxter Lie algebras, that is, we show that there is a Rota-Baxter group with the underlying group structure given by the Baker-Campbell-Hausdorff formula, associated to any

Kota Tanabe, Masahiro Suzuki, Hiroki Sakaji, Itsuki Noda

We construct an instruction dataset for the large language model (LLM) in the Japanese finance domain. Domain adaptation of language models, including LLMs, is receiving more attention as language models become more popular. This study demonstrates the effectiveness of domain adaptation through instruction tuning. To achieve this, we propose an instruction t

Yu Qiao, Huy Q. Le, Mengchun Zhang, Apurba Adhikary

Federated learning (FL) facilitates a privacy-preserving neural network training paradigm through collaboration between edge clients and a central server. One significant challenge is that the distributed data is not independently and identically distributed (non-IID), typically including both intra-domain and inter-domain heterogeneity. However, recent rese

Chen Deng, Yong-Feng Huang, Chen Du, Pei Wang

Some fast radio bursts (FRBs) exhibit repetitive behaviors and their origins remain enigmatic. It has been argued that repeating FRBs could be produced by the interaction between a neutron star and an asteroid belt. Here we consider the systems in which an asteroid belt dwells around a massive star, while a neutron star, as a companion of the massive star, i

Asmita Bhandare, Benoît Commerçon, Guillaume Laibe, Mario Flock

Signposts of early planet formation are ubiquitous in substructured young discs. Dense, hot and high-pressure regions formed during gravitational collapse process, integral to star formation, facilitate dynamical mixing of dust within the protostellar disc. This provides an incentive to constrain the role of gas-dust interaction and resolve zones of dust con