July 2023 arXiv papers — page 131

Rao Fu, Cheng Wen, Qian Li, Xiao Xiao

This paper proposes BPNet, a novel end-to-end deep learning framework to learn B\'ezier primitive segmentation on 3D point clouds. The existing works treat different primitive types separately, thus limiting them to finite shape categories. To address this issue, we seek a generalized primitive segmentation on point clouds. Taking inspiration from B\'ezier d

Alice E. A. Allen, Nicholas Lubbers, Sakib Matin, Justin Smith

The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverage the plethora of data available as they require that each dataset be generated using the same QM method. Taki

Qiang Wang, Pablo Martinez Ulloa, Robert Burke, David Cordova Bulens

The ability to detect slip, particularly incipient slip, enables robotic systems to take corrective measures to prevent a grasped object from being dropped. Therefore, slip detection can enhance the overall security of robotic gripping. However, accurately detecting incipient slip remains a significant challenge. In this paper, we propose a novel learning-ba

Maria Luisa Taccari, Oded Ovadia, He Wang, Adar Kahana

This paper presents a comprehensive comparison of various machine learning models, namely U-Net, U-Net integrated with Vision Transformers (ViT), and Fourier Neural Operator (FNO), for time-dependent forward modelling in groundwater systems. Through testing on synthetic datasets, it is demonstrated that U-Net and U-Net + ViT models outperform FNO in accuracy

Sean Flynn, Francesco Bronzino, Paul Schmitt

Internet privacy is increasingly important on the modern Internet. Users are looking to control the trail of data that they leave behind on the systems that they interact with. Multi-Party Relay (MPR) architectures lower the traditional barriers to adoption of privacy enhancing technologies on the Internet. MPRs are unique from legacy architectures in that t

Md. Mostafizer Rahman, Yutaka Watanobe, Atsushi Shirafuji, Mohamed Hamada

The automated code evaluation system (AES) is mainly designed to reliably assess user-submitted code. Due to their extensive range of applications and the accumulation of valuable resources, AESs are becoming increasingly popular. Research on the application of AES and their real-world resource exploration for diverse coding tasks is still lacking. In this s

Belinda Z. Li, Jason Eisner, Adam Pauls, Sam Thomson

Voice dictation is an increasingly important text input modality. Existing systems that allow both dictation and editing-by-voice restrict their command language to flat templates invoked by trigger words. In this work, we study the feasibility of allowing users to interrupt their dictation with spoken editing commands in open-ended natural language. We intr

Andrés Pinto, Zhibo Wu, Fabrice Balli, Nicolas Berger

When a measurement of a physical quantity is reported, the total uncertainty is usually decomposed into statistical and systematic uncertainties. This decomposition is not only useful to understand the contributions to the total uncertainty, but also required to propagate these contributions in subsequent analyses, such as combinations or interpretation fits

Nan Chen, Evelyn Lunasin, Stephen Wiggins

Lagrangian descriptors provide a global dynamical picture of the geometric structures for arbitrarily time-dependent flows with broad applications. This paper develops a mathematical framework for computing Lagrangian descriptors when uncertainty appears. The uncertainty originates from estimating the underlying flow field as a natural consequence of data as

Rineke Verbrugge

The TARK conference (Theoretical Aspects of Rationality and Knowledge) is a conference that aims to bring together researchers from a wide variety of fields, including computer science, artificial intelligence, game theory, decision theory, philosophy, logic, linguistics, and cognitive science. Its goal is to further our understanding of interdisciplinary is

Harnaik Dhami, Vishnu D. Sharma, Pratap Tokekar

Next-Best View (NBV) planning is a long-standing problem of determining where to obtain the next best view of an object from, by a robot that is viewing the object. There are a number of methods for choosing NBV based on the observed part of the object. In this paper, we investigate how predicting the unobserved part helps with the efficiency of reconstructi

Jean-Pierre Magnot

We describe a framework for random pairwise comparisons matrices, inspired by selected constructions releted to the so called inconsistency reduction of pairwise comparisons (PC) matrices. In to build up structures on random pairwise comparisons matrices, the set up for (deterministic) PC matrices for non-reciprocal PC matrices is completed. The extension of

T. Tsuji, T. Sekiguchi, T. Iwasaki, M. Hatano

Extending the spin-dephasing time (T2*) of perfectly aligned nitrogen-vacancy (NV) centers in large-volume chemical vapor deposition (CVD) diamonds leads to enhanced DC magnetic sensitivity. However, T2* of the NV centers is significantly reduced by the stress distribution in the diamond film as its thickness increases. To overcome this issue, we developed a

Jiaqi Zou, Songlin Sun, Christos Masouros, Yuanhao Cui

In this paper, we investigate the design of energy-efficient beamforming for an ISAC system, where the transmitted waveform is optimized for joint multi-user communication and target estimation simultaneously. We aim to maximize the system energy efficiency (EE), taking into account the constraints of a maximum transmit power budget, a minimum required signa

Peihao Wang, Shenghao Yang, Shu Li, Zhangyang Wang

Set representation has become ubiquitous in deep learning for modeling the inductive bias of neural networks that are insensitive to the input order. DeepSets is the most widely used neural network architecture for set representation. It involves embedding each set element into a latent space with dimension $L$, followed by a sum pooling to obtain a whole-se

Antonello Tamburrino, Carlo Forestiere, Giovanni Miano, Guglielmo Rubinacci

Resonant modes determine the response of electromagnetic devices, including dielectric and plasmonic resonators. Relying on the degrees of freedom that metamaterials provide, this contribution shows how to design, at will, the resonant modes of a dielectric object placed in an unbounded space. Specifically, the proposed method returns in analytical form the

Rachid El Aitouni, Miloud Mekkaoui, Ahmed Jellal, Michael Schreiber

We study the transport properties of Dirac fermions through gapped graphene through a magnetic barrier irradiated by a laser field oscillating in time. We use Floquet theory and the solution of Weber's differential equation to determine the energy spectrum corresponding to the three regions composing the system. The boundary conditions and the transfer matri

Liqi Xue, Tianyi Xu, Yongbao Song, Yan Liu

The display devices like HDR10 televisions are increasingly prevalent in our daily life for visualizing high dynamic range (HDR) images. But the majority of media images on the internet remain in 8-bit standard dynamic range (SDR) format. Therefore, converting SDR images to HDR ones by inverse tone mapping (ITM) is crucial to unlock the full potential of abu

Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin

A major challenge in reinforcement learning is to develop practical, sample-efficient algorithms for exploration in high-dimensional domains where generalization and function approximation is required. Low-Rank Markov Decision Processes -- where transition probabilities admit a low-rank factorization based on an unknown feature embedding -- offer a simple, y

Saifullah Mahbub, Md. Easin Arafat, Chowdhury Rafeed Rahman, Zannatul Ferdows

Code review is considered a key process in the software industry for minimizing bugs and improving code quality. Inspection of review process effectiveness and continuous improvement can boost development productivity. Such inspection is a time-consuming and human-bias-prone task. We propose a semi-supervised learning based system ReviewRanker which is aimed

V. Ananiev, A. L. Read

Approximating significance scans of searches for new particles in high-energy physics experiments as Gaussian fields is a well-established way to estimate the trials factors required to quantify global significances. We propose a novel, highly efficient method to estimate the covariance matrix of such a Gaussian field. The method is based on the linear appro

Luca Ciccone

Sessions are a fundamental notion in message-passing systems. A session is an abstract notion of communication between parties where each one owns an endpoint. Session types are types that are assigned to the endpoints and that are used to statically and dynamically enforce some desired properties of the communications, such as the absence of deadlocks. Prop

Arnav Goel, Medha Hira, Avinash Anand, Siddhesh Bangar

The previous work on controllable text generation is organized using a new schema we provide in this study. Seven components make up the schema, and each one is crucial to the creation process. To accomplish controlled generation for scientific literature, we describe the various modulation strategies utilised to modulate each of the seven components. We als

Saurabh Amin, Patrick Jaillet, Haripriya Pulyassary, Manxi Wu

We study a market mechanism that sets edge prices to incentivize strategic agents to efficiently share limited network capacity. In this market, agents form coalitions, with each coalition sharing a unit capacity of a selected route and making payments to cover edge prices. Our focus is on the existence and computation of market equilibrium, where challenges

Andrew Kean Gao

Ribozymes, RNA molecules with distinct 3D structures and catalytic activity, have widespread applications in synthetic biology and therapeutics. However, relatively little research has focused on leveraging deep learning to enhance our understanding of ribozymes. This study implements Word2Vec, an unsupervised learning technique for natural language processi

Christopher Heil, Pu-Ting Yu

A subset $M$ of a separable Hilbert space $H$ is $\ell^1$-bounded if there exists a Riesz basis $\mathcal{F} = \{e_n\}_{n \in \mathbb{N}}$ for $H$ such that $\sup_{x \in M} \sum_{n \in \mathbb{N}} |\langle x, e_n\rangle| < \infty.$ A similar definition for $\ell^1$-frame-bounded sets is made by replacing Riesz bases with frames. This paper derives properties

Alessandro Scirè, Valerio Annovazzi-Lodi

This work concerns a many-body deterministic model that displays life-like properties as emergence, complexity, self-organization, spontaneous compartmentalization, and self-regulation. The model portraits the dynamics of an ensemble of locally coupled polar phase oscillators, moving in a two-dimensional space, that in certain conditions exhibit emergent sup

Tong Li, Hansen Feng, Lizhi Wang, Zhiwei Xiong

Image denoising is a fundamental problem in computational photography, where achieving high perception with low distortion is highly demanding. Current methods either struggle with perceptual quality or suffer from significant distortion. Recently, the emerging diffusion model has achieved state-of-the-art performance in various tasks and demonstrates great

Paulo Lima-Filho

This paper utilizes the properties of transforms of currents under equidimensional cycles, as introduced in \cite{MR4498559}, to establish the multiplicative nature of the resulting regulator map, in the derived category. The construction relies on a synthetic presentation of the fundamental triples of currents from \cite{MR4498559}, which exhibits group-lik

Mohammad Taghi Dabiri, Mazen Hasna

As an alternative solution for quick disaster recovery of backhaul/fronthaul links, in this paper, a dynamic unmanned aerial vehicles (UAV)-assisted heterogeneous (HetNet) network equipped with directional terahertz (THz) antennas is studied to solve the problem of transferring traffic of distributed small cells. To this end, we first characterize a detailed

Ganglai Wang, Peng Zhang, Junwen Xiong, Feihan Yang

DeepFake based digital facial forgery is threatening public media security, especially when lip manipulation has been used in talking face generation, and the difficulty of fake video detection is further improved. By only changing lip shape to match the given speech, the facial features of identity are hard to be discriminated in such fake talking face vide

João Paulo Dias, Hermano Frid

In this paper we introduce a model of relativistic short wave-long wave interaction where the short waves are described by the massless $1+3$-dimensional Thirring model of nonlinear Dirac equation and the long waves are described by the $1+3$-dimensional relativistic Euler equations. The interaction coupling terms are modeled by a potential proportional to t

Wenjin Li, Kai Li

Autonomous driving technology has five levels, from L0 to L5. Currently, only the L2 level (partial automation) can be achieved, and there is a long way to go before reaching the final level of L5 (full automation). The key to crossing these levels lies in training the autonomous driving model. However, relying solely on real-world road data to train the mod

Neeraj Varshney, Wenlin Yao, Hongming Zhang, Jianshu Chen

Recently developed large language models have achieved remarkable success in generating fluent and coherent text. However, these models often tend to 'hallucinate' which critically hampers their reliability. In this work, we address this crucial problem and propose an approach that actively detects and mitigates hallucinations during the generation process.

Stefan Ivanov, Nikola Stanchev

Curvature properties of a metric connection with totally skew-symmetric torsion are investigated. It is shown that if either the 3-form $T$ is harmonic, $dT=\delta T=0$ or the curvature of the torsion connection $R\in S^2\Lambda^2$ then the scalar curvature of a $\nabla$-Einstein manifold is determined by the norm of the torsion up to a constant. It is prove

Somsawat Rattanasoon, Eugene Semenko, David Mkrtichian, Saran Poshyachinda

The National Astronomical Research Institute of Thailand (NARIT) has a manifold network of small telescopes installed worldwide. These telescopes serve educational and research purposes and are equipped mainly with CCD detectors for direct imaging and photometry. To extend the possible field of applications, several telescopes were fitted with commercially a

Alexander Botros, Barry Gilhuly, Nils Wilde, Armin Sadeghi

We study the problem of deploying a fleet of mobile robots to service tasks that arrive stochastically over time and at random locations in an environment. This is known as the Dynamic Vehicle Routing Problem (DVRP) and requires robots to allocate incoming tasks among themselves and find an optimal sequence for each robot. State-of-the-art approaches only co

Ren Jie Tee, Mengmi Zhang

Humans engage in learning and reviewing processes with curricula when acquiring new skills or knowledge. This human learning behavior has inspired the integration of curricula with replay methods in continual learning agents. The goal is to emulate the human learning process, thereby improving knowledge retention and facilitating learning transfer. Existing

Yanshi Sun, Wei Cao, Momiao Zhou, Zhiguo Ding

The aim of this paper is to reveal the importance of hybrid successive interference cancellation (SIC) and power adaptation (PA) for improving transmission robustness of uplink non-orthogonal multiple access (NOMA). Particularly, a cognitive radio inspired uplink NOMA communication scenario is considered, where one primary user is allocated one dedicated res

Eugene Semenko, Manfred Cuntz

BINA-3 has been the third workshop of this series involving scientists from India and Belgium aimed at fostering future joint research in the view of cutting-edge observatories and advances in theory. BINA-3 was held at the Graphic Era Hill University, 22-24 March 2023 at Bhimtal (near Nainital), Uttarakhand, India. A major event was the inauguration of the

Yuqian Chen, Leo R. Zekelman, Chaoyi Zhang, Tengfei Xue

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize pointwise tissue microstructure and positional information from all

L Bhargava Kumar, Ramavath Prasad Naik, Datta Choudhari, Prabu Krishnan

This paper contemplates a relay-assisted visible light communication (VLC) system, where the light source (Table lamp) acts as a relay node and cooperates with the main light source. Following the IEEE 802.15.7r1 VLC reference channel model, we assume that there are two different light sources present in an office room. The first one is the source terminal p

Sahil Gangurde

In city, information about urban objects such as water supply, railway lines, power lines, buildings, roads, etc., is necessary for city planning. In particular, information about the spread of these objects, locations and capacity is needed for the policymakers to make impactful decisions. This thesis aims to segment the building and roads from the aerial i

M. Adamoudis, K. A. Draziotis, D. Poulakis

In this paper, we present a deterministic attack on (EC)DSA signature scheme, providing that several signatures are known such that the corresponding ephemeral keys share a certain amount of bits without knowing their value. By eliminating the shared blocks of bits between the ephemeral keys, we get a lattice of dimension equal to the number of signatures ha

Vincenzo Marra, Matías Menni

General theory determines the notion of separable MV-algebra (equivalently, of separable unital lattice-ordered Abelian group). We establish the following structure theorem: An MV-algebra is separable if, and only if, it is a finite product of algebras of rational numbers, i.e., of subalgebras of the MV-algebra $[0,1]\cap\mathbb{Q}$. Beyond its intrinsic alg

Jagannath Bhanja

For a set $A$ of $k$ elements from an additive abelian group $G$ and a positive integer $r \leq k$, we consider the set of elements of $G$ that can be written as a sum of $h$ elements of $A$ with at least $r$ distinct elements. We denote this set by $h^{(\geq r)}A$. The set $h^{(\geq r)}A$ generalizes the classical sumsets $hA$ and $h\hat{}A$ for $r=1$ and $

Timo Schorlepp, Pavel Sasorov, Baruch Meerson

Using the optimal fluctuation method, we evaluate the short-time probability distribution $P (\bar{H}, L, t=T)$ of the spatially averaged height $\bar{H} = (1/L) \int_0^L h(x, t=T) \, dx$ of a one-dimensional interface $h(x, t)$ governed by the Kardar-Parisi-Zhang equation $$ \partial_th=\nu \partial_x^2h+\frac{\lambda}{2} \left(\partial_xh\right)^2+\sqrt{D}

Jeffrey M. La Fortune

Galaxy and galaxy clusters exhibit tight robust physical scaling relations between baryons and system dynamics. One such phenomenon is mass discrepancy with two leading solution spaces occupied by LCDM and MOND. Here, we propose an alternative solution to this puzzling problem exclusively based on application of the scalar virial theorem. For these dynamical

Maximiliano Cristiá, Catherine Dubois

Many representations for sets are available in programming languages libraries. The paper focuses on sparse sets used, e.g., in some constraint solvers for representing integer variable domains which are finite sets of values, as an alternative to range sequence. We propose in this paper verified implementations of sparse sets, in three deductive formal veri

Shuang Wu, Bo Yu, Shaoshan Liu, Yuhao Zhu

With the advancement of robotics and AI technologies in the past decade, we have now entered the age of autonomous machines. In this new age of information technology, autonomous machines, such as service robots, autonomous drones, delivery robots, and autonomous vehicles, rather than humans, will provide services. In this article, through examining the tech

Fanyi Qu, Chenming Tang, Yunfang Wu

Large-scale language models (LLMs) has shown remarkable capability in various of Natural Language Processing (NLP) tasks and attracted lots of attention recently. However, some studies indicated that large language models fail to achieve promising result beyond the state-of-the-art models in English grammatical error correction (GEC) tasks. In this report, w

Sara Ayhan

The origins of proof-theoretic semantics lie in the question of what constitutes the meaning of the logical connectives and its response: the rules of inference that govern the use of the connective. However, what if we go a step further and ask about the meaning of a proof as a whole? In this paper we address this question and lay out a framework to disting

Parosh Aziz Abdulla, Mohamed Faouzi Atig Bui Phi Diep, Lukáš Holík, Petr Janků

We address the satisfiability problem for string constraints that combine relational constraints represented by transducers, word equations, and string length constraints. This problem is undecidable in general. Therefore, we propose a new decidable fragment of string constraints, called weakly chaining string constraints, for which we show that the satisfia

Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld

Despite its fundamental importance and widespread use for assessing reaction success in organic chemistry, deducing chemical structures from nuclear magnetic resonance (NMR) measurements has remained largely manual and time consuming. To keep up with the accelerated pace of automated synthesis in self driving laboratory settings, robust computational algorit

Y. K. Negi, N. Balakrishnan, S. M. Rao

In this paper, we propose a new multilevel power series solution method for solving a large surface and volume electric field integral equation based H-Matrix. The proposed solution method converges in a fixed number of iterations and is solved at each level of the H-Matrix computation.The solution method avoids the computation of a full matrix, as it can be

Logan Nye

Digital twin technology has is anticipated to transform healthcare, enabling personalized medicines and support, earlier diagnoses, simulated treatment outcomes, and optimized surgical plans. Digital twins are readily gaining traction in industries like manufacturing, supply chain logistics, and civil infrastructure. Not in patient care, however. The challen

Ahmad Sajedi, Samir Khaki, Konstantinos N. Plataniotis, Mahdi S. Hosseini

Multilabel representation learning is recognized as a challenging problem that can be associated with either label dependencies between object categories or data-related issues such as the inherent imbalance of positive/negative samples. Recent advances address these challenges from model- and data-centric viewpoints. In model-centric, the label correlation

Nischal Ashok Kumar, Nitin Gupta, Shanmukha Guttula, Hima Patel

In mapping enterprise applications, data mapping remains a fundamental part of integration development, but its time consuming. An increasing number of applications lack naming standards, and nested field structures further add complexity for the integration developers. Once the mapping is done, data transformation is the next challenge for the users since e

Morgan T. Chidester, F. X. Timmes, Ebraheem Farag

We consider the combined effects that overshooting and the $^{12}$C($\alpha$, $\gamma$)$^{16}$O reaction rate have on variable white dwarf stellar models. We find that carbon-oxygen white dwarf models continue to yield pulsation signatures of the current experimental $^{12}$C($\alpha$, $\gamma$)$^{16}$O reaction rate probability distribution function when ov

Yindi Weng

Let $G$ be a nontrivial connected and vertex-colored graph. A vertex subset $X$ is called rainbow if any two vertices in $X$ have distinct colors. The graph $G$ is called \emph{rainbow vertex-disconnected} if for any two vertices $x$ and $y$ of $G$, there exists a vertex subset $S$ such that when $x$ and $y$ are nonadjacent, $S$ is rainbow and $x$ and $y$ be

Stefanos Aretakis, Gaurav Khanna, Subir Sabharwal

We consider scalar perturbations of the Reissner--Nordstr\"{o}m family and the Kerr family. We derive a characteristic expression of the radiation field, at any given unit solid angle of future null infinity, and numerically show that its amplitude gets excited only in the extremal case. Our work, therefore, identifies an observational signature for extremal

Seung-Hyun Nam, Hyun-Young Park, Si-Hyeon Lee

We consider a discrete distribution estimation problem under a local differential privacy (LDP) constraint in the presence of shared randomness. By exploiting the shared randomness, we suggest a new method for constructing LDP schemes which achieve the exactly optimal privacy-utility trade-off (PUT) with the communication cost of less than or equal to the in

Taro Fujisawa, Chikara Nakayama

We prove that a projective semistable morphism of fs log analytic spaces yields polarized log Hodge structures in the canonical way.

Eddy Ella-Mintsa

Consider a diffusion process X=(X_t), with t in [0,1], observed at discrete times and high frequency, solution of a stochastic differential equation whose drift and diffusion coefficients are assumed to be unknown. In this article, we focus on the nonparametric esstimation of the diffusion coefficient. We propose ridge estimators of the square of the diffusi

Jia Yu, Mengjun Ding, Weiqiang Sun, Weisheng Hu

The improvement of living standards and awareness of chronic diseases have increased the importance of community sports organizations in promoting the physical activity levels of the public. However, limited understanding of human behavior in this context often leads to suboptimal resource utilization. In this study, we analyzed the participation behavior of

Markus Dahlmanns, Constantin Sander, Robin Decker, Klaus Wehrle

Containerization allows bundling applications and their dependencies into a single image. The containerization framework Docker eases the use of this concept and enables sharing images publicly, gaining high momentum. However, it can lead to users creating and sharing images that include private keys or API secrets-either by mistake or out of negligence. Thi

Nguyen Van Phu, Nguyen Quang Dieu

In this paper, we first study subextensions in the classes $\mathcal{F}_{m}(\Omega)$ and $\mathcal{E}_{m,\chi}(\Omega)$. These results are then used to study approximation in the classes $\mathcal{F}_{m}(\Omega)$ and $\mathcal{E}_{m,\chi}(\Omega)$.

Frank den Hollander, Daoyi Wang

We study the total mass of the solution to the parabolic Anderson model on a regular tree with an i.i.d. random potential whose marginal distribution is double-exponential. In earlier work we identified two terms in the asymptotic expansion for large time of the total mass under the quenched law, i.e., conditional on the realisation of the random potential.

Nguyen Van Phu, Nguyen Quang Dieu

In this paper, we first study the comparison principle for the operator $H_{\chi,m}$. This result is used to solve certain weighted complex $m-$ Hessian equations.

A. S. Ianovskaia, A. M. Bobkov, I. V. Bobkova

Superconductor$-$ferromagnetic-insulator heterostructures are paradigmatic systems for studying the mutual influence of superconductivity and magnetism via proximity effects. In particular, spin-split superconductivity is realized in such structures. Recent experiments and theories demonstrate a rich variety of transport phenomena occurring in devices based

Nikita Medvedev

This is the user manual for the hybrid code XTANT-3, simulating intense femtosecond X-ray irradiation of matter. The code combines a few models into one with feedbacks: transport Monte Carlo simulation, Boltzmann collision integrals, and tight binding molecular dynamics. Such a combination allows the simulation of nonequilibrium, nonadiabatic, and nonthermal

Yu Ji, Wen Wu, Hong Zheng, Yi Hu

In recent years, personality has been regarded as a valuable personal factor being incorporated into numerous tasks such as sentiment analysis and product recommendation. This has led to widespread attention to text-based personality recognition task, which aims to identify an individual's personality based on given text. Considering that ChatGPT has recentl

Leonardo Di Giustino, Stanley J. Brodsky, Philip G. Ratcliffe, Xing-Gang Wu

A key issue in making precise predictions in QCD is the uncertainty in setting the renormalization scale $\mu_R$ and thus determining the correct values of the QCD running coupling $\alpha_s(\mu_R^2)$ at each order in the perturbative expansion of a QCD observable. It has often been conventional to simply set the renormalization scale to the typical scale of

Kim A. Frøyshov

Using instanton homology with coefficients in $Z/2$ we construct a homomorphism $q_2$ from the homology cobordism group in dimension 3 to the integers which is not a rational linear combination of the instanton $h$--invariant and the Heegaard Floer correction term $d$. If an oriented homology $3$--sphere $Y$ bounds a smooth, compact, negative definite $4$--m

Marco Baldovin, Raffaele Marino, Angelo Vulpiani

In the framework of statistical mechanics the properties of macroscopic systems are deduced starting from the laws of their microscopic dynamics. One of the key assumptions in this procedure is the ergodic property, namely the equivalence between time averages and ensemble averages. This property can be proved only for a limited number of systems; however, a

George Tom, Minesh Mathew, Sergi Garcia, Dimosthenis Karatzas

Text and signs around roads provide crucial information for drivers, vital for safe navigation and situational awareness. Scene text recognition in motion is a challenging problem, while textual cues typically appear for a short time span, and early detection at a distance is necessary. Systems that exploit such information to assist the driver should not on

Luca Battistella, Sebastian Bozlee

We produce a flexible tool for contracting subcurves of logarithmic hyperelliptic curves, which is local around the subcurve and commutes with arbitrary base-change. As an application, we prove that hyperelliptic multiscale differentials determine a sequence of Gorenstein contractions of the underlying nodal curve, whose dualising bundle they descend to gene

A. Bhattacharyya, D. T. Adroja, A. D. Hillier, P. K. Biswas

Filled skutterudite compounds have gained attention recently as an innovative platforms for studying intriguing low-temperature superconducting properties. Regarding the symmetry of the superconducting gap, contradicting findings from several experiments have been made for LaRu$_{4}$As$_{12}$ and its isoelectronic counterpart, LaOs$_{4}$As$_{12}$. In this ve

Gerardo Adesso

This paper presents a novel approach to scientific discovery using an artificial intelligence (AI) environment known as ChatGPT, developed by OpenAI. This is the first paper entirely generated with outputs from ChatGPT. We demonstrate how ChatGPT can be instructed through a gamification environment to define and benchmark hypothetical physical theories. Thro

Khouloud Abdelli, Carsten Tropschug, Helmut Griesser, Stephan Pachnicke

Passive optical network (PON) systems are vulnerable to a variety of failures, including fiber cuts and optical network unit (ONU) transmitter/receiver failures. Any service interruption caused by a fiber cut can result in huge financial losses for service providers or operators. Identifying the faulty ONU becomes difficult in the case of nearly equidistant

Jie Qian, Jie Li, Shi-Yao Zhu, J. Q. You

Light-matter interaction is crucial to both understanding fundamental phenomena and developing versatile applications. Strong coupling, robustness, and controllability are the three most important aspects in realizing light-matter interactions. Topological and non-Hermitian photonics, have provided frameworks for robustness and extensive control freedom, res

Sandip Maiti, Debasish Banerjee, Shailesh Chandrasekharan, Marina K. Marinkovic

We propose a two-dimensional hard-core loop-gas model as a way to regularize the asymptotically free massive continuum quantum field theory that emerges at the Berezinskii-Kosterlitz-Thouless transition. Without fine-tuning, our model can reproduce the universal step-scaling function of the classical lattice XY model in the massive phase as we approach the p

Huizhen Qiu, Shanghua Zheng, Yangfan Dan

Differential operators and integral operators are linked together by the first fundamental theorem of calculus. Based on this principle, the notion of a differential Rota-Baxter algebra was proposed by Guo and Keigher from an algebraic abstraction point of view. Recently, the subject has attracted more attention since it is associated with many areas in math

Yuxuan Song, Xinyue Li, Lin Qi

The task of Camouflaged Object Detection (COD) aims to accurately segment camouflaged objects that integrated into the environment, which is more challenging than ordinary detection as the texture between the target and background is visually indistinguishable. In this paper, we proposed a novel Feature Grafting and Distractor Aware network (FDNet) to handle

Yi Zhong, Mengqiu Xu, Kongming Liang, Kaixin Chen

Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections. Existing medical image segmentation methods are almost uni-modal methods based on image. However, these image-only methods tend to produce inaccurate results unless trained with large amounts of annotated data. To overcome this

Dawen Zhang, Pamela Finckenberg-Broman, Thong Hoang, Shidong Pan

The Right to be Forgotten (RTBF) was first established as the result of the ruling of Google Spain SL, Google Inc. v AEPD, Mario Costeja Gonz\'alez, and was later included as the Right to Erasure under the General Data Protection Regulation (GDPR) of European Union to allow individuals the right to request personal data be deleted by organizations. Specifica

Rima Alaifari, Francesca Bartolucci, Matthias Wellershoff

Sampled Gabor phase retrieval - the problem of recovering a square-integrable signal from the magnitude of its Gabor transform sampled on a lattice - is a fundamental problem in signal processing, with important applications in areas such as imaging and audio processing. Recently, a classification of square-integrable signals which are not phase retrievable

Lisi Xia, Peter J. M. van der Slot, Maximilian Timmerkamp, Bert Bastiaens

We investigate on-chip spectral control of supercontinuum generation, taking advantage of the additional spatial degree of freedom in strongly-coupled dual-core waveguides. Using numerical integration of the multi-mode generalized nonlinear Schr\"odinger equation, we show that, with proper waveguide cross-section design, selective excitation of supermodes ca

Zheng Xu

In this paper, we prove the abundance conjecture for threefolds over a perfect field $k$ of characteristic $p > 3$ in the case of numerical dimension equals to $2$. More precisely, we prove that if $(X,B)$ be a projective lc threefold pair over $k$ such that $K_{X}+B$ is nef and $\nu(K_{X}+B)=2$, then $K_{X}+B$ is semiample.

Chee Wei Tan, Shangxin Guo, Man Fai Wong, Ching Nam Hang

This paper presents an AI-assisted programming tool called Copilot for Xcode for program composition and design to support human software developers. By seamlessly integrating cloud-based Large Language Models (LLM) with Apple's local development environment, Xcode, this tool enhances productivity and unleashes creativity for software development in Apple so

Shixuan Liu, Changjun Fan, Kewei Cheng, Yunfei Wang

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs

Olga Krestinskaya, Li Zhang, Khaled Nabil Salama

The amount of data processed in the cloud, the development of Internet-of-Things (IoT) applications, and growing data privacy concerns force the transition from cloud-based to edge-based processing. Limited energy and computational resources on edge push the transition from traditional von Neumann architectures to In-memory Computing (IMC), especially for ma

Iqbal Nouyed, Na Zhang

Unconstrained face recognition is an active research area among computer vision and biometric researchers for many years now. Still the problem of face recognition in low quality photos has not been well-studied so far. In this paper, we explore the face recognition performance on low quality photos, and we try to improve the accuracy in dealing with low qua

Colin Chan

Liquidity providers are currently incentivised to provide liquidity through the LP Incentives Programme on dYdX. Based on the various parameters - makerVolume, depths and spreads, they are rewarded accordingly based on their activities. Given the maturity of the BTC and ETH markets, alongside other altcoins which enjoy a consistent amount of liquidity, this

Joran Deschamps, Damian Dalle Nogare, Florian Jug

In the past decade, enormous progress has been made in advancing the state-of-the-art in bioimage analysis - a young computational field that works in close collaboration with the life sciences on the quantitative analysis of scientific image data. In many cases, tremendous effort has been spent to package these new advances into usable software tools and, a

Musen Li, Michael J. Ford, Rika Kobayashi, Roger D. Amos

Optimizing density-functional theory (DFT) and G0W0 calculations present coupled problems as orbitals from DFT are needed as G0W0 starting points. Applied to 341 two-dimensional (2D) materials, we demonstrate that CAM-B3LYP provides minimal changes in bandgap (e.g., mean absolute deviation of 0.23 eV) when used to start G0W0 calculations, compared to traditi

Dongyue Sun, Shiyao Jiang, Lin Qi

Existing edge-aware camouflaged object detection (COD) methods normally output the edge prediction in the early stage. However, edges are important and fundamental factors in the following segmentation task. Due to the high visual similarity between camouflaged targets and the surroundings, edge prior predicted in early stage usually introduces erroneous for

Lingling Gao, Junwen Lai, Dong Chen, Cuiying Pei

Recently, the giant intrinsic anomalous Hall effect (AHE) has been observed in the materials with kagome lattice. In this study, we systematically investigate the influence of high pressure on the AHE in the ferromagnet LiMn6Sn6 with clean Mn kagome lattice. Our in-situ high-pressure Raman spectroscopy indicates that the crystal structure of LiMn6Sn6 maintai

Vikas Natesh, Andrew Sabot, H. T. Kung, Mark Ting

We propose Rosko -- row skipping outer products -- for deriving sparse matrix multiplication (SpMM) kernels in reducing computation and memory access requirements of deep neural networks (DNNs). Rosko allows skipping of entire row computations during program execution with low sparsity-management overheads. We analytically derive sparse CPU kernels that adap

April Chen, Ryan A. Rossi, Namyong Park, Puja Trivedi

Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the l