July 2023 arXiv papers — page 129

A. Bravetti, S. Grillo, J. C. Marrero, E. Padron

In this paper, we discuss the reduction of symplectic Hamiltonian systems by scaling and standard symmetries which commute. We prove that such a reduction process produces a so-called Kirillov Hamiltonian system. Moreover, we show that if we reduce first by the scaling symmetries and then by the standard ones or in the opposite order, we obtain equivalent Ki

Marco Campetella, Giovanni Marini, Jianqiang Sky Zhou, Matteo Calandra

The compound CuTe (vulcanite) undergoes a quasi one dimensional charge density wave (CDW) at $T< T_{\mathrm{CDW}}=335$ K with a $5\times1\times2$ periodicity. The mechanism at its origin is debated. Several theoretical works claimed that semilocal functionals are unable to describe its occurrence and ascribed its formation only to strong electron-electron in

Linda Cai, S. Matthew Weinberg, Evan Wildenhain, Shirley Zhang

We consider the problem of repeatedly auctioning a single item to multiple i.i.d buyers who each use a no-regret learning algorithm to bid over time. In particular, we study the seller's optimal revenue, if they know that the buyers are no-regret learners (but only that their behavior satisfies some no-regret property -- they do not know the precise algorith

C. Marty, C. Reichl, S. Parolo, I. Grandt-Ionita

We observe nearly vanishing Hall resistances for integer filling factors in a counterflow (CF) experiment on a density balanced 2D bilayer system. Filling factor dependent equilibration lengths demonstrate enhanced 1D coupling via edge-channels. Due to the narrow barrier the edge-modes of the two 2DEGs are in close proximity allowing for 1D excitonic correla

Ilan Doron-Arad, Ariel Kulik, Hadas Shachnai

We study budgeted variants of well known maximization problems with multiple matroid constraints. Given an $\ell$-matchoid $\cm$ on a ground set $E$, a profit function $p:E \rightarrow \mathbb{R}_{\geq 0}$, a cost function $c:E \rightarrow \mathbb{R}_{\geq 0}$, and a budget $B \in \mathbb{R}_{\geq 0}$, the goal is to find in the $\ell$-matchoid a feasible se

Rao Ma, Mengjie Qian, Potsawee Manakul, Mark Gales

ASR error correction is an interesting option for post processing speech recognition system outputs. These error correction models are usually trained in a supervised fashion using the decoding results of a target ASR system. This approach can be computationally intensive and the model is tuned to a specific ASR system. Recently generative large language mod

LaNell A. Williams, Andreas Neophytou, Rees F. Garmann, Dwaipayan Chakrabarti

Self-assembly is a vital part of the life cycle of certain icosahedral RNA viruses. Furthermore, the assembly process can be harnessed to make icosahedral virus-like particles (VLPs) from coat protein and RNA in vitro. Although much previous work has explored the effects of RNA-protein interactions on the assembly products, relatively little research has exp

Kaihui Li, Long-Jing Yin, Chenglong Che, Xueying Liu

Strongly correlated states are commonly emerged in twisted bilayer graphene (TBG) with magic-angle, where the electron-electron (e-e) interaction U becomes prominent relative to the small bandwidth W of the nearly flat band. However, the stringent requirement of this magic angle makes the sample preparation and the further application facing great challenges

Sanchari Bhattacharyya

We investigate a Left-Right symmetric model respecting $SU(3)_C \otimes SU(2)_L \otimes U(1)_L \otimes SU(2)_R \otimes U(1)_R$ local gauge symmetry. We study the interactions of the heavy neutral and charged scalars of this model along with their production at the hadron collider and their subsequent decays. We analyze the collider searches of two heavy scal

Rui Chen, Qi Huang

In this work, we adopt the one-boson-exchange model to study the $Y_cK^{(*)} (Y_c=\Lambda_c, \Sigma_c)$ interactions. After considering both of the $S-D$ wave mixing effects and the coupled channel effects, we can predict several possible open-charm molecular pentaquarks, i.e., the single $\Sigma_cK^*$ molecular states with $I(J^P)=1/2(1/2^-)$, $1/2(3/2^-)$

Anubhab Das, Sanja Šćepanović, Luca Maria Aiello, Remington Mallett

Dreaming is a fundamental but not fully understood part of human experience that can shed light on our thought patterns. Traditional dream analysis practices, while popular and aided by over 130 unique scales and rating systems, have limitations. Mostly based on retrospective surveys or lab studies, they struggle to be applied on a large scale or to show the

Derick Nganyu Tanyu, Isabel Michel, Andreas Rademacher, Jörg Kuhnert

Classical physical modelling with associated numerical simulation (model-based), and prognostic methods based on the analysis of large amounts of data (data-driven) are the two most common methods used for the mapping of complex physical processes. In recent years, the efficient combination of these approaches has become increasingly important. Continuum mec

Bowen Yi, Ian R. Manchester

The inertial measurement unit (IMU) preintegration approach nowadays is widely used in various robotic applications. In this article, we revisit the preintegration theory and propose a novel interpretation to understand it from a nonlinear observer perspective, specifically the parameter estimation-based observer (PEBO). We demonstrate that the preintegratio

M. Lanza de Cristoforis

We prove the validity of regularizing properties of the boundary integral operator corresponding to the double layer potential associated to the fundamental solution of a {\em nonhomogeneous} second order elliptic differential operator with constant coefficients in H\"{o}lder spaces by exploiting an estimate on the maximal function of the tangential gradient

Olexandr Vyshnevetskiy, Alexander Bendikov

We consider random walk on a finite group $G$ as follows. We can consider $G$ as a group of substitutions. Randomly (i.e. with probability $U(g)=|G|^{-1}$ ) we choose a substitution $g \in G$ and execute it twice in a row, i.e. execute a substitution $g^2 \in G$ . Then the set of squares of elements of the group $G$ be a carrier of a probability $P(g)=\frac{

Manfred Einsiedler, Elon Lindenstrauss

We present a new argument in the study of positive entropy measures for higher rank diagonalisable actions. The argument relies on a quantitative form of recurrence along unipotent directions (that are not known to preserve the measure). Using this argument we prove a classification of positive entropy measures for any higher rank action on an irreducible ar

Matthieu George, Frédéric Nallet, Pascale Fabre

Plastic pollution in the aquatic environment has been assessed for many years by ocean waste collection expeditions around the globe or by river sampling. While the total amount of plastic produced worldwide is well documented, the amount of plastic found in the ocean, the distribution of particles on its surface and its evolution over time are still the sub

F. Dai, V. Temlyakov

Recently, it has been discovered that results on universal sampling discretization of the square norm are useful in sparse sampling recovery with error being measured in the square norm. It was established that a simple greedy type algorithm -- Weak Orthogonal Matching Pursuit -- based on good points for universal discretization provides effective recovery i

Christian Corda

The Schr\"odinger equation of the Schwarzschild black hole (BH) shows that a BH is composed of a particle, the "electron", interacting with a central field, the "nucleus". Via de Broglie's hypothesis, one interprets the "electron" in terms of BH horizon's modes. Quantum gravity effects modify the BH semi-classical structure at the Schwarzschild scale rather

Jordan Conrad-Burton, Alon Shabi, Sivan Ginzburg

Black widows and redbacks are binary millisecond pulsars with close low-mass companions that are irradiated and gradually ablated by the pulsar's high-energy luminosity $L_{\rm irr}$. These binaries evolve primarily through magnetic braking, which extracts orbital angular momentum and pushes the companion to overflow its Roche lobe. Here, we use the stellar

Xavier Bou, Aitor Artola, Thibaud Ehret, Gabriele Facciolo

Detecting relevant changes is a fundamental problem of video surveillance. Because of the high variability of data and the difficulty of properly annotating changes, unsupervised methods dominate the field. Arguably one of the most critical issues to make them practical is to reduce their false alarm rate. In this work, we develop a method-agnostic weakly su

G. Falcioni, F. Herzog, S. Moch, A. Vogt

We have computed the even-$N$ moments $N \leq 20$ of the gluon-to-quark splitting function $P_{\rm qg}$ at the fourth order of perturbative QCD via the renormalization of off-shell operator matrix elements. Our results, derived analytically for a general gauge group, agree with all results obtained for this function so far, in particular with the lowest five

Dan Ruta, Gemma Canet Tarrés, Andrew Gilbert, Eli Shechtman

Neural Style Transfer (NST) is the field of study applying neural techniques to modify the artistic appearance of a content image to match the style of a reference style image. Traditionally, NST methods have focused on texture-based image edits, affecting mostly low level information and keeping most image structures the same. However, style-based deformati

Florin Avram, Rim Adenane, Dan Goreac, Andrei Halanay

A very large class of ODE epidemic models (2.2) discussed in this paper enjoys the property of admitting also an integral renewal formulation, with respect to an "age of infection kernel" a(t) which has a matrix exponential form (3.2). We observe first that a very short proof of this fact is available when there is only one susceptible compartment, and when

Deepam Patel, K. V. Shuddhodan

In this article, we characterize the image of the Brylinski-Radon transform in characteristic $p>0$ via Beilinson's theory of singular supports. We also provide an alternate proof of Brylinski's results over $\mathbb{C}$, which also works for sheaves with finite coefficients. Along the way, we also obtain a microlocal criterion for the descent of perverse sh

Matteo Dora, Stéphane Jaffard, David Holcman

Electroencephalogram (EEG) signals reflect brain activity across different brain states, characterized by distinct frequency distributions. Through multifractal analysis tools, we investigate the scaling behaviour of different classes of EEG signals and artifacts. We show that brain states associated to sleep and general anaesthesia are not in general charac

Ana Carpio, Gema Duro

Two phase solid-fluid mixture models are ubiquitous in biological applications. For instance, models for growth of tissues and biofilms combine time dependent and quasi-stationary boundary value problems set in domains whose boundary moves in response to variations in the mechano-chemical variables. For a model of biofilm spread, we show how to obtain better

M. Lanza de Cristoforis

In this paper we consider an elliptic operator with constant coefficients and we estimate the maximal function of the tangential gradient of the kernel of the double layer potential with respect to its first variable. As a consequence, we deduce the validity of a continuity property of the double layer potential in H\"{o}lder spaces on the boundary that exte

Shuo Yang, Xinran Zheng, Zhengzhuo Xu, Xingjun Wang

Network Intrusion Detection (NID) works as a kernel technology for the security network environment, obtaining extensive research and application. Despite enormous efforts by researchers, NID still faces challenges in deploying on resource-constrained devices. To improve detection accuracy while reducing computational costs and model storage simultaneously,

E. F. Ocran, M. Vaccari, J. M. Stil, A. R. Taylor

We measure the specific star formation rates of \textit{K}-band selected galaxies from the ELAIS-N1 by stacking GMRT data at 610 MHz. We identify a sample of SFGs, spanning $\rm{0.1\leq\,\textit{z}\,\leq\,1.5}$ and $\rm{10^{8.5}<\,{\textit{M}_{\star}}/{\textit{M}_{\odot}}<10^{12.4}}$, using a combination of multi-wavelength diagnostics obtained from the deep

You-Jin Jong, Yong-Jin Kim, Ok-Chol Ri

The first automated essay scoring system was developed 50 years ago. Automated essay scoring systems are developing into systems with richer functions than the previous simple scoring systems. Its purpose is not only to score essays but also as a learning tool to improve the writing skill of users. Feedback is the most important aspect of making an automated

Ra'Fat Al-Msie'deen

Requirements traceability is an important activity to reach an effective requirements management method in the requirements engineering. Requirement-to-Code Traceability Links (RtC-TLs) shape the relations between requirement and source code artifacts. RtC-TLs can assist engineers to know which parts of software code implement a specific requirement. In addi

Raihaneh Moti, Ali Shojai

We study the thermodynamic properties of a freely falling ensemble of gyroscopes after the passage of a weak gravitational wave. Due to the precession memory effect, the thermodynamic quantities will experience a change because of the space-time perturbation. We discuss that this GravoThermo memory effect potentially can be used for the detection of the grav

Djamel Dou

We review the different proposals and attempts to identify the ``horizon molecules" that would give a kinematical estimation for the black hole entropy in causal set theory. The proposals are presented according to their chronological appearance in scientific literature. The review is neither very technical nor merely descriptive; it is aimed to provide the

Ayush Singh, Yash Bhambhu, Himanshu Buckchash, Deepak K. Gupta

Global contexts in images are quite valuable in image-to-image translation problems. Conventional attention-based and graph-based models capture the global context to a large extent, however, these are computationally expensive. Moreover, the existing approaches are limited to only learning the pairwise semantic relation between any two points on the image.

Anurag Dhote, Mohammed Javed, David S Doermann

Figures visually represent an essential piece of information and provide an effective means to communicate scientific facts. Recently there have been many efforts toward extracting data directly from figures, specifically from tables, diagrams, and plots, using different Artificial Intelligence and Machine Learning techniques. This is because removing inform

Luca Cuomo, Claudio Scordino, Alessandro Ottaviano, Nils Wistoff

The complexity of automotive systems is increasing quickly due to the integration of novel functionalities such as assisted or autonomous driving. However, increasing complexity poses considerable challenges to the automotive supply chain since the continuous addition of new hardware and network cabling is not considered tenable. The availability of modern h

Anurag Dhote, Mohammed Javed, David S Doermann

Charts represent an essential source of visual information in documents and facilitate a deep understanding and interpretation of information typically conveyed numerically. In the scientific literature, there are many charts, each with its stylistic differences. Recently the document understanding community has begun to address the problem of automatic char

Tshimankinda Jerome Ngoy, Mike Nkongolo

This project introduces a groundbreaking approach to address the challenge of periodic signal compression. By proposing a novel adaptive coding method, coupled with hardware-assisted data compression, we have developed a new architecture model tailored for efficient data compression. The selected compression scheme has demonstrated remarkable results, showca

Boris Houska

This paper is about output-feedback control problems for general linear systems in the presence of given state-, control-, disturbance-, and measurement error constraints. Because the traditional separation theorem in stochastic control is inapplicable to such constrained systems, a novel information-theoretic framework is proposed. It leads to an intrinsic

Maks Pecnik Bambic, Nuno A. M. Araújo, Benjamin J. Walker, Duncan R. Hewitt

Kirigami-inspired designs can enable self-folding three-dimensional materials from flat, two-dimensional sheets. Hierarchical designs of connected levels increase the diversity of possible target structures, yet they can lead to longer folding times in the presence of fluctuations. Here, we study the effect of rotational coupling between levels on the self-f

Ok-Chol Ri, Yong-Jin Kim, You-Jin Jong

Traditional load balancers used in server clusters have problems such as lack of flexibility, high cost, etc. To overcome these problems, research has been conducted to apply a load balancer using software-defined network (SDN) to the server cluster. Under this trend, in this paper, we proposed a hybrid load balancing method with failover capability in the s

Qian Li, Chen Ma, Yu Zhang, Zhi-Wen Lin

This paper studies the black hole shadow, absorption cross section, and Hawking radiation of a massless scalar field in the background of a static spherically symmetric black hole spacetime that is surrounded by a cloud of strings in Rastall gravity. Specifically, the effects of the parameters $a$ and $\beta$ on the photon sphere and shadow radii are investi

Mateusz Kwaśnicki

We prove a non-extinction result for Fleming-Viot-type systems of two particles with dynamics described by an arbitrary symmetric Hunt process under the assumption that the reference measure is finite. Additionally, we describe an invariant measure for the system, we discuss its ergodicity, and we prove that the reference measure is a stationary measure for

P. Sivakumar, A. Ramamoorthy, R. Muthucumaraswamy, R. M. Madhusudhan

The topic of flow across an infinitely wide parabolic vertical sheet with accelerating reactions of chemicals and heating is addressed in this article. The Laplace transform method is used to rectify the dimensioned equations that govern of movement into a set of non-dimensional regulating mathematical equations of motion. It is found that thermal energy as

Qian Li, Chen Ma, Yu Zhang, Zhi-Wen Lin

The gray-body factor and the absorption cross section of the 4D ESTGB gravity with a mode of nonlinear electrodynamics for the massless Dirac field are studied in this paper. The magnetic charge value varies between $-2^{(\frac{5}{3})}/3$ and $0$ as well as the ADM mass is set to $1$, which corresponds to a non-extreme black hole. The gray-body factor is obt

Kostyantyn Anatolievich Malyshenko, Majid Mohammad Shafiee, Vadim Anatolievich Malyshenko, Marina Viktorovna Anashkina

Developing a system of indicators that reflects the degree to which the securities market fulfils its key functions, is essential to assess the level of its development. In the conditions of asymmetric information it can also provide effective policies for securities market development. This paper is aimed to develop a set of indicators to assess the securit

S. M. Masrur Ahmed, Eshaan Tanzim Sabur

Emotion has a significant influence on how one thinks and interacts with others. It serves as a link between how a person feels and the actions one takes, or it could be said that it influences one's life decisions on occasion. Since the patterns of emotions and their reflections vary from person to person, their inquiry must be based on approaches that are

Ran Duan, Jiayi Mao, Xinkai Shu, Longhui Yin

In undirected graphs with real non-negative weights, we give a new randomized algorithm for the single-source shortest path (SSSP) problem with running time $O(m\sqrt{\log n \cdot \log\log n})$ in the comparison-addition model. This is the first algorithm to break the $O(m+n\log n)$ time bound for real-weighted sparse graphs by Dijkstra's algorithm with Fibo

Prakhar Ganesh, Hongyan Chang, Martin Strobel, Reza Shokri

Statistical measures for group fairness in machine learning reflect the gap in performance of algorithms across different groups. These measures, however, exhibit a high variance between different training instances, which makes them unreliable for empirical evaluation of fairness. What causes this high variance? We investigate the impact on group fairness o

Quoc Hung Cao, Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen, Xuan Phong Nguyen

In recent years, artificial intelligence is increasingly being applied widely in many different fields and has a profound and direct impact on human life. Following this is the need to understand the principles of the model making predictions. Since most of the current high-precision models are black boxes, neither the AI scientist nor the end-user deeply un

Yilan Zhang, Jianqi Chen, Ke Wang, Fengying Xie

Skin image datasets often suffer from imbalanced data distribution, exacerbating the difficulty of computer-aided skin disease diagnosis. Some recent works exploit supervised contrastive learning (SCL) for this long-tailed challenge. Despite achieving significant performance, these SCL-based methods focus more on head classes, yet ignoring the utilization of

Qiyao Liu, Taeheon Kim, Kyusup Lee, Dongsheng Yang

We examine the high-frequency optical mode of {\alpha}-Fe2O3 and report that Dzyaloshinskii-Moriya (DM) interaction generates a new type of torque on the magnetic resonance. Using a continuous-wave terahertz interferometer, we measure the optical mode spectra, where the asymmetric absorption with a large amplitude and broad linewidth is observed near the mag

Priti Kaushik, Randhir Singh Baghel

In this work, long-term spatiotemporal changes in rainfall are analysed and evaluated using whole-year data from Rajasthan, India, at the meteorological divisional level. In order to determine how the rainfall pattern has changed over the past 10 years, I examined the data from each of the thirteen tehsils in the Jaipur district. For the years 2012 through 2

Danny Fainozzi, Laura Foglia, Riccardo Mincigrucci, Nupur N. Khatu

We crossed two femtosecond extreme ultraviolet (EUV) pulses in a beta - Ga2O3 (001) single crystal to create transient gratings (TG) of light intensity with sub-100 nm spatial periodicity. The EUV TG excitation launches phonon modes, whose dynamics were revealed via the backward diffraction of a third, time-delayed, EUV probe pulse. In addition to the modes

Yuanheng Zhang, Nan Jiang, Zhaoheng Xie, Junying Cao

Accurately annotated ultrasonic images are vital components of a high-quality medical report. Hospitals often have strict guidelines on the types of annotations that should appear on imaging results. However, manually inspecting these images can be a cumbersome task. While a neural network could potentially automate the process, training such a model typical

Gerard Calvo Bartra, Filip Lemic, Guillem Pascual, Aina Pérez Rodas

Contemporary research advances in nanotechnology and material science are rooted in the emergence of nanodevices as a versatile tool that harmonizes sensing, computing, wireless communication, data storage, and energy harvesting. These devices offer novel pathways for disease diagnostics, treatment, and monitoring within the bloodstreams. Ensuring precise lo

Amrit Diggavi Seshadri, Alessandra Russo

In this work, following the intuition that adverbs describing scene-sequences are best identified by reasoning over high-level concepts of object-behavior, we propose the design of a new framework that reasons over object-behaviours extracted from raw-video-clips to recognize the clip's corresponding adverb-types. Importantly, while previous works for genera

Yiyang Zhao, Tian Guo

This work presents a novel approach to neural architecture search (NAS) that aims to reduce energy costs and increase carbon efficiency during the model design process. The proposed framework, called carbon-efficient NAS (CE-NAS), consists of NAS evaluation algorithms with different energy requirements, a multi-objective optimizer, and a heuristic GPU alloca

Yue Shao, Yidong Xu, Yougang Wang, Wenxiu Yang

The absorption features in spectra of high-redshift background radio sources, caused by hyperfine structure lines of hydrogen atoms in the intervening structures, are known collectively as the 21-cm forest. They provide a unique probe of small-scale structures during the epoch of reionization, and can be used to constrain the properties of the dark matter (D

Zhiyu Zhu, Junhui Hou, Dapeng Oliver Wu

This paper addresses the problem of cross-modal object tracking from RGB videos and event data. Rather than constructing a complex cross-modal fusion network, we explore the great potential of a pre-trained vision Transformer (ViT). Particularly, we delicately investigate plug-and-play training augmentations that encourage the ViT to bridge the vast distribu

Ao Shen, Yijie Zhu, Richard Jiang

Marine debris is an important issue for environmental protection, but current methods for locating marine debris are yet limited. In order to achieve higher efficiency and wider applicability in the localization of Marine debris, this study tries to combine the instance segmentation of YOLOv7 with different attention mechanisms and explores the best model. B

Archit Vidyarthi

Among the vast variety of proposals put forward by the community to resolve tree-level unitarity violations in Higgs inflation models, there exists the concept of self-healing. It heals the theory from supposed tree-level violations for elastic scattering processes by summing over successive vacuum polarization loop corrections. In this work, we examine this

Hao Zheng, Regina Lee, Yuqian Lu

Understanding comprehensive assembly knowledge from videos is critical for futuristic ultra-intelligent industry. To enable technological breakthrough, we present HA-ViD - the first human assembly video dataset that features representative industrial assembly scenarios, natural procedural knowledge acquisition process, and consistent human-robot shared annot

Wenchuan Tian, Changliang Wang

Gromov and Sormani conjectured that a sequence of three dimensional Riemannian manifolds with nonnegative scalar curvature and some additional uniform geometric bounds should have a subsequence which converges in some sense to a limit space with generalized notion of nonnegative scalar curvature. In this paper, we study the pre-compactness of a sequence of t

Chengwu Liu

Firstly, we get the completely bouncing criteria Cr for droplet on moving substrate. The bouncing without splashing condition is $\mathrm{Cr}>1$. Then, we mainly research the effect of wind field for droplet, and get the completely bouncing criteria $\mathrm{Cr}_{\mathrm{wind}}$ for droplet with wind. Lastly, we get the contact angle of droplet on the moving

N. A. Usov

Standard power series are used to construct and analyze angular and radial spheroidal functions, which are necessary for solving boundary value problems for Helmholtz equation in a spheroid. With an advanced approach the low-lying energy levels of a deep spheroidal quantum well are calculated as a function of the spheroid semiaxes ratio a/b. The well-known r

Jonathan E. Avila, Nigel G. Ward

Speech-to-speech translation systems today do not adequately support use for dialog purposes. In particular, nuances of speaker intent and stance can be lost due to improper prosody transfer. We present an exploration of what needs to be done to overcome this. First, we developed a data collection protocol in which bilingual speakers re-enact utterances from

Shulin Tian, Yufei Wang, Renjie Wan, Wenhan Yang

Low-light image enhancement task is essential yet challenging as it is ill-posed intrinsically. Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss, which limits the capacity of recovering the brightness, contrast, and texture details due to the small number of income photons. In this work, we propose a n

Rajat Arora

Physics informed neural networks (PINNs) have emerged as a powerful tool to provide robust and accurate approximations of solutions to partial differential equations (PDEs). However, PINNs face serious difficulties and challenges when trying to approximate PDEs with dominant hyperbolic character. This research focuses on the development of a physics informed

Linghua Xie, Nicola R. Napolitano, Xiaotong Guo, Crescenzo Tortora

The Kilo Degree Survey (KiDS) is currently the only sky survey providing optical ($ugri$) plus near-infrared (NIR, $ZYHJK_S$) seeing matched photometry over an area larger than 1000 $\rm deg^2$. This is obtained by incorporating the NIR data from the VISTA Kilo Degree Infrared Galaxy (VIKING) survey, covering the same KiDS footprint. As such, the KiDS multi-

Haruka Tomita

In categorical realizability, it is common to construct categories of assemblies and categories of modest sets from applicative structures. These categories have structures corresponding to the structures of applicative structures. In the literature, classes of applicative structures inducing categorical structures such as Cartesian closed categories and sym

Qingran Wang, Jia Yu, Mengjun Ding, Weiqiang Sun

Backbone members are recognized as essential parts of an organization, yet their role and mechanisms of functioning in networks are not fully understood. In this paper, we propose a new framework called Twotier to analyze the evolution of community sports organizations (CSOs) and the role of backbone members. Tier-one establishes a dynamic user interaction n

Mingxu Sun, Bingqiu Chen, Helong Guo, He Zhao

Modern large-scale photometric surveys have provided us with multi-band photometries of billions of stars. Determining the stellar atmospheric parameters, such as the effective temperature (\teff) and metallicities (\feh), absolute magnitudes ($M_{G}$), distances ($d$) and reddening values (\ebr) is fundamental to study the stellar populations, structure, ki

B. C. Yu, J. Y. Yang, D. J. Gawryluk, Y. Xu

We report on the geometrically frustrated two-dimensional triangular-lattice magnets $A_2$La$_2$NiW$_2$O$_{12}$ ($A$ = Sr, Ba) studied mostly by means of neutron powder diffraction (NPD) and muon-spin rotation and relaxation ($\mu$SR) techniques. The chemical pressure induced by the Ba-for-Sr substitution suppresses the ferromagnetic (FM) transition from 6.3

Aleksander Horawa, Kartik Prasanna

We study the coherent cohomology of automorphic sheaves corresponding to Siegel modular forms $f$ of low weight on ${\rm GSp}(4)$ Shimura varieties. Inspired by the work of Prasanna--Venkatesh on singular cohomology of locally symmetric spaces, we propose a conjecture that explains all the contributions of a Hecke eigensystem to coherent cohomology in terms

Zihao Jiang, Yunkai Dang, Dong Pang, Huishuai Zhang

Few-shot learning aims to train models that can be generalized to novel classes with only a few samples. Recently, a line of works are proposed to enhance few-shot learning with accessible semantic information from class names. However, these works focus on improving existing modules such as visual prototypes and feature extractors of the standard few-shot l

Kazuya Nishimura, Ami Katanaya, Shinichiro Chuma, Ryoma Bise

Detection of mitosis events plays an important role in biomedical research. Deep-learning-based mitosis detection methods have achieved outstanding performance with a certain amount of labeled data. However, these methods require annotations for each imaging condition. Collecting labeled data involves time-consuming human labor. In this paper, we propose a m

Péter L. Erdős, Ervin Győri, Tamás Róbert Mezei, Nika Salia

An independent vertex subset $S$ of the directed graph $G$ is a kernel if the set of out-neighbors of $S$ is $V(G)\setminus S$. An independent vertex subset $Q$ of $G$ is a quasi-kernel if the union of the first and second out-neighbors contains $V(G)\setminus S$ as a subset. Deciding whether a directed graph has a kernel is an NP-hard problem. In stark cont

Yi Liu, Stefano Moretti, Harri Waltari

The seesaw scale is a priori unknown. If the seesaw scale is low, one may directly look for the new particles predicted by seesaw models. If the seesaw scale is high, such an approach is unfeasible. We show that in some supersymmetric seesaw models the large Yukawa couplings of high scale seesaw models leave their fingerprints to Higgs-slepton couplings and

José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin, Luc Le Magoarou

We study model-based end-to-end learning in the context of integrated sensing and communication (ISAC) under hardware impairments. Hardware impairments are usually addressed by means of array calibration with a focus on communication performance. However, residual impairments may exist that affect sensing performance. This paper proposes a data-driven framew

Molla Basir Ahamed, Sanju Mandal

The main purpose of this article is concerned with the existence and the precise forms of the transcendental solutions of several refined versions of Fermat-type functional equations with polynomial coefficients in several complex variables by utilizing the Nevanlinna theory of meromorphic functions in several complex variables. In fact, we investigate the e

Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki

We introduce a novel grid-independent model for learning partial differential equations (PDEs) from noisy and partial observations on irregular spatiotemporal grids. We propose a space-time continuous latent neural PDE model with an efficient probabilistic framework and a novel encoder design for improved data efficiency and grid independence. The latent sta

Anurabh Charkravarty, Gnanam Ramasamy

Type 2 diabetes mellitus is a global pandemic, it is a chronic, progressive and an incompletely understood metabolic condition. The disease is characterized by higher levels of sugar in blood caused either due to insufficient production of insulin or because of insulin resistance. Major drugs used for the treatment of the condition are fraught with side effe

Yoav Kolumbus, Menahem Levy, Noam Nisan

We study Proportional Response Dynamics (PRD) in linear Fisher markets where participants act asynchronously. We model this scenario as a sequential process in which in every step, an adversary selects a subset of the players that will update their bids, subject to liveness constraints. We show that if every bidder individually uses the PRD update rule whene

Galina Weinstein

This paper explores the ER bridges theory and its relationship with quantum phenomena. An argument can be made that the ER bridges theory does not explicitly address quantum phenomena and implies that Einstein intended to differentiate between individual particles within the ER bridges theory and the systems involved in the EPR paradox. However, this paper c

Chi-Yeh Chen

This paper focuses on the problem of coflow scheduling with precedence constraints in identical parallel networks, which is a well-known $\mathcal{NP}$-hard problem. Coflow is a relatively new network abstraction used to characterize communication patterns in data centers. Both flow-level scheduling and coflow-level scheduling problems are examined, with the

Jiayu Yang, Enze Xie, Miaomiao Liu, Jose M. Alvarez

Recent vision-only perception models for autonomous driving achieved promising results by encoding multi-view image features into Bird's-Eye-View (BEV) space. A critical step and the main bottleneck of these methods is transforming image features into the BEV coordinate frame. This paper focuses on leveraging geometry information, such as depth, to model suc

Chia-Yuan Chang, Yu-Neng Chuang, Kwei-Herng Lai, Xiaotian Han

Despite the impressive prediction ability, machine learning models show discrimination towards certain demographics and suffer from unfair prediction behaviors. To alleviate the discrimination, extensive studies focus on eliminating the unequal distribution of sensitive attributes via multiple approaches. However, due to privacy concerns, sensitive attribute

Constantin Habes, Alexandra von Kameke, Mohammed Elwardi Fadeli, Holger Marschall

To facilitate the understanding and to quantitatively assess the material transport in fluids, a modern characterisation method has emerged in fluid dynamics in the last decades footed in dynamical systems theory. It allows to examine the most influential material lines which are called Lagrangian Coherent Structures (LCS) and order the material transport in

Christoph Lenzen, Moti Medina, Mehrdad Saberi, Stefan Schmid

With the increasing scale of communication networks, the likelihood of failures grows as well. Since these networks form a critical backbone of our digital society, it is important that they rely on robust routing algorithms which ensure connectivity despite such failures. While most modern communication networks feature robust routing mechanisms, these mech

Zhijian Liu, Nian Cai, Wensheng Ouyang, Chengbin Zhang

Automatic hardhat wearing detection can strengthen the safety management in construction sites, which is still challenging due to complicated video surveillance scenes. To deal with the poor generalization of previous deep learning based methods, a novel anchor-free deep learning framework called CA-CentripetalNet is proposed for hardhat wearing detection. T

Jason Alfonso, Ricardo Baptista, Anupam Bhakta, Noam Gal

Sampling conditional distributions is a fundamental task for Bayesian inference and density estimation. Generative models, such as normalizing flows and generative adversarial networks, characterize conditional distributions by learning a transport map that pushes forward a simple reference (e.g., a standard Gaussian) to a target distribution. While these ap

Zhiling Guo, Xiaodan Shi, Haoran Zhang, Dou Huang

The development of remote sensing and deep learning techniques has enabled building semantic segmentation with high accuracy and efficiency. Despite their success in different tasks, the discussions on the impact of spatial resolution on deep learning based building semantic segmentation are quite inadequate, which makes choosing a higher cost-effective data

Nati Ofir, Jean-Christophe Nebel

Multispectral imaging is an important task of image processing and computer vision, which is especially relevant to applications such as dehazing or object detection. With the development of the RGBT (RGB & Thermal) sensor, the problem of visible (RGB) to Near Infrared (NIR) image fusion has become particularly timely. Indeed, while visible images see color,

Tao Wu, Tie Luo, Donald C. Wunsch

Adversarial examples (AE) with good transferability enable practical black-box attacks on diverse target models, where insider knowledge about the target models is not required. Previous methods often generate AE with no or very limited transferability; that is, they easily overfit to the particular architecture and feature representation of the source, whit

Andreas Metzger, Jan Laufer, Felix Feit, Klaus Pohl

Online reinforcement learning (RL) is increasingly used for realizing adaptive systems in the presence of design time uncertainty. Online RL facilitates learning from actual operational data and thereby leverages feedback only available at runtime. However, Online RL requires the definition of an effective and correct reward function, which quantifies the fe

Fareed Sheriff

Markov chain Monte Carlo (MCMC) methods have existed for a long time and the field is well-explored. The purpose of MCMC methods is to approximate a distribution through repeated sampling; most MCMC algorithms exhibit asymptotically optimal behavior in that they converge to the true distribution at the limit. However, what differentiates these algorithms are

Chen Ling, Ningyuan Yao

Let $K$ be a henselian valued field with ${\cal O}_K$ its valuation ring, $\Gamma$ its value group, and $\boldsymbol{k}$ its residue field. We study the definable subsets of ${\cal O}_K$ and algebraic groups definable over ${\cal O}_K$ in the case where $\boldsymbol{k}$ is algebraically closed and $\Gamma$ is a $\mathbb Z$-group. We first describe the defina

Feng Xiao, Ruoyu Sun, Jicong Fan

We present a simple framework for one-class classification and anomaly detection. The core idea is to learn a mapping to transform the unknown distribution of training (normal) data to a known target distribution. Crucially, the target distribution should be sufficiently simple, compact, and informative. The simplicity is to ensure that we can sample from th