May 2023 arXiv papers — page 150

Cunyuan Jiang, Alessio Zaccone, Chandan Setty, Matteo Baggioli

Phasons are collective low-energy modes that appear in disparate condensed matter systems such as quasicrystals, incommensurate structures, fluctuating charge density waves, and Moir\'e superlattices. They share several similarities with acoustic phonon modes, but they are not protected by any exact translational symmetry. As a consequence, they are subject

Arthur Genthon, Takashi Nozoe, Luca Peliti, David Lacoste

Cell lineage statistics is a powerful tool for inferring cellular parameters, such as division rate, death rate or the population growth rate. Yet, in practice such an analysis suffers from a basic problem: how should we treat incomplete lineages that do not survive until the end of the experiment? Here, we develop a model-independent theoretical framework t

Andrzej Turko, Jarosław Byrka

We study an envy-free pricing problem, in which each buyer wishes to buy a shortest path connecting her individual pair of vertices in a network owned by a single vendor. The vendor sets the prices of individual edges with the aim of maximizing the total revenue generated by all buyers. Each customer buys a path as long as its cost does not exceed her indivi

Wenjie Liu, Panos Papadimitratos

Global Navigation Satellite Systems (GNSS) are integrated into many devices. However, civilian GNSS signals are usually not cryptographically protected. This makes attacks that forge signals relatively easy. Considering modern devices often have network connections and onboard sensors, the proposed here Probabilistic Detection of GNSS Spoofing (PDS) scheme i

Simon Razniewski, Hiba Arnaout, Shrestha Ghosh, Fabian Suchanek

General-purpose knowledge bases (KBs) are a cornerstone of knowledge-centric AI. Many of them are constructed pragmatically from Web sources, and are thus far from complete. This poses challenges for the consumption as well as the curation of their content. While several surveys target the problem of completing incomplete KBs, the first problem is arguably t

Guy Horowitz, Stav Yanovsky Daye, Noa Avigdor-Elgrabli, Ariel Raviv

The categorization of massive e-Commerce data is a crucial, well-studied task, which is prevalent in industrial settings. In this work, we aim to improve an existing product categorization model that is already in use by a major web company, serving multiple applications. At its core, the product categorization model is a text classification model that takes

Wei Xue, Yiwen Wang, Qifeng Liu, Yike Guo

The virtual world is being established in which digital humans are created indistinguishable from real humans. Producing their audio-related capabilities is crucial since voice conveys extensive personal characteristics. We aim to create a controllable audio-form virtual singer; however, supervised modeling and controlling all different factors of the singin

Georg Siedel, Weijia Shao, Silvia Vock, Andrey Morozov

Robustness is a fundamental property of machine learning classifiers required to achieve safety and reliability. In the field of adversarial robustness of image classifiers, robustness is commonly defined as the stability of a model to all input changes within a p-norm distance. However, in the field of random corruption robustness, variations observed in th

Safia Kedad-Sidhoum, Anton Medvedev, Frédéric Meunier

Two-stage robust optimization is a fundamental paradigm for modeling and solving optimization problems with uncertain parameters. A now classical method within this paradigm is finite adaptability, introduced by Bertsimas and Caramanis (IEEE Transactions on Automatic Control, 2010). It consists in restricting the recourse to a finite number $k$ of possible v

Arnab Maiti, Yuki Koyano, Hiroyuki Kitahata, Krishna Kanti Dey

We show that the force generated by active enzyme molecules are strong enough to influence the dynamics of their surroundings under artificial crowded environments. We measured the behavior of polymer microparticles in a quasi-two-dimensional system under aqueous environment, at various area fraction values of particles. In the presence of enzymatic activity

Vittorio Vitale, Tiago Mendes-Santos, Alex Rodriguez, Marcello Dalmonte

Topology plays a fundamental role in our understanding of many-body physics, from vortices and solitons in classical field theory, to phases and excitations in quantum matter. Topological phenomena are intimately connected to the distribution of information content - that, differently from ordinary matter, is now governed by non-local degrees of freedom. How

Amedeo Balbi, Manasvi Lingam

The probability that life spontaneously emerges in a suitable environment (abiogenesis) is one of the major unknowns in astrobiology. Assessing its value is impeded by the lack of an accepted theory for the origin of life, and is further complicated by the existence of selection biases. Appealing uncritically to some version of the ``Principle of Mediocrity'

Jan C. Louw, Sizheng Cao, Xian-Hui Ge

Motivated by recent analogies between the large-$q$ cSYK model and charged black holes, we aim to find a concrete gravitation theory with a matching partition function. Our main focus is to match the thermodynamics of the $(0+1)$-dimensional cSYK model, with that of a $(1+1)$-dimensional gravitational model. We focus on a model of deformed JT gravity, charac

Yixiao Ma, Yueyue Wu, Weihang Su, Qingyao Ai

Legal case retrieval is a critical process for modern legal information systems. While recent studies have utilized pre-trained language models (PLMs) based on the general domain self-supervised pre-training paradigm to build models for legal case retrieval, there are limitations in using general domain PLMs as backbones. Specifically, these models may not f

Zeming Wei, Jingyu Zhu, Yihao Zhang

Sharpness-Aware Minimization (SAM) is an effective method for improving generalization ability by regularizing loss sharpness. In this paper, we explore SAM in the context of adversarial robustness. We find that using only SAM can achieve superior adversarial robustness without sacrificing clean accuracy compared to standard training, which is an unexpected

Jincenzi Wu, Zhuang Chen, Jiawen Deng, Sahand Sabour

Theory of mind (ToM) refers to humans' ability to understand and infer the desires, beliefs, and intentions of others. The acquisition of ToM plays a key role in humans' social cognition and interpersonal relations. Though indispensable for social intelligence, ToM is still lacking for modern AI and NLP systems since they cannot access the human mental state

Kevin Mika, René Griessl, Nils Kucza, Florian Porrmann

The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a mod

Adil Jueid, Jinheung Kim, Soojin Lee, Jeonghyeon Song

We comprehensively study the potential for discovering lepton flavor violation (LFV) phenomena associated with the $Z$ and Higgs bosons at the LHeC and FCC-he. Our meticulous investigation reveals the remarkable suitability of electron-proton colliders for probing these rare new physics signals. This is due to the distinct advantages they offer, including ne

Cheng Chen, Shoki Ohta, Takayuki Nishio, Mehdi Bennis

Introducing CSI-Inpainter, a pioneering approach for occlusion removal using Channel State Information (CSI) time sequences, this work propels the application of wireless signal processing into the realm of visual scene recovery. Departing from traditional occlusion removal, CSI-Inpainter leverages CSI data to construct and refine obscured visual elements in

Giovanni Masciocchi, Mouad Fattouhi, Elizaveta Spetzler, Maria-Andromachi Syskaki

In this work, we propose and evaluate an inexpensive and CMOS-compatible method to locally apply strain on a Si/SiOx substrate. Due to high growth temperatures and different thermal expansion coefficients, a SiN passivation layer exerts a compressive stress when deposited on a commercial silicon wafer. Removing selected areas of the passivation layer alters

Sushma Palimar

A conjecture of Odoni stated over Hilbertian fields $K$ of characteristic zero asserts that for every positive integer $d$, there exists a polynomial $f\in K[x]$ of degree $d$ such that for every positive integer $n$, each iterate $f^{\circ n}$ of $f$ is irreducible and the Galois group of the splitting field of $f^{\circ n}$ is isomorphic to $[S_d]^{n}$, th

Qiwei Lang, Jingbo Zhou, Haoyi Wang, Shiqi Lyu

The number of web pages is growing at an exponential rate, accumulating massive amounts of data on the web. It is one of the key processes to classify webpages in web information mining. Some classical methods are based on manually building features of web pages and training classifiers based on machine learning or deep learning. However, building features m

Jingyue Liu, Pablo Borja, Cosimo Della Santina

This work concerns the application of physics-informed neural networks to the modeling and control of complex robotic systems. Achieving this goal required extending Physics Informed Neural Networks to handle non-conservative effects. We propose to combine these learned models with model-based controllers originally developed with first-principle models in m

Zhen Cao, F. Aharonian, Q. An, Axikegu

The diffuse Galactic $\gamma$-ray emission, mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a very important probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. In this work we report the measurements of diffuse $\gamma$-rays from the Galactic plane between 10 Te

Patricia Puchhammer, Peter Filzmoser

Most multivariate outlier detection procedures ignore the spatial dependency of observations, which is present in many real data sets from various application areas. This paper introduces a new outlier detection method that accounts for a (continuously) varying covariance structure, depending on the spatial neighborhood of the observations. The underlying es

Chen Peng, Xianzhong Long, Yun Li

Self-supervised methods based on contrastive learning have achieved great success in unsupervised visual representation learning. However, most methods under this framework suffer from the problem of false negative samples. Inspired by the mean shift for self-supervised learning, we propose a new simple framework, namely Multiple Sample Views and Queues (MSV

Jiuyang Liang, Zhenli Xu, Qi Zhou

This paper provides an error estimate for the u-series method of the Coulomb interaction in molecular dynamics simulations. We show that the number of truncated Gaussians $M$ in the u-series and the base of interpolation nodes $b$ in the bilateral serial approximation are two key parameters for the algorithm accuracy, and that the errors converge as $\mathca

Jingbo Zhou, Yixuan Du, Ruqiong Zhang, Jun Xia

Graph Neural Networks (GNNs), a type of neural network that can learn from graph-structured data through neighborhood information aggregation, have shown superior performance in various downstream tasks. However, as the number of layers increases, node representations become indistinguishable, which is known as over-smoothing. To address this issue, many res

Shengxuan Tang, Liming Zhang, Shuo Jiang, Ming Cai

Assessing advancements of technology is essential for creating science and technology policies and making informed investments in the technology market. However, current methods primarily focus on the characteristics of the technologies themselves, making it difficult to accurately assess technologies across various fields and generations. To address this ch

Feng Wang, Xiaohua Zhu

We prove that the Gromov-Hausdorff limit of K\"ahler-Ricci flow on a $\mathbf G$-spherical Fano manifold $X$ is a $\mathbf G$-spherical $\mathbb Q$-Fano variety $X_{\infty}$, which admits a (singular) K\"ahler-Ricci soliton. Moreover, the $\mathbf G$-spherical variety structure of $X_{\infty}$ can be constructed as a center of torus $\mathbb C^*$-degeneratio

Yi-Huang Shen, Guangjun Zhu

Connected bipartite graphs whose binomial edge ideals are Cohen--Macaulay have been classified by Bolognini et al. In this paper, we compute the depth, Castelnuovo--Mumford regularity, and dimension of the generalized binomial edge ideals of these graphs.

Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz, Wen-tau Yih

In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples. The possibility to parameterise an LLM through such in-context examples widens their capability at a much lower cost than finetuning. We extend this line of reasoning and present a method which further ex

Mohammad K. Mardini, Anna Frebel, Leyatt Betre, Heather Jacobson

Based on high-resolution spectra obtained with Magellan/MIKE, we present a chemo-dynamical analysis for 27 near main-sequence turnoff metal-poor stars, including 20 stars analyzed for the first time. The sample spans a range in [Fe/H] from -2.5 to -3.6, with 44% having [Fe/H] <-2.9. We derived chemical abundances for 17 elements, including strontium and bari

Pablo Benítez-Llambay, Leonardo Krapp, Ximena S. Ramos, Kaitlin M. Kratter

The study of many astrophysical flows requires computational algorithms that can capture high Mach number flows, while resolving a large dynamic range in spatial and density scales. In this paper we present a novel method, RAM: Rapid Advection Algorithm on Arbitrary Meshes. RAM is a time-explicit method to solve the advection equation in problems with large

David Forsman

The concept of a variance on a category is introduced as a two-sided strict factorization system. By employing variances, we define functors of variance in a more general setting than is usually considered, thereby eliminating the need for their domains to be product categories. Heuristic natural transformations are defined between functors of variance, whos

R. Sant, A. De Vita, V. Polewczyk, G. Pierantozzi

Polarization dependent x-ray absorption spectroscopy was used to study the magnetic ground state and the orbital occupation in bulk-phase VI$_3$ van der Waals crystals below and above the ferromagnetic and structural transitions. X-ray natural linear dichroism and X-ray magnetic circular dichroism spectra acquired at the V $L_{2,3}$ edges are compared agains

Barış Göktepe, Cornelius Hellge, Tatiana Rykova, Thomas Schierl

We provide a mathematical framework to analyze the limits of Hybrid Automatic Repeat reQuest (HARQ) and derive analytical expressions for the most powerful test for estimating the decodability under maximum-likelihood decoding and $t$-error decoding. Furthermore, we numerically approximate the most powerful test for sum-product decoding. We compare the perfo

Marco Peer, Florian Kleber, Robert Sablatnig

This paper presents an unsupervised approach for writer retrieval based on clustering SIFT descriptors detected at keypoint locations resulting in pseudo-cluster labels. With those cluster labels, a residual network followed by our proposed NetRVLAD, an encoding layer with reduced complexity compared to NetVLAD, is trained on 32x32 patches at keypoint locati

Song Ling Liu, Xin Yu Luo, Jing Shan Cao, Zhao Yuan Liu

The formation of bulk metallic glass requires the constituent elements to have a negative heat of mixing but has no restrictions on its magnitude. An understanding of this issue is lacking due to the absence of a valid method for describing chemical ordering of metallic glasses. For example, the radial distribution function is ineffective in identifying the

Shuting Xia, Tingyu Fan, Yiling Xu, Jenq-Neng Hwang

3D dynamic point cloud (DPC) compression relies on mining its temporal context, which faces significant challenges due to DPC's sparsity and non-uniform structure. Existing methods are limited in capturing sufficient temporal dependencies. Therefore, this paper proposes a learning-based DPC compression framework via hierarchical block-matching-based inter-pr

Marco Arazzi, Mauro Conti, Antonino Nocera, Stjepan Picek

Recently, researchers have successfully employed Graph Neural Networks (GNNs) to build enhanced recommender systems due to their capability to learn patterns from the interaction between involved entities. In addition, previous studies have investigated federated learning as the main solution to enable a native privacy-preserving mechanism for the constructi

Yunke Ao, Hooman Esfandiari, Fabio Carrillo, Yarden As

Spinal fusion surgery requires highly accurate implantation of pedicle screw implants, which must be conducted in critical proximity to vital structures with a limited view of anatomy. Robotic surgery systems have been proposed to improve placement accuracy, however, state-of-the-art systems suffer from the limitations of open-loop approaches, as they follow

Richard Santiago, Ivan Sergeev, Rico Zenklusen

The Matroid Secretary Conjecture is a notorious open problem in online optimization. It claims the existence of an $O(1)$-competitive algorithm for the Matroid Secretary Problem (MSP). Here, the elements of a weighted matroid appear one-by-one, revealing their weight at appearance, and the task is to select elements online with the goal to get an independent

Qinghua Lu, Liming Zhu, Xiwei Xu, Yue Liu

The recent release of large language model (LLM) based chatbots, such as ChatGPT, has attracted huge interest in foundation models. It is widely believed that foundation models will serve as the fundamental building blocks for future AI systems. As foundation models are in their early stages, the design of foundation model based systems has not yet been syst

Caiyang Yu, Xianggen Liu, Yifan Wang, Yun Liu

Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically. Although neural architectures have achieved human-level performances in several tasks, few of them are obtained from the NAS method. The main reason is the huge search space of neural architectures, making NAS algorith

Jie Liu, Zifeng Ye, Kun Chen, Panpan Zhang

Motivated by the connections between collaborative filtering and network clustering, we consider a network-based approach to improving rating prediction in recommender systems. We propose a novel Bipartite Mixed-Membership Stochastic Block Model ($\mathrm{BM}^2$) with a conjugate prior from the exponential family. We derive the analytical expression of the m

Saja Tawalbeh, José Oramas

Capsule Networks (CapsNets) have been re-introduced as a more compact and interpretable alternative to standard deep neural networks. While recent efforts have proved their compression capabilities, to date, their interpretability properties have not been fully assessed. Here, we conduct a systematic and principled study towards assessing the interpretabilit

Jiacheng Yao, Jindan Xu, Wei Xu, Derrick Wing Kwan Ng

In this paper, we consider the robust beamforming design in a reconfigurable intelligent surface (RIS)-aided cell-free (CF) system considering the channel state information (CSI) uncertainties of both the direct channels and cascaded channels at the transmitter with capacity-limited backhaul. We jointly optimize the precoding at the access points (APs) and t

R. D. Strauss, N. Dresing, I. G. Richardson, J. P. van den Berg

The processes responsible for the acceleration of solar energetic particles (SEPs) are still not well understood, including whether SEP electrons and protons are accelerated by common or separate processes. Using a numerical particle transport model that includes both pitch-angle and perpendicular spatial diffusion, we simulate, amongst other quantities, the

Nikita Kalinin, Vladislav Khramov

For a finite connected graph $G$ and a non-empty subset $S$ of its vertices thought of sinks, the so-called critical group (or sandpile group) $C(G, S)$ has been studied for a long time. We present a class of graphs where such an extension can be made in a unified way. Similar extension was made by Maes, C. and Redig, F. and Saada, E., but we propose a more

Sheng Xiang, Dawei Cheng, Chencheng Shang, Ying Zhang

The price movement prediction of stock market has been a classical yet challenging problem, with the attention of both economists and computer scientists. In recent years, graph neural network has significantly improved the prediction performance by employing deep learning on company relations. However, existing relation graphs are usually constructed by han

Ermes Franch, Chunlei Li

Due to their weak algebraic structure, low rank parity check (LRPC) codes have been employed in several post-quantum cryptographic schemes. In this paper we propose new improved decoding algorithms for (n, k) LRPC codes of dual rank weight d. The proposed algorithms can efficiently decode LRPC codes with the parameters satisfying n - k = rd - c, where r is t

Chuanfei Hu, Tianyi Xia, Ying Cui, Quchen Zou

Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the complementary multi-phase information for liver tumor segmentation (LiTS), which are crucial to assist the diagnosis of liver cancer clinically. However, the performances of existing multi-phase liver tumor segmentation (MPLiTS)-based methods suffer from redundancy and weak inte

Libor Polčák, Kamil Jeřábek

Network Error Logging helps web server operators detect operational problems in real-time to provide fast and reliable services. This paper analyses Network Error Logging from two angles. Firstly, this paper overviews Network Error Logging from the data protection view. The ePrivacy Directive requires consent for non-essential access to the end devices. Neve

Maryam Olyaee, Juan P. Pena-Martin, F. J. Lopez-Martinez, Juan M. Romero-Jerez

We introduce and characterize the Multi-cluster Two-Wave (MTW) fading model, which generalizes \textit{both} the Durgin's Two-Wave with Diffuse Power (TWDP) and the $\kappa$-$\mu$ models under a common umbrella. The MTW model consists of an arbitrary number of clusters of waves each of which may include one or two dominant (specular) components. The chief pr

Bharat Kumar, Deepmala, A. K. Das

In this article, we establish a class of new projected type iteration methods based on matrix spitting for solving the linear complementarity problem. Also, we provide a sufficient condition for the convergence analysis when the system matrix is an $H_+$-matrix. We show the efficiency of the proposed method by using two numerical examples for different param

Luca Mariot, Federico Mazzone

Motivated by applications to noncoherent network coding, we study subspace codes defined by sets of linear cellular automata (CA). As a first remark, we show that a family of linear CA where the local rules have the same diameter -- and thus the associated polynomials have the same degree -- induces a Grassmannian code. Then, we prove that the minimum distan

Iztok Banič, Goran Erceg, Judy Kennedy

The Lelek fan $L$ is usually constructed as a subcontinuum of the Cantor fan in such a way that the set of the end-points of $L$ is dense in $L$. It easily follows that the Lelek fan is embeddable into the Cantor fan. {It is also a well-known fact that the Cantor fan is embeddable into the Lelek fan, but this is less obvious. When proving this, one usually u

Nuo Chen, Linjun Shou, Ming Gong, Jian Pei

Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities. Lots of approaches based on pre-trained language models (PLMs) and contrastive learning have achieved promising results on this task. Experimentally, we observe that the over-smoothing problem reduces the capacity of these powerful

Mengxiang Liu, Fei Teng, Zhenyong Zhang, Pudong Ge

The rapid development of information and communications technology has enabled the use of digital-controlled and software-driven distributed energy resources (DERs) to improve the flexibility and efficiency of power supply, and support grid operations. However, this evolution also exposes geographically-dispersed DERs to cyber threats, including hardware and

Tiago B. Gonçalves, João Luís Rosa, Francisco S. N. Lobo

In this work, we use the dynamical system approach to explore the cosmological background evolution of the scalar-tensor representation of $f(R,T)$ gravity, where $R$ is the Ricci scalar and $T$ is the trace of the stress-energy tensor. The motivation for this work resides in finding dynamical cosmological behaviors comparable with the $\Lambda$CDM model wit

Marc Briant, Nicolas Meunier

This paper deals with the existence and uniqueness of solutions to kinetic equations describing alignment of self-propelled particles. The particularity of these models is that the velocity variable is not on the euclidean space but constrained on the unit sphere (the self-propulsion constraint). Two related equations are considered : the first one in which

Sougata Saha, Rohini Srihari

The subtle human values we acquire through life experiences govern our thoughts and gets reflected in our speech. It plays an integral part in capturing the essence of our individuality and making it imperative to identify such values in computational systems that mimic human actions. Computational argumentation is a field that deals with the argumentation c

Sougata Saha, Rohini Srihari

Effective argumentation is essential towards a purposeful conversation with a satisfactory outcome. For example, persuading someone to reconsider smoking might involve empathetic, well founded arguments based on facts and expert opinions about its ill-effects and the consequences on one's family. However, the automatic generation of high-quality factual argu

Taeho Ryu, Julian Krolik, Tsvi Piran, Scott Noble

Accretion of debris seems to be the natural mechanism to power the radiation emitted during a tidal disruption event (TDE), in which a supermassive black hole tears apart a star. However, this requires the prompt formation of a compact accretion disk. Here, using a fully relativistic global simulation for the long-term evolution of debris in a TDE with reali

Ali Gholami, Kai Wan, Tayyebeh Jahani-Nezhad, Hua Sun

In a typical formulation of the private information retrieval (PIR) problem, a single user wishes to retrieve one out of $ K$ files from $N$ servers without revealing the demanded file index to any server. This paper formulates an extended model of PIR, referred to as multi-message private computation (MM-PC), where instead of retrieving a single file, the u

Hanqi Yan, Lin Gui, Menghan Wang, Kun Zhang

Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanation generation or leverage the attention mechanism to extract important text spans from reviews as explanations. The extracted rationales ar

Daniele Girolimetto, Tommaso Di Fonzo

Forecast reconciliation is the post-forecasting process aimed to revise a set of incoherent base forecasts into coherent forecasts in line with given data structures. Most of the point and probabilistic regression-based forecast reconciliation results ground on the so called "structural representation" and on the related unconstrained generalized least squar

Simone Laterza, Antonio Caretta, Richa Bhardwaj, Paolo Moras

The investigation of the properties of metal-semiconductor interfaces has gained significant attention due to the unique features that emerge from the combination of both metal and semiconductor attributes. In this report, the magnetic properties of Ni/Si interfaces utilizing X-ray magnetic circular dichroism (XMCD) spectroscopy at the Ni and Si edges have b

Kalel L. Rossi, Roberto C. Budzinski, Everton S. Medeiros, Bruno R. R. Boaretto

Metastability, characterized by a variability of regimes in time, is a ubiquitous type of neural dynamics. It has been formulated in many different ways in the neuroscience literature, however, which may cause some confusion. In this Perspective, we discuss metastability from the point of view of dynamical systems theory. We extract from the literature a ver

Samuel E. Jackson, David C. Woods

Statistical models typically capture uncertainties in our knowledge of the corresponding real-world processes, however, it is less common for this uncertainty specification to capture uncertainty surrounding the values of the inputs to the model, which are often assumed known. We develop general modelling methodology with uncertain inputs in the context of t

Yuta Nakayama

We show a degree formula for a type of orthogonal Deligne--Lusztig varieties and their Pl\"ucker embeddings. This is an analog of work of Li on a unitary case.

Ilija Tavchioski, Marko Robnik-Šikonja, Senja Pollak

As the impact of technology on our lives is increasing, we witness increased use of social media that became an essential tool not only for communication but also for sharing information with community about our thoughts and feelings. This can be observed also for people with mental health disorders such as depression where they use social media for expressi

Lee R. Liu, Dina Rosenberg, P. Bryan Changala, Philip J. D. Crowley

Ergodicity, the central tenet of statistical mechanics, requires that an isolated system will explore all of its available phase space permitted by energetic and symmetry constraints. Mechanisms for violating ergodicity are of great interest for probing non-equilibrium matter and for protecting quantum coherence in complex systems. For decades, polyatomic mo

Jian Bao, Wenlu Zhang, Ding Li, Zhihong Lin

The energetic electrons (EEs) generated through auxiliary heating have been found to destabilize various Alfven eigenmodes (AEs) in recent experiments, which in turn lead to the EE transport and degrade the plasma energy confinement. In this work, we propose a global fluid-kinetic hybrid model for studying corresponding kinetic-magnetohydrodynamic (MHD) proc

Tianlun Zheng, Zhineng Chen, Jinfeng Bai, Hongtao Xie

Text irregularities pose significant challenges to scene text recognizers. Thin-Plate Spline (TPS)-based rectification is widely regarded as an effective means to deal with them. Currently, the calculation of TPS transformation parameters purely depends on the quality of regressed text borders. It ignores the text content and often leads to unsatisfactory re

Marvellous Ajala, Blessing Oko, David Oba-Fidelis, Joycelyn Iyasele

Starches are important energy sources found in plants with many uses in the pharmaceutical industry such as binders, disintegrants, bulking agents in drugs and thus require very careful physicochemical analysis for proper identification and verification which includes microscopy. In this work, we applied artificial intelligence techniques (using transfer lea

W. Lu, X. Wu, X. W. Cao, G. J. Luo

Partial spread is important in finite geometry and can be used to construct linear codes. From the results in (Designs, Codes and Cryptography 90:1-15, 2022) by Xia Li, Qin Yue and Deng Tang, we know that if the number of the elements in a partial spread is ``big enough", then the corresponding linear code is minimal. They used the sufficient condition in (I

C. Bernardin, P. Gonçalves, S. Olla

We consider the macroscopic limit for the space-time density fluctuations in the open symmetric simple exclusion in the quasi-static scaling limit. We prove that the distribution of these fluctuations converge to a gaussian space-time field that is delta correlated in time but with long-range correlations in space.

Jetze T. Schuurmans, Kim Batselier, Julian F. P. Kooij

Tensor decompositions have been successfully applied to compress neural networks. The compression algorithms using tensor decompositions commonly minimize the approximation error on the weights. Recent work assumes the approximation error on the weights is a proxy for the performance of the model to compress multiple layers and fine-tune the compressed model

X. Wu, W. Lu, X. P. Qin, X. W. Cao

J. Y. Hyun, et al. (Des. Codes Cryptogr., vol. 88, pp. 2475-2492, 2020) constructed some optimal and minimal binary linear codes generated by one or two order ideals in hierarchical posets of two levels. At the end of their paper, they left an open problem: it also should be interesting to investigate the cases of more than two orders in hierarchical posets

Michael C. Kopreski

Given a compact surface $\Sigma$ with boundary and a relation $\Gamma$ on $\pi_0(\partial\Sigma)$, we define the prescribed arc graph $\mathscr A(\Sigma,\Gamma)$ to be the full subgraph of the arc graph $\mathscr A(\Sigma)$ containing only classes of arcs between boundary components in $\Gamma$. We prove that $\mathscr(\Sigma,\Gamma)$ is connected and infini

Anna Pandolfi, Laurent Stainier, Michael Ortiz

We formulate a class of velocity-free finite-particle methods for mass transport problems based on a time-discrete incremental variational principle that combines entropy and the cost of particle transport, as measured by the Wasserstein metric. The incremental functional is further spatially discretized into finite particles, i.e., particles characterized b

Olga Fourkioti, Matt De Vries, Chen Jin, Daniel C. Alexander

The visual examination of tissue biopsy sections is fundamental for cancer diagnosis, with pathologists analyzing sections at multiple magnifications to discern tumor cells and their subtypes. However, existing attention-based multiple instance learning (MIL) models used for analyzing Whole Slide Images (WSIs) in cancer diagnostics often overlook the context

Nwankwo Linus, Elmar Rueckert

Simultaneous localization and mapping (SLAM) algorithms are essential for the autonomous navigation of mobile robots. With the increasing demand for autonomous systems, it is crucial to evaluate and compare the performance of these algorithms in real-world environments. In this paper, we provide an evaluation strategy and real-world datasets to test and eval

Gerhard Jung, Thomas Franosch

We present mode-coupling theory (MCT) results for densely packed hard-sphere fluids confined between two parallel walls and compare them quantitatively to computer simulations. The numerical solution of MCT is calculated for the first time using the full system of matrix-valued integro-differential equations. We investigate several dynamical properties of su

Daniel Fernández-González

Structured sentiment analysis (SSA) aims to automatically extract people's opinions from a text in natural language and adequately represent that information in a graph structure. One of the most accurate methods for performing SSA was recently proposed and consists of approaching it as a dependency graph parsing task. Although we can find in the literature

Chandra Sekhar Sanaboina, Tejeswar Eluri

In Internet of Things (IoT), the simple IPv6 capable electronic devices with limited hardware resources like memory and power resources are called constrained devices. Congestion is a major issue in network communications of these devices. To solve congestion in networks of constrained devices, Internet Engineering Task Force (IETF) had designed Constrained

Franco Oberti, Ernesto Sanchez, Alessandro Savino, Filippo Parisi

As more cars connect to the internet and other devices, the automotive market has become a lucrative target for cyberattacks. This has made the industry more vulnerable to security threats. As a result, car manufacturers and governments are working together to reduce risks and prevent cyberattacks in the automotive sector. However, existing attack feasibilit

Chandra Sekhar Sanaboina, Pallamsetty Sanaboina

The main theme of this paper is to implement the mobility model in Cooja simulator and to investigate the impact of the mobility on the performance of Routing Protocol over Low power Lossy networks (RPL) in the IoT environment. In the real world, mobility occurs frequently. Therefore in this paper, a frequently used mobility model -- Random Way Point (RWP) i

Susana Furtado, Charles Johnson

Efficient vectors are the natural set from which to choose a cardinal ranking vector for a pairwise comparison matrix. Such vectors are the key to certain business project selection models. Many ways to construct specific efficient vectors have been proposed. Yet, no previous method to produce all efficient vectors was known. Here, using some graph theoretic

Chandra Sekhar Sanaboina, Pallamsetty Sanaboina

Internet of Things (IoT) consists of a wide variety of devices with limited power sources. Due to the adhered reason, energy consumption is considered as one of the major challenges in the IoT environment. In this research article, an attempt is made to optimize the existing Routing Protocol (RPL) towards a green technology. It focuses on finding the most si

Miguel Montenegro, Francisco J. Galindo-Rosales

Due to their nature, using shear thickening fluids (STFs) in engineering applications has sparked an interest in developing energy-dissipating systems, such as damping devices or shock absorbers. The Rheinforce technology allows the design of customized energy dissipative composites by embedding microfluidic channels filled with STFs in a scaffold material.

Kevin M. Hickson, Juan Carlos San Vicente Veliz, Debasish Koner, Markus Meuwly

The reaction N(4S) + NO -> O(3P) + N2 plays a pivotal role in the conversion of atomic to molecular nitrogen in dense interstellar clouds and in the atmosphere. Here we report a joint experimental and computational investigation of the N + NO reaction with the aim of providing improved constraints on its low temperature reactivity. Thermal rates were measure

Freddie Illingworth, Lukas Michel, Alex Scott

The free semigroup $\mathcal{F}$ over a finite alphabet $\mathcal{A}$ is the set of all finite words with letters from $\mathcal{A}$ equipped with the operation of concatenation. A subset $S$ of $\mathcal{F}$ is $k$-product-free if no element of $S$ can be obtained by concatenating $k$ words from $S$, and strongly $k$-product-free if no element of $S$ is a (

Efstratios Voulgaris, Ilias Dimitriadis, Dimitrios P. Giakatos, Athena Vakali

The need for a more energy efficient future is now more evident than ever and has led to the continuous growth of sectors with greater potential for energy savings, such as smart buildings, energy consumption meters, etc. The large volume of energy related data produced is a huge advantage but, at the same time, it creates a new problem; The need to structur

Eliya Habba, Renana Keydar, Dan Bareket, Gabriel Stanovsky

We develop computational models to analyze court statements in order to assess judicial attitudes toward victims of sexual violence in the Israeli court system. The study examines the resonance of "rape myths" in the criminal justice system's response to sex crimes, in particular in judicial assessment of victim's credibility. We begin by formulating an onto

Clémentin Boittiaux, Claire Dune, Maxime Ferrera, Aurélien Arnaubec

Visual localization plays an important role in the positioning and navigation of robotics systems within previously visited environments. When visits occur over long periods of time, changes in the environment related to seasons or day-night cycles present a major challenge. Under water, the sources of variability are due to other factors such as water condi

Leonardo Sán. Hernández, Tonatiuh Matos

Recently, the Scalar Field Dark Matter (SFDM) model (also known as Fuzzy, Wave, Bose-Einstein, Ultra-light Dark Matter) has gained a lot of attention because it has provided simpler and more natural explanations for various phenomena observed in galaxies, as a natural explanation for the center of galaxies, the number of satellite galaxies around their host

Saeed Farajollahi, Zhiwei Fang, Jintian Lin, Shahin Honari

Polygon and star modes enable unidirectional emission and single-frequency lasing in whispering gallery microcavities. To understand their properties and facilitate design, we have adopted both two-dimensional and three-dimensional full-wave perturbation methods to simulate these modes. Our simulation demonstrates that a tapered optical fiber can be used as