March 2023 arXiv papers — page 40

Bui Khac Thach, Le Nhat Tan, Do Quang Minh, Ly Cam Hung

The Solar energy production is growing quickly for the global demand of renewa-ble one, decrease the dependence on fossil fuels. However, disposing of used pho-tovoltaic (PV) panels will be a serious environmental challenge in the future dec-ades since the solar panels would eventually become a source of hazardous waste. The potential of waste solar panel gl

Wenqiang Xu, Zhenjun Yu, Han Xue, Ruolin Ye

Tactile sensing is one of the modalities humans rely on heavily to perceive the world. Working with vision, this modality refines local geometry structure, measures deformation at the contact area, and indicates the hand-object contact state. With the availability of open-source tactile sensors such as DIGIT, research on visual-tactile learning is becoming m

Zeeshan Ahmed

In recent years there has been considerable interest in using photonic thermometers such as Fiber Bragg grating (FBG) and silicon ring resonators as an alternative technology to resistance-based legacy thermometers. Although FBG thermometers have been commercially available for decades their metrological performance remains poorly understood, hindered in par

G. R. Aizin, J. Mikalopas, M. Shur

We developed a theory of collective plasma oscillations in a dc current-biased field effect transistor with interdigitated dual grating gate and demonstrated a new mechanism of electron plasma instability in this structure. The instability in the plasmonic crystal formed in the transistor channel develops due to conversion of the kinetic energy carried by th

Wenjing Chen, Shiqi Zhang

In this paper, we establish a weighted Adams' inequality in some appropriate weighted Sobolev space in $\mathbb{R}^4$. Then we give an improvement inequality by proving the concentration-compactness result. In the last part, we consider an application to elliptic equation involving new exponential growth at infinity in $\mathbb{R}^4$.

Rattana Pukdee, Dylan Sam, J. Zico Kolter, Maria-Florina Balcan

As larger deep learning models are hard to interpret, there has been a recent focus on generating explanations of these black-box models. In contrast, we may have apriori explanations of how models should behave. In this paper, we formalize this notion as learning from explanation constraints and provide a learning theoretic framework to analyze how such exp

Xinhua Shen, Hongpeng Sun, Xuecheng Tai

In this work, we propose and study a preconditioned framework with a graphic Ginzburg-Landau functional for image segmentation and data clustering by parallel computing. Solving nonlocal models is usually challenging due to the huge computation burden. For the nonconvex and nonlocal variational functional, we propose several damped Jacobi and generalized Ric

Luiz Faria, Carlos Perez-arancibia, Catalin Turc

We analyze the well posedness of certain field-only boundary integral equations (BIE) for frequency domain electromagnetic scattering from perfectly conducting spheres. Starting from the observations that (1) the three components of the scattered electric field $\mathbf{E}^s(\mathbf{x})$ and (2) scalar quantity $\mathbf{E}^s(\mathbf{x})\cdot\mathbf{x}$ are r

I V Barashenkov, Frank Smuts, Alexander Chernyavsky

We consider $\mathcal{PT}$-symmetric ring-like arrays of optical waveguides with purely nonlinear gain and loss. Regardless of the value of the gain-loss coefficient, these systems are protected from spontaneous $\mathcal{PT}$-symmetry breaking. If the nonhermitian part of the array matrix has cross-compensating structure, the total power in such a system re

Bakir Farhi

The purpose of the present paper is to show that in certain classes of real (or complex) functions, the Bernoulli polynomials are essentially the only ones satisfying the Raabe functional equation. For the class of the real $1$-periodic functions which are expandable as Fourier series, we point out new solutions of the Raabe functional equation, not relating

Xian Xu, Aoyu Wu, Leni Yang, Zheng Wei

Data videos are becoming increasingly popular in society and academia. Yet little is known about how to create endings that strengthen a lasting impression and persuasion. To fulfill the gap, this work aims to develop guidelines for data video endings by drawing inspiration from cinematic arts. To contextualize cinematic endings in data videos, 111 film endi

Xi-Hong Luo, Shuo Xiao, Shi-Jie Zheng, Ming-Yu Ge

The determination of the absolute and relative position of a spacecraft is critical for its operation, observations, data analysis, scientific studies, as well as deep space exploration in general. A spacecraft that can determine its own absolute position autonomously may perform more than that must rely on transmission solutions. In this work, we report an

Pathma Eswaran, Shradha Mishra

Many microorganisms use chemical `signaling' - a quintessential self-organizing strategy in non-equilibrium - that can induce spontaneous aggregation and coordination in behavior. Using synthetic signaling as a design principle, we construct a minimal model of active Brownian particles (ABPs) having soft repulsive interactions on a chemically quenched patter

Bowei Du, Yecheng Huang, Jiaxin Chen, Di Huang

Object detection on drone images with low-latency is an important but challenging task on the resource-constrained unmanned aerial vehicle (UAV) platform. This paper investigates optimizing the detection head based on the sparse convolution, which proves effective in balancing the accuracy and efficiency. Nevertheless, it suffers from inadequate integration

Zirui Fu, Aleksandre Avaliani, Marco Donato

Executing machine learning inference tasks on resource-constrained edge devices requires careful hardware-software co-design optimizations. Recent examples have shown how transformer-based deep neural network models such as ALBERT can be used to enable the execution of natural language processing (NLP) inference on mobile systems-on-chip housing custom hardw

R. A. Ryutin

Central exclusive diffractive production (CEDP) of proton anti-proton pairs was calculated in the Regge-eikonal approach taking into account continuum and possible $f_0(2100)$ resonance. We use the simple model with the ``scalar'' proton. Data from ISR and STAR were analysed and compared with theoretical description. Some predictions for the LHC at 13~TeV ar

Sebastian T. Braun, Jan Stuhler

With 70 million dead, World War II remains the most devastating conflict in history. Among the survivors, millions were displaced, returned maimed from the battlefield, or endured years of captivity. We examine the effects of such war exposures on labor market careers, showing that they often become apparent only at certain life stages. While war injuries re

Sarah Good, Anthony O'Hare

Determining who is at risk from a disease is important in order to protect vulnerable subpopulations during an outbreak. We are currently in a SARS-COV-2 (commonly referred to as COVID-19) pandemic which has had a massive impact across the world, with some communities and individuals seen to have a higher risk of severe outcomes and death from the disease co

Parker K. Hamilton, Jaime M. Moya, Alannah M. Hallas, E. Morosan

Symmetry-adapted distortion modes provide a natural way to describe distorted structures derived from higher-symmetry parent phases. Structural refinements using symmetry-mode amplitudes as fit variables have been used for at least 10 years in Rietveld refinements of the average crystal structure from diffraction data; more recently, this approach has also b

Guangyin Jin, Yuxuan Liang, Yuchen Fang, Zezhi Shao

With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal data is an important yet demanding aspect of urban computing, which can enhance intelligent management decisions in various fields, including transportation, environment, climate

Alborz Aghamaleki Sarvestani, Felix Ruppert, Alexander Badri-Spröwitz

Ground reaction force sensing is one of the key components of gait analysis in legged locomotion research. To measure continuous force data during locomotion, we present a novel compound instrumented treadmill design. The treadmill is 1.7m long, with a natural frequency of 170Hz and an adjustable range that can be used for humans and small robots alike. Here

Yukang Zhang, Hanzi Wang

For the visible-infrared person re-identification (VIReID) task, one of the major challenges is the modality gaps between visible (VIS) and infrared (IR) images. However, the training samples are usually limited, while the modality gaps are too large, which leads that the existing methods cannot effectively mine diverse cross-modality clues. To handle this l

Zhouhong Gu, Sihang Jiang, Jingping Liu, Yanghua Xiao

Taxonomy is formulated as directed acyclic concepts graphs or trees that support many downstream tasks. Many new coming concepts need to be added to an existing taxonomy. The traditional taxonomy expansion task aims only at finding the best position for new coming concepts in the existing taxonomy. However, they have two drawbacks when being applied to the r

Caner Ozer, Arda Guler, Aysel Turkvatan Cansever, Ilkay Oksuz

Medical image quality assessment is an important aspect of image acquisition, as poor-quality images may lead to misdiagnosis. Manual labelling of image quality is a tedious task for population studies and can lead to misleading results. While much research has been done on automated analysis of image quality to address this issue, relatively little work has

Yu Chen, Gim Hee Lee

Recent works such as BARF and GARF can bundle adjust camera poses with neural radiance fields (NeRF) which is based on coordinate-MLPs. Despite the impressive results, these methods cannot be applied to Generalizable NeRFs (GeNeRFs) which require image feature extractions that are often based on more complicated 3D CNN or transformer architectures. In this w

Kevin R. Payne, Davide Francesco Redaelli

This work is dedicated to foundational aspects of general (nonlinear second order) potential theories and fully nonlinear elliptic PDEs. In particular, we systematically develop the fundamental role played by semiconvex functions as a bridge between the classical and viscosity theory of generalized subharmonics determined by a given subequation constraint se

Can Liu, Yu Zhang, Cong Wu, Chen Li

We propose a spatial-constraint approach for modeling spatial-based interactions and enabling interactive visualizations, which involves the manipulation of visualizations through selection, filtering, navigation, arrangement, and aggregation. We proposes a system that activates static visualizations by adding intelligent interactions, which is achieved by a

Md Abul Bashar, Richi Nayak, Gareth Knapman, Paul Turnbull

Among the pressing issues facing Australian and other First Nations peoples is the repatriation of the bodily remains of their ancestors, which are currently held in Western scientific institutions. The success of securing the return of these remains to their communities for reburial depends largely on locating information within scientific and other literat

Lei Wang, Piotr Koniusz

Many skeletal action recognition models use GCNs to represent the human body by 3D body joints connected body parts. GCNs aggregate one- or few-hop graph neighbourhoods, and ignore the dependency between not linked body joints. We propose to form hypergraph to model hyper-edges between graph nodes (e.g., third- and fourth-order hyper-edges capture three and

Bernd Hofmann, Hans-Jürgen Fischer, Robert Plato

Numerical differentiation of a function, contaminated with noise, over the unit interval $[0,1] \subset \mathbb{R}$ by inverting the simple integration operator $J:L^2([0,1]) \to L^2([0,1])$ defined as $[Jx](s):=\int_0^s x(t) dt$ is discussed extensively in the literature. The complete singular system of the compact operator $J$ is explicitly given with sing

Yuri Yakubovich

We present an explicit construction of a Markovian random growth process on integer partitions such that given it visits some level $n$, it passes through any partition $\lambda$ of $n$ with equal probabilities. The construction has continuous time, but we also investigate its discrete time jump chain. The jump probabilities are given by explicit but complic

Kartik Teotia, Mallikarjun B R, Xingang Pan, Hyeongwoo Kim

Multi-view volumetric rendering techniques have recently shown great potential in modeling and synthesizing high-quality head avatars. A common approach to capture full head dynamic performances is to track the underlying geometry using a mesh-based template or 3D cube-based graphics primitives. While these model-based approaches achieve promising results, t

Yikai Wang, Wenbing Huang, Yinpeng Dong, Fuchun Sun

Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation. This paper is motivated by a previously revealed phenomenon that the binary kernels in successful BNNs are nearly power-law distributed: their values are mostly clustered into a small number of codewords. This phenomenon enc

Philippe Brax, Clare Burrage, Jose A. R. Cembranos, Patrick Valageas

We analyse the dynamics of a light scalar field responsible for the $\mu$ term of the Higgs potential and coupled to matter via the Higgs-portal mechanism. We find that this dilaton model is stable under radiative corrections induced by the standard model particle masses. When the background value of the scalar field is stabilised at the minimum of the scala

Jongmin Lee, Byungjin Kim, Seungwook Kim, Minsu Cho

Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a self-supervised learning framework to extract discriminative rotation-invariant descriptors using group-equivariant CNNs. Thanks to employing group-equivariant CNNs, our method effe

Wessel P. Bruinsma, Stratis Markou, James Requiema, Andrew Y. K. Foong

Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractive meta-learning models which produce well-calibrated predictions and are trainable via a simple maximum likelihood procedure. Although CNPs have many advantages, they are unable to model dependencies in their predictions. Various works propose solutions to this, but these come at the cos

Tommaso Lanciano, Atsushi Miyauchi, Adriano Fazzone, Francesco Bonchi

The Densest Subgraph Problem requires to find, in a given graph, a subset of vertices whose induced subgraph maximizes a measure of density. The problem has received a great deal of attention in the algorithmic literature since the early 1970s, with many variants proposed and many applications built on top of this basic definition. Recent years have witnesse

Baruch Rosenstein, B. Ya. Shapiro

Novel "quasi two dimensional" typically layered (semi) metals offer a unique opportunity to control the density and even the topology of the electronic matter. Along with doping and gate voltage, a robust tuning is achieved by application of the hydrostatic pressure. In Weyl semi - metals the tilt of the dispersion relation cones, k , increases with pressure

Tan Wang, Kevin Lin, Linjie Li, Chung-Ching Lin

This study explores the concept of equivariance in vision-language foundation models (VLMs), focusing specifically on the multimodal similarity function that is not only the major training objective but also the core delivery to support downstream tasks. Unlike the existing image-text similarity objective which only categorizes matched pairs as similar and u

Emilia Przybysz, Bimal Bhattarai, Cosimo Persia, Ana Ozaki

Tsetlin Machines (TsMs) are a promising and interpretable machine learning method which can be applied for various classification tasks. We present an exact encoding of TsMs into propositional logic and formally verify properties of TsMs using a SAT solver. In particular, we introduce in this work a notion of similarity of machine learning models and apply o

Robert Salzwedel, Andreas Knorr, Dominik Hoeing, Holger Lange

We provide a microscopic approach to describe the onset of radial oscillation of a silver nanoparticle. Using the Heisenberg equation of motion framework, we find that the coupled ultrafast dynamics of coherently excited electron occupation and the coherent phonon amplitude initiate periodic size oscillations of the nanoparticle. Compared to the established

Lukas Koch, Felix Otto

These lecture notes present the quantitative harmonic approximation result for quadratic optimal transport and general measures obtained by Goldman and Otto. The aim is to give a clear presentation of the proof of the main theorem with more motivations, less PDE machinery, and a number of simplifications.

Ashok Urlana, Sahil Manoj Bhatt, Nirmal Surange, Manish Shrivastava

The ILSUM shared task focuses on text summarization for two major Indian languages- Hindi and Gujarati, along with English. In this task, we experiment with various pretrained sequence-to-sequence models to find out the best model for each of the languages. We present a detailed overview of the models and our approaches in this paper. We secure the first ran

Zeming Wei, Yifei Wang, Yiwen Guo, Yisen Wang

Adversarial training has been widely acknowledged as the most effective method to improve the adversarial robustness against adversarial examples for Deep Neural Networks (DNNs). So far, most existing works focus on enhancing the overall model robustness, treating each class equally in both the training and testing phases. Although revealing the disparity in

Naihuan Jing, Ning Liu

A Pfaffian-type Murnaghan-Nakayama rule is derived for the Hecke-Clifford algebra $\mathcal{H}^c_n$ based on the Frobenius formula and vertex operators, and this leads to a combinatorial version via the tableaux realization of Schur's $Q$-functions. As a consequence, a general formula for the irreducible characters $\zeta^{\la}_{\mu}(q)$ using partition-valu

Önder Nomaler, Bart Verspagen

In this paper, we investigate the nature of the density metric, which is employed in the literature on smart specialization and the product space. We find that although density is supposed to capture relatedness between a country's current specialization pattern and potential products that it may diversify into, density is also correlated strongly to the lev

Tianxiang Ren, Jubo Yu, Shihui Guo, Ying Ma

In-betweening is a technique for generating transitions given initial and target character states. The majority of existing works require multiple (often $>$10) frames as input, which are not always accessible. Our work deals with a focused yet challenging problem: to generate the transition when given exactly two frames (only the first and last). To cope wi

Jakub Breier, Dirmanto Jap, Xiaolu Hou, Shivam Bhasin

Model extraction attacks have been widely applied, which can normally be used to recover confidential parameters of neural networks for multiple layers. Recently, side-channel analysis of neural networks allows parameter extraction even for networks with several multiple deep layers with high effectiveness. It is therefore of interest to implement a certain

Tjaart P. J. Krüger, B. Trevor Sewell, Lawrence Norris

This is a provisional status report of biophysics activities in Africa. We start by highlighting the importance of biophysics research and development for every country's economy in the 21st century. Yet, the amount of biophysics activity in African countries varies between woefully little to nothing at all. We present a scope of biophysics research on the c

Daniele Boffi, Abdul Halim, Gopal Priyadarshi

In this article we apply reduced order techniques for the approximation of parametric eigenvalue problems. The effect of the choice of sampling points is investigated. Here we use the standard proper orthogonal decomposition technique to obtain the basis of the reduced space and Galerking orthogonal technique is used to get the reduced problem. We present so

Warut Suksompong, Nicholas Teh

We study the problem of fairly allocating indivisible goods to agents with weights corresponding to their entitlements. Previous work has shown that, when agents have binary additive valuations, the maximum weighted Nash welfare rule is resource-, population-, and weight-monotone, satisfies group-strategyproofness, and can be implemented in polynomial time.

Yilun Jin, Yang Liu, Kai Chen, Qiang Yang

Data privacy has become an increasingly important concern in real-world big data applications such as machine learning. To address the problem, federated learning (FL) has been a promising solution to building effective machine learning models from decentralized and private data. Existing federated learning algorithms mainly tackle the supervised learning pr

Meiru Zhang, Yixuan Su, Zaiqiao Meng, Zihao Fu

Event extraction is a complex information extraction task that involves extracting events from unstructured text. Prior classification-based methods require comprehensive entity annotations for joint training, while newer generation-based methods rely on heuristic templates containing oracle information such as event type, which is often unavailable in real-

Vishnu A Pai, Titus K Mathew

Recent studies indicate that, near equilibrium condition could not be maintained for bulk viscous matter models during the accelerated expansion of the universe in the context of Einstein's gravity, without including the cosmological constant. But from our investigation in $f(R,T)$ gravity, it is observed that, this condition can be satisfied in this modifie

Mehdi Sadeghi, Ramin Anvari Asl, Mohammad Shamseh

In this paper, we consider Einstein-Hilbert gravity in the presence of cosmological constant with cylindrical symmetry to introduce the black hole solution of this model. Here, we solve the Einstein's vacuum field equation, and then we calculate the appropriate metric for this problem.

Martin Campos Pinto, Frederik Schnack

We present commuting projection operators on de Rham sequences of two-dimensional multipatch spaces with local tensor-product parametrization and non-matching interfaces. Our construction yields projection operators which are local and stable in any $L^p$ norm with $p \in [1,\infty]$: it applies to shape-regular spline patches with different mappings and loc

Trevor McCourt, Ila R. Fiete, Isaac L. Chuang

Noise is a ubiquitous feature of the physical world. As a result, the first prerequisite of life is fault tolerance: maintaining integrity of state despite external bombardment. Recent experimental advances have revealed that biological systems achieve fault tolerance by implementing mathematically intricate error-correcting codes and by organizing in a modu

Carlos Ferreira

The bidding is the Public Administration's administrative process and other designated persons by law to select the best proposal, through objective and impersonal criteria, for contracting services and purchasing goods. In times of globalization, it is common for companies seeking to expand their business by participating in biddings. Brazilian legislation

Ralf Wimmer, Ming-Yi Hu

Several effective preprocessing techniques for Boolean formulas with and without quantifiers use unit propagation to simplify the formula. Among these techniques are vivification, unit propagation look-ahead (UPLA), and the identification of redundant clauses as so-called quantified resolution asymmetric tautologies (QRAT). For quantified Boolean formulas (Q

Ntebatseng Mahlake, Topside E. Mathonsi, Tonderai Muchenje, Deon Du Plessis

The Internet of Things (IoT) is a futuristic technology that promises to connect tons of devices via the internet. As more individuals connect to the internet, it is believed that communication will generate mountains of data. IoT is currently leveraging Wireless Sensor Networks (WSNs) to collect, monitor, and transmit data and sensitive data across wireless

Constantin Ulrich, Fabian Isensee, Tassilo Wald, Maximilian Zenk

The medical imaging community generates a wealth of datasets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmentation. Current practices continue to limit model training and supervised pre-training to one or a few similar datasets, neglecting the synergistic potential of other available ann

Jaouad Danane, Delfim F. M. Torres

Our study focuses on analyzing the behavior of a stochastic predator-prey model with a time delay and logistic growth of prey, influenced by L\'{e}vy noise. Initially, we establish the existence, uniqueness, and boundedness of a positive solution that spans globally. Subsequently, we explore the conditions under which extinction occurs, and identify adequate

Thorsten Eisenhofer, Erwin Quiring, Jonas Möller, Doreen Riepel

The number of papers submitted to academic conferences is steadily rising in many scientific disciplines. To handle this growth, systems for automatic paper-reviewer assignments are increasingly used during the reviewing process. These systems use statistical topic models to characterize the content of submissions and automate the assignment to reviewers. In

Xiao L. Pan, Hao Wang, Lei L. Zhang, Yu F. Wang

Ordered intermetallics are long believed to be the final products of the aging of U-Nb solid solutions at low temperatures, a crucial property for the practical applications of this alloy in engineering and industry. However, such conjectured ordered compounds have not been experimentally or theoretically established. Herein, numerical evidence for ordered i

Nombulelo Zulu, Deon P. Du Plessis, Topside E. Mathonsi, Tshimangadzo M. Tshilongamulenzhe

Wireless Body Sensor Networks (WBSNs) is one of the greatest growing technology for sensing and performing various tasks. The information transmitted in the WBSNs is vulnerable to cyber-attacks, therefore security is very important. Denial of Service (DoS) attacks are considered one of the major threats against WBSNs security. In DoS attacks, an adversary ta

Atefeh Akbarnia Dafrazi, Tahmineh Mehrabi, Fatemeh Malekinejad

Background: The aim of this study is to use bioinformatics to discover the biomarkers associated with patients with Pancreatic Ductal Adenocarcinoma(PDAC). Material and Methods: GSE28735, GSE15471, and GSE62452 are gene microarray datasets drived from the GEO database, included 153 PDAC samples and 145 normal samples. By analyzing both Gene Ontology (GO) and

A. I. Fedyushkin, N. G. Bourago

Vibrational melt flows in Czochralski crystal growth are investigated numerically on the basis of unsteady Navier-Stokes-Boussinesq formulation for incompressible fluid. The finite element code ASTRA is used for calculations. It is found that the vibrations provide much more effective mixing of the melt flow compared to the rotation of the crystal and the cr

Javier Ron, César Soto-Valero, Long Zhang, Benoit Baudry

As all software, blockchain nodes are exposed to faults in their underlying execution stack. Unstable execution environments can disrupt the availability of blockchain nodes interfaces, resulting in downtime for users. This paper introduces the concept of N-version Blockchain nodes. This new type of node relies on simultaneous execution of different implemen

Franco Coltraro, Jaume Amorós, Maria Alberich-Carramiñana, Carme Torras

In this work, we introduce a collision model specifically tailored for the simulation of inextensible textiles. The model considers friction, contacts, and inextensibility constraints all at the same time without any decoupling. Self-collisions are modeled in a natural way that allows considering the thickness of cloth without introducing unwanted oscillatio

Beauty L. Komane, Topside E. Mathonsi

Every human being in this world produces waste. South Africa is a developing country with many townships that have limited waste resources. Over-increasing population growth overpowers the volume of most municipal authorities to provide even the most essential services. Waste in townships is produced via littering, dumping of bins, cutting of trees, dumping

Zhiwen Yan, Chen Li, Gim Hee Lee

Dynamic Neural Radiance Field (NeRF) is a powerful algorithm capable of rendering photo-realistic novel view images from a monocular RGB video of a dynamic scene. Although it warps moving points across frames from the observation spaces to a common canonical space for rendering, dynamic NeRF does not model the change of the reflected color during the warping

Marcel F. Langer, Florian Knoop, Christian Carbogno, Matthias Scheffler

The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials. However, it requires an accurate description of the potential-energy surface and carefully converged statistics. Machine-learning potentials can achieve the accuracy of first-principles simulations while allowing to reach well beyond their simulation time and leng

Jihyo Kim, Jeonghyeon Kim, Sangheum Hwang

Active learning aims to identify the most informative data from an unlabeled data pool that enables a model to reach the desired accuracy rapidly. This benefits especially deep neural networks which generally require a huge number of labeled samples to achieve high performance. Most existing active learning methods have been evaluated in an ideal setting whe

Julien Genovese, Francesco Ballarin, Gianluigi Rozza, Claudio Canuto

In this manuscript we propose and analyze weighted reduced order methods for stochastic Stokes and Navier-Stokes problems depending on random input data (such as forcing terms, physical or geometrical coefficients, boundary conditions). We will compare weighted methods such as weighted greedy and weighted POD with non-weighted ones in case of stochastic para

Raghvendra Posti, Abhishek Kumar, Mayank Baghoria, Bhanu Prakash

Type-x device attracts considerable interest in the field of spintronics due to its robust spin-orbit torque (SOT) induced magnetization switching, and easy deposition technique. However, universally applicable and straightforward detection of type-X magnetization reversal is still elusive, unlike type-Z switching, which employs DC-based anomalous Hall effec

Zhouzheng Li, Hao Liu

Beta-VAE is a very classical model for disentangled representation learning, the use of an expanding bottleneck that allow information into the decoder gradually is key to representation disentanglement as well as high-quality reconstruction. During recent experiments on such fascinating structure, we discovered that the total amount of latent variables can

Yaroslav Zharov, Evelina Ametova, Rebecca Spiecker, Tilo Baumbach

Noise is an important issue for radiographic and tomographic imaging techniques. It becomes particularly critical in applications where additional constraints force a strong reduction of the Signal-to-Noise Ratio (SNR) per image. These constraints may result from limitations on the maximum available flux or permissible dose and the associated restriction on

Samidh Pal

This paper presents a new nested production function that is specifically designed for analyzing capital and labor intensity of manufacturing industries in developing and developed regions. The paper provides a rigorous theoretical foundation for this production function, as well as an empirical analysis of its performance in a sample of industries. The anal

Chaoyi Gu, Varuna De Silva, Corentin Artaud, Rafael Pina

Artificial Intelligence has been used to help human complete difficult tasks in complicated environments by providing optimized strategies for decision-making or replacing the manual labour. In environments including multiple agents, such as football, the most common methods to train agents are Imitation Learning and Multi-Agent Reinforcement Learning (MARL)

Yanis Taege, Tim Samuel Winter, Sophia Laura Schulz, Bernhard Messerschmidt

One-dimensional Airy beams allow the generation of thin light-sheets without scanning, simplifying the complex optical arrangements of light-sheet microscopes (LSM) with an extended field-of-view (FOV). However, their uniaxial acceleration limits the maximum numerical aperture of the detection objective in order to keep both the active and inactive axes with

Sheetal, Pabitra K. Biswas, K. Yokoyama, D. T. Adroja

The disordered pyrochlore oxide Dy$_{2}$Zr$_{2}$O$_{7}$ shows the signatures of field-induced spin freezing with remnant zero-point spin-ice entropy at 5 kOe magnetic field. We have performed zero-field and longitudinal field Muon spin relaxation ($\mu$SR) studies on Dy$_{2}$Zr$_{2}$O$_{7}$. Our zero field studies reveal the absence of both long-range orderi

Zhouhong Gu, Sihang Jiang, Wenhao Huang, Jiaqing Liang

The model's ability to understand synonymous expression is crucial in many kinds of downstream tasks. It will make the model to better understand the similarity between context, and more robust to the synonym substitution attack. However, many Pretrained Language Model (PLM) lack synonym knowledge due to limitation of small-scale synsets and PLM's pretrainin

Giuseppe Lancia, Marcello Dalpasso

We discuss the way to group all 25 possible 4-OPT moves into 7 orbits of equivalent moves. We then describe two implementations, one for a $\Theta(n^3)$ algorithm by de Berg's et al. and one of a $\Theta(n^2)$ algorithm by Glover, for finding the best 4-OPT move via dynamic programming.

Yuliang Cai, Jesse Thomason, Mohammad Rostami

The size and the computational load of fine-tuning large-scale pre-trained neural network are becoming two major obstacles in adopting machine learning in many applications. Continual learning (CL) can serve as a remedy through enabling knowledge-transfer across sequentially arriving tasks which relaxes the need to fine-tune all network weights from scratch.

Kewei Sun, Kaijun Shen, Maxim F. Gelin, Yang Zhao

We have developed an ab-initio-based fully-quantum numerically-accurate methodology for the simulation of the exciton dynamics and time- and frequency-resolved fluorescence spectra of the cavity-controlled two-dimensional materials at finite temperature and applied this methodology to the single-layer WSe2 system. This allowed us to establish dynamical and s

Hannes Vandecasteele, Giovanni Samaey

We introduce a generalised micro-macro Markov chain Monte Carlo (mM-MCMC) method with pseudo-marginal approximation to the free energy, that is able to accelerate sampling of the microscopic Gibbs distributions when there is a time-scale separation between the macroscopic dynamics of a reaction coordinate and the remaining microscopic degrees of freedom. The

Dominik J. Mühlematter, Nina Wiedemann, Yanan Xin, Martin Raubal

In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable transportation. Research on short-term prediction and optimization methods has improved operations and fleet control of car-sharing services; however, long-term projections and spat

Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao

Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public. However, existing models often generate images that do not align well with human preferences, such as awkward combinations of limbs and facial expressions. To address this issue, we collect a dataset of human choices

Hao Wang

Online rating platform represents the new trend of online cultural and commercial goods consumption. The user rating data on such platforms are foods for recommender system algorithms. Understanding the evolution pattern and its underlying mechanism is the key to understand the structures of input data for recommender systems. Prior research on input data an

Hongbin Zhang, Baocheng Zhang

We investigate the effect of superradiance shielding for the analogue rotating black holes simulated by optical vortices by calculating the radial motion of massless particles in such spacetime background. We add the conditions $E<L\Omega_{r_{e}}$ and $L>0$ to judge the classically forbidden region of superradiance. It is found that the superradiance forbidd

Hao Wang

Recommender system is the most successful commercial technology in the past decade. Technical mammoth such as Temu, TikTok and Amazon utilize the technology to generate enormous revenues each year. Although there have been enough research literature on accuracy enhancement of the technology, explainable AI is still a new idea to the field. In 2022, the autho

Baker Alhasn, Mohammad Qatawneh, Wesam Alkmobaideen

The paper proposes a Blockchain (BC) system to prevent counterfeiting in health insurance sector. The results show the system strength in terms of achieving data integrity and privacy of data. Moreover, the results show that the consensus algorithm can effectively reduce the total validation time for the proposed system.

Su Houng Lee

The masses of hadrons in the vacuum, where the chiral symmetry is restored, and in the medium are in general different even when the changes in the order parameters of chiral symmetry are the same. Here, we first discuss the relation between the hadron masses and the chiral symmetry breaking in approaches based on operator product expansion (OPE). We then di

Dinesh Krishnamoorthy, Joel A. Paulson

Bayesian optimization (BO) is a powerful black-box optimization framework that looks to efficiently learn the global optimum of an unknown system by systematically trading-off between exploration and exploitation. However, the use of BO as a tool for coordinated decision-making in multi-agent systems with unknown structure has not been widely studied. This p

Satsuki Matsuno, Fumihiro Ueno

Exact solutions to an Einstein-Maxwell(E-D-M) system with an electric current are simple models of global cosmic magnetic phenomena in the universe. In this paper, we consider an E-D-M system with two chiral spinors $ \psi^\pm$ coupled with a gauge field $ A$ and an electromagnetic field $F=dA$. We construct a family of exact solutions to the E-D-M system on

Han Xue, Zhiwu Huang, Qianru Sun, Li Song

Typical layout-to-image synthesis (LIS) models generate images for a closed set of semantic classes, e.g., 182 common objects in COCO-Stuff. In this work, we explore the freestyle capability of the model, i.e., how far can it generate unseen semantics (e.g., classes, attributes, and styles) onto a given layout, and call the task Freestyle LIS (FLIS). Thanks

Leonardo Iurada, Silvia Bucci, Timothy M. Hospedales, Tatiana Tommasi

Deep learning-based recognition systems are deployed at scale for several real-world applications that inevitably involve our social life. Although being of great support when making complex decisions, they might capture spurious data correlations and leverage sensitive attributes (e.g. age, gender, ethnicity). How to factor out this information while keepin

Filip K. Malinowski

We present a model of two Anderson impurities coupled to and through a superconducting island. The model parametrizes the strength of the coupling between impurity sites, allowing it to represent a variable distance between the impurities. We systematically explore the effect of the model parameters in the subspaces with total even and odd occupancy, identif

Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar

Recent vision architectures and self-supervised training methods enable vision models that are extremely accurate and general, but come with massive parameter and computational costs. In practical settings, such as camera traps, users have limited resources, and may fine-tune a pretrained model on (often limited) data from a small set of specific categories

Ziqin Wang, Bowen Cheng, Lichen Zhao, Dong Xu

The task of 3D semantic scene graph (3DSSG) prediction in the point cloud is challenging since (1) the 3D point cloud only captures geometric structures with limited semantics compared to 2D images, and (2) long-tailed relation distribution inherently hinders the learning of unbiased prediction. Since 2D images provide rich semantics and scene graphs are in