April 2023 arXiv papers — page 103

Mahendran NV

Convolutional neural networks learns spatial features and are heavily interlinked within kernels. The SE module have broken the traditional route of neural networks passing the entire result to next layer. Instead SE only passes important features to be learned with its squeeze and excitation (SE) module. We propose variations of the SE module which improvis

Filip Wudarski, Yaxing Zhang, M. I. Dykman

Slow fluctuations of a qubit frequency are one of the major problems faced by quantum computers. To understand their origin it is necessary to go beyond the analysis of their spectra. We show that characteristic features of the fluctuations can be revealed using comparatively short sequences of periodically repeated Ramsey measurements, with the sequence dur

A. Barletta, M. Celli, S. Lazzari, P. V. Brandão

The mixed convection flow in a plane channel with adiabatic boundaries is examined. The boundaries have an externally prescribed relative velocity defining a Couette-like setup for the flow. A stationary flow regime is maintained with a constant velocity difference between the boundaries, considered as thermally insulated. The effect of viscous dissipation i

Lorenzo Dello Schiavo, Jan Maas, Francesco Pedrotti

This paper deals with local criteria for the convergence to a global minimiser for gradient flow trajectories and their discretisations. To obtain quantitative estimates on the speed of convergence, we consider variations on the classical Kurdyka--{\L}ojasiewicz inequality for a large class of parameter functions. Our assumptions are given in terms of the in

Inês Lourenço, Andreea Bobu, Cristian R. Rojas, Bo Wahlberg

Robots have been increasingly better at doing tasks for humans by learning from their feedback, but still often suffer from model misalignment due to missing or incorrectly learned features. When the features the robot needs to learn to perform its task are missing or do not generalize well to new settings, the robot will not be able to learn the task the hu

Xiaojin Zhang, Lixin Fan, Siwei Wang, Wenjie Li

In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of \textit{privacy leakage} cannot be ignored in the presence of \textit{semi-honest} adversaries. Existing research has focused either on designing protection mechanisms or on inventing attacking mechanisms. While the battle between defenders and att

David Bruce Cousins, Yuriy Polyakov, Ahmad Al Badawi, Matthew French

Secure computation is of critical importance to not only the DoD, but across financial institutions, healthcare, and anywhere personally identifiable information (PII) is accessed. Traditional security techniques require data to be decrypted before performing any computation. When processed on untrusted systems the decrypted data is vulnerable to attacks to

Jijie Tang, Fangyuan Ma, Feng Li, Honglian Guo

Circuits provide ideal platforms of topological phases and matter, yet the study of topological circuits in the strongly nonlinear regime, has been lacking. We propose and experimentally demonstrate strongly nonlinear topological phases and transitions in one-dimensional electrical circuits composed of nonlinear capacitors. Nonlinear topological interface mo

Marzia Mazzotta, Bernard Rybołowicz, Paola Stefanelli

A dual weak brace is an algebraic structure $\left(S,\,+,\,\circ\right)$ including skew braces and giving rise to a set-theoretic solution of the Yang-Baxter equation. We show that such a map belongs to a family of set-theoretic solutions, called deformed solutions, that are defined on $S$ and depending on certain parameters. We prove these elements are exac

Qiao Jin, Qinghua Zhang, Bai He, Yuting Zou

The magnetic correlations at the superconductor/ferromagnet (S/F) interfaces play a crucial role in realizing dissipation-less spin-based logic and memory technologies, such as triplet-supercurrent spin-valves and "{\pi}" Josephson junctions. Here we report the coexistence of an induced large magnetic moment and a crypto ferromagnetic state at high-quality n

Roman Macháček, Leila Mozaffari, Zahra Sepasdar, Sravanthi Parasa

In order to take advantage of AI solutions in endoscopy diagnostics, we must overcome the issue of limited annotations. These limitations are caused by the high privacy concerns in the medical field and the requirement of getting aid from experts for the time-consuming and costly medical data annotation process. In computer vision, image synthesis has made a

Waleed Iqbal, Vahid Ghafouri, Gareth Tyson, Guillermo Suarez-Tangil

From health to education, income impacts a huge range of life choices. Earlier research has leveraged data from online social networks to study precisely this impact. In this paper, we ask the opposite question: do different levels of income result in different online behaviors? We demonstrate it does. We present the first large-scale study of Nextdoor, a po

Vineet Dawara, Ashok Bajantri, Harish Singh Dhami, SVS Narayana Murty

Material deformation and failure under impact loading is a subject of active investigation in space science and often requires very specialized equipment for testing. In this work, we present the design, operational analysis and application of a low-velocity ($\sim 100$ m/s) projectile impact framework for evaluating the deformation and failure of space-grad

Lucia Janičková, Evelin Csókási

A Pythagorean triple is a triple of positive integers $(x,y,z)$ such that $x^2+y^2=z^2$. If $x,y$ are coprime and $x$ is odd, then it is called a primitive Pythagorean triple. Berggren showed that every primitive Pythagorean triple can be generated from triple $(3,4,5)$ using multiplication by uniquely number and order of three $3\times3$ matrices, which yie

Laure Daviaud, David Purser, Marie Tcheng

We show that the big-O problem for max-plus automata is decidable and PSPACE-complete. The big-O (or affine domination) problem asks whether, given two max-plus automata computing functions f and g, there exists a constant c such that f < cg+ c. This is a relaxation of the containment problem asking whether f < g, which is undecidable. Our decidability resul

Driss Bennis, Rachid EL Maaouy, Juan Ramon Garcia Rozas, Luis Oyonarte

In this paper we introduce and study the weak Gorenstein global dimension of a ring $R$ with respect to a left $R$-module $C$. We provide several characterizations of when this homological invariant is bounded. Two main applications are given: first, we prove that the weak Gorenstein global dimension of $R$ relative to a semidualizing $(R,S)$-bimodule $C$ ca

Udrea Păun

The $G^{+}$ method is a new method, a powerful one, for the study of (homogeneous and nonhomogeneous) products of nonnegative matrices -- for problems on the products of nonnegative matrices. To study such products, new classes of matrices are introduced: that of the sum-positive matrices, that of the $\left[ \Delta \right] $-positive matrices on partitions

Ashutosh Dubey, Arnab Pal

We study the statistical properties of first-passage Brownian functionals (FPBFs) of an Ornstein-Uhlenbeck (OU) process in the presence of stochastic resetting. We consider a one dimensional set-up where the diffusing particle sets off from $x_0$ and resets to $x_R$ at a certain rate $r$. The particle diffuses in a harmonic potential (with strength $k$) whic

Arthur Vereijken, Shahriyar Jafarzade, Milena Piotrowska, Francesco Giacosa

Glueballs remain an experimentally undiscovered expectation of QCD. Lattice QCD (As well as other theoretical approaches) predicts a spectrum of glueballs, with the tensor ($J^{PC}=2^{++}$) glueball being the second lightest, behind the scalar glueball. Here, using a chiral hadronic model, we compute decay ratios of the tensor glueball into various meson dec

Rafiah Ansari

Around ten percent of children may present with a disorder where language does not develop as expected. This often affects vocabulary skills, i.e., finding the words to express wants, needs and ideas, which can influence behaviours linked to wellbeing and daily functioning, such as concentration, independence, social interactions and managing emotions. Witho

Sohan Salahuddin Mugdho, Hafiz Imtiaz

Building a recommendation system involves analyzing user data, which can potentially leak sensitive information about users. Anonymizing user data is often not sufficient for preserving user privacy. Motivated by this, we propose a privacy-preserving recommendation system based on the differential privacy framework and matrix factorization, which is one of t

Xiaoqing Huang, Andersen Ang, Kun Huang, Jie Zhang

We study estimation of piecewise smooth signals over a graph. We propose a $\ell_{2,0}$-norm penalized Graph Trend Filtering (GTF) model to estimate piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness across the nodes. We prove that the proposed GTF model is simultaneously a k-means clustering on the signal over the nodes and a min

Kyle L. Walker, Adam A. Stokes, Aristides Kiprakis, Francesco Giorgio-Serchi

When deploying robots in shallow ocean waters, wave disturbances can be significant, highly dynamic and pose problems when operating near structures; this is a key limitation of current control strategies, restricting the range of conditions in which subsea vehicles can be deployed. To improve dynamic control and offer a higher level of robustness, this work

Hadeel Al-Negheimish, Pranava Madhyastha, Alessandra Russo

Large pre-trained language models such as BERT have been widely used as a framework for natural language understanding (NLU) tasks. However, recent findings have revealed that pre-trained language models are insensitive to word order. The performance on NLU tasks remains unchanged even after randomly permuting the word of a sentence, where crucial syntactic

Yang Li, Ligong Bian, Yongtao Jia

Domain wall networks are two-dimensional topological defects generally predicted in many beyond standard model physics. In this Letter, we propose to solve the domain wall problem with the first-order phase transition. We numerically study the phase transition dynamics, and for the first time show that the domain walls reached scaling regime can be diluted t

Abhishek Sinha

Classic no-regret multi-armed bandit algorithms, including the Upper Confidence Bound (UCB), Hedge, and EXP3, are inherently unfair by design. Their unfairness stems from their objective of playing the most rewarding arm as frequently as possible while ignoring the rest. In this paper, we consider a fair prediction problem in the stochastic setting with a gu

Shu Chen, Junyao Li, Yang Zhang, Beiji Zou

With dense inputs, Neural Radiance Fields (NeRF) is able to render photo-realistic novel views under static conditions. Although the synthesis quality is excellent, existing NeRF-based methods fail to obtain moderate three-dimensional (3D) structures. The novel view synthesis quality drops dramatically given sparse input due to the implicitly reconstructed i

Charles Kind

Magnetic skyrmions are nano-scale spin textures whose stability, size and ease of manipulation make them strong contenders for next generation data and logic applications. Here we illustrate fine motion control of skyrmions, we show how they can be moved in any direction and for any distance, in the plane. We demonstrate, using simulations, two novel skyrmio

Ensheng Shi, Yanlin Wang, Hongyu Zhang, Lun Du

Recently, fine-tuning pre-trained code models such as CodeBERT on downstream tasks has achieved great success in many software testing and analysis tasks. While effective and prevalent, fine-tuning the pre-trained parameters incurs a large computational cost. In this paper, we conduct an extensive experimental study to explore what happens to layer-wise pre-

Keumgang Cha, Junghoon Seo, Taekyung Lee

As the potential of foundation models in visual tasks has garnered significant attention, pretraining these models before downstream tasks has become a crucial step. The three key factors in pretraining foundation models are the pretraining method, the size of the pretraining dataset, and the number of model parameters. Recently, research in the remote sensi

Oksana Shadura, Alexander Held

The IRIS-HEP Analysis Grand Challenge (AGC) is designed to be a realistic environment for investigating how analysis methods scale to the demands of the HL-LHC. The analysis task is based on publicly available Open Data and allows for comparing the usability and performance of different approaches and implementations. It includes all relevant workflow aspect

Debaprasanna Kar

We study the boundary behavior of the Kobayashi--Fuks metric on the class of h-extendible domains. Here, we derive the non-tangential boundary asymptotics of the Kobayashi--Fuks metric and its Riemannian volume element by the help of some maximal domain functions and then using their stability results on h-extendible local models.

Jun Wang, Omran Alamayreh, Benedetta Tondi, Mauro Barni

Classification of AI-manipulated content is receiving great attention, for distinguishing different types of manipulations. Most of the methods developed so far fail in the open-set scenario, that is when the algorithm used for the manipulation is not represented by the training set. In this paper, we focus on the classification of synthetic face generation

Guodong Pang, Etienne Pardoux

We study an individual-based stochastic spatial epidemic model where the number of locations and the number of individuals at each location both grow to infinity. Each individual is associated with a random infection-age dependent infectivity function. Individuals are infected through interactions across the locations with heterogeneous effects. The epidemic

Dominique Sommers, Natalia Sidorova, Boudewijn F. van Dongen

The execution of different cases of a process is often restricted by inter-case dependencies through e.g., queueing or shared resources. Various high-level Petri net formalisms have been proposed that are able to model and analyze coevolving cases. In this paper, we focus on a formalism tailored to conformance checking through alignments, which introduces ch

Muzaffer Adak, Nese Ozdemir, Caglar Pala

We consider a Weyl-Lorentz-$U(1)$-invariant gravity model written in terms of a scalar field, electromagnetic field and nonmetricity without torsion and curvature, the so-called symmetric teleparallel geometry, in three dimensions. Firstly, we obtain variational field equations from a Lagrangian. Then, we find some classes of circularly symmetric rotating so

Xiaoxiang Chai

We show a spacetime positive mass theorem for asymptotically flat initial data sets with a noncompact boundary. We develop a mass type invariant and a boundary dominant energy condition. Our proof is based on spinors.

Zhaoyuan Shi, Huabing Lu, Xianzhong Xie, Helin Yang

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS's amplification matrix and phase shift matrix is formulated

Konstantin Hemker, Zohreh Shams, Mateja Jamnik

Rule-based surrogate models are an effective and interpretable way to approximate a Deep Neural Network's (DNN) decision boundaries, allowing humans to easily understand deep learning models. Current state-of-the-art decompositional methods, which are those that consider the DNN's latent space to extract more exact rule sets, manage to derive rule sets at hi

Lu Han, Han-Jia Ye, De-Chuan Zhan

Multivariate time series data comprises various channels of variables. The multivariate forecasting models need to capture the relationship between the channels to accurately predict future values. However, recently, there has been an emergence of methods that employ the Channel Independent (CI) strategy. These methods view multivariate time series data as s

Alexandre Heuillet, Ahmad Nasser, Hichem Arioui, Hedi Tabia

In the past few years, Differentiable Neural Architecture Search (DNAS) rapidly imposed itself as the trending approach to automate the discovery of deep neural network architectures. This rise is mainly due to the popularity of DARTS, one of the first major DNAS methods. In contrast with previous works based on Reinforcement Learning or Evolutionary Algorit

Tan-Minh Nguyen, Thai-Binh Nguyen, Hoang-Trung Nguyen, Hai-Long Nguyen

Multi-document summarization is challenging because the summaries should not only describe the most important information from all documents but also provide a coherent interpretation of the documents. This paper proposes a method for multi-document summarization based on cluster similarity. In the extractive method we use hybrid model based on a modified ve

Dina Barak-Pelleg, Daniel Berend

Despite their frequency, denial-of-service (DoS\blfootnote{Denial of Service (DoS), Distributed Denial of Service (DDoS), Probabilistic Packet Marking (PPM), coupon collector's problem (CCP)}) and distributed-denial-of-service (DDoS) attacks are difficult to prevent and trace, thus posing a constant threat. One of the main defense techniques is to identify t

Mathew S. Madhavacheril, Frank J. Qu, Blake D. Sherwin, Niall MacCrann

We present cosmological constraints from a gravitational lensing mass map covering 9400 sq. deg. reconstructed from CMB measurements made by the Atacama Cosmology Telescope (ACT) from 2017 to 2021. In combination with BAO measurements (from SDSS and 6dF), we obtain the amplitude of matter fluctuations $\sigma_8 = 0.819 \pm 0.015$ at 1.8% precision, $S_8\equi

Frank J. Qu, Blake D. Sherwin, Mathew S. Madhavacheril, Dongwon Han

We present new measurements of cosmic microwave background (CMB) lensing over $9400$ sq. deg. of the sky. These lensing measurements are derived from the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) CMB dataset, which consists of five seasons of ACT CMB temperature and polarization observations. We determine the amplitude of the CMB lensing power s

Haoyu Ren, Darko Anicic, Thomas A. Runkler

Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask whether TinyML applications can benefit from aggregating their knowledge. Federated learning (FL) enables decentralized agents to jointly l

Piergiorgio Ladisa, Serena Elisa Ponta, Antonino Sabetta, Matias Martinez

This work discusses open-source software supply chain attacks and proposes a general taxonomy describing how attackers conduct them. We then provide a list of safeguards to mitigate such attacks. We present our tool "Risk Explorer for Software Supply Chains" to explore such information and we discuss its industrial use-cases.

Thomas W. Owen, Vytene Janiukstyte, Gerard R. Hall, Fahmida A. Chowdhury

Intracranial EEG (iEEG) is the gold standard technique for epileptogenic zone (EZ) localisation, but requires a hypothesis of which tissue is epileptogenic, guided by qualitative analysis of seizure semiology and other imaging modalities such as magnetoencephalography (MEG). We hypothesised that if quantifiable MEG band power abnormalities were sampled by iE

Weiyang Jin

Rolling bearings are critical components in rotating machinery, and their faults can cause severe damage. Early detection of abnormalities is crucial to prevent catastrophic accidents. Traditional and intelligent methods have been used to analyze time series data, but in real-life scenarios, sensor data is often noisy and cannot be accurately characterized i

Haoran Li, Dadi Guo, Wei Fan, Mingshi Xu

With the rapid progress of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts. Though model developers and researchers work hard on dialog safety to avoid generating harmful content from LLMs, it is still challenging to steer AI-generated content (AIGC) for the human good. As powerful LLMs are devouring exist

Niall MacCrann, Blake D. Sherwin, Frank J. Qu, Toshiya Namikawa

We investigate the impact and mitigation of extragalactic foregrounds for the CMB lensing power spectrum analysis of Atacama Cosmology Telescope (ACT) data release 6 (DR6) data. Two independent microwave sky simulations are used to test a range of mitigation strategies. We demonstrate that finding and then subtracting point sources, finding and then subtract

Anda Cheng, Zhen Wang, Yaliang Li, Jian Cheng

Numerous research studies in the field of federated learning (FL) have attempted to use personalization to address the heterogeneity among clients, one of FL's most crucial and challenging problems. However, existing works predominantly focus on tailoring models. Yet, due to the heterogeneity of clients, they may each require different choices of hyperparame

Alessandro Lucchesini

Using the Tunable Diode Laser Absorption Spectroscopy (TDLAS) 82 CH$_3$I ro-vibrational overtone absorption were detected for the first time between 11660 and 11840 cm$^{-1}$ (844 -- 857 nm), with strengths estimated to be around $10^{-27}$ -- 10$^{-26}$ cm/molecule. The lines have been measured utilizing commercial heterostructure F-P type diode lasers, mul

Jian Wang, Shangqing Liu, Xiaofei Xie, Yi Li

Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with ChatGPT emerging as a leading AIGC model that produces high-quality responses across various applications, including software development and maintenance. Despite its potential, the misuse of ChatGPT poses significant concerns, especially

Thomas W. Owen, Vytene Janiukstyte, Gerard R. Hall, Jonathan J. Horsley

Successful epilepsy surgery depends on localising and resecting cerebral abnormalities and networks that generate seizures. Abnormalities, however, may be widely distributed across multiple discontiguous areas. We propose spatially constrained clusters as candidate areas for further investigation, and potential resection. We quantified the spatial overlap be

Takashi Horiuchi, Hidekazu Hanayama, Masatoshi Ohishi, Tatsuya Nakaoka

This study provides the results of simultaneous multicolor observations for the first Visorsat (STARLINK-1436) and the ordinary Starlink satellite, STARLINK-1113 in the $U$, $B$, $V$, $g'$, $r$, $i$, $R_{\rm C}$, $I_{\rm C}$, $z$, $J$, $H$, and $K_s$ bands to quantitatively investigate the extent to which Visorsat reduces its reflected light. Our results are

Kshitish Kumar Pradhan, Bhagyarathi Sahoo, Dushmanta Sahu, Raghunath Sahoo

Studying the thermodynamics of the systems produced in ultra-relativistic heavy-ion collisions is crucial in understanding the QCD phase diagram. Recently, a new avenue has opened regarding the implications of large initial angular momentum and subsequent vorticity in the medium evolution in high-energy collisions. This adds a new type of chemical potential

Fernando Delbianco, Fernando Tohmé

The problem of individualized prediction can be addressed using variants of conformal prediction, obtaining the intervals to which the actual values of the variables of interest belong. Here we present a method based on detecting the observations that may be relevant for a given question and then using simulated controls to yield the intervals for the predic

Enrique Álvarez, Jesús Anero, Carmelo P. Martin, Eduardo Velasco-Aja

When implementing a non-linear constraint in quantum field theory by means of a Lagrange multiplier, $\l(x)$, it is often the case that quantum dynamics induce quadratic and even higher order terms in $\l(x)$, which then does not enforce the constraint anymore. This is illustrated in the case of Unimodular Gravity, where the constraint is that the metric ten

Jeremy Bernstein, Chris Mingard, Kevin Huang, Navid Azizan

The architecture of a deep neural network is defined explicitly in terms of the number of layers, the width of each layer and the general network topology. Existing optimisation frameworks neglect this information in favour of implicit architectural information (e.g. second-order methods) or architecture-agnostic distance functions (e.g. mirror descent). Mea

Ofer Neufeld, Hannes Hübener, Gregor Jotzu, Umberto De Giovannini

We study low-frequency linearly-polarized laser-dressing in materials with valley (graphene and hexagonal-Boron-Nitride), and topological (Dirac- and Weyl-semimetals), properties. In Dirac-like linearly-dispersing bands, the laser substantially moves the Dirac nodes away from their original position, and the movement direction can be fully controlled by rota

Peter Goričan, Žiga Virk

We consider persistent homology obtained by applying homology to the open Rips filtration of a compact metric space $(X,d)$. We show that each decrease in zero-dimensional persistence and each increase in one-dimensional persistence is induced by local minima of the distance function $d$. When $d$ attains local minimum at only finitely many pairs of points,

M. Y. Avetisyan, R. L. Mkrtchyan

The partition function of refined Chern-Simons theory on 3d sphere for the exceptional $E_n$ gauge algebras is presented in terms of multiple sine functions. Gopakumar-Vafa (BPS) approximation is calculated and presented in the form of some refined topological string partition function.

Yvon Bossut

We present here some known and some new examples of non-simple NSOP1 theories and some behaviour that Kim-forking can exhibit in these theories, in particular that Kim-forking after forcing base monotonicity can or can not satisfy extension (on arbitrary sets). This study is based on the results of Chernikov, Ramsey, Dobrowolski and Granger.

Syllas R. C. Magalhaes, Suzan Bayhan, Geert Heijenk

While non-orthogonal multiple access (NOMA) improves spectral efficiency, it results in a complexity at the receivers due to successive interference cancellation (SIC). Prior studies on the energy efficiency of NOMA overlook the SIC overhead and rely on simplistic power consumption models (PCM). To fill this gap, we first introduce PCM-$\kappa$ that accounts

Arda Gulersoy, Ahmet Berk Tuzcu, Doga Gunduzalp, Koray Kavakl

In this work, we developed a wearable, head-mounted device that automatically calculates the precise Relative Afferent Pupillary Defect (RAPD) value of a patient. The device consists of two RGB LEDs, two infrared cameras, and one microcontroller. In the RAPD test, the parameters like LED on-off durations, brightness level, and color of the light can be contr

Hung M. Bui, R. R. Hall

Using the twisted fourth moment of the Riemann zeta-function we study large gaps between consecutive zeros of the derivatives of Hardy's function $Z(t)$, improving upon previous results of Conrey and Ghosh [J. London Math. Soc. 32 (1985), 193--202], and of the second named author [Acta Arith. 111 (2004), 125--140]. We also exhibit small distances between the

Sabine Tostain

Research activities are generally observed and evaluated through the prism of their production and financial elements or team composition. In addition to standardized management indicators and bibliometrics, the French National Research Institute for Sustainable Development (IRD) has been building new indicators for the last ten years, based on the annual re

Isaac Bird, Jordan Williamson

We develop an alternative approach to the homological spectrum of a tensor-triangulated category through the lens of definable subcategories. This culminates in a proof that the homological spectrum is homeomorphic to a quotient of the Ziegler spectrum. Along the way, we characterise injective objects in homological residue fields in terms of the definable s

Hung M. Bui, R. R. Hall

Let $Z^{(k)}(t)$ be the $k$-th derivative of Hardy's $Z$-function. The numerics seem to suggest that if $k$ and $\ell$ have the same parity, then the zeros of $Z^{(k)}(t)$ and $Z^{(\ell)}(t)$ come in pairs which are very close to each other. That is to say that $Z^{(k)}(t)Z^{(\ell)}(t)$ has constant sign for the majority, if not almost all, of values $t$. In

El-Mehdi El Arar, Devan Sohier, Pablo de Oliveira Castro, Eric Petit

The main objective of this work is to investigate non-linear errors and pairwise summation using stochastic rounding (SR) in variance computation algorithms. We estimate the forward error of computations under SR through two methods: the first is based on a bound of the variance and Bienaym{\'e}-Chebyshev inequality, while the second is based on martingales

YanMing Hu, Chuan Chen, BoWen Deng, YuJing Lai

Anomaly detection on attributed graphs is a crucial topic for its practical application. Existing methods suffer from semantic mixture and imbalance issue because they mainly focus on anomaly discrimination, ignoring representation learning. It conflicts with the assortativity assumption that anomalous nodes commonly connect with normal nodes directly. Addit

Qi Wang, Wenchuan Wu, Chenhui Lin, Shuwei Xu

Storage-concerned Optimal Power Flow (OPF) with complementarity constraints is highly non-convex and intractable. In this paper, we propose two generalized sufficient conditions which guarantee no simultaneous charging and discharging (SCD) in the relaxed multi-period OPF excluding the complementarity constraints. Moreover, we prove that the regions on the l

Tatiana Gonzalez Grandon, Johannes Schwenzer, Thomas Steens, Julia Breuing

This article presents a novel hybrid approach using statistics and machine learning to forecast the national demand of electricity. As investment and operation of future energy systems require long-term electricity demand forecasts with hourly resolution, our mathematical model fills a gap in energy forecasting. The proposed methodology was constructed using

Ahmet Iscen, Alireza Fathi, Cordelia Schmid

Retrieval augmented models are becoming increasingly popular for computer vision tasks after their recent success in NLP problems. The goal is to enhance the recognition capabilities of the model by retrieving similar examples for the visual input from an external memory set. In this work, we introduce an attention-based memory module, which learns the impor

Hui Li, Tianyang Xu, Xiao-Jun Wu, Jiwen Lu

Deep learning based fusion methods have been achieving promising performance in image fusion tasks. This is attributed to the network architecture that plays a very important role in the fusion process. However, in general, it is hard to specify a good fusion architecture, and consequently, the design of fusion networks is still a black art, rather than scie

Maximilian Xiling Li, Onur Celik, Philipp Becker, Denis Blessing

Learning skills by imitation is a promising concept for the intuitive teaching of robots. A common way to learn such skills is to learn a parametric model by maximizing the likelihood given the demonstrations. Yet, human demonstrations are often multi-modal, i.e., the same task is solved in multiple ways which is a major challenge for most imitation learning

Yutao Cui, Chenkai Zeng, Xiaoyu Zhao, Yichun Yang

Multi-object tracking in sports scenes plays a critical role in gathering players statistics, supporting further analysis, such as automatic tactical analysis. Yet existing MOT benchmarks cast little attention on the domain, limiting its development. In this work, we present a new large-scale multi-object tracking dataset in diverse sports scenes, coined as

Alexander F. Sax

Bonding in the C$_2$ molecule is investigated with CAS(8,8) wave functions using canonical MOs. In a subsequent step, orthogonal atomic orbitals are constructed by localizing the CASSCF MOs on the two carbon atoms with an orthogonal transformation. This orbital transformation causes an orthogonal transformation of the configuration state functions (CSF) span

Bojan Arbutina

A well-known paradigm about the origin of Galactic cosmic rays (CRs) is that these high-energy particles are accelerated in the process of diffusive shock acceleration (DSA) at collisionless shocks (at least up to the so-called "knee"energy of $10^{15}$ eV). Knowing the details of injection of electrons, protons and heavier nuclei into the DSA, their initial

Manasi Pranav, Thomas Hultzsch, Artem Musiienko, Bowen Sun

Understanding the origin of inefficient photocurrent generation in organic solar cells with low energy offset remains key to realizing high performance donor-acceptor systems. Here, we probe the origin of field-dependent free charge generation and photoluminescence in non-fullerene acceptor (NFA) based organic solar cells using the polymer PM6 and NFA Y5 - a

Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora, Arkady Zgonnikov

Predicting the future behavior of human road users is an important aspect for the development of risk-aware autonomous vehicles. While many models have been developed towards this end, effectively capturing and predicting the variability inherent to human behavior still remains an open challenge. This paper proposes TrajFlow - a new approach for probabilisti

Mengyao Xie, Zongbo Han, Changqing Zhang, Yichen Bai

Classifying incomplete multi-view data is inevitable since arbitrary view missing widely exists in real-world applications. Although great progress has been achieved, existing incomplete multi-view methods are still difficult to obtain a trustworthy prediction due to the relatively high uncertainty nature of missing views. First, the missing view is of high

Maximilian Nielsen, Laura Wenderoth, Thilo Sentker, René Werner

Is self-supervised deep learning (DL) for medical image analysis already a serious alternative to the de facto standard of end-to-end trained supervised DL? We tackle this question for medical image classification, with a particular focus on one of the currently most limiting factors of the field: the (non-)availability of labeled data. Based on three common

Sarah E. I. Bosman, Frederick B. Davies, Eduardo Bañados

Koptelova et al. 2022 (K22) recently claimed a new quasar discovery at $z=7.46$. After careful consideration of the publicly-available data underlying K22's claim, we find that the observations were contaminated by a moving Solar System object, likely a main-belt asteroid. In the absence of the contaminated photometry, there is no evidence for the nearby, pe

Almudena Rouco, Mohammad Rajabdorri, Lukas Sigrist, Enrique Lobato

This letter presents a corrective frequency-constrained UC (C-FCUC) for island power systems implementing analytical constraints on underfrequency load shedding (UFLS). Since UFLS is inevitable for sufficiently large disturbances, one can argue that less spinning reserve could be held back since UFLS takes place anyway. Congruently, the reserve criterion sho

A. Malagón-Romero, A. Luque

We investigate the effect of humidity on the propagation of streamers in air. We present a minimal set of chemical reactions that takes into account the presence of water in a nonthermal air plasma and considers ionization, attachment, detachment, recombination and ion conversion including water cluster formation. We find differences in streamer propagation

Cristian Ortiz, Carlos Varea

In this paper we describe all invariant complex Dirac structures with constant real index on a maximal flag manifold in terms of the roots of the Lie algebra which defines the flag manifold. We also completely classify these structures under the action of $B$-transformations.

Daniel Torres-Salinas, Domingo Docampo, Wenceslao Arroyo-Machado, Nicolas Robinson-Garcia

Altmetrics have led to new quantitative studies of science through social media interactions. However, there are no models of science communication that respond to the multiplicity of non-academic channels. Using the 3653 authors with the highest volume of altmetrics mentions from the main channels (Twitter, News, Facebook, Wikipedia, Blog, Policy documents,

Xinyue Zhang, Liangzu Peng, Wanting Xu, Laurent Kneip

Branch-and-bound-based consensus maximization stands out due to its important ability of retrieving the globally optimal solution to outlier-affected geometric problems. However, while the discovery of such solutions caries high scientific value, its application in practical scenarios is often prohibited by its computational complexity growing exponentially

Trish Velan Dcruz, Cicero Estibeiro, Anil Shankar, Mangal Das

Autonomous robots can help people explore parts of the ocean that would be hard or impossible to get to otherwise. The increase in the availability of low-cost components has made it possible to innovate, design, and implement new and innovative ideas for underwater robotics. Cost-effective and open solutions that are available today can be used to replace e

Miao Zhang, Y. F. Wang, X. Y. Peng, X. N. Feng

We introduce a spin-based receiver to sensitively detect the electric fields of millimeter (mm) waves by using quantum interferometric approach. The proposed quantum sensor consists of many surface-state electrons trapped individually on liquid helium by an electrode-network at the bottom of the liquid helium film. A dc-current in this chip is biased to gene

Tony Ma, Songze Li, Yisong Xiao, Shunchang Liu

The transferability of adversarial examples is a crucial aspect of evaluating the robustness of deep learning systems, particularly in black-box scenarios. Although several methods have been proposed to enhance cross-model transferability, little attention has been paid to the transferability of adversarial examples across different tasks. This issue has bec

Omar S. M. El Nahhas, Chiara M. L. Loeffler, Zunamys I. Carrero, Marko van Treeck

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesized that regression-based DL outperforms classification-based DL. Therefore, we developed and evaluated a ne

Shiyu Tang, Ting Sun, Juncai Peng, Guowei Chen

The success of transformers in computer vision has led to several attempts to adapt them for mobile devices, but their performance remains unsatisfactory in some real-world applications. To address this issue, we propose PP-MobileSeg, a semantic segmentation model that achieves state-of-the-art performance on mobile devices. PP-MobileSeg comprises three nove

Yaling Ke

The hierarchical equations of motion (HEOM) method is a numerically exact open quantum system dynamics approach. The method is rooted in an exponential expansion of the bath correlation function, which in essence strategically reshapes a continuous environment into a set of effective bath modes that allow for more efficient cutoff at finite temperatures. Bas

G Palacios, Sumanta Kundu, L A P Santos, M A F Gomes

We investigate the transport properties of a complex porous structure with branched fractal architectures formed due to the gradual deposition of dimers in a model of multilayer adsorption. We thoroughly study the interplay between the orientational anisotropy parameter $p_0$ of deposited dimers and the formation of porous structures, as well as its impact o

F Rousse, M Fasi, A Dmytryshyn, M Gulliksson

The Gaussian phase-space representation can be used to implement quantum dynamics for fermionic particles numerically. To improve numerical results, we explore the use of dynamical diffusion gauges in such implementations. This is achieved by benchmarking quantum dynamics of few-body systems against independent exact solutions. A diffusion gauge is implement

Yiquan Chen, Zhen Jin, Yijing Wang, Yi Chen

NVMe(Non-Volatile Memory Express) is an industry standard for solid-state drives (SSDs) that has been widely adopted in data centers. NVMe virtualization is crucial in cloud computing as it allows for virtualized NVMe devices to be used by virtual machines (VMs), thereby improving the utilization of storage resources. However, traditional software-based solu

Roberto Casadei

Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence,