October 2023 arXiv papers — page 143

Andreas Löhne

We provide a solution method for the polyhedral convex set optimization problem, that is, the problem to minimize a set-valued mapping with polyhedral convex graph with respect to a set ordering relation which is generated by a polyhedral convex cone . The method is proven to be correct and finite without any further assumption to the problem.

Pritish Urumkar, Ashwini Gade

The essential activities such as communication via email, surfing the world wide web, watching ones preferred Film or television series a large majority of people impaired by neurolocomotor disorders including those paralyzed by accident do not access machines. It was inferred from a previous research review those eyeballs are really an exceptional contender

Ke Wang, Guillermo Ortiz-Jimenez, Rodolphe Jenatton, Mark Collier

Label noise is a pervasive problem in deep learning that often compromises the generalization performance of trained models. Recently, leveraging privileged information (PI) -- information available only during training but not at test time -- has emerged as an effective approach to mitigate this issue. Yet, existing PI-based methods have failed to consisten

Christiaan Verwijs, Daniel Russo

In the era of Agile methodologies, organizations are exploring strategies to scale development across teams. Various scaling strategies have emerged, from "SAFe" to "LeSS", with some organizations creating their own methods. Despite numerous studies on organizational challenges with these approaches, none have empirically compared their impact on Agile team

Shashikant Kumar, Xin Jing, John E. Pask, Andrew J. Medford

We present a $\Delta$-machine learning model for obtaining Kohn-Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine learned force field (MLFF) scheme based on the kernel method to capture the difference between Kohn-Sham and orbital-free DFT energies/forces. We implement this model in the context o

Xiao Liu, Jie-Dong Jiang, Lin-Jing Qi, Yang-Yang Xu

In the present work, based on Wentzel-Kramers-Brillouin theory, we systematically study the cluster radioactivity half-lives of 22 nuclei ranging from $^{221}$$\rm{Fr}$ to $^{242}$$\rm{Cm}$ by using a phenomenological model, which considers the screened electrostatic effect of Coulomb potential. In this model, there are two adjustable parameters i.e. the par

Nicola Pranzini, Esko Keski-Vakkuri

We present and investigate two issues within the measurement scheme for QFT presented by J. Polo-G\'omez, L. J. Garay and E. Mart\'in-Mart\'inez in "A detector-based measurement theory for quantum field theory". We point out some discrepancies that arise when the measurement scheme is applied to contextual field states and show that $n$-point function assign

Jorge J. Garcés, Mykola Khrypchenko

We study bounded bilinear maps on a C$^*$-algebra $A$ having product property at $c\in A$. This leads us to the question of when a C$^*$-algebra is determined by products at $c.$ In the first part of our paper, we investigate this question for compact C$^*$-algebras, and in the second part, we deal with von Neumann algebras having non-trivial atomic part. Ou

Ning Liao, Shaofeng Zhang, Renqiu Xia, Min Cao

There is an emerging line of research on multimodal instruction tuning, and a line of benchmarks has been proposed for evaluating these models recently. Instead of evaluating the models directly, in this paper, we try to evaluate the Vision-Language Instruction-Tuning (VLIT) datasets. Also, we seek the way of building a dataset for developing an all-powerful

A. Dey, V. Keus, S. Moretti, C. Shepherd-Themistocleous

We analyse new signals of a 3-Higgs Doublet Model (3HDM) at the Large Hadron Collider (LHC) where only one doublet acquires a Vacuum Expectation Value (VEV), preserving a $Z_2$ parity. The other two doublets are \textit{inert} and do not develop a VEV, leading to a \textit{dark scalar sector} controlled by $Z_2$, with the lightest CP-even dark scalar $H_1$ b

Wim Beenakker, David Venhoek

We investigate accelerated Unruh-deWitt detectors as a model for particle decay. We find non-trivial decay rates, including a pattern of peaks in decay rate that extends to lower accelerations. Applying our model to the alpha decay of $\mathrm{^{210}Po}$, we find that effects could be observed with an acceleration of $a\approx 10^{26} \frac{\mathrm{m}}{\math

Dario Mazzoleni, Giorgio Tortone, Bozhidar Velichkov

In this paper, we prove estimates on the dimension of the singular part of the free boundary for solutions to shape optimization problems with measure constraints. The focus is on the heat conduction problem studied by Aguilera, Caffarelli, and Spruck and the one-phase Bernoulli problem with measure constraint introduced by Aguilera, Alt and Caffarelli. To e

Dennis Fucci, Marco Gaido, Matteo Negri, Mauro Cettolo

Automatic speech recognition (ASR) systems are known to be sensitive to the sociolinguistic variability of speech data, in which gender plays a crucial role. This can result in disparities in recognition accuracy between male and female speakers, primarily due to the under-representation of the latter group in the training data. While in the context of hybri

G. Bouzerar, M. Thumin

Unconventional flat band (FB) superconductivity, as observed in van der Waals heterostructures, could open promising avenues towards high-T$_c$ materials. In FBs, pairings and superfluid weight scale linearly with the interaction parameter, such an unusual behaviour justifies and encourages strategies to promote FB engineering. Bipartite lattices (BLs) which

Yupei Du, Albert Gatt, Dong Nguyen

Despite the massive success of fine-tuning Pre-trained Language Models (PLMs), they remain susceptible to out-of-distribution input. Dataset cartography is a simple yet effective dual-model approach that improves the robustness of fine-tuned PLMs. It involves fine-tuning a model on the original training set (i.e. reference model), selecting a subset of impor

Andreas Risch

Two popular methods to reduce discretisation effects are Symanzik improvement and gauge field smearing in the Dirac operator. Tree-level $O(a^2)$-improved Wilson fermions can be obtained from $O(a)$-improved Wilson fermions by adding one dimension-6 operator to the action. For gauge field smearing one wants to avoid the situation when too much smearing leads

Judhajeet Basu, M. Pavana, G. C. Anupama, Sudhanshu Barway

We report the optical, UV, and soft X-ray observations of the $2017-2022$ eruptions of the recurrent nova M31N 2008-12a. We infer a steady decrease in the accretion rate over the years based on the inter-eruption recurrence period. We find a ``cusp'' feature in the $r'$ and $i'$ band light curves close to the peak, which could be associated to jets. Spectral

Giulio Giacomuzzos, Ruggero Carli, Diego Romeres, Alberto Dalla Libera

Learning the inverse dynamics of robots directly from data, adopting a black-box approach, is interesting for several real-world scenarios where limited knowledge about the system is available. In this paper, we propose a black-box model based on Gaussian Process (GP) Regression for the identification of the inverse dynamics of robotic manipulators. The prop

Muhammad R. Hasyim, Kranthi K. Mandadapu

In supercooled liquids, dynamical facilitation refers to a phenomenon where microscopic motion begets further motion nearby, resulting in spatially heterogeneous dynamics. This is central to the glassy relaxation dynamics of such liquids, which show super-Arrhenius growth of relaxation timescales with decreasing temperature. Despite the importance of dynamic

Sheryl L. Sanchez, Elham Foadian, Maxim Ziatdinov, Jonghee Yang

The unique aspect of the hybrid perovskites is their tunability, allowing to engineer the bandgap via substitution. From application viewpoint, this allows creation of the tandem cells between perovskites and silicon, or two or more perovskites, with associated increase of efficiency beyond single-junction Schokley-Queisser limit. However, the concentration

Madeleine Darbyshire, Elizabeth Sklar, Simon Parsons

Advancements in machine vision that enable detailed inferences to be made from images have the potential to transform many sectors including agriculture. Precision agriculture, where data analysis enables interventions to be precisely targeted, has many possible applications. Precision spraying, for example, can limit the application of herbicide only to wee

Alejandra Garrido, Zoran Šunić

A theoretical framework is established for explicitly calculating rigid kernels of self-similar regular branch groups. This is applied to a new infinite family of branch groups in order to provide the first examples of self-similar, branch groups with infinite rigid kernel. The groups are analogs of the Hanoi Towers group on 3 pegs, based on the standard act

Lisa Marquand, Stevell Muller

We classify finite groups that act faithfully by symplectic birational transformations on an irreducible holomorphic symplectic (IHS) manifold of OG10 type. In particular, if X is an IHS manifold of OG10 type and G a finite subgroup of symplectic birational transformations of X, then the action of G on H2(X, Z) is conjugate to a subgroup of one of 375 groups

Achiel Colpaert, Zhuangzhuang Cui, Evgenii Vinogradov, Sofie Pollin

Unmanned aerial vehicles (UAVs) have gained popularity in the communications research community because of their versatility in placement and potential to extend the functions of communication networks. However, there remains still a gap in existing works regarding detailed and measurement-verified air-to-ground (A2G) Massive Multi-Input Multi-Output (MaMIMO

Yunhui Zhou, Dongqi Han, Yuguo Yu

Human vision incorporates non-uniform resolution retina, efficient eye movement strategy, and spiking neural network (SNN) to balance the requirements in visual field size, visual resolution, energy cost, and inference latency. These properties have inspired interest in developing human-like computer vision. However, existing models haven't fully incorporate

Fei Wang, Kongzhang Tang, Hefeng Wu, Baoquan Zhao

Reconstructing 3D human shapes from 2D images has received increasing attention recently due to its fundamental support for many high-level 3D applications. Compared with natural images, freehand sketches are much more flexible to depict various shapes, providing a high potential and valuable way for 3D human reconstruction. However, such a task is highly ch

Alexis Gaget, Adelino Gomes, Yves Lussignol

CEA Irfu Saclay is involved as partner in the ESS accelerator construction through different work-packages: controls for several RF test stands, for cryomodule demonstrators, for the RFQ coupler test and for the conditioning around 120 couplers and the tests of 8 cryomodules. Due to the high number of components it is really crucial to automatize the conditi

Andrés Aceña, Bruno Cardin Guntsche, Ivan Gentile de Austria

We present a no-go theorem for spherically symmetric configurations of two charged fluid species in equilibrium. The fluid species are assumed to be dusts, that is, perfect fluids without pressure, and the equilibrium can be attained for a single dust from the balance of electrostatic repulsion and gravitational attraction. We show that this is impossible fo

Ayshah Chan, Maja Schneider, Marco Körner

We propose an approach for early crop classification through identifying important timesteps with eXplainable AI (XAI) methods. Our approach consists of training a baseline crop classification model to carry out layer-wise relevance propagation (LRP) so that the salient time step can be identified. We chose a selected number of such important time indices to

Yan-Hong Yang, Ying-Hui Shao

Determining the reputation of academic journals is an crucial issue. The Author Affiliation Index (AAI) was proposed as a novel indicator for judging journal quality in many academic disciplines. Nevertheless, the original AAI has several potential limitations, some of which have been discussed and addressed in previous studies. In this paper, we modified th

Ali Asad, Romain de Loubens, Laurent François, Marc Massot

We investigate the modeling and simulation of ionic transport and charge conservation in lithium-ion batteries (LIBs) at the microscale. It is a multiphysics problem that involves a wide range of time scales. The associated computational challenges motivate the investigation of numerical techniques that can decouple the time integration of the governing equa

Geoffrey Daniel, Mohamed Bahi Yahiaoui, Claude Comtat, Sebastien Jan

This article presents a physics-informed deep learning method for the quantitative estimation of the spatial coordinates of gamma interactions within a monolithic scintillator, with a focus on Positron Emission Tomography (PET) imaging. A Density Neural Network approach is designed to estimate the 2-dimensional gamma photon interaction coordinates in a fast

Rémy Chapelle, Bruno Falissard

Introduction: The amount of data generated by original research is growing exponentially. Publicly releasing them is recommended to comply with the Open Science principles. However, data collected from human participants cannot be released as-is without raising privacy concerns. Fully synthetic data represent a promising answer to this challenge. This approa

S. Saha Ray, Akanksha Singh

This paper presents an efficient numerical method for solving fractional optimal control problems using an operational matrix for a fractional wavelet. Using well-known formulae such as Caputo and Riemann-Liouville operators to determine fractional derivatives and integral fractional wavelets, operational matrices were devised and utilised to solve fractiona

Rong-Long Ma, Ao-Ran Li, Chu Wang, Zhen-Zhen Kong

Preserving qubit coherence and maintaining high-fidelity qubit control under complex noise environment is an enduring challenge for scalable quantum computing. Here we demonstrate an addressable fault-tolerant single spin qubit with an average control fidelity of 99.12% via randomized benchmarking on a silicon quantum dot device with an integrated micromagne

Christopher J. Moore, Eliot Finch, Antoine Klein, Valeriya Korol

LISA will detect $\sim \! 10^4$ Galactic binaries, the majority being double white dwarfs. However, approximately $\sim \! 1 \textrm{--} 5 \%$ of these systems will contain neutron stars which, if they can be correctly identified, will provide new opportunities for studying binary evolution pathways involving mass reversal and supernovae as well as being pro

Marouane Il Idrissi, Nicolas Bousquet, Fabrice Gamboa, Bertrand Iooss

Performing an additive decomposition of arbitrary functions of random elements is paramount for global sensitivity analysis and, therefore, the interpretation of black-box models. The well-known seminal work of Hoeffding characterized the summands in such a decomposition in the particular case of mutually independent inputs. Going beyond the framework of ind

Jiajun Zhang, Georgina Cosma, Sarah Bugby, Jason Watkins

Image retrieval is the process of searching and retrieving images from a database based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or medical images by extracting features from the images, such as deep features, colour-based features, shape-based features and

Yun-Hao Shi, Zheng-Hang Sun, Yong-Yi Wang, Zheng-An Wang

Characterizing the nature of hydrodynamical transport properties in quantum dynamics provides valuable insights into the fundamental understanding of exotic non-equilibrium phases of matter. Experimentally simulating infinite-temperature transport on large-scale complex quantum systems is of considerable interest. Here, using a controllable and coherent supe

Geoff Goehle, Christopher Griffin

Persuasion is the process of changing an agent's belief distribution from a given (or estimated) prior to a desired posterior. A common assumption in the acceptance of information or misinformation as fact is that the (mis)information must be consistent with or familiar to the individual who accepts it. We model the process as a control problem in which the

Thu Ha Trieu

We prove that under certain explicit conditions, the Mahler measure of a three-variable polynomial can be expressed in terms of elliptic curve $L$-values and Bloch-Wigner dilogarithmmic values, conditionally on Beilinson's conjecture. In some cases, these dilogarithmic values simplify to Dirichlet $L$-values. The proof involves a construction of an element i

Xiao Liu, Antanas Kascenas, Hannah Watson, Sotirios A. Tsaftaris

For brain tumour segmentation, deep learning models can achieve human expert-level performance given a large amount of data and pixel-level annotations. However, the expensive exercise of obtaining pixel-level annotations for large amounts of data is not always feasible, and performance is often heavily reduced in a low-annotated data regime. To tackle this

Bin Guo, Song-Yan Xie

By means of hypercyclic operator theory, we complement our previous results on hypercyclic holomorphic maps between complex Euclidean spaces having slow growth rates,by showing {\it abstract abundance} rather than {\it explicit existence}. Next, we establish that, in the space of holomorphic maps from $\mathbb{C}^n$ to any connected Oka manifold $Y$, equippe

Yujia Kang, Thomas Selig, Guanyi Yang, Yanting Zhang

In parking problems, a given number of cars enter a one-way street sequentially, and try to park according to a specified preferred spot in the street. Various models are possible depending on the chosen rule for collisions, when two cars have the same preferred spot. In classical parking functions, if a car's preferred spot is already occupied by a previous

Valeriya Korol, Andrei P. Igoshev, Silvia Toonen, Nikolaos Karnesis

Neutron star-white dwarf (NS+WD) binaries offer a unique opportunity for studying NS-specific phenomena with gravitational waves. In this paper, we employ the binary population synthesis technique to study the Galactic population of NS+WDs with the future Laser Interferometer Space Antenna (LISA). We anticipate approximately $\mathcal{O}(10^2)$ detectable NS

E. Solano, I. Novalbos, A. J. Ros, M. Cortés-Contreras

The aim of this paper is, making use of the Gaia DR3 catalogue and Virtual Observatory tools, to confirm and characterize 428 binary and multiple stellar systems classified as neglected (only one observation) in the Washington Double Star Catalogue (WDS). The components of the stellar systems have the same parallax and proper motion (within the errors) and a

Suruchi Kumari, Pravendra Singh

The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing e

Fiona Draxler, Daniel Buschek, Mikke Tavast, Perttu Hämäläinen

Large Language Models (LLMs) such as ChatGPT have become increasingly integrated into critical activities of daily life, raising concerns about equitable access and utilization across diverse demographics. This study investigates the usage of LLMs among 1,500 representative US citizens. Remarkably, 42% of participants reported utilizing an LLM. Our findings

Mattes Ohlenbusch, Christian Rollwage, Simon Doclo

Many hearables contain an in-ear microphone, which may be used to capture the own voice of its user. However, due to the hearable occluding the ear canal, the in-ear microphone mostly records body-conducted speech, typically suffering from band-limitation effects and amplification at low frequencies. Since the occlusion effect is determined by the ratio betw

Dingran Yuan, Xinyi Yu, Shaoyuan Li, Xiang Yin

Ensuring safety for vehicle overtaking systems is one of the most fundamental and challenging tasks in autonomous driving. This task is particularly intricate when the vehicle must not only overtake its front vehicle safely but also consider the presence of potential opposing vehicles in the opposite lane that it will temporarily occupy. In order to tackle t

Joseph S. Boyle, Antanas Kascenas, Pat Lok, Maria Liakata

The task of assigning diagnostic ICD codes to patient hospital admissions is typically performed by expert human coders. Efforts towards automated ICD coding are dominated by supervised deep learning models. However, difficulties in learning to predict the large number of rare codes remain a barrier to adoption in clinical practice. In this work, we leverage

Gaia Collaboration, K. Weingrill, A. Mints, J. Castañeda

Gaia's readout window strategy is challenged by very dense fields in the sky. Therefore, in addition to standard Gaia observations, full Sky Mapper (SM) images were recorded for nine selected regions in the sky. A new software pipeline exploits these Service Interface Function (SIF) images of crowded fields (CFs), making use of the availability of the full t

Rajesh Dey, Kashyap Rajeevsarathy

Let $\mathrm{Mod}(S_g)$ be the mapping class group of the closed orientable surface of genus $g \geq 2$. In this article, we derive necessary and sufficient conditions under which two torsion elements in $\mathrm{Mod}(S_g)$ will have conjugates that generate a finite symmetric or an alternating subgroup of $\mathrm{Mod}(S_g)$. Furthermore, we characterize wh

Lukas Struppek, Dominik Hintersdorf, Kristian Kersting

Label smoothing -- using softened labels instead of hard ones -- is a widely adopted regularization method for deep learning, showing diverse benefits such as enhanced generalization and calibration. Its implications for preserving model privacy, however, have remained unexplored. To fill this gap, we investigate the impact of label smoothing on model invers

Holger Boche, Andrea Grigorescu, Rafael F. Schaefer, H. Vincent Poor

This paper explores the computational complexity involved in determining the capacity of the band-limited additive colored Gaussian noise (ACGN) channel and its capacity-achieving power spectral density (p.s.d.). The study reveals that when the noise p.s.d. is a strictly positive computable continuous function, computing the capacity of the band-limited ACGN

Weimin Xiong, Yifan Song, Peiyi Wang, Sujian Li

Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations. Recent CRE studies have found that catastrophic forgetting arises from the model's lack of robustness against future analogous relations. To address the issue, we introduce rationale, i.e., the explanations of relatio

Haeyun Choi, Jio Gim, Yuho Lee, Youngin Kim

This paper proposes a simple and robust zero-shot voice conversion system with a cycle structure and mel-spectrogram pre-processing. Previous works suffer from information loss and poor synthesis quality due to their reliance on a carefully designed bottleneck structure. Moreover, models relying solely on self-reconstruction loss struggled with reproducing d

Nikhil Bharti

In this paper, a normality criterion concerning a sequence of meromorphic functions and their differential polynomials is obtained. Precisely, we have proved: Let $\left\{f_j\right\}$ be a sequence of meromorphic functions in the open unit disk $\mathbb{D}$ such that, for each $j,$ $f_j$ has poles of multiplicity at least $m,~m\in\mathbb{N}.$ Let $\left\{h_j

Naoki Takeuchi, Taiki Yamae, Taro Yamashita, Tsuyoshi Yamamoto

Cryogenic qubit controllers (QCs) are the key to build large-scale superconducting quantum processors. However, developing scalable QCs is challenging because the cooling power of a dilution refrigerator is too small (~10 $\mu$W at ~10 mK) to operate conventional logic families, such as complementary metal-oxide-semiconductor logic and superconducting single

Shiqing Liu, Xueming Yan, Yaochu Jin

In recent years, there has been a notable surge in research on machine learning techniques for combinatorial optimization. It has been shown that learning-based methods outperform traditional heuristics and mathematical solvers on the Traveling Salesman Problem (TSP) in terms of both performance and computational efficiency. However, most learning-based TSP

Andrey Ryabichev

Suppose for closed surfaces $M,N$ there exists a continuous map $f:M\to N$ of geometric degree $d>0$. Then $\chi(M)\le d\cdot\chi(N)$. This inequality was first proved by Kneser in case of orientable surfaces and by Edmonds for arbitrary $M,N$. We give a new simple proof of this result. Our proof is completely elementary and does not use additional technique

Dingxu Guo, Jian Xu, Shu Zhang

The mode shape function is difficult to determine in modeling manipulators with flexible links using the assumed mode method. In this paper, for a planar 3-RRR parallel manipulator with flexible actuation links, we provide a data-driven method to identify the mode shape of the flexible links and propose a model-based controller for the vibration suppression.

Gyu Seon Kim, JaeHyun Chung, Soohyun Park

The advent of reusable rockets has heralded a new era in space exploration, reducing the costs of launching satellites by a significant factor. Traditional rockets were disposable, but the design of reusable rockets for repeated use has revolutionized the financial dynamics of space missions. The most critical phase of reusable rockets is the landing stage,

Daria-Mihaela Broscoteanu, Radu Tudor Ionescu

To increase revenue, news websites often resort to using deceptive news titles, luring users into clicking on the title and reading the full news. Clickbait detection is the task that aims to automatically detect this form of false advertisement and avoid wasting the precious time of online users. Despite the importance of the task, to the best of our knowle

Fabrice Martins

Wolf-Rayet (WR) stars of the WNh category contain a significant fraction of hydrogen at their surface. They can be hydrogen-burning, very massive stars or stars in a post-main sequence phase of evolution. Also, WNh stars are sometimes not included in population synthesis models. We aim to better characterise the properties of single WNh stars in the Galaxy a

Tiia-Maria Pasanen, Jouni Helske, Harri Högmander, Tarmo Ketola

Infections are known to interact as previous infections may have an effect on risk of succumbing to a new infection. The co-dynamics can be mediated by immunosuppression or -modulation, shared environmental or climatic drivers, or competition for susceptible hosts. Research and statistical methods in epidemiology often concentrate on large pooled datasets, o

Kirill Skovpen

Communicating science through mobile smartphone and tablet applications is one of the most efficient ways to reach general public of diverse background and age coverage. The Higgsy project was created in 2022 to celebrate the 10th anniversary of the discovery of the Higgs boson at CERN. This project introduces a mobile game to search for the Higgs boson prod

Shuangshuang Yuan, Peng Wu, Yuehui Chen

Data class imbalance is a common problem in classification problems, where minority class samples are often more important and more costly to misclassify in a classification task. Therefore, it is very important to solve the data class imbalance classification problem. The SMART dataset exhibits an evident class imbalance, comprising a substantial quantity o

Sofie Labat, Thomas Demeester, Véronique Hoste

Due to the rise of user-generated content, social media is increasingly adopted as a channel to deliver customer service. Given the public character of these online platforms, the automatic detection of emotions forms an important application in monitoring customer satisfaction and preventing negative word-of-mouth. This paper introduces EmoTwiCS, a corpus o

Maxime Le Ster, Paweł Dabrowski, Paweł Krukowski, Maciej Rogala

Incommensurate heterostructures of two-dimensional (2D) materials, despite their attractive electronic behaviour, are challenging to simulate because of the absence of translation symmetry. Experimental investigations of these structures often employ scanning tunneling microscopy (STM), however there is to date no comprehensive theory to simulate an STM imag

Guangfu Gao, Peng Wu, Hussain Dawood

Large scale data storage is susceptible to failure. As disks are damaged and replaced, traditional machine learning models, which rely on historical data to make predictions, struggle to accurately predict disk failures. This paper presents a novel method for predicting disk failures by leveraging multi-layer domain adaptive learning techniques. First, disk

Mohamed Maama, Ajay Jasra, Kengo Kamatani

In this paper we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applications, of which we focus upon the case of neuroscience. The data are assumed to be observed regularly in time and driven by the SDE model with

Saeed Razavikia, José Mairton Barros Da Silva Júnior, Carlo Fischione

Over-the-air computation (AirComp) is a well-known technique by which several wireless devices transmit by analog amplitude modulation to achieve a sum of their transmit signals at a common receiver. The underlying physical principle is the superposition property of the radio waves. Since such superposition is analog and in amplitude, it is natural that AirC

Erwan Taillanter, Andreas Schadschneider, Marc Barthelemy

The macroscopic fundamental diagram (MFD) is a large scale description of the traffic in a urban area and relates the average car flow to the average car density. This MFD has been observed empirically in several cities but how its properties are related to the structure of the road network has remained unclear so far. The MFD displays in general a maximum f

Wai Kin Wong, Huaijin Wang, Zongjie Li, Zhibo Liu

A C decompiler converts an executable into source code. The recovered C source code, once re-compiled, is expected to produce an executable with the same functionality as the original executable. With over twenty years of development, C decompilers have been widely used in production to support reverse engineering applications. Despite the prosperous develop

William Orivaldo Faria Carvalho, Edwin Moncada-Villa, Jorge Ricardo Mejía-Salazar, Danilo Henrique Spadoti

In this work, we introduce a concept to enable dynamic beamforming of terahertz (THz) wavefronts using applied magnetic fields (B). The proposed system exploits the magnetically switchable hyperbolic dispersion of the InSb semiconductor. This phenomenology, combined with diffractive surfaces and magnetic tilting of scattered fields, allows the design of a me

Ulrich Kohlenbach, Pedro Pinto

In this paper we introduce a localized and relativized generalization of the usual concept of Fej\'er monotonicity together with uniform and quantitative versions thereof and show that the main quantitative results obtained by the 1st author together with Nicolae and Leu\c{s}tean in 2018 and with L\'opez-Acedo and Nicolae in 2019 respectively, extend to this

Zi-Kui Liu, Nigel L. E. Hew, Shun-Li Shang

Based on statistical mechanics, a macroscopically homogeneous system, i.e., a single phase in the present context, is composed of many independent configurations that the system embraces. The macroscopical properties of the system are determined by the properties and statistical probabilities of those configurations with respect to external conditions. The v

Jin Sun, Zhi-Peng Xing

Motivated by the most recent measurement of tau polarization in $Z\to \tau^+\tau^-$ by CMS, we have introduced a new $U(1)_X$ gauge boson field X, which can have renormalizable kinetic mixing with the standard model $U(1)_Y$ gauge boson field Y. In addition to the kinetic mixing of the dark photon, denoted as $\sigma$, there may also be mass mixing introduce

Xiaochen Ma, Jizhe Zhou, Xiong Xu, Zhuohang Jiang

Nowadays, multimedia forensics faces unprecedented challenges due to the rapid advancement of multimedia generation technology thereby making Image Manipulation Localization (IML) crucial in the pursuit of truth. The key to IML lies in revealing the artifacts or inconsistencies between the tampered and authentic areas, which are evident under pixel-level fea

Mikhail Smagin, Ivan Toftul, Konstantin Y. Bliokh, Mihail Petrov

Acoustic forces and torques are of immense importance for manipulation of particles, in particular in biomedical applications. While such forces and torques are well understood for small spherical particles with lowest-order monopole and dipole responses, the higher-order effects for larger anisotropic particles have not been properly investigated. Here we e

Omid Amini, Noema Nicolussi

We introduce higher rank inner products on real and complex vector spaces and study their corresponding Voronoi tilings. We use the framework to describe metric degenerations of polarized tori and Hausdorff limits of Voronoi tilings of discrete sets.

Shreyank N Gowda, Xinyue Hao, Gen Li, Shashank Narayana Gowda

Deep learning models have revolutionized various fields, from image recognition to natural language processing, by achieving unprecedented levels of accuracy. However, their increasing energy consumption has raised concerns about their environmental impact, disadvantaging smaller entities in research and exacerbating global energy consumption. In this paper,

Deepti Rana, Soumyaranjan Dash, Monika Bhakar, Rajeshwari Roy Chowdhury

Motivated by the observation of Skyrmion-like magnetic textures in 2D itinerant ferromagnets Fe$_n$GeTe$_2$ ($n \geq3$), we develop a microscopic model combining itinerant magnetism and spin-orbit coupling on a triangular lattice. The ground state of the model in the absence of magnetic field consists of filamentary magnetic domain walls revealing a striking

Callum Wilkinson, Alfonso Garcia Soto

The Forward Physics Facility (FPF) plans to use neutrinos produced at the Large Hadron Collider (LHC) to make a variety of measurements at previously unexplored TeV energies. Its primary goals include precision measurements of the neutrino cross section and using the measured neutrino flux both to uncover information about far-forward hadron production and t

Majid Hashemi, Laleh Roushandel

In this work, we present a search strategy for heavy charged Higgs boson at Compact Linear Collider (CLIC) as a future $e^+e^-$ collider. The signal is charged Higgs boson pair production in two Higgs doublet model (2HDM) followed by $H^{\pm}\to W^{\pm}H$ and $H\to b\bar{b}$. Here, $H$ denotes the heavy CP-even neutral Higgs boson of the model. The collider

Feng Shi, Haoxiang Chang, Le Zhang, Huanyuan Shan

The deep learning technique has been employed in removing foreground contaminants from 21 cm intensity mapping, but its effectiveness is limited by the large dynamic range of the foreground amplitude. In this study, we develop a novel foreground removal technique grounded in U-Net networks. The essence of this technique lies in introducing an innovative data

Swathi Anil, Jennifer D'Souza

Noninvasive brain stimulation (NIBS) encompasses transcranial stimulation techniques that can influence brain excitability. These techniques have the potential to treat conditions like depression, anxiety, and chronic pain, and to provide insights into brain function. However, a lack of standardized reporting practices limits its reproducibility and full cli

Peter J. Cameron, Hiranya Kishore Dey

This paper provides a bridge between two active areas of research, the spectrum (set of element orders) and the power graph of a finite group. The order sequence of a finite group $G$ is the list of orders of elements of the group, arranged in non-decreasing order. Order sequences of groups of order $n$ are ordered by elementwise domination, forming a partia

Csaba Kozma, Gabrielle Schroeder, Tom Owen, Jane de Tisi

Identifying abnormal electroencephalographic activity is crucial in diagnosis and treatment of epilepsy. Recent studies showed that decomposing brain activity into periodic (oscillatory) and aperiodic (trend across all frequencies) components may illuminate drivers of changes in spectral activity. Using iEEG data from 234 subjects, we constructed a normative

Yang Zhang, Yawei Li, Hannah Brown, Mina Rezaei

Feature attribution explains neural network outputs by identifying relevant input features. The attribution has to be faithful, meaning that the attributed features must mirror the input features that influence the output. One recent trend to test faithfulness is to fit a model on designed data with known relevant features and then compare attributions with

Yangqing Fu, Ming Sun, Buqing Nie, Yue Gao

Monte Carlo Tree Search (MCTS) algorithms such as AlphaGo and MuZero have achieved superhuman performance in many challenging tasks. However, the computational complexity of MCTS-based algorithms is influenced by the size of the search space. To address this issue, we propose a novel probability tree state abstraction (PTSA) algorithm to improve the search e

Damiano Piovesan, Davide Zago, Parnal Joshi, M. Clara De Paolis Kaluza

We present CAFA-evaluator, a powerful Python program designed to evaluate the performance of prediction methods on targets with hierarchical concept dependencies. It generalizes multi-label evaluation to modern ontologies where the prediction targets are drawn from a directed acyclic graph and achieves high efficiency by leveraging matrix computation and top

Varinder Singh, Vahid Shaghaghi, Tanmoy Pandit, Cameron Beetar

We present a detailed study of an asymmetrically driven quantum Otto engine with a time-dependent harmonic oscillator as its working medium. We obtain analytic expressions for the upper bounds on the efficiency of the engine for two different driving schemes having asymmetry in the expansion and compression work strokes. We show that the Otto cycle under con

Dong Bok Lee, Seanie Lee, Joonho Ko, Kenji Kawaguchi

Dataset distillation methods have achieved remarkable success in distilling a large dataset into a small set of representative samples. However, they are not designed to produce a distilled dataset that can be effectively used for facilitating self-supervised pre-training. To this end, we propose a novel problem of distilling an unlabeled dataset into a set

Yao Lu, Yutian Huang, Jiaqi Nie, Zuohui Chen

Recently, the field of machine learning has undergone a transition from model-centric to data-centric. The advancements in diverse learning tasks have been propelled by the accumulation of more extensive datasets, subsequently facilitating the training of larger models on these datasets. However, these datasets remain relatively under-explored. To this end,

Yuxuan Wang

We investigate the interaction of two oncoming shock waves in spherical symmetry for an ideal barotropic fluid. Our research problem is how to establish a local in time solution after the interaction point and determine the state behind the shock waves. This problem is a double free boundary problem, as the position of the shock waves in space time is unknow

Oliver J. Sutton, Qinghua Zhou, Alexander N. Gorban, Ivan Y. Tyukin

High dimensional data can have a surprising property: pairs of data points may be easily separated from each other, or even from arbitrary subsets, with high probability using just simple linear classifiers. However, this is more of a rule of thumb than a reliable property as high dimensionality alone is neither necessary nor sufficient for successful learni

Margherita Andrao, Barbara Treccani, Massimo Zancanaro

This paper presents a qualitative study that investigates the effects of some language choices in expressing the trigger part of a trigger-action rule on the users' mental models. Specifically, we explored how 11 non-programmer participants articulated the definition of trigger-action rules in different contexts by choosing among alternative conjunctions, ve

Guillem Bonafos, Jean-Marc Freyermuth, Pierre Pudlo, Samuel Tronçon

Topological Data Analysis (TDA) has been successfully used for various tasks in signal/image processing, from visualization to supervised/unsupervised classification. Often, topological characteristics are obtained from persistent homology theory. The standard TDA pipeline starts from the raw signal data or a representation of it. Then, it consists in buildi