November 2022 arXiv papers — page 171

Dhruv Nandakumar, Robert Schiller, Christopher Redino, Kevin Choi

The proliferation of zero-day threats (ZDTs) to companies' networks has been immensely costly and requires novel methods to scan traffic for malicious behavior at massive scale. The diverse nature of normal behavior along with the huge landscape of attack types makes deep learning methods an attractive option for their ability to capture highly-nonlinear

Jaemin Jung, Youkyum Kim, Jihwan Park, Youshin Lim

The goal of this work is to detect new spoken terms defined by users. While most previous works address Keyword Spotting (KWS) as a closed-set classification problem, this limits their transferability to unseen terms. The ability to define custom keywords has advantages in terms of user experience. In this paper, we propose a metric learning-based training s

Adriano Viganò

Multi-black hole solutions play a relevant role both from the theoretical and the phenomenological point of view. In this Thesis, we construct some regular multi-black hole spacetimes in pure Einstein's General Relativity with the aid of solution generating techniques. We begin with a perspective on the history of solution generating techniques, and then

Dominikus Noll

We discuss a variant of the Banach-Mazur game which has applications to topological open mapping and closed graph theorems.

Sruthy Poulose, Jennifer A. Quirke, Plamen Stamenov, Matthias E. Möbius

Pendant droplets of water and paramagnetic solutions are studied in the presence of uniform and nonuniform magnetic fields produced by small permanent magnet arrays, both in static conditions and during dynamic pinch-off. Static measurements of the droplet shape are analysed in terms of an apparent surface tension γapp or an effective density \r{ho}eff. The

Dou Hu, Xiaolong Hou, Xiyang Du, Mengyuan Zhou

Pre-trained language models have achieved promising performance on general benchmarks, but underperform when migrated to a specific domain. Recent works perform pre-training from scratch or continual pre-training on domain corpora. However, in many specific domains, the limited corpus can hardly support obtaining precise representations. To address this issu

Bo You, Fang Li

This paper is concerned with the application of Stackelberg-Nash strategies to control fourth order linear and semi-linear parabolic equations. We assume that the system is acted through a hierarchy of distributed controls: one main control (the leader) that is responsible for an exact controllability property; and a couple of secondary controls (the followe

Xiaoqiong Ran, Rong Luo

Recently, subfiled codes of linear code over GF$ (q) $ with good parameters were studied, and many optimal subfield codes were obtained. In this paper, Our mainly motivation is to generlize the results of the subfield codes of hyperoval in Ding and Heng (Finite Fields Their Appl. 56, 308-331 (2019)), and generlize the results of two families of subfield code

Tobias Rindlisbacher, Niko Jokela, Arttu Pönni, Kari Rummukainen

The determination of entanglement measures in SU(N) gauge theories is a non-trivial task. With the so-called "replica trick", a family of entanglement measures, known as "Rényi entropies", can be determined with lattice Monte Carlo. Unfortunately, the standard implementation of the replica method for SU(N) lattice gauge theories suffers from

Haowei Li, Wei Yi

In a recent experiment, a dissipative Aharaonov-Bohm (AB) chain was implemented in the momentum space of a Bose-Einstein condensate. Formed by a series of dissipative AB rings threaded by synthetic magnetic flux, the chain exhibits the non-Hermitian skin effect, necessitating the non-Bloch band theory to account for its topology. In this work, we systematica

Zhicheng Wang, Tianyu Shi, Liyin Xiao, Cong Liu

We propose a novel algorithm that improves on the previous neural span-based CKY decoder for constituency parsing. In contrast to the traditional span-based decoding, where spans are combined only based on the sum of their scores, we introduce an order-sensitive strategy, where the span combination scores are more carefully derived from an order-sensitive ba

Noushin Raeisi Kheirabadi, Alessandro Chioleriob, Neil Phillipsa, Andrew Adamatzky

Colloids submitted to electrical stimuli exhibit a reconfiguration that could be used to store information and, potentially, compute. We investigated learnign, memorization, and time and stimulation's voltage dependence of conductive network formation in a colloidal suspension of ZnO nanoparticles in DMSO. Relations between critical resistance and stimul

Areeda Ayoub, Javed Akram

We investigate the thermal entanglement in two superconducting qubits for arbitrary interaction strength and ground state frequencies. We calculate the concurrence of the system to quantify the thermal entanglement. We suggest a scheme, where an external tunable coupler qubit sandwich between two superconducting qubits generates entanglement. The behavior of

Joonas Tikkanen, Jan Morelius, Teemu Siiskonen

In radiation beam-profile measurements, an accurate positioning of the detector with high position resolution is essential. For this purpose, we built a scanning device capable of moving a detector in three dimensions using mainly parts from a commercial 3D-printer. The accuracy and repeatability of movement was tested with caliper, laser displacement sensor

John T. H. Wong, Matthias Hei Man, Alex Li Cheuk Hung

We exploit the heterogeneous impact of the Roe v. Wade ruling by the US Supreme Court, which ruled most abortion restrictions unconstitutional. Our identifying assumption is that states which had not liberalized their abortion laws prior to Roe would experience a negative birth shock of greater proportion than states which had undergone pre-Roe reforms. We e

Mengdie Wang, Liyuan Shang, Suyun Zhao, Yiming Wang

Deep clustering aims to learn a clustering representation through deep architectures. Most of the existing methods usually conduct clustering with the unique goal of maximizing clustering performance, that ignores the personalized demand of clustering tasks.% and results in unguided clustering solutions. However, in real scenarios, oracles may tend to cluste

Ekaterina Smirnova, Yongqi Zhong, Rasha Alsaadawi, Xu Ning

Integrative analysis of datasets generated by multiple cohorts is a widely-used approach for increasing sample size, precision of population estimators, and generalizability of analysis results in epidemiological studies. However, often each individual cohort dataset does not have all variables of interest for an integrative analysis collected as a part of a

Amal K. Giri, Paolo Malgaretti, Dirk Peschka, Marcello Sega

The molecular structure of moving contact lines (MCLs) and the emergence of a corresponding macroscopic dissipation have made the MCL a paradigm of fluid dynamics. Through novel averaging techniques that remove capillary waves smearing we achieve an unprecedented resolution in molecular dynamics (MD) simulations and find that they match with the continuum de

Javed Akram

We study the quantum dynamics of the two impurities in a trapped quasi-one-dimensional Bose-Einstein condensate (BEC). We explore the effect of impurity-BEC and impurity-impurity interaction strengths on the dynamics of impurities inside the Bose-Einstein condensate. By studying the auto-correlation function of impurities and the BEC, we analyze and quantify

Kate A. Womack, Fiorenzo Vincenzo, Brad K. Gibson, Benoit Côté

Fluorine has many different potential sites and channels of production, making narrowing down a dominant site of fluorine production particularly challenging. In this work, we investigate which sources are the dominant contributors to the galactic fluorine by comparing chemical evolution models to observations of fluorine abundances in Milky Way stars coveri

Tiago A. Morgado, Mário G. Silveirinha

Here, we investigate the nonreciprocal propagation and amplification of surface plasmons in drift-current biased graphene, using both Galilean and relativistic-type Doppler shift transformations of the graphene's conductivity. Consistent with previous studies, both conductivity models predict strongly nonreciprocal propagation of surface plasmons due to

A. Lapi, T. Ronconi, L. Danese

In the framework of the stochastic theory for hierarchical clustering, we investigate the time-dependent solutions of the Fokker-Planck equation describing the statistics of dark matter halos, and discuss the typical timescales needed for these to converge toward stationary states, far away enough from initial conditions. Although we show that the stationary

Wenhao Huang, Tathagata Paul, Kenji Watanabe, Takashi Taniguchi

Electron-electron interactions in graphene are sufficiently strong to induce a correlated and momentum-conserving flow such that charge carriers behave similarly to the Hagen-Poiseuille flow of a classical fluid. In the current work, we investigate the electronic signatures of such a viscous charge flow in high-mobility graphene FETs. In two complementary me

Sarvesh Gharat, Yogesh Dandawate

In recent decades, large-scale sky surveys such as Sloan Digital Sky Survey (SDSS) have resulted in generation of tremendous amount of data. The classification of this enormous amount of data by astronomers is time consuming. To simplify this process, in 2007 a volunteer-based citizen science project called Galaxy Zoo was introduced, which has reduced the ti

Lubomir T. Dechevsky, Kristoffer M. Tangrand

This is the first paper in a sequence of studies in which we introduce a new type of neural networks (NNs) -- wavelet-based neural networks (WBNNs) -- and study their properties and potential for applications. We begin this study with a comparison to the currently existing type of wavelet neural networks (WNNs) and show that WBNNs vastly outperform WNNs. One

Hamid Amiriara

Hyperloop is a sonic-speed train transporting passengers and freights in a vacuum tube without friction or air resistance. Two essential communications in such vehicles are central control connection and real-time dispatching, also an optional data connection for passengers is welcome. The high mobility of Hyperloop imposes a severe impact on the performance

Andrea Pilzer, Yuxin Hou, Niki Loppi, Arno Solin

We introduce visual hints expansion for guiding stereo matching to improve generalization. Our work is motivated by the robustness of Visual Inertial Odometry (VIO) in computer vision and robotics, where a sparse and unevenly distributed set of feature points characterizes a scene. To improve stereo matching, we propose to elevate 2D hints to 3D points. Thes

Alexey Mironov, Ilnur Khuziev

CatBoost is a popular machine learning library. CatBoost models are based on oblivious decision trees, making training and evaluation rapid. CatBoost has many applications, and some require low latency and high throughput evaluation. This paper investigates the possibilities for improving CatBoost's performance in single-core CPU computations. We explore

Javed Akram, Asad Hussain, Muhammad Nouman, Jameel Hussain

The postulates of the eigenstate thermalization hypothesis (ETH) express that thermalization occurs due to the individual eigenstate of the system's Hamiltonian. But the ETH put no light on the dynamics that lead toward thermalization. In this paper, we observe the thermalization of a Bose-Einstein Condensate (BEC) confined in an optical lattice potentia

Valentin Hamaide, Kevin Souris, Damien Dasnoy, Francois Glineur

Purpose: To improve target coverage and reduce the dose in the surrounding organs-at-risks (OARs), we developed an image-guided treatment method based on a precomputed library of treatment plans controlled and delivered in real-time. Methods: A library of treatment plans is constructed by optimizing a plan for each breathing phase of a 4DCT. Treatments are d

Larissa C. Shimomura, Nikolay Yakovets, George Fletcher

Graph Generating Dependencies (GGDs) informally express constraints between two (possibly different) graph patterns which enforce relationships on both graph's data (via property value constraints) and its structure (via topological constraints). Graph Generating Dependencies (GGDs) can express tuple- and equality-generating dependencies on property grap

André Maia, Christelle Kadlec, Maxim Savinov, Rui Vilarinho

Type-II multiferroics, where spin interactions induce a ferroelectric polarization, are interesting for new device functionalities due to large magnetoelectric coupling. We report on a new type of multiferroicity in the quadruple-perovskite $\text{BiMn}_{\text{3}}\text{Cr}_{\text{4}}\text{O}_{\text{12}}$, where an antiferromagnetic phase is induced by the st

Jeonghyun Kim, Kaichun Mo, Minhyuk Sung, Woontack Woo

We propose Seg&Struct, a supervised learning framework leveraging the interplay between part segmentation and structure inference and demonstrating their synergy in an integrated framework. Both part segmentation and structure inference have been extensively studied in the recent deep learning literature, while the supervisions used for each task have not be

Elise Deen, Leo van Iersel, Remie Janssen, Mark Jones

The maximum parsimony distance $d_{\textrm{MP}}(T_1,T_2)$ and the bounded-state maximum parsimony distance $d_{\textrm{MP}}^t(T_1,T_2)$ measure the difference between two phylogenetic trees $T_1,T_2$ in terms of the maximum difference between their parsimony scores for any character (with $t$ a bound on the number of states in the character, in the case of $

Wamidh Jalil Mazher, hadeel Tariq Ibrahim

Digital drone cameras with free-space optical (FSO) communication networks have been proposed to be promising for air surveillance. In the FSO channel, atmospheric turbulence (AT) degrades the signal. In this study, we combined the parameters of the digital drone camera and the optical channel to mitigate the AT effect. The digital drone camera parameters ar

Prabhant Singh, Joaquin Vanschoren

Automated Machine Learning has grown very successful in automating the time-consuming, iterative tasks of machine learning model development. However, current methods struggle when the data is imbalanced. Since many real-world datasets are naturally imbalanced, and improper handling of this issue can lead to quite useless models, this issue should be handled

Samer Fatayri, Kirill Serykh, Egor Gumin

In soccer games, the goalkeeper's performance is an important factor to the success of the whole team. Despite the goalkeeper's importance, little attention has been paid to their performance in events and tracking data. Here, we developed a model to predict which movements would be most effective for shot-stopping and compare it to the real-life beh

Zheng Li, Nicolás Saldías-Vallejos, María Andrea Rodríguez, Austen Rainer

Cloud computing has made federated database systems (FDBS) significantly more practical to implement than in the past. As part of a recent Web-based Geographic Information System (WebGIS) project, we are employing cloud-native technologies (from the container ecosystem) to develop a federated database (DB) infrastructure, to help manage and utilise the distr

Sushmitha Shree S, Avijit Mandal, Avhishek Chatterjee, Krishna Jagannathan

We consider maximizing the long-term average reward in a single server queue, where the reward obtained for a job is a non-increasing function of its sojourn time. The motivation behind this work comes from multiple applications, including quantum information processing and multimedia streaming. We introduce a new service discipline, shortest predicted sojou

Ekaterina Shumitskaya, Anastasia Antsiferova, Dmitriy Vatolin

Universal adversarial perturbation attacks are widely used to analyze image classifiers that employ convolutional neural networks. Nowadays, some attacks can deceive image- and video-quality metrics. So sustainability analysis of these metrics is important. Indeed, if an attack can confuse the metric, an attacker can easily increase quality scores. When deve

Jean-Christophe Pain

In this note, we present a simple summation formula for $k$-bonacci numbers. The derivation consists in obtaining the generating function of such numbers, and noting that its evaluation at a particular value yields a formula generalizing a known expression for Fibonacci numbers.

Semyon Goncharov, Kilian Fritsch, Oleg Pronin

We report supercontinuum generation and pulse compression in two stacked multipass cells based on dielectric mirrors. The 230 fs pulses at 1 MHz containing 12 $μ$J were compressed by factor 33 down to 7 fs, corresponding to 1.0 GW peak power and overall transmission of 84 %. The source is particularly interesting for such applications as time-resolved ARPES,

Zheng Li, Austen Rainer

Background: Academic search engines (i.e., digital libraries and indexers) play an increasingly important role in systematic reviews however these engines do not seem to effectively support such reviews, e.g., researchers confront usability issues with the engines when conducting their searches. Aims: To investigate whether the usability issues are bugs (i.e

Ayse Yilmazer-Metin

Asymmetric sharing is a dynamic sharing model, where a shared data is heavily accessed by a (local) sharer, and rarely accessed by other (remote) sharers. On GPUs, without special support, asymmetric sharing requires heavily loaded synchronization on every access. With the introduction of Remote Scope Promotion (RSP), access to the local sharer is allowed wi

Anna Fedotova, Luca Carletti, Attilio Zilli, Frank Setzpfandt

The rapid development of metasurfaces - 2D ensembles of engineered nanostructures - is presently fostering a steady drive towards the miniaturization of many optical functionalities and devices to a subwavelength size. The material platforms for optical metasurfaces are rapidly expanding and for the past few years, we are seeing a surge in establishing meta-

Andreas Irrgang, Norbert Przybilla, Georges Meynet

A vital condition for life on Earth is the steady supply of radiative heat by the Sun. Like all other stars, the Sun generates its emitted energy in its central regions where densities and temperatures are high enough for nuclear fusion processes to take place. Because stellar cores are usually covered by an opaque envelope, most of our knowledge about them

J. Jiménez Vicente, E. Mediavilla

We introduce the use of the Fast Multipole Method (FMM) to speed up gravitational lensing ray tracing calculations. The method allows very fast calculation of ray deflections when a large number of deflectors, $N_*$, is involved, while keeping rigorous control on the errors. In particular, we apply this method, in combination with the Inverse Polygon Mapping

Akanksha Saran, Kush Desai, Mai Lee Chang, Rudolf Lioutikov

Humans use audio signals in the form of spoken language or verbal reactions effectively when teaching new skills or tasks to other humans. While demonstrations allow humans to teach robots in a natural way, learning from trajectories alone does not leverage other available modalities including audio from human teachers. To effectively utilize audio cues acco

Haiming Yao, Xue Wang, Wenyong Yu

Unsupervised visual anomaly detection conveys practical significance in many scenarios and is a challenging task due to the unbounded definition of anomalies. Moreover, most previous methods are application-specific, and establishing a unified model for anomalies across application scenarios remains unsolved. This paper proposes a novel hybrid framework term

Hanan Herzig Sheinfux, Minwoo Jung, Lorenzo Orsini, Matteo Ceccanti

Photonic crystals and metamaterials are two overarching paradigms for manipulating light. Combining the two approaches leads to hypercrystals: hyperbolic dispersion metamaterials that undergo periodic modulation and mix photonic-crystal-like aspects with hyperbolic dispersion physics. So far, there has been limited experimental realization of hypercrystals d

Kenji Bekki

We discuss a GC formation scenario in which the first generation (1G) of single asymptotic giant branch (AGB) stars and intermediate-mass close binaries (IMCBs) eject gas, from which the second generation (2G) of stars can be formed. The two key parameters in the scenario are the fractions of binary stars (f_b) and the slopes (alpha) of the stellar initial m

Michael Hinz, Jörn Kommer

We study differential complexes of Kolmogorov-Alexander-Spanier type on metric measure spaces associated with unbounded non-local operators, such as operators of fractional Laplacian type. We define Hilbert complexes, observe invariance properties and obtain self-adjoint non-local analogues of Hodge Laplacians. For $d$-regular measures and operators of fract

Badis Ydri

The correspondence between quantum mechanics and noncommutative geometry is illustrated in the context of the noncommutative ${\rm AdS}^2_θ/{\rm CFT_1}$ duality where ${\rm CFT}_1$ is identified as conformal quantum mechanics. This model is conjectured to describe the gauge/gravity correspondence in one dimension. Applications to quantum black holes and emer

Ivan Kaygorodov, Cándido Martín González, Pilar Páez-Guillán

In this article, we develop a further adaptation of the method of Skjelbred-Sund to construct central extensions of axial algebras. We use our method to prove that all axial central extensions (with respect to a maximal set of axes) of complex simple finite-dimensional Jordan algebras are split and that all non-split axial central extensions of dimension $n\

Kamran Ali Khan Niazi, Marta Victoria

Agrivoltaics (APV) is the dual use of land by combining agricultural crop production and photovoltaic (PV) systems. In this work, we have analyzed three different APV configurations: static with optimal tilt, vertically-mounted bifacial, and single-axis horizontal tracking. A model is developed to calculate the shadowing losses on the PV panels along with th

Ben Kenwright

We present a realistic, robust, and computationally fast method of solving highly non-linear inverse kinematic problems with angular limits using the Gauss-Seidel iterative method. Our method is ideally suited towards character based interactive applications such as games. To achieve interactive simulation speeds, numerous acceleration techniques are employe

Kevin Kamm, Michelle Muniz

In this paper, we model the rating process of an entity by using a geometrical approach. We model rating transitions as an SDE on a Lie group. Specifically, we focus on calibrating the model to both historical data (rating transition matrices) and market data (CDS quotes) and compare the most popular choices of changes of measure to switch from the historica

Yuhang Yang, Haihua Xu, Hao Huang, Eng Siong Chng

To let the state-of-the-art end-to-end ASR model enjoy data efficiency, as well as much more unpaired text data by multi-modal training, one needs to address two problems: 1) the synchronicity of feature sampling rates between speech and language (aka text data); 2) the homogeneity of the learned representations from two encoders. In this paper we propose to

Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang

Diffusion models have been recently employed to improve certified robustness through the process of denoising. However, the theoretical understanding of why diffusion models are able to improve the certified robustness is still lacking, preventing from further improvement. In this study, we close this gap by analyzing the fundamental properties of diffusion

Jianfei Zhang, Jun Bai, Chenghua Lin, Yanmeng Wang

Variational autoencoders (VAEs) are one of the powerful unsupervised learning frameworks in NLP for latent representation learning and latent-directed generation. The classic optimization goal of VAEs is to maximize the Evidence Lower Bound (ELBo), which consists of a conditional likelihood for generation and a negative Kullback-Leibler (KL) divergence for r

Yanyan Huang, Yong Wang, Kun Shi, Chaojie Gu

Gait recognition is widely used in diversified practical applications. Currently, the most prevalent approach is to recognize human gait from RGB images, owing to the progress of computer vision technologies. Nevertheless, the perception capability of RGB cameras deteriorates in rough circumstances, and visual surveillance may cause privacy invasion. Due to

Masud An-Nur Islam Fahim, Jani Boutellier

Methods for improving deep neural network training times and model generalizability consist of various data augmentation, regularization, and optimization approaches, which tend to be sensitive to hyperparameter settings and make reproducibility more challenging. This work jointly considers two recent training strategies that address model generalizability:

Indu Kant Deo, Rui Gao, Rajeev Jaiman

There is a critical need for efficient and reliable active flow control strategies to reduce drag and noise in aerospace and marine engineering applications. While traditional full-order models based on the Navier-Stokes equations are not feasible, advanced model reduction techniques can be inefficient for active control tasks, especially with strong non-lin

Aritra Dhar, Supraja Sridhara, Shweta Shinde, Srdjan Capkun

Modern data centers have grown beyond CPU nodes to provide domain-specific accelerators such as GPUs and FPGAs to their customers. From a security standpoint, cloud customers want to protect their data. They are willing to pay additional costs for trusted execution environments such as enclaves provided by Intel SGX and AMD SEV. Unfortunately, the customers

Anton Vasiliuk, Daria Frolova, Mikhail Belyaev, Boris Shirokikh

When applying a Deep Learning model to medical images, it is crucial to estimate the model uncertainty. Voxel-wise uncertainty is a useful visual marker for human experts and could be used to improve the model's voxel-wise output, such as segmentation. Moreover, uncertainty provides a solid foundation for out-of-distribution (OOD) detection, improving th

Adi Wijaya, Noor Akhmad Setiawan, Asma Hayati Ahmad, Rahimah Zakaria

Background: Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) due to the high rate of progression from MCI to AD. Sensitive neural biomarkers may provide a tool for an accurate MCI diagnosis, enabling earlier and perhaps more effective treatment. Despite the availability of numerous neuroscience techniques, elect

Enwei Zhu, Yiyang Liu, Ming Jin, Jinpeng Li

Many recent named entity recognition (NER) studies criticize flat NER for its non-overlapping assumption, and switch to investigating nested NER. However, existing nested NER models heavily rely on training data annotated with nested entities, while labeling such data is costly. This study proposes a new subtask, nested-from-flat NER, which corresponds to a

Mohsen Khodadi

By exploiting the extreme environment of the black hole (BH) as a potential place for axion-photon interaction, we use an axion-producing model of the magnetized plasma to study the shadow of an asymptotically flat rotating BH immersed into an axion-plasmon cloud. By aiming to reveal footprints of axion in the dark shadow of BH, we in this paper explore the

Weizhu Bao, Yifei Li

We propose and analyze structure-preserving parametric finite element methods (SP-PFEM) for evolution of a closed curve under different geometric flows with arbitrary anisotropic surface energy $γ(\boldsymbol{n})$ for $\boldsymbol{n}\in \mathbb{S}^1$ representing the outward unit normal vector. By introducing a novel surface energy matrix $\boldsymbol{G}_k(\

Mohamed Maama, Ajay Jasra, Hernando Ombao

In this paper we consider Bayesian parameter inference for partially observed fractional Brownian motion (fBM) models. The approach we follow is to time-discretize the hidden process and then to design Markov chain Monte Carlo (MCMC) algorithms to sample from the posterior density on the parameters given data. We rely on a novel representation of the time di

Abhilasha Ravichander, Matt Gardner, Ana Marasović

The full power of human language-based communication cannot be realized without negation. All human languages have some form of negation. Despite this, negation remains a challenging phenomenon for current natural language understanding systems. To facilitate the future development of models that can process negation effectively, we present CONDAQA, the firs

Wenhao Wu, Wei Li, Jiachen Liu, Xinyan Xiao

Despite being able to generate fluent and grammatical text, current Seq2Seq summarization models still suffering from the unfaithful generation problem. In this paper, we study the faithfulness of existing systems from a new perspective of factual robustness which is the ability to correctly generate factual information over adversarial unfaithful informatio

Wang-Chen Xue, Xiao-Bo Li, Shao-Lin Xiong, Yong Chen

The Earth occultation technique has broad applications in both astronomy and atmospheric density measurements. We construct the background model during the occultation of the Crab Nebula observed by the Insight-Hard X-ray Modulation Telescope (Insight-HXMT) at energies between 6 keV and 100 keV. We propose a Bayesian atmospheric density retrieval method base

Mohammad W. Alomari, Mohammad Sababheh, Cristian Conde, Hamid Reza Moradi

In this paper, we introduce the $f-$operator radius of Hilbert space operators as a generalization of the Euclidean operator radius and the $q-$operator radius. Properties of the newly defined radius are discussed, emphasizing how it extends some known results in the literature.

Meena Arunachalam, Vrushabh Sanghavi, Yi A Yao, Yi A Zhou

End-to-end (E2E) artificial intelligence (AI) pipelines are composed of several stages including data preprocessing, data ingestion, defining and training the model, hyperparameter optimization, deployment, inference, postprocessing, followed by downstream analyses. To obtain efficient E2E workflow, it is required to optimize almost all the stages of pipelin

Saubhagyalaxmi Singh

In this current study, the most apparent aspect is to submit a new geometric sequence space. We investigate its topological properties , inclusion relations, Geometric statistical convergence and Geometric property of Orlicz function and . Moreover, we also check its dual properties.

Brian L. Flores, D. John Hillier, Luc Dessart

The spectra of Wolf-Rayet (WR) stars exhibit strong, broad emission lines that originate in the wind. These winds are radiatively driven and are susceptible to hydrodynamic instabilities that result in the formation of clumps. When modelling spectra of WR stars the volume-filling factor (VFF) approach is usually employed to treat clumpy winds. However, it is

Kosuke Nomura

Reflection asymmetric, octupole shapes in nuclei are a prominent aspect of nuclear structure, and have been recurrently studied over the decades. Recent experiments using radioactive-ion beams have provided evidence for stable octupole shapes. A variety of nuclear models have been employed for the related theoretical analyses. We review recent studies on the

Rui-Xin Yang, Fei Xie, Dao-Jun Liu

Unimodular gravity is a modified theory with respect to general relativity by an extra condition that the determinant of the metric is fixed. Especially, if the energy-momentum tensor is not imposed to be conserved separately, a new geometric structure appears with potentially observational signatures. In this paper, we study tidal deformability of compact s

Jinyin Chen, Jie Ge, Haibin Zheng

Widespread applications of deep neural networks (DNNs) benefit from DNN testing to guarantee their quality. In the DNN testing, numerous test cases are fed into the model to explore potential vulnerabilities, but they require expensive manual cost to check the label. Therefore, test case prioritization is proposed to solve the problem of labeling cost, e.g.,

Bo Liang, Purui Wang, Renjie Zhao, Heyu Guo

RFID localization is considered the key enabler of automating the process of inventory tracking and management for high-performance logistic network. A practical and deployable RFID localization system needs to meet reliability, throughput, and range requirements. This paper presents RF-Chord, the first RFID localization system that simultaneously meets all

Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu

Adversarial training (AT) with imperfect supervision is significant but receives limited attention. To push AT towards more practical scenarios, we explore a brand new yet challenging setting, i.e., AT with complementary labels (CLs), which specify a class that a data sample does not belong to. However, the direct combination of AT with existing methods for

Santiago Pinzon

Let $\mathbb K=(K,+,\cdot,v,Γ)$ be a valued algebraically closed field of characteristic and $(G,\oplus)$ be a $\mathcal K$-interpretable group that is either locally isomorphic to $(K,+)$ or to $(K,\cdot)$. Then if $\mathcal G=(G,\oplus,\ldots)$ is a strongly minimal non locally modular structure intepretable in $\mathbb K$, it interprets a field. We also p

Minoru Hirose, Hideki Murahara, Shingo Saito

We define the interpolated polynomial multiple zeta values as a generalization of all of multiple zeta values, multiple zeta-star values, interpolated multiple zeta values, symmetric multiple zeta values, and polynomial multiple zeta values. We then compute the generating function of the sum of interpolated polynomial multiple zeta values of fixed weight, de

Elad Segal, Ben Bogin, Jonathan Berant

Standard practice in pretraining multimodal models, such as vision-language models, is to rely on pairs of aligned inputs from both modalities, for example, aligned image-text pairs. However, such pairs can be difficult to obtain in low-resource settings and for some modality pairs (e.g., structured tables and images). In this work, we investigate the extent

Yue Yu, Xuan Kan, Hejie Cui, Ran Xu

Functional magnetic resonance imaging (fMRI) has become one of the most common imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have been adopted for fMRI analysis with superior performance. Unfortunately, traditional functional brain networks are mainly constructed based on similarities among region of interests (ROI), w

Daniel Carter

We prove that $\{\overline{K_3}, H\}$-free graphs are not counterexamples to Hadwiger's Conjecture, where $H$ is any one of 33 graphs on seven, eight, or nine vertices, or $H=K_8$. This improves on past results of Plummer-Stiebitz-Toft, Kriesell, and Bosse. The proofs are mostly computer-assisted.

Hong Tang, Santosh Neupane, Li Yin, Jason M. Breslin

A WSe$_2$ monolayer shows many interesting properties due to its spin-orbit coupling induced spin splitting in bands around the Fermi level and the spin-valley configuration. The orientation of the spin polarization in the relevant bands is crucial for the nature of exciton states and the optical valley selectivity. In this work, we studied the WSe$_2$ nanor

Wei Kang, Daniel M. Tartakovsky, Apoorv Srivastava

We introduce a mathematical formulation of feature-informed data assimilation (FIDA). In FIDA, the information about feature events, such as shock waves, level curves, wavefronts and peak value, in dynamical systems are used for the estimation of state variables and unknown parameters. The observation operator in FIDA is a set-valued functional, which is fun

Xiaolong Yang, Jialu Ji, Ravi Joshi, Jun Yang

X-shaped radio galaxies (XRGs) are those that exhibit two pairs of unaligned radio lobes (main radio lobes and "wings"), one of the promising models for the peculiar morphology is jet re-orientation. To clarify it, we conducted the European VLBI Network (EVN) 5 GHz observation of an XRG J0725+5835, which resembles the archetypal binary AGNs 0402+379

Dan T. Abell, Alex J. Dragt

To a very good approximation, particularly for hadron machines, charged-particle trajectories in accelerators obey Hamiltonian mechanics. During routine storage times of eight hours or more, such particles execute some $10^{8}$ revolutions about the machine, $10^{10}$ oscillations about the design orbit, and $10^{13}$ passages through various bending and foc

Xinyu Li, Yilin Li, Qing Cui, Longfei Li

In the era of big data, the explosive growth of multi-source heterogeneous data offers many exciting challenges and opportunities for improving the inference of conditional average treatment effects. In this paper, we investigate homogeneous and heterogeneous causal data fusion problems under a general setting that allows for the presence of source-specific

J. García, C. A. Gómez, F. Luca

In this paper, we prove identities for members of the k-generalized Fibonacci sequence with negative indices and we apply these identities to deduce an exact formula for its zero-multiplicity.

Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex Lamb

Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that are capable of solving multiple tasks and reach a diverse set of objectives. How to \textit{specify} and \textit{ground} these goals in such a way that we can both reliably reach goals during training as well as generalize to new goals during evaluation remains an

James Scully, Jassem Bourahmah, David Bloom, Andrey L Shilnikov

The purpose of this paper is trifold -- to serve as an instructive resource and a reference catalog for biologically plausible modeling with i) conductance-based models, coupled with ii) strength-varying slow synapse models, culminating in iii) two canonical pair-wise rhythm-generating networks. We document the properties of basic network components: cell mo

Jiyun Kim, Byounghan Lee, Kyung-Ah Sohn

In a hate speech detection model, we should consider two critical aspects in addition to detection performance-bias and explainability. Hate speech cannot be identified based solely on the presence of specific words: the model should be able to reason like humans and be explainable. To improve the performance concerning the two aspects, we propose Masked Rat

Abrar Faiyaz, Md Nasir Uddin, Giovanni Schifitto

Artificial Intelligence (Deep Learning(DL)/ Machine Learning(ML)) techniques are widely being used to address and overcome all kinds of ill-posed problems in medical imaging which was or in fact is seemingly impossible. Reducing gradient directions but harnessing high angular resolution(HAR) diffusion data in MR that retains clinical features is an important

Tomokatsu Takahashi, Masanori Yamada, Yuuki Yamanaka, Tomoya Yamashita

Adversarial training is the most promising method for learning robust models against adversarial examples. A recent study has shown that knowledge distillation between the same architectures is effective in improving the performance of adversarial training. Exploiting knowledge distillation is a new approach to improve adversarial training and has attracted

Federico Sabbatini, Roberta Calegari

As opaque decision systems are being increasingly adopted in almost any application field, issues about their lack of transparency and human readability are a concrete concern for end-users. Amongst existing proposals to associate human-interpretable knowledge with accurate predictions provided by opaque models, there are rule extraction techniques, capable

An Y. Li, Edwin S. Kite, Katarina Keating

The Medusae Fossae Formation (MFF) is an enigmatic sedimentary unit near the equator of Mars, with an uncertain formation process and absolute age. Due to the heavily wind-eroded surface, it is difficult to determine the absolute model age of the MFF using a one-parameter model based on the crater size-frequency distribution function with existing crater cou