August 2022 arXiv papers — page 33

Matthew C. Chan, John P. Stott

We introduce AutoEnRichness, a hybrid approach that combines empirical and analytical strategies to determine the richness of galaxy clusters (in the redshift range of $0.1 \leq z \leq 0.35$) using photometry data from the Sloan Digital Sky Survey Data Release 16, where cluster richness can be used as a proxy for cluster mass. In order to reliably estimate c

Ippei Obayashi, Masao Kimura

This paper proposes a data analysis method using persistent homology and nonnegative matrix factorization. A concatenated persistence image technique is used to extract coexisting structures from the persistence diagrams of different dimensions hidden behind the data. To demonstrate the potential of our method, we apply the method to 3D voxel data of iron or

Walter Bridges, Kathrin Bringmann

We apply the new framework for modularity of false theta functions developed by the second author and Nazaroglu to study the asymptotic behavior of Taylor coefficients of false Jacobi forms. The examples we study generate moments of the rank for unimodal sequences. For two types of unimodal sequences, we prove asymptotic series for the rank moments.

Harriet Apel, Toby Cubitt

Analogue Hamiltonian simulation is a promising near-term application of quantum computing and has recently been put on a theoretical footing. In Hamiltonian simulation, a physical Hamiltonian is engineered to have identical physics to another - often very different - Hamiltonian. This is qualitatively similar to the notion of duality in physics, whereby two

Rebecca M. C. Taylor, Johan A. du Preez

Rail breaks are one of the most common causes of derailments internationally. This is no different for the South African Iron Ore line. Many rail breaks occur as a heavy-haul train passes over a crack, large defect or defective weld. In such cases, it is usually too late for the train to slow down in time to prevent a de-railment. Knowing the risk of a rail

Giovanni Denaro, Rahim Heydarov, Ali Mohebbi, Mauro Pezzè

This paper presents PREVENT, an approach for predicting and localizing failures in distributed enterprise applications by combining unsupervised techniques. Software failures can have dramatic consequences in production, and thus predicting and localizing failures is the essential step to activate healing measures that limit the disruptive consequences of fa

Juan González-Meneses, Ivan Marin

In this paper we introduce a class of `parabolic' subgroups for the generalized braid group associated to an arbitrary irreducible complex reflection group, which maps onto the collection of parabolic subgroups of the reflection group. Except for one case, which is proven separately elsewhere, we prove that this collection forms a lattice, so that intersecti

Jack Saunders

We compute the dimensions of $\operatorname{Ext}_G^n(V, W)$ for all irreducible $V$, $W$ lying in $r$-blocks of cyclic defect in the simple groups $\operatorname{Sz}(q)$, $\operatorname{PSU}_3(q)$ and $\operatorname{{}^2G}_2(q)$ in cross characteristic, obtaining in particular the dimensions of all cohomology groups for such modules. Along the way, we also o

Nick Zhang

This is an evolving document. It is devoted to summarizing patterns and laws of knowledge growth. By examining a variety of parameters in data sources such as Wikipedia and Microsoft Academic Graph, we can get deeper insights of how knowledge evolves.

Avval Amil, Shashank Gupta

Random number generators are imperfect due to manufacturing bias and technological imperfections. These imperfections are removed using post-processing algorithms that in general compress the data and do not work in every scenario. In this work, we present a universal whitening algorithm using n-qubit permutation matrices to remove the imperfections in comme

Jay Morgan, Adeline Paiement, Christian Klinke

We explore different strategies to integrate prior domain knowledge into the design of a deep neural network (DNN). We focus on graph neural networks (GNN), with a use case of estimating the potential energy of chemical systems (molecules and crystals) represented as graphs. We integrate two elements of domain knowledge into the design of the GNN to constrai

Saeed Montazeri Moghadam, Päivi Nevalainen, Nathan J. Stevenson, Sampsa Vanhatalo

Objective: To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. Methods: A deep learning -based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an external dataset from 30 polysom

Shrish Nath Upadhyay, Srimanta Pakhira

TMDs are a new family of 2D materials with features that make them appealing for potential applications in nanomaterials science and engineering. Although, the edges of the 2D TMDs show excellent electrocatalytic performance, their basal plane is inert which hinders the industrial applications for electrocatalysis. Here, we have computationally designed the

Eren Cakmak, Johannes Fuchs, Dominik Jäckle, Tobias Schreck

Many data analysis problems rely on dynamic networks, such as social or communication network analyses. Providing a scalable overview of long sequences of such dynamic networks remains challenging due to the underlying large-scale data containing elusive topological changes. We propose two complementary pixel-based visualizations, which reflect occurrences o

Guillaume Valette

We prove a trace formula for integration by parts on subanalytic bounded submanifolds of $\mathbb{R}^n$, possibly non closed. We also establish density results for $\mathbf{W}^{1,p}_\nabla (M)$, $M$ bounded subanalytic manifold, which is the space of the $L^p$ tangent vector fields $v$ on $M$ for which $\nabla v$ is $L^p$, where $\nabla$ is the divergence op

Sanghoon Lee, Alexei Andreanov, Sergej Flach

We study the effect of quasiperiodic perturbations on one-dimensional all-bands-flat lattice models. Such networks can be diagonalized by a finite sequence of local unitary transformations parameterized by angles $\theta_i$. Without loss of generality, we focus on the case of two bands with bandgap $\Delta$. Weak perturbations lead to an effective Hamiltonia

Kisung You, Dennis Shung

The von Mises-Fisher (vMF) distribution has long been a mainstay for inference with data on the unit hypersphere in directional statistics. The performance of statistical inference based on the vMF distribution, however, may suffer when there are significant outliers and noise in the data. Based on an analogy of the median as a robust measure of central tend

Arnd Hartmanns, Bram Kohlen

Backwards reachability is an efficient zone-based approach for model checking probabilistic timed automata w.r.t. PTCTL properties. Current implementations, however, are restricted to maximum probabilities of reachability properties. In this paper, we report on our new implementation of backwards reachability as part of the Modest Toolset. Its support for mi

Matilda Tamm, Olivia Shamon, Hector Anadon Leon, Konrad Tollmar

Modern video games are rapidly growing in size and scale, and to create rich and interesting environments, a large amount of content is needed. As a consequence, often several thousands of detailed 3D assets are used to create a single scene. As each asset's polygon mesh can contain millions of polygons, the number of polygons that need to be drawn every fra

Tiberiu Burlacu, Danny Lathouwers, Zoltán Perkó

In this paper we propose a solution to the need for a fast particle transport algorithm in Online Adaptive Proton Therapy capable of cheaply, but accurately computing the changes in patient dose metrics as a result of changes in the system parameters. We obtain the proton phase-space density through the product of the numerical solution to the one-dimensiona

Shion Ishikawa, Young-joo Chung, Yu Hirate

Recently online advertisers utilize Recommender systems (RSs) for display advertising to improve users' engagement. The contextual bandit model is a widely used RS to exploit and explore users' engagement and maximize the long-term rewards such as clicks or conversions. However, the current models aim to optimize a set of ads only in a specific domain and do

Jiaxu Li, Jing Li, Boqiang Lü

The full compressible Navier-Stokes system (FNS) describing the motion of a viscous, compressible, heat-conductive, and Newtonian polytropic fluid in a three-dimensional (3D) exterior domain is studied. For the initial-boundary-value problem with the slip boundary conditions on the velocity and the Neumann one on the temperature, it is shown that there exist

Rahul Roy, Subir Kumar Bhandari

Here we address dependence among the test statistics in connection with asymptotically Bayes' optimal tests in presence of sparse alternatives. Extending the setup in Bogdan et.al. (2011) we consider an equicorrelated ( with equal correlation $\rho$ ) multivariate normal assumption on the joint distribution of the test statistics, while conditioned on the me

Wenze Chen, Yuewen Hou, Dong Yao

In the standard SIR model, infected vertices infect their neighbors at rate $\lambda$ independently across each edge. They also recover at rate $\gamma$. In this work we consider the SIR-$\omega$ model where the graph structure itself co-evolves with the SIR dynamics. Specifically, $S-I$ connections are broken at rate $\omega$. Then, with probability $\alpha

Fengkui Ju, Woxuan Zhou

In this paper, we do three kinds of work. First, we recognize four notions of necessity and two notions of possibility related to time flow, namely strong/weak historical/temporal necessities, as well as historical/temporal possibilities, which are motivated more from a linguistic perspective than from a philosophical one. Strong/weak historical necessities

Pengcheng Tang, Xuejun Zhang

Let $\mu$ be a finite positive Borel measure on the interval $[0,1)$ and $f(z)=\sum_{n=0}^{\infty}a_{n}z^{n} \in H(\mathbb{D})$. The Ce\`{a}sro-like operator is defined by $$ \mathcal{C}_\mu(f)(z)=\sum^\infty_{n=0}\mu_n\left(\sum^n_{k=0}a_k\right)z^n, \ z\in \mathbb{D}, $$ where, for $n\geq 0$, $\mu_n$ denotes the $n$-th moment of the measure $\mu$, that is,

Chunyan Zeng, Shuai Kong, Zhifeng Wang, Xiangkui Wan

Most digital audio tampering detection methods based on electrical network frequency (ENF) only utilize the static spatial information of ENF, ignoring the variation of ENF in time series, which limit the ability of ENF feature representation and reduce the accuracy of tampering detection. This paper proposes a new method for digital audio tampering detectio

Taro Fukazawa, Takashi Miyake

We propose a scheme for investigating the correlation and trade-off among target variables using a multi-objective Bayesian optimization (MBO). We discuss the features of the Pareto front (PF) of ThMn12-type compounds, (R, Z)(Fe,Co,Ti)12 (R = Y, Nd, Sm; Z = Zr, Dy) in terms of magne- tization, Curie temperature, and a price index by using data from first-pri

Cui-Wen Zhang, Mamat-Ali Bake, Hong Xiao, Hai-Bo Sang

We use numerical simulations to demonstrate that a source of bright collimated vortex $\gamma$-ray with large orbital angular momentum can be achieved by irradiating a circularly polarized laser with an intensity about $10^{22}\rm{W/{cm^2}}$ on a cone-fan target. In the studied setup, electron beam of energy of hundreds of MeV and vortex laser pulse are form

Fengkui Ju

Weak ontic necessity is the ontic necessity expressed by ``should'' or ``ought to'' in English. An example of it is ``I should be dead by now''. A feature of this necessity is whether it holds does not have anything to do with whether its prejacent holds. In this paper, we present a logical theory for conditional weak ontic necessity based on context update.

Yuri N. Lima, André V. Giannini, Victor P. Goncalves

The production of the $K_S^0$ meson in high multiplicity $pp$ collisions at $\sqrt{s} =$ 13 TeV is investigated considering the hybrid formalism and the solution of the running coupling Balitsky - Kovchegov equation. The associated cross section is estimated and compared with the experimental data for the transverse momentum spectrum. Moreover, we analyze th

O. Kirmizitas, S. Cavus, F. Kahraman Aliçavuş}

Pulsating stars are remarkable objects for stellar astrophysics. Their pulsation frequencies allow us to probe the internal structure of stars. One of the most known groups of pulsating stars is $\delta$ Scuti variables which could be used to understand the energy transfer mechanism in A-F type stars. Therefore, in the current study, we focused on the discov

Valentin Niess

We present an algorithm for simulating reverse Monte Carlo decays given an existing forward Monte Carlo decay engine. This algorithm is implemented in the Alouette library, a TAUOLA thin wrapper for simulating decays of tau-leptons. We provide a detailed description of Alouette, as well as validation results.

Ziming Wang, Junjie Zhao, Zihe An, Lijing Shao

Compact binaries are an important class of gravitational-wave (GW) sources that can be detected by current and future GW observatories. They provide a testbed for general relativity (GR) in the highly dynamical strong-field regime. Here, we use GWs from inspiraling binary neutron stars and binary black holes to investigate dipolar gravitational radiation (DG

Jooyeon Geem, Masateru Ishiguro, Jun Takahashi, Hiroshi Akitaya

We report on the first polarimetric study of (3200) Phaethon, the target of JAXA's DESTINY$^+$ mission, in the negative branch to ensure its anhydrous nature and to derive an accurate geometric albedo. We conducted observations at low phase angles (Sun-target-observer angle, alpha = 8.8-32.4 deg) from 2021 October to 2022 January and found that Phaethon has

Chunyan Zeng, Shixiong Feng, Zhifeng Wang, Xiangkui Wan

The existing source cell-phone recognition method lacks the long-term feature characterization of the source device, resulting in inaccurate representation of the source cell-phone related features which leads to insufficient recognition accuracy. In this paper, we propose a source cell-phone recognition method based on spatio-temporal representation learnin

Alain Mazzolo, Cécile Monthus

For a diffusion process $X(t)$ of drift $\mu(x)$ and of diffusion coefficient $D=1/2$, we study the joint distribution of the two local times $A(t)= \int_{0}^{t} d\tau \delta(X(\tau)) $ and $B(t)= \int_{0}^{t} d\tau \delta(X(\tau)-L) $ at positions $x=0$ and $x=L$, as well as the simpler statistics of their sum $ \Sigma(t)=A(t)+B(t)$. Their asymptotic statis

Jinhong Kim, Yongjun Ahn, Byonghyo Shim

As an entirely-new paradigm to design the communication systems, deep learning (DL), an approach that the machine learns the desired wireless function, has received much attention recently. In order to fully realize the benefit of DL-aided wireless system, we need to collect a large number of training samples. Unfortunately, collecting massive samples in the

Yizheng Ouyang, Tianjin Zhang, Weibo Gu, Hongfa Wang

Temporal action localization aims to predict the boundary and category of each action instance in untrimmed long videos. Most of previous methods based on anchors or proposals neglect the global-local context interaction in entire video sequences. Besides, their multi-stage designs cannot generate action boundaries and categories straightforwardly. To addres

Ryohei Umatani, Takashi Imai, Kaoru Kawamoto, Shutaro Kunimasa

In this paper, we consider the task of clustering a set of individual time series while modeling each cluster, that is, model-based time series clustering. The task requires a parametric model with sufficient flexibility to describe the dynamics in various time series. To address this problem, we propose a novel model-based time series clustering method with

Chen-Yu Wang, Da-Shin Lee, Chi-Yong Lin

We study the null and time-like geodesics of the light and the neutral particles respectively in the exterior of Kerr-Newman black holes. The geodesic equations are known to be written as a set of first-order differential equations in Mino time from which the angular and radial potentials can be defined. We classify the roots for both potentials, and mainly

Yiming Wang, Qingzhe Gao, Libin Liu, Lingjie Liu

We propose a new method for learning a generalized animatable neural human representation from a sparse set of multi-view imagery of multiple persons. The learned representation can be used to synthesize novel view images of an arbitrary person from a sparse set of cameras, and further animate them with the user's pose control. While existing methods can eit

Gayan K. Kulatilleke, Sugandika Samarakoon

With growing credit card transaction volumes, the fraud percentages are also rising, including overhead costs for institutions to combat and compensate victims. The use of machine learning into the financial sector permits more effective protection against fraud and other economic crime. Suitably trained machine learning classifiers help proactive fraud dete

Kushagra Sharma, Rasit Eskicioglu

The number of IoT devices in healthcare is expected to rise sharply due to increased demand since the COVID-19 pandemic. Deep learning and IoT devices are being employed to monitor body vitals and automate anomaly detection in clinical and non-clinical settings. Most of the current technology requires the transmission of raw data to a remote server, which is

Sophie Buckeridge, Pamela Carreno-Medrano, Akansel Cosgun, Elizabeth Croft

For autonomous robots navigating in urban environments, it is important for the robot to stay on the designated path of travel (i.e., the footpath), and avoid areas such as grass and garden beds, for safety and social conformity considerations. This paper presents an autonomous navigation approach for unknown urban environments that combines the use of seman

Pascal Gutjahr, Jean-Marco Alameddine, Alexander Sandrock, Jan Soedingrekso

Large scale neutrino detectors and muon tomography rely on the muon direction in the detector to infer the muon's or parent neutrino's origin. However, muons accumulate deflections along their propagation path prior to entering the detector, which may need to be accounted for as an additional source of uncertainty. In this paper, the deflection of muons is s

Yiran Guan, Jiejun Zhang, Lingzhi Li, Ruidong Cao

The synthetic dimension opens new horizons in quantum physics and topological photonics by enabling new dimensions for field and particle manipulations. The most appealing property of the photonic synthetic dimension is its ability to emulate high-dimensional optical behavior in a unitary physical system. Here we show that the photonic synthetic dimension ca

Gayan K. Kulatilleke

Machine learning has opened up new tools for financial fraud detection. Using a sample of annotated transactions, a machine learning classification algorithm learns to detect frauds. With growing credit card transaction volumes and rising fraud percentages there is growing interest in finding appropriate machine learning classifiers for detection. However, f

G. G. L. Nashed, W. El Hanafy

We investigated Rastall gravity, for an anisotropic star with a static spherical symmetry, whereas the matter-geometry coupling as assumed in Rastall Theory (RT) is expected to play a crucial role in differentiating RT from General Relativity (GR). Indeed, all the obtained results confirm that RT is not equivalent to GR, however, it produces the same amount

Kumpei Shiraishi, Yusuke Hara, Hideyuki Mizuno

Glasses exhibit spatially localized vibrations in the low-frequency regime. These localized modes emerge below the boson peak frequency $\omega_\text{BP}$, and their vibrational densities of state follow $g(\omega) \propto \omega^4$ ($\omega$ is frequency). Here, we attempt to address how the localized vibrations behave through the ideal glass transition. To

Mohammad Tahaei, Kami Vaniea, Awais Rashid

To make privacy a first-class citizen in software, we argue for equipping developers with usable tools, as well as providing support from organizations, educators, and regulators. We discuss the challenges with the successful integration of privacy features and propose solutions for stakeholders to help developers perform privacy-related tasks.

Juan de Dios Pérez, David Pérez-López

We consider real hypersurfaces $M$ in complex projective space equipped with both the Levi-Civita and generalized Tanaka-Webster connections. For any nonnull constant $k$ and any symmetric tensor field of type (1,1) $L$ on $M$ we can define two tensor fields of type (1,2) on $M$, $L_F^{(k)}$ and $L_T^{(k)}$, related to both connections. We study the behaviou

Hiroyuki Umeeda

We study violation of quark-hadron duality for $\bar{B}\to X_ul\nu$ and $D\to X_dl\nu$ decays in the presence of the right-handed current operator. For this case, we show that duality violation has some aspects different from the standard model due to the existence of an instanton. In particular, the fermionic zero mode of an intermediate light quark can giv

M. Kasalo, S. Wurster, M. Stückler, M. Zawodzki

Severe plastic deformation using high-pressure torsion of ternary Cu-based materials (CuFeCo and CuFeNi) was used to fabricate bulk samples with a nanocrystalline microstructure. The goal was to produce materials featuring the granular giant magnetoresistance effect, requiring interfaces between ferro- and nonmagnetic materials. This magnetic effect was foun

Guo-Peng Li

Most of the binary black hole (BBH) mergers detected by LIGO and Virgo could be explained by first-generation mergers formed from the collapse of stars, while others might come from second (or higher) generation mergers, namely hierarchical mergers, with at least one of the black holes (BHs) being the remnant of a previous merger. A primary condition for the

Ming Jiang, Shaoxiong Ji

Multimodal sentiment analysis is an important research task to predict the sentiment score based on the different modality data from a specific opinion video. Many previous pieces of research have proved the significance of utilizing the shared and unique information across different modalities. However, the high-order combined signals from multimodal data w

Tomoya Kemmochi

In this paper, we will show the $L^p$-resolvent estimate for the finite element approximation of the Stokes operator for $p \in \left( \frac{2N}{N+2}, \frac{2N}{N-2} \right)$, where $N \ge 2$ is the dimension of the domain. It is expected that this estimate can be applied to error estimates for finite element approximation of the non-stationary Navier--Stoke

Miguel A. Mendez

This chapter reviews the fundamentals of continuous and discrete Linear Time-Invariant (LTI) systems with Single Input-Single Output (SISO). We start from the general notions of signals and systems, the signal representation problem and the related orthogonal bases in discrete and continuous forms. We then move to the key properties of LTI systems and discus

Mostafa Eghbali Zarch, Reece Neff, Michela Becchi

Over the past few years, there has been an increased interest in including FPGAs in data centers and high-performance computing clusters along with GPUs and other accelerators. As a result, it has become increasingly important to have a unified, high-level programming interface for CPUs, GPUs and FPGAs. This has led to the development of compiler toolchains

Takuya Matsumoto

The Lie superalgebra $\mathfrak{psl}(2|2)$ is recognized as a pretty special one in both mathematics and theoretical physics. In this paper, we present the Drinfeld realization of the Yangian algebra associated with the centrally extended Lie superalgebra $\mathfrak{psl}(2|2)$. Furthermore, we show that it possesses the Hopf algebra structures, particularly

Waqas Ahmed, Muhammad Moosa, Shoaib Munir, Umer Zubair

We analyse the shifted hybrid inflation in a no-scale SU(5) model with supersymmetry, which naturally circumvents the monopole problem. The no-scale framework is derivable as the effective field theory of the supersymmetric (SUSY) compactifications of string theory, and it yields a flat potential with no anti-de Sitter vacua, resolving the $\eta$ problem. Th

Abhilash Singh, J. Amutha, Jaiprakash Nagar, Sandeep Sharma

Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at the border areas and in the defense establishments. The border areas are stretched in hundreds to thousands of miles, hence, it is not possible to patrol the enti

Rohan Sarkar, Sreelekshmi R, Abhijit Nayek, Anirban Bhowmick

Foraging and acquiring of food is a delicate balance between managing the costs, both energy and social, and individual preferences. Previous research on the solitary foraging of free ranging dogs showed that they prioritized the nutritionally highest valued food patch first but do not ignore other less valuable food either, displaying typical scavenger beha

Ke Xu, Junsheng Feng, Hongjun Xiang

Magnetics, ferroelectrics and multiferroics have attracted great attentions because they are not only extremely important for investigating fundamental physics, but also have important applications in information technology. Here, recent computational studies on magnetism and ferroelectricity are reviewed. We first give a brief introduction to magnets, ferro

Melissa E. Swift, Wyatt Ayers, Sophie Pallanck, Scott Wehrwein

What can we learn about a scene by watching it for months or years? A video recorded over a long timespan will depict interesting phenomena at multiple timescales, but identifying and viewing them presents a challenge. The video is too long to watch in full, and some occurrences are too slow to experience in real-time, such as glacial retreat. Timelapse vide

Jungmin Kim, Dayeong Lee, Sunkyu Yu, Namkyoo Park

The temporal degree of freedom in photonics has been a recent research hotspot due to its analogy with spatial axes, causality, and open-system characteristics. In particular, the temporal analogues of photonic crystals have stimulated the design of momentum gaps and their extension to topological and non-Hermitian photonics. Although recent studies have als

Lei Qu, Edwin Moncada-Villa, Jie-Long Fang, Yong Zhang

Recently, the application of an external magnetic field to actively control the near-field heat transfer has emerged as an appealing and promising technique. Existing studies have shown that an external static magnetic field tends to reduce the subwavelength radiative flux exchanged between two planar structures containing magneto-optical (MO) materials, but

Hao Ge, Zi-Wei Long, Xiang-Yuan Xu, Yang Liu

Local density-of-states (LDOS) is a fundamental spectral property that plays a central role in various physical phenomena such as wave-matter interactions. Here, we report on the direct measurement of the LDOS of acoustic systems and derive from which the fractional topological number in an acoustic Su-Schrieffer-Heeger system. The acoustic LDOS is quantifie

Roberto García, Ana Cediel, Mercè Teixidó, Rosa Gil

Recent initiatives related to the Metaverse focus on better visualisation, like augmented or virtual reality, but also persistent digital objects. To guarantee real ownership of these digital objects, open systems based on public blockchains and Non-Fungible Tokens (NFTs) are emerging together with a nascent decentralized and open creator economy. To manage

Xiangyu Chen, Zolboo Byambadorj, Takeaki Yajima, Hisashi Inoue

Conventional neural structures tend to communicate through analog quantities such as currents or voltages, however, as CMOS devices shrink and supply voltages decrease, the dynamic range of voltage/current-domain analog circuits becomes narrower, the available margin becomes smaller, and noise immunity decreases. More than that, the use of operational amplif

Man-Sheng Chen, Tuo Liu, Chang-Dong Wang, Dong Huang

Multiview clustering has been extensively studied to take advantage of multi-source information to improve the clustering performance. In general, most of the existing works typically compute an n * n affinity graph by some similarity/distance metrics (e.g. the Euclidean distance) or learned representations, and explore the pairwise correlations across views

Fysol Ibna Abbas, Kazuhisa Hoshi, Yuki Nakahira, Miku Yoshida

The superconducting properties and structural parameters, including the Gr\"uneisen parameter ({\gamma}G), of the BiCh2-based (Ch: S, Se) layered superconductor LaO0.5F0.5BiS2-xSex were investigated. The superconducting transition temperature (Tc) increased with increasing Se concentration (x), and bulk superconductivity was induced by Se substitution. {\gam

Lindon Roberts, Edward Smyth

In distributed learning, a central server trains a model according to updates provided by nodes holding local data samples. In the presence of one or more malicious servers sending incorrect information (a Byzantine adversary), standard algorithms for model training such as stochastic gradient descent (SGD) fail to converge. In this paper, we present a simpl

Rishikesh Kakade, Joey Chou, Shannon Torcato

The operation of many network communication protocols require accurate time synchronization between nodes. In the automotive space, IEEE 802.3bw (commonly referred to as automotive ethernet) is quickly becoming the most popular in-vehicle communication protocol between electronic control units (ECUs). The rapid advance of autonomous vehicles is predicated on

Yunpu Zhang, Changsheng You

Prior studies on intelligent reflecting surface (IRS) have mostly considered wireless communication systems aided by a single passive IRS, which, however, has limited control over wireless propagation environment and suffers from product-distance path-loss. To address these issues, we propose in this paper a new hybrid active/passive IRS aided wireless commu

Zhixun Lu, Qihua Feng, Peiya Li

Applying encryption technology to image retrieval can ensure the security and privacy of personal images. The related researches in this field have focused on the organic combination of encryption algorithm and artificial feature extraction. Many existing encrypted image retrieval schemes cannot prevent feature leakage and file size increase or cannot achiev

Oswin Aichholzer, Kristin Knorr, Wolfgang Mulzer, Nicolas El Maalouly

For a simple drawing $D$ of the complete graph $K_n$, two (plane) subdrawings are compatible if their union is plane. Let $\mathcal{T}_D$ be the set of all plane spanning trees on $D$ and $\mathcal{F}(\mathcal{T}_D)$ be the compatibility graph that has a vertex for each element in $\mathcal{T}_D$ and two vertices are adjacent if and only if the corresponding

Yuta Hyodo, Teruyuki Kitabayashi

The magic textures are successful candidates of the correct texture for the Majorana neutrinos. In this study, we show that some types of magic texture for Majorana neutrinos approximately immanent in the flavor mass matrix for Dirac neutrinos. In addition, it turned out that the normal mass ordering of the Dirac neutrino masses is slightly preferable to the

Hengyu Liu, Qiang Fu, Lun Du, Tiancheng Zhang

Learning rate is one of the most important hyper-parameters that has a significant influence on neural network training. Learning rate schedules are widely used in real practice to adjust the learning rate according to pre-defined schedules for fast convergence and good generalization. However, existing learning rate schedules are all heuristic algorithms an

Osamu Fujino

We show that log canonical thresholds for complex analytic spaces satisfy the ACC.

Yuan-Zhu Wang, Yin-Jie Li, Jorick S. Vink, Yi-Zhong Fan

The origins of coalescing binary black holes (BBHs) detected by the advanced LIGO/Virgo are still under debate, and clues may be present in the joint mass-spin distribution of these merger events. Here we construct phenomenological models containing two sub-populations to investigate the BBH population detected in gravitational wave observations. We find tha

Zhe Huang, Mary-Joy Sidhom, Benjamin S. Wessler, Michael C. Hughes

Semi-supervised learning (SSL) promises improved accuracy compared to training classifiers on small labeled datasets by also training on many unlabeled images. In real applications like medical imaging, unlabeled data will be collected for expediency and thus uncurated: possibly different from the labeled set in classes or features. Unfortunately, modern dee

Gianmarco Bet, Anna Gallo, Seonwoo Kim

We consider the Potts model on a two-dimensional periodic rectangular lattice with general coupling constants $J_{ij}>0$, where $i,j\in\{1,2,3\}$ are the possible spin values (or colors). The resulting energy landscape is thus significantly more complex than in the original Ising or Potts models. The system evolves according to a Glauber-type spin-flipping d

Puneet Kumar, Sarthak Malik, Balasubramanian Raman

This paper proposes a multimodal emotion recognition system based on hybrid fusion that classifies the emotions depicted by speech utterances and corresponding images into discrete classes. A new interpretability technique has been developed to identify the important speech & image features leading to the prediction of particular emotion classes. The propose

Chengdong Yao, Cuihua Wang

In some areas, such as Cognitive Linguistics, researchers are still using traditional techniques based on manual rules and patterns. Since the definition of shell noun is rather subjective and there are many exceptions, this time-consuming work had to be done by hand in the past when Deep Learning techniques were not mature enough. With the increasing number

Zongren Zou, Xuhui Meng, Apostolos F Psaros, George Em Karniadakis

Uncertainty quantification (UQ) in machine learning is currently drawing increasing research interest, driven by the rapid deployment of deep neural networks across different fields, such as computer vision, natural language processing, and the need for reliable tools in risk-sensitive applications. Recently, various machine learning models have also been de

Jianhao Jiao, Hexiang Wei, Tianshuai Hu, Xiangcheng Hu

Combining multiple sensors enables a robot to maximize its perceptual awareness of environments and enhance its robustness to external disturbance, crucial to robotic navigation. This paper proposes the FusionPortable benchmark, a complete multi-sensor dataset with a diverse set of sequences for mobile robots. This paper presents three contributions. We firs

Eduard Navas, Ebner Pineda, Wilfredo O. Urbina

In this paper we prove the boundedness of the Gaussian Riesz potentials $I_{\beta}$, for $\beta\geq 1$ on $L^{p(\cdot)}(\gamma_d)$, the Gaussian variable Lebesgue spaces under a certain additional condition of regularity on $p(\cdot)$ following \cite{DalSco}. Additionally, this result trivially gives us an alternative proof of the boundedness of Gaussian Rie

Kyeongpung Lee, Yutack Park, Seungwu Han

While several studies confirmed that machine-learned potentials (MLPs) can provide accurate free energies for determining phase stabilities, the abilities of MLPs for efficiently constructing a full phase diagram of multi-component systems are yet to be established. In this work, by employing neural network interatomic potentials (NNPs), we demonstrate const

Hichem Hajaiej, Linjie Song

We first give an abstract framework to show the uniqueness of Ground State Solutions (GSS) for a large class of PDEs. To the best of our knowledge, all the existing results in the literature only addressed particular cases. Moreover, our self-contained approach offers a general framework to study the existence/non-existence and multiplicity of normalized sol

Zheng Cao, Wenyu Du, Kirill V. Golubnichiy

This paper presents a novel way to apply mathematical finance and machine learning (ML) to forecast stock options prices. Following results from the paper Quasi-Reversibility Method and Neural Network Machine Learning to Solution of Black-Scholes Equations (appeared on the AMS Contemporary Mathematics journal), we create and evaluate new empirical mathematic

Mitsuhiro Itoh, Hiroyasu Satoh

In this article, we present recent developments of information geometry, namely, geometry of the Fisher metric, dualistic structures and divergences on the space of probability measures, particularly the theory of geodesics of the Fisher metric. Moreover, we consider several facts concerning the barycenter of probability measures on the ideal boundary of a H

Xuefeng Jiang, Sheng Sun, Yuwei Wang, Min Liu

Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner. However, since high-quality labeled data require expensive human intelligence and efforts, data with incorrect labels (called noisy labels) are ubiquitous in reality, which inevitably cause performance degradatio

Yuan Gao, Jian-Guo Liu

This paper reinterprets Freidlin-Wentzell's variational construction of the rate function in the large deviation principle for invariant measures from the weak KAM perspective. Through a one-dimensional irreversible diffusion process on a torus, we explicitly characterize essential concepts in the weak KAM theory, such as the Peierls barrier and the projecte

Yan-Ying Bai, Xuan-Rui Chen, Zhen-Ming Xu, Bin Wu

The thermodynamics of the BTZ black holes are revisited with variable Newton constant. A new pair of conjugated variables, the central charge $C$ and the chemical potential $\mu$, is introduced as thermodynamic variables. The first law of thermodynamics and the Euler relation, instead of the Smarr relation in the extended phase space formalism, are matched p

Tuowen Zhao, Tobi Popoola, Mary Hall, Catherine Olschanowsky

This paper presents a code generator for sparse tensor contraction computations. It leverages a mathematical representation of loop nest computations in the sparse polyhedral framework (SPF), which extends the polyhedral model to support non-affine computations, such as arise in sparse tensors. SPF is extended to perform layout specification, optimization, a

Mengnan Du, Fengxiang He, Na Zou, Dacheng Tao

Large language models (LLMs) have achieved state-of-the-art performance on a series of natural language understanding tasks. However, these LLMs might rely on dataset bias and artifacts as shortcuts for prediction. This has significantly affected their generalizability and adversarial robustness. In this paper, we provide a review of recent developments that

Wesley P. Chan, Morgan Crouch, Khoa Hoang, Charlie Chen

When humans work together to complete a joint task, each person builds an internal model of the situation and how it will evolve. Efficient collaboration is dependent on how these individual models overlap to form a shared mental model among team members, which is important for collaborative processes in human-robot teams. The development and maintenance of

Farhad Aghili

This paper presents Lidar-based Simultaneous Localization and Mapping (SLAM) for autonomous driving vehicles. Fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF) plus the observability of the system are investigated. In addition to the vehicle's states and landmark positions, a self-tuning filt

Yue Cao, Shaoshi Yang, Zhiyong Feng, Lihua Wang

A distributed spatio-temporal information based cooperative positioning (STICP) algorithm is proposed for wireless networks that require three-dimensional (3D) coordinates and operate in the global navigation satellite system (GNSS) denied environments. Our algorithm supports any type of ranging measurements that can determine the distance between nodes. We