July 2023 arXiv papers — page 32

Tokio Kajitsuka, Issei Sato

Existing analyses of the expressive capacity of Transformer models have required excessively deep layers for data memorization, leading to a discrepancy with the Transformers actually used in practice. This is primarily due to the interpretation of the softmax function as an approximation of the hardmax function. By clarifying the connection between the soft

Tomasz Śmierzchalski, Zakaria Mzaouali, Sebastian Deffner, Bartłomiej Gardas

We investigate the computational efficiency and thermodynamic cost of the D-Wave quantum annealer under reverse-annealing with and without pausing. Our experimental results demonstrate that the combination of reverse-annealing and pausing leads to improved computational efficiency while minimizing the thermodynamic cost compared to reverse-annealing alone. M

Huzheng Yang, Jianbo Shi, James Gee

Brain encoding models aim to predict brain voxel-wise responses to stimuli images, replicating brain signals captured by neuroimaging techniques. There is a large volume of publicly available data, but training a comprehensive brain encoding model is challenging. The main difficulties stem from a) diversity within individual brain, with functional heterogene

Ioanna Mitropoulou, Amir Vaxman, Olga Diamanti, Benjamin Dillenburger

Strip-decomposable quadrilateral (SDQ) meshes, i.e., quad meshes that can be decomposed into two transversal strip networks, are vital in numerous fabrication processes; examples include woven structures, surfaces from sheets, custom rebar, or cable-net structures. However, their design is often challenging and includes tedious manual work, and there is a la

Yongzhe Yuan, Yue Wu, Maoguo Gong, Qiguang Miao

The precision of unsupervised point cloud registration methods is typically limited by the lack of reliable inlier estimation and self-supervised signal, especially in partially overlapping scenarios. In this paper, we propose an effective inlier estimation method for unsupervised point cloud registration by capturing geometric structure consistency between

Sayyed Mehrab Ramezani

In this paper, we define the concept of soft $g$-frame in soft Hilbert spaces by extending the concept of soft from frame to $g$-frame. We then show some properties of the soft $g$-frames in soft Hilbert spaces. Among other results, we show that the $g$-frame operator is associated with a soft, finite, self-adjoining, reversible, and finite inverse $g$-frame

Kenta Asakawa, Hideki Ishihara, Makoto Tsubota

We investigate the collective mode of a self-gravitating Bose-Einstein condensate (BEC) described by the Gross-Pitaevskii-Poisson (GPP) equations. The self-gravitating BEC has garnered considerable attention in cosmology and astrophysics, being proposed as a plausible candidate for dark matter. Our inquiry delves into the breathing and anisotropic collective

Stephan Rinner, Florian Burger, Andreas Gritsch, Jonas Schmitt

Quantum memories integrated into nanophotonic silicon devices are a promising platform for large quantum networks and scalable photonic quantum computers. In this context, erbium dopants are particularly attractive, as they combine optical transitions in the telecommunications frequency band with the potential for second-long coherence time. Here we show tha

Lei Shen, Jianlong Jin, Ruixin Zhang, Huaen Li

Palmprint recently shows great potential in recognition applications as it is a privacy-friendly and stable biometric. However, the lack of large-scale public palmprint datasets limits further research and development of palmprint recognition. In this paper, we propose a novel realistic pseudo-palmprint generation (RPG) model to synthesize palmprints with ma

Pengfu Tian, Ping Zhang, Wei Wang, Pei Wang

Powerful relativistic jets are one of the ubiquitous features of accreting black holes in all scales. GRS 1915+105 is a well-known fast-spinning black-hole X-ray binary with a relativistic jet, termed as a ``microquasar'', as indicated by its superluminal motion of radio emission. It exhibits persistent x-ray activity over the last 30 years, with quasi-perio

Yuki Sakamoto, Masahito Ueda

We consider an iterated multiplayer prisoner's dilemma game on a square lattice and regular graphs based on the pairwise-Fermi update rule, and obtain heat-maps of the fraction of cooperators and the correlation of neighboring pairs. In the heat-map, there is a mixed region where cooperators and defectors coexist, and in the mixed region the correlation betw

E. A. O'Brien, Sunil Kumar Prajapati, Ayush Udeep

Over the years various errors have been identified in the 1980 paper of James on the groups of order $p^6$, where $p$ is an odd prime. Here we summarise them.

Xingyu Chen, Fei Ma, Amy Bastine, Prasanga Samarasinghe

Accurate estimation of the sound field around a rigid sphere necessitates adequate sampling on the sphere, which may not always be possible. To overcome this challenge, this paper proposes a method for sound field estimation based on a physics-informed neural network. This approach integrates physical knowledge into the architecture and training process of t

Anders Sjöberg, Jakob Lindqvist, Magnus Önnheim, Mats Jirstrand

Diffusion models can be parameterized in terms of either score or energy function. The energy parameterization is attractive as it enables sampling procedures such as Markov Chain Monte Carlo (MCMC) that incorporates a Metropolis--Hastings (MH) correction step based on energy differences between proposed samples. Such corrections can significantly improve sa

Carole Porrier

Penrose tilings are the most famous aperiodic tilings, and they have been studied extensively. In particular, patterns composed with hexagons (H), boats (B) and stars (S) were soon exhibited and many physicists published on what they later called HBS tilings, but no article or book combines all we know about them. This work is done here, before introducing n

Mingjin Zhang, Chi Zhang, Qiming Zhang, Jie Guo

Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. However, the prevailing CNN-based approaches have shown limitations in building long-range dependencies and capturing interaction information between spectral features. This results in inadequate utilization of

Tianyu Liu, Hao Zhao, Yang Yu, Guyue Zhou

Compositional neural scene graph studies have shown that radiance fields can be an efficient tool in an editable autonomous driving simulator. However, previous studies learned within a sequence of autonomous driving datasets, resulting in unsatisfactory blurring when rotating the car in the simulator. In this letter, we propose a pipeline for learning uncon

Zhipeng Huang, Zhizheng Zhang, Cuiling Lan, Zheng-Jun Zha

Recent vision transformers, large-kernel CNNs and MLPs have attained remarkable successes in broad vision tasks thanks to their effective information fusion in the global scope. However, their efficient deployments, especially on mobile devices, still suffer from noteworthy challenges due to the heavy computational costs of self-attention mechanisms, large k

Dimitris Vartziotis, Doris Bohnet

Triangles are everywhere in the virtual world. The surface of nearly every graphical object is saved as a triangular mesh on a computer. Light effects and movements of virtual objects are computed on the basis of triangulations. Besides computer graphics, triangulated surfaces are used for the simulations of physical processes, like heating or cooling of obj

Walter Bridges, William Craig

The norm of an integer partition is defined as the product of its parts. This statistic was recently introduced by Schneider in connection to partition zeta functions. In this note, we use the method of moments to study the distribution of the norm under the uniform probability measure on partitions of $n$ as $n \to \infty$. We use singularity analysis to pr

Xinshun Wang, Qiongjie Cui, Chen Chen, Shen Zhao

Existing Graph Convolutional Networks to achieve human motion prediction largely adopt a one-step scheme, which output the prediction straight from history input, failing to exploit human motion patterns. We observe that human motions have transitional patterns and can be split into snippets representative of each transition. Each snippet can be reconstructe

Lida Aleksanyan, Armen E. Allahverdyan

We propose an unsupervised, corpus-independent method to extract keywords from a single text. It is based on the spatial distribution of words and the response of this distribution to a random permutation of words. As compared to existing methods (such as e.g. YAKE) our method has three advantages. First, it is significantly more effective at extracting keyw

Yarik Menchaca Resendiz, Roman Klinger

Models for affective text generation have shown a remarkable progress, but they commonly rely only on basic emotion theories or valance/arousal values as conditions. This is appropriate when the goal is to create explicit emotion statements ("The kid is happy."). Emotions are, however, commonly communicated implicitly. For instance, the emotional interpretat

Shan-Ping Wu, Shao-Wen Wei

Understanding the light ring, one kind fundamental orbit, shall provide us with novel insight into the astronomical phenomena, such as the ringdown of binary merger and shadow of black holes. Recently, topological approach has preliminarily demonstrated its potential advantages on the properties of the light rings. However, for the black holes without $\math

Thierry Huillet, Servet Martinez

We study the number of individuals per level defined by excursions of random walks with state dependent jump law. These level numbersdetermine the probability of the excursion, and the set of transformations preserving the level numbers is, generically, the set of transformation that preserve the probability law of excursions.We compute the number of excursi

Clarissa Astuto, Mohammed Lemou, Giovanni Russo

In this paper we propose a numerical method to solve a 2D advection-diffusion equation, in the highly oscillatory regime. We use an efficient and robust integrator which leads to an accurate approximation of the solution without any time step-size restriction. Uniform first and second order numerical approximations in time are obtained with errors, and at a

Christian Herglotz, André Kaup

In this paper, we show that processor events like instruction counts or cache misses can be used to accurately estimate the processing energy of software video decoders. Therefore, we perform energy measurements on an ARM-based evaluation platform and count processor level events using a dedicated profiling software. Measurements are performed for various co

Weixin Wu, Hankz Hankui Zhuo

Large-scale pre-trained language models such as BERT have contributed significantly to the development of NLP. However, those models require large computational resources, making it difficult to be applied to mobile devices where computing power is limited. In this paper we aim to address the weakness of existing input-adaptive inference methods which fail t

Xiaozhu Fang, Tianshi Chen

Through one decade's development, the kernel-based regularization method (KRM) has become a complement to the classical maximum likelihood/prediction error method and an emerging new system identification paradigm. One recent example is its application in the non-causal system identification, and the key issue lies in the design and analysis of kernels for n

Soowon Kim, Young-Eun Lee, Seo-Hyun Lee, Seong-Whan Lee

Decoding EEG signals for imagined speech is a challenging task due to the high-dimensional nature of the data and low signal-to-noise ratio. In recent years, denoising diffusion probabilistic models (DDPMs) have emerged as promising approaches for representation learning in various domains. Our study proposes a novel method for decoding EEG signals for imagi

Huang Yin

Starting from a classic financial optimization problem, we first propose a cutting plane algorithm for this problem. Then we use spectral decomposition to tranform the problem into an equivalent D.C. programming problem, and the corresponding upper bound estimate is given by the SCO algorithm; then the corresponding lower bound convex relaxation is given by

Bo Jiang, Ming Li, Ravi Tandon

Publishing streaming data in a privacy-preserving manner has been a key research focus for many years. This issue presents considerable challenges, particularly due to the correlations prevalent within the data stream. Existing approaches either fall short in effectively leveraging these correlations, leading to a suboptimal utility-privacy tradeoff, or they

Fredrik Brange, Riya Baruah, Christian Flindt

Recent experiments have observed Cooper pair splitting in quantum dots coupled to superconductors, and efficient schemes for controlling and timing the splitting process are now called for. Here, we propose and analyze an adiabatic Cooper pair splitter that can produce a regular flow of spin-entangled electrons in response to a time-dependent and periodic ga

Shuxian Niu, Qian Liu, Yang Zhou, Min Li

In this paper, we apply a Threshold-Decreasing Algorithm to maximize $k$-submodular functions under a matroid constraint, which reduces the query complexity of the algorithm compared to the greedy algorithm with little loss in approximation ratio. We give a $(\frac{1}{2} - \epsilon)$-approximation algorithm for monotone $k$-submodular function maximization,

Daniel Kazenwadel, Christoph V. Steinert

OpenAI's ChatGPT language model has gained popularity as a powerful tool for complex problem-solving and information retrieval. However, concerns arise about the reproduction of biases present in the language-specific training data. In this study, we address this issue in the context of the Israeli-Palestinian and Turkish-Kurdish conflicts. Using GPT-3.5, we

Guogang Zhu, Xuefeng Liu, Shaojie Tang, Jianwei Niu

Personalized federated learning (PFL) is a popular framework that allows clients to have different models to address application scenarios where clients' data are in different domains. The typical model of a client in PFL features a global encoder trained by all clients to extract universal features from the raw data and personalized layers (e.g., a classifi

Dinu Gavojdian, Teddy Lazebnik, Madalina Mincu, Ariel Oren

There is a critical need to develop and validate non-invasive animal-based indicators of affective states in livestock species, in order to integrate them into on-farm assessment protocols, potentially via the use of precision livestock farming (PLF) tools. One such promising approach is the use of vocal indicators. The acoustic structure of vocalizations an

Maxime Leiber, Yosra Marnissi, Axel Barrau, Mohammed El Badaoui

In this paper, we propose a differentiable version of the short-time Fourier transform (STFT) that allows for gradient-based optimization of the hop length or the frame temporal position by making these parameters continuous. Our approach provides improved control over the temporal positioning of frames, as the continuous nature of the hop length allows for

Florian Simon, Mark O. Goerbig, Marc Gabay

We theoretically investigate the influence of the normal state quantum geometry on the superconducting phase in (111) oriented oxide interfaces and discuss some of the implications for the $\text{LaAlO}_3/\text{SrTiO}_3$ (LAO/STO) heterostructure. From a tight-binding modeling of the interface, we derive a two-band low-energy model, allowing us to analytical

Kexuan Zhang, Qiyu Sun, Chaoqiang Zhao, Yang Tang

Deep learning has revolutionized the field of artificial intelligence. Based on the statistical correlations uncovered by deep learning-based methods, computer vision has contributed to tremendous growth in areas like autonomous driving and robotics. Despite being the basis of deep learning, such correlation is not stable and is susceptible to uncontrolled f

Junwon Seo, Taekyung Kim, Seongyong Ahn, Kiho Kwak

Autonomous navigation in off-road conditions requires an accurate estimation of terrain traversability. However, traversability estimation in unstructured environments is subject to high uncertainty due to the variability of numerous factors that influence vehicle-terrain interaction. Consequently, it is challenging to obtain a generalizable model that can a

Maxime Leiber, Yosra Marnissi, Axel Barrau, Mohammed El Badaoui

This paper presents a gradient-based method for on-the-fly optimization for both per-frame and per-frequency window length of the short-time Fourier transform (STFT), related to previous work in which we developed a differentiable version of STFT by making the window length a continuous parameter. The resulting differentiable adaptive STFT possesses commenda

Philipp Rüßmann, Masoud Bahari, Stefan Blügel, Björn Trauzettel

Multi-band effects in hybrid structures provide a rich playground for unconventional superconductivity. We combine two complementary approaches based on density-functional theory (DFT) and effective low-energy model theory in order to investigate the proximity effect in a Rashba surface state in contact to an $s$-wave superconductor. We discuss these synergi

Miriam Anschütz, Diego Miguel Lozano, Georg Groh

Large language models underestimate the impact of negations on how much they change the meaning of a sentence. Therefore, learned evaluation metrics based on these models are insensitive to negations. In this paper, we propose NegBLEURT, a negation-aware version of the BLEURT evaluation metric. For that, we designed a rule-based sentence negation tool and us

Henry Fetsch, Nathaniel J. Fisch

The fast ignition paradigm for inertial fusion offers increased gain and tolerance of asymmetry by compressing fuel at low entropy and then quickly igniting a small region. Because this hotspot rapidly disassembles, the ions must be heated to ignition temperature as quickly as possible, but most ignitor designs directly heat electrons. A constant-power ignit

Derya Malak

In this work, we consider the problem of distributed computing of functions of structured sources, focusing on the classical setting of two correlated sources and one user that seeks the outcome of the function while benefiting from low-rate side information provided by a helper node. Focusing on the case where the sources are jointly distributed according t

Ganping Li, Yoshito Otake, Mazen Soufi, Masashi Taniguchi

Purpose: Manual annotations for training deep learning (DL) models in auto-segmentation are time-intensive. This study introduces a hybrid representation-enhanced sampling strategy that integrates both density and diversity criteria within an uncertainty-based Bayesian active learning (BAL) framework to reduce annotation efforts by selecting the most informa

Ryota Iijima, Miki Tanaka, Sayaka Shiota, Hitoshi Kiya

Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In addition, AEs have adversarial transferability, which means AEs generated for a source model can fool another black-box model (target model) with a non-trivial probability. In previous studies, it was confirmed that the vision transformer (ViT) is more robust agains

Carles Batlle, Víctor Campello, Joaquim Gomis

We construct a Lorentz invariant massive particle model in (2+1) space-time with an enlarged set of symmetries which includes Bondi-Metzner-Sachs (BMS) translations (supertranslations), using the non-linear realization framework. The Hamiltonian formalism for the resulting Lagrangian is constructed, and the infinite phase-space constraints and the set of gau

Nicolò De Ponti, Sara Farinelli, Ivan Yuri Violo

We prove the Pleijel theorem in non-collapsed RCD spaces, providing an asymptotic upper bound on the number of nodal domains of Laplacian eigenfunctions. As a consequence, we obtain that the Courant nodal domain theorem holds except at most for a finite number of eigenvalues. More in general, we show that the same result is valid for Neumann (resp. Dirichlet

Yossi Rosenzweig, Dmitriy Tokar, Igor Shcerback, Menachem Givon

Optically pumped magnetometers are ultra-sensitive devices, but this sensitivity can significantly degrade due to heading errors, whereby a change in the angle between the pumping laser and the magnetic field translates to a change in the magnetic field readout. We present a portable all-optical single-beam magnetometer with a reduced heading error due to a

Yuni Lai, Marcin Waniek, Liying Li, Jingwen Wu

Random Walks-based Anomaly Detection (RWAD) is commonly used to identify anomalous patterns in various applications. An intriguing characteristic of RWAD is that the input graph can either be pre-existing or constructed from raw features. Consequently, there are two potential attack surfaces against RWAD: graph-space attacks and feature-space attacks. In thi

Wei Sun, Wen Wen, Xiongkuo Min, Long Lan

Blind video quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users' viewing experience in various real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. Thus, it is crucial to gain a better understanding of

Jakub Slezak, Ralf Metzler

We introduce the stochastic process of incremental multifractional Brownian motion (IMFBM), which locally behaves like fractional Brownian motion with a given local Hurst exponent and diffusivity. When these parameters change as function of time the process responds to the evolution gradually: only new increments are governed by the new parameters, while sti

Yash Bhargava, Sudip Bhattacharyya, Jeroen Homan, Mayukh Pahari

Understanding the spectral evolution along the `Z'-shaped track in the hardness-intensity diagram of Z-sources, which are a class of luminous neutron star low-mass X-ray binaries, is crucial to probe accretion processes close to the neutron star. Here, we study the horizontal branch (HB) and the normal branch (NB) of the Z source GX 340+0 using $AstroSat$ da

Mahyar Abbasian, Taha Rajabzadeh, Ahmadreza Moradipari, Seyed Amir Hossein Aqajari

Generative Adversarial Networks (GAN) have emerged as a formidable AI tool to generate realistic outputs based on training datasets. However, the challenge of exerting control over the generation process of GANs remains a significant hurdle. In this paper, we propose a novel methodology to address this issue by integrating a reinforcement learning (RL) agent

Spandan Bisoyi, Murali Mohan Pasupuleti, Kiran Kuchi, Koteswara Rao

The white paper focuses on several candidate technologies that could play a crucial role in the development of 6G systems. Two of the key technologies explored in detail are Orthogonal Time Frequency Division Multiplexing (OTFDM) waveform and Structural MIMO (S-MIMO).

Chencheng Tang, Matthias Althoff

Verifying the correct behavior of robots in contact tasks is challenging due to model uncertainties associated with contacts. Standard methods for testing often fall short since all (uncountable many) solutions cannot be obtained. Instead, we propose to formally and efficiently verify robot behaviors in contact tasks using reachability analysis, which enable

Barbara Baumeister, Timothy C. Burness, Robert M. Guralnick, Hung P. Tong-Viet

In this paper, we determine the finite groups with a Sylow $r$-subgroup contained in a unique maximal subgroup. The proof involves a reduction to almost simple groups, and our main theorem extends earlier work of Aschbacher in the special case $r=2$. Several applications are presented. This includes some new results on weakly subnormal subgroups of finite gr

Ali Gürbüz, Andrew Lemus, Ebru Demir, On Shun Pak

Swimming at the microscale has recently garnered substantial attention due to the fundamental biological significance of swimming microorganisms and the wide range of biomedical applications for artificial microswimmers. These microswimmers invariably find themselves surrounded by different confining boundaries, which can impact their locomotion in significa

Yao Yao, Jie Song, Xu Kong, Guanwen Fang

Galaxy morphology is one of the most fundamental ways to describe galaxy properties, but the morphology we observe may be affected by wavelength and spatial resolution, which may introduce systematic bias when comparing galaxies at different redshift. Taking advantage of the broad wavelength coverage from optical to near-IR and high resolution NIRCam instrum

Jiawen Zhu, Zhenyu Chen, Zeqi Hao, Shijie Chang

Visual object tracking is a fundamental video task in computer vision. Recently, the notably increasing power of perception algorithms allows the unification of single/multiobject and box/mask-based tracking. Among them, the Segment Anything Model (SAM) attracts much attention. In this report, we propose HQTrack, a framework for High Quality Tracking anythin

Teppei Saito, Nao Hirokawa

We show that weighted path orders are special instances of a variant of semantic path orders. Exploiting this fact, we introduce a generalization of weighted path orders that goes beyond the realm of simple termination. Experimental data show that generalized weighted path orders are viable.

Xiao Yan Chew, Kok-Geng Lim

We numerically construct the asymptotically flat solutions of hairy black holes supported by a symmetric inverted Mexican hat potential with a local minimum and two degenerate global maxima of a real scalar field that contains a quartic self-interaction term. The solutions of hairy black holes emerge from the Schwarzschild black hole when the non-trivial sca

Sjoerd Bouma, Anna Nelles

The IceCube-Gen2 facility will extend the energy range of IceCube to ultra-high energies. The key component to detect neutrinos with energies above 10 PeV is a large array of in-ice radio detectors. In previous work, direction reconstruction algorithms using the forward-folding technique have been developed for both shallow ($\lesssim 20$ m) and deep in-ice

Rakesh Kumar

Let $S_g$ denote a closed oriented surface of genus $g \geq 2$. A set $\Omega = \{ c_1, \dots, c_d\}$ of pairwise non-homotopic simple closed curves on $S_g$ is called a filling system or simply a filling of $S_g$, if $S_g\setminus \Omega$ is a union of $\ell$ topological discs for some $\ell\geq 1$. For $1\leq i\leq d$, let $T_{c_i}$ denotes the Dehn twist

Lars Heuermann

IceAct is an array of compact Imaging Air Cherenkov Telescopes at the ice surface as part of the IceCube Neutrino Observatory. The telescopes, featuring a camera of 61 silicon photomultipliers and fresnel-lens-based optics, are optimized to be operated in harsh environmental conditions, such as at the South Pole. Since 2019, the first two telescopes have bee

Xuhai Xu, Bingsheng Yao, Yuanzhe Dong, Saadia Gabriel

Advances in large language models (LLMs) have empowered a variety of applications. However, there is still a significant gap in research when it comes to understanding and enhancing the capabilities of LLMs in the field of mental health. In this work, we present a comprehensive evaluation of multiple LLMs on various mental health prediction tasks via online

Takol Tangphati, Butsayapat Chaihao, Daris Samart, Phongpichit Channuie

In this work, we study the rotating wormhole geometries supported by a three-form field. We demonstrate for particular choices of parameters that it is possible for the matter fields threading the wormhole to satisfy the null and weak energy conditions throughout the spacetime, when the three-form field is present. In this case, the form field is interpreted

Shao-Ping Li, Xun-Jie Xu

We investigate the impact of new light particles, carrying significant energy in the early universe after neutrino decoupling, on the cosmological effective relativistic neutrino species, $N_{{\rm eff}}$. If the light particles are produced from decoupled neutrinos, $N_{{\rm eff}}$ is predominantly modified through the dilution-resistant effect. This effect

Romuald Meango

Can stated preferences help in counterfactual analyses of actual choice? This research proposes a novel approach to researchers who have access to both stated choices in hypothetical scenarios and actual choices. The key idea is to use probabilistic stated choices to identify the distribution of individual unobserved heterogeneity, even in the presence of me

Larissa Paul

The IceCube Neutrino Observatory is equipped with the unique possibility to measure cosmic ray induced air showers simultaneously by their particle footprint on the surface with the IceTop detector and by the high-energy muonic shower component at a depth of more than 1.5 km. Since 2019 additionally two Imaging Air Cherenkov Telescopes, called IceAct, measur

Elizaveta Savchenko, Ariel Rosenfeld, Svetlana Bunimovich-Mendrazitsky

Cancer is one of the most widespread diseases around the world with millions of new patients each year. Bladder cancer is one of the most prevalent types of cancer affecting all individuals alike with no obvious prototypical patient. The current standard treatment for BC follows a routine weekly Bacillus Calmette-Guerin (BCG) immunotherapy-based therapy prot

Arun Anil, Manoj Changat

Cover-Incomparability graphs (C-I graphs) are an interesting class of graphs from posets. A C-I graph is a graph from a poset $P=(V,\le)$ with vertex set $V$, and the edge-set is the union of edge sets of the cover graph and the incomparability graph of the poset. The recognition of the C-I graphs is known to be NP-complete (Maxov\'{a} et al., Order 26(3), 2

Borys Álvarez-Samaniego, Wilson P. Álvarez-Samaniego, Kevin Lloacana-Unda

Following closely the analysis performed by Andrew C. Fowler to derive the first canonical equation for nonlinear dune dynamics, but considering some appropriate changes of variables, suitable scalings, and by neglecting higher order terms, we obtain an adaptation of the aforementioned equation, which contains an additional term, to describe dune morphodynam

Dheeraj Kulkarni, Tanushree Shah, Monika Yadav

In this article, we introduce a non-negative integer-valued function that measures the obstruction for converting topological isotopy between two Legendrian knots into a Legendrian isotopy. We refer to this function as the Cost function. We show that the Cost function induces a metric on the set of topologically isotopic Legendrian knots. Hence, the set of t

Chao Zhang, Xinyu Chen, Wensheng Li, Lixue Liu

In this paper, we measure the linear separability of hidden layer outputs to study the characteristics of deep neural networks. In particular, we first propose Minkowski difference based linear separability measures (MD-LSMs) to evaluate the linear separability degree of two points sets. Then, we demonstrate that there is a synchronicity between the linear s

R. Trappen, X. Dai, M. A. Yurtalan, D. Melanson

Quantum annealing is a method to solve optimization problems that leverages quantum tunneling in a coupled qubit system. We present a detailed study of the coherence of a tunable capacitively-shunted flux qubit, designed for coherent quantum annealing applications. We find that for high qubit frequencies, thermal noise in the bias line makes a significant co

Tianyi He, Weihua Su

This paper presents a method of data-driven parametric Dynamic Mode Decomposition (p-DMD) to derive a linear parameter-varying reduced-order model (LPV-ROM) for the nonlinear aeroelasticity of highly flexible aircraft. It directly uses the data snapshots obtained at varying flight conditions, and encodes the physical understanding of the nonlinear model's po

B. O'Connor, E. Gogus, J. Hare, K. Mukai

Here, we present the results of our multi-wavelength campaign aimed at classifying \textit{Swift} J170800$-$402551.8 as part of the \textit{Swift} Deep Galactic Plane Survey (DGPS). We utilized Target of Opportunity (ToO) observations with \textit{Swift}, \textit{NICER}, \textit{XMM-Newton}, \textit{NuSTAR}, and the Southern African Large Telescope (SALT), a

Zitong Yu, Rizhao Cai, Yawen Cui, Ajian Liu

Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, multimodal face data collected from the real world is often imperfect due to missing modalities from various imaging sensors. Recently, flexible-modal FAS~\cite{yu2023flexible} has attracted more attention,

Yuxi Mi, Hongquan Liu, Yewei Xia, Yiheng Sun

The emergence of vertical federated learning (VFL) has stimulated concerns about the imperfection in privacy protection, as shared feature embeddings may reveal sensitive information under privacy attacks. This paper studies the delicate equilibrium between data privacy and task utility goals of VFL under differential privacy (DP). To address the generality

Xinzhu Liu, Di Guo, Huaping Liu

Multi-agent embodied tasks have recently been studied in complex indoor visual environments. Collaboration among multiple agents can improve work efficiency and has significant practical value. However, most of the existing research focuses on homogeneous multi-agent tasks. Compared with homogeneous agents, heterogeneous agents can leverage their different c

Muhammad Waseem, Irfan Mahmood, Hira Sohail

In this article, we present a new quantum Painlev\'e II Lax pair which explicitly involves the Planck constant $ \hbar $ and an arbitrary field variable $v$ so these two objects make this new pair different from Flaschka-Newell Painlev\'e II Lax pair and that pair appears as particular case of our's pair which consolidates the Painlev\'e II equation from qua

P. P. Li, L. Tao, L. Zhang, Q. C. Bu

MAXI J181-195 is a newly discovered accreting millisecond X-ray pulsar that went outburst in June 2022. Through timing analysis with NICER and NuSTAR observations, we find a transient modulation at ~2.5 Hz during the decay period of MAXI J1816-195. The modulation is strongly correlated with a spectral hardening, and its fractional rms amplitude increases wit

Yui Hayashi, Yuya Tanizaki, Hiromasa Watanabe

We study the phase structure of bifundamental quantum chromodynamics (QCD(BF)), which is the $4$-dimensional $SU(N) \times SU(N)$ gauge theory coupled with the bifundamental fermion. Firstly, we refine constraints on its phase diagram from 't Hooft anomalies and global inconsistencies, and we find more severe constraints than those in previous literature abo

Walaa I. Eshraim

A non-Abelian theory of fermions interacting with gauge bosons, the constrained system, is studied. The equations of motion for a singular system are obtained as total differential equations in many variables. The integrability conditions are investigated and the set of equations of motion is integrable. The Senjanovic and the canonical methods are used to q

Liao Qu, Xianwei Zou, Xiang Li, Yandong Wen

This work unveils the enigmatic link between phonemes and facial features. Traditional studies on voice-face correlations typically involve using a long period of voice input, including generating face images from voices and reconstructing 3D face meshes from voices. However, in situations like voice-based crimes, the available voice evidence may be short an

Bhaskar Tejaswi, Mohammad Mannan, Amr Youssef

A diverse set of Internet of Things (IoT) devices are becoming an integrated part of daily lives, and playing an increasingly vital role in various industry, enterprise and agricultural settings. The current IoT ecosystem relies on several IoT management platforms to manage and operate a large number of IoT devices, their data, and their connectivity. Consid

Dmitry Chirkin, Martin Rongen

A precise understanding of the optical properties of the instrumented Antarctic ice sheet is crucial to the performance of the IceCube Neutrino Observatory, a cubic-kilometer Cherenkov array of 5,160 digital optical modules (DOMs) deployed in the deep ice below the geographic South Pole. We present an update to the description of the ice tilt, which describe

Milad Ramezani, Ethan Griffiths, Maryam Haghighat, Alex Pitt

The success of re-localisation has crucial implications for the practical deployment of robots operating within a prior map or relative to one another in real-world scenarios. Using single-modality, place recognition and localisation can be compromised in challenging environments such as forests. To address this, we propose a strategy to prevent lidar-based

Huazheng Wang, Daixuan Cheng, Haifeng Sun, Jingyu Wang

Transformer-based pretrained language models (PLMs) have achieved great success in modern NLP. An important advantage of PLMs is good out-of-distribution (OOD) robustness. Recently, diffusion models have attracted a lot of work to apply diffusion to PLMs. It remains under-explored how diffusion influences PLMs on OOD data. The core of diffusion models is a f

Xiang Li, Yandong Wen, Muqiao Yang, Jinglu Wang

Previous works on voice-face matching and voice-guided face synthesis demonstrate strong correlations between voice and face, but mainly rely on coarse semantic cues such as gender, age, and emotion. In this paper, we aim to investigate the capability of reconstructing the 3D facial shape from voice from a geometry perspective without any semantic informatio

Jaeung Lee, Keunho Byeon, Jin Tae Kwak

Cancer grading is an essential task in pathology. The recent developments of artificial neural networks in computational pathology have shown that these methods hold great potential for improving the accuracy and quality of cancer diagnosis. However, the issues with the robustness and reliability of such methods have not been fully resolved yet. Herein, we p

Guojie Liu, Haipeng Qu, Lijian An

The Chermak-Delgado lattice of a finite group G is a self-dual sublattice of the subgroup lattice of G. In this paper, we determine finite p-groups with at most p2 + p subgroups not in Chermak-Delgado lattice.

Zhenxiao Yin, Xiaobing Dai, Zewen Yang, Yang Shen

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to i

Yixuan Ma, Xiaolin Zhang, Peng Zhang, Kun Zhan

Contrastive learning on graphs aims at extracting distinguishable high-level representations of nodes. In this paper, we theoretically illustrate that the entropy of a dataset can be approximated by maximizing the lower bound of the mutual information across different views of a graph, \ie, entropy is estimated by a neural network. Based on this finding, we

Fengchun Qiao, Xi Peng

Out-of-distribution (OOD) generalization is a challenging machine learning problem yet highly desirable in many high-stake applications. Existing methods suffer from overly pessimistic modeling with low generalization confidence. As generalizing to arbitrary test distributions is impossible, we hypothesize that further structure on the topology of distributi

Xuezhang Chen, Wei Wei

We first establish local $C^2$ estimates of solutions to the $\sigma_2$-curvature equation with nonlinear Neumann boundary condition. Then, under assumption that the mean curvature of a background metric is nonnegative on totally non-umbilic boundary, for dimensions three and four there exists a conformal metric having a prescribed positive $\sigma_2$-curvat

Keisuke Kimura, Shoichi Koyama, Hiroshi Saruwatari

A sound field synthesis method enhancing perceptual quality is proposed. Sound field synthesis using multiple loudspeakers enables spatial audio reproduction with a broad listening area; however, synthesis errors at high frequencies called spatial aliasing artifacts are unavoidable. To minimize these artifacts, we propose a method based on the combination of