March 2023 arXiv papers — page 81

Yinsheng He, Xingyu Li

Recently, various deep learning methods have shown significant successes in medical image analysis, especially in the detection of cancer metastases in hematoxylin and eosin (H&E) stained whole-slide images (WSIs). However, in order to obtain good performance, these research achievements rely on hundreds of well-annotated WSIs. In this study, we tackle the t

José Cantarero, Germán Combariza

Given a saturated fusion system $\mathcal{F}$ over a finite $p$-group $S$, we provide criteria to determine when uniqueness of factorization into irreducible $\mathcal{F}$--invariant representations holds. We use them to prove uniqueness of factorization when the order of $S$ is at most $p^3$. We also describe an example where the monoid of fusion-invariant

Wenwen Tong, Jiangwei Xie, Tianyu Li, Hanming Deng

Driving scenes are extremely diverse and complicated that it is impossible to collect all cases with human effort alone. While data augmentation is an effective technique to enrich the training data, existing methods for camera data in autonomous driving applications are confined to the 2D image plane, which may not optimally increase data diversity in 3D re

Zahra Haghani, Tiberiu Harko

We consider the structure and physical properties of specific classes of neutron, quark, and Bose-Einstein Condensate stars in the conformally invariant Weyl geometric gravity theory. The basic theory is derived from the simplest conformally invariant action, constructed, in Weyl geometry, from the square of the Weyl scalar, the strength of the Weyl vector,

Saptarshi Purkayastha, Rohan Isaac, Sharon Anthony, Shikhar Shukla

Radiology AI models have made significant progress in near-human performance or surpassing it. However, AI model's partnership with human radiologist remains an unexplored challenge due to the lack of health information standards, contextual and workflow differences, and data labeling variations. To overcome these challenges, we integrated an AI model servic

M. Marinucci, V. Desjacques, A. Benson

We use $z=1$ mock galaxy catalogues produced with the semi-analytic code GALACTICUS to study the dependence of the non-Gaussian bias parameter $b_\phi$ on the mass assembly history of the host halos. We generate large sets of merger trees and measure the non-Gaussian assembly bias $\Delta b_\phi$ for galaxies selected by color magnitude and emission line lum

Denisa Qori McDonald, Richard Valett, Lev Saunders, Genevieve Dion

Developments in touch-sensitive textiles have enabled many novel interactive techniques and applications. Our digitally-knitted capacitive active sensors can be manufactured at scale with little human intervention. Their sensitive areas are created from a single conductive yarn, and they require only few connections to external hardware. This technique incre

Su Zhang, Ziyuan Zhao, Cuntai Guan

We used two multimodal models for continuous valence-arousal recognition using visual, audio, and linguistic information. The first model is the same as we used in ABAW2 and ABAW3, which employs the leader-follower attention. The second model has the same architecture for spatial and temporal encoding. As for the fusion block, it employs a compact and straig

Zhaozheng Chen, Qianru Sun

Extracting class activation maps (CAM) from a classification model often results in poor coverage on foreground objects, i.e., only the discriminative region (e.g., the "head" of "sheep") is recognized and the rest (e.g., the "leg" of "sheep") mistakenly as background. The crux behind is that the weight of the classifier (used to compute CAM) captures only t

Zhaohu Xing, Lei Zhu, Lequan Yu, Zhiheng Xing

Masked image modeling (MIM) with transformer backbones has recently been exploited as a powerful self-supervised pre-training technique. The existing MIM methods adopt the strategy to mask random patches of the image and reconstruct the missing pixels, which only considers semantic information at a lower level, and causes a long pre-training time.This paper

Xue-Chao Feng, Ke-Wei Wei

In this work, the mass spectrum of the first radial excitation axial vector meson nonet is considered in the framework of the nonrelativistic constituent quark model and Regge phenomenology. After that, we investigate the strong decay characteristics of these states within the $^{3}P_{0}$ model. The results are compared to values from other phenomenological

M. Haddadi, Kh. Keshvardoost, N. S. Razmara

The category ${\rm Rel}(\mathcal{C})$ may be formed for any category $\mathcal{C}$ with finite limits using the same objects as $\mathcal{C}$ but whose morphisms from $X$ to $Y$ are binary relations in $\mathcal{C}$, that is, subobjects of $X\times Y$. In this paper, concerning the topos ${\bf Nom}$, we study the category ${\bf Rel}({\bf Nom})$. In this cate

Hongyi Yuan, Keming Lu, Zheng Yuan

Biomedical entity linking (EL) consists of named entity recognition (NER) and named entity disambiguation (NED). EL models are trained on corpora labeled by a predefined KB. However, it is a common scenario that only entities within a subset of the KB are precious to stakeholders. We name this scenario partial knowledge base inference: training an EL model w

Jonathan M. Larson, Hans A. Bechtel, Robert Kostecki

Researchers from a broad spectrum of scientific and engineering disciplines are increasingly using near-field infrared spectroscopic techniques to characterize materials nondestructively and with nanoscale spatial resolution. However, sub-optimal understanding of a technique's implementation can complicate data interpretation and even act as a barrier to ent

Hongyi Yuan, Yaoyun Zhang, Fei Huang, Songfang Huang

Automatic evaluation metrics have been facilitating the rapid development of automatic summarization methods by providing instant and fair assessments of the quality of summaries. Most metrics have been developed for the general domain, especially news and meeting notes, or other language-generation tasks. However, these metrics are applied to evaluate summa

Yue Meng, Chuchu Fan

Hybrid systems are prevalent in robotics. However, ensuring the stability of hybrid systems is challenging due to sophisticated continuous and discrete dynamics. A system with all its system modes stable can still be unstable. Hence special treatments are required at mode switchings to stabilize the system. In this work, we propose a hierarchical, neural net

David Ceddia, Alaleh Aminzadeh, Philip K. Cook, Daniele Pelliccia

The penetrating power of X rays underpins important applications such as medical radiography. However, this same attribute makes it challenging to achieve flexible on-demand patterning of X-ray beams. One possible path to this goal is ``ghost projection'', a method which may be viewed as a reversed form of classical ghost imaging. This technique employs mult

Zhaohu Xing, Liang Wan, Huazhu Fu, Guang Yang

In recent years, Denoising Diffusion Models have demonstrated remarkable success in generating semantically valuable pixel-wise representations for image generative modeling. In this study, we propose a novel end-to-end framework, called Diff-UNet, for medical volumetric segmentation. Our approach integrates the diffusion model into a standard U-shaped archi

Guandong Li, Xian Yang

Companies use banners extensively to promote their products, and the intelligent automatic synthesis of banners is a challenging event. Under the premise of inputting only a small amount of information such as product, text and size, it can synthesize styles with high freedom and richness, but at the same time, it must satisfy the design specifications of ad

Qingfang Wang, Dong Ye

In this paper, we consider the following nonlinear Schr\"odinger system in $R^3$: \begin{align*} -\Delta u_j +P_j(x) u=\mu_j u_j^3+\sum\limits_{i=1,i\neq j}^N\beta_{ij}u_i^2u_j, \end{align*} where $N\geq3$, $P_j$ are nonnegative radial potentials, $\mu_j>0$ and $\beta_{ij}=\beta_{ji}$ are coupling constants. This type of systems have been widely studied in t

Mingjie Li, Bingqian Lin, Zicong Chen, Haokun Lin

Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the severe visual and textual bias in this task. The structures of such graphs are exploited by using the clini

Himali Singh, Kumar Vijay Mishra, Arpan Chattopadhyay

Recent research in inverse cognition with cognitive radar has led to the development of inverse stochastic filters that are employed by the target to infer the information the cognitive radar may have learned. Prior works addressed this inverse cognition problem by proposing inverse Kalman filter (I-KF) and inverse extended KF (I-EKF), respectively, for line

Peiwen Pan, Huan Wang, Chenyi Wang, Chang Nie

Infrared small target detection (ISTD) has a wide range of applications in early warning, rescue, and guidance. However, CNN based deep learning methods are not effective at segmenting infrared small target (IRST) that it lack of clear contour and texture features, and transformer based methods also struggle to achieve significant results due to the absence

Hui Rao, Zhi-Ying Wen, Qihan Yuan, Yuan Zhang

In this paper, we use a class of finite state automata, called topology automaton, to study the metric classification of a special class of post-critically finite self-similar sets. As an application, we prove that the conformal dimension of post-critically finite self-similar dendrites and fractal gasket with connected component is 1.

Jaydeep Chipalkatti

Given six points $A,B,C,D,E,F$ on a nonsingular conic in the complex projective plane, Pascal's theorem says that the three intersection points $AE \cap BF, BD \cap CE, AD \cap CF$ are collinear. The line containing them is called a pascal, and we get altogether $60$ such lines by permuting the points. In this paper, we consider the enumerative problem of fi

Shengqin Jiang, Yuan Gao, Bowen Li, Fengna Cheng

Efficient models for remote sensing object counting are urgently required for applications in scenarios with limited computing resources, such as drones or embedded systems. A straightforward yet powerful technique to achieve this is knowledge distillation, which steers the learning of student networks by leveraging the experience of already-trained teacher

Luís F. V. Thomazini, Alexandre F. Fonseca

Macroscopic assemblies of carbon nanotubes (CNTs) are desirable materials because of the excellent CNT properties. Amongst the methods of production of these CNT materials, the dry-draw fabrication where CNT fibers (CNTFs) are directly pulled out from a CNT forest is known to provide good physical properties. Although it is known that vertical alignment of C

Sharmistha Chakrabarti, Yuanmeng Yang, Shuman Zhang, Md Shah Naoaj

This study re-examines the impact of natural disasters on economic growth in the perspective of developed and developing countries. Based on panel data consisting of developing and developed countries over the period 1990-2019 and using different statistical techniques we find that i) Developing countries agricultural growth impacted twice as much severely a

Yi-Han Lin, Xunquan Chen, Ryoichi Takashima, Tetsuya Takiguchi

This paper introduces a zero-shot sound event classification (ZS-SEC) method to identify sound events that have never occurred in training data. In our previous work, we proposed a ZS-SEC method using sound attribute vectors (SAVs), where a deep neural network model infers attribute information that describes the sound of an event class instead of inferring

Md Shah Naoaj

This study investigates the factors that influence the capital adequacy of commercial banks in Bangladesh using panel data from 28 banks over the period of 2013-2019. Three analytical methods, including the Fixed Effect model, Random Effect model, and Pooled Ordinary Least Square (POLS) method, are employed to analyze two versions of the capital adequacy rat

Youshan Zhang

Lung cancer has emerged as a severe disease that threatens human life and health. The precise segmentation of lung regions is a crucial prerequisite for localizing tumors, which can provide accurate information for lung image analysis. In this work, we first propose a lung image segmentation model using the NASNet-Large as an encoder and then followed by a d

Takanori Anegawa, Norihiro Iizuka, Kazumi Okuyama, Kazuhiro Sakai

We study the late time behavior of $n$-point spectral form factors (SFFs) in two-dimensional Witten-Kontsevich topological gravity, which includes both Airy and JT gravities as special cases. This is conducted in the small $\hbar$ expansion, where $\hbar \sim e^{- {1}/{G_N}}$ is the genus counting parameter and nonperturbative in Newton's constant $G_N$. For

Yang Jingzhe, Zeng Zhi, Dai Wenhan, Yang Mingxin

In order to suppress the background in rare event detection experiments such as 0{\nu}\b{eta}\b{eta}, this paper developed a set of single/multi-site event pulse shape discrimination methods suitable for strip multi-electrode high-purity germanium detectors. In the simulation of 228Th, this method achieves 7.92 times suppression of SEP events at 2103 keV wit

Jie Hu

Drug combination therapy is a powerful solution for the treatment of complex disease such as cancers due to its capability of therapeutic efficacy and reducing side effects. Nevertheless, it is very difficult to screen all drug combinations by experiments since the vast number of possible combinations. Currently, computational methods, especially graph neura

Punyajoy Saha, Kiran Garimella, Narla Komal Kalyan, Saurabh Kumar Pandey

Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle techniques are being deployed. One of the most striking among these is fear speech. Fear speech, as the name sugg

Firas Al-Hindawi, Md Mahfuzur Rahman Siddiquee, Teresa Wu, Han Hu

The ability to classify images is dependent on having access to large labeled datasets and testing on data from the same domain that the model can train on. Classification becomes more challenging when dealing with new data from a different domain, where gathering and especially labeling a larger image dataset for retraining a classification model requires a

Mohammadjavad Mirzazadeh Moallem, Mehdi Korki

In this paper, we investigate the diffusion least mean square (DLMS) algorithm over fading channel, where in addition to channel noise and path-loss the inter-node-interference (INI) among neighboring nodes of a host node is also taken into account. We also analyze the mean-square convergence behavior of DLMS algorithm, under such condition. In addition, bas

Tianyu Li

The problem of multiphase materials (fluid or solid) interacting with the rigid body structure is studied by proposing a novel VMS-FEM (variational multi-scale finite element method) in the Eulerian framework using the fixed mesh. The incompressible N-S equation with high Reynolds number is stabilized using the idea of the VMS stabilization. To model the mul

Jianye Yi, Xiaopin Zhong, Weixiang Liu, Wenxuan Zhu

Semantic segmentation is a classic and fundamental computer vision problem dedicated to assigning each pixel with its corresponding class. Some recent methods introduce edge-based information for improving the segmentation performance. However these methods are specific and limited to certain network architectures, and they can not be transferred to other mo

Denis Chetverikov, Jinyong Hahn, Zhipeng Liao, Andres Santos

We examine asymptotic properties of the OLS estimator when the values of the regressor of interest are assigned randomly and independently of other regressors. We find that the OLS variance formula in this case is often simplified, sometimes substantially. In particular, when the regressor of interest is independent not only of other regressors but also of t

Matteo Baggioli

In the context of bottom-up holography, we demonstrate the power of mixed boundary conditions to promote the boundary gauge field to be dynamical. We provide two concrete applications of this idea. First, we consider a holographic dual for a strongly coupled plasma described by dissipative magnetohydrodynamics. Second, we reveal the expected features of the

Wenxiong Chen, Lingwei Ma

In this paper, we consider the dual fractional parabolic problem in the right half space. We prove that the positive solutions are strictly increasing in $x_1$ direction without assuming the solutions be bounded. So far as we know, this is the first paper to explore the monotonicity of possibly unbounded solutions for the nonlocal parabolic problem involving

Amol Khanna, Fred Lu, Edward Raff

Linear $L_1$-regularized models have remained one of the simplest and most effective tools in data analysis, especially in information retrieval problems where n-grams over text with TF-IDF or Okapi feature values are a strong and easy baseline. Over the past decade, screening rules have risen in popularity as a way to reduce the runtime for producing the sp

Manav Vora, Pranay Thangeda, Michael N. Grussing, Melkior Ornik

Partially Observable Markov Decision Processes (POMDPs) provide an efficient way to model real-world sequential decision making processes. Motivated by the problem of maintenance and inspection of a group of infrastructure components with independent dynamics, this paper presents an algorithm to find the optimal policy for a multi-component budget-constraine

David Chester, Alessio Marrani, Daniele Corradetti, Raymond Aschheim

We present three new coset manifolds named Dixon-Rosenfeld lines that are similar to Rosenfeld projective lines except over the Dixon algebra $\mathbb{C}\otimes\mathbb{H}\otimes\mathbb{O}$. Three different Lie groups are found as isometry groups of these coset manifolds using Tits' formula. We demonstrate how Standard Model interactions with the Dixon algebr

Mingpu Qin

We propose a new scheme to implement the self-consistent optimization of the trial wave-function in constrained path auxiliary field Quantum Monte Carlo (CP-AFQMC) in the framewok of natural orbitals. In this scheme, a new trial wave-function in the form of Slater determinant is constructed from the CP-AFQMC results by diagonalizing the mixed estimator of th

Jerry Zhang, Dong Wang, Weiwei Jin, Annie Xia

Jammed packings of granular materials display complex mechanical response. For example, the ensemble-averaged shear modulus $\left\langle G \right\rangle$ increases as a power-law in pressure $p$ for static packings of soft spherical particles that can rearrange during compression. We seek to design granular materials with shear moduli that can either increa

X. X. Shang, N. N. Xu, J. Guo, S. Sun

Niobium telluride (NbTe$_2$), an emerging transition metal dichalcogenide material, has been theoretically predicted to have nonlinear absorption properties and excellent optical response. However, only a few studies of the utilization of NbTe$_2$ in ultrafast photonics have been reported. In this work, a NbTe$_2$-based saturable absorber (SA) was applied in

Genki Omori, Naoki Sakata

We give Dehn twist--crosscap slide presentations for involutions on non-orientable surfaces of genera up to 5.

Santhosh Ganapa

The Fermi-Pasta-Ulam-Tsingou (FPUT) problem addresses fundamental questions in statistical physics, and attempts to understand the origin of recurrences in the system have led to many great advances in nonlinear dynamics and mathematical physics. In this work we revisit the problem and study quasiperiodic recurrences in the weakly nonlinear $\alpha-$FPUT sys

Thaisa C. da C. Guio, Gláuber C. Dorsch

We present the second part of a series of papers proposing a novel teaching sequence for Particle Physics in high school. The topic of the present work is Nuclear Physics. The goal of the sequence is to approach the subject in a way as to stimulate scientific literacy, from a perspective involving Science, Technology, Society and Environment (STSE). We evalu

Poramet Pathumsoot, Theerapat Tansuwannont, Naphan Benchasattabuse, Ryosuke Satoh

A quantum network is expected to enhance distributed quantum computing and quantum communication over a long distance while providing unconditional security. As quantum entanglement is essential for a quantum network, major issues from various types of noise and decoherence prevent it from being realized, and research has been intensively active to obtain op

Daniel L. Silver, Rinda Digamarthi

Objective: To develop machine learning models that can predict the number of COVID-19 cases per day given the last 14 days of environmental and mobility data. Approach: COVID-19 data from four counties around Toronto, Ontario, were used. Data were prepared into daily records containing the number of new COVID case counts, patient demographic data, outdoor we

Jacob Knaup, Panagiotis Tsiotras

This work considers the optimal covariance steering problem for systems subject to both additive noise and uncertain parameters which may enter multiplicatively with the state and the control. The unknown parameters are modeled as a constant random variable sampled from a distribution with known moments. The optimal covariance steering problem is formulated

Yaman Kindap, Simon Godsill

Generalised hyperbolic (GH) processes are a class of stochastic processes that are used to model the dynamics of a wide range of complex systems that exhibit heavy-tailed behavior, including systems in finance, economics, biology, and physics. In this paper, we present novel simulation methods based on subordination with a generalised inverse Gaussian (GIG)

Terence Jie Chua, Wenhan Yu, Jun Zhao

Development of defenses against physical world attacks such as adversarial patches is gaining traction within the research community. We contribute to the field of adversarial patch detection by introducing an uncertainty-based adversarial patch localizer which localizes adversarial patch on an image, permitting post-processing patch-avoidance or patch-recon

Armine Bagyan, Donald Richards

For $d \ge 2$, let $X$ be a random vector having a Bingham distribution on $\mathcal{S}^{d-1}$, the unit sphere centered at the origin in $\R^d$, and let $\Sigma$ denote the symmetric matrix parameter of the distribution. Let $\Psi(\Sigma)$ be the normalizing constant of the distribution and let $\nabla \Psi_d(\Sigma)$ be the matrix of first-order partial de

Terence Jie Chua, Wenhan Yu, Jun Zhao

The Metaverse play-to-earn games have been gaining popularity as they enable players to earn in-game tokens which can be translated to real-world profits. With the advancements in augmented reality (AR) technologies, users can play AR games in the Metaverse. However, these high-resolution games are compute-intensive, and in-game graphical scenes need to be o

Terence Jie Chua, Wenhan Yu, Jun Zhao

Real-time Digital Twinning of physical world scenes onto the Metaverse is necessary for a myriad of applications such as augmented-reality (AR) assisted driving. In AR assisted driving, physical environment scenes are first captured by Internet of Vehicles (IoVs) and are uploaded to the Metaverse. A central Metaverse Map Service Provider (MMSP) will aggregat

Michael Levine, Donald Richards, Jianxi Su

This article considers exponential families of truncated multivariate normal distributions with one-sided truncation for some or all coordinates. We observe that if all components are one-sided truncated then this family is not full. The family of truncated multivariate normal distributions is extended to a full family, and the extended family is investigate

Antonio O. Bouzas, F. Larios

We summarize the quantitative results of our analysis [1] of top-pair photoproduction in semileptonic mode in $pe$ collisions at the LHeC and FCC-he. We define three photoproduction regions, based on the rapidity acceptance range of the electron tagger, that provide different degrees of sensitivity to top-quark effective couplings. We focus on the $t\bar{t}\

Andrey Bychkov, Opal Issan, Gleb Pogudin, Boris Kramer

Quadratization of polynomial and nonpolynomial systems of ordinary differential equations is advantageous in a variety of disciplines, such as systems theory, fluid mechanics, chemical reaction modeling and mathematical analysis. A quadratization reveals new variables and structures of a model, which may be easier to analyze, simulate, control, and provides

Chuanlian Xiao, Hongguang Wang, Peter A. van Aken, Robert Usiskin

We carefully investigated the storage of lithium in titania films on various substrates as a function of thickness. The experiments enable us to precisely separate contributions from bulk and boundary storage. The battery capacity measurements are complemented by bias dependent measurements of impedance, yielding interfacial resistance as well as interfacial

Haitz Sáez de Ocáriz Borde, Pietro Innocenzi, Flavio Savarino

The typical size of computational meshes needed for realistic geometries and high-speed flow conditions makes Computational Fluid Dynamics (CFD) impractical for full-mission performance prediction and control. Reduced-Order Models (ROMs) in low-speed aerodynamics have come a long way in terms of reconstructing coherent flow patterns, thus enabling aerodynami

M. Williams, B. Davids, G. Lotay, N. Nishimura

We have measured the cross section of the $^{83}$Rb(p,$\gamma)^{84}$Sr radiative capture reaction in inverse kinematics using a radioactive beam of $^{83}$Rb at incident energies of 2.4 and $2.7 A$ MeV. Prior to the radioactive beam measurement, the $^{84}$Kr(p,$\gamma)^{85}$Rb radiative capture reaction was measured in inverse kinematics using a stable beam

Davide Mattiolo, Giuseppe Mazzuoccolo, Jozef Rajník, Gloria Tabarelli

Let $r \geq 2$ be a real number. A complex nowhere-zero $r$-flow on a graph $G$ is an orientation of $G$ together with an assignment $\varphi\colon E(G)\to \mathbb{C}$ such that, for all $e \in E(G)$, the modulus of the complex number $\varphi(e)$ lies in the interval $[1,r-1]$ and, for every vertex, the incoming flow is equal to the outgoing flow. The compl

Arun V. Reddy, Ketul Shah, William Paul, Rohita Mocharla

Human action recognition is a challenging problem, particularly when there is high variability in factors such as subject appearance, backgrounds and viewpoint. While deep neural networks (DNNs) have been shown to perform well on action recognition tasks, they typically require large amounts of high-quality labeled data to achieve robust performance across a

Boris Deroo, Erwin Aertbeliën, Wilm Decré, Herman Bruyninckx

This paper focusses on the energy-efficient control of a cable-driven robot for tasks that only require precise positioning at few points in their motion, and where that accuracy can be obtained through contacts. This includes the majority of pick-and-place operations. Knowledge about the task is directly taken into account when specifying the control execut

Nahid Binandeh Dehaghani, A. Pedro Aguiar, Rafal Wisniewski

We investigate a time and energy minimization optimal control problem for open quantum systems, whose dynamics is governed through the Lindblad (or Gorini-Kossakowski-Sudarshan-Lindblad) master equation. The dissipation is Markovian time-independent, and the control is governed by the Hamiltonian of a quantum-mechanical system. We are specifically interested

Arunn Suntharalingam, Lucas Fernández-Alcázar, Rodion Kononchuck, Tsampikos Kottos

Exceptional point degeneracies (EPD) of linear non-Hermitian systems have been recently utilized for hypersensitive sensing. This proposal exploits the sublinear response that the degenerate frequencies experience once the system is externally perturbed. The enhanced sensitivity, however, might be offset by excess (fundamental and/or technical) noise. Here,

Tianhao Wei, Shucheng Kang, Ruixuan Liu, Changliu Liu

Safety is critical in robotic tasks. Energy function based methods have been introduced to address the problem. To ensure safety in the presence of control limits, we need to design an energy function that results in persistently feasible safe control at all system states. However, designing such an energy function for high-dimensional nonlinear systems rema

Chen Li, Edward Jones, Steve Furber

This paper presents a new methodology to alleviate the fundamental trade-off between accuracy and latency in spiking neural networks (SNNs). The approach involves decoding confidence information over time from the SNN outputs and using it to develop a decision-making agent that can dynamically determine when to terminate each inference. The proposed method,

Renat Bashirov, Alexey Larionov, Evgeniya Ustinova, Mikhail Sidorenko

We present a system to create Mobile Realistic Fullbody (MoRF) avatars. MoRF avatars are rendered in real-time on mobile devices, learned from monocular videos, and have high realism. We use SMPL-X as a proxy geometry and render it with DNR (neural texture and image-2-image network). We improve on prior work, by overfitting per-frame warping fields in the ne

Alireza Ahmadi, Jean-Pierre Magnot

We consider a differential geometric setting on power sets and Borel algebras. Our chosen framework is based on diffeologies, and we make a link between the various diffeological structures that we propose, having in mind set-valued maps, relations, set-valued gradients, differentiable measures, and shape analysis. This work intends to establish rigorous pro

Gautier Dietrich

In this short note, we compute the conformal stereographic projection on the standard metric of a sphere quotient. The result is a Majumdar-Papapetrou metric, which might be useful.

Oliver Jeong, Richard Plambeck, Christopher Raum, Aritoki Suzuki

We present a broadband plasma spray anti-reflection (AR) coating technology for millimeter-wave astrophysics experiments with large-format, cryogenic optics. By plasma spraying alumina- and silica-based powders, we have produced coatings of tunable index of refraction and thickness, low loss, and coefficient of thermal expansion matched to alumina substrates

Ronaldo A. Ortez, John B. Rundle

We extend our previous model, avalanche-burst invasion percolation (AIP) by introducing long-range correlations between sites described by fractional Brownian statistics. In our previous models with independent, random site strengths, we reproduced a unique set of power-laws consistent with some of the b-values observed during induced seismicity. We expand u

Charles Qi, Yi Wang, Hui Wang, Yang Lu

State-of-art NPUs are typically architected as a self-contained sub-system with multiple heterogeneous hardware computing modules, and a dataflow-driven programming model. There lacks well-established methodology and tools in the industry to evaluate and compare the performance of NPUs from different architectures. We present an event-based performance model

Kevin Johnston, Musabbir Ahmed Arrafi, Krishna B Kidambi, Madhur Tiwari

This paper presents an adaptive modified Robust Inverse of Signum Error (AM-RISE) control method, which achieves reliable trajectory tracking control for a quadrotor unmanned aerial vehicle. The proposed method systematically accounts for gyroscopic effects, rotor dynamics, parametric uncertainties, and external disturbances, ensuring robust performance acro

Deepanshu Trivedi, Leonid Belostotski, Arjuna Madanayake, Alex Krasnok

Magneto-optical isolators and circulators have been widely used to safeguard quantum devices from reflections and noise in the readout stage. However, these devices have limited bandwidth, low tunability, are bulky, and suffer from high losses, making them incompatible with planar technologies such as circuit QED. To address these limitations, we propose a n

Angelo Gilio, David E. Over, Niki Pfeifer, Giuseppe Sanfilippo

In this paper we recall some results for conditional events, compound conditionals, conditional random quantities, p-consistency, and p-entailment. Then, we show the equivalence between bets on conditionals and conditional bets, by reviewing de Finetti's trivalent analysis of conditionals. But our approach goes beyond de Finetti's early trivalent logical ana

Yuncheng You, Jing Tian, Junyi Tu

A new mathematical model of neural networks described by diffusive FitzHugh-Nagumo equations with memristors and linear synaptic coupling is proposed and investigated. The existence of absorbing set for the solution semiflow in the energy space is proved and global dynamics of the memristive neural networks are dissipative. Through uniform estimates and mane

Matej Fekete, Clio Azina, Pavel Ondračka, Lukas Löfler

Thermal shock resistance is one of the performance-defining properties for applications where extreme temperature gradients are required. The thermal shock resistance of a material can be described by means of the thermal shock parameter RT. Here, the thermo-mechanical properties required for the calculation of RT are quantum-mechanically predicted, experime

Mikhail Genkin, Frank Dehne, Anousheh Shahmirza, Pablo Navarro

The big data software stack based on Apache Spark and Hadoop has become mission critical in many enterprises. Performance of Spark and Hadoop jobs depends on a large number of configuration settings. Manual tuning is expensive and brittle. There have been prior efforts to develop on-line and off-line automatic tuning approaches to make the big data stack les

Camron Farhang, Jingyuan Wang, Brenden R. Ortiz, Stephen D. Wilson

Kagome metals AV3Sb5 (A = K, Cs, Rb) provide a rich platform for intertwined orders, where evidence for time-reversal symmetry breaking, likely due to the long-sought loop currents, has emerged in STM and muon spin relaxation experiments. An isotropic component in the spontaneous optical rotation has also been reported, and it was interpreted as the magneto-

J. E. Horvath, L. S. Rocha, L. M. de Sá, P. H. R. S. Moraes

Context: Recently, Doroshenko and collaborators reported a very low-mass compact star, a Central Compact Object named XMMU J173203.3-344518 inside the supernova remnant HESS J1731-347. Its tiny mass is at odds with all calculations of minimum masses of neutron stars generated by iron cores, therefore (and even if not compellingly) it has been suggested to be

Haoran Li, Jingfeng Wu, Vladimir Braverman

We consider a continual learning (CL) problem with two linear regression tasks in the fixed design setting, where the feature vectors are assumed fixed and the labels are assumed to be random variables. We consider an $\ell_2$-regularized CL algorithm, which computes an Ordinary Least Squares parameter to fit the first dataset, then computes another paramete

Feras Al Taha, Francesca Parise

We consider network games where a large number of agents interact according to a network sampled from a random network model, represented by a graphon. By exploiting previous results on convergence of such large network games to graphon games, we examine a procedure for estimating unknown payoff parameters, from observations of equilibrium actions, without t

Sachin Vaidya, Ali Ghorashi, Thomas Christensen, Mikael C. Rechtsman

Photonic crystals (PhCs) have emerged as a popular platform for realizing various topological phases due to their flexibility and potential for device applications. In this article, we present a comprehensive classification of topological bands in one- and two dimensional photonic crystals, with and without time-reversal symmetry. Our approach exploits the s

Aren Karapetyan, Diego Bolliger, Anastasios Tsiamis, Efe C. Balta

Online learning algorithms for dynamical systems provide finite time guarantees for control in the presence of sequentially revealed cost functions. We pose the classical linear quadratic tracking problem in the framework of online optimization where the time-varying reference state is unknown a priori and is revealed after the applied control input. We show

Prasit Bhattacharya, Foling Zou

In this paper, we view the equivariant orientation theory of equivariant vector bundles from the lenses of equivariant Picard spectra. This viewpoint allows us to identify, for a finite group $\mathrm{G}$, a precise condition under which an $\mathrm{R}$-orientation of a $\mathrm{G}$-equivariant vector bundle is encoded by a Thom class. Consequently, we are a

A. Salch

We investigate the question of how to compute the cotensor product, and more generally the derived cotensor (i.e., Cotor) groups, of a tensor product of comodules. In particular, we determine the conditions under which there is a K\"{u}nneth formula for Cotor. We show that there is a simple K\"{u}nneth theorem for Cotor groups if and only if an appropriate c

Ruimeng Hu, Mathieu Laurière

Stochastic optimal control and games have a wide range of applications, from finance and economics to social sciences, robotics, and energy management. Many real-world applications involve complex models that have driven the development of sophisticated numerical methods. Recently, computational methods based on machine learning have been developed for solvi

Jochen Stiasny, Baosen Zhang, Spyros Chatzivasileiadis

The dynamic behaviour of a power system can be described by a system of differential-algebraic equations. Time-domain simulations are used to simulate the evolution of these dynamics. They often require the use of small time step sizes and therefore become computationally expensive. To accelerate these simulations, we propose a simulator - PINNSim - that all

Zidi Xiu, Kai-Chen Cheng, David Q. Sun, Jiannan Lu

With the growing popularity of intelligent assistants (IAs), evaluating IA quality becomes an increasingly active field of research. This paper identifies and quantifies the feedback effect, a novel component in IA-user interactions: how the capabilities and limitations of the IA influence user behavior over time. First, we demonstrate that unhelpful respons

Aleksei Ponomarenko-Timofeev, Olga Galinina, Ravikumar Balakrishnan, Nageen Himayat

Federated systems enable collaborative training on highly heterogeneous data through model personalization, which can be facilitated by employing multi-task learning algorithms. However, significant variation in device computing capabilities may result in substantial degradation in the convergence rate of training. To accelerate the learning procedure for di

Mark Beliaev, Negar Mehr, Ramtin Pedarsani

In this paper, we study the pickup and delivery problem with multiple transportation modalities, and address the challenge of efficiently allocating transportation resources while price matching users with their desired delivery modes. More precisely, we consider that orders are demanded by a heterogeneous population of users with varying trade-offs between

Anirban Sarkar, Matthew Groth, Ian Mason, Tomotake Sasaki

Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios. In this paper, we introduce a tool to visualize and understand such failures. We draw inspiration from concepts from neural electrophysiology, which are based on inspecting the internal functioning of a neural networks by analyzing the feature tuning and invariances of individual units.

H. Lv, A. da Silva, A. I. Figueroa, C. Guillemard

Van der Waals (vdW) heterostructures combining layered ferromagnets and other two-dimensional (2D) crystals are promising building blocks for the realization of ultra-compact devices with integrated magnetic, electronic and optical functionalities. Their implementation in various technologies depends strongly on the development of a bottom-up scalable synthe

Victor Dorobantu, Charlotte Borcherds, Yisong Yue

We propose conformal generative modeling, a framework for generative modeling on 2D surfaces approximated by discrete triangle meshes. Our approach leverages advances in discrete conformal geometry to develop a map from a source triangle mesh to a target triangle mesh of a simple manifold such as a sphere. After accounting for errors due to the mesh discreti