October 2022 arXiv papers — page 27

Chen Bao, Xudong Zhang, Jiazhou Chen, Yongwei Miao

Instance-level image retrieval in fashion is a challenging issue owing to its increasing importance in real-scenario visual fashion search. Cross-domain fashion retrieval aims to match the unconstrained customer images as queries for photographs provided by retailers; however, it is a difficult task due to a wide range of consumer-to-shop (C2S) domain discre

Zhenting Wang, Kai Mei, Hailun Ding, Juan Zhai

Deep Neural Networks are vulnerable to Trojan (or backdoor) attacks. Reverse-engineering methods can reconstruct the trigger and thus identify affected models. Existing reverse-engineering methods only consider input space constraints, e.g., trigger size in the input space. Expressly, they assume the triggers are static patterns in the input space and fail t

Mansooreh Montazerin, Elahe Rahimian, Farnoosh Naderkhani, S. Farokh Atashzar

Development of advance surface Electromyogram (sEMG)-based Human-Machine Interface (HMI) systems is of paramount importance to pave the way towards emergence of futuristic Cyber-Physical-Human (CPH) worlds. In this context, the main focus of recent literature was on development of different Deep Neural Network (DNN)-based architectures that perform Hand Gest

Cunxi Dai, Xiaohan Liu, Jianxiang Zhou, Zhengtao Liu

This article presents the design, implementation, and performance evaluation of SWheg, a novel modular wheel-leg transformable robot family with minimalist actuator realization. SWheg takes advantage of both wheeled and legged locomotion by seamlessly integrating them on a single platform. In contrast to other designs that use multiple actuators, SWheg uses

Zohreh HajiAkhondi-Meybodi, Arash Mohammadi, Ming Hou, Jamshid Abouei

Mobile Edge Caching (MEC) is a revolutionary technology for the Sixth Generation (6G) of wireless networks with the promise to significantly reduce users' latency via offering storage capacities at the edge of the network. The efficiency of the MEC network, however, critically depends on its ability to dynamically predict/update the storage of caching nodes

Alberto Hernandez, Tim Mueller

In recent years there has been great progress in the use of machine learning algorithms to develop interatomic potential models. Machine-learned potential models are typically orders of magnitude faster than density functional theory but also orders of magnitude slower than physics-derived models such as the embedded atom method. In our previous work, we use

Hakseok Ko, Junghoon Kim, Jin-Hee Hong, Junyeob Cheon

The imaging depth of deep-tissue optical microscopy is governed by the performance of the gating operation that suppresses the multiply scattered waves obscuring the ballistic waves. Although various gating operations based on confocal, time-resolved/coherence-gated, and polarization-selective detections have proven to be effective, each has its own limitati

Yiqiang Yi, Xu Wan, Kangfei Zhao, Le Ou-Yang

Binding affinity prediction of three-dimensional (3D) protein ligand complexes is critical for drug repositioning and virtual drug screening. Existing approaches transform a 3D protein-ligand complex to a two-dimensional (2D) graph, and then use graph neural networks (GNNs) to predict its binding affinity. However, the node and edge features of the 2D graph

Fengyi Yu

In alloy solidification, the transport processes of heat and solute result in morphological instability of the interface, forming different patterns of solidification structure and determining the mechanical properties of components. As the first observable phenomenon of the morphological instabilities, the planar instability influences the subsequent stages

Malak Abid Ali Khan, Hongbin Ma, Syed Muhammad Aamir, Cekderi Anil Baris

LoRa lacks the sensing capabilities of channel status. Received signal strength indicator (RSSI) decreases due to collision, interference, and near-far effect while for signal-to-noise ratio (SNR), the packets are rejected by decreasing the transmission power (TP) at a higher spreading factor (SF). To overcome these challenges in the case of electric shelf l

Aritro Roy Arko, James J. Little, Kwang Moo Yi

We propose a bootstrapping framework to enhance human optical flow and pose. We show that, for videos involving humans in scenes, we can improve both the optical flow and the pose estimation quality of humans by considering the two tasks at the same time. We enhance optical flow estimates by fine-tuning them to fit the human pose estimates and vice versa. In

Susheel Suresh, Danny Godbout, Arko Mukherjee, Mayank Shrivastava

Federated graph representation learning (FedGRL) brings the benefits of distributed training to graph structured data while simultaneously addressing some privacy and compliance concerns related to data curation. However, several interesting real-world graph data characteristics viz. label deficiency and downstream task heterogeneity are not taken into consi

Soheil Zabihi, Elahe Rahimian, Amir Asif, Arash Mohammadi

Advancements in Biological Signal Processing (BSP) and Machine-Learning (ML) models have paved the path for development of novel immersive Human-Machine Interfaces (HMI). In this context, there has been a surge of significant interest in Hand Gesture Recognition (HGR) utilizing Surface-Electromyogram (sEMG) signals. This is due to its unique potential for de

Seiji Zenitani, Shin'ya Nakano

A procedure for loading particle velocities from a relativistic kappa distribution in particle-in-cell (PIC) and Monte Carlo simulations is presented. It is based on the rejection method and the beta prime distribution. The rejection part extends earlier method for the Maxwell-Juttner distribution, and then the acceptance rate reaches ~95%. Utilizing the gen

Luke Wood, Kevin Anderson, Peter Gerstoft, Richard Bell

Traditionally source identification is solved using threshold based energy detection algorithms. These algorithms frequently sum up the activity in regions, and consider regions above a specific activity threshold to be sources. While these algorithms work for the majority of cases, they often fail to detect signals that occupy small frequency bands, fail to

Wenchen Luo, Wei Zhang, Yutao Hu, Hao Wang

We numerically study a 5/2 fractional quantum Hall system with even number of electrons using the exact diagonalization where both the strong Landau level (LL) mixing and a finite width of the quantum well have been considered and adapted into a screened Coulomb interaction. With the principal component analysis, we are able to recognize a compressible-incom

Gopika Lakshmi Bhai, Hiroto Mukai, Tsuyoshi Yamamoto, Jaw-Shen Tsai

We perform the noise spectroscopy of a Josephson parametric oscillator (JPO) by implementing a microwave homodyne interferometric measurement scheme. We observe the fluctuations in the self-oscillating output field of the JPO for a long 10 s time interval in a single shot measurement and characterize the phase and amplitude noise. Furthermore, we investigate

Elvin Lo, Pin-Yu Chen

Molecule optimization is an important problem in chemical discovery and has been approached using many techniques, including generative modeling, reinforcement learning, genetic algorithms, and much more. Recent work has also applied zeroth-order (ZO) optimization, a subset of gradient-free optimization that solves problems similarly to gradient-based method

Yue Lang, Zhaoyang Peng, Zengxiu Zhao

Using one-dimensional semiconductor Bloch equations, we investigate the multiband dynamics of electrons in a cutoff extension scheme employing an infrared pulse with additional UV injection. An extended three-step model is firstly validated to play a dominant role in emitting harmonics in the second plateau. Surprisingly, further analysis employing the accel

Jinyang Li, Gregorio R. M. da Silva, Schuyler Kain, Selim M. Shahriar

We show that using the process of one-axis-twist squeezing in an echo configuration, it is possible to control the orientation of the macroscopic magnetic moment of a large number of atoms by manipulating the quantum state of a single atom that is physically isolated from the ensemble. With this control technique, it is also possible to entangle an ensemble

Piotr Indyk, Sandeep Silwal

The distance matrix of a dataset $X$ of $n$ points with respect to a distance function $f$ represents all pairwise distances between points in $X$ induced by $f$. Due to their wide applicability, distance matrices and related families of matrices have been the focus of many recent algorithmic works. We continue this line of research and take a broad view of

Hyeonbae Kang, Shigeru Sakaguchi

We consider the Cauchy problem for the heat diffusion equation in the whole Euclidean space consisting of two media with different constant conductivities, where initially one medium has temperature 0 and the other has temperature 1. Under the assumptions that one medium is bounded and the interface is of class $C^{2,\alpha}$, we show that if the interface i

Jun Zhang, Ping Li, Wei Wang

Recent advances in neural networks have been successfully applied to many tasks in online recommendation applications. We propose a new framework called cone latent mixture model which makes use of hand-crafted state being able to factor distinct dependencies among multiple related documents. Specifically, it uses discriminative optimization techniques in or

Yuxuan Sun, Bowen Xie, Sheng Zhou, Zhisheng Niu

Edge intelligence is an emerging paradigm for real-time training and inference at the wireless edge, thus enabling mission-critical applications. Accordingly, base stations (BSs) and edge servers (ESs) need to be densely deployed, leading to huge deployment and operation costs, in particular the energy costs. In this article, we propose a new framework calle

Ge-Peng Ji, Mingcheng Zhuge, Dehong Gao, Deng-Ping Fan

We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation. Technically, we simply utilize vision transformer architecture for replacing the BERT in the pre-training model, making MVLT the first end-to-end framework for the fashion domain. Besides, we designed masked image reconstruction (MIR) for a fine-grained un

Pengyuan Zhou, Jinjing Zhu, Yiting Wang, Yunfan Lu

Since 2021, the term "Metaverse" has been the most popular one, garnering a lot of interest. Because of its contained environment and built-in computing and networking capabilities, a modern car makes an intriguing location to host its own little metaverse. Additionally, the travellers don't have much to do to pass the time while traveling, making them ideal

Mithunjha Anandakumar, Jathurshan Pradeepkumar, Simon L. Kappel, Chamira U. S. Edussooriya

Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG is among popular alternatives. However, t

Toranosuke Matsubara, Akihisa Koga, Sam Coates

We study the tight-binding model with two distinct hoppings $(t_L, t_S)$ on the two-dimensional hexagonal golden-mean tiling and examine the confined states with $E=0$, where $E$ is the eigenenergy. Some confined states found in the case $t_L=t_S$ are exact eigenstates even for the system with $t_L \neq t_S$, where their amplitudes are smoothly changed. By c

Xiaoyang Huang, Yi Zhang, Bingbing Ni, Teng Li

Recent years we have witnessed rapid development in NeRF-based image rendering due to its high quality. However, point clouds rendering is somehow less explored. Compared to NeRF-based rendering which suffers from dense spatial sampling, point clouds rendering is naturally less computation intensive, which enables its deployment in mobile computing device. I

Liang Chen, Xuan Guo

The dispersion of quasiparticles in topological node-line semimetals is significantly different in different directions. In a certain direction, the quasiparticles behave like relativistic particles with constant velocity. In other directions, they act as two-dimensional electron gas. The competition between relativistic and nonrelativistic dispersions can i

Ian U. Roederer, John J. Cowan, Marco Pignatari, Timothy C. Beers

We present new observational benchmarks of rapid neutron-capture process (r-process) nucleosynthesis for elements at and between the first (A ~ 80) and second (A ~ 130) peaks. Our analysis is based on archival ultraviolet and optical spectroscopy of eight metal-poor stars with Se (Z = 34) or Te (Z = 52) detections, whose r-process enhancement varies by more

Piyush Behre, Sharman Tan, Amy Shah, Harini Kesavamoorthy

Punctuation and Segmentation are key to readability in Automatic Speech Recognition (ASR), often evaluated using F1 scores that require high-quality human transcripts and do not reflect readability well. Human evaluation is expensive, time-consuming, and suffers from large inter-observer variability, especially in conversational speech devoid of strict gramm

Tao Fu, Haode Yan

Recently, the investigation of Partially APN functions has attracted a lot of attention. In this paper, with the help of resultant elimination and MAGMA, we propose several new infinite classes of 0-APN power functions over $\mathbb{F}_{2^{n}}$. By the main result in [4], these $0$-APN power functions are CCZ-inequivalent to the known ones. Moreover, these i

João Henrique Andrade, Juncheng Wei

We classify the local asymptotic behavior of positive singular solutions to a class of subcritical sixth order equations on the punctured ball. Initially, using a version of the integral moving spheres technique, we prove that solutions are asymptotically radially symmetric solutions with respect to the origin. We divide our approach into some cases concerni

Karen Butt

The marked length spectrum (MLS) of a closed negatively curved manifold $(M, g)$ is known to determine the metric $g$ under various circumstances. We show that in these cases, (approximate) values of the MLS on a sufficiently large finite set approximately determine the metric. Our approach is to recover the hypotheses of our main theorems in arXiv:2203.1212

S. Yu. Orevkov

The following problem is studied: describe the triplets $(\Omega,g,\mu)$, $\mu=\rho\,dx$, where $g= (g^{ij}(x))$ is the (co)metric associated with the symmetric second order differential operator $L(f) = \frac{1}{\rho}\sum_{ij} \partial_i (g^{ij} \rho\,\partial_j f)$ defined on a domain $\Omega$ of $\mathbb R^n$ and such that there exists an orthonormal basi

Cuong Pham, Tuan Hoang, Thanh-Toan Do

Knowledge distillation which learns a lightweight student model by distilling knowledge from a cumbersome teacher model is an attractive approach for learning compact deep neural networks (DNNs). Recent works further improve student network performance by leveraging multiple teacher networks. However, most of the existing knowledge distillation-based multi-t

Michelle Zhao, Reid Simmons, Henny Admoni

Human and robot partners increasingly need to work together to perform tasks as a team. Robots designed for such collaboration must reason about how their task-completion strategies interplay with the behavior and skills of their human team members as they coordinate on achieving joint goals. Our goal in this work is to develop a computational framework for

Elliot Murphy, Emma Holmes, Karl Friston

Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, w

Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang

Given a language model (LM), maximum probability is a poor decoding objective for open-ended generation, because it produces short and repetitive text. On the other hand, sampling can often produce incoherent text that drifts from the original topics. We propose contrastive decoding (CD), a reliable decoding approach that optimizes a contrastive objective su

Utkarsh Soni, Nupur Thakur, Sarath Sreedharan, Lin Guan

There is a growing interest in developing automated agents that can work alongside humans. In addition to completing the assigned task, such an agent will undoubtedly be expected to behave in a manner that is preferred by the human. This requires the human to communicate their preferences to the agent. To achieve this, the current approaches either require t

Jiseong Kim

In this paper, we investigate the Rankin-Selberg problem over short intervals in families of holomorphic modular forms and Hecke-Maass cusp forms. Our investigation assumes a Lindel\"of-on-average bound for holomorphic modular forms, and for Hecke-Maass cusp forms, we make no assumptions.

Baithi Mallesh, Ngoc Toan Dang, Tuan Anh Tran, Dinh Hoa Luong

Although the magnetic order is suppressed by a strong magnetic frustration, it is maintained but appears in complex order forms such as a cycloid or spin density wave in weakly frustrated systems. Herein, we report a weakly magnetic-frustrated two-dimensional van der Waals material CrPSe$_3$. Polycrystalline CrPSe$_3$ was synthesized at an optimized temperat

Manoosh Samiei, James J. Clark

Most studies in computational modeling of visual attention encompass task-free observation of images. Free-viewing saliency considers limited scenarios of daily life. Most visual activities are goal-oriented and demand a great amount of top-down attention control. Visual search task demands more top-down control of attention, compared to free-viewing. In thi

Zhiqi Shao, Andi Han, Dai Shi, Andrey Vasnev

This paper introduces a novel Framelet Graph approach based on p-Laplacian GNN. The proposed two models, named p-Laplacian undecimated framelet graph convolution (pL-UFG) and generalized p-Laplacian undecimated framelet graph convolution (pL-fUFG) inherit the nature of p-Laplacian with the expressive power of multi-resolution decomposition of graph signals.

Enamundram Naga Karthik, Anne Kerbrat, Pierre Labauge, Tobias Granberg

Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem. Several deep-learning-based methods have been proposed in recent years. However, most methods tend to be static, that is, a single model trained on a large, specialized dataset, which does not generalize well. Instead, the model should learn across datasets arriving sequentially from d

So-Myoung Park, Jihye Shin, Rory Smith, Kyungwon Chun

We investigate the properties of globular clusters in a galaxy cluster, using the particle tagging method with a semi-analytical approach in a cosmological context. We assume globular clusters form from dark matter halo mergers and their metallicity is assigned based on the stellar mass of the host dark matter halos and the formation redshift of GCs. Dynamic

Qiang Zhang, Wei Tian, Roshan K. Nepal, Ashfia Huq

Spinel compounds AB$_{2}$X$_{4}$ consist of both tetrahedral (AX$_{4}$) and octahedral (BX$_{6}$) environments with the former forming a diamond lattice and the latter a geometrically frustrated pyrochlore lattice. Exploring the fascinating properties and their correlations with structural features is critical in understanding these materials. FeMn$_{2}$O$_{

Qiushi Huang, Yu Zhang, Tom Ko, Xubo Liu

Persona-based dialogue systems aim to generate consistent responses based on historical context and predefined persona. Unlike conventional dialogue generation, the persona-based dialogue needs to consider both dialogue context and persona, posing a challenge for coherent training. Specifically, this requires a delicate weight balance between context and per

Shaowei Jiang, Chengfei Guo, Tianbo Wang, Jia Liu

Blu-ray drive is an engineering masterpiece that integrates disc rotation, pickup head translation, and three lasers in a compact and portable format. Here we integrate a blood-coated image sensor with a modified Blu-ray drive for high-throughput cytometric analysis of various bio-specimens. In this device, samples are mounted on the rotating Blu-ray disc an

Zhisong Qu, Matthew Hole

The shear Alfv\'en continuum spectrum is studied for a tokamak with a single island chain using the ideal Magnetohydrodynamics (MHD) theory. We have taken into account the toroidal geometry and toroidal mode coupling with the island considered as a highly-shaped stellarator. Various new frequency gaps open up inside the island due to its asymmetry both poloi

Mohammadhadi Mohandes, Behnam Moradi, Kamal Gupta, Mehran Mehrandezh

We present a robot-to-human object handover algorithm and implement it on a 7-DOF arm equipped with a 3-finger mechanical hand. The system performs a fully autonomous and robust object handover to a human receiver in real-time. Our algorithm relies on two complementary sensor modalities: joint torque sensors on the arm and an eye-in-hand RGB-D camera for sen

Yunis Xanthos, Wanting Lyu, Songjie Yang, Chadi Assi

Reconfigurable intelligent surface (RIS) is an attractive technology to improve the transmission rate of millimetre-wave (mmWave) communication systems. The previous {research} on RIS technology mainly focused on improving the transmission rate and security rate of the mmWave communication systems. Since the emergence of RIS technology creates the conditions

Huan Zhang, Wei Ye, Shoukang Chang, Ying Xia

We propose a method to generate the multi-mode entangled catalysis squeezed vacuum states (MECSVS) by embedding the cross-Kerr nonlinear medium into the Mach-Zehnder interferometer. This method realizes the exchange of quantum states between different modes based on Fredkin gate. In addition, we study the MECSVS as the probe state of multi-arm optical interf

Il-Seung Jang, Jae-Hoon Kwon, Akito Uruno

We construct a crystal base of $U_q(\mathfrak{gl}(m|n))^-$, the negative half of the quantum superalgebra $U_q(\mathfrak{gl}(m|n))$. We give a combinatorial description of the associated crystal $\mathscr{B}_{m|n}(\infty)$, which is equal to the limit of the crystals of the ($q$-deformed) Kac modules $K(λ)$. We also construct a crystal base of a parabolic Ve

Xing He, Naveen Kumar Tailor, Soumitra Satapathi, Jakoah Brgoch

To assess the potential optoelectronic applications of metal-halide perovskites, it is critical to have a detailed understanding of the nature, strength, and dynamics of the interactions between carriers and the polar lattices. Here, we report the electronic and structural dynamics of bismuth-based perovskite $\textrm{Cs}_{3}\textrm{Bi}_{2}\textrm{I}_{9}$ re

Ruiwen Dong

We consider two algorithmic problems concerning sub-semigroups of Heisenberg groups and, more generally, two-step nilpotent groups. The first problem is Intersection Emptiness, which asks whether a finite number of given finitely generated semigroups have empty intersection. This problem was first studied by Markov in the 1940s. We show that Intersection Emp

Thomas Chen, Ryan Denlinger, Nataša Pavlović

We prove the existence of a class of large global scattering solutions of Boltzmann's equation with constant collision kernel in two dimensions. These solutions are found for $L^2$ perturbations of an underlying initial data which is Gaussian jointly in space and velocity. Additionally, the perturbation is required to satisfy natural physical constraints

Benjamin Bacq-Labreuil, Benjamin Lenz, Silke Biermann

Obtaining an accurate theoretical description of the emergent phenomena in oxide heterostructures is a major challenge. Recently, intriguing paramagnetic spin and pinned orbital moments have been discovered by x-ray magnetic circular dichro\"ism measurements at the Cu $L_{2,3}$-edge of a tetragonal CuO/SrTiO$_3$ heterostructure. Using first principles calcul

Carey E. Priebe, Ningyuan Huang, Soledad Villar, Cong Mu

Deep neural networks (DNNs) are capable of perfectly fitting the training data, including memorizing noisy data. It is commonly believed that memorization hurts generalization. Therefore, many recent works propose mitigation strategies to avoid noisy data or correct memorization. In this work, we step back and ask the question: Can deep learning be robust ag

Zhifeng Tang, Nan Yang, Xiangyun Zhou, Jemin Lee

In this paper, we analyze the non-linear age of information (AoI) performance in a point-to-point short packet communication system, where a transmitter generates packets based on status updates and transmits the packets to a receiver. Specifically, we investigate three packet management strategies, namely, the non-preemption with no buffer strategy, the non

Nan Wang, Ricardo G. Sanfelice

This paper proposes a rapidly-exploring random trees (RRT) algorithm to solve the motion planning problem for hybrid systems. At each iteration, the proposed algorithm, called HyRRT, randomly picks a state sample and extends the search tree by flow or jump, which is also chosen randomly when both regimes are possible. Through a definition of concatenation of

Bolun Liu, Shane Lubold, Adrian E. Raftery, Tyler H. McCormick

Multidimensional scaling (MDS) is a widely used approach to representing high-dimensional, dependent data. MDS works by assigning each observation a location on a low-dimensional geometric manifold, with distance on the manifold representing similarity. We propose a Bayesian approach to multidimensional scaling when the low-dimensional manifold is hyperbolic

John P. Perdew, Shah Tanvir ur Rahman Chowdhury, Chandra Shahi, Aaron D. Kaplan

The SCAN (strongly constrained and appropriately normed) meta-generalized gradient approximation (meta-GGA), which satisfies all 17 exact constraints that a meta-GGA can satisfy, accurately describes equilibrium bonds that are normally correlated. With symmetry breaking, it also accurately describes some sd equilibrium bonds that are strongly correlated. Whi

Jim Alves-Foss, Varsah Venugopal

The effectiveness of binary analysis tools and techniques is often measured with respect to how well they map to a ground truth. We have found that not all ground truths are created equal. This paper challenges the binary analysis community to take a long look at the concept of ground truth, to ensure that we are in agreement with definition(s) of ground tru

Zhifeng Tang, Nan Yang, Parastoo Sadeghi, Xiangyun Zhou

We analytically decide whether the broadcast transmission scheme or the unicast transmission scheme achieves the optimal age of information (AoI) performance of a multiuser system where a base station (BS) generates and transmits status updates to multiple user equipments (UEs). In the broadcast transmission scheme, the status update for all UEs is jointly e

Gábor Rácz, Alina Kiessling, István Csabai, István Szapudi

Cosmic variance limits the accuracy of cosmological N-body simulations, introducing bias in statistics such as the power spectrum, halo mass function, or the cosmic shear. We provide new methods to measure and reduce the effect of cosmic variance in existing and new simulations. We ran pairs of simulations using phase-shifted initial conditions with matching

Noga Alon, Peter Frankl

We determine the maximum possible number of edges of a graph with $n$ vertices, matching number at most $s$ and clique number at most $k$ for all admissible values of the parameters.

Pier Franco Nali

Lima and Monteiro [1] derived the buoyant force in a nonuniform gravitational field by applying a gradient version of the divergence theorem to the surface integral of the pressure forces [2]. Here it is outlined an alternate approach in the framework of fluid mechanics, based on the energy formulations of the equations of state for compressible fluids.

Hritam Basak, Soumitri Chattopadhyay, Rohit Kundu, Sayan Nag

Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level segmentation tasks due to their inability to mine local features. To this end, we extend the concept of metric

Fu Yu, Jie Zhao, Yang Su, Xiaoshuai Zhu

Magnetic field extrapolation is a fundamental tool to reconstruct the three-dimensional magnetic field above the solar photosphere. However, the prevalently used force-free field model might not be applicable in the lower atmosphere with non-negligible plasma \b{eta}, where the crucial process of flux rope formation and evolution could happen. In this work,

Matt Lourens, Ilya Sinayskiy, Daniel K. Park, Carsten Blank

Machine learning with hierarchical quantum circuits, usually referred to as Quantum Convolutional Neural Networks (QCNNs), is a promising prospect for near-term quantum computing. The QCNN is a circuit model inspired by the architecture of Convolutional Neural Networks (CNNs). CNNs are successful because they do not need manual feature design and can learn h

Pranav Pidathala, Dawson Franz, James Waller, Raja Kushalnagar

Few VR applications and games implement captioning of speech and audio cues, which either inhibits or prevents access of their application by deaf or hard of hearing (DHH) users, new language learners, and other caption users. Additionally, little to no guidelines exist on how to implement live captioning on VR headsets and how it may differ from traditional

Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal

We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the \emph{underdamped} Langevin (stochastic) dynamic. Our detector achieves state-of-the-art performance in terms of symbol error rate (SER) while keeping the computational complexity in check. Indeed, our method can be easily tuned to strike the right balance between

Yui Muto, Takumi Nakaso, Motoya Shinozaki, Takumi Aizawa

Charge state recognition in quantum dot devices is important in the preparation of quantum bits for quantum information processing. Toward auto-tuning of larger-scale quantum devices, automatic charge state recognition by machine learning has been demonstrated. For further development of this technology, an understanding of the operation of the machine learn

Caden Farley, Tara Holm, Nicki Magill, Jemma Schroder

The ellipsoid embedding function of a symplectic four-manifold measures the amount by which its symplectic form must be scaled in order for it to admit an embedding of an ellipsoid of varying eccentricity. This function generalizes the Gromov width and ball packing numbers. In the one continuous family of symplectic four-manifolds that has been analyzed, one

Pengyue Hou, Jie Han, Xingyu Li

Deep Neural Networks are vulnerable to adversarial attacks. Among many defense strategies, adversarial training with untargeted attacks is one of the most effective methods. Theoretically, adversarial perturbation in untargeted attacks can be added along arbitrary directions and the predicted labels of untargeted attacks should be unpredictable. However, we

Chao Jiang, Wei Xu, Samuel Stevens

Scientific publications are the primary means to communicate research discoveries, where the writing quality is of crucial importance. However, prior work studying the human editing process in this domain mainly focused on the abstract or introduction sections, resulting in an incomplete picture. In this work, we provide a complete computational framework fo

Ruoyuan Liu

We study low regularity local well-posedness of the nonlinear Schr\"odinger equation (NLS) with the quadratic nonlinearity $\overline{u}^2$, posed on one-dimensional and two-dimensional tori. While the relevant bilinear estimate with respect to the $X^{s, b}$-space is known to fail when the regularity $s$ is below some threshold value, we establish local wel

Timothy Draher, Tomas Polakovic, Juliang Li, Yi Li

Titanium nitride is a material of interest for many superconducting devices such as nanowire microwave resonators and photon detectors. Thus, controlling the growth of TiN thin films with desirable properties is of high importance. In previous work on niobium nitride, ion beam-assisted sputtering (IBAS) reduced nitrogen sensitivity during deposition in tande

Patrick L. Combettes, Jean-Christophe Pesquet, Audrey Repetti

Neural networks have become ubiquitous tools for solving signal and image processing problems, and they often outperform standard approaches. Nevertheless, training neural networks is a challenging task in many applications. The prevalent training procedure consists of minimizing highly non-convex objectives based on data sets of huge dimension. In this cont

Sharman Tan, Piyush Behre, Nick Kibre, Issac Alphonso

Features such as punctuation, capitalization, and formatting of entities are important for readability, understanding, and natural language processing tasks. However, Automatic Speech Recognition (ASR) systems produce spoken-form text devoid of formatting, and tagging approaches to formatting address just one or two features at a time. In this paper, we unif

Romeo Brunetti, Andrea Moro

Inspired by the recent algebraic approach to classical field theory, we propose a more general setting based on the manifold of smooth sections of a non-trivial fiber bundle. Central is the notion of observables over such sections, i.e. appropriate smooth functions on them. The kinematic will be further specified by means of the Peierls brackets, which in tu

Stephanie Monty, David Yong, Anna F. Marino, Amanda I. Karakas

The view of globular clusters (GCs) as simple systems continues to unravel, revealing complex objects hosting multiple chemical peculiarities. Using differential abundance analysis, we probe the chemistry of the Type I GC, NGC 288 and the Type II GC, NGC 362 at the 2\% level for the first time. We measure 20 elements and find differential measurement uncerta

Song Wei, Chaofan Huang

We present a novel scheme to boost detection power for kernel maximum mean discrepancy based sequential change-point detection procedures. Our proposed scheme features an optimal sub-sampling of the history data before the detection procedure, in order to tackle the power loss incurred by the random sub-sample from the enormous history data. We apply our pro

Mohammad Samiul Arshad, William J. Beksi

Real-world 3D data may contain intricate details defined by salient surface gaps. Automated reconstruction of these open surfaces (e.g., non-watertight meshes) is a challenging problem for environment synthesis in mixed reality applications. Current learning-based implicit techniques can achieve high fidelity on closed-surface reconstruction. However, their

Claudio Battiloro, Zhiyang Wang, Hans Riess, Paolo Di Lorenzo

In this work we introduce a convolution operation over the tangent bundle of Riemannian manifolds exploiting the Connection Laplacian operator. We use the convolution to define tangent bundle filters and tangent bundle neural networks (TNNs), novel continuous architectures operating on tangent bundle signals, i.e. vector fields over manifolds. We discretize

Lajos Diósi

Based on the assumption that the standard Schr\"odinger equation becomes gravitationally modified for massive macroscopic objects, two independent proposals has survived from the nineteen-eighties. The Schr\"odinger--Newton equation (1984) provides well-localized solitons for free macro-objects but lacks the mechanism how extended wave functions collapse on

Xiaomin Lin, Cheng Liu, Allen Pattillo, Miao Yu

Unmanned Aerial Vehicles (UAVs) are known for their fast and versatile applicability. With UAVs' growth in availability and applications, they are now of vital importance in serving as technological support in search-and-rescue(SAR) operations in marine environments. High-resolution cameras and GPUs can be equipped on the UAVs to provide effective and effici

Yanbo Xu, Alind Khare, Glenn Matlin, Monish Ramadoss

Machine Learning (ML) research has focused on maximizing the accuracy of predictive tasks. ML models, however, are increasingly more complex, resource intensive, and costlier to deploy in resource-constrained environments. These issues are exacerbated for prediction tasks with sequential classification on progressively transitioned stages with ''happens-befo

Pedram Agand, Mahdi Aliyari Shoorehdeli

Since batch algorithms suffer from lack of proficiency in confronting model mismatches and disturbances, this contribution proposes an adaptive scheme based on continuous Lyapunov function for online robot dynamic identification. This paper suggests stable updating rules to drive neural networks inspiring from model reference adaptive paradigm. Network struc

Tristram de Piro

We prove there exists a charge solution to the 1-dimensional wave equation, and a corresponding current, such that the pair satisfy the continuity equation. We show that when they are extended to a smooth solution of the continuity equation on a vanishing annulus containing the unit circle, with a corresponding causal solution to Maxwell's equations, obtaine

Troy J. Sewell, Ning Bao, Stephen P. Jordan

Here, we investigate the use of deep multi-scale entanglement renormalization (DMERA) circuits as a variational ansatz for ground states of gapless systems. We use the exactly-solvable one-dimensional critical transverse-field Ising model as a testbed. Numerically exact simulation of the ansatz can in this case be carried out to hundreds of qubits by exploit

Christian Baer, Penelope Gehring

Non-local boundary conditions, such as the Atiyah-Patodi-Singer (APS) conditions, for Dirac operators on Riemannian manifolds are well understood while not much is known for such operators on spacetimes with timelike boundary. We define a class of Lorentzian boundary conditions that are local in time and non-local in the spatial directions and show that they

Marco Schreyer, Hamed Hemati, Damian Borth, Miklos A. Vasarhelyi

The International Standards on Auditing require auditors to collect reasonable assurance that financial statements are free of material misstatement. At the same time, a central objective of Continuous Assurance is the real-time assessment of digital accounting journal entries. Recently, driven by the advances in artificial intelligence, Deep Learning techni

Hyunwook Lee, Chunggi Lee, Hongkyu Lim, Sungahn Ko

Time-series forecasting has gained increasing attention in the field of artificial intelligence due to its potential to address real-world problems across various domains, including energy, weather, traffic, and economy. While time-series forecasting is a well-researched field, predicting complex temporal patterns such as sudden changes in sequential data st

Nam H. Le, Dat T. Tran, Ha T. N. Tran

In this paper, we investigate the properties of well-rounded twists of a given ideal lattice of an imaginary quadratic field $K$. We show that every ideal lattice $I$ of $K$ has at least one well-rounded twist lattice. Moreover, we provide an explicit algorithm to compute all well-rounded twists of $I$.

Urchade Zaratiana, Niama El Khbir, Dennis Núñez, Pierre Holat

Extractive question answering (ExQA) is an essential task for Natural Language Processing. The dominant approach to ExQA is one that represents the input sequence tokens (question and passage) with a pre-trained transformer, then uses two learned query vectors to compute distributions over the start and end answer span positions. These query vectors lack the

Francesco C. De Vecchi, Luca Fresta, Massimiliano Gubinelli

Building on previous work on the stochastic analysis for Grassmann random variables, we introduce a forward-backward stochastic differential equation (FBSDE) which provides a stochastic quantisation of Grassmann measures. Our method is inspired by the so-called continuous renormalisation group, but avoids the technical difficulties encountered in the direct

Daoguan Ning, Yuriy Shoshin

In the present work, the discrete flame model [1] is augmented by introducing the thermal inertia of particles in the preheating zone. The effect of particle thermal inertia on flame speed, propagation limits, and near-limits dynamics of one-dimensional discrete combustion waves is studied using the new model. It is found that, with the increase of particle