October 2022 arXiv papers — page 111

Lizhi Bai, Jun Yang, Chunqi Tian, Yaoru Sun

Combining RGB images and the corresponding depth maps in semantic segmentation proves the effectiveness in the past few years. Existing RGB-D modal fusion methods either lack the non-linear feature fusion ability or treat both modal images equally, regardless of the intrinsic distribution gap or information loss. Here we find that depth maps are suitable to

Hao Cui, Rahmadi Trimananda, Scott Jordan, Athina Markopoulou

Privacy policies disclose how an organization collects and handles personal information. Recent work has made progress in leveraging natural language processing (NLP) to automate privacy policy analysis and extract data collection statements from different sentences, considered in isolation from each other. In this paper, we view and analyze, for the first t

Andrei Mikhailov

We develop a general framework for the insertion of vertex operator on the string worldsheet, in BV formalism. Such insertions correspond to deformations of the Master Action which breaks the gauge symmetry to a subgroup, and then restoring the full gauge symmetry by integrating over a cycle in the space of Lagrangian submanifolds. We provide the general con

Jonathan Klawitter, Felix Klesen, Moritz Niederer, Alexander Wolff

We consider the problem of drawing multiple gene trees inside a single species tree in order to visualize multispecies coalescent trees. Specifically, the drawing of the species tree fills a rectangle in which each of its edges is represented by a smaller rectangle, and the gene trees are drawn as rectangular cladograms (that is, orthogonally and downward, w

Himanshu Gaur, Urjit A. Yajnik

In this paper, we study the symmetry decomposition of R\'enyi negativity into charge imbalance sectors for the 1+1 dimensional free compact boson field with a global U(1) symmetry in the ground state for the case of two disjoint intervals. We obtain multi-charged and charged R\'enyi negativity moments by computing the four-point correlator of flux-generating

Xue Yang, Gefan Zhang, Wentong Li, Xuehui Wang

Oriented object detection emerges in many applications from aerial images to autonomous driving, while many existing detection benchmarks are annotated with horizontal bounding box only which is also less costive than fine-grained rotated box, leading to a gap between the readily available training corpus and the rising demand for oriented object detection.

Chao Ma, Lexing Ying

In this paper, we show that structures similar to self-attention are natural to learn many sequence-to-sequence problems from the perspective of symmetry. Inspired by language processing applications, we study the orthogonal equivariance of seq2seq functions with knowledge, which are functions taking two inputs -- an input sequence and a ``knowledge'' -- and

Ting Lv, Yu-Bin Liu, Tian-Cheng Yi, Liangsheng Li

We have investigated scaling properties near the quantum critical point between the extended phase and the critical phase in the Aubry-Andr\'{e}-Harper model with p-wave pairing, which have rarely been exploited as most investigations focus on the localization transition from the critical phase to the localized phase. We find that the spectrum averaged entan

Janio Carlos Nascimento Silva, Uéverton S. Souza

Measures of circuit complexity are usually analyzed to ensure the computation of Boolean functions with economy and efficiency. One of these measures is energy complexity, which is related to the number of gates that output true in a circuit for an assignment. The idea behind energy complexity comes from the counting of `firing' neurons in a natural neural n

Yash Chaurasia, Visvesh Subramanian, Sujit Gujar

Bitcoin is the first of its kind, a truly decentralized and anonymous cryptocurrency. To realize it, it has developed blockchain technology using the concept of `Proof of Work' (PoW). The miners, nodes responsible for writing transaction databases, solve a cryptographic puzzle to claim the right to write to the database. Though bitcoin and many other relevan

Yuhang Wu, Zeyu Zheng, Yingfei Wang, Guangyu Zhang

We consider stochastic optimization problems with the dual tasks of (i) effectively finding the optimizer and (ii) reliably conducting statistical inference for the optimal objective function value. We find that classical simulation optimization and stochastic optimization algorithms, despite of their fast convergence rates to the optimizer under strong conv

Ekaterina V. Shishkina, Serge N. Gavrilov, Yulia A. Mochalova

We introduce a new wave phenomenon, which can be observed in continuum and discrete systems, where a trapped mode exists under certain conditions, namely, the anti-localization of non-stationary linear waves. This is zeroing of the non-localized propagating component of the wave-field in a neighbourhood of an inclusion. In other words, it is a tendency for n

Manish Kr. Singh, Jayashree Pati, Deepak Seth, Jagdees Prasad

We report the electrochemical investigation of 5% Al doped MoS$_2$@rGO composite as a high-performance anode for sodium (Na)-ion batteries. The x-ray diffraction (XRD), Raman spectroscopy and high-resolution transmission electron microscopy characterizations reveal that the Al doping increase the interlayer spacing of (002) plane of MoS$_2$ nanosheets and fo

Aayushman Sharma, Zirui Mao, Haiying Yang, Suman Chakravorty

In this paper, we consider the optimal control of material micro-structures. Such material micro-structures are modeled by the so-called phase field model. We study the underlying physical structure of the model and propose a data based approach for its optimal control, along with a comparison to the control using a state of the art Reinforcement Learning (R

Sudipta Mallik, Bahattin Yildiz

Binary codes are constructed from incidence matrices of hypergraphs. A combinatroial description is given for the minimum distances of such codes via a combinatorial tool called ``eonv". This combinatorial approach provides a faster alternative method of finding the minimum distance, which is known to be a hard problem. This is demonstrated on several classe

Ozgur Guldogan, Yuchen Zeng, Jy-yong Sohn, Ramtin Pedarsani

Devising a fair classifier that does not discriminate against different groups is an important problem in machine learning. Although researchers have proposed various ways of defining group fairness, most of them only focused on the immediate fairness, ignoring the long-term impact of a fair classifier under the dynamic scenario where each individual can imp

Baitian Tang, Jiajun Zhang, Zhiqiang Yan, Zhiyu Zhang

Owing to the recent identification of major substructures in our Milky Way (MW), the astronomical community has started to reevaluate the importance of dissolved and existing dwarf galaxies. In this work, we investigate up to 13 elements in 43 giant stars of the Sculptor dwarf galaxy (Scl) using high-signal-to-noise-ratio near-infrared (NIR) APOGEE spectra.

Yujiao Zhao, Linfang Xiao, Vick Lau, Yilong Liu

At present, MRI scans are performed inside a fully-enclosed RF shielding room, posing stringent installation requirement and unnecessary patient discomfort. We aim to develop an electromagnetic interference (EMI) cancellation strategy for MRI with no or incomplete RF shielding. In this study, a simultaneous sensing and deep learning driven EMI cancellation s

Ehsan Hallaji, Roozbeh Razavi-Far, Meng Wang, Mehrdad Saif

This paper presents a novel data-driven framework to aid in system state estimation when the power system is under unobservable false data injection attacks. The proposed framework dynamically detects and classifies false data injection attacks. Then, it retrieves the control signal using the acquired information. This process is accomplished in three main m

Moses Charikar, Zhihao Jiang, Kirankumar Shiragur, Aaron Sidford

We provide an efficient unified plug-in approach for estimating symmetric properties of distributions given $n$ independent samples. Our estimator is based on profile-maximum-likelihood (PML) and is sample optimal for estimating various symmetric properties when the estimation error $\epsilon \gg n^{-1/3}$. This result improves upon the previous best accurac

Lu Chen, Guozhen Lu, Hanli Tang

In this paper, we are concerned with the stability problem for endpoint conformally invariant cases of the Sobolev inequality on the sphere $\mathbb{S}^n$. Namely, we will establish the stability for Beckner's log-Sobolev inequality and Beckner's Moser-Onofri inequality on the sphere. We also prove that the sharp constant of global stability for the log-Sobo

Shiyang Li, Jianshu Chen, Yelong Shen, Zhiyu Chen

Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations. In this paper, we consider the problem of leveraging the explanations generated by LLM to improve the training of small reasoners, which are more favorable in real-production deployment

Prasann Singhal, Jarad Forristal, Xi Ye, Greg Durrett

While pretrained language models have exhibited impressive generalization capabilities, they still behave unpredictably under certain domain shifts. In particular, a model may learn a reasoning process on in-domain training data that does not hold for out-of-domain test data. We address the task of predicting out-of-domain (OOD) performance in a few-shot fas

Ryan S. Brill, Sameer K. Deshpande, Abraham J. Wyner

As a baseball game progresses, batters appear to perform better the more times they face a particular pitcher. The apparent drop-off in pitcher performance from one time through the order to the next, known as the Time Through the Order Penalty (TTOP), is often attributed to within-game batter learning. Although the TTOP has largely been accepted within base

Junyu Liu, Frederik Wilde, Antonio Anna Mele, Xin Jin

Saddle points constitute a crucial challenge for first-order gradient descent algorithms. In notions of classical machine learning, they are avoided for example by means of stochastic gradient descent methods. In this work, we provide evidence that the saddle points problem can be naturally avoided in variational quantum algorithms by exploiting the presence

Qian Huang, Hongyu Ren, Jure Leskovec

Few-shot knowledge graph (KG) completion task aims to perform inductive reasoning over the KG: given only a few support triplets of a new relation $\bowtie$ (e.g., (chop,$\bowtie$,kitchen), (read,$\bowtie$,library), the goal is to predict the query triplets of the same unseen relation $\bowtie$, e.g., (sleep,$\bowtie$,?). Current approaches cast the problem

Luis Daniel Abreu

We confirm Flandrin's prediction for the expected average of local maxima of spectrograms of complex white noise with Gaussian windows (Gaussian spectrograms or, equivalently, modulus of weighted Gaussian Entire Functions), a consequence of the conjectured double honeycomb mean model for the network of zeros and local maxima, where the area of local maxima c

Akhil Pandey Akella

Dimensionality reduction techniques map data represented on higher dimensions onto lower dimensions with varying degrees of information loss. Graph dimensionality reduction techniques adopt the same principle of providing latent representations of the graph structure with minor adaptations to the output representations along with the input data. There exist

Seongmin Park, Jihwa Lee

In weakly-supervised text classification, only label names act as sources of supervision. Predominant approaches to weakly-supervised text classification utilize a two-phase framework, where test samples are first assigned pseudo-labels and are then used to train a neural text classifier. In most previous work, the pseudo-labeling step is dependent on obtain

Xiao Zhang, Ninglu Shao, Zihua Si, Jun Xu

Contextual batched bandit (CBB) is a setting where a batch of rewards is observed from the environment at the end of each episode, but the rewards of the non-executed actions are unobserved, resulting in partial-information feedback. Existing approaches for CBB often ignore the rewards of the non-executed actions, leading to underutilization of feedback info

Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell

We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has access to an offline dataset and the ability to collect experience via real-world online interaction. The framework mitigates the challenges that arise in both pure offline and online RL settings, allowing for the design of simple and highly effective algorithms, in both t

Bilguun Bayarsaikhan, Sunly Khimphun, Phearun Rithy, Gansukh Tumurtushaa

We investigate the regularized four-dimensional Einstein-Gauss-Bonnet ($4D$ EGB) gravity with a non-minimal scalar coupling function, which is an extension of the regularized $4D$ EGB theory. By introducing non-minimal coupling to the Gauss-Bonnet term, we demonstrate the additional contribution to the dynamical equations which is otherwise absent in the dim

Zhe Yang, Qingkai Fang, Yang Feng

How to achieve neural machine translation with limited parallel data? Existing techniques often rely on large-scale monolingual corpora, which is impractical for some low-resource languages. In this paper, we turn to connect several low-resource languages to a particular high-resource one by additional visual modality. Specifically, we propose a cross-modal

Najiya V K, Chithra A

Cospectral graphs are a fascinating concept in graph theory, where two non-isomorphic graphs possess identical sets of eigenvalues. In this paper, we compute the $A_\alpha$-characteristic polynomial of neighbour and non-neighbour splitting join, neighbour and non-neighbour shadow join, central vertex and edge join and duplicate join of two graphs. In additio

Diane Castonguay, Erika M. M. Coelho, Hebert Coelho, Julliano R. Nascimento

In Partition Into Complementary Subgraphs (Comp-Sub) we are given a graph $G=(V,E)$, and an edge set property $\Pi$, and asked whether $G$ can be decomposed into two graphs, $H$ and its complement $\overline{H}$, for some graph $H$, in such a way that the edge cut $[V(H),V(\overline{H})]$ satisfies the property $\Pi$. Motivated by previous work, we consider

Nicholas Chimitt, Xingguang Zhang, Zhiyuan Mao, Stanley H. Chan

Numerical simulation of atmospheric turbulence is one of the biggest bottlenecks in developing computational techniques for solving the inverse problem in long-range imaging. The classical split-step method is based upon numerical wave propagation which splits the propagation path into many segments and propagates every pixel in each segment individually via

Mubarak Alqahtani

Using anisotropic hydrodynamics, we examine the existence of early-time attractors of non-conformal systems undergoing Bjorken expansion. In the case of a constant mass, we find that the evolution of the scaled longitudinal pressure is insensitive to variations of initial conditions converging onto an early-time universal curve and eventually merging with th

Fotis Iliopoulos, Vasilis Kontonis, Cenk Baykal, Gaurav Menghani

Distillation with unlabeled examples is a popular and powerful method for training deep neural networks in settings where the amount of labeled data is limited: A large ''teacher'' neural network is trained on the labeled data available, and then it is used to generate labels on an unlabeled dataset (typically much larger in size). These labels are then util

Wenhu Chen

Recent literature has shown that large language models (LLMs) are generally excellent few-shot reasoners to solve text reasoning tasks. However, the capability of LLMs on table reasoning tasks is yet to be explored. In this paper, we aim at understanding how well LLMs can perform table-related tasks with few-shot in-context learning. Specifically, we evaluat

Yongjing Yin, Yafu Li, Fandong Meng, Jie Zhou

Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks. However, they have recently been shown to suffer limitation in compositional generalization, failing to effectively learn the translation of atoms (e.g., words) and their semantic composition (e.g., modification) from seen compounds (e.g., phrases), a

L. Aldoroty, L. Wang, P. Hoeflich, J. Yang

We apply the color-magnitude intercept calibration method (CMAGIC) to the Nearby Supernova Factory SNe Ia spectrophotometric dataset. The currently existing CMAGIC parameters are the slope and intercept of a straight line fit to the first linear region in the color-magnitude diagram, which occurs over a span of approximately 30 days after maximum brightness.

Yanjing Li, Sheng Xu, Baochang Zhang, Xianbin Cao

The large pre-trained vision transformers (ViTs) have demonstrated remarkable performance on various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-constrained devices. Among the powerful compression approaches, quantization extremely reduces the computation and memory consumption by low-bit parameter

Hong Liu, Zhijian Ou, Yi Huang, Junlan Feng

Recently, there has been progress in supervised funetuning pretrained GPT-2 to build end-to-end task-oriented dialog (TOD) systems. However, online reinforcement learning of a GPT-2 based dialog system (DS), together with a end-to-end user simulator (US), has not ever been explored. Moreover, a drawback with existing GPT-2 based TOD systems is that they most

Satyen Kale, Jason D. Lee, Chris De Sa, Ayush Sekhari

Stochastic Gradient Descent (SGD) has been the method of choice for learning large-scale non-convex models. While a general analysis of when SGD works has been elusive, there has been a lot of recent progress in understanding the convergence of Gradient Flow (GF) on the population loss, partly due to the simplicity that a continuous-time analysis buys us. An

Mikhail Galkin, Zhaocheng Zhu, Hongyu Ren, Jian Tang

Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural methods achieved impressive results in link prediction and complex query answering tasks by learning representations of entities, relations, and queries. Still, most existing query answ

Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie

Deep neural network (DNN) classifiers are vulnerable to backdoor attacks. An adversary poisons some of the training data in such attacks by installing a trigger. The goal is to make the trained DNN output the attacker's desired class whenever the trigger is activated while performing as usual for clean data. Various approaches have recently been proposed to

Gabriel L. Duarte, Uéverton S. Souza

In 2021, Duarte, Oliveira, and Souza [MFCS 2021] showed some problems that are FPT when parameterized by the treewidth of the complement graph (called co-treewidth). Since the degeneracy of a graph is at most its treewidth, they also introduced the study of co-degeneracy (the degeneracy of the complement graph) as a parameter. In 1976, Bondy and Chv\'{a}tal

Andrew Zhao, Matthieu Gaetan Lin, Yangguang Li, Yong-Jin Liu

Unsupervised reinforcement learning aims at learning a generalist policy in a reward-free manner for fast adaptation to downstream tasks. Most of the existing methods propose to provide an intrinsic reward based on surprise. Maximizing or minimizing surprise drives the agent to either explore or gain control over its environment. However, both strategies rel

Huiyuan Yang, Han Yu, Akane Sano

Deep learning has performed remarkably well on many tasks recently. However, the superior performance of deep models relies heavily on the availability of a large number of training data, which limits the wide adaptation of deep models on various clinical and affective computing tasks, as the labeled data are usually very limited. As an effective technique t

Xiaozhen Ge, Lijun Liu, Shuming Cheng

Multipartite entanglement is an indispensable resource in quantum communication and computation, however, it is a challenging task to faithfully quantify this global property of multipartite quantum systems. In this work, we study the concurrence fill, which admits a geometric interpretation to measure genuine tripartite entanglement for the three-qubit syst

Yue Bai, Huan Wang, Xu Ma, Yitian Zhang

A deeper network structure generally handles more complicated non-linearity and performs more competitively. Nowadays, advanced network designs often contain a large number of repetitive structures (e.g., Transformer). They empower the network capacity to a new level but also increase the model size inevitably, which is unfriendly to either model restoring o

Shaahin Angizi, Mehrdad Morsali, Sepehr Tabrizchi, Arman Roohi

In this work, a high-speed and energy-efficient comparator-based Near-Sensor Local Binary Pattern accelerator architecture (NS-LBP) is proposed to execute a novel local binary pattern deep neural network. First, inspired by recent LBP networks, we design an approximate, hardware-oriented, and multiply-accumulate (MAC)-free network named Ap-LBP for efficient

Junyi Shen, Itaru Kitahara, Shinichi Koyama, Qiaoge Li

The emotional response of users induced by VR scenarios has become a topic of interest, however, whether changing the size of objects in VR scenes induces different levels of anxiety remains a question to be studied. In this study, we conducted an experiment to initially reveal how the size of a large object in a VR environment affects changes in participant

Huize Li, Hai Jin, Long Zheng, Yu Huang

The attention mechanism requires huge computational efforts to process unnecessary calculations, significantly limiting the system's performance. Researchers propose sparse attention to convert some DDMM operations to SDDMM and SpMM operations. However, current sparse attention solutions introduce massive off-chip random memory access. We propose CPSAA, a no

Boning Di, Ryan Frier

In this paper, we consider the Strichartz inequality for a fourth-order Schr\"odinger equation on $\mathbb{R}^{2+1}$. We show that extremizers exist using a linear profile decomposition which follows from the endpoint version decomposition and the stationary phase method. Based on the existence of extremizers, we investigate the associated Euler-Lagrange equ

Zhaowei Wang, Hongming Zhang, Tianqing Fang, Yangqiu Song

In this paper, we propose a new task of sub-event generation for an unseen process to evaluate the understanding of the coherence of sub-event actions and objects. To solve the problem, we design SubeventWriter, a sub-event sequence generation framework with a coherence controller. Given an unseen process, the framework can iteratively construct the sub-even

Qipeng Liu

QROM (quantum random oracle model), introduced by Boneh et al. (Asiacrypt 2011), captures all generic algorithms. However, it fails to describe non-uniform quantum algorithms with preprocessing power, which receives a piece of bounded classical or quantum advice. As non-uniform algorithms are largely believed to be the right model for attackers, starting fro

Kaiyang Guo, Yunfeng Shao, Yanhui Geng

Model-based offline reinforcement learning (RL) aims to find highly rewarding policy, by leveraging a previously collected static dataset and a dynamics model. While the dynamics model learned through reuse of the static dataset, its generalization ability hopefully promotes policy learning if properly utilized. To that end, several works propose to quantify

Xinyue Evelyn Zhao, Long-Qing Chen, Wenrui Hao, Yanxiang Zhao

Phase field method is playing an increasingly important role in understanding and predicting morphological evolution in materials and biological systems. Here, we develop a new analytical approach based on bifurcation analysis to explore the mathematical solution structure of phase field models. Revealing such solution structures not only is of great mathema

Meng Lu, Qiang Li, Zhengyun You, Ce Zhang

Novel collision methods and rich phenomena are crucial to keeping high-energy collision physics more robust and attractive. In this document, we present a staged blueprint for future high-energy colliders: from neutrino-neutrino collision, neutrino-lepton collision to electron-muon and muon-muon collisions. Neutrino beam from TeV scale muons is a good candid

Endong Wang, Yi Gao

Nanoscale confinement provides an ideal platform to rouse some exceptional reactions which cannot happen in the open space. Intuitively, H2 and H$_2$O cannot react. Herein, through utilizing small-sized fullerenes (C$_{24}$, C$_{26}$, C$_{28}$, and C$_{30}$) as nanoreactors, we demonstrate that a hyperhydrogenated water species, H$_4$O, can be easily formed

Weijun Tan, Qi Yao, Jingfeng Liu

Current weakly supervised video anomaly detection algorithms mostly use multiple instance learning (MIL) or their varieties. Almost all recent approaches focus on how to select the correct snippets for training to improve the performance. They overlook or do not realize the power of video classification in boosting the performance of anomaly detection. In th

Noah Perry, Norman Matloff, Patrick Tendick

A novel variation of the data swapping approach to statistical disclosure control is presented, aimed particularly at preservation of multivariate relations in the original dataset. A theorem is proved in support of the method, and extensive empirical investigation is reported.

Yufei Guo, Liwen Zhang, Yuanpei Chen, Xinyi Tong

Brain-inspired spiking neural networks (SNNs) have recently drawn more and more attention due to their event-driven and energy-efficient characteristics. The integration of storage and computation paradigm on neuromorphic hardwares makes SNNs much different from Deep Neural Networks (DNNs). In this paper, we argue that SNNs may not benefit from the weight-sh

Anqi Wang, Yupeng Li, Guang Yang, Dayu Yan

The interplay between topology and interaction always plays an important role in condensed matter physics and induces many exotic quantum phases, while rare transition metal layered material (TMLM) has been proved to possess both. Here we report a TMLM Ta$_2$Pd$_3$Te$_5$ has the two-dimensional second-order topology (also a quadrupole topological insulator)

Shreyas Devaraju, Alexander Ihler, Sunil Kumar

UAV networks consisting of reduced size, weight, and power (low SWaP) fixed-wing UAVs are used for civilian and military applications such as search and rescue, surveillance, and tracking. To carry out these operations efficiently, there is a need to develop scalable, decentralized autonomous UAV network architectures with high network connectivity. However,

Michael Guevarra, Srijita Das, Christabel Wayllace, Carrie Demmans Epp

We propose an AI-based pilot trainer to help students learn how to fly aircraft. First, an AI agent uses behavioral cloning to learn flying maneuvers from qualified flight instructors. Later, the system uses the agent's decisions to detect errors made by students and provide feedback to help students correct their errors. This paper presents an instantiation

Kohou Wang, Zhaoxiang Liu, Shiguo Lian

In practical applications especially with safety requirement, some hand-held actions need to be monitored closely, including smoking cigarettes, dialing, eating, etc. Taking smoking cigarettes as example, existing smoke detection algorithms usually detect the cigarette or cigarette with hand as the target object only, which leads to low accuracy. In this pap

Xuan Kan, Wei Dai, Hejie Cui, Zilong Zhang

Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transformer-based models have been studied over different types of data, including graphs, shown to bring performance gains widely. In this work, we study Transformer-based models for brain

Norman Matloff

The old empathetic adage, ``Walk a mile in their shoes,'' asks that one imagine the difficulties others may face. This suggests a new ML counterfactual fairness criterion, based on a \textit{group} level: How would members of a nonprotected group fare if their group were subject to conditions in some protected group? Instead of asking what sentence would a p

Xiaoyu Xia, Sheik Mohammad Mostakim Fattah, Muhammad Ali Babar

Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, d

Fang Gao, Dejian Huang, Ziwei Zhao, Wei Dai

A series of hybrid quantum-classical generalized Benders decomposition (GBD) algorithms are proposed to address unit commitment (UC) problems under centralized, distributed, and partially distributed frameworks. In the centralized approach, the quantum GBD transforms the master problem (MP) into a quadratic unconstrained binary optimization form suitable for

Irteza Enan Kabir

Ultrasound elastography is the method to image the elasticity of compliant tissues due to a mechanical compression applied to it. In elastography, the local strain of explored tissue is estimated by analyzing the echo signals. This is accomplished by a sonographer who uses ultrasound transducer to apply pressure on the tissue area causing displacement of the

Berkay Kaplan, Jingyu Qian, Israel J Lopez-Toledo, Carl A. Gunter

The number of IoT devices in smart homes is increasing. This broad adoption facilitates users' lives, but it also brings problems. One such issue is that some IoT devices may invade users' privacy. Some reasons for this invasion can stem from obscure data collection practices or hidden devices. Specific IoT devices can exist out of sight and still collect us

Haicen Yue, Justin C. Burton, Daniel M. Sussman

Droplet coalescence is essential in a host of biological and industrial processes, involving complex systems as diverse as cellular aggregates, colloidal suspensions, and polymeric liquids. Classical solutions for the time evolution of coalescing clusters are typically based on tractable limiting physics, such as analytical solutions to the Stokes equation.

Vikrant Gupta, Larry K. B. Li, Shiyi Chen, Minping Wan

Reservoir computing is a powerful tool for forecasting turbulence because its simple architecture has the computational efficiency to handle large systems. Its implementation, however, often requires full state-vector measurements and knowledge of the system nonlinearities. We use nonlinear projector functions to expand the system measurements to a high dime

Khondker Fariha Hossain, Sharif Amit Kamran, Prithul Sarker, Philip Pavilionis

Sport-related concussion (SRC) depends on sensory information from visual, vestibular, and somatosensory systems. At the same time, the current clinical administration of Vestibular/Ocular Motor Screening (VOMS) is subjective and deviates among administrators. Therefore, for the assessment and management of concussion detection, standardization is required t

Kazue Matsuyama, Jeff Greensite

The usual Hartree-Fock approximation to the Hubbard model is based on eigenstates of the electron number operator. But this formulation is not unique. A different (and inequivalent) version, formulated in terms of Nambu two-component spinors, is based on eigenstates of the difference between the numbers of spin up and spin down electrons, with electron densi

Yuxuan Zhao, Alex Townsend, Madeleine Udell

Many real-world datasets contain missing entries and mixed data types including categorical and ordered (e.g. continuous and ordinal) variables. Imputing the missing entries is necessary, since many data analysis pipelines require complete data, but this is challenging especially for mixed data. This paper proposes a probabilistic imputation method using an

Geoffrey Wolfer, Pierre Alquier

An important feature of kernel mean embeddings (KME) is that the rate of convergence of the empirical KME to the true distribution KME can be bounded independently of the dimension of the space, properties of the distribution and smoothness features of the kernel. We show how to speed-up convergence by leveraging variance information in the reproducing kerne

Aditya Kumar Akash, Sixu Li, Nicolás García Trillos

Based on the concepts of Wasserstein barycenter (WB) and Gromov-Wasserstein barycenter (GWB), we propose a unified mathematical framework for neural network (NN) model fusion and utilize it to reveal new insights about the linear mode connectivity of SGD solutions. In our framework, the fusion occurs in a layer-wise manner and builds on an interpretation of

Khondker Fariha Hossain, Alireza Tavakkoli, Shamik Sengupta

In recent times deep learning has been widely used for automating various security tasks in Cyber Domains. However, adversaries manipulate data in many situations and diminish the deployed deep learning model's accuracy. One notable example is fooling CAPTCHA data to access the CAPTCHA-based Classifier leading to the critical system being vulnerable to cyber

Tom Kennedy, Slava Rychkov

We continue our study of rigorous renormalization group (RG) maps for tensor networks that was begun in arXiv:2107.11464. In this paper we construct a rigorous RG map for 2D tensor networks whose domain includes tensors that represent the 2D Ising model at low temperatures with a magnetic field $h$. We prove that the RG map has two stable fixed points, corre

Sapan Karki, Brett Altschul

It is known that in Lorentz-violating effective field theory, there is a classical equivalence between certain coefficients ($c$ and $f$), in spite of the fact that the operators the two types of coefficients describe appear to have opposite behaviors under $\textbf{CPT}$. This paper is a continuation of previous work extending this equivalence to the quantu

Ricky Sinclair, Muhammad Ashad Kabir

The Munsell soil color chart (MSCC) is a in laboratories under controlled conditions. To support an appbased solution, this paper explores three research areas including: (i) identifying the most effective color space, (ii) establishing then important reference for many professionals in the area of soil color analysis. Currently, the functionality to identif

Phil Attard

In the previous paper (Attard 2022d)on the fountain pressure in superfluid helium, it was shown that the experimentally confirmed expression of H. London (1939) was thermodynamically equivalent to equality of chemical potential. However this theoretical equivalence was not reflected in the experimental data. The problem has now been traced to errors in the e

Casey Brinkman, Lauren M. Weiss, Fei Dai, Daniel Huber

TOI-561 is a galactic thick disk star hosting an ultra-short period (0.45 day orbit) planet with a radius of 1.37 R$_{\oplus}$, making it one of the most metal-poor ([Fe/H] = -0.41) and oldest ($\sim$10 Gyr) sites where an Earth-sized planet has been found. We present new simultaneous radial velocity measurements (RVs) from Gemini-N/MAROON-X and Keck/HIRES,

Charis Y. N. Chiang, Fabian Braeu, Thanadet Chuangsuwanich, Royston K. Y. Tan

Purpose: (1) To develop a deep learning algorithm to automatically segment structures of the optic nerve head (ONH) and macula in 3D wide-field optical coherence tomography (OCT) scans; (2) To assess whether 3D macula or ONH structures (or the combination of both) provide the best diagnostic power for glaucoma. Methods: A cross-sectional comparative study wa

Zhibo Chen, Frank Pfenning

Logical frameworks provide natural and direct ways of specifying and reasoning within deductive systems. The logical framework LF and subsequent developments focus on finitary proof systems, making the formalization of circular proof systems in such logical frameworks a cumbersome and awkward task. To address this issue, we propose CoLF, a conservative exten

Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani

Learning the continuous dynamics of a system from snapshots of its temporal marginals is a problem which appears throughout natural sciences and machine learning, including in quantum systems, single-cell biological data, and generative modeling. In these settings, we assume access to cross-sectional samples that are uncorrelated over time, rather than full

Yoshifumi Nakata, Takaya Matsuura, Masato Koashi

Among various classes of quantum error correcting codes (QECCs), non-stabilizer codes have rich properties and are of theoretical and practical interest. Decoding non-stabilizer codes is, however, a highly non-trivial task. In this paper, we show that a decoding circuit for Calderbank-Shor-Steane (CSS) codes can be straightforwardly extended to handle genera

F. Flores

Given a Fell bundle $\mathscr C\overset{q}{\to}\Xi$ over the discrete groupoid $\Xi$, we study the symmetry of the associated Hahn algebra $\ell^{\infty,1}(\Xi\!\mid\!\mathscr C)$ in terms of the isotropy subgroups of $\Xi$. We prove that $\Xi$ is symmetric (resp. hypersymmetric) if and only if all of the isotropy subgroups are symmetric (resp. hypersymmetri

Maying Shen, Hongxu Yin, Pavlo Molchanov, Lei Mao

Structural pruning can simplify network architecture and improve inference speed. We propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a global resource allocation optimization problem, aiming at maximizing the accuracy while constraining latency under a predefined budget on targeting device. For filter importance ranking, H

Ahmet Orun

The human's cognitive capacity for problem solving is always limited to his/her educational background, skills, experiences, etc. Hence, it is often insufficient to bring solution to extraordinary problems especially when there is a time restriction. Nowadays this sort of personal cognitive limitations are overcome at some extend by the computational utiliti

Diana Kim, Virgil Watkins, Laszlo Cline, Jingxian Li

Electrochemical random-access memory (ECRAM) is a recently developed and highly promising analog resistive memory element for in-memory computing. One longstanding challenge of ECRAM is attaining retention time beyond a few hours. This short retention has precluded ECRAM from being considered for inference classification in deep neural networks, which is lik

L. R. Cullinane, A. D. Mackey, G. S. Da Costa, S. E. Koposov

We use data from the Magellanic Edges Survey (MagES) in combination with Gaia EDR3 to study the extreme southern outskirts of the Small Magellanic Cloud (SMC), focussing on a field at the eastern end of a long arm-like structure which wraps around the southern periphery of the Large Magellanic Cloud (LMC). Unlike the remainder of this structure, which is tho

Ahmed Benfaid, Nadia Adem, Abdurrahman Elmaghbub

Rendering to their abilities to provide ubiquitous connectivity, flexibly and cost effectively, unmanned aerial vehicles (UAVs) have been getting more and more research attention. To take the UAVs' performance to the next level, however, they need to be merged with some other technologies like non-orthogonal multiple access (NOMA) and millimeter wave (mmWave

Dian Yu, Mingqiu Wang, Yuan Cao, Izhak Shafran

Knowledge (including structured knowledge such as schema and ontology, and unstructured knowledge such as web corpus) is a critical part of dialog understanding, especially for unseen tasks and domains. Traditionally, such domain-specific knowledge is encoded implicitly into model parameters for the execution of downstream tasks, which makes training ineffic

Partiswari Maharana, Sabita Sahoo

The interest in orthogonal polynomials and random Fourier series in numerous branches of science and a few studies on random Fourier series in orthogonal polynomials inspired us to focus on random Fourier series in Jacobi polynomials. In the present note, an attempt has been made to investigate the stochastic convergence of some random Jacobi series. We look

Dulaji Hidellaarachchi, John Grundy, Rashina Hoda, Ingo Mueller

Context: Requirements engineering (RE) is an important part of Software Engineering (SE), consisting of various human-centric activities that require the frequent collaboration of a variety of roles. Prior research has shown that personality is one such human aspect that has a huge impact on the success of a software project. However, a limited number of emp