October 2022 arXiv papers — page 41

Chutian Ma

In this paper, we study the defocusing cubic Schr\"{o}dinger equation on three dimensional hyperbolic space $\mathbb{H}^3$ with radial initial data in the Sobolev Space $H^s(0<s<1)$. Our main result is that the initial value problem is globally wellposed and scatters for $\frac{15}{16}<s<1$. This is an extension of the work of Staffilani and Yu to the three

N. Yang, I. B. Gorshkov, A. M. Staroletov, A. V. Vasil'ev

The spectrum of a finite group is the set of its element orders. We give an affirmative answer to Problem 20.58(a) from the Kourovka Notebook proving that for every positive integer $k$, the $k$-th direct power of the simple linear group $L_{n}(2)$ is uniquely determined by its spectrum in the class of finite groups provided $n$ is a power of $2$ greater tha

Meihong Wang, Miao Zhang, Zhongzhong Qin, Qiang Zhang

The squeezed cat state, an essential quantum resource, can be used for quantum error correction and slowing decoherence of the optical cat state. However, preparing a squeezed cat state with high generation rate, and effectively manipulating it, remain challenging. In this work, a high-performance all-optical in-line squeezer is developed to prepare a squeez

Ashish Ledalla, Vineet Singh, Deepak Mishra

Finding the similarity between two workload behaviors is helpful in 1. creating proxy workloads 2. characterizing an unknown workload's behavior by matching its behavior against known workloads. In this article, we propose a method to measure the similarity between two workloads using machine learning-based analysis of the performance telemetry data collecte

Kun Zhou, Berrak Sisman, Carlos Busso, Bin Ma

Emotional voice conversion (EVC) traditionally targets the transformation of spoken utterances from one emotional state to another, with previous research mainly focusing on discrete emotion categories. This paper departs from the norm by introducing a novel perspective: a nuanced rendering of mixed emotions and enhancing control over emotional expression. T

Thomas Kesselheim, Marco Molinaro, Sahil Singla

Vector norms play a fundamental role in computer science and optimization, so there is an ongoing effort to generalize existing algorithms to settings beyond $\ell_\infty$ and $\ell_p$ norms. We show that many online and bandit applications for general norms admit good algorithms as long as the norm can be approximated by a function that is ``gradient-stable

Saman Habibi Esfahani

We prove the existence of non-trivial irreducible SU(2)-monopoles with Dirac singularities on any rational homology 3-sphere, equipped with any Riemannian metric, using a gluing construction.

A. Miyazaki, M. Tsuge, H. Hidaka, Y. Nakai

Using a combination of photostimulated desorption and resonance-enhanced multiphoton ionization methods, the behaviors of OH radicals on the surface of interstellar ice analog was monitored at temperatures between 54 and 80 K. The OH number density on the surface of ultraviolet (UV)-irradiated compact amorphous solid water gradually decreased at temperatures

Juan Nathaniel, Levente J. Klein, Campbell D. Watson, Gabrielle Nyirjesy

The global carbon cycle is a key process to understand how our climate is changing. However, monitoring the dynamics is difficult because a high-resolution robust measurement of key state parameters including the aboveground carbon biomass (AGB) is required. Here, we use deep neural network to generate a wall-to-wall map of AGB within the Continental USA (CO

Zhen-Peng Xu

Quantum networks are of high interest nowadays. In short, they describe the distribution of quantum sources represented by edges to different parties represented by nodes in the networks. Bundles of tools have been developed recently to characterize quantum states from the network in the ideal case. However, features of quantum networks in the noisy intermed

George D. J. Phillies

Polymer Physics: Phenomenology of Polymeric Fluid Simulations is a review volume that I am writing. I anticipate it will take a while to complete, so I am supplying individual chapters when each is more-or-less completed. This Chapter considers collective coordinates and collective models for isolated polymer chains. Collective coordinates can be effective t

Marzena Karpinska, Nishant Raj, Katherine Thai, Yixiao Song

While machine translation evaluation metrics based on string overlap (e.g., BLEU) have their limitations, their computations are transparent: the BLEU score assigned to a particular candidate translation can be traced back to the presence or absence of certain words. The operations of newer learned metrics (e.g., BLEURT, COMET), which leverage pretrained lan

Robert Joseph George, Xinwei Yu

The utilization of renewable energy technologies, particularly hydrogen, has seen a boom in interest and has spread throughout the world. Ethanol steam reformation is one of the primary methods capable of producing hydrogen efficiently and reliably. This paper provides an in-depth study of the reformulated system both theoretically and numerically, as well a

Rang Liu, Zhu Bo, Ming Li, Qian Liu

Conventional symbol-level precoding (SLP) designs assume fixed modulations and detection rules at the receivers for simplifying the transmit precoding optimizations, which greatly limits the flexibility of SLP and the communication quality-of-service (QoS). To overcome the performance bottleneck of these approaches, in this letter we propose an end-to-end le

Jiongyu Guo, Defang Chen, Can Wang

Graph neural networks (GNNs) have become one of the most popular research topics in both academia and industry communities for their strong ability in handling irregular graph data. However, large-scale datasets are posing great challenges for deploying GNNs in edge devices with limited resources and model compression techniques have drawn considerable resea

Kanishka Perera

We prove a general perturbation theorem that can be used to obtain pairs of nontrivial solutions of a wide range of local and nonlocal nonhomogeneous elliptic problems. Applications to critical $p$-Laplacian problems, $p$-Laplacian problems with critical Hardy-Sobolev exponents, critical fractional $p$-Laplacian problems, and critical $(p,q)$-Laplacian probl

Zhao Yang, Fengyang Deng, Linxi Han

The behavior of malware threats is gradually increasing, heightened the need for malware detection. However, existing malware detection methods only target at the existing malicious samples, the detection of fresh malicious code and variants of malicious code is limited. In this paper, we propose a novel scheme that detects malware and its variants efficient

Sandip Chajjed, Mohammad Khalil, Dominique Poirel, Chris Pettit

Zimmerman and Weissenburger's flutter margin method is widely used to estimate the aeroelastic coalescence flutter speed. In contrast to aeroelastic decay rates, the flutter margin exhibits monotonic decay with respect to airspeed redering it effective in extrapolating the flutter speed using flight test data conducted at pre-flutter airspeeds. This paper re

Antonino Marciano, Emanuele Zappala, Tommaso Torda, Matteo Lulli

Deep Neural Networks miss a principled model of their operation. A novel framework for supervised learning based on Topological Quantum Field Theory that looks particularly well suited for implementation on quantum processors has been recently explored. We propose using this framework to understand the problem of generalisation in Deep Neural Networks. More

Liu Cao, Abbas Kiani, Amanda Xiang, Kaippallimalil John

5th Generation Mobile Communication Technology (5G) utilizes the Access Traffic Steering, Switching, and Splitting (ATSSS) rule to enable multi-path data transmission, which is currently being standardized. Recently, the 3rd Generation Partnership Project (3GPP) SA1 and SA2 have been working on the multi-path solution for possible improvement from different

Thatchaphol Saranurak, Sorrachai Yingchareonthawornchai

In the vertex connectivity problem, given an undirected $n$-vertex $m$-edge graph $G$, we need to compute the minimum number of vertices that can disconnect $G$ after removing them. This problem is one of the most well-studied graph problems. From 2019, a new line of work [Nanongkai et al.~STOC'19;SODA'20;STOC'21] has used randomized techniques to break the

Shukai Ma, Thomas M. Antonsen, Steven M. Anlage

Electromagnetic (EM) wave scattering in electrically large, irregularly shaped, environments is a common phenomenon. The deterministic, or first principles, study of this process is usually computationally expensive and the results exhibit extreme sensitivity to scattering details. For this reason, the deterministic approach is often dropped in favor of a st

Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle

Practitioners frequently observe that pruning improves model generalization. A long-standing hypothesis based on bias-variance trade-off attributes this generalization improvement to model size reduction. However, recent studies on over-parameterization characterize a new model size regime, in which larger models achieve better generalization. Pruning models

Aditya Balu, Manoj R. Rajanna, Joel Khristy, Fei Xu

Immersogeometric analysis (IMGA) is a geometrically flexible method that enables one to perform multiphysics analysis directly using complex computer-aided design (CAD) models. In this paper, we develop a novel IMGA approach for simulating incompressible and compressible flows around complex geometries represented by point clouds. The point cloud object's ge

Yang Shi

In this paper, we review the properties and representations of the Weyl groups relevant in the study of discrete integrable systems. Previously in \cite{jns4, Shi:19}, properties of Weyl groups of type $ADE$ (known as simply-laced) were shown to be useful in characterizing and establishing relations between different integrable systems. Here we extend the fo

Rodrigo Angelo, Max Wenqiang Xu

We prove that if a polynomial has a root mod $p$ for every large prime $p$, then it has a real root. As an application, we show that the primes can't be covered by finitely many positive definite binary quadratic forms.

Peiyuan Zhang, Wei Lu

Few-shot relation extraction aims to learn to identify the relation between two entities based on very limited training examples. Recent efforts found that textual labels (i.e., relation names and relation descriptions) could be extremely useful for learning class representations, which will benefit the few-shot learning task. However, what is the best way t

Dhruv Vyas, Erik Jorgensen, Yu-Hsiang Wu, Octav Chipara

Adults with mild-to-moderate hearing loss can use over-the-counter hearing aids to treat their hearing loss at a fraction of traditional hearing care costs. These products incorporate self-fitting methods that allow end-users to configure their hearing aids without the help of an audiologist. A self-fitting method helps users configure the gain-frequency res

Luis Verde-Star

We obtain weight functions associated with $q$-linear and $q$-quadratic lattices that yield discrete orthogonality with respect to a quasi-definite moment functional for the Askey-Wilson polynomials and all the polynomial sequences in the q-Askey scheme, with the exception of the continuous $q$-Hermite polynomials.

Zhangjie Peng, Tianshu Li, Cunhua Pan, Xianfu Lei

This paper considers an active reconfigurable intelligent surface (RIS)-aided communication system, where an M-antenna base station (BS) transmits data symbols to a single-antenna user via an N-element active RIS. We use two-timescale channel state information (CSI) in our system, so that the channel estimation overhead and feedback overhead can be decreased

Hiya Gada, Pieter van Goor, Ravi Banavar, Robert Mahony

Observers for systems with Lie group symmetries are an active area of research that is seeing significant impact in a number of practical domains, including aerospace, robotics, and mechatronics. This paper builds on the theory of the recently proposed Equivariant Filter (EqF), which is a general observer design for systems on homogeneous spaces that takes a

Qiuyue Ma, Yingmei Li, Guochun Yang, Yong Liu

Two-dimensional (2D) half-metallic materials that have sparked intense interest in advanced spintronic applications are essential to the developing next-generation nanospintronic devices. Here we have adopted a first-principles calculation method to predict the magnetic properties of intrinsic, Se-doped, and biaxial strain tuning Cr$_2$AsP monolayer. The Jan

Kazuyoshi Yoshimi, Takahiro Misawa, Takao Tsumuraya, Hitoshi Seo

An $ab$ $initio$ investigation of the family of molecular compounds TM$_2$$X$ is conducted, where TM is either TMTSF or TMTTF and $X$ takes centrosymmetric monovalent anions. By deriving the extended Hubbard-type Hamiltonians from first-principles band calculations and evaluating not only the intermolecular transfer integrals but also the Coulomb parameters,

Yinan Fang, Pericles Philippopoulos, Dimitrie Culcer, W. A. Coish

In recent years, hole-spin qubits based on semiconductor quantum dots have advanced at a rapid pace. We first review the main potential advantages of these hole-spin qubits with respect to their electron-spin counterparts, and give a general theoretical framework describing them. The basic features of spin-orbit coupling and hyperfine interaction in the vale

Yunrong Luo, Xuemei Wang, Jia Yi, Wenjuan Li

Exact solutions for spin-orbit (SO) coupled cold atomic systems are very important and rare in physics. In this paper, we propose a simple method of combined modulations to generate the analytic exact solutions for an SO-coupled boson held in a driven double well. For the cases of synchronous combined modulations and the spin-conserving tunneling, we obtain

Dapeng Feng, Yuhua Qi, Shipeng Zhong, Zhiqiang Chen

The burgeoning demand for collaborative robotic systems to execute complex tasks collectively has intensified the research community's focus on advancing simultaneous localization and mapping (SLAM) in a cooperative context. Despite this interest, the scalability and diversity of existing datasets for collaborative trajectories remain limited, especially in

Hu Wang, Hui Li, Sourav S Bhowmick, Zihao Ma

Off-the-shelf RDBMS typically expose only the query execution plan (QEP) of an SQL query, without presenting information about representative alternative query plans (AQPs) considered during plan selection in a user-friendly manner. Providing easy access to representative AQPs is valuable in database education, as it helps learners understand the plan choice

Yanwu Yang, Xutao Guo, Zhikai Chang, Chenfei Ye

Multi-modal neuroimaging technology has greatlly facilitated the efficiency and diagnosis accuracy, which provides complementary information in discovering objective disease biomarkers. Conventional deep learning methods, e.g. convolutional neural networks, overlook relationships between nodes and fail to capture topological properties in graphs. Graph neura

Rutger Campbell, Marc Distel, J. Pascal Gollin, Daniel J. Harvey

A graph class $\mathcal{G}$ has linear growth if, for each graph $G \in \mathcal{G}$ and every positive integer $r$, every subgraph of $G$ with radius at most $r$ contains $O(r)$ vertices. In this paper, we show that every graph class with linear growth has bounded treewidth.

Yingcui Zhao, Lidong Wang, Nan Wang

We study relationships between a set-valued map and its inverse limits about the notion of periodic point set, transitivity, sensitivity and Devaney chaos. Density of periodic point set of a set-valued map and its inverse limits implies each other. Sensitivity of a set-valued map and its inverse limits does not imply each other. Transitivity and Devaney chao

Zhipeng Hu, Wei Zhang, Lincheng Li, Yu Ding

Since Facial Action Unit (AU) annotations require domain expertise, common AU datasets only contain a limited number of subjects. As a result, a crucial challenge for AU detection is addressing identity overfitting. We find that AUs and facial expressions are highly associated, and existing facial expression datasets often contain a large number of identitie

Zeyang Sun, Pengjie Zhang, Fuyu Dong, Ji Yao

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio $R$ between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, the decay rate of gravitational potential and $E_G$ to test gravity. However, simply taking the ratio of the two data sets is biased, sin

Chuanfei Hu, Hang Shao, Bo Dong, Zhe Wang

Representing the spatial properties of facial attributes is a vital challenge for facial attribute recognition (FAR). Recent advances have achieved the reliable performances for FAR, benefiting from the description of spatial properties via extra prior information. However, the extra prior information might not be always available, resulting in the restricte

Jianhao Shen, Chenguang Wang, Ye Yuan, Jiawei Han

This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM parameters, we only tune a few new parameters while keeping the original LM parameters fixed. We establish this via reformulating KG completion as a "fill-in-the-blank" task, and

Jiazheng Zhou, Xiao Xu, Huijue Wu, Jinlin Wang

Phase evolution during the selenization is crucial for high-quality kesterite Cu2ZnSn(S, Se)4 (CZTSSe) absorbers and efficient solar cells. Herein, we regulate kinetic process of phase evolution from Cu+-Sn4+-MOE (MOE: 2-methoxyethanol) system by precisely controlling positive chamber pressure. We found that, at the heating-up stage, Se vapor concentration i

Andrei Nomerotski, Matthew Chekhlov, Denis Dolzhenko, Rene Glazenborg

We describe a fast data-driven optical camera, Tpx3Cam, with nanosecond scale timing resolution and 80 Mpixel/sec throughput. After the addition of intensifier, the camera is single photon sensitive with quantum efficiency determined primarily by the intensifier photocathode. The single photon performance of the camera was characterized with results on the g

Jonathan Li, Rohan Bhambhoria, Xiaodan Zhu

Seeking legal advice is often expensive. Recent advancements in machine learning for solving complex problems can be leveraged to help make legal services more accessible to the public. However, real-life applications encounter significant challenges. State-of-the-art language models are growing increasingly large, making parameter-efficient learning increas

Rong Ma, Eric D. Sun, James Zou

Dimension reduction and data visualization aim to project a high-dimensional dataset to a low-dimensional space while capturing the intrinsic structures in the data. It is an indispensable part of modern data science, and many dimensional reduction and visualization algorithms have been developed. However, different algorithms have their own strengths and we

Haibin Zheng, Haiyang Xiong, Jinyin Chen, Haonan Ma

Graph neural network (GNN) with a powerful representation capability has been widely applied to various areas, such as biological gene prediction, social recommendation, etc. Recent works have exposed that GNN is vulnerable to the backdoor attack, i.e., models trained with maliciously crafted training samples are easily fooled by patched samples. Most of the

Ali Garjani, Atoosa Malemir Chegini, Mohammadreza Salehi, Alireza Tabibzadeh

The influenza virus hemagglutinin is an important part of the virus attachment to the host cells. The hemagglutinin proteins are one of the genetic regions of the virus with a high potential for mutations. Due to the importance of predicting mutations in producing effective and low-cost vaccines, solutions that attempt to approach this problem have recently

Junfan Liu, Zonglin Gu, Mengru Duan, Pei Li

Ultrahigh water permeance, together with a high rejection rate through nanofiltration and separation membranes1,2, is crucial but still challenging for multivalent ion sieving in water treatment processes of desalination, separation, and purification3,4. To date, no theory or equation has ever been quantitatively clarified the mechanism of water permeance in

Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia

We study the following fundamental hypothesis testing problem, which we term Gaussian mean testing. Given i.i.d. samples from a distribution $p$ on $\mathbb{R}^d$, the task is to distinguish, with high probability, between the following cases: (i) $p$ is the standard Gaussian distribution, $\mathcal{N}(0,I_d)$, and (ii) $p$ is a Gaussian $\mathcal{N}(\mu,\Si

Chien Thai, Viet Tran, Minh Bui, Huong Ninh

Human head pose estimation is an essential problem in facial analysis in recent years that has a lot of computer vision applications such as gaze estimation, virtual reality, and driver assistance. Because of the importance of the head pose estimation problem, it is necessary to design a compact model to resolve this task in order to reduce the computational

Jian Wang, Miaomiao Zhang

Deformable shapes provide important and complex geometric features of objects presented in images. However, such information is oftentimes missing or underutilized as implicit knowledge in many image analysis tasks. This paper presents Geo-SIC, the first deep learning model to learn deformable shapes in a deformation space for an improved performance of imag

Jacob Askew, Daniel Reardon, Ryan Shannon

Long-term studies of binary millisecond pulsars (MSPs) provide precise tests of strong-field gravity and can be used to measure neutron-star masses. PSR~J1909$-$3744, a binary MSP has been the subject of several pulsar timing analyses. The edge-on orbit enables measurement of its mass using the Shapiro delay; however, there is degeneracy in the sense of the

Ankur Handa, Arthur Allshire, Viktor Makoviychuk, Aleksei Petrenko

Recent work has demonstrated the ability of deep reinforcement learning (RL) algorithms to learn complex robotic behaviours in simulation, including in the domain of multi-fingered manipulation. However, such models can be challenging to transfer to the real world due to the gap between simulation and reality. In this paper, we present our techniques to trai

Hung-Ting Chen, Michael J. Q. Zhang, Eunsol Choi

Question answering models can use rich knowledge sources -- up to one hundred retrieved passages and parametric knowledge in the large-scale language model (LM). Prior work assumes information in such knowledge sources is consistent with each other, paying little attention to how models blend information stored in their LM parameters with that from retrieved

Greg Taylor

This paper is concerned with modelling multiple claim arrays that are subject to one or more common shocks. It uses a structure that involves very general forms both idiosyncratic and common shock components of cell means. The dependencies between arrays, or between cells within an array, generated by the shocks are also of very general form. All of this app

Jessica E. Thorne, Aaron S. G. Robotham, Sabine Bellstedt, Luke J. M. Davies

Gas-phase metallicities of galaxies are typically measured through auroral or nebular emission lines, but metallicity also leaves an imprint on the overall spectral energy distribution (SED) of a galaxy and can be estimated through SED fitting. We use the ProSpect SED fitting code with a flexible parametric star formation history and an evolving metallicity

Jing Yuan, Shaojie Tang

In this paper, we study the adaptive submodular cover problem under the worst-case setting. This problem generalizes many previously studied problems, namely, the pool-based active learning and the stochastic submodular set cover. The input of our problem is a set of items (e.g., medical tests) and each item has a random state (e.g., the outcome of a medical

Peng Shi, Rui Zhang, He Bai, Jimmy Lin

In-context learning using large language models has recently shown surprising results for semantic parsing tasks such as Text-to-SQL translation. Prompting GPT-3 or Codex using several examples of question-SQL pairs can produce excellent results, comparable to state-of-the-art finetuning-based models. However, existing work primarily focuses on English datas

Diego Martinez-Taboada, Dino Sejdinovic

The problem of sequentially maximizing the expectation of a function seeks to maximize the expected value of a function of interest without having direct control on its features. Instead, the distribution of such features depends on a given context and an action taken by an agent. In contrast to Bayesian optimization, the arguments of the function are not un

T. Chetry, L. El Fassi, W. K. Brooks, R. Dupré

We report results of $\Lambda$ hyperon production in semi-inclusive deep-inelastic scattering off deuterium, carbon, iron, and lead targets obtained with the CLAS detector and the Continuous Electron Beam Accelerator Facility 5.014~GeV electron beam. These results represent the first measurements of the $\Lambda$ multiplicity ratio and transverse momentum br

Andrés Aldana, Andrea Falcón-Cortés, Hernán Larralde

The costs and impacts of government corruption range from impairing a country's economic growth to affecting its citizens' well-being and safety. Public contracting between government dependencies and private sector instances, referred to as public procurement, is a fertile land of opportunity for corrupt practices, generating substantial monetary losses wor

Quan Wang, Yiling Huang, Han Lu, Guanlong Zhao

While recent research advances in speaker diarization mostly focus on improving the quality of diarization results, there is also an increasing interest in improving the efficiency of diarization systems. In this paper, we demonstrate that a multi-stage clustering strategy that uses different clustering algorithms for input of different lengths can address m

Ahamed Mustak, Hongbin Ma, Lepeng Song, Ying Jin

In agriculture, crops need to apply pesticide spraying flow control precisely to reduce costs, protect the environment, and increase yield production. Although there have several variable control methods for spraying flow control because indirect control flow techniques and having a slow response could cause inaccuracy and mismanagement, also noted that thos

Jiang-Yuan Lian, Xia Li, Tian-Yu Ye

In this paper, we put forward a multi-party quantum private comparison (MQPC) protocol with two semi-honest third parties (TPs) by adopting d-dimensional Bell states, which can judge the size relationship of private integers from more than two users within one execution of protocol. Each TP is permitted to misbehave on her own but cannot collude with others.

Konstantinos Pelechrinis

Implicit biases occur automatically and unintentionally and are particularly present when we have to make split second decisions. One such situations appears in refereeing, where referees have to make an instantaneous decision on a potential violation. In this work we revisit and extend some of the existing work on implicit biases in refereeing. In particula

Anwaar Ulhaq

Due to the numerous potential applications in visual surveillance and nighttime driving, recognizing human action in low-light conditions remains a difficult problem in computer vision. Existing methods separate action recognition and dark enhancement into two distinct steps to accomplish this task. However, isolating the recognition and enhancement impedes

Junyi Li, Heng Huang

Recommender systems are widely used in industry to improve user experience. Despite great success, they have recently been criticized for collecting private user data. Federated Learning (FL) is a new paradigm for learning on distributed data without direct data sharing. Therefore, Federated Recommender (FedRec) systems are proposed to mitigate privacy conce

Honglei Feng, Gang Shi, Dayu Yan, Yong Li

All van der Waals (vdW) Fe3GeTe2/Cr2Ge2Te6/graphite magnetic heterojunctions have been fabricated via mechanical exfoliation and stacking, and their magnetotransport properties are studied in detail. At low bias voltages large negative junction magnetoresistances have been observed and are attributed to spin-conserving tunneling transport across the insulati

Anthony Corso, Yizheng Wang, Markus Zechner, Jef Caers

Geological carbon capture and sequestration (CCS), where CO$_2$ is stored in subsurface formations, is a promising and scalable approach for reducing global emissions. However, if done incorrectly, it may lead to earthquakes and leakage of CO$_2$ back to the surface, harming both humans and the environment. These risks are exacerbated by the large amount of

Gholamreza Hajargasht

The Fisher and GEKS are celebrated as ideal bilateral and multilateral indexes due to their superior axiomatic and econ-theoretic properties. The Fisher index is the main index used for constructing CPI by statistical agencies and the GEKS is the index used for compiling PPPs in World Bank's International Comparison Program (ICP). Despite such a high status

Wissam Kontar, Soyoung Ahn

This paper proposes a methodology to estimate uncertainty in automated vehicle (AV) dynamics in real time via Bayesian inference. Based on the estimated uncertainty, the method aims to continuously monitor the car-following (CF) performance of the AV to support strategic actions to maintain a desired performance. Our methodology consists of three sequential

Vinesh Sridhar, Erica Blum, Jonathan Katz

The HashGraph Protocol is a Byzantine fault tolerant atomic broadcast protocol. Its novel use of locally stored metadata allows parties to recover a consistent ordering of their log just by examining their local data, removing the need for a voting protocol. Our paper's first contribution is to present a rewritten proof of security for the HashGraph Protocol

Yaoyi Bai, Songyin Wu, Zheng Zeng, Beibei Wang

Parametric Bidirectional Scattering Distribution Functions (BSDFs) are pervasively used because of their flexibility to represent a large variety of material appearances by simply tuning the parameters. While efficient evaluation of parametric BSDFs has been well-studied, high-quality importance sampling techniques for parametric BSDFs are still scarce. Exis

Ziyu Huang, Thomas Michael Keller, Shane Kissinger, Wen Plotnick

In this paper we continue the study of prime graphs of finite solvable groups. The prime graph, or Gruenberg-Kegel graph, of a finite group G has vertices consisting of the prime divisors of the order of G and an edge from primes p to q if and only if G contains an element of order pq. Since the discovery of a simple, purely graph theoretical characterizatio

Chang-Yeon Chough

We show that almost perfect complexes of commutative ring spectra satisfy excision and $v$-descent. These results generalize Milnor excision for perfect complexes of ordinary commutative rings and $v$-descent for almost perfect complexes of locally noetherian derived stacks by Halpern-Leistner and Preygel, respectively.

Gyuwan Kim, Jinhyuk Lee, Barlas Oguz, Wenhan Xiong

Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search. However, these procedures are often misaligned in that training objectives do not exactly reflect the retrieval scenario at inference time. In this paper, we explore how the gap between training and in

Diogo Boito, Maarten Golterman, Kim Maltman, Santiago Peris

We develop a number of sum rules comparing spectral integrals involving judiciously chosen weights to integrals over the corresponding Euclidean two-point function. The applications we have in mind are to the hadronic vacuum polarization that determines the most important hadronic correction $a_\mu^{\rm HVP}$ to the muon anomalous magnetic moment. First, we

Ningna Wang, Bin Wang, Wenping Wang, Xiaohu Guo

We propose a novel framework for computing the medial axis transform of 3D shapes while preserving their medial features via restricted power diagram (RPD). Medial features, including external features such as the sharp edges and corners of the input mesh surface and internal features such as the seams and junctions of medial axis, are important shape descri

Angus McAndrew

A theorem of Grothendieck tells us that if the Galois action on the Tate module of an abelian variety factors through a smaller field, then the abelian variety, up to isogeny and finite extension of the base, is itself defined over the smaller field. Inspired by this, we give a Galois descent datum for a motive $H$ over a field by asking that the Galois acti

Praful Gagrani, Eric Smith

We first derive the Hamilton-Jacobi theory underlying continuous-time Markov processes, and then use the construction to develop a variational algorithm for estimating escape (least improbable or first passage) paths for a generic stochastic chemical reaction network that exhibits multiple fixed points. The design of our algorithm is such that it is independ

Tze Yeung Mathew Yu, Brad Hansen, Yasuhiro Hasegawa

We present a physically motivated model for the manner in which a stellar magnetic field sculpts the inner edge of a protoplanetary disk, and examine the consequence for the migration and stopping of sub-Neptune and super-Earth planets. This model incorporates a transition zone exterior to the inner truncation of the disk, where the surface density profile i

Peng Xu, Mostofa Patwary, Shrimai Prabhumoye, Virginia Adams

Parameter efficient learning methods (PERMs) have recently gained significant attention as they provide an efficient way for pre-trained language models (PLMs) to adapt to a downstream task. However, these conclusions are mostly drawn from in-domain evaluations over the full training set. In this paper, we present comparisons between PERMs and finetuning fro

Xuanyu Fang, Yunzhu Pan, Hongjun Wu

The technological advancement in data analysis and sensor technology has contributed to a growth in knowledge of the surrounding environments. Feng Shui, the Chinese philosophy of evaluating a certain environment and how it influences human well-being, can only be determined by self-claimed specialists for the past thousands of years. We developed a device a

Yoshihiro Shirai

Dynamic spectral risk measures define a claim's valuation bounds as supremum and infimum of expectations of the claim's payoff over a dominated set of measures. The measures at which such extrema are attained are called extreme measures. We determine explicit expressions for their Radon-Nykodim derivatives with respect to the common dominating measure. Based

Sergio Demian Lerner, Federico Jinich, Diego Masini, Shreemoy Mishra

Uncontrolled growth of blockchain state can adversely affect client performance, decentralization and security. Previous attempts to introduce duration-based state storage pricing or 'storage rent' in Ethereum have stalled, partly because of complexity. We present a new approach with finer granularity to "spread" rent payments across peers. Our proposal shif

Tuhin Chakrabarty, Vishakh Padmakumar, He He

Recent work in training large language models (LLMs) to follow natural language instructions has opened up exciting opportunities for natural language interface design. Building on the prior success of LLMs in the realm of computer-assisted creativity, we aim to study if LLMs can improve the quality of user-generated content through collaboration. We present

Prithul Sarker, Sushmita Sarker, George Bebis, Alireza Tavakkoli

Deep learning has made a breakthrough in medical image segmentation in recent years due to its ability to extract high-level features without the need for prior knowledge. In this context, U-Net is one of the most advanced medical image segmentation models, with promising results in mammography. Despite its excellent overall performance in segmenting multimo

Clary Rodriguez-Cruz, Mehdi Molaei, Amruthesh Thirumalaiswamy, Klebert Feitosa

Many soft and biological materials display so-called 'soft glassy' dynamics; their constituents undergo anomalous random motions and complex cooperative rearrangements. A recent simulation model of one soft glassy material, a coarsening foam, suggested that the random motions of its bubbles are due to the system configuration moving over a fractal energy lan

Yugo Abe, Takeo Inami, Keisuke Izumi

We obtain the matter-graviton scattering amplitude in the gravitational theory of quadratic curvature, which has $R_{\mu\nu}^2$ term in the action. Unitarity bound is not satisfied because of the existence of negative norm states, while an analog of unitarity bound for $S$-matrix unitarity holds due to the cancelation among the positive norm states and negat

Quy C. Tran, Nam H. Nguyen, Thach A. Nguyen, Trung Phan

We re-examine a familiar problem given in introductory physics courses, about determining the induced charge distribution on an uncharged ``infinitely-large'' conducting plate when placing parallel to it a uniform charged dielectric plate of the same size. We show that, no matter how large the plates are, the edge effect will always be strong enough to influ

Jean-Rémy Conti, Nathan Noiry, Vincent Despiegel, Stéphane Gentric

In spite of the high performance and reliability of deep learning algorithms in a wide range of everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against specific subgroups of the population (e.g. gender, ethnicity). This urges the practitioner to develop fair systems with a uniform/comparable perform

Yingqi Tang, Yunfei Ma, Yanmei Liu

Over the past few of years, we have witnessed increasing interests in the use cases of multi-path QUIC from both industry and academia. For example, Alibaba deployed XLINK, a QoE-driven multi-path QUIC solution, in Taobao short video and showed benefits in both reduced tail latency and video re-buffering. For the time being, the multi-path QUIC protocol is i

Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi

Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy risks in machine learning. However, exactly how small the privacy parameter $\epsilon$ needs to be to protect against certain privacy risks in practice is still not well-understood. In this work, we study data reconstruction attacks for discrete data and analyze it

A. Finn Hackett, Joshua Rowe, Markus Alexander Kuppe

Beyond implementation correctness of a distributed system, it is equally important to understand exactly what users should expect to see from that system. Even if the system itself works as designed, insufficient understanding of its user-visible semantics can cause bugs in its dependencies. By focusing a formal specification effort on precisely defining the

Ying Yuan, Giovanni Apruzzese, Mauro Conti

Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks that break every ML model, or defenses that withstand most attacks. Unfortunately, little consideration is given to the actual feasibility of the attack or the defense. Moreover, adversarial samples are often crafted in the "feature-space", making the corresponding eva

Bartosz Grabowski, Maciej Ziaja, Michal Kawulok, Nicolas Longépé

Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can help to reduce the amount of data to downlink by pruning the cloudy areas, or to make a satellite more autonomous through data-driven acquisition re-scheduling of the cloudy areas.

Xiaobing Feng, Huicong Zhong

In this paper, we propose and study a fast multilevel dimension iteration (MDI) algorithm for computing arbitrary $d$-dimensional integrals based on tensor product approximations. It reduces the computational complexity (in terms of the CPU time) of a tensor product method from the exponential order $O(N^d)$ to the polynomial order {\color{black} $O(d^3N^2)$