May 2022 arXiv papers — page 19

Mehrdad Nasernejad, Somayeh Bandari, Leslie G. Roberts

In this paper, we first give some sufficient criteria for normality of monomial ideals. As applications, we show that closed neighborhood ideals of complete bipartite graphs are normal, and hence satisfy the (strong) persistence property. We also prove that dominating ideals of complete bipartite graphs are nearly normally torsion-free. In addition, we show

Jiaxin Yu, Deqing Yang, Shuyu Tian

Compared with traditional sentence-level relation extraction, document-level relation extraction is a more challenging task where an entity in a document may be mentioned multiple times and associated with multiple relations. However, most methods of document-level relation extraction do not distinguish between mention-level features and entity-level feature

Marcello Ortaggio

We study four-dimensional Einstein-Maxwell fields for which any higher-order corrections to the field equations effectively reduces to just a rescaling of the gravitational and the cosmological constant. These configurations are thus simultaneous solutions of (virtually) any modified theory of gravity coupled (possibly non-minimally) to any electrodynamics.

Yu. M. Poluektov, A. A. Soroka

For a system of a large number of Bose particles, a chain of coupled equations for the averages of field operators is obtained. In the approximation where only the averages of one field operator and the averages of products of two operators at zero temperature are taken into account, there is derived a closed system of dynamic equations. Taking into account

Thomas Bonis, Frédéric Chazal, Bertrand Michel, Wojciech Reise

We consider a signal composed of several periods of a periodic function, of which we observe a noisy reparametrisation. The phase estimation problem consists of finding that reparametrisation, and, in particular, the number of observed periods. Existing methods are well-suited to the setting where the periodic function is known, or at least, simple. We consi

Mina Seifi, Ali Soltanmanesh, Afshin Shafiee

Recently, it has been suggested that ion channel selectivity filter may exhibit quantum coherence, which may be appropriate to explain ion selection and conduction processes. Potassium channels play a vital role in many physiological processes. One of their main physiological functions is the efficient and highly selective transfer of K+ ions through the mem

Davide A. Bignamini, Simone Ferrari

We prove Schauder type estimates for solutions of stationary and evolution equations driven by weak generators of transition semigroups associated to a semilinear stochastic partial differential equations with values in a separable Hilbert space.

Kei Ikegami, Atsushi Iwasaki, Akira Matsushita, Kyohei Okumura

Cap-based regulations are widely used to address distributional disparities in matching markets, but their efficiency relative to alternative instruments such as subsidies remains poorly understood. This paper develops a framework for evaluating policy interventions by incorporating regional constraints into a transferable utility matching model. We show tha

Ruixin Li, Viktor M. Kalita, Hennagii Fylymonov, Wei Xu

Recently, the possibility of exploiting the phenomenon of spin transition (ST) has been intensively investigated, therefore, it is particularly important to study the behavior of ST under various stimuli. Here, the shape and content of the intermediate phase of ST in Hoffmann-like compounds [Fe(Fpz)2M(CN)4](M = Pt, Pd) under external stimuli are studied. For

Sebastian Gwizdek

Sequences of $n$-th order bidual Banach spaces, called tower systems and their direct and inverse limits are considered. Motivated by recent applications in uniform algebras, we introduce two functors: $\textrm{Dir}$ and $\textrm{Inv}$ assigning to Banach spaces (and to bounded linear operators) some new Banach spaces and operators. Of particular interest is

Leiming Chen, Chiu Fan Lee, Ananyo Maitra, John Toner

We study the moving phase of two-dimensional (2D) incompressible polar active fluids in the presence of both quenched and annealed disorder. We show that long-range polar order persists even in this defect-ridden two-dimensional system. We obtain the large-distance, long-time scaling laws of the velocity fluctuations using three distinct dynamic renormalizat

Devansh Gupta, Aditya Saini, Drishti Bhasin, Sarthak Bhagat

Retrieving facial images from attributes plays a vital role in various systems such as face recognition and suspect identification. Compared to other image retrieval tasks, facial image retrieval is more challenging due to the high subjectivity involved in describing a person's facial features. Existing methods do so by comparing specific characteristics fro

Robert Fleischer

CP violation offers a powerful probe for testing the Standard Model. In this endeavour, non-leptonic decays of B mesons play a key role. I will discuss benchmark decays and puzzling patterns emerging from theoretical analyses of the current data.

Orhan Donmez, Fatih Dogan, Tugba Sahin

Understanding the physical structures of the accreated matter very close to the black hole in quasars and active galactic nucleus (AGNs) is an important milestone to constrain the activities occurring in their centers. In this paper, we numerically investigate the effects of the asymptotic velocities on the physical structures of the accretion disk around th

A. D. Patel, A. Amardas, N. Ramasubramanian

A Multi-dipole line cusp configured Plasma Device (MPD) having six electromagnets with embedded Vacoflux-50 as a core material has been operated with a capability to experimentally control the field-free region, the radial profile of magnetic field, and pole magnetic field by changing magnet current. For the validation of multi-dipole line cusp magnetic fiel

Yaochen Zhu, Xubin Ren, Jing Yi, Zhenzhong Chen

Traditional content-based tag recommender systems directly learn the association between user-generated content (UGC) and tags based on collected UGC-tag pairs. However, since a UGC uploader simultaneously creates the UGC and selects the corresponding tags, her personal preference inevitably biases the tag selections, which prevents these recommenders from l

Andreas Papaefstathiou, Tania Robens, Graham White

We consider an extension of the scalar sector of the Standard Model (SM) by an additional gauge singlet, which mixes with a part of the SM-like Higgs doublet. Within this model, parameter-space regions exist that can lead to a strong first-order electro-weak phase transition, a necessary condition for electro-weak baryogenesis. We discuss how such regions of

Tsuyoshi Tokuoka, Akio K. Inoue, Takuya Hashimoto, Richard S. Ellis

We present new observations with the Atacama Large Millimeter/submillimeter Array for a gravitationally-lensed galaxy at $z=9.1$, MACS1149-JD1. [O III] 88-$\mu$m emission is detected at 10$\sigma$ with a spatial resolution of $\sim0.3$ kpc in the source plane, enabling the most distant morpho-kinematic study of a galaxy. The [O III] emission is distributed s

Kai Hu, Yu Liu, Renhe Liu, Wei Lu

Facial semantic guidance (including facial landmarks, facial heatmaps, and facial parsing maps) and facial generative adversarial networks (GAN) prior have been widely used in blind face restoration (BFR) in recent years. Although existing BFR methods have achieved good performance in ordinary cases, these solutions have limited resilience when applied to fa

Marziyeh Beygi Khormaei, Ashkan Nikseresht, Shohreh Namazi

In this paper, we study some repeated-root two-dimensional cyclic and constacyclic codes over a finite field $F=\mathbb{F}_q$. We obtain the generator matrices and generator polynomials of these codes and their duals. We also investigate when such codes are self-dual. Moreover, we prove that if there exists an asymptotically good family of one-sided repeated

Pranav Jeevan, Kavitha Viswanathan, Anandu A S, Amit Sethi

We propose a novel neural architecture for computer vision -- WaveMix -- that is resource-efficient and yet generalizable and scalable. While using fewer trainable parameters, GPU RAM, and computations, WaveMix networks achieve comparable or better accuracy than the state-of-the-art convolutional neural networks, vision transformers, and token mixers for sev

Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour

Neural code intelligence (CI) models are opaque black-boxes and offer little insight on the features they use in making predictions. This opacity may lead to distrust in their prediction and hamper their wider adoption in safety-critical applications. Recently, input program reduction techniques have been proposed to identify key features in the input progra

Kanak Kanti Baishya, Sanjib Kr Jana, Manoj Ray Bakshi, Malay Pain

In this work, we have introduced and studied some basic geometric properties of extended weakly symmetric spaces. After classification of this structure we have also established the existence of such a space by presenting a non-trivial example.

Maben Rabi

The step and impulse responses of a proper, rational transfer function are well-behaved analytic functions. We prove that such a response cannot have an inflection point such that the tangent at that point is parallel to the time-axis. Hence when a step or impulse response of a finite-dimensional LTI system crosses any given level, that crossing must be tran

Nuno Saavedra, João F. Ferreira

Infrastructure as Code (IaC) is the process of managing IT infrastructure via programmable configuration files (also called IaC scripts). Like other software artifacts, IaC scripts may contain security smells, which are coding patterns that can result in security weaknesses. Automated analysis tools to detect security smells in IaC scripts exist, but they fo

J. Rozehnal, M. Brož, D. Nesvorný, K. J. Walsh

We studied impact processes by means of smoothed-particle hydrodynamics (SPH) simulations. The method was applied to modeling formation of main-belt families during the cometary bombardment (either early or late, ${\sim}\,3.85\,{\rm Gy}$ ago). If asteroids were bombarded by comets, as predicted by the Nice model, hundreds of asteroid families (catastrophic d

Shunsaku Nagasawa, Tomoko Kawate, Noriyuki Narukage, Tadayuki Takahashi

We conduct a wide-band X-ray spectral analysis in the energy range of 1.5-100 keV to study the time evolution of the M7.6 class flare of 2016 July 23, with the Miniature X-ray Solar Spectrometer (MinXSS) CubeSat and the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) spacecraft. With the combination of MinXSS for soft X-rays and RHESSI for hard

Zhongyu Huang, Yingheng Wang, Chaozhuo Li, Huiguang He

The invariance to permutations of the adjacency matrix, i.e., graph isomorphism, is an overarching requirement for Graph Neural Networks (GNNs). Conventionally, this prerequisite can be satisfied by the invariant operations over node permutations when aggregating messages. However, such an invariant manner may ignore the relationships among neighboring nodes

Jie Dong, Hailong Peng, Hui Wang, Yang Tong

Experimentally resolving atomic-scale structural changes of a deformed glass remains challenging owing to the disordered nature of glass structure. Here, we show that the structural anisotropy emerges as a general hallmark for different types of glasses (metallic glasses, oxide glass, amorphous selenium, and polymer glass) after thermo-mechanical deformation

Bastian Telgen, Ole Sigmund, Dennis M. Kochmann

We introduce a computational framework for the topology optimization of cellular structures with spatially varying architecture, which is applied to functionally graded truss lattices under quasistatic loading. We make use of a first-order homogenization approach, which replaces the discrete truss by an effective continuum description to be treated by finite

A Mani, Sushmita Mitra

Rational approximations are introduced and studied in granular graded rough sets and generalizations thereof by the first author in recent research papers. The concept of rationality is determined by related ontologies and coherence between granularity, mereology and approximations in the context. In addition, a framework for rational approximations is intro

Tobias Frohoff-Hülsmann, Max Philipp Holl, Edgar Knobloch, Svetlana V. Gurevich

We demonstrate that several nonvariational continuum models commonly used to describe active matter as well as other active systems exhibit nongeneric behavior: each model supports asymmetric but stationary localized states even in the absence of pinning at heterogeneities. Moreover, such states only begin to drift following a drift-transcritical bifurcation

Ulisse Munari, Fulvio Tabacco

RS Oph has persistently displayed flickering at optical wavelengths when observed away from its repeating nova outbursts. During the 2006 eruption the flickering disappeared, and this repeated during the recent 2021 event. We have been monitoring RS Oph looking for the reappearance of flickering at B-band following the 2021 outburst. The flickering was still

Kamolphat Intawong, Noboru Ito

Topological polymers have various topological types, and they are expressed by graphs. However, the Jones polynomial, we have a difficulty to compute it; computational time is growing exponentially with respect to the crossing number. The simplest Vassiliev invariant is the linking number and thus we will seek a next simple one is as the Milnor's triple link

Jing Jiang, Weihong Deng

In this paper, we aim to improve the performance of in-the-wild Facial Expression Recognition (FER) by exploiting semi-supervised learning. Large-scale labeled data and deep learning methods have greatly improved the performance of image recognition. However, the performance of FER is still not ideal due to the lack of training data and incorrect annotations

Nickos Papadatos

George R. Terrell (1983, {Ann. Probab., vol. 11(3), pp. 823--826) showed that the Pearson coefficient of correlation of an ordered pair from a random sample of size two is at most one-half, and the equality is attained only for rectangular (uniform over some interval) distributions. In the present note it is proved that the same is true for the discrete case

Liming Ling, Xiaoen Zhang

Multi-soliton and high-order soliton solutions are two type of famous ones in the integrable focusing nonlinear Schr\"odinger equation. The dynamics of multi-soliton was well known to us since 70s of the last century by the determinant analysis. However, there is few progress on the high-order solitons. In this work, we would like to analyze the large order

Seyed A. Esmaeili, Sharmila Duppala, John P. Dickerson, Brian Brubach

Numerous algorithms have been produced for the fundamental problem of clustering under many different notions of fairness. Perhaps the most common family of notions currently studied is group fairness, in which proportional group representation is ensured in every cluster. We extend this direction by considering the downstream application of clustering and h

Tatiana V. Nizkaya, Evgeny S. Asmolov, Olga I. Vinogradova

Self-propelling particles or microswimmers have opened a new field of investigation with both fundamental and practical perspectives. They represent very convenient model objects for experimental studies of active matter, and have implications in nano-robotics, drug delivery, and more. Here, we summarize recent advances in theoretical description of the self

Naoki Kubota

We consider the simple random walk on the $d$-dimensional lattice $\mathbb{Z}^d$ ($d \geq 1$), traveling in potentials which are Bernoulli distributed. The so-called Lyapunov exponent describes the cost of traveling for the simple random walk in the potential, and it is known that the Lyapunov exponent is strictly monotone in the parameter of the Bernoulli d

Naoki Kubota, Masato Takei

We consider Bernoulli first-passage percolation on the $d$-dimensional hypercubic lattice with $d \geq 2$. The passage time of edge $e$ is $0$ with probability $p$ and $1$ with probability $1-p$, independently of each other. Let $p_c$ be the critical probability for percolation of edges with passage time $0$. When $0\leq p<p_c$, there exists a nonrandom, non

Vincent M. Rossi, Katherine C. Davidson, Lauren E. Moore

In order to image live cells for prolonged periods of time, an Arduino-based, low-cost imaging incubator was constructed. The imaging incubator keeps cells viable by controlling for temperature and CO2 in order to maintain physiological conditions for cells during imaging. All devices and parts employed in the build were typical maker-type components in orde

Yangyang Xu, Xiangtai Li, Haobo Yuan, Yibo Yang

Previous multi-task dense prediction studies developed complex pipelines such as multi-modal distillations in multiple stages or searching for task relational contexts for each task. The core insight beyond these methods is to maximize the mutual effects of each task. Inspired by the recent query-based Transformers, we propose a simple pipeline named Multi-Q

Byoung S. Ham

The heart of quantum mechanics is quantum superposition between orthogonal bases of a single particle. In the particle nature of quantum mechanics, quantum superposition is represented by probability amplitudes between mutually exclusive natures such as orthogonal polarization bases. The delayed-choice quantum eraser is for the post-determination of the phot

Amey Gohil, Manan Tayal, Tezan Sahu, Vyankatesh Sawalpurkar

The Traveling Salesman Problem (often called TSP) is a classic algorithmic problem in the field of computer science and operations research. It is an NP-Hard problem focused on optimization. TSP has several applications even in its purest formulation, such as planning, logistics, and the manufacture of microchips; and can be slightly modified to appear as a

Boris O. Volkov

An infinite dimensional Laplacian defined as the Ces\'aro mean of the second order directional derivatives on manifold is considered. This Laplacian is parameterized by the choice of a curve in the group of orthogonal rotations. It is shown that, under certain conditions on the curve, this operator is related to instantons on a 4-dimensional manifold.

Ilya Chevyrev

We consider a non-linear heat equation $\partial_t u = \Delta u + B(u,Du)+P(u)$ posed on the $d$-dimensional torus, where $P$ is a polynomial of degree at most $3$ and $B$ is a bilinear map that is not a total derivative. We show that, if the initial condition $u_0$ is taken from a sequence of smooth Gaussian fields with a specified covariance, then $u$ exhi

Shuang Li, Ke Li, Wei Li

Constraint violation has been a building block to design evolutionary multi-objective optimization algorithms for solving constrained multi-objective optimization problems. However, it is not uncommon that the constraint violation is hardly approachable in real-world black-box optimization scenarios. It is unclear that whether the existing constrained evolut

Rongchang Liu, Kening Lu

We consider the incompressible 2D Navier-Stokes equations on the torus driven by a deterministic time quasi-periodic force and a noise that is white in time and degenerate in Fourier space. We show that the asymptotic statistical behavior is characterized by a quasi-periodic invariant measure that exponentially attracts the law of all solutions. The result i

Kundan Sai Prabhu Thota, Sungho Suh, Bo Zhou, Paul Lukowicz

The estimation of 3D human body shape and clothing measurements is crucial for virtual try-on and size recommendation problems in the fashion industry but has always been a challenging problem due to several conditions, such as lack of publicly available realistic datasets, ambiguity in multiple camera resolutions, and the undefinable human shape space. Exis

L. V. Begunovich, M. M. Korshunov, S. G. Ovchinnikov

Electronic structure and magnetic properties of Fe$_3$Se$_4$ are calculated using the density functional approach. Due to the metallic properties, magnetic moments of the iron atoms in two nonequivalent positions in the unit cell are different from ionic values for Fe$^{3+}$ and Fe$^{2+}$ and are equal to $M_1=2.071 \mu_B$ and $M_2=-2.042 \mu_B$, making the

Christopher W. F. Parsonson, Alexandre Laterre, Thomas D. Barrett

Combinatorial optimisation problems framed as mixed integer linear programmes (MILPs) are ubiquitous across a range of real-world applications. The canonical branch-and-bound algorithm seeks to exactly solve MILPs by constructing a search tree of increasingly constrained sub-problems. In practice, its solving time performance is dependent on heuristics, such

Renzhi Chen, Ke Li

Data-driven evolutionary multi-objective optimization (EMO) has been recognized as an effective approach for multi-objective optimization problems with expensive objective functions. The current research is mainly developed for problems with a 'regular' triangle-like Pareto-optimal front (PF), whereas the performance can significantly deteriorate when the PF

João Araújo, Fernando Maia Ferreira, Michael Kinyon

A magma (or groupoid) is a set with a binary operation $(A,f)$. Roughly speaking, a magma is said to be lazy if compositions such as $f(x,f(f(y,z),u))$ depend on at most two variables. Recently, Kaprinai, Machida and Waldhauser described the lattice of all the varieties of lazy groupoids. A forbidden structure theorem is one that charcaterizes a smaller clas

C. S. Davies, J. H. Mentink, A. V. Kimel, Th. Rasing

We review and discuss the process of single-shot helicity-independent all-optical switching of magnetization by which a single suitably-ultrafast excitation, under the right conditions, toggles magnetization from one stable state to another. For almost a decade, this phenomenon was only consistently observed in specific rare-earth-transition-metal ferrimagne

Guillaume Barnier, Ettore Biondi, Robert G. Clapp, Biondo Biondi

We describe a new method, full waveform inversion by model extension (FWIME) that recovers accurate acoustic subsurface velocity models from seismic data, when conventional methods fail. We leverage the advantageous convergence properties of wave-equation migration velocity analysis (WEMVA) with the accuracy and high-resolution nature of acoustic full wavefo

Tan Chen, Zhe Huang, James Motes, Junyi Geng

Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technologies and their application to challenging industrial environments. This paper presents lessons from a collaborative assembly project between three academic research groups and an

Yucheng Guo, Mason Klemm, Ji Seop Oh, Yaofeng Xie

The emergence of unconventional superconductivity in proximity to intertwined electronic orders is especially relevant in the case of iron-based superconductors. Such order consists of an electronic nematic order and a spin density wave in these systems. BaNi$_2$As$_2$, like its well-known iron-based analog BaFe$_2$As$_2$, also hosts a symmetry-breaking stru

Jiantao Wu, Shentong Mo

Self-supervised pre-training for images without labels has recently achieved promising performance in image classification. The success of transformer-based methods, ViT and MAE, draws the community's attention to the design of backbone architecture and self-supervised task. In this work, we show that current masked image encoding models learn the underlying

Shiwei Zeng, Jie Shen

Robust mean estimation is one of the most important problems in statistics: given a set of samples in $\mathbb{R}^d$ where an $\alpha$ fraction are drawn from some distribution $D$ and the rest are adversarially corrupted, we aim to estimate the mean of $D$. A surge of recent research interest has been focusing on the list-decodable setting where $\alpha \in

Rui Liu, Young Jin Kim, Alexandre Muzio, Hany Hassan Awadalla

Sparsely activated transformers, such as Mixture of Experts (MoE), have received great interest due to their outrageous scaling capability which enables dramatical increases in model size without significant increases in computational cost. To achieve this, MoE models replace the feedforward sub-layer with Mixture-of-Experts sub-layer in transformers and use

Feiran Zhao, Xingyun Fu, Keyou You

System stabilization via policy gradient (PG) methods has drawn increasing attention in both control and machine learning communities. In this paper, we study their convergence and sample complexity for stabilizing linear time-invariant systems in terms of the number of system rollouts. Our analysis is built upon a discounted linear quadratic regulator (LQR)

Stephanie Lin, Jacob Hilton, Owain Evans

We show that a GPT-3 model can learn to express uncertainty about its own answers in natural language -- without use of model logits. When given a question, the model generates both an answer and a level of confidence (e.g. "90% confidence" or "high confidence"). These levels map to probabilities that are well calibrated. The model also remains moderately ca

Chenze Shao, Xuanfu Wu, Yang Feng

Non-autoregressive neural machine translation (NAT) suffers from the multi-modality problem: the source sentence may have multiple correct translations, but the loss function is calculated only according to the reference sentence. Sequence-level knowledge distillation makes the target more deterministic by replacing the target with the output from an autoreg

Wanshui Gan, Hongbin Xu, Yi Huang, Shifeng Chen

Neural radiance fields have made a remarkable breakthrough in the novel view synthesis task at the 3D static scene. However, for the 4D circumstance (e.g., dynamic scene), the performance of the existing method is still limited by the capacity of the neural network, typically in a multilayer perceptron network (MLP). In this paper, we utilize 3D Voxel to mod

Renith G, Harikrishna Warrier, Yogesh Gupta

Survival analysis is playing a major role in manufacturing sector by analyzing occurrence of any unwanted event based on the input data. Predictive maintenance, which is a part of survival analysis, helps to find any device failure based on the current incoming data from different sensor or any equipment. Deep learning techniques were used to automate the pr

Qiang Zhang, Seung-Hwan Baek, Szymon Rusinkiewicz, Felix Heide

We propose a differentiable rendering algorithm for efficient novel view synthesis. By departing from volume-based representations in favor of a learned point representation, we improve on existing methods more than an order of magnitude in memory and runtime, both in training and inference. The method begins with a uniformly-sampled random point cloud and l

Jian Luo, Jianzong Wang, Ning Cheng, Haobin Tang

In this paper, we investigated a speech augmentation based unsupervised learning approach for keyword spotting (KWS) task. KWS is a useful speech application, yet also heavily depends on the labeled data. We designed a CNN-Attention architecture to conduct the KWS task. CNN layers focus on the local acoustic features, and attention layers model the long-time

Qiang Zhou, Chaohui Yu, Zhibin Wang, Hao Li

Rotated object detection in aerial images is still challenging due to arbitrary orientations, large scale and aspect ratio variations, and extreme density of objects. Existing state-of-the-art rotated object detection methods mainly rely on angle-based detectors. However, angle regression can easily suffer from the long-standing boundary problem. To tackle t

Navdeep Kumar, Kfir Levy, Kaixin Wang, Shie Mannor

Robust Markov decision processes (MDPs) provide a general framework to model decision problems where the system dynamics are changing or only partially known. Efficient methods for some \texttt{sa}-rectangular robust MDPs exist, using its equivalence with reward regularized MDPs, generalizable to online settings. In comparison to \texttt{sa}-rectangular robu

Jian Luo, Jianzong Wang, Ning Cheng, Zhenpeng Zheng

Low resource automatic speech recognition (ASR) is a useful but thorny task, since deep learning ASR models usually need huge amounts of training data. The existing models mostly established a bottleneck (BN) layer by pre-training on a large source language, and transferring to the low resource target language. In this work, we introduced an adaptive activat

Ryuta Tamura, Yuichi Takano, Ryuhei Miyashiro

We study the mixed-integer optimization (MIO) approach to feature subset selection in nonlinear kernel support vector machines (SVMs) for binary classification. First proposed for linear regression in the 1970s, this approach has recently moved into the spotlight with advances in optimization algorithms and computer hardware. The goal of this paper is to est

Wei Dong, Yuting Liang, Ke Yi

In this paper, we present two new algorithms for covariance estimation under concentrated differential privacy (zCDP). The first algorithm achieves a Frobenius error of $\tilde{O}(d^{1/4}\sqrt{\mathrm{tr}}/\sqrt{n} + \sqrt{d}/n)$, where $\mathrm{tr}$ is the trace of the covariance matrix. By taking $\mathrm{tr}=1$, this also implies a worst-case error bound

Chi Zhang, Olga Papaemmanouil, Josiah P. Hanna, Aditya Akella

Machine learning is rapidly being used in database research to improve the effectiveness of numerous tasks included but not limited to query optimization, workload scheduling, physical design, etc. Currently, the research focus has been on replacing a single database component responsible for one task by its learning-based counterpart. However, query perform

J. K. Nangoi, M. Gaowei, A. Galdi, J. M. Maxson

Mono- and bi-alkali antimonides, X$_2$YSb (X and Y from Group I), are promising for next-generation electron emitters due to their capability of producing high-quality electron beams. However, these materials are not yet well understood, in part due to the technical challenges in growing pure, ordered alkali antimonides. For example, in the current literatur

Xinyu Zou, Zhi Hu, Yiming Zhao, Xuchu Ding

Multi-scenario learning (MSL) enables a service provider to cater for users' fine-grained demands by separating services for different user sectors, e.g., by user's geographical region. Under each scenario there is a need to optimize multiple task-specific targets e.g., click through rate and conversion rate, known as multi-task learning (MTL). Recent soluti

Changjiang Cai, Pan Ji, Qingan Yan, Yi Xu

This paper presents a learning-based method for multi-view depth estimation from posed images. Our core idea is a "learning-to-optimize" paradigm that iteratively indexes a plane-sweeping cost volume and regresses the depth map via a convolutional Gated Recurrent Unit (GRU). Since the cost volume plays a paramount role in encoding the multi-view geometry, we

Jinli Liao, Yikang Ding, Yoli Shavit, Dihe Huang

Recently, Transformers were shown to enhance the performance of multi-view stereo by enabling long-range feature interaction. In this work, we propose Window-based Transformers (WT) for local feature matching and global feature aggregation in multi-view stereo. We introduce a Window-based Epipolar Transformer (WET) which reduces matching redundancy by using

Ansong Ni, Jeevana Priya Inala, Chenglong Wang, Oleksandr Polozov

Pretrained language models have shown superior performance on many natural language processing tasks, yet they still struggle at multi-step formal reasoning tasks like grade school math problems. One key challenge of finetuning them to solve such math reasoning problems is that many existing datasets only contain one reference solution for each problem, desp

Diptesh Das, Eugene Ndiaye, Ichiro Takeuchi

In predictive modeling for high-stake decision-making, predictors must be not only accurate but also reliable. Conformal prediction (CP) is a promising approach for obtaining the confidence of prediction results with fewer theoretical assumptions. To obtain the confidence set by so-called full-CP, we need to refit the predictor for all possible values of pre

Hanwei Li, Aiguo Xu, Ge Zhang, Yiming Shan

The Rayleigh-Taylor Instability (RTI) under multi-mode perturbation in compressible flow is probed via the Discrete Boltzmann Modeling (DBM) with tracers. The distribution of tracers provides clear boundaries between light and heavy fluids in the position space. Besides, the position-velocity phase space offers a new perspective for understanding the flow be

Kan Xie, Zhe Zhang, Bo Li, Jiawen Kang

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from dis

Yoshikazu Giga, Jun Okamoto, Koya Sakakibara, Masaaki Uesaka

By introducing a new topology, a representation formula of the Gamma limit of the Kobayashi-Warren-Carter energy is given in a multi-dimensional domain. A key step is to study the Gamma limit of a single-well Modica-Mortola functional. The convergence introduced here is called the sliced graph convergence, which is finer than conventional $L^1$ convergence,

Zeshi Yang, KangKang Yin, Libin Liu

Learning dexterous manipulation skills is a long-standing challenge in computer graphics and robotics, especially when the task involves complex and delicate interactions between the hands, tools and objects. In this paper, we focus on chopsticks-based object relocation tasks, which are common yet demanding. The key to successful chopsticks skills is steady

Sepehr Assadi, Vikram Kher, George Li, Ariel Schvartzman

Multi-item revenue-optimal mechanisms are known to be extremely complex, often offering buyers randomized lotteries of goods. In the standard buy-one model, it is known that optimal mechanisms can yield revenue infinitely higher than that of any "simple" mechanism -- the ones with size polynomial in the number of items -- even with just two items and a singl

Yujie Qian, Jiang Guo, Zhengkai Tu, Zhening Li

Molecular structure recognition is the task of translating a molecular image into its graph structure. Significant variation in drawing styles and conventions exhibited in chemical literature poses a significant challenge for automating this task. In this paper, we propose MolScribe, a novel image-to-graph generation model that explicitly predicts atoms and

Allen Schmaltz, Danielle Rasooly

A typical desideratum for quantifying the uncertainty from a classification model as a prediction set is class-conditional singleton set calibration. That is, such sets should map to the output of well-calibrated selective classifiers, matching the observed frequencies of similar instances. Recent works proposing adaptive and localized conformal p-values for

Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan

Recent works on neural contextual bandits have achieved compelling performances due to their ability to leverage the strong representation power of neural networks (NNs) for reward prediction. Many applications of contextual bandits involve multiple agents who collaborate without sharing raw observations, thus giving rise to the setting of federated contextu

Chaojin Qing, Lei Dong, Li Wang, Guowei Ling

Data-nulling superimposed pilot (DNSP) effectively alleviates the superimposed interference of superimposed training (ST)-based channel estimation (CE) in orthogonal frequency division multiplexing (OFDM) systems, while facing the challenges of the estimation accuracy and computational complexity. By developing the promising solutions of deep learning (DL) i

Shi-Hai Dong, Biswanath Rath

We propose a new "superpotential" and find that neither the supersymmetric energy conditions nor the associated shape invariance condition remain valid. On the other hand a new energy condition $E_{n}^{+}-E_{n}^{(-)}=2$ between the two partner Hamiltonian $H^{(\pm)}$ emerges. Mathematical proof supported the present findings with examples are presented. It i

Xueyuan Lin, Chengjin Xu, Haihong E, Fenglong Su

Multi-hop logical reasoning over knowledge graph (KG) plays a fundamental role in many artificial intelligence tasks. Recent complex query embedding (CQE) methods for reasoning focus on static KGs, while temporal knowledge graphs (TKGs) have not been fully explored. Reasoning over TKGs has two challenges: 1. The query should answer entities or timestamps; 2.

Jay S. Zou, Helen S. Ansell, István A. Kovács

Quantifying entanglement of multiple subsystems is a challenging open problem in interacting quantum systems. Here, we focus on two subsystems of length $\ell$ separated by a distance $r=\alpha\ell$ and quantify their entanglement negativity (${\cal E}$) and mutual information (${\cal I}$) in critical random Ising chains. Both the disorder averaged ${\cal E}

Shi-Yang Wang

KPI anomaly detection is one important function of network management system. Traditional methods either require prior knowledge or manually set thresholds. To overcome these shortcomings, we propose the Ensemble2 framework, which applies ensemble learning to improve exogenous capabilities. Meanwhile, automatically adjusts thresholds based on extreme value t

Yangming Zhou, Qichao Ying, Zhenxing Qian, Sheng Li

Multimodal fake news detection has attracted many research interests in social forensics. Many existing approaches introduce tailored attention mechanisms to guide the fusion of unimodal features. However, how the similarity of these features is calculated and how it will affect the decision-making process in FND are still open questions. Besides, the potent

Yimei Zheng, Caiyan Jia, Jian Yu, Xuanya Li

Many complex systems in the real world can be characterized by attributed networks. To mine the potential information in these networks, deep embedded clustering, which obtains node representations and clusters simultaneously, has been paid much attention in recent years. Under the assumption of consistency for data in different views, the cluster structure

Shanjin Wu, Koichi Murase, Shian Tang, Huichao Song

The light-nuclei yield ratio is one of the candidates to probe the critical fluctuations of hot QCD matter. In this paper, we investigate the \textit{background effects}, namely the non-critical effects coming from the non-trivial thermal background, on the light-nuclei production within the framework of the coalescence model. Specifically, we analyze the im

Jian Li, Dongxiao Zhang, Tianhao He, Qiang Zheng

Uncertainty quantification (UQ) of subsurface two-phase flow usually requires numerous executions of forward simulations under varying conditions. In this work, a novel coupled theory-guided neural network (TgNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The core notion of this proposed me

Annan Yu, Yunan Yang, Alex Townsend

Small generalization errors of over-parameterized neural networks (NNs) can be partially explained by the frequency biasing phenomenon, where gradient-based algorithms minimize the low-frequency misfit before reducing the high-frequency residuals. Using the Neural Tangent Kernel (NTK), one can provide a theoretically rigorous analysis for training where data

Li Chen, Ningyuan Huang, Cong Mu, Hayden S. Helm

Deep neural networks are susceptible to label noise. Existing methods to improve robustness, such as meta-learning and regularization, usually require significant change to the network architecture or careful tuning of the optimization procedure. In this work, we propose a simple hierarchical approach that incorporates a label hierarchy when training the dee

Mingchen Li, Di Zhuang, J. Morris Chang

With the development of machine learning and data science, data sharing is very common between companies and research institutes to avoid data scarcity. However, sharing original datasets that contain private information can cause privacy leakage. A reliable solution is to utilize private synthetic datasets which preserve statistical information from origina