March 2023 arXiv papers — page 35

Tarek Saier, Johan Krause, Michael Färber

Large-scale data sets on scholarly publications are the basis for a variety of bibliometric analyses and natural language processing (NLP) applications. Especially data sets derived from publication's full-text have recently gained attention. While several such data sets already exist, we see key shortcomings in terms of their domain and time coverage, citat

Xuanfan Ni, Piji Li, Huayang Li

Text structuralization is one of the important fields of natural language processing (NLP) consists of information extraction (IE) and structure formalization. However, current studies of text structuralization suffer from a shortage of manually annotated high-quality datasets from different domains and languages, which require specialized professional knowl

Naoyuki Hirata

Asteroid 4 Vesta has a set of parallel troughs aligned with its equator. Although previous evaluations suggest that it is of shock fracturing tectonic origin, we propose that the equatorial troughs can be created by secondary cratering from the largest impact basin, Rheasilvia. We calculated the trajectories of ejecta particles from Rheasilvia by considering

Alexander Ivanov

This collection bundles the following memos dedicated to Fast-speed (100 Mb/s) and Original-speed (10 Mb/s) Ethernet protocols, including upgradeable from Manchester (ME/DME) into Like-a-Manchester (LaM) encoding schemes: [1] "Data Coding Means and Event Coding Means Multiplexed Over the 100BASE-T1 PMA Sublayer" (extends the IEEE 802.3bw protocol, uses a non

Ming Wang, Xianda Guo, Beibei Lin, Tian Yang

Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. Compared with other biometric technologies, gait recognition is more difficult to disguise and can be applied to the condition of long-distance without the cooperation of subjects. Thus, it has unique potential and wide application for crime prev

Tobias Frederico, Mario Gattobigio

We demonstrate that a four-boson limit-cycle independent of the Efimov one appears in Hamiltonian systems at the unitary limit. The model interaction contains two-, three- and four-body short-range potentials, which disentangle the interwoven three- and four-boson cycles, for the universal trimer and tetramer energy levels, respectively. The limit-cycle asso

Suman Adhya, Avishek Lahiri, Debarshi Kumar Sanyal, Partha Pratim Das

Topic modeling has emerged as a dominant method for exploring large document collections. Recent approaches to topic modeling use large contextualized language models and variational autoencoders. In this paper, we propose a negative sampling mechanism for a contextualized topic model to improve the quality of the generated topics. In particular, during mode

Chen Cheng, Linjie Wen, Jinglai Li

In this work we study systems consisting of a group of moving particles. In such systems, often some important parameters are unknown and have to be estimated from observed data. Such parameter estimation problems can often be solved via a Bayesian inference framework. However in many practical problems, only data at the aggregate level is available and as a

Robert Wild, Markus Nötzold, Malcolm Simpson, Thuy Dung Tran

Quantum tunneling reactions play a significant role in chemistry when classical pathways are energetically forbidden, be it in gas phase reactions, surface diffusion, or liquid phase chemistry. In general, such tunneling reactions are challenging to calculate theoretically, given the high dimensionality of the quantum dynamics, and also very difficult to ide

Xiaogeng Liu, Minghui Li, Haoyu Wang, Shengshan Hu

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge

Lukas Jürgensmeier, Bernd Skiera

Digital platforms use recommendations to facilitate exchanges between platform actors, such as trade between buyers and sellers. Aiming to protect consumers and guarantee fair competition on platforms, legislators increasingly require that recommendations on market-dominating platforms be free from self-preferencing. That is, platforms that also act as selle

Rongyu Zhang, Xiaowei Chi, Guiliang Liu, Wenyi Zhang

Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement. Meanwhile, federated learning (FL) addresses the data sharing problem, enabling privacy-preserved collaborative training to provide sufficient precious data. Great potential, therefore, arises with the confluence of them, kn

Chao-Ming Li, Chong Ge, Ruo-Yu Liu

HESS J1809-193 is an extended TeV $\gamma$-ray source and the origin of its $\gamma$-ray emission remains ambiguous. Pulsar wind nebula (PWN) of PSR J1809-1917 laying inside the extended $\gamma$-ray emission is a possible candidate. Powered by the central pulsar, ultrarelativistic electrons in PWN can produce radio to X-ray emission through synchrotron and

Roberto Casadio, Octavian Micu

The idea that gravity can act as a regulator of ultraviolet divergences is almost a century old and has inspired several approaches to quantum gravity. In fact, a minimum Planckian length can be shown to emerge from the nonlinear dynamics of gravity in the effective field theory approach to gravitational scatterings at Planckian energies. A simple quantum de

Tomohiro Oda, Gaël Dur, Stéphane Ducasse, Hugo Daniel Macedo

Formal specification is a basis for rigorous software implementation. VDM-SL is a formal specification language with an extensive executable subset. Successful cases of VDM-family including VDM-SL have shown that producing a well-tested executable specification can reduce the cost of the implementation phase. This paper introduces and discusses the reversed

Haobo Zhang, Yicheng Li, Qian Lin

In the misspecified spectral algorithms problem, researchers usually assume the underground true function $f_{\rho}^{*} \in [\mathcal{H}]^{s}$, a less-smooth interpolation space of a reproducing kernel Hilbert space (RKHS) $\mathcal{H}$ for some $s\in (0,1)$. The existing minimax optimal results require $\|f_{\rho}^{*}\|_{L^{\infty}}<\infty$ which implicitly

E Carlini, Francisco José Silva, Ahmad Zorkot

In this work, we consider a first order mean field games system with non-local couplings. A Lagrange-Galerkin scheme for the continuity equation, coupled with a semi-Lagrangian scheme for the Hamilton-Jacobi-Bellman equation, is proposed to discretize the mean field games system. The convergence of solutions to the scheme towards a solution to the mean field

Filippo Alberto Edoardo Nuccio Mortarino Majno Di Capriglio, Tadashi Ochiai, Jishnu Ray

For a given Coleman family of modular forms, we construct a formal modeland prove the existence of a family of Galois representations associated to the Colemanfamily. As an application, we study the variations of Iwasawa $\lambda$- and $\mu$-invariants of dualfine (strict) Selmer groups over the cyclotomic Zp-extension of Q in Coleman families ofmodular form

Williams Rizzi, Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi

Recent papers have introduced a novel approach to explain why a Predictive Process Monitoring (PPM) model for outcome-oriented predictions provides wrong predictions. Moreover, they have shown how to exploit the explanations, obtained using state-of-the art post-hoc explainers, to identify the most common features that induce a predictor to make mistakes in

Bo'az Klartag

We prove that the Bourgain slicing conjecture and the Kannan-Lov\'asz-Simonovits (KLS) isoperimetric conjecture in $\mathbb{R}^n$ hold true up to a factor of $\sqrt{\log n}$. A new ingredient used in the proof is an improved log-concave Lichnerowicz inequality.

Caglar Aytekin

In this paper, we propose LEURN: a neural network architecture that learns univariate decision rules. LEURN is a white-box algorithm that results into univariate trees and makes explainable decisions in every stage. In each layer, LEURN finds a set of univariate rules based on an embedding of the previously checked rules and their corresponding responses. Bo

Victor Gandarillas, John T. Hwang

We present the Toolbox for Analysis and Large-scale Optimization of Spacecraft (TALOS), a framework designed for applying large-scale multidisciplinary design optimization (MDO) to spacecraft design problems. The framework is built using the Computational System Design Language (CSDL), with abstractions for users to describe systems at a high level. CSDL is

Phuc Nguyen, Nam Tuan Ly, Hideaki Takeda, Atsuhiro Takasu

Table answering questions from business documents has many challenges that require understanding tabular structures, cross-document referencing, and additional numeric computations beyond simple search queries. This paper introduces a novel pipeline, named TabIQA, to answer questions about business document images. TabIQA combines state-of-the-art deep learn

Junyi Li, Zhilu Zhang, Xiaoyu Liu, Chaoyu Feng

Significant progress has been made in self-supervised image denoising (SSID) in the recent few years. However, most methods focus on dealing with spatially independent noise, and they have little practicality on real-world sRGB images with spatially correlated noise. Although pixel-shuffle downsampling has been suggested for breaking the noise correlation, i

Zicheng Zhang, Wei Wu, Wei Sun, Dangyang Tu

User-generated content (UGC) live videos are often bothered by various distortions during capture procedures and thus exhibit diverse visual qualities. Such source videos are further compressed and transcoded by media server providers before being distributed to end-users. Because of the flourishing of UGC live videos, effective video quality assessment (VQA

Nouman Khan, Vijay Subramanian

The work studies the problem of decentralized constrained POMDPs in a team-setting where multiple nonstrategic agents have asymmetric information. Using an extension of Sion's Minimax theorem for functions with positive infinity and results on weak-convergence of measures, strong duality is established for the setting of infinite-horizon expected total disco

Sachchidanand Prasad

Associated to every closed, embedded submanifold $N$ of a connected Riemannian manifold $M$, there is the distance function $d_N$ which measures the distance of a point in $M$ from $N$. We analyze the square of this function and show that it is Morse-Bott on the complement of the cut locus $\mathrm{Cu}(N)$ of $N$, provided $M$ is complete. Moreover, the grad

David Pershouse, Feras Dayoub, Dimity Miller, Niko Sünderhauf

We address the challenging problem of open world object detection (OWOD), where object detectors must identify objects from known classes while also identifying and continually learning to detect novel objects. Prior work has resulted in detectors that have a relatively low ability to detect novel objects, and a high likelihood of classifying a novel object

Hongying Lin, Bo Zhou

Given a $k$-uniform hypergraph $G$ with vertex set $[n]$ and edge set $E(G)$, the ABC tensor $\mathcal{ABC}(G)$ of $G$ is the $k$-order $n$-dimensional tensor with \[ \mathcal{ABC}(G)_{i_1, \dots, i_k}= \begin{cases} \dfrac{1}{(k-1)!}\sqrt[k]{\dfrac{\sum_{i\in e}d_{i}-k}{\prod_{i\in e}d_{i}}} & \mbox{if $e\in E(G)$} 0 & \mbox{otherwise} \end{cases} \] for $i

Eugene Goldberg

Quantifier elimination (QE) is an important problem that has numerous applications. Unfortunately, QE is computationally very hard. Earlier we introduced a generalization of QE called $\mathit{partial}$ QE (or PQE for short). PQE allows to unquantify a $\mathit{part}$ of the formula. The appeal of PQE is twofold. First, many important problems can be solved

M. A. Souza, H. Miyake

In a phenomenological approach, the $\alpha$ + core structure is investigated in the $^{20}$Ne, $^{44}$Ti, $^{94}$Mo, $^{104}$Te, and $^{212}$Po nuclei through the local potential model using a double-folding nuclear potential with effective nucleon-nucleon interaction of M3Y + $c_{\mathrm{sat}}\delta(s)$ type, where the term $c_{\mathrm{sat}}\delta(s)$ acts

Clinton Ansun Mo, Kun Hu, Chengjiang Long, Zhiyong Wang

Deriving sophisticated 3D motions from sparse keyframes is a particularly challenging problem, due to continuity and exceptionally skeletal precision. The action features are often derivable accurately from the full series of keyframes, and thus, leveraging the global context with transformers has been a promising data-driven embedding approach. However, exi

Giulian Wiggins

This paper studies abelian categories that can be decomposed into smaller abelian categories via iterated recollements - such a decomposition we call a stratification. Examples include the categories of (equivariant) perverse sheaves and epsilon-stratified categories (in particular highest weight categories) in the sense of Brundan-Stroppel (2018). We give n

Xudong Wang, Yao Chen, Wanli Wang

Anomalous diffusion phenomena have been observed in many complex physical and biological systems. One significant advance recently is the physical extension of particle's motion in static medium to uniformly (and even nonuniformly) expanding medium. The dynamic mechanism of particle's motion in the nonuniformly expanding medium has only been investigated in

Filip Rozpędek, Kaushik P. Seshadreesan, Paul Polakos, Liang Jiang

Long distance quantum communication will require the use of quantum repeaters to overcome the exponential attenuation of signal with distance. One class of such repeaters utilizes quantum error correction to overcome losses in the communication channel. Here we propose a novel strategy of using the bosonic Gottesman-Kitaev-Preskill (GKP) code in a two-way re

Xingbin Liu, Huafeng Kuang, Xianming Lin, Yongjian Wu

Adversarial training can improve the robustness of neural networks. Previous methods focus on a single adversarial training strategy and do not consider the model property trained by different strategies. By revisiting the previous methods, we find different adversarial training methods have distinct robustness for sample instances. For example, a sample ins

James T. Wheeler

We study sources for torsion in Poincare gauge theory of any dimension, signature, and spin. We find that symmetric kinetic terms for non-Yang-Mills bosonic fields of arbitrary rank drive torsion. Our detailed discussion of spin-3/2 Rarita-Schwinger fields shows that they source all independent parts of the torsion. We develop systematic notation for spin-(2

Christopher McMaster, David FL Liew, Douglas EV Pires

We propose an approach for adapting the DeBERTa model for electronic health record (EHR) tasks using domain adaptation. We pretrain a small DeBERTa model on a dataset consisting of MIMIC-III discharge summaries, clinical notes, radiology reports, and PubMed abstracts. We compare this model's performance with a DeBERTa model pre-trained on clinical texts from

Chenxi Huang, Liang Xie, Yibo Yang, Wenxiao Wang

One of the challenges in federated learning is the non-independent and identically distributed (non-iid) characteristics between heterogeneous devices, which cause significant differences in local updates and affect the performance of the central server. Although many studies have been proposed to address this challenge, they only focus on local training and

Wee Teck Gan

This is an expanded set of notes based on two lectures given by the author at the 2022 IHES summer school on the Langlands program, on explicit techniques for the construction of automorphic representations.

Wee Teck Gan

This is a set of notes on automorphic forms and theta correspondence, based on my lectures at the 2022 Arizona Winter School.

Slava G. Turyshev, Darren Garber, Louis D. Friedman, Andreas M. Hein

Recently, we witnessed how the synergy of small satellite technology and solar sailing propulsion enables new missions. Together, small satellites with lightweight instruments and solar sails offer affordable access to deep regions of the solar system, also making it possible to realize hard-to-reach trajectories that are not constrained to the ecliptic plan

Keri D'Angelo, Alexandra Silva, Gerco van Heerdt, Leon Witzman

Compact representations of automata are important for efficiency. In this paper, we study methods to compute reduced automata, in which no two states accept the same language. We do this for finitary automata (FA), an abstract definition that encompasses probabilistic and weighted automata. Our procedure makes use of Milius' locally finite fixpoint. We prese

Najiya V K, Chithra A

The $k$-coalescence of two graphs is obtained by merging a $k$-clique of each graph. The $A_\alpha$-matrix of a graph is the convex combination of its degree matrix and adjacency matrix. In this paper, we present some structural properties of a non-regular graph which is obtained from the $k$-coalescence of two graphs. Also, we derive the $A_\alpha$-characte

Ned Cooper

The 2019/20 Black Summer bushfires in Australia demonstrated the brutal and disastrous consequences of changing the technological world without considering linkages with the biophysical, ecological or human worlds. An emerging more-than-human design philosophy encourages designers to consider such interrelations between humans and non-human entities. Yet, th

Mao Shi

By studying the existing higher order derivation formulas of rational B\'{e}zier curves, we find that they fail when the order of the derivative exceeds the degree of the curves. In this paper, we present a new derivation formula for rational B\'{e}zier curves that overcomes this drawback and show that the $k$th degree derivative of a $n$th degree rational B

Sang Quang Dinh, Hieu Minh Tran

The updated analysis of the LHCb Collaboration on the lepton flavor violation suggests that the new physics should couple to muons and electrons with comparable magnitudes, resulting in the anomalies in both rare decay channels, $b \rightarrow s \mu^+ \mu^-$ and $b \rightarrow s e^+ e^-$. Meanwhile, the recent result of the Muon $g-2$ experiment with higher

N. K. Karn, M. M. Sharma, V. P. S. Awana

Here we report, the successful synthesis of single crystals of topological semimetal (TSM) candidate, PdSn$_4$ using the self-grown route. The synthesized crystal is well characterized through X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and X-ray photoelectron spectroscopy (XPS). Detailed Rietveld analysis of the powder XRD

Guodong Zhang, Kapil Khandelwal, Tong Guo

This paper proposes a computational framework for the design optimization of stable structures under large deformations by incorporating nonlinear buckling constraints. A novel strategy for suppressing spurious buckling modes related to low-density elements is proposed. The strategy depends on constructing a pseudo-mass matrix that assigns small pseudo masse

Ivailo Hartarsky

In modified two-neighbour bootstrap percolation in two dimensions each site of $\mathbb Z^2$ is initially independently infected with probability $p$ and on each discrete time step one additionally infects sites with at least two non-opposite infected neighbours. In this note we establish that for this model the second term in the asymptotics of the infectio

Achintya Kundu, Laura Wynter, Rhui Dih Lee, Luis Angel Bathen

Weight-sharing neural architecture search aims to optimize a configurable neural network model (supernet) for a variety of deployment scenarios across many devices with different resource constraints. Existing approaches use evolutionary search to extract models of different sizes from a supernet trained on a very large data set, and then fine-tune the extra

Zhemin Li, Hongxia Wang, Deyu Meng

This paper proposes a regularizer called Implicit Neural Representation Regularizer (INRR) to improve the generalization ability of the Implicit Neural Representation (INR). The INR is a fully connected network that can represent signals with details not restricted by grid resolution. However, its generalization ability could be improved, especially with non

Akira Iyo, Hiraku Ogino, Shigeyuki Ishida, Hiroshi Eisaki

Graphite intercalation compounds (GICs) have a variety of functions due to their rich material variations, and thus,innovative methods for their synthesis are desired for practical applications. We have discovered that Na has a catalytic property that dramatically accelerates the formation of GICs. We demonstrate that LiC6n (n = 1, 2), KC8, KC12n (n = 2, 3,

Haobin Huo, Leon L. Shaw, Karoly Nemeth

Several effective methods have been developed recently to demonstrate simultaneous high energy and high power density in Li - carbon fluoride (CF$_x$) batteries. These methods can achieve as high as 1000 Wh/kg energy density at 60-70 kW/kg power density (40-50 C rate) in coin cells and 750 Wh/kg energy density at 12.5 kW/kg power density (20 C rate) in pouch

Lin Zhao, Haixing Dai, Zihao Wu, Dajiang Zhu

The evolution of convolutional neural networks (CNNs) can be largely attributed to the design of its architecture, i.e., the network wiring pattern. Neural architecture search (NAS) advances this by automating the search for the optimal network architecture, but the resulting network instance may not generalize well in different tasks. To overcome this, expl

Soichiro Fujii, Keisuke Hoshino, Yuki Maehara

We study weakly invertible cells in weak $\omega$-categories in the sense of Batanin-Leinster, adopting the coinductive definition of weak invertibility. We show that weakly invertible cells in a weak $\omega$-category are closed under globular pasting. Using this, we generalise elementary properties of weakly invertible cells known to hold in strict $\omega

Xuerui Li, Yanyan Liu, Yankai Peng, Jing Zhang

How to select the active variables which have significant impact on the event of interest is a very important and meaningful problem in the statistical analysis of ultrahigh-dimensional data. Sure independent screening procedure has been demonstrated to be an effective method to reduce the dimensionality of data from a large scale to a relatively moderate sc

Jeffrey D Vaaler

We prove a fairly general inequality that estimates the number of lattice points in a ball of positive radius in general position in a Euclidean space. The bound is uniform over lattices induced by a matrix having a bounded operator norm.

Jiaqing Liu, Chong Deng, Qinglin Zhang, Qian Chen

Meetings are increasingly important for collaborations. Action items in meeting transcripts are crucial for managing post-meeting to-do tasks, which usually are summarized laboriously. The Action Item Detection task aims to automatically detect meeting content associated with action items. However, datasets manually annotated with action item detection label

Christopher DiLullo, Whitham D. Reeve, Brian C. Hicks, Jayce Dowell

We present recent 2-port vector network analyzer (VNA) measurements of the complete set of scattering parameters for the antenna used within the Long Wavelength Array (LWA) and the associated front end electronics (FEEs). Full scattering parameter measurements of the antenna yield not only the reflection coefficient for each polarization, S11 and S22, but al

Cheng Chen, Kevin P. Nuckolls, Shuhan Ding, Wangqian Miao

The unusual properties of superconductivity in magic-angle twisted bilayer graphene (MATBG) have sparked enormous research interest. However, despite the dedication of intensive experimental efforts and the proposal of several possible pairing mechanisms, the origin of its superconductivity remains elusive. Here, utilizing angle-resolved photoemission spectr

Mikko Korhonen

Let $G$ be a simple algebraic group over an algebraically closed field $K$ with Lie algebra $\mathfrak{g}$. For unipotent elements $u \in G$ and nilpotent elements $e \in \mathfrak{g}$, the Jordan block sizes of $\operatorname{Ad}(u)$ and $\operatorname{ad}(e)$ are known in most cases. In the cases that remain, the group $G$ is of classical type in bad chara

Ryo Toda, Hayato Itoh, Masahiro Oda, Yuichiro Hayashi

This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes. One major role of chest CT scanning in COVID-19 diagnoses is identification of an inflammation particular to the disease. This task is generally performed by radiologists through an interpretation of the CT volume

Yiming Ma, Hang Liu, Shanyong Wang

Linear regression models, especially the extended STIRPAT model, are routinely-applied for analyzing carbon emissions data. However, since the relationship between carbon emissions and the influencing factors is complex, fitting a simple parametric model may not be an ideal solution. This paper investigated various nonparametric approaches in statistics and

Qiuyue Liu, Yuqin Zhang, Zhanyuan Cai

In 1980, V. I. Arnold studied the classification problem for convex lattice polygons of given area. Since then, this problem and its analogues have been studied by many authors, including $\mathrm{B\acute{a}r\acute{a}ny}$, Lagarias, Pach, Santos, Ziegler and Zong. Recently, Zong proposed two computer programs to prove Hadwiger's covering conjecture and Borsu

Ruijie Wang, Zheng Li, Jingfeng Yang, Tianyu Cao

This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones. The cross-lingual distillation ability across TKGs becomes increasingly crucial, in light of the unsatisfying performance o

Xianfan Gu, Chuan Wen, Weirui Ye, Jiaming Song

Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning. To tackle this task and empower robots with the ability to foresee the future, we propose a sample and computation-efficient model

Xiaofan Li, Xuan Li, Guangfa Lv, Yongzheng Zhang

Dynamic data flow analysis has been widely used to guide greybox fuzzing. However, traditional dynamic data flow analysis tends to go astray in the massive path tracking and requires to process a large volume of data, resulting in low efficiency in reaching the target location. In this paper, we propose a directed greybox fuzzer based on dynamic constraint f

Md Solimul Chowdhury, Cayden R. Codel, Marijn J. H. Heule

The Divide and Distribute Fixed Weights algorithm (ddfw) is a dynamic local search SAT-solving algorithm that transfers weight from satisfied to falsified clauses in local minima. ddfw is remarkably effective on several hard combinatorial instances. Yet, despite its success, it has received little study since its debut in 2005. In this paper, we propose thre

Xiaoyan Qian, Chang Liu, Xiaojuan Qi, Siew-Chong Tan

3D automatic annotation has received increased attention since manually annotating 3D point clouds is laborious. However, existing methods are usually complicated, e.g., pipelined training for 3D foreground/background segmentation, cylindrical object proposals, and point completion. Furthermore, they often overlook the inter-object feature relation that is p

Thomas Goodwillie, Kiyoshi Igusa, Cary Malkiewich, Mona Merling

We construct an $(\infty,1)$-functor that takes each smooth $G$-manifold with corners $M$ to the space of equivariant smooth $h$-cobordisms ${\mathcal H}_{\mathrm{Diff}}(M)$. We also give a stable analogue ${\mathcal H}^{\mathcal U}_{\mathrm{Diff}}(M)$ where the manifolds are stabilized with respect to representation discs. The functor structure is subtle to

Yosuke Harashima, Hiroaki Koga, Zeyuan Ni, Takehiro Yonehara

The structural stabilities of the monoclinic and tetragonal phases of Si-doped HfO$_{2}$ at finite temperatures were analyzed using a computational scheme to assess the effects of impurity doping. The finite temperature effects considered in this work represented lattice vibration and impurity configuration effects. The results show that 6% Si doping stabili

Abhijeet Alase, Salini Karuvade, Carlo Maria Scandolo

This document is our reply to the Comment (Miloslav Znojil 2023 J. Phys. A: Math. Theor. 56, 038001) on our recent work titled `The operational foundations of PT-symmetric and quasi-Hermitian quantum theory'. The original Comment consists of three addenda to our work. The first addendum claims that our work is ill-motivated as the motivating question, namely

Minting Pan, Xiangming Zhu, Yitao Zheng, Yunbo Wang

World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios like autonomous driving, noncontrollable dynamics that are independent or sparsely dependent on action signals often exist, making it challenging to learn effective world models. To address this issue, we propose Iso-Dream++, a model-based rein

Ruoqi Yin, Jianqin Yin

In this paper, we concern on the bottom-up paradigm in multi-person pose estimation (MPPE). Most previous bottom-up methods try to consider the relation of instances to identify different body parts during the post processing, while ignoring to model the relation among instances or environment in the feature learning process. In addition, most existing works

Yasuyuki Hatsuda, Tadashi Okazaki

We study the Schur line defect correlation functions in $\mathcal{N}=4$ and $\mathcal{N}=2^*$ $U(N)$ super Yang-Mills (SYM) theory. We find exact closed-form formulae of the correlation functions of the Wilson line operators in the fundamental, antisymmetric and symmetric representations via the Fermi-gas method in the canonical and grand canonical ensembles

Sanjaya Paudel, Suk-Jin Yoon, Jaewon Yoo, Rory Smith

We present an extensive catalog of 5405 early-type dwarf (dE) galaxies located in the various environments, i.e., clusters, groups and fields, of the local universe ($z$ $<$ 0.01). The dEs are selected through visual inspection of the Legacy survey's $g$-$r$-$z$ combined tri-color images. The inspected area, covering a total sky area of 7643 deg$^{2}$, encom

Karren Yang, Ting-Yao Hu, Jen-Hao Rick Chang, Hema Swetha Koppula

Adapting generic speech recognition models to specific individuals is a challenging problem due to the scarcity of personalized data. Recent works have proposed boosting the amount of training data using personalized text-to-speech synthesis. Here, we ask two fundamental questions about this strategy: when is synthetic data effective for personalization, and

Fan Yang, Lei Hu, Xinwu Liu, Shuangping Huang

Table recognition (TR) is one of the research hotspots in pattern recognition, which aims to extract information from tables in an image. Common table recognition tasks include table detection (TD), table structure recognition (TSR) and table content recognition (TCR). TD is to locate tables in the image, TCR recognizes text content, and TSR recognizes spati

Sandra Q. Liu, Yuxiang Ma, Edward H. Adelson

The synthesis of tactile sensing with compliance is essential to many fields, from agricultural usages like fruit picking, to sustainability practices such as sorting recycling, to the creation of safe home-care robots for the elderly to age with dignity. From tactile sensing, we can discern material properties, recognize textures, and determine softness, wh

Shikhar Tuli, Niraj K. Jha

Automated co-design of machine learning models and evaluation hardware is critical for efficiently deploying such models at scale. Despite the state-of-the-art performance of transformer models, they are not yet ready for execution on resource-constrained hardware platforms. High memory requirements and low parallelizability of the transformer architecture e

Peiyu Liu, Ze-Feng Gao, Yushuo Chen, Wayne Xin Zhao

In this paper, we propose a highly parameter-efficient approach to scaling pre-trained language models (PLMs) to a deeper model depth. Unlike prior work that shares all parameters or uses extra blocks, we design a more capable parameter-sharing architecture based on matrix product operator (MPO). MPO decomposition can reorganize and factorize the information

Shan-Liang Zhang, Enke Wang, Hongxi Xing, Ben-Wei Zhang

We investigate the flavor dependence of jet quenching, by performing a systematic analysis of medium modifications on the inclusive jet, $\gamma$+jet, and $b$-jet in Pb+Pb collisions at the LHC. Our results from MadGraph+PYTHIA exhibit excellent agreement with experimental measurements of the inclusive jet, $\gamma$+jet and $b$-jet simultaneously in p+p coll

Duc M. Nguyen, Mohammad Nazeri, Amirreza Payandeh, Aniket Datar

Humans are well-adept at navigating public spaces shared with others, where current autonomous mobile robots still struggle: while safely and efficiently reaching their goals, humans communicate their intentions and conform to unwritten social norms on a daily basis; conversely, robots become clumsy in those daily social scenarios, getting stuck in dense cro

Angelo Giorgio Cavaliere, Federico Ricci-Tersenghi

Random constraint satisfaction problems can display a very rich structure in the space of solutions, with often an ergodicity breaking -- also known as clustering or dynamical -- transition preceding the satisfiability threshold when the constraint-to-variables ratio $\alpha$ is increased. However, smart algorithms start to fail finding solutions in polynomi

Yanlai Chen, Shawn Koohy

Physics-Informed Neural Network (PINN) has proven itself a powerful tool to obtain the numerical solutions of nonlinear partial differential equations (PDEs) leveraging the expressivity of deep neural networks and the computing power of modern heterogeneous hardware. However, its training is still time-consuming, especially in the multi-query and real-time s

Lixue Cheng, Yu-Qin Chen, Shi-Xin Zhang, Shengyu Zhang

Combinatorial optimization problems are ubiquitous and computationally hard to solve in general. Quantum approximate optimization algorithm (QAOA), one of the most representative quantum-classical hybrid algorithms, is designed to solve combinatorial optimization problems by transforming the discrete optimization problem into a classical optimization problem

Yuta Ito

Gravitational polarization is examined for equilibrium self-gravitating polytropic sheets perturbed by gravitational field due to test mass sheet. We find equilibrium solutions to the corresponding perturbed Lane-Emden equations for non-negative polytropic indexes. It is shown that gravitational polarization may be observed even in a finite extent of self-gr

Yuming Qin, Qitao Cai, Ming Mei, Ke Wang

In this paper, we mainly study the regularity of pullback $\mathcal{D}$-attractors for a nonautonomous nonclassical diffusion equation with delay term $b(t,u_t)$ which contains some hereditary characteristics. Under a critical nonlinearity $f$, a time-dependent force $g(t,x)$ with exponential growth and a delayed force term $b(t,u_t)$, we prove that there ex

Marco Faroni, Alessandro Umbrico, Manuel Beschi, Andrea Orlandini

Combining symbolic and geometric reasoning in multi-agent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility that is intrinsic to these systems because of the interaction between agents and the environment. We propose a combined t

Yuming Qin, Xiaoling Chen, Ke Wang

In this paper, we discuss the long-time behavior of solutions to the nonclassical diffusion equation with fading memory when the nonlinear term $f$ fulfills the polynomial growth of arbitrary order and the external force $ g(x)\in L^{2}(\Omega)$. In the framework of time-dependent spaces, we verify the existence and uniqueness of strong solutions by the Gale

Cong Ma, Wang Gao, Qing Jiang

Stacking fault energy (SFE) is an essential parameter for characterizing mechanical properties. However, in high entropy alloys (HEAs), the local chemical environment varies significantly across different stacking fault planes, resulting in a substantial fluctuation of SFE values rather than a unique value, which prohibits the prediction of the local SFE. He

Xu Liu, Mengyue Zhou, Gaosheng Shi, Yu Du

Linking computational natural language processing (NLP) models and neural responses to language in the human brain on the one hand facilitates the effort towards disentangling the neural representations underpinning language perception, on the other hand provides neurolinguistics evidence to evaluate and improve NLP models. Mappings of an NLP model's represe

Satoshi Kataoka, Youngseog Chung, Seyed Kamyar Seyed Ghasemipour, Pannag Sanketi

Most successes in robotic manipulation have been restricted to single-arm gripper robots, whose low dexterity limits the range of solvable tasks to pick-and-place, inser-tion, and object rearrangement. More complex tasks such as assembly require dual and multi-arm platforms, but entail a suite of unique challenges such as bi-arm coordination and collision av

Qixin Hu, Yixiong Chen, Junfei Xiao, Shuwen Sun

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors; (II) effective for training AI models, which can perform liver t

Joshua C. Zhao, Ahmed Roushdy Elkordy, Atul Sharma, Yahya H. Ezzeldin

Secure aggregation promises a heightened level of privacy in federated learning, maintaining that a server only has access to a decrypted aggregate update. Within this setting, linear layer leakage methods are the only data reconstruction attacks able to scale and achieve a high leakage rate regardless of the number of clients or batch size. This is done thr

Jose Blanchet, Alexander Shapiro

The goal of this paper is to develop methodology for the systematic analysis of asymptotic statistical properties of data driven DRO formulations based on their corresponding non-DRO counterparts. We illustrate our approach in various settings, including both phi-divergence and Wasserstein uncertainty sets. Different types of asymptotic behaviors are obtaine

Jinjin Xie, Juan Li, Junzhi Wang, Shu Liu

Protostellar outflows are considered a signpost of star formation. These outflows can cause shocks in the molecular gas and are typically traced by the line wings of certain molecules. HNCO (4--3) has been regarded as a shock tracer because of the high abundance in shocked regions. Here we present the first imaging results of HNCO (4--3) line wings toward ni

Yuxiao Chen, Jianbo Yuan, Yu Tian, Shijie Geng

Contrastive learning-based vision-language pre-training approaches, such as CLIP, have demonstrated great success in many vision-language tasks. These methods achieve cross-modal alignment by encoding a matched image-text pair with similar feature embeddings, which are generated by aggregating information from visual patches and language tokens. However, dir

Jesús D. Cifuentes, Tuomo Tanttu, Will Gilbert, Jonathan Y. Huang

Spins of electrons in CMOS quantum dots combine exquisite quantum properties and scalable fabrication. In the age of quantum technology, however, the metrics that crowned Si/SiO2 as the microelectronics standard need to be reassessed with respect to their impact upon qubit performance. We chart the spin qubit variability due to the unavoidable atomic-scale r