November 2022 arXiv papers — page 7

Matilde Manzaroli

It goes back to Ahlfors that a real algebraic curve $C$ admits a separating morphism $f$ to the complex projective line if and only if the real part of the curve disconnects its complex part, i.e. the curve is \textit{separating}. The degree of such $f$ is bounded from below by the number $l$ of real connected components of $ \mathbb{R} C$. The sharpness of

Ryosuke Shimmura, Joe Suzuki

In this paper, we propose new methods to efficiently solve convex optimization problems encountered in sparse estimation, which include a new quasi-Newton method that avoids computing the Hessian matrix and improves efficiency, and we prove its fast convergence. We also prove the local convergence of the Newton method under weaker assumptions. Our proposed m

Alexis Sáez, Brice Lecampion

Injection-induced aseismic slip plays an important role in a broad range of human-made and natural systems, from the exploitation of geo-resources to the understanding of earthquakes. Recent studies have shed light on how aseismic slip propagates in response to continuous fluid injections. Yet much less is known about the response of faults after the injecti

Shao-Ping Li

It is generically believed that the two-body scattering is suppressed by higher-order weak couplings with respect to the two-body decay. We show that this does not always hold when a heavy particle is produced by forbidden decay in a thermal plasma, where the scattering shares the same order of couplings with the decay. We find that there is a simple and clo

Souvik Banerjee, Bamdev Mishra, Pratik Jawanpuria, Manish Shrivastava

This paper aims to provide an unsupervised modelling approach that allows for a more flexible representation of text embeddings. It jointly encodes the words and the paragraphs as individual matrices of arbitrary column dimension with unit Frobenius norm. The representation is also linguistically motivated with the introduction of a novel similarity metric.

Koichi Hamaguchi, Natsumi Nagata, Genta Osaki, Shih-Yen Tseng

We examine the lepton dipole moments in an extension of the Standard Model (SM), which contains vector-like leptons that couple only to the second-generation SM leptons. The model naturally leads to sizable contributions to the muon $g-2$ and the muon electric dipole moment (EDM). One feature of this model is that a sizable electron EDM is also induced at th

Bin Tan, Nan Xue, Tianfu Wu, Gui-Song Xia

This paper studies the challenging two-view 3D reconstruction in a rigorous sparse-view configuration, which is suffering from insufficient correspondences in the input image pairs for camera pose estimation. We present a novel Neural One-PlanE RANSAC framework (termed NOPE-SAC in short) that exerts excellent capability to learn one-plane pose hypotheses fro

Wonjoon Jin, Nuri Ryu, Geonung Kim, Seung-Hwan Baek

While 3D GANs have recently demonstrated the high-quality synthesis of multi-view consistent images and 3D shapes, they are mainly restricted to photo-realistic human portraits. This paper aims to extend 3D GANs to a different, but meaningful visual form: artistic portrait drawings. However, extending existing 3D GANs to drawings is challenging due to the in

Ayaka Okuya, Shigeru Ida, Ryuki Hyodo, Satoshi Okuzumi

A growing number of debris discs have been detected around metal-polluted white dwarfs. They are thought to be originated from tidally disrupted exoplanetary bodies and responsible for metal accretion onto host WDs. To explain (1) the observationally inferred accretion rate higher than that induced by Poynting-Robertson drag, $\dot{M}_{\rm PR}$, and (2) refr

Bo Wang, Yihong Wang, Xiubao Sui, Yuan Liu

Guided image filter is a well-known local filter in image processing. However, the presence of halo artifacts is a common issue associated with this type of filter. This paper proposes an algorithm that utilizes gradient information to accurately identify the edges of an image. Furthermore, the algorithm uses weighted information to distinguish flat areas fr

Bacon R., Accardo M., Adjali L., Anwand H.

The Multi Unit Spectroscopic Explorer (MUSE) is a second-generation VLT panoramic integral-field spectrograph currently in manufacturing, assembly and integration phase. MUSE has a field of 1x1 arcmin2 sampled at 0.2x0.2 arcsec2 and is assisted by the VLT ground layer adaptive optics ESO facility using four laser guide stars. The instrument is a large assemb

Byeonghee Yu, Uros Seljak, Yin Li, Sukhdeep Singh

We present growth of structure constraints from the cosmological analysis of the power spectrum multipoles of SDSS-III BOSS DR12 galaxies. We use the galaxy power spectrum model of Hand et al. (2017), which decomposes the galaxies into halo mass bins, each of which is modeled separately using the relations between halo biases and halo mass. The model combine

Sascha Kurz, Ivan Landjev, Francesco Pavese, Assia Rousseva

In this paper, we give a geometric construction of the three strong non-lifted $(3\mod{5})$-arcs in $\operatorname{PG}(3,5)$ of respective sizes 128, 143, and 168, and construct an infinite family of non-lifted, strong $(t\mod{q})$-arcs in $\operatorname{PG}(r,q)$ with $t=(q+1)/2$ for all $r\ge3$ and all odd prime powers $q$.

Katarzyna Grabowska, Janusz Grabowski

We show that contact reductions can be described in terms of symplectic reductions in the traditional Marsden-Weinstein-Meyer as well as the constant rank picture. The point is that we view contact structures as particular (homogeneous) symplectic structures. A group action by contactomorphisms is lifted to a Hamiltonian action on the corresponding symplecti

Jing Tang, Bo Tao, Zeyu Gong, Zhouping Yin

In recent years, multi-scale generative adversarial networks (GANs) have been proposed to build generalized image processing models based on single sample. Constraining on the sample size, multi-scale GANs have much difficulty converging to the global optimum, which ultimately leads to limitations in their capabilities. In this paper, we pioneered the introd

Shivani Bhandari, Alexa C. Gordon, Danica R. Scott, Lachlan Marnoch

We present the discovery of as-of-yet non-repeating Fast Radio Burst (FRB), FRB 20210117A, with the Australian Square Kilometer Array Pathfinder (ASKAP) as a part of the Commensal Real-time ASKAP Fast Transients (CRAFT) Survey. The sub-arcsecond localization of the burst led to the identification of its host galaxy at a $z=0.214(1)$. This redshift is much lo

Kate Reidy, Wouter Mortelmans, Seong Soon Jo, Aubrey Penn

Layered transition metal dichalcogenide (TMD) semiconductors oxidize readily in a variety of conditions, and a thorough understanding of this oxide formation is required for the advancement of TMD-based microelectronics. Here, we combine scanning transmission electron microscopy (STEM) with spectroscopic ellipsometry (SE) to investigate oxide formation at th

Pranay Patil, Markus Heyl, Fabien Alet

We present the analysis of the slowing down exhibited by stochastic dynamics of a ring-exchange model on a square lattice, by means of numerical simulations. We find the preservation of coarse-grained memory of initial state of density-wave types for unexpectedly long times. This behavior is inconsistent with the prediction from a low frequency continuum the

Thomas Lam

The Number Rotation Puzzle (NRP) is a combination puzzle in which the goal is to rearrange a scrambled rectangular grid of numbers back into order via moves that consist of rotating square blocks of numbers of fixed size. Over all possible boards and rotating block sizes, we find all solvable initial configurations and provide algorithms to solve such config

Jiaxing Li, Chenqi Kong, Shiqi Wang, Haoliang Li

The image recapture attack is an effective image manipulation method to erase certain forensic traces, and when targeting on personal document images, it poses a great threat to the security of e-commerce and other web applications. Considering the current learning-based methods suffer from serious overfitting problem, in this paper, we propose a novel two-b

Misha Urooj Khan, Mahnoor Dil, Muhammad Zeshan Alam, Farooq Alam Orakazi

The increasing prevalence of unmanned aerial vehicles (UAVs), commonly known as drones, has generated a demand for reliable detection systems. The inappropriate use of drones presents potential security and privacy hazards, particularly concerning sensitive facilities. To overcome those obstacles, we proposed the concept of MultiFeatureNet (MFNet), a solutio

Yuxin Dong, Tieliang Gong, Shujian Yu, Hong Chen

The matrix-based R\'enyi's entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This intriguing property makes it widely applied in statistical inference and machine learning tasks. However, this information theoretical quantity is not robust against noise in the

Yingshu Yang, Stefano Dal Forno, Marco Battiato

Spintronic THz emitters (STE) are efficient THz sources constructed using thin heavy-metal (HM) and ferromagnetic-metal (FM) layers. To improve the performance of the STE, different structuring methods (trilayers, stacked bilayers) have been experimentally applied. A theoretical description of the overall THz emission process is necessary to optimize the eff

Guillermo Cabrera, Sungwook E. Hong, Lilianne Nakazono, David Parkinson

Machine Learning is a powerful tool for astrophysicists, which has already had significant uptake in the community. But there remain some barriers to entry, relating to proper understanding, the difficulty of interpretability, and the lack of cohesive training. In this discussion session we addressed some of these questions, and suggest how the field may mov

A. Gade, B. A. Brown, D. Weisshaar, D. Bazin

A novel pathway for the formation of multi-particle-multi-hole (np-mh) excited states in rare isotopes is reported from highly energy- and momentum-dissipative inelastic-scattering events measured in reactions of an intermediate-energy beam of 38Ca on a Be target. The negative-parity,complex-structure final states in 38Ca were observed following the in-beam

Quyen Tran, Hai Nguyen, Hoang Phan, Quan Dao

In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because previous samples have limited replay value when learning a new task. Prior research has typically relied on either a single adaptive centroid or multiple fixed centroids to represent each class in the latent space. Howeve

Zizhang Wu, Yunzhe Wu, Jian Pu, Xianzhi Li

Monocular 3D object detection is a low-cost but challenging task, as it requires generating accurate 3D localization solely from a single image input. Recent developed depth-assisted methods show promising results by using explicit depth maps as intermediate features, which are either precomputed by monocular depth estimation networks or jointly evaluated wi

Yao Zhu, Yuefeng Chen, Xiaodan Li, Rong Zhang

Out-Of-Distribution (OOD) detection has received broad attention over the years, aiming to ensure the reliability and safety of deep neural networks (DNNs) in real-world scenarios by rejecting incorrect predictions. However, we notice a discrepancy between the conventional evaluation vs. the essential purpose of OOD detection. On the one hand, the convention

Ya-Dong Wu

Bosonic fault tolerant quantum computing requires preparations of Bosonic code states like cat states and GKP states with high fidelity and reliable quantum certification of these states. Although many proposals on preparing these states have been developed, few investigation has been done on how to reliably certify these experimental states. In this paper,

Jie Liu, Chao Chen, Jie Tang, Gangshan Wu

Image super-resolution (SR) serves as a fundamental tool for the processing and transmission of multimedia data. Recently, Transformer-based models have achieved competitive performances in image SR. They divide images into fixed-size patches and apply self-attention on these patches to model long-range dependencies among pixels. However, this architecture d

S K H Auluck

This paper, Part I of a series, describes a kinematic framework for the theory of a Dense Plasma Focus which is very similar to the GV model in spirit but which differs in its scope in four respects. First, the GV model derives most of its results from the mathematical properties of the solution of a certain partial differential equation derived from assumpt

M. Tessler, M. Paul, S. Halfon, Y. Kashiv

The stable gallium isotopes, $^{69,71}$Ga, are mostly produced by the weak slow ($s$) process in massive stars. We report here on measurements of astrophysically-relevant neutron capture cross sections of the $^{69,71}$Ga$(n,\gamma)$ reactions. The experiments were performed by the activation technique using a high-intensity ($3-5\times10^{10}$ n/s), quasi-M

Xiao Yu, Qingyang Wu, Kun Qian, Zhou Yu

In task-oriented dialogs (TOD), reinforcement learning (RL) algorithms train a model to directly optimize response for task-related metrics. However, RL needs to perform exploration, which can be time-consuming due to the slow auto-regressive sequence generation process. We investigate an approach to create a more efficient RL-based algorithm to improve TOD

Hidetoshi Awata, Koji Hasegawa, Hiroaki Kanno, Ryo Ohkawa

We show the relation of the non-stationary difference equation proposed by one of the authors and the quantized discrete Painlev\'e VI equation. The five-dimensional Seiberg-Witten curve associated with the difference equation has a consistent four-dimensional limit. We also show that the original equation can be factorized as a coupled system for a pair of

Ziqi Gao, Yifan Niu, Jiashun Cheng, Jianheng Tang

Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation a burning issue. Until recently, many works notice that GNNs are coupled with spectral concentration, which means the spectrum obtained by GNNs concentrates on a local part in spec

Moran Jia, Huixia Li, Defu Hou

We present an event-by-event study of photon production in early stage of high energy nuclear collisions, where the system is dominant by highly occupied of gluons and initialized by McLerran-Venugopalan model. The photons are produced through the gluon fusion and splitting processes when strong magnetic field is included. We study the spectra and collective

Zhengcong Fei, Mingyuan Fan, Li Zhu, Junshi Huang

It is well believed that the higher uncertainty in a word of the caption, the more inter-correlated context information is required to determine it. However, current image captioning methods usually consider the generation of all words in a sentence sequentially and equally. In this paper, we propose an uncertainty-aware image captioning framework, which par

Gui-hong Huang, Wei Jiang, Liang-jian Wen, Yi-fang Wang

High precision vertex and energy reconstruction is crucial for large liquid scintillator detectors such as JUNO, especially for the determination of the neutrino mass ordering by analyzing the energy spectrum of reactor neutrinos. This paper presents a data-driven method to obtain more realistic and more accurate expected PMT response of positron events in J

Seung-Ho Baek, Sergey L. Bud'ko, Paul C. Canfield, F. Borsa

We measured the central ($1/2\leftrightarrow -1/2$) and first satellite ($\pm3/2\leftrightarrow \pm1/2$) lines of the \la\ NMR spectra as a function of temperature in LaAgSb$_2$, in order to elucidate the origin and nature of the charge-density-wave (CDW) transitions at $T_\text{CDW1}=207$ K and $T_\text{CDW2}=186$ K. In the normal state, the Knight shift K

Xiaoyan Jing, Aixian Zhang, Keqin Feng

Binary $m$-sequences are ones with the largest period $n=2^m-1$ among the binary sequences produced by linear shift registers with length $m$. They have a wide range of applications in communication since they have several desirable pseudorandomness such as balance, uniform pattern distribution and ideal (classical) autocorrelation. In his reseach on arithme

James T. Farmer, Azarin Zarassi, Sadman Shanto, Darian Hartsell

We report continuous measurements of quasiparticles trapping and clearing from Andreev Bound States in aluminum nanobridge Josephson junctions integrated into a superconducting-qubit-like device. We find that trapping is well modeled by independent spontaneous emission events. Above 80 mK the clearing process is well described by absorption of thermal phonon

Andong Li, Guochen Yu, Chengshi Zheng, Wenzhe Liu

While deep neural networks have facilitated significant advancements in the field of speech enhancement, most existing methods are developed following either empirical or relatively blind criteria, lacking adequate guidelines in pipeline design. Inspired by Taylor's theorem, we propose a general unfolding framework for both single- and multi-channel speech e

R. A. Vargas-Hernández, K. Jorner, R. Pollice, A. Aspuru-Guzik

Semi-empirical quantum chemistry has recently seen a renaissance with applications in high-throughput virtual screening and machine learning. The simplest semi-empirical model still in widespread use in chemistry is H\"uckel's $\pi$-electron molecular orbital theory. In this work, we implemented a H\"uckel program using differentiable programming with the JA

Siyu Ren, Junhui Hou, Xiaodong Chen, Ying He

We present a learning-based method, namely GeoUDF,to tackle the long-standing and challenging problem of reconstructing a discrete surface from a sparse point cloud.To be specific, we propose a geometry-guided learning method for UDF and its gradient estimation that explicitly formulates the unsigned distance of a query point as the learnable affine averagin

Aakriti Sharma, Ajay K. Sharma, M. Mursaleen

In this paper, we characterize Carleson measure and vanishing Carleson measure on Bergman spaces with admissible weights in terms of {\it t-Berezin transform} and {\it averaging function} as key tools. Moreover, power bounded and power compact weighted composition operators are characterized as application of Carleson measure and vanishing Carleson measure r

Dongwon Kim, Namyup Kim, Suha Kwak

Cross-modal retrieval across image and text modalities is a challenging task due to its inherent ambiguity: An image often exhibits various situations, and a caption can be coupled with diverse images. Set-based embedding has been studied as a solution to this problem. It seeks to encode a sample into a set of different embedding vectors that capture differe

Petr Opletal, Hironori Sakai, Yoshinori Haga, Yoshifumi Tokiwa

We investigate the physical properties of a single crystal of uranium telluride U$_{7}$Te$_{12}$. We have confirmed that U$_{7}$Te$_{12}$ crystallizes in the hexagonal structure with three nonequivalent crystallographic uranium sites. The paramagnetic moments are estimated to be approximately 1 $\mu_{\rm B}$ per the uranium site, assuming a uniform moment on

Angel Yanguas-Gil, Sandeep Madireddy

We have developed a model for online continual or lifelong reinforcement learning (RL) inspired on the insect brain. Our model leverages the offline training of a feature extraction and a common general policy layer to enable the convergence of RL algorithms in online settings. Sharing a common policy layer across tasks leads to positive backward transfer, w

Jarn de Jong, Frederik Hahn, Nikolay Tcholtchev, Manfred Hauswirth

Quantum information processing architectures typically only allow for nearest-neighbour entanglement creation. In many cases, this prevents the direct generation of GHZ states, which are commonly used for many communication and computation tasks. Here, we show how to obtain GHZ states between nodes in a network that are connected in a straight line, naturall

Alexander Vidal, Samy Wu Fung, Luis Tenorio, Stanley Osher

A normalizing flow (NF) is a mapping that transforms a chosen probability distribution to a normal distribution. Such flows are a common technique used for data generation and density estimation in machine learning and data science. The density estimate obtained with a NF requires a change of variables formula that involves the computation of the Jacobian de

Mengjuan Liu, Zhengning Hu, Zhi Lai, Daiwei Zheng

Bidding strategies that help advertisers determine bidding prices are receiving increasing attention as more and more ad impressions are sold through real-time bidding systems. This paper first describes the problem and challenges of optimizing bidding strategies for individual advertisers in real-time bidding display advertising. Then, several representativ

Chengming Xu, Chen Liu, Siqian Yang, Yabiao Wang

Positive-Unlabeled (PU) learning aims to learn a model with rare positive samples and abundant unlabeled samples. Compared with classical binary classification, the task of PU learning is much more challenging due to the existence of many incompletely-annotated data instances. Since only part of the most confident positive samples are available and evidence

Aakriti Sharma, Ajay K. Sharma

In this paper, we completely characterize nuclear Volterra composition operators $T^\phi_g : \mathcal H^\infty_\nu \longrightarrow \mathcal H^\infty_\mu$ and $S^\phi_g : \mathcal H^\infty_\nu \longrightarrow \mathcal H^\infty_\mu$ acting between weighted type spaces in terms of the symbols $g$ and $\phi$ of $T^\phi_g$ and $S^\phi_g$ and weights $\nu$ and $\m

De-yu Zhong, Guang-qian Wang

The kinetic equation is crucial for understanding the statistical properties of stochastic processes, yet current equations, such as the classical Fokker-Planck, are limited to local analysis. This paper derives a new kinetic equation for stochastic systems on vector bundles, addressing global scale randomness. The kinetic equation was derived by cumulant ex

Bin Shan, Ye Li, Shengjun Huang

Although physics-informed neural networks(PINNs) have progressed a lot in many real applications recently, there remains problems to be further studied, such as achieving more accurate results, taking less training time, and quantifying the uncertainty of the predicted results. Recent advances in PINNs have indeed significantly improved the performance of PI

Ritu Belani, Jeffrey Flanigan

Code-switching, or switching between languages, occurs for many reasons and has important linguistic, sociological, and cultural implications. Multilingual speakers code-switch for a variety of purposes, such as expressing emotions, borrowing terms, making jokes, introducing a new topic, etc. The reason for code-switching may be quite useful for analysis, bu

Guergana Petrova, Przemysław Wojtaszczyk

We prove Carl's type inequalities for the error of approximation of compact sets K by deep and shallow neural networks. This in turn gives lower bounds on how well we can approximate the functions in K when requiring the approximants to come from outputs of such networks. Our results are obtained as a byproduct of the study of the recently introduced Lipschi

Qiaodan Luo, Leonardo Christino, Fernando V Paulovich, Evangelos Milios

Dimensionality reduction has become an important research topic as demand for interpreting high-dimensional datasets has been increasing rapidly in recent years. There have been many dimensionality reduction methods with good performance in preserving the overall relationship among data points when mapping them to a lower-dimensional space. However, these ex

Hussein Darir, Nikita Borisov, Geir Dullerud

Tor is the most popular anonymous communication network. It has millions of daily users seeking privacy while browsing the internet. It has thousands of relays to route and anonymize the source and destinations of the users packets. To create a path, Tor authorities generate a probability distribution over relays based on the estimates of the capacities of t

Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans

Score-based modeling through stochastic differential equations (SDEs) has provided a new perspective on diffusion models, and demonstrated superior performance on continuous data. However, the gradient of the log-likelihood function, i.e., the score function, is not properly defined for discrete spaces. This makes it non-trivial to adapt \textcolor{\cdiff}{t

Jiaqi Gu, Ben Keller, Jean Kossaifi, Anima Anandkumar

Transformers have attained superior performance in natural language processing and computer vision. Their self-attention and feedforward layers are overparameterized, limiting inference speed and energy efficiency. Tensor decomposition is a promising technique to reduce parameter redundancy by leveraging tensor algebraic properties to express the parameters

Akatoki Noboriguchi, Tohru Nagao, Yoshiki Toba, Kohei Ichikawa

We report optical spectroscopic observations of four blue-excess dust-obscured galaxies (BluDOGs) identified by Subaru Hyper Suprime-Cam. BluDOGs are a sub-class of dust-obscured galaxies (DOGs, defined with the extremely red color $(i-[22])_{\rm AB} \geq 7.0$; Toba et al. 2015), showing a significant flux excess in the optical $g$- and $r$-bands over the po

Calvin Beideman, Karthekeyan Chandrasekaran, Weihang Wang

We show that every $\alpha$-approximate minimum cut in a connected graph is the unique minimum $(S,T)$-terminal cut for some subsets $S$ and $T$ of vertices each of size at most $\lfloor2\alpha\rfloor+1$. This leads to an alternative proof that the number of $\alpha$-approximate minimum cuts in a $n$-vertex connected graph is $n^{O(\alpha)}$ and they can all

Adit Magotra, Aagat Gedam, Tanush Savadi, Emily Li

Optical Coherence Tomography is a technique used to scan the Retina of the eye and check for tears. In this paper, we develop a Convolutional Neural Network Architecture for OCT scan classification. The model is trained to detect Retinal tears from an OCT scan and classify the type of tear. We designed a block-based approach to accompany a pre-trained VGG-19

Seungjun Ahn, Tyler Grimes, Somnath Datta

The differential network (DN) analysis identifies changes in measures of association among genes under two or more experimental conditions. In this article, we introduce a Pseudo-value Regression Approach for Network Analysis (PRANA). This is a novel method of differential network analysis that also adjusts for additional clinical covariates. We start from m

Ruiqi Bao, S. Mandal, Huawen Xu, Xingran Xu

We consider theoretically a system of exciton-polariton micropillars arranged in a honeycomb lattice. The naturally present TE-TM splitting and an alternating Zeeman splitting, where the different sublattices experience opposite Zeeman splitting, shifts the Dirac points in energy, giving rise to antichiral behavior. In a strip geometry having zigzag edges, t

Bo-Qiang Lu, Da Huang

It is widely believed that extensions of the minimal Higgs sector is one of the promising directions for resolving many puzzles beyond the Standard Model (SM). In this work, we study the unitarity bounds on the models by extending the two-Higgs-doublet model with an additional real or complex Higgs triplet scalar. By noting that the SM gauge symmetries $SU(2

Bozhen Hu, Jun Xia, Jiangbin Zheng, Cheng Tan

The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding. Recent advances have proved the power of language models (LMs) in processing the protein sequence databases, which inherit the advantages of attention networks and capture useful information in learnin

Raghvendra Sahai, Valentin Bujarrabal, Guillermo Quintana-Lacaci, Nicole Reindl

The planetary nebula (PN) NGC3132 is a striking example of the dramatic but poorly understood, mass-loss phenomena that (1-8) Msun stars undergo during their death throes as they evolve into white dwarfs (WDs). From an analysis of JWST multiwavelength (0.9-18 micron) imaging of NGC3132, we report the discovery of an asymmetrical dust cloud around the WD cent

Zhangir Azerbayev, Ansong Ni, Hailey Schoelkopf, Dragomir Radev

Large language models (LLMs) can acquire strong code-generation capabilities through few-shot learning. In contrast, supervised fine-tuning is still needed for smaller models to achieve good performance. Such fine-tuning demands a large number of task-specific NL-code pairs, which are expensive to obtain. In this paper, we attempt to transfer the code genera

Yifen Ke, Changfeng Ma, Zhigang Jia, Yajun Xie

To address the non-negativity dropout problem of quaternion models, a novel quasi non-negative quaternion matrix factorization (QNQMF) model is presented for color image processing. To implement QNQMF, the quaternion projected gradient algorithm and the quaternion alternating direction method of multipliers are proposed via formulating QNQMF as the non-conve

Chuang Yang, Haozhao Ma, Qi Wang

Contour-based instance segmentation methods include one-stage and multi-stage schemes. These approaches achieve remarkable performance. However, they have to define plenty of points to segment precise masks, which leads to high complexity. We follow this issue and present a single-shot method, called \textbf{VeinMask}, for achieving competitive performance i

Tiziano Schiavone, Giovanni Montani, Flavio Bombacigno

We analyse the $f(R)$ gravity in the so-called Jordan frame, as implemented to the isotropic Universe dynamics. The goal of the present study is to show that, according to recent data analyses of the supernovae Ia Pantheon sample, it is possible to account for an effective redshift dependence of the Hubble constant. This is achieved via the dynamics of a non

Elnaz Yousefzadeh Barri, Steven Farber, Hadi Jahanshahi, Eda Beyazit

Building an accurate model of travel behaviour based on individuals' characteristics and built environment attributes is of importance for policy-making and transportation planning. Recent experiments with big data and Machine Learning (ML) algorithms toward a better travel behaviour analysis have mainly overlooked socially disadvantaged groups. Accordingly,

Aditya Basu, John Sampson, Zhiyun Qian, Trent Jaeger

File name confusion attacks, such as malicious symbolic links and file squatting, have long been studied as sources of security vulnerabilities. However, a recently emerged type, i.e., case-sensitivity-induced name collisions, has not been scrutinized. These collisions are introduced by differences in name resolution under case-sensitive and case-insensitive

J. Egedal, H. Gurram, S. Greess, W. Daughton

Fully kinetic simulations are applied to the study of 2D anti-parallel reconnection, elucidating the dynamics by which the electron fluid maintains force balance within both the electron diffusion region (EDR) and the ion diffusion region (IDR). Inside the IDR, magnetic field-aligned electron pressure anisotropy ($p_{e\parallel}\gg p_{e\perp})$ develops upst

Hayafumi Watanabe

To understand the growing phenomena of new vocabulary on nationwide online social media, we analyzed monthly word count time series extracted from approximately 1 billion Japanese blog articles from 2007 to 2019. In particular, we first introduced the extended logistic equation by adding one parameter to the original equation and showed that the model can co

Anneliese Rilinger, Catherine Espaillat, Zihua Xin, Álvaro Ribas

We present spectral energy distribution (SED) modeling of 338 disks around T Tauri stars from eleven star-forming regions, ranging from $\sim$0.5 to 10 Myr old. The disk masses we infer from our SED models are typically greater than those reported from (sub)mm surveys by a factor of 1.5-5, with the discrepancy being generally higher for the more massive disk

Valentina Giunchiglia, Chirag Varun Shukla, Guadalupe Gonzalez, Chirag Agarwal

With the increasing use of Graph Neural Networks (GNNs) in critical real-world applications, several post hoc explanation methods have been proposed to understand their predictions. However, there has been no work in generating explanations on the fly during model training and utilizing them to improve the expressive power of the underlying GNN models. In th

Qi Lü, Yu Wang

A widely used stochastic plate equation is the classical plate equation perturbed by a term of It\^o's integral. However, it is known that this equation is not exactly controllable even if the controls are effective everywhere in both the drift and the diffusion terms and also on the boundary. In some sense, this means that some key feature has been ignored

Yan Jiang, Hongyu Liu, Jiachuan Zhang, Kai Zhang

We present a comprehensive study of new discoveries on the spectral patterns of elastic transmission eigenfunctions, including boundary localisation, surface resonance, and stress concentration. In the case where the domain is radial and the underlying parameters are constant, we give rigorous justifications and derive a thorough understanding of those intri

Dibyayan Chakraborty, Carl Feghali, Reem Mahmoud

A \emph{Kempe chain} on colors $a$ and $b$ is a component of the subgraph induced by colors $a$ and $b$. A \emph{Kempe change} is the operation of interchanging the colors of some Kempe chain. For a list-assignment $L$ and an $L$-coloring $\varphi$, a Kempe change is \emph{$L$-valid} for $\varphi$ if performing the Kempe change yields another $L$-coloring. T

Everett Schlawin, Thomas Beatty, Brian Brooks, Nikolay K. Nikolov

We use JWST NIRCam short wavelength photometry to capture a transit lightcurve of the exoplanet HAT-P-14 b to assess performance as part of instrument commissioning. The short wavelength precision is 152 ppm per 27 second integration as measured over the full time series compared to a theoretical limit of 107 ppm, after corrections to spatially correlated 1/

Haichao Yu, Haoxiang Li, Gang Hua, Gao Huang

Early-exiting dynamic neural networks (EDNN), as one type of dynamic neural networks, has been widely studied recently. A typical EDNN has multiple prediction heads at different layers of the network backbone. During inference, the model will exit at either the last prediction head or an intermediate prediction head where the prediction confidence is higher

Max Goldberg, Konstantin Batygin

Short-period super-Earths and mini-Neptunes encircle more than $\sim50\%$ of Sun-like stars and are relatively amenable to direct observational characterization. Despite this, environments in which these planets accrete are difficult to probe directly. Nevertheless, pairs of planets that are close to orbital resonances provide a unique window into the inner

Khushboo Suman, Sachin Shanbhag, Yogesh M. Joshi

We investigate the nonlinear viscoelastic behavior of a colloidal dispersion at the critical gel state using large amplitude oscillatory shear (LAOS) rheology. The colloidal gel at the critical point is subjected to oscillatory shear flow with increasing strain amplitude at different frequencies. We observe that the first harmonic of the elastic and viscous

Juan Valero, Juan Sosa, Carolina Luque

In this paper, a Bayesian spatial voting model is applied for the first time to characterize the legislative behavior of the Senate of the Republic of Colombia for the period 2006-2010. The analysis is carried out based on the plenary nominal votes of the Senate. The estimation of the model is done using Markov Monte Carlo chain algorithms. The estimated ide

Yu Lu, Huicheng Yin

In the previous paper [Ding Bingbing, Lu Yu, Yin Huicheng, On the critical exponent $p_c$ of the 3D quasilinear wave equation $-\big(1+(\partial_t\phi)^p\big)\partial_t^2\phi+\Delta\phi=0$ with short pulse initial data. I, global existence, Preprint, 2022], for the 3D quasilinear wave equation $-\big(1+(\partial_t\phi)^p\big)\partial_t^2\phi+\Delta\phi=0$ wi

Troi Williams, Po-Lun Chen, Sparsh Bhogavilli, Vaibhav Sanjay

We present DyFOS, an active perception method that dynamically finds optimal states to minimize localization uncertainty while avoiding obstacles and occlusions. We consider the scenario where a perception-denied rover relies on position and uncertainty measurements from a viewer robot to localize itself along an obstacle-filled path. The position uncertaint

Si Wu, Tengfei Liu, Magnus Egerstedt, Zhong-Ping Jiang

This paper studies the control problem for safety-critical multi-agent systems based on quadratic programming (QP). Each controlled agent is modeled as a cascade connection of an integrator and an uncertain nonlinear actuation system. In particular, the integrator represents the position-velocity relation, and the actuation system describes the dynamic respo

Zekun Zhuang

Motivated by the thermal transport problem in the Kitaev spin liquids, we consider a nearest-neighbor tight-binding model on the honeycomb lattice in the presence of random uncorrelated $\pi$-fluxes. We employ different numerical methods to study its transport properties near half-filling. The zero-temperature DC conductivity away from the Dirac point is fou

Youngdae Kim, Debojyoti Ghosh, Emil M. Constantinescu, Ramesh Balakrishnan

This paper explores strategies to transform an existing CPU-based high-performance computational fluid dynamics solver, HyPar, for compressible flow simulations on emerging exascale heterogeneous (CPU+GPU) computing platforms. The scientific motivation for developing a GPU-enhanced version of HyPar is to simulate canonical turbulent flows at the highest reso

Zhen Yang, Fandong Meng, Yingxue Zhang, Ernan Li

We report the result of the first edition of the WMT shared task on Translation Suggestion (TS). The task aims to provide alternatives for specific words or phrases given the entire documents generated by machine translation (MT). It consists two sub-tasks, namely, the naive translation suggestion and translation suggestion with hints. The main difference is

Ziyan Zhao, Li Zhang, Xiaoyun Gao, Xiaoli Lian

Software requirements specification is undoubtedly critical for the whole software life-cycle. Nowadays, writing software requirements specifications primarily depends on human work. Although massive studies have been proposed to fasten the process via proposing advanced elicitation and analysis techniques, it is still a time-consuming and error-prone task t

Caleb Ju, Guanghui Lan

Reinforcement learning (RL) problems over general state and action spaces are notoriously challenging. In contrast to the tableau setting, one can not enumerate all the states and then iteratively update the policies for each state. This prevents the application of many well-studied RL methods especially those with provable convergence guarantees. In this pa

Zijun Cui, Tian Gao, Kartik Talamadupula, Qiang Ji

Deep learning models, though having achieved great success in many different fields over the past years, are usually data hungry, fail to perform well on unseen samples, and lack of interpretability. Various prior knowledge often exists in the target domain and their use can alleviate the deficiencies with deep learning. To better mimic the behavior of human

Boyuan Zhang

The assumption of group heterogeneity has become popular in panel data models. We develop a constrained Bayesian grouped estimator that exploits researchers' prior beliefs on groups in a form of pairwise constraints, indicating whether a pair of units is likely to belong to a same group or different groups. We propose a prior to incorporate the pairwise cons

Hisashi Kasuya, Natsuo Miyatake

We give a criterion for compact Sasakian manifolds to be deformed to Sasakian manifolds which are locally isomorphic to circle bundles of anti-canonical bundles over Hermitian symmetric spaces as a Sasakian analogue of Simpson's uniformization results related to variations of Hodge structure and Higgs bundles.

Hao Zhang, Nan Zhang, Ruixin Zhang, Lei Shen

In recent years, molecular graph representation learning (GRL) has drawn much more attention in molecular property prediction (MPP) problems. The existing graph methods have demonstrated that 3D geometric information is significant for better performance in MPP. However, accurate 3D structures are often costly and time-consuming to obtain, limiting the large

Yuxuan Jiang, Tianhao Zhao, Luojia Zhang, Qiang Chen

We report the temperature evolution of the Dirac band in semiconducting zirconium pentatelluride (ZrTe$_5$) using magneto-infrared spectroscopy. We find that the band gap is temperature independent at low temperatures and increases with temperature at elevated temperatures. Although such an observation seems to support a weak topological insulator phase at a