May 2023 arXiv papers — page 159

Yilei Shi, Qinyu Li, Xiaoxiang Zhu

Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. Recent developments in deep convolutional neural networks (DCNNs) have enabled accurate pixel-level labeling tasks.

Zhongjun Ni, Chi Zhang, Magnus Karlsson, Shaofang Gong

Digital transformation in buildings accumulates massive operational data, which calls for smart solutions to utilize these data to improve energy performance. This study has proposed a solution, namely Deep Energy Twin, for integrating deep learning and digital twins to better understand building energy use and identify the potential for improving energy eff

A V Subramanyam, Niranjan Sundararajan, Vibhu Dubey, Brejesh Lall

Text-to-Image (T2I) ReID has attracted a lot of attention in the recent past. CUHK-PEDES, RSTPReid and ICFG-PEDES are the three available benchmarks to evaluate T2I ReID methods. RSTPReid and ICFG-PEDES comprise of identities from MSMT17 but due to limited number of unique persons, the diversity is limited. On the other hand, CUHK-PEDES comprises of 13,003 i

Yuan Yue, Peng-Bo Ding, Yong-Qiang Wang

In this paper, we revisit the model of bosonic matter in the form of a free complex scalar field with a nontrivial wormhole spacetime topology supported by a free phantom field. We obtain a new type of boson star with wormhole solutions, in which the complex scalar field possess full parity-odd symmetry with respect to the two asymptotically flat spacetime r

Shubham Kumar, Deepmala

In this paper, we discussed the unique solvability of the two absolute value matrix equations. The unique solvability condition $\rho (\vert A^{-1} B \vert)<1$ is provided for the generalized absolute value matrix equation (GAVME) $AX + B \vert X \vert = F$. This condition is superior to that of Kumar et al. [J. Numer. Anal. Approx. Theory, 51(1) (2022) 83-8

M. Khosravi, A. Sheikhhosseini, S. Malekinejad

In this note, some inequalities involving operator means of sectorial matrices are proved which are generalizations and refinements of previous known results. Among them, let $A$ and $B$ be two accretive matrices with $A,B\in\mathcal{S}_{\theta}$, $0 < mI \leqslant A, B \leqslant MI$ for positive real numbers $ M, m, \, \sigma$ be an operator mean and $\sigm

Guangsheng Bao, Zhiyang Teng, Yue Zhang

Sequence-to-sequence (seq2seq) models have been widely used for natural language processing, computer vision, and other deep learning tasks. We find that seq2seq models trained with early-stopping suffer from issues at the token level. In particular, while some tokens in the vocabulary demonstrate overfitting, others underfit when training is stopped. Experi

Wei Liu, Haozhao Wang, Jun Wang, Ruixuan Li

Rationalization is to employ a generator and a predictor to construct a self-explaining NLP model in which the generator selects a subset of human-intelligible pieces of the input text to the following predictor. However, rationalization suffers from two key challenges, i.e., spurious correlation and degeneration, where the predictor overfits the spurious or

Zhuoyue Jia, Chi Sin Tang, Jing Wu, Changjian Li

The integration of high-temperature superconducting YBa2Cu3O6+x (YBCO) into flexible electronic devices has the potential to revolutionize the technology industry. The effective preparation of high-quality flexible YBCO films therefore plays a key role in this development. We present a novel approach for transferring water-sensitive YBCO films onto flexible

Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li

The keyphrase extraction task refers to the automatic selection of phrases from a given document to summarize its core content. State-of-the-art (SOTA) performance has recently been achieved by embedding-based algorithms, which rank candidates according to how similar their embeddings are to document embeddings. However, such solutions either struggle with t

Mark J. Ablowitz, Justin T. Cole, S. D. Nixon

A honeycomb Floquet lattice with helically rotating waveguides and an interface separating two counter-propagating subdomains is analyzed. Two topologically protected localized waves propagate unidirectionally along the interface. Switching can occur when these interface modes reach the edge of the lattice and the light splits into waves traveling in two opp

Radha Ramakrishnan, Rabeetha Velsamy

We introduce the Zak transform on $L^{2}(\mathbb{R}^{2n})$ associated with the Weyl transform. By making use of this transform, we define a bracket map and prove that the system of twisted translates $\{T^{t}_{(k,l)}\phi : k,l\in \mathbb{Z}^{n}\}$ is a frame sequence iff $0<A\leq \left[\phi,\phi\right](\xi,\xi^{'})\leq B<\infty,$ for a.e $(\xi,\xi^{'})\in \O

Hitendra Sarkar, Madhurjya P. Bora

The excitation of nonlinear wave structures in a dusty plasma caused by a moving external charge perturbation is examined in this work, which uses a 1-D flux corrected transport simulation. The plasma responds uniquely to different nature of the moving charge, depending on which, for small amplitude perturbations, pinned envelope solitons are generated and e

Gianluca Viggiano

The Taylor expansion is a widely used and powerful tool in all branches of Mathematics, both pure and applied. In Probability and Mathematical Statistics, however, a stronger version of Taylor's classical theorem is often needed, but only tacitly assumed. In this note, we provide an elementary proof of this measurable Taylor's theorem, which guarantees that

Alexey Glazyrin

Recently, Arman, Bondarenko, and Prymak constructed a constant width body in $\mathbb{R}^n$ whose illumination number is exponential in $n$. In this note, we improve their bound by generalizing the construction. In particular, we construct a constant width body in $\mathbb{R}^n$ whose illumination number is at least $(\tau+o(1))^n$, where $\tau\approx 1.047$

Bon-Chul Koo, Yong-Hyun Lee, Jae-Joon Lee, Sung-Chul Yoon

The Cassiopeia A supernova remnant has a complex structure, manifesting the multidimensional nature of core-collapse supernova explosions. To further understand this, we carried out near-infrared multi-object spectroscopy on the ejecta knots located in the "northeastern (NE) jet" and the "Fe K plume" regions, which are two distinct features in the outer east

James Rowan, Lizhe Wan

We consider the two dimensional pure gravity water waves with nonzero constant vorticity in infinite depth, working in the holomorphic coordinates introduced by Hunter, Ifrim, and Tataru. We show that close to the critical velocity corresponding to zero frequency, a solitary wave exists. We use a fixed point argument to construct the solitary wave whose prof

K. H. Thong, A. Melatos, L. V. Drummond

The coupled, time-dependent Gross-Pitaevskii and Ginzburg-Landau equations are solved simultaneously in three dimensions to investigate the equilibrium state and far-from-equilibrium, spin-down dynamics of an interpenetrating neutron superfluid and proton type-II superconductor, as an idealized description of the outer core of a neutron star. The simulations

Rajiuddin Sk, Prasanta K. Panigrahi

The primary objective of quantum Shannon theory is to evaluate the capacity of quantum channels. In spite of the existence of rigorous coding theorems that quantify the transmission of information through quantum channels, superadditivity effects limit our understanding of the channel capacities. In this paper, we mainly focus on a family of channels known a

Ohad Kammar, Katarzyna Marek

Regular expressions -- regexes -- are widely used not only for validating, but also for parsing textual data. Generally, regex parsers output a loose structure, e.g. an unstructured list of matches, leaving it up to the user to validate the output's properties and transform it into the desired structure. Since the regex itself carries information about the s

Kayvan Sadeghi, Terry Soo

Causal intervention is an essential tool in causal inference. It is axiomatized under the rules of do-calculus in the case of structure causal models. We provide simple axiomatizations for families of probability distributions to be different types of interventional distributions. Our axiomatizations neatly lead to a simple and clear theory of causality that

Xingyu Zhong, Wenbiao Han, Ziren Luo, Yueliang Wu

A supermassive binary black-hole candidate SDSS J1430+2303 reported recently motivates us to investigate an imminent binary of supermassive black holes as potential gravitational wave source, the radiated gravitational waves at the end of the merger are shown to be in the band of space-borne detectors. We provide a general analysis on the required detecting

Rushuai Yang, Chenjia Bai, Hongyi Guo, Siyuan Li

In reinforcement learning, unsupervised skill discovery aims to learn diverse skills without extrinsic rewards. Previous methods discover skills by maximizing the mutual information (MI) between states and skills. However, such an MI objective tends to learn simple and static skills and may hinder exploration. In this paper, we propose a novel unsupervised s

Ruiqi Li, Rongjie Huang, Lichao Zhang, Jinglin Liu

The speech-to-singing (STS) voice conversion task aims to generate singing samples corresponding to speech recordings while facing a major challenge: the alignment between the target (singing) pitch contour and the source (speech) content is difficult to learn in a text-free situation. This paper proposes AlignSTS, an STS model based on explicit cross-modal

Jyun-Yi Chen, Saeed Saeedvand, I-Wei Lai

This paper introduces the Adaptive Learning Path Navigation (ALPN) system, a novel approach for enhancing E-learning platforms by providing highly adaptive learning paths for students. The ALPN system integrates the Attentive Knowledge Tracing (AKT) model, which assesses students' knowledge states, with the proposed Entropy-enhanced Proximal Policy Optimizat

Chaoya Jiang, Haiyang Xu, Chenliang Li, Miang Yan

Vision Transformers (ViTs) have been widely used in large-scale Vision and Language Pre-training (VLP) models. Though previous VLP works have proved the effectiveness of ViTs, they still suffer from computational efficiency brought by the long visual sequence. To tackle this problem, in this paper, we propose an efficient vision-and-language pre-training mod

Wenbin Hu, Yudi Wang, Mingxian Huang, Huaiwu Zhang

The field-dependence elastic modulus of magnetostrictive films, also called {\Delta}E or {\Delta}G effect, is crucial for ultrasensitive magnetic field sensors based on surface acoustic waves (SAWs). In spite of a lot of successful demonstrations, rare attention was paid to the frequency-dependence of {\Delta}E or {\Delta}G effect. In current work, shear hor

Na Wang

3D (dimensional) Young diagrams are a generalization of 2D Young diagrams. We want to construct the structures on 3D Young diagrams paralleled to that on 2D Young diagrams. We have already obtained the 3D Bosons, 3D Fermions and 3-Jack polynomials, which are the symmetric functions on 3D Young diagrams. In this paper, we realize the KP hierarchy by the field

Mengsong Xue, Kenji Watanabe, Takashi Taniguchi, Ryo Kitaura

We have developed a microspectroscopy technique for measuring gate-modulated reflectance to probe excitonic states in two-dimensional transition metal dichalcogenides. Successfully observing excited states of excitons from cryogenic to room temperature showed that this method is more sensitive to excitonic signals than traditional reflectance spectroscopy. O

Yuanyou Xu, Zongxin Yang, Yi Yang

In this paper, we introduce semi-supervised video object segmentation (VOS) to panoptic wild scenes and present a large-scale benchmark as well as a baseline method for it. Previous benchmarks for VOS with sparse annotations are not sufficient to train or evaluate a model that needs to process all possible objects in real-world scenarios. Our new benchmark (

Longwen Zhang, Zijun Zhao, Xinzhou Cong, Qixuan Zhang

Significant advancements have been made in developing parametric models for digital humans, with various approaches concentrating on parts such as the human body, hand, or face. Nevertheless, connectors such as the neck have been overlooked in these models, with rich anatomical priors often unutilized. In this paper, we introduce HACK (Head-And-neCK), a nove

Yungi Jeong, Eunseok Yang, Jung Hyun Ryu, Imseong Park

Mechanical defects in real situations affect observation values and cause abnormalities in multivariate time series, such as sensor values or network data. To perceive abnormalities in such data, it is crucial to understand the temporal context and interrelation between variables simultaneously. The anomaly detection task for time series, especially for unla

Yuwen Cao, Tomoaki Ohtsuki, Setareh Maghsudi, Tony Q. S. Quek

In this paper, we develop a deep learning (DL)-guided hybrid beam and power allocation approach for multiuser millimeter-wave (mmWave) networks, which facilitates swift beamforming at the base station (BS). The following persisting challenges motivated our research: (i) User and vehicular mobility, as well as redundant beam-reselections in mmWave networks, d

Michael E. Hoenk

Stability and photometric accuracy of silicon imaging detectors are essential for the Habitable Worlds Observatory and a range of NASA missions that will explore time domain astrophysics and astronomy over a spectral range spanning soft X-rays through the ultraviolet, visible, and near infrared. Detector stability is one of the oldest and most challenging pr

Didi Zhu, Yinchuan Li, Yunfeng Shao, Jianye Hao

We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target labels including unknown categories. GUDA bridges the gap between label distribution shift-based and label space mismatch-based variants, essentially categorizing them as a unified prob

Zecheng Tang, Pinzheng Wang, Keyan Zhou, Juntao Li

Diffusion models have been successfully adapted to text generation tasks by mapping the discrete text into the continuous space. However, there exist nonnegligible gaps between training and inference, owing to the absence of the forward process during inference. Thus, the model only predicts based on the previously generated reverse noise rather than the noi

Valentina Di Marco, Andrew Zic, Matthew T. Miles, Daniel J. Reardon

The recent observation of a common red-noise process in pulsar timing arrays (PTAs) suggests that the detection of nanohertz gravitational waves might be around the corner. However, in order to confidently attribute this red process to gravitational waves, one must observe the Hellings-Downs curve -- the telltale angular correlation function associated with

A. Lourdusamy, I. Dhivviyanandam, Lian Mathew

Let $G$ be a connected graph. A pebbling move is defined as taking two pebbles from one vertex and placing one pebble to an adjacent vertex and throwing away the other pebble. The non-split domination cover pebbling number, $\psi_{ns}(G)$, of a graph $G$ is the minimum of pebbles that must be placed on $V(G)$ such that after a sequence of pebbling moves, the

Jon McCormack, Camilo Cruz Gambardella, Stephen James Krol

In creative design, where aesthetics play a crucial role in determining the quality of outcomes, there are often multiple worthwhile possibilities, rather than a single ``best'' design. This challenge is compounded in the use of computational generative systems, where the sheer number of potential outcomes can be overwhelming. This paper introduces a method

Xin-Yang Zheng, Hao Pan, Peng-Shuai Wang, Xin Tong

Although the recent rapid evolution of 3D generative neural networks greatly improves 3D shape generation, it is still not convenient for ordinary users to create 3D shapes and control the local geometry of generated shapes. To address these challenges, we propose a diffusion-based 3D generation framework -- locally attentional SDF diffusion, to model plausi

Bhanu Prakash Voutharoja, Lizhen Qu, Fatemeh Shiri

Recent works on form understanding mostly employ multimodal transformers or large-scale pre-trained language models. These models need ample data for pre-training. In contrast, humans can usually identify key-value pairings from a form only by looking at layouts, even if they don't comprehend the language used. No prior research has been conducted to investi

Zhao Liu, Nikhil V. Medhekar

Here we demonstrate the occurrence of robust itinerant ferromagnetism in Mott-Hubbard systems at both low and high doping concentrations. Specifically, we study the effect of hole doping on the experimentally synthesized LaCrAsO via first-principles calculations and observe that the parent G-type antiferromagnetism vanishes quickly at low doping concentratio

Mohammed Zia Jalaludeen, Shilong Li, Ke Tian, Toshio Sasaki

Whispering gallery mode (WGM) microbubble cavities are a versatile optofluidic sensing platform owing to their hollow core geometry. To increase the light-matter interaction and, thereby, achieve a higher sensitivity, thin-walled microbubbles are desirable. However, a lack of knowledge about the precise geometry of hollow microbubbles prevents us from having

Cheng Yang, Lijing Liang, Zhixun Su

Real-world image denoising is an extremely important image processing problem, which aims to recover clean images from noisy images captured in natural environments. In recent years, diffusion models have achieved very promising results in the field of image generation, outperforming previous generation models. However, it has not been widely used in the fie

Arghya Mallick, Abhishek Mishra, Ashish R. Hota, Prabodh Bajpai

With the phenomenal growth in renewable energy generation, the conventional synchronous generator-based power plants are gradually getting replaced by renewable energy sources-based microgrids. Such transition gives rise to the challenges of procuring various ancillary services from microgrids. We propose a distributed optimization framework that coordinates

Vladimir Kremenetski, Anuj Apte, Tad Hogg, Stuart Hadfield

The Quantum Approximate Optimization Algorithm and its generalization to Quantum Alternating Operator Ansatz (QAOA) is a promising approach for applying quantum computers to challenging problems such as combinatorial optimization and computational chemistry. In this paper, we study the underlying mechanisms governing the behavior of QAOA circuits beyond shal

Carlo Silvestri, Xiaoqiong Qi, Thomas Taimre, Aleksandar D. Rakić

This study investigates the interaction between frequency combs and optical feedback effects in Quantum Cascade Lasers (QCLs). The theoretical analysis reveals new phenomena arising from the interplay between comb generation and feedback. By considering the bias current corresponding to free-running single mode emission, the introduction of optical feedback

Ruoyu Wu, Wei Bao, Liming Ge

We study a new online assignment problem, called the Online Task Assignment with Controllable Processing Time. In a bipartite graph, a set of online vertices (tasks) should be assigned to a set of offline vertices (machines) under the known adversarial distribution (KAD) assumption. We are the first to study controllable processing time in this scenario: The

Ingrid Beltita, Daniel Beltita

We obtain a Lie theoretic intrinsic characterization of the connected and simply connected solvable Lie groups whose regular representation is a factor representation. When this is the case, the corresponding von Neumann algebras are isomorphic to the hyperfinite II$_\infty$ factor, and every Casimir function is constant. We thus obtain a family of geometric

Anran Lin, Nanxuan Zhao, Shuliang Ning, Yuda Qiu

Virtual try-on attracts increasing research attention as a promising way for enhancing the user experience for online cloth shopping. Though existing methods can generate impressive results, users need to provide a well-designed reference image containing the target fashion clothes that often do not exist. To support user-friendly fashion customization in fu

Saizo Aoyagi, Satoshi Fukumori, Kenji Hirose, Takayoshi Kitamura

Virtual re-creations of real-world places are attractive and becoming more popular. Attractiveness of re-created place is commonly determined by the degree of visual similarity to the original places. Even if the re-creation has poor similarity to the original, there are cases in which people recognize and allow the re-creation to have similarity as the same

Bao Thach, Brian Y. Cho, Shing-Hei Ho, Tucker Hermans

Applications in fields ranging from home care to warehouse fulfillment to surgical assistance require robots to reliably manipulate the shape of 3D deformable objects. Analytic models of elastic, 3D deformable objects require numerous parameters to describe the potentially infinite degrees of freedom present in determining the object's shape. Previous attemp

Diego Di Carlo, Aditya Arie Nugraha, Mathieu Fontaine, Mathieu Fontaine

We address the problem of accurately interpolating measured anechoic steering vectors with a deep learning framework called the neural field. This task plays a pivotal role in reducing the resource-intensive measurements required for precise sound source separation and localization, essential as the front-end of speech recognition. Classical approaches to in

Junyu Lu, Bo Xu, Xiaokun Zhang, Changrong Min

The widespread dissemination of toxic online posts is increasingly damaging to society. However, research on detecting toxic language in Chinese has lagged significantly. Existing datasets lack fine-grained annotation of toxic types and expressions, and ignore the samples with indirect toxicity. In addition, it is crucial to introduce lexical knowledge to de

Davin Choo, Kirankumar Shiragur

Recovering causal relationships from data is an important problem. Using observational data, one can typically only recover causal graphs up to a Markov equivalence class and additional assumptions or interventional data are needed for complete recovery. In this work, under some standard assumptions, we study causal graph discovery via adaptive interventions

Penghui Li

We give a combinatorial description of the dg category of character sheaves on a complex reductive group $G$, extending results of [Li] for $G$ simply-connected. We also explicitly identify the parabolic induction/restriction functors.

Jiarui Sun, Girish Chowdhary

Human motion prediction aims to forecast an upcoming pose sequence given a past human motion trajectory. To address the problem, in this work we propose FreqMRN, a human motion prediction framework that takes into account both the kinematic structure of the human body and the temporal smoothness nature of motion. Specifically, FreqMRN first generates a fixed

Guo-Jian Qiao, Zhi-Lei Zhang, Sheng-Wen Li, C. P. Sun

The Josephson junction is typically tuned by a magnetic field or electrostatic gates to realize a superconducting transistor, which manipulates the supercurrent in integrated superconducting circuits. However, this tunable method does not achieve simultaneous control for the supercurrent phase (phase difference between two superconductors) and magnitude. Her

Wenkai Dong, Song Xue, Xiaoyue Duan, Shumin Han

Recently large-scale language-image models (e.g., text-guided diffusion models) have considerably improved the image generation capabilities to generate photorealistic images in various domains. Based on this success, current image editing methods use texts to achieve intuitive and versatile modification of images. To edit a real image using diffusion models

Zhicheng Wang, Liwen Xiao, Zhiguo Cao, Hao Lu

Class-agnostic counting (CAC) aims to count objects of interest from a query image given few exemplars. This task is typically addressed by extracting the features of query image and exemplars respectively and then matching their feature similarity, leading to an extract-then-match paradigm. In this work, we show that CAC can be simplified in an extract-and-

Goyal Keshav, Duc Tu Dao, Han Mao Kiah, Mladen Kovacevic

Analytic combinatorics in several variables refers to a suite of tools that provide sharp asymptotic estimates for certain combinatorial quantities. In this paper, we apply these tools to determine the Gilbert-Varshamov (GV) bound for the sticky insertion and the constrained-synthesis channel.

Noah G. Singer

Motivated by recent works on streaming algorithms for constraint satisfaction problems (CSPs), we define and analyze oblivious algorithms for the Max-$k$AND problem. This generalizes the definition by Feige and Jozeph (Algorithmica '15) of oblivious algorithms for Max-DICUT, a special case of Max-$2$AND. Oblivious algorithms round each variable with probabil

Ross Dempsey, Igor R. Klebanov, Silviu S. Pufu, Benjamin T. Søgaard

We examine the phase structure of the two-flavor Schwinger model as a function of the $\theta$-angle and the two masses, $m_1$ and $m_2$. In particular, we find interesting effects at $\theta=\pi$: along the $SU(2)$-invariant line $m_1 = m_2 = m$, in the regime where $m$ is much smaller than the charge $g$, the theory undergoes logarithmic RG flow of the Ber

Zhaoxia Yin, Shaowei Zhu, Hang Su, Jianteng Peng

Deep Neural Networks (DNNs) have recently made significant progress in many fields. However, studies have shown that DNNs are vulnerable to adversarial examples, where imperceptible perturbations can greatly mislead DNNs even if the full underlying model parameters are not accessible. Various defense methods have been proposed, such as feature compression an

Ruoyu Meng, Aditya Ramamoorthy

We consider a quantum and classical version multi-party function computation problem with $n$ players, where players $2, \dots, n$ need to communicate appropriate information to player 1, so that a "generalized" inner product function with an appropriate promise can be calculated. The communication complexity of a protocol is the total number of bits that ne

Dallan Goldblatt, Calvin Vuong, Michael Rabinovich

This paper considers the phenomenon where a single probe to a target generates multiple, sometimes numerous, packets in response -- which we term "blowback". Understanding blowback is important because attackers can leverage it to launch amplified denial of service attacks by redirecting blowback towards a victim. Blowback also has serious implications for I

Anjali Parashar, Priyank Srivastava, Anuradha M. Annaswamy

Convex optimization with equality and inequality constraints is a ubiquitous problem in several optimization and control problems in large-scale systems. Recently there has been a lot of interest in establishing accelerated convergence of the loss function. A class of high-order tuners was recently proposed in an effort to lead to accelerated convergence for

Kazuki Takahashi, Tomoki Fukai, Yutaka Sakai, Takashi Takekawa

The agent learns to organize decision behavior to achieve a behavioral goal, such as reward maximization, and reinforcement learning is often used for this optimization. Learning an optimal behavioral strategy is difficult under the uncertainty that events necessary for learning are only partially observable, called as Partially Observable Markov Decision Pr

Harshat Kumar, Alejandro Parada-Mayorga, Alejandro Ribeiro

In this paper we propose a framework to leverage Lie group symmetries on arbitrary spaces exploiting \textit{algebraic signal processing} (ASP). We show that traditional group convolutions are one particular instantiation of a more general Lie group algebra homomorphism associated to an algebraic signal model rooted in the Lie group algebra $L^{1}(G)$ for gi

Han Zhou, Wei Dong, Yangyi Liu, Jun Chen

Haze usually leads to deteriorated images with low contrast, color shift and structural distortion. We observe that many deep learning based models exhibit exceptional performance on removing homogeneous haze, but they usually fail to address the challenge of non-homogeneous dehazing. Two main factors account for this situation. Firstly, due to the intricate

Yang Wu, Yanyan Zhao, Zhongyang Li, Bing Qin

Instruction tuning has been shown to be able to improve cross-task generalization of language models. However, it is still challenging for language models to complete the target tasks following the instructions, as the instructions are general and lack intermediate steps. To address this problem, we propose to incorporate the step-by-step instructions to hel

Frank Oertel

Within the framework of the search for the still unknown exact value of the real and complex Grothendieck constant $K_G^\mathbb{F}$ in the famous Grothendieck inequality (unsolved since 1953), where $\mathbb{F}$ denotes either the real or the complex field, we concentrate our search on their smallest upper bound. To this end, we establish a basic framework,

Alejandro Allendes, Gilberto Campaña, Enrique Otarola

In two-dimensional Lipschitz domains, we analyze a Brinkman--Darcy--Forchheimer problem on the weighted spaces $\mathbf{H}_0^1(\omega,\Omega) \times L^2(\omega,\Omega)/\mathbb{R}$, where $\omega$ belongs to the Muckenhoupt class $A_2$. Under a suitable smallness assumption, we prove the existence and uniqueness of a solution. We propose a finite element meth

Wenhao Hu

Face recognition in complex scenes suffers severe challenges coming from perturbations such as pose deformation, ill illumination, partial occlusion. Some methods utilize depth estimation to obtain depth corresponding to RGB to improve the accuracy of face recognition. However, the depth generated by them suffer from image blur, which introduces noise in sub

Seyed Ehsan Ghasempouri, Gerhard W. Dueck, Stijn De Baerdemacker

The variational quantum eigensolver (VQE) algorithm recently became a popular method to compute quantum chemical properties of molecules on noisy intermediate scale quantum (NISQ) devices. In order to avoid noise accumulation from the NISQ device in the circuit, it is important to keep the so-called quantum depth of the circuit at a minimum, defined as the m

Zinan Zhao, Ding Hu, Xue Fu, Kaijuan Zhou

An experimental determination of electronic phase diagrams of high-transition temperature (high-$T_c$) superconductors forms the basis for a microscopic understanding of unconventional superconductivity. For most high-$T_c$ superconductors, the electronic phase diagrams are established through partial chemical substitution, which also induces lattice disorde

Junchang Sun, Shuai Ma, Ruixin Yang, Yang Tingting

Unmanned aerial vehicle (UAV) holds immense potential in integrated sensing and communication (ISAC) systems for the Internet of Things (IoT). In this paper, we propose a UAV-aided ISAC framework and investigate three robust power allocation schemes. First, we derive an explicit expression of the Cram\'er-Rao bound (CRB) based on time-of-arrival (ToA) estima

Linglin Zhang, Beatrice Brown-Mulry, Vineela Nalla, InChan Hwang

Although deep learning models for abnormality classification can perform well in screening mammography, the demographic, imaging, and clinical characteristics associated with increased risk of model failure remain unclear. This retrospective study uses the Emory BrEast Imaging Dataset(EMBED) containing mammograms from 115931 patients imaged at Emory Healthca

Eric C. Cyr

Solving optimization problems with transient PDE-constraints is computationally costly due to the number of nonlinear iterations and the cost of solving large-scale KKT matrices. These matrices scale with the size of the spatial discretization times the number of time steps. We propose a new two level domain decomposition preconditioner to solve these linear

Yi Yu, Shengyue Yao, Juanjuan Li, Fei-Yue Wang

Data trading has been hindered by privacy concerns associated with user-owned data and the infinite reproducibility of data, making it challenging for data owners to retain exclusive rights over their data once it has been disclosed. Traditional data pricing models relied on uniform pricing or subscription-based models. However, with the development of Priva

Preeti Kharb, Silpa Sasikumar, Janhavi Baghel, Salmoli Ghosh

This is a technical report for band 4 (550-900 MHz) polarization data with the upgraded GMRT (uGMRT). The report describes the band 4 polarization data analysis procedure and includes notes for observers who are planning polarization observations with the uGMRT. A few pipelines that are currently being used and tested by astronomers at NCRA are discussed as

Yi Chou, Yao-Wun Jhang

The 4U 1820-30 is a ultra-compact low mass X-ray binary (LMXB) near the center of the globular cluster NGC 6624. Its negative orbital period derivative, observed from the phase evolution of its sinusoidal-like orbital variation, contradicts the positive value obtained from the theoretical prediction. In this paper, we present the analysis of the 4U 1820-30 o

Makiko Mase

We discuss properties of the Seifert form for simple $K3$ singularities, and of the Picard lattices of families of weighted $K3$ surfaces. We study a collection $\mathcal{M}_{(\rho,\,\delta)}$ of $K3$ surfaces polarized by their Picard lattices that are in the set $\mathcal{L}_{(\rho,\,\delta)}$ of certain lattices. We also report a numerical formula that re

Jaromir Savelka

We evaluated the capability of a state-of-the-art generative pre-trained transformer (GPT) model to perform semantic annotation of short text snippets (one to few sentences) coming from legal documents of various types. Discussions of potential uses (e.g., document drafting, summarization) of this emerging technology in legal domain have intensified, but to

Xiangdong Ji, Yizhuang Liu, Yushan Su

Parton distribution functions (PDFs) at large $x$ are poorly constrained by high-energy experimental data, but extremely important for probing physics beyond standard model at colliders. We study the calculation of PDFs at large-$x$ through large-momentum $P^z$ expansion of the lattice quasi PDFs. Similar to deep-inelastic scattering, there are two distinct

C. J. Hao, Y. Xu, L. G. Hou, Z. H. Lin

Open clusters (OCs) are infrequent survivors of embedded clusters gestated in molecular clouds. Up to now, little is known about the initial conditions for the formation of OCs. Here, we studied this issue using high-precision astrometric parameters provided by Gaia data release 3. The statistics show that the peculiar motion velocities of OCs vary little fr

Hao Chang, Alva Kosasih, Wibowo Hardjawana, Xinwei Qu

The orthogonal time frequency space (OTFS) symbol detector design for high mobility communication scenarios has received numerous attention lately. Current state-of-the-art OTFS detectors mainly can be divided into two categories; iterative and training-based deep neural network (DNN) detectors. Many practical iterative detectors rely on minimum-mean-square-

Mehedi H. Raju, Lee Friedman, Dillon Lohr, Oleg Komogortsev

Prior research states that frequencies below 75 Hz in eye-tracking data represent the primary eye movement termed ``signal'' while those above 75 Hz are deemed ``noise''. This study examines the biometric significance of this signal-noise distinction and its privacy implications. There are important individual differences in a person's eye movement, which le

Letian Wang, Jie Liu, Hao Shao, Wenshuo Wang

When autonomous vehicles are deployed on public roads, they will encounter countless and diverse driving situations. Many manually designed driving policies are difficult to scale to the real world. Fortunately, reinforcement learning has shown great success in many tasks by automatic trial and error. However, when it comes to autonomous driving in interacti

Samuel E. Armstrong, Mitchell A. Klusty, Aaron D. Mullen, Jeffery C. Talbert

Developing and enforcing study protocols is crucial in medical research, especially as interactions with participants become more intricate. Traditional rules-based systems struggle to provide the automation and flexibility required for real-time, personalized data collection. We introduce SmartState, a state-based system designed to act as a personal agent

Yankai Chen, Yifei Zhang, Menglin Yang, Zixing Song

Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models. Despite the superior performance for item recommendations, these methods however implicitly deprioritize the modeling of user-wise similarity in the embedding space; consequently, identifying similar users is underperforming, and additiona

Jounghun Lee, Jun-Sung Moon

In the linear theory, the galaxy angular momentum vectors which originate from the initial tidal interactions with surrounding matter distribution intrinsically develop perpendicular alignments with the directions of maximum matter compression, regardless of galaxy mass. In simulations, however, the galaxy spins exhibit parallel alignments in the mass-range

Hanlan Yang, Shohin Mukherjee, Maxim Likhachev

Anytime search algorithms are useful for planning problems where a solution is desired under a limited time budget. Anytime algorithms first aim to provide a feasible solution quickly and then attempt to improve it until the time budget expires. On the other hand, parallel search algorithms utilize the multithreading capability of modern processors to speed

Logan Gates

We present the fully analytical result for the next-to-next-to-leading nonfactorizable QCD corrections to Higgs boson production via vector boson fusion computed to the leading power in the ratio of the jet transverse momentum to the partonic center-of-mass energy.

Aaditya Chandrasekhar

In this paper, we propose a topology optimization (TO) framework where the design is parameterized by a set of convex polygons. Extending feature mapping methods in TO, the representation allows for direct extraction of the geometry. In addition, the method allows one to impose geometric constraints such as feature size control directly on the polygons that

Frederik Geth, Sander Claeys, Rahmat Heidari

This report serves as a technology description of a Julia-based re-implementation of the fixed-point current injection algorithm, available in PowerModelsDistribution.jl [1]. This report does not describe a novel method for solving unbalanced power flow problems. It merely provides a description of the fixed point iteration variant of the current injection m

Harini Desiraju, Tomas Lasic Latimer, Pieter Roffelsen

Building upon the recent works of Bertola; Fasondini, Olver and Xu, we define a class of orthogonal polynomials on elliptic curves and establish a corresponding Riemann-Hilbert framework. We then focus on the special case, defined by a constant weight function, and use the Riemann-Hilbert problem to derive recurrence relations and differential equations for

Ryusuke Sugimoto, Terry Chen, Yiti Jiang, Christopher Batty

We introduce the walk-on-boundary (WoB) method for solving boundary value problems to computer graphics. WoB is a grid-free Monte Carlo solver for certain classes of second order partial differential equations. A similar Monte Carlo solver, the walk-on-spheres (WoS) method, has been recently popularized in computer graphics due to its advantages over traditi

Md. Mehedi Hasan, Md. Ali Hossain, Azmain Yakin Srizon, Abu Sayeed

The application of the deep learning model in classification plays an important role in the accurate detection of the target objects. However, the accuracy is affected by the activation function in the hidden and output layer. In this paper, an activation function called TaLU, which is a combination of Tanh and Rectified Linear Units (ReLU), is used to impro

Jannatul Nayem, Sayed Sahriar Hasan, Noshin Amina, Bristy Das

Deep learning becomes an elevated context regarding disposing of many machine learning tasks and has shown a breakthrough upliftment to extract features from unstructured data. Though this flourishing context is developing in the medical image processing sector, scarcity of problem-dependent training data has become a larger issue in the way of easy applicat