March 2023 arXiv papers — page 68

Tamitake Itoh, Yuko S. Yamamoto

We investigated the origin of the identical polarization angle dependences between surface-enhanced resonant Raman scattering (SERRS) and plasmon resonance for two types of single silver nanoparticle aggregates. The first type (Type I), in which the SERRS spectral envelopes are similar to the plasmon resonance elastic scattering spectra, shows the identical

Siddique Latif, Aun Zaidi, Heriberto Cuayahuitl, Fahad Shamshad

The remarkable success of transformers in the field of natural language processing has sparked the interest of the speech-processing community, leading to an exploration of their potential for modeling long-range dependencies within speech sequences. Recently, transformers have gained prominence across various speech-related domains, including automatic spee

Jingi Ju, Hyeoncheol Noh, Yooseung Wang, Minseok Seo

Semi-supervised semantic segmentation learns a model for classifying pixels into specific classes using a few labeled samples and numerous unlabeled images. The recent leading approach is consistency regularization by selftraining with pseudo-labeling pixels having high confidences for unlabeled images. However, using only highconfidence pixels for self-trai

Shouvik Datta Choudhury

The paper introduces a new elliptic operator called the two-radical Laplace operator, which has a positive eigenvalue equal to the positive square root of the eigenvalue of the Laplace operator. The author provide several theorems that serve as counterparts to those associated with the Laplace operator.

Khawar Islam

Video-based person re-identification (video re-ID) has lately fascinated growing attention due to its broad practical applications in various areas, such as surveillance, smart city, and public safety. Nevertheless, video re-ID is quite difficult and is an ongoing stage due to numerous uncertain challenges such as viewpoint, occlusion, pose variation, and un

Fumihiko Nakamura, Hiroki Ogura, Tatsuhiro Sakami, Takashi Suzuki

We have studied the magnetic properties of pressure-induced ferromagnet Ca2RuO4 to reveal the uniqueness of the 4d-electron ferromagnetism in the quasi-two-dimensional conductor. The magnetic parameters have been estimated from the paramagnetic susceptibility and the magnetisation process under pressure up to 2 GPa. The parameters can well be interpreted on

Zhaosheng Li, Lucien Kuiper, Mingyu Ge, Maurizio Falanga

We studied the broadband X-ray timing and spectral behaviors of the newly confirmed accreting millisecond X-ray pulsar MAXI J1816$-$195 during its 2022 outburst. We used the data from Insight-HXMT ME/HE, NICER and NuSTAR which cover the energy range between 0.8$-$210 keV. A coherent timing analysis of solely Insight-HXMT HE data across the full outburst reve

Nilin Abrahamsen, Zhiyan Ding, Gil Goldshlager, Lin Lin

We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem. We study both the energy minimization phase and the supervised pre-training phase that is commonly used prior to energy minimization. For the energy minimization phase, the standard

Yan Meng, Wei Wei, Xiangzhuo Xing, Xiaolei Yi

Superconducting transition temperature (Tc) and critical current density (Jc) are two key factors that are not only crucial for probing high-temperature superconducting mechanisms, but also for practical applications. The simple crystal structure of FeSe is very favorable for the fabrication of thin films and wires, but its application is limited by the rela

Sungwoo Lee, Woojin Choi, Yeongjae Kim, Young-Kyun Kwon

In unconventional high-$T_c$ cuprate superconductors, the intricate interplay between the non-ergodic bad metal and the strange metal state has remained enigmatic. Herein, we unravel this mystery using ab initio molecular dynamics simulations and the temperature-dependent effective potential method. Our investigation, centered on YBa$_2$Cu$_3$O$_7$ , provide

Xinjie Zhang, Jiawei Shao, Jun Zhang

Prevalent predictive coding-based video compression methods rely on a heavy encoder to reduce temporal redundancy, which makes it challenging to deploy them on resource-constrained devices. Since the 1970s, distributed source coding theory has indicated that independent encoding and joint decoding with side information (SI) can achieve high-efficient compres

Xiaolei Yi, Xiangzhuo Xing, Yan Meng, Nan Zhou

The second magnetization peak (SMP) appears in most superconductors and is crucial for the understanding of vortex physics as well as the application. Although it is well known that the SMP is related to the type and quantity of disorder/defects, the mechanism has not been universally understood. In this work, we selected three stoichiometric superconducting

Alex Stivala

A geodesic cycle in a graph is a cycle with no shortcuts, so that the shortest path between any two nodes in the cycle is the path along the cycle itself. A recently published paper used random graph models to investigate the geodesic cycle length distributions of a unique set of delusional social networks, first examined in an earlier work, as well as some

Wenkai Zhu, Yingmei Zhu, Tong Zhou, Xianpeng Zhang

Magnetic tunnel junctions (MTJs) with conventional bulk ferromagnets separated by a nonmagnetic insulating layer are key building blocks in spintronics for magnetic sensors and memory. A radically different approach of using atomically-thin van der Waals (vdW) materials in MTJs is expected to boost their figure of merit, the tunneling magnetoresistance (TMR)

Yiming Chen, Jinyu Tian, Xiangyu Chen, Jiantao Zhou

Since training a deep neural network (DNN) is costly, the well-trained deep models can be regarded as valuable intellectual property (IP) assets. The IP protection associated with deep models has been receiving increasing attentions in recent years. Passport-based method, which replaces normalization layers with passport layers, has been one of the few prote

Yuanyuan Mao, Shuang Liu, Pengshuai Zhao, Qin Ni

Theory of Mind (ToM) is the ability to attribute mental states to others, the basis of human cognition. At present, there has been growing interest in the AI with cognitive abilities, for example in healthcare and the motoring industry. Beliefs, desires, and intentions are the early abilities of infants and the foundation of human cognitive ability, as well

Yasuhiro Yoshikai, Tadahaya Mizuno, Shumpei Nemoto, Hiroyuki Kusuhara

Recent years have seen rapid development of descriptor generation based on representation learning of extremely diverse molecules, especially those that apply natural language processing (NLP) models to SMILES, a literal representation of molecular structure. However, little research has been done on how these models understand chemical structure. To address

Hochang Rhee, Seyun Kim, Nam Ik Cho

Recent deep-learning-based video compression methods brought coding gains over conventional codecs such as AVC and HEVC. However, learning-based codecs generally require considerable computation time and model complexity. In this paper, we propose a new lightweight hybrid video codec consisting of a conventional video codec(HEVC / VVC), a lossless image code

Yuzhi Zhao, Lai-Man Po, Kangcheng Liu, Xuehui Wang

In this paper, we propose a scribble-based video colorization network with temporal aggregation called SVCNet. It can colorize monochrome videos based on different user-given color scribbles. It addresses three common issues in the scribble-based video colorization area: colorization vividness, temporal consistency, and color bleeding. To improve the coloriz

Will Sharpless, Nikhil Shinde, Matthew Kim, Yat Tin Chow

The Hopf formula for Hamilton-Jacobi reachability (HJR) analysis has been proposed to solve high-dimensional differential games, producing the set of initial states and corresponding controller required to reach (or avoid) a target despite bounded disturbances. As a space-parallelizable method, the Hopf formula avoids the curse of dimensionality that afflict

Junyi Zhang, Jiaqi Guo, Shizhao Sun, Jian-Guang Lou

Creating graphic layouts is a fundamental step in graphic designs. In this work, we present a novel generative model named LayoutDiffusion for automatic layout generation. As layout is typically represented as a sequence of discrete tokens, LayoutDiffusion models layout generation as a discrete denoising diffusion process. It learns to reverse a mild forward

Peng Gao, Liangyi Zhao

We evaluate the first moment of central values of the family of quadratic Dirichlet $L$-functions using the method of double Dirichlet series. Under the generalized Riemann hypothesis, we prove an asymptotic formula with an error term of size that is the fourth root of that of the primary main term.

Anarul Islam Mondal, Sangita Jha

This article aims to study fractal interpolation functions corresponding to a sequence of iterated function systems (IFSs). For a suitable choice of a sequence of IFS parameters, the corresponding non-stationary fractal function is a better approximant for the non-smooth approximant. In this regard, we first construct the non-stationary interpolant in the Li

Aneesha Manocha, Neha Patankar, Jesse D. Jenkins

Expanding transmission capacity is likely a bottleneck that will restrict variable renewable energy (VRE) deployment required to achieve ambitious emission reduction goals. Grid interconnection and inter-regional transmission capacity may be reduced by the optimal sizing of VREs to grid connection or co-location of VRE and battery resources behind the grid i

Shan-Feng Shao, Xiao-Yu Cao, Yuan-Mei Xie, Jie Gu

Quantum key distribution provides a promising solution for sharing secure keys between two distant parties with unconditional security. Nevertheless, quantum key distribution is still severely threatened by the imperfections of devices. In particular, the classical pulse correlation threatens security when sending decoy states. To address this problem and si

Tamás Darvas, Eleonora Di Nezza, Chinh H. Lu

Given a compact K\"ahler manifold, we survey the study of complex Monge-Amp\`ere type equations with prescribed singularity type, developed by the authors in a series of papers. In addition, we give a general answer to a question of Guedj--Zeriahi about the finite energy range of the complex Monge-Amp\`ere operator.

Hongjin Wang, Jingyi Zhuang, Zhen Zhang, Qi Zhang

The quasi-harmonic approximation (QHA) is a powerful method that uses the volume dependence of non-interacting phonons to compute the free energy of materials at high pressures (P) and temperatures (T). However, anharmonicity, electronic excitations in metals, or both, introduce an intrinsic T-dependence on phonon frequencies, rendering the QHA inadequate. H

Ethan Che, Hongseok Namkoong

Standard bandit algorithms that assume continual reallocation of measurement effort are challenging to implement due to delayed feedback and infrastructural/organizational difficulties. Motivated by practical instances involving a handful of reallocation epochs in which outcomes are measured in batches, we develop a computation-driven adaptive experimentatio

Sourayan Banerjee, Vivek Sadhu

In this article, we show that for a quasicompact scheme $X$ and $n>0,$ the $n$-th $K$-group $K_{n}(X)$ is a $\lambda$-module over a $\lambda$-ring $K_{0}(X)$ in the sense of Hesselholt.

Daniel S Johnson, Igor L Markov

Many ML applications and products train on medium amounts of input data but get bottlenecked in real-time inference. When implementing ML systems, conventional wisdom favors segregating ML code into services queried by product code via Remote Procedure Call (RPC) APIs. This approach clarifies the overall software architecture and simplifies product code by a

Wenkang Shan, Zhenhua Liu, Xinfeng Zhang, Zhao Wang

In this paper, a novel Diffusion-based 3D Pose estimation (D3DP) method with Joint-wise reProjection-based Multi-hypothesis Aggregation (JPMA) is proposed for probabilistic 3D human pose estimation. On the one hand, D3DP generates multiple possible 3D pose hypotheses for a single 2D observation. It gradually diffuses the ground truth 3D poses to a random dis

Bang-Yen Chen, Shihshu Walter Wei

By studying cohomology classes that are related with $p$-harmonic morphisms, $F$-harmonic maps, and $f$-harmonic maps, we extend several of our previous results on Riemannian submersions and $p$-harmonic morphisms to $F$-harmonic maps, and $f$-harmonic maps which are submersions.

Wenqian Chen, Panos Stinis

Physics-informed neural networks have emerged as an alternative method for solving partial differential equations. However, for complex problems, the training of such networks can still require high-fidelity data which can be expensive to generate. To reduce or even eliminate the dependency on high-fidelity data, we propose a novel multi-fidelity architectur

Dawid Czapla

In this paper, we consider a subclass of piecewise deterministic Markov processes with a Polish state space that involve a deterministic motion punctuated by random jumps, occurring in a Poisson-like fashion with some state-dependent rate, between which the trajectory is driven by one of the given semiflows. We prove that there is a one-to-one correspondence

Jingjie Li, Sunpreet Singh Arora, Kassem Fawaz, Younghyun Kim

Users readily embrace the rapid advancements in virtual reality (VR) technology within various everyday contexts, such as gaming, social interactions, shopping, and commerce. In order to facilitate transactions and payments, VR systems require access to sensitive user data and assets, which consequently necessitates user authentication. However, there exists

Dugang Liu, Pengxiang Cheng, Zinan Lin, Xiaolian Zhang

Debiased recommendation with a randomized dataset has shown very promising results in mitigating the system-induced biases. However, it still lacks more theoretical insights or an ideal optimization objective function compared with the other more well studied route without a randomized dataset. To bridge this gap, we study the debiasing problem from a new pe

Girish Narayanswamy, Yujia Liu, Yuzhe Yang, Chengqian Ma

Understanding of human visual perception has historically inspired the design of computer vision architectures. As an example, perception occurs at different scales both spatially and temporally, suggesting that the extraction of salient visual information may be made more effective by paying attention to specific features at varying scales. Visual changes i

Ya Qiao, Yajun Zhang, Zhe Yuan

Traditional artificial intelligence implemented in software is usually executed on accurate digital computers. Nevertheless, the nanoscale devices for the implementation of neuromorphic computing may not be ideally identical, and the performance is reduced by nonuniform devices. In biological brains, information is usually encoded by a cluster of neurons suc

Dongdong Liu, Bo Wang

It has been proposed that accretion-induced collapse (AIC) of massive white dwarfs (WDs) is an indispensable path for the formation of neutron star (NS) binaries. Although there are still no direct evidence for the existence of AIC events, several kinds of NS systems are suggested to originate from the AIC processes. One of the representative evidence is the

Min Chen, Weizhuo Gao, Gaoyang Liu, Kai Peng

The practical needs of the ``right to be forgotten'' and poisoned data removal call for efficient \textit{machine unlearning} techniques, which enable machine learning models to unlearn, or to forget a fraction of training data and its lineage. Recent studies on machine unlearning for deep neural networks (DNNs) attempt to destroy the influence of the forget

Amar Ghar, Sanyogita, Utpal Das, P. K. Panigrahi

This work represents a particular application of waveguide fabricated by femtosecond laser micromachining technology. More specifically, we report the development of an optical modulator based on the fabrication of single-mode optical waveguide structures buried in X-cut lithium niobate crystal with the femtosecond laser direct writing method. Here, change i

Jianing Qiu, Lin Li, Jiankai Sun, Jiachuan Peng

Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people's imagination about the

Shuai Li, Minghan Li, Ruihuang Li, Chenhang He

One-to-one (o2o) label assignment plays a key role for transformer based end-to-end detection, and it has been recently introduced in fully convolutional detectors for end-to-end dense detection. However, o2o can degrade the feature learning efficiency due to the limited number of positive samples. Though extra positive samples are introduced to mitigate thi

Yinan Hu, Quanyan Zhu

Human-sensor systems have a wide range of applications in fields such as robotics, healthcare, and finance. These systems utilize sensors to observe the true state of nature and generate strategically designed signals, aiding humans in making more accurate decisions regarding the state of nature. We adopt a Bayesian persuasion framework that is integrated wi

Li Luo, Zhiyi Mi, Junyi Huang, Zhichao Ruan

Recently, spatial photonic Ising machines (SPIMs) have demonstrated the abilities to compute the Ising Hamiltonian of large-scale spin systems, with the advantages of ultrafast speed and high power efficiency. However, such optical computations have been limited to specific Ising models with fully connected couplings. Here we develop a wavelength-division mu

Abraham Sánchez, Raúl Nanclares, Alexander Quevedo, Ulises Pelagio

The responsible and sustainable agave-tequila production chain is fundamental for the social, environment and economic development of Mexico's agave regions. It is therefore relevant to develop new tools for large scale automatic agave region monitoring. In this work, we present an Agave tequilana Weber azul crop segmentation and maturity classification usin

Hankyu Jang, Sulyun Lee, D. M. Hasibul Hasan, Philip M. Polgreen

As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare professionals, prescription data, procedures data, and data on patient admission, discharge, and transfers. Th

Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu

Label noise poses a serious threat to deep neural networks (DNNs). Employing robust loss functions which reconcile fitting ability with robustness is a simple but effective strategy to handle this problem. However, the widely-used static trade-off between these two factors contradicts the dynamics of DNNs learning with label noise, leading to inferior perfor

Yifu Tang, Claudia Kirch, Jeong Eun Lee, Renate Meyer

Based on a novel dynamic Whittle likelihood approximation for locally stationary processes, a Bayesian nonparametric approach to estimating the time-varying spectral density is proposed. This dynamic frequency-domain based likelihood approximation is able to depict the time-frequency evolution of the process by utilizing the moving periodogram previously int

Harry Dobbs, Oliver Batchelor, Richard Green, James Atlas

This paper introduces Smart-Tree, a supervised method for approximating the medial axes of branch skeletons from a tree point cloud. Smart-Tree uses a sparse voxel convolutional neural network to extract the radius and direction towards the medial axis of each input point. A greedy algorithm performs robust skeletonization using the estimated medial axis. Ou

Bernard Shiffman, Steve Zelditch

We provide a survey of results on the statistics of random sections of holomorphic line bundles on K\"ahler manifolds, with an emphasis on the resulting asymptotics when a line bundle is raised to increasing tensor powers. We conclude with a brief discussion of the `Bergman' K\"ahler metrics induced by these random sections.

Weijie Dong, Xiaoxi Zhou, Xinyang Pan, Haitao Li

In conventional plasmonic media and plasmonic metamaterials, such as metallic wire mesh, longitudinal mode of electromagnetic wave manifests itself in frequency overlapping transverse modes, which impedes clear observation of longitudinal-mode-specific physical effects. Through interlacing two sets of wire meshes, an ideal band for longitudinal mode is achie

I. S. Morrison, B. Crosse, G. Sleap, R. B. Wayth

We describe the design, validation, and commissioning of a new correlator termed "MWAX" for the Murchison Widefield Array (MWA) low-frequency radio telescope. MWAX replaces an earlier generation MWA correlator, extending correlation capabilities and providing greater flexibility, scalability, and maintainability. MWAX is designed to exploit current and futur

Samer Israwi, Youssef Khalifeh, Dimitrios Mitsotakis

A generalized version of the $abcd$-Boussinesq class of systems is derived to accommodate variable bottom topography in two-dimensional space. This extension allows for the conservation of suitable energy functionals in some cases and enables the description of water waves in closed basins with well-justified slip-wall boundary conditions. The derived system

Kelly Gourdji, Antonia Rowlinson, Ralph Wijers, Jess Broderick

The electromagnetic counterparts to gravitational wave (GW) merger events hold immense scientific value, but are difficult to detect due to the typically large localisation errors associated with GW events. The Low-Frequency Array (LOFAR) is an attractive GW follow-up instrument owing to its high sensitivity, large instantaneous field of view, and ability to

Jose Reinaldo Cunha Santos A V Silva Neto, Tomoya Nakamura, Yasushi Makihara, Yasushi Yagi

The freedom of design of coded masks used by mask-based lensless cameras is an advantage these systems have when compared to lens-based ones. We leverage this freedom of design to propose a shape-preserving optimization scheme for a radial-type amplitude coded mask. Due to the depth-independency of the radial mask's point spread function, they can be used fo

Daniel Gonzalez Cedre, Justus Isaiah Hibshman, Timothy La Fond, Grant Boquet

Context-free graph grammars have shown a remarkable ability to model structures in real-world relational data. However, graph grammars lack the ability to capture time-changing phenomena since the left-to-right transitions of a production rule do not represent temporal change. In the present work, we describe dynamic vertex-replacement grammars (DyVeRG), whi

Zhaodi Zhang, Zhiyi Xue, Yang Chen, Si Liu

This paper proposes a novel, abstraction-based, certified training method for robust image classifiers. Via abstraction, all perturbed images are mapped into intervals before feeding into neural networks for training. By training on intervals, all the perturbed images that are mapped to the same interval are classified as the same label, rendering the varian

Akash Gupta, Rohun Tripathi, Wondong Jang

Lack of audio-video synchronization is a common problem during television broadcasts and video conferencing, leading to an unsatisfactory viewing experience. A widely accepted paradigm is to create an error detection mechanism that identifies the cases when audio is leading or lagging. We propose ModEFormer, which independently extracts audio and video embed

Kelei Tian, Guangmiao Lai, Ge Yi, Ying Xu

In this paper, we prove the existence of tau functions of the discrete modified KP hierarchy and define the squared eigenfunction symmetry. Meanwhile, the Fay identity with its difference form, the squared eigenfunction potentials and the symmetry flow acting on tau functions are obtained.

Yan Pan, Heng Wang, Yun Shao, Yaodi Pi

To reduce the influence of random channel polarization variation, especially fast polarization perturbation,for continuous-variable quantum key distribution (CV-QKD) systems, a simple and fast polarization tracking algorithm is proposed and experimentally demonstrated. The algorithm is implemented by an orthogonal pilot tone scheme, one of the pilot tones is

Sahil Goyal, Shagun Uppal, Sarthak Bhagat, Yi Yu

Several works have developed end-to-end pipelines for generating lip-synced talking faces with various real-world applications, such as teaching and language translation in videos. However, these prior works fail to create realistic-looking videos since they focus little on people's expressions and emotions. Moreover, these methods' effectiveness largely dep

Dan Hawes, Ali Arya

Recent research indicates that most post-secondary students in North America "felt overwhelming anxiety" in the past few years, negatively affecting well-being and academic performance. Further research revealed that other emotions, biases, perceptions, and negative thoughts, can similarly affect student academic performance. To address this problem, we clas

Sunghwan Kim, Dae-hwan Kim, Hoseong Kim

Deep Neural Networks (DNNs)-based semantic segmentation models trained on a source domain often struggle to generalize to unseen target domains, i.e., a domain gap problem. Texture often contributes to the domain gap, making DNNs vulnerable to domain shift because they are prone to be texture-biased. Existing Domain Generalized Semantic Segmentation (DGSS) m

Dongyeun Lee, Jae Young Lee, Doyeon Kim, Jaehyun Choi

Recent studies show strong generative performance in domain translation especially by using transfer learning techniques on the unconditional generator. However, the control between different domain features using a single model is still challenging. Existing methods often require additional models, which is computationally demanding and leads to unsatisfact

Kazunari Iwasaki, Hiroshi Kobayashi, Aya E. Higuchi, Yuri Aikawa

The faint CO gases in debris disks are easily dissolved into C by UV irradiation, while CO can be reformed via reactions with hydrogen. The abundance ratio of C/CO could thus be a probe of the amount of hydrogen in the debris disks. We conduct radiative transfer calculations with chemical reactions for debris disks. For a typical dust-to-gas mass ratio of de

Wenyu Zhang, Kaiyuan Bai, Sherali Zeadally, Haijun Zhang

Semantic communication is a new paradigm that exploits deep learning models to enable end-to-end communications processes, and recent studies have shown that it can achieve better noise resiliency compared with traditional communication schemes in a low signal-to-noise (SNR) regime. To achieve multiple access in semantic communication, we propose a deep lear

Cornelia Mihaila, Brian Seguin

Here we introduce a fractional notion of $k$-dimensional measure, $0\leq k<n$, that depends on a parameter $\sigma$ that lies between $0$ and $1$. When $k=n-1$ this coincides with the fractional notions of area and perimeter, and when $k=1$ this coincides with the fractional notion of length. It is shown that, when multiplied by the factor $1-\sigma$, this $

E. Acalapati, H. S. Ramadhan

We apply the {\it first-order formalism} method to obtaining BPS equations for Alice string. This is done by generalizing the well-known first-order formalism to the case of non-Abelian strings. We do not assume any specific gauge group nor the shape of the kinetic term function, but require only that the fields are axially-symmetric and static. With this fo

Huimin Qiang, Zhiyuan Guo, Shiyuan Xie, Xiaodong Peng

Incorporating the dynamics knowledge into the model is critical for achieving accurate trajectory prediction while considering the spatial and temporal characteristics of the vessel. However, existing methods rarely consider the underlying dynamics knowledge and directly use machine learning algorithms to predict the trajectories. Intuitively, the vessel's m

Yoichi Tamura, Tom J. L. C. Bakx, Akio K. Inoue, Takuya Hashimoto

We present the results of 300 pc resolution ALMA imaging of the [OIII] 88 $\mu$m line and dust continuum emission from a $z = 8.312$ Lyman break galaxy MACS0416_Y1. The velocity-integrated [OIII] emission has three peaks which are likely associated with three young stellar clumps of MACS0416_Y1, while the channel map shows a complicated velocity structure wi

Cédric Lorcé, Qin-Tao Song

We study the energy-momentum tensor of spin-$0$ and spin-$\frac{1}{2}$ hadrons in momentum space. We parametrize this object in terms of so-called gravitational transverse-momentum distributions, and we identify in the quark sector the relations between the latter and the usual transverse-momentum distributions. Focusing on particular components of the energ

Shaoxu Li, Ye Pan

In this paper, we propose a method that enables interactive geometry editing for neural radiance fields manipulation. We use two proxy cages(inner cage and outer cage) to edit a scene. The inner cage defines the operation target, and the outer cage defines the adjustment space. Various operations apply to the two cages. After cage selection, operations on th

Abel C. H. Chen

With the maturity of web services, containers, and cloud computing technologies, large services in traditional systems (e.g. the computation services of machine learning and artificial intelligence) are gradually being broken down into many microservices to increase service reusability and flexibility. Therefore, this study proposes an efficiency analysis fr

Tao Yang, Chuang Liu, Xiaofeng Ma, Weijia Lu

Complex continuous or mixed joint distributions (e.g., P(Y | z_1, z_2, ..., z_N)) generally lack closed-form solutions, often necessitating approximations such as MCMC. This paper proposes Indeterminate Probability Theory (IPT), which makes the following contributions: (1) An observer-centered framework in which experimental outcomes are represented as distr

Muhammad Kazim, Michael Muldoon, Kwang-Ki K. Kim

This paper considers the problem of managing single or multiple robots and proposes a cloud-based robot fleet manager, Adaptive Goal Management (AGM) System, for teams of unmanned mobile robots. The AGM system uses an adaptive goal execution approach and provides a restful API for communication between single or multiple robots, enabling real-time monitoring

Juan Maldacena

During the past decades, theorists have been studying quantum mechanical systems that are believed to describe black holes. We review one of the simplest examples. It involves a collection of interacting oscillators and Majorana fermions. It is conjectured to describe a black hole in an emergent universe governed by Einstein equations. Based on previous nume

Imam Nugraha Albania, Masaru Nagisa

We compute the operator $(p,q)$-norm of some $n\times n$ complex matrices, which can be seen as bounded linear operators from the $n$ dimensional Banach space $\ell^p(n)$ to $\ell^q(n)$. We have shown that a special matrix $A=\begin{pmatrix} 8 & 1 & 6 \\ 3 & 5 & 7 \\ 4 & 9 & 2 \end{pmatrix}$ which corresponds to a magic square has $\|A\|_{p,p} = \max \{\|A\x

Garrett Ervin

We show that if $\mathbb{R} = A \cup B$ is a partition of $\mathbb{R}$ into two suborders $A$ and $B$, then there is an open interval $I$ such that $A \cap I$ is not order-isomorphic to $B \cap I$. The proof depends on the completeness of $\mathbb{R}$, and we show in contrast that there is a partition of the irrationals $\mathbb{R} \setminus \mathbb{Q} = A \

Yang Li, Yang Liu, Daiheng Ni, Ang Ji

Existing research on merging behavior generally prioritize the application of various algorithms, but often overlooks the fine-grained process and analysis of trajectories. This leads to the neglect of surrounding vehicle matching, the opaqueness of indicators definition, and reproducible crisis. To address these gaps, this paper presents a reproducible appr

Ruiqi Wang, Akshay Gadi Patil, Fenggen Yu, Hao Zhang

We introduce the first active learning (AL) model for high-accuracy instance segmentation of moveable parts from RGB images of real indoor scenes. Specifically, our goal is to obtain fully validated segmentation results by humans while minimizing manual effort. To this end, we employ a transformer that utilizes a masked-attention mechanism to supervise the a

Patrick Rehill

Counterfactual fairness is an approach to AI fairness that tries to make decisions based on the outcomes that an individual with some kind of sensitive status would have had without this status. This paper proposes Double Machine Learning (DML) Fairness which analogises this problem of counterfactual fairness in regression problems to that of estimating coun

Jun He, Dong Zhao, Hong Liu, Jinghua Teng

Planar lenses with optimized but disordered structures can focus light beyond the diffraction limit. However, these disordered structures have inevitably destroyed wide-field imaging capability, limiting their applications in microscopy. Here we introduce information entropy S to evaluate the disorder of an objective chip by using the probability of its stru

Muhammad Riaz Hasib Hossain, Muhammad Ashad Kabir

Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in smartphone technologies and computer vision have demonstrated a non-destructive nature of soil properties, inc

Yiqing Zhang, Xinming Huang, Ziming Zhang

The Lucas-Kanade (LK) method is a classic iterative homography estimation algorithm for image alignment, but often suffers from poor local optimality especially when image pairs have large distortions. To address this challenge, in this paper we propose a novel Deep Star-Convexified Lucas-Kanade (PRISE) method for multimodel image alignment by introducing st

Vithursan Thangarasa, Shreyas Saxena, Abhay Gupta, Sean Lie

Recent research has focused on weight sparsity in deep neural network training to reduce FLOPs, aiming for improved efficiency (test accuracy w.r.t training FLOPs). However, sparse weight training often compromises accuracy, requiring extended training schedules to attain the accuracy of dense models. In contrast, our approach, Sparse Iso-FLOP Transformation

Huaiqian Li, Bin Qian

We study the Li--Yau inequality for the heat equation corresponding to the Dunkl harmonic oscillator, which is a non-local Schr\"{o}dinger operator parameterized by reflections and multiplicity functions. In the particular case when the reflection group is isomorphic to $\mathbb{Z}_2^d$, the result is sharp in the sense that equality is achieved by the heat

Pooja Sekhar, Connor Fredrick, David R. Carlson, Zachary Newman

Frequency combs with mode spacing in the range of 10 to 20 gigahertz (GHz) are critical for increasingly important applications such as astronomical spectrograph calibration, high-speed dual-comb spectroscopy, and low-noise microwave generation. While electro-optic modulators and microresonators can provide narrowband comb sources at this repetition rate, a

Devansh Jalota, Haoyuan Sun, Navid Azizan

The computation of equilibrium prices at which the supply of goods matches their demand typically relies on complete information on agents' private attributes, e.g., suppliers' cost functions, which are often unavailable in practice. Motivated by this practical consideration, we consider the problem of learning equilibrium prices over a horizon of $T$ period

Tomohiro Matsuda

This paper proposes a basic method for understanding stationary particle production on manifolds by means of the Stokes phenomenon. We studied the Stokes phenomena of the Schwinger effect, the Unruh effect and Hawking radiation in detail focusing on the origin of their continuous particle production. We found a possibility that conventional calculations may

Zhangchi Lu, Mertcan Cokbas, Prakash Ishwar, Jansuz Konrad

Unobtrusive monitoring of distances between people indoors is a useful tool in the fight against pandemics. A natural resource to accomplish this are surveillance cameras. Unlike previous distance estimation methods, we use a single, overhead, fisheye camera with wide area coverage and propose two approaches. One method leverages a geometric model of the fis

Yin Shi, Alexey Arefiev, Jue Xuan Hao, Jian Zheng

Strong laser-driven magnetic fields are crucial for high-energy-density physics and laboratory astrophysics research, but generation of axial multi-kT fields remains a challenge. The difficulty comes from the inability of a conventional linearly polarized laser beam to induce the required azimuthal current or, equivalently, angular momentum (AM). We show tha

Sigrun Ortleb

A fully discrete energy stability analysis is carried out for linear advection-diffusion problems discretized by generalized upwind summation-by-parts~(upwind gSBP) schemes in space and implicit-explicit Runge-Kutta~(IMEX-RK) schemes in time. Hereby, advection terms are discretized explicitly while diffusion terms are solved implicitly. In this context, spec

Eric J. Hanson

Reading's "shard intersection order" on the symmetric group can be realized as the "lattice of wide subcategories" of the corresponding preprojective algebra. In this paper, we first use Bancroft's combinatorial model for the shard intersection order to associate a unique shard to each downward cover relation. We then show that, under the correspondence with

Fulin Liu, Yinlin Hu, Mathieu Salzmann

Most modern image-based 6D object pose estimation methods learn to predict 2D-3D correspondences, from which the pose can be obtained using a PnP solver. Because of the non-differentiable nature of common PnP solvers, these methods are supervised via the individual correspondences. To address this, several methods have designed differentiable PnP strategies,

Jan Heiland, Steffen W. R. Werner

Nonlinear feedback design via state-dependent Riccati equations is well established but unfeasible for large-scale systems because of computational costs. If the system can be embedded in the class of linear parameter-varying (LPV) systems with the parameter dependency being affine-linear, then the nonlinear feedback law has a series expansion with constant

Sarah Bradley, Albertus Alvin Janitra, Babar Shahzaad, Balsam Alkouz

We present a demonstration of service-based trajectory planning for a drone delivery system in a multi-drone skyway network. We conduct several experiments using Crazyflie drones to collect the drone's position data, wind speed and direction, and wind effects on voltage consumption rates. The experiments are run for a varying number of recharging stations, w

Eric J. Hanson, Xinrui You

The bricks over preprojective algebras of type A are known to be in bijection with certain combinatorial objects called "arcs". In this paper, we show how one can use arcs to compute bases for the Hom-spaces and first extension spaces between bricks. We then use this description to classify the "weak exceptional sequences" over these algebras. Finally, we ex

Andreas Frommer, Mostafa Nasr Khalil

Hutchinson's method estimates the trace of a matrix function $f(D)$ stochastically using samples $\tau^Hf(D)\tau$, where the components of the random vectors $\tau$ obey an isotropic probability distribution. Estimating the trace of the inverse of a discretized Dirac operator or variants thereof have become a major challenge in lattice QCD simulations, as th

Ka-Ho Chow, Ling Liu, Wenqi Wei, Fatih Ilhan

Federated Learning (FL) has been gaining popularity as a collaborative learning framework to train deep learning-based object detection models over a distributed population of clients. Despite its advantages, FL is vulnerable to model hijacking. The attacker can control how the object detection system should misbehave by implanting Trojaned gradients using o