March 2023 arXiv papers — page 61

Nhat Le, Thang Pham, Tuong Do, Erman Tjiputra

Music-driven choreography is a challenging problem with a wide variety of industrial applications. Recently, many methods have been proposed to synthesize dance motions from music for a single dancer. However, generating dance motion for a group remains an open problem. In this paper, we present $\rm AIOZ-GDANCE$, a new large-scale dataset for music-driven g

Siyuan Feng, Taijie Chen, Yuhao Zhang, Jintao Ke

On-demand ride services or ride-sourcing services have been experiencing fast development in the past decade. Various mathematical models and optimization algorithms have been developed to help ride-sourcing platforms design operational strategies with higher efficiency. However, due to cost and reliability issues (implementing an immature algorithm for real

Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li

While semantic communication succeeds in efficiently transmitting due to the strong capability to extract the essential semantic information, it is still far from the intelligent or human-like communications. In this paper, we introduce an essential component, memory, into semantic communications to mimic human communications. Particularly, we investigate a

Desmond Coles, Netanel Friedenberg

We develop a method for subdividing polyhedral complexes in a way that restricts the possible recession cones and allows one to work with a fixed class of polyhedron. We use these results to construct locally finite completions of rational polyhedral complexes whose recession cones lie in a fixed fan, locally finite polytopal completions of polytopal complex

Hongchen Chu, Xiang Xiong, Nicholas X. Fang, Feng Wu

The traditional wisdom for achieving transparency is to minimize disordered scattering within and on the surface of materials, so as to avoid translucency. However, the lack of disordered scattering also deprives the possibility of achieving a matte surface, resulting in the specular reflection and glare on transparent materials as a severe light pollution i

Wulian Yun, Mengshi Qi, Chuanming Wang, Huadong Ma

Weakly-supervised temporal action localization aims to locate action regions and identify action categories in untrimmed videos simultaneously by taking only video-level labels as the supervision. Pseudo label generation is a promising strategy to solve the challenging problem, but the current methods ignore the natural temporal structure of the video that c

Hiroshi Nozaki

Let $X$ be a finite set in the Euclidean space $\mathbb{R}^d$. If the squared distance between any two distinct points in $X$ is an odd integer, then the cardinality of $X$ is bounded above by $d+2$, as shown by Rosenfeld (1997) or Smith (1995). They proved that there exists a $(d+2)$-point set $X$ in $\mathbb{R}^d$ having only odd integral squared distances

Saurav Pandey, Junaid Majeed Bhat, Abhishek Dhar, Sheldon Goldstein

We study the time evolution of the Boltzmann entropy of a microstate during the non-equilibrium free expansion of a one-dimensional quantum ideal gas. This quantum Boltzmann entropy, $S_B$, essentially counts the "number" of independent wavefunctions (microstates) giving rise to a specified macrostate. It generally depends on the choice of macrovariables, su

Tarcisio Mendes de Farias, Julien Wollbrett, Marc Robinson-Rechavi, Frederic Bastian

Background, enhancing interoperability of bioinformatics knowledge bases is a high priority requirement to maximize data reusability, and thus increase their utility such as the return on investment for biomedical research. A knowledge base may provide useful information for life scientists and other knowledge bases, but it only acquires exchange value once

Ahmet M. Elbir, Kumar Vijay Mishra, Asmaa Abdallah, Abdulkadir Celik

As the demand for wireless connectivity continues to soar, the fifth generation and beyond wireless networks are exploring new ways to efficiently utilize the wireless spectrum and reduce hardware costs. One such approach is the integration of sensing and communications (ISAC) paradigms to jointly access the spectrum. Recent ISAC studies have focused on uppe

Vladislav Ryzhov

Positioning problem can be solved with several approaches in 5G. This paper introduces RT (Ray Tracing) technique for small-cell Outdoor positioning and tracking for systems with distributed architecture with multipath processing gain. Proposed approach exploits high-resolution angular spectrum estimation and known radio propagation environment. It solves po

Ziyang Yuan, Yiming Zhu, Yu Li, Hongyu Liu

3D GAN inversion aims to achieve high reconstruction fidelity and reasonable 3D geometry simultaneously from a single image input. However, existing 3D GAN inversion methods rely on time-consuming optimization for each individual case. In this work, we introduce a novel encoder-based inversion framework based on EG3D, one of the most widely-used 3D GAN model

Meghana Nasre, Prajakta Nimbhorkar, Keshav Ranjan

We study the many-to-many bipartite matching problem in the presence of preferences where ties, as well as lower quotas, may appear on both sides of the bipartition. The input is a bipartite graph $G=(A \cup B, E)$, where each vertex in $A \cup B$ has a positive upper quota and a non-negative lower quota denoting the maximum and minimum number of vertices th

Zhidong He

The emergence of congestion is a critical phenomenon in transport systems. Transport is organized along pathways abstracted by links, which connect different nodes as regions to form the network. The modeling of traffic has so far mainly been based on one-dimensional lattices or networked queuing systems, which restricts mechanistic insights and analytical t

Yong-Guang Su, Ruifeng Lu, Hantao Lu, Can Shao

We investigate the non-equilibrium dynamics of the one-dimensional extended Hubbard model after interaction quenches. In strong-coupling regime with large on-site interaction, the ground states of this model with small and large nearest-neighbor interactions are in spin-density-wave and charge-density-wave phases, respectively. Combining twisted boundary con

Haruhiro Kubo, Takayuki Ishitobi, Kazumasa Hattori

We study the electronic origin of parasitic ferroic quadrupole moments in antiferroic quadrupole orders by extending a model studied in G. Chen et al., Phys. Rev. B 82, 174440 (2010) with the effective angular momentum $J_{\rm eff}=3/2$ quartet ground states. Taking into account the first crystalline-electric-field (CEF) excited doublet, cubic anisotropy in

B. Eslam Panah, M. E. Rodrigues

In this paper, we obtain exact phantom (A)dS black hole solutions in the context of $F(R)$ gravity with topological spacetime in four dimensions. Then, we study the effects of different parameters on the event horizon. In the following, we calculate the conserved and thermodynamic quantities of the system and check the first law of thermodynamics for these k

Rui Zhang, Zhen Zhang

The origin and nature of dark energy is one of the most significant challenges in modern science. This research aims to investigate dark energy on astrophysical scales and provide a cosmology-independent method to measure its equation-of-state parameter $w$. To accomplish this, we introduce the concept of a perfect fluid in any static, curved spacetime, and

Dhaval Taunk, Lakshya Khanna, Pavan Kandru, Vasudeva Varma

Commonsense question-answering (QA) methods combine the power of pre-trained Language Models (LM) with the reasoning provided by Knowledge Graphs (KG). A typical approach collects nodes relevant to the QA pair from a KG to form a Working Graph (WG) followed by reasoning using Graph Neural Networks(GNNs). This faces two major challenges: (i) it is difficult t

Guangzheng Hu, Haoran Li, Shasha Liu, Mingjun Ma

Multi-agent reinforcement learning (MARL) has achieved remarkable success in various challenging problems. Meanwhile, more and more benchmarks have emerged and provided some standards to evaluate the algorithms in different fields. On the one hand, the virtual MARL environments lack knowledge of real-world tasks and actuator abilities, and on the other hand,

Gaurav Harsha, Thomas M. Henderson, Gustavo E. Scuseria

Wave-function methods have offered a robust, systematically improvable means to study ground-state properties in quantum many-body systems. Theories like coupled cluster and their derivatives provide highly accurate approximations to the energy landscape at a reasonable computational cost. Analogs of such methods to study thermal properties, though highly de

SangMook Kim, Sangmin Bae, Hwanjun Song, Se-Young Yun

Although federated learning has made awe-inspiring advances, most studies have assumed that the client's data are fully labeled. However, in a real-world scenario, every client may have a significant amount of unlabeled instances. Among the various approaches to utilizing unlabeled data, a federated active learning framework has emerged as a promising soluti

Vikas C. Raykar, Arindam Jati, Sumanta Mukherjee, Nupur Aggarwal

A trustworthy machine learning model should be accurate as well as explainable. Understanding why a model makes a certain decision defines the notion of explainability. While various flavors of explainability have been well-studied in supervised learning paradigms like classification and regression, literature on explainability for time series forecasting is

Denis Gokhfeld

Holes drilled in a type-II superconductor trap the magnetic flux. Following Clem's flux pinning model, we consider surface pinning as a mechanism for compressing the magnetic flux in the holes. Estimations of the trapped magnetic flux demonstrate that the holes with the diameter up to 2 mm are advantageous for bulk single-crystal REBCO samples. The REBCO fil

Kaihang Pan, Juncheng Li, Hongye Song, Jun Lin

Prompt tuning is a parameter-efficient method, which learns soft prompts and conditions frozen language models to perform specific downstream tasks. Though effective, prompt tuning under few-shot settings on the one hand heavily relies on a good initialization of soft prompts. On the other hand, it can easily overfit to few-shot training samples, thereby und

Haipeng Zhou, Lei Zhu, Yuyin Zhou

The Diffusion Probabilistic Model (DPM) has emerged as a highly effective generative model in the field of computer vision. Its intermediate latent vectors offer rich semantic information, making it an attractive option for various downstream tasks such as segmentation and detection. In order to explore its potential further, we have taken a step forward and

W. J. Schill, M. R. Armstrong, J. H. Nguyen, D. M. Sterbentz

The classical Richtmyer-Meshkov instability is a hydrodynamic instability characterizing the evolution of an interface following shock loading. In contrast to other hydrodynamic instabilities such as Rayleigh-Taylor, it is known for being unconditionally unstable: regardless of the direction of shock passage, any deviations from a flat interface will be ampl

Jun Li, Che Liu, Sibo Cheng, Rossella Arcucci

The electrocardiogram (ECG) is one of the most commonly used non-invasive, convenient medical monitoring tools that assist in the clinical diagnosis of heart diseases. Recently, deep learning (DL) techniques, particularly self-supervised learning (SSL), have demonstrated great potential in the classification of ECG. SSL pre-training has achieved competitive

Kaniz Mishty, Mehdi Sadi

SoCs are now designed with their own AI accelerator segment to accommodate the ever-increasing demand of Deep Learning (DL) applications. With powerful MAC engines for matrix multiplications, these accelerators show high computing performance. However, because of limited memory resources (i.e., bandwidth and capacity), they fail to achieve optimum system per

Kazue Kudo

Quantum dynamics in a strongly disordered quantum many-body system show localization properties. The initial state memory is maintained owing to slow relaxation when the system is in the localized regime. This work demonstrates how localization can be observed using a noisy quantum computer by evaluating the magnetization and twist overlap in a quantum spin

Dhaval Taunk, Shivprasad Sagare, Anupam Patil, Shivansh Subramanian

Lack of encyclopedic text contributors, especially on Wikipedia, makes automated text generation for low resource (LR) languages a critical problem. Existing work on Wikipedia text generation has focused on English only where English reference articles are summarized to generate English Wikipedia pages. But, for low-resource languages, the scarcity of refere

Yanbiao Ma, Licheng Jiao, Fang Liu, Maoji Wen

To address the challenges of long-tailed classification, researchers have proposed several approaches to reduce model bias, most of which assume that classes with few samples are weak classes. However, recent studies have shown that tail classes are not always hard to learn, and model bias has been observed on sample-balanced datasets, suggesting the existen

Haiquan Qiu, Yongqi Zhang, Yong Li, Quanming Yao

Rule learning is critical to improving knowledge graph (KG) reasoning due to their ability to provide logical and interpretable explanations. Recently, Graph Neural Networks (GNNs) with tail entity scoring achieve the state-of-the-art performance on KG reasoning. However, the theoretical understandings for these GNNs are either lacking or focusing on single-

George Dadunashvili, Timon Idema

Animal cells are both encapsulated and subdivided by lipid bilayer membranes. Beyond just acting as boundaries, these membranes' shapes influence the function of cells and their compartments. Physically, membranes are two-dimensional fluids with complex elastic behavior, which makes it impossible, for all but a few simple cases, to predict membrane shapes an

Jiahao Bao, Kaiqiang Chen, Xian Sun, Liangjin Zhao

Siamese network based trackers develop rapidly in the field of visual object tracking in recent years. The majority of siamese network based trackers now in use treat each channel in the feature maps generated by the backbone network equally, making the similarity response map sensitive to background influence and hence challenging to focus on the target reg

Jiafeng Lu, Shiyin Shen, Fangting Yuan, Qi Zeng

Using a sample of face-on star-forming galaxies selected from the Sloan Digital Sky Survey, we statistically derive the typical optical depth $\tau_{\rm{cl}}$ of individual HII regions based on the ``Chocolate Chip Cookie" model of Lu2022. By binning galaxies into stellar mass and gas-phase metallicity bins and interpreting $\tau_{\rm{cl}}$ as the dust to ga

Andrew Cook, Andy Hammerlindl, Warwick Tucker

We define a family of $C^1$ functions which we call "nowhere coexpanding functions" that is closed under composition and includes all $C^3$ functions with non-positive Schwarzian derivative. We establish results on the number and nature of the fixed points of these functions, including a generalisation of a classic result of Singer.

Thomas Templin, Milad Memarzadeh, Walter Vinci, P. Aaron Lott

Deep generative learning cannot only be used for generating new data with statistical characteristics derived from input data but also for anomaly detection, by separating nominal and anomalous instances based on their reconstruction quality. In this paper, we explore the performance of three unsupervised deep generative models -- variational autoencoders (V

Joy Datta, Nikhil Koratkar, Dibakar Datta

Lithium-ion batteries (LIBs) are ubiquitous in everyday applications. However, Lithium (Li) is a limited resource on the planet and is therefore not sustainable. As an alternative to lithium, earth-abundant and cheaper multivalent metals such as aluminum (Al) and calcium (Ca) have been actively researched in battery systems. However, finding suitable interca

Zeyu Ren, Nurmement Yolwas, Huiru Wang, Wushour Slamu

In recent years, End-to-End speech recognition technology based on deep learning has developed rapidly. Due to the lack of Turkish speech data, the performance of Turkish speech recognition system is poor. Firstly, this paper studies a series of speech recognition tuning technologies. The results show that the performance of the model is the best when the da

S. G. Salnikov, A. I. Milstein

We show that the recent experimental data on the cross section of the process $e^{+}e^{-}\to\Lambda\bar{\Lambda}$ near the threshold can be perfectly explained by the final-state interaction of $\Lambda$ and $\bar{\Lambda}$. The enhancement of the cross section is related to the existence of low-energy real or virtual state in the corresponding potential. We

Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim

While having options could be liberating, too many options could lead to the sub-optimal solution being chosen. This is not an exception in the software engineering domain. Nowadays, API has become imperative in making software developers' life easier. APIs help developers implement a function faster and more efficiently. However, given the large number of o

Lianke Qin, Zhao Song, Ruizhe Zhang

Matrix sensing has many real-world applications in science and engineering, such as system control, distance embedding, and computer vision. The goal of matrix sensing is to recover a matrix $A_\star \in \mathbb{R}^{n \times n}$, based on a sequence of measurements $(u_i,b_i) \in \mathbb{R}^{n} \times \mathbb{R}$ such that $u_i^\top A_\star u_i = b_i$. Previ

Thomas Sayer, Yusef R. Farah, Rachelle Austin, Justin Sambur

Monolayer transition metal dichalcogenides (TMDs) have the potential to unlock novel photonic and chemical technologies if their optoelectronic properties can be understood and controlled. Yet, recent work has offered contradictory explanations for how TMD absorption spectra change with carrier concentration, fluence, and time. Here, we test our hypothesis t

Yu Qiao, Seong-Bae Park, Sun Moo Kang, Choong Seon Hong

Federated learning (FL) is a distributed machine learning technique in which multiple clients cooperate to train a shared model without exchanging their raw data. However, heterogeneity of data distribution among clients usually leads to poor model inference. In this paper, a prototype-based federated learning framework is proposed, which can achieve better

Shawn Priore, Meeko Oishi

While many techniques have been developed for chance constrained stochastic optimal control with Gaussian disturbance processes, far less is known about computationally efficient methods to handle non-Gaussian processes. In this paper, we develop a method for solving chance constrained stochastic optimal control problems for linear time-invariant systems wit

Xiaomeng Ma, Lingyu Gao

Neural network models have been proposed to explain the grapheme-phoneme mapping process in humans for many alphabet languages. These models not only successfully learned the correspondence of the letter strings and their pronunciation, but also captured human behavior in nonce word naming tasks. How would the neural models perform for a non-alphabet languag

Xinwei Ou, Zhangxin Chen, Ce Zhu, Yipeng Liu

Deep neural networks have achieved great success in many data processing applications. However, the high computational complexity and storage cost makes deep learning hard to be used on resource-constrained devices, and it is not environmental-friendly with much power cost. In this paper, we focus on low-rank optimization for efficient deep learning techniqu

Matteo Zallio, Takumi Ohashi, P. John Clarkson

The metaverse and digital, virtual environments have been part of recent history as places in which people can socialize, work and spend time playing games. However, the infancy of the development of these digital, virtual environments brings some challenges that are still not fully depicted. With this article, we seek to identify and map the currently avail

Minzhe Zhu

In this paper, we investigate the explicit birational geometry for projective $\epsilon$-lc varieties polarised by nef and big Weil divisors. We show that if $X$ is a projective $\epsilon$-lc variety, $H$ is a nef and big Weil divisor with $\dim\overline{\varphi_{H}(X)}\geq n-1$ and $L$ is an effective Weil divisor such that $|L-K_X|\neq \emptyset$ or $L-K_X

Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu

Both long-tailed and noisily labeled data frequently appear in real-world applications and impose significant challenges for learning. Most prior works treat either problem in an isolated way and do not explicitly consider the coupling effects of the two. Our empirical observation reveals that such solutions fail to consistently improve the learning when the

M. Cvetic, M. M. Stetsko

We study the separability of the Dirac equation for the four-dimensional STU black hole space-time. In particular, we analyze in detail the separability conditions in the pair-wise equal charge STU black hole space-time \cite{Cvetic_PRD96}. While in the latter case the minimally coupled Dirac equation is not separable the introduction of a specific torsion t

Qiming Ye, Yuxiang Feng, Jose Javier Escribano Macias, Marc Stettler

The deployment of Autonomous Vehicles (AVs) poses considerable challenges and unique opportunities for the design and management of future urban road infrastructure. In light of this disruptive transformation, the Right-Of-Way (ROW) composition of road space has the potential to be renewed. Design approaches and intelligent control models have been proposed

Xiaoming Tan

Given a compact Riemannian manifold $(M,g)$ with smooth boundary $\partial M$, we give an explicit expression for full symbol of the thermoelastic Dirichlet-to-Neumann map $\Lambda_g$ with variable coefficients $\lambda,\mu,\alpha,\beta \in C^{\infty}(\bar{M})$. We prove that $\Lambda_g$ uniquely determines partial derivatives of all orders of the coefficien

Dylan J. Foster, Noah Golowich, Sham M. Kakade

We consider the problem of decentralized multi-agent reinforcement learning in Markov games. A fundamental question is whether there exist algorithms that, when adopted by all agents and run independently in a decentralized fashion, lead to no-regret for each player, analogous to celebrated convergence results in normal-form games. While recent work has show

Chuanhong Liu, Caili Guo, Yang Yang, Wanli Ni

Task-oriented semantic communication has gained increasing attention due to its ability to reduce the amount of transmitted data without sacrificing task performance. Although some prior efforts have been dedicated to developing semantic communications, the semantics in these works remains to be unexplainable. Challenges related to explainable semantic repre

Dimitris Bertsimas, Leonard Boussioux, Cynthia Zeng

This paper presents a data-driven approach to mitigate the effects of air pollution from industrial plants on nearby cities by linking operational decisions with weather conditions. Our method combines predictive and prescriptive machine learning models to forecast short-term wind speed and direction and recommend operational decisions to reduce or pause the

Luciano Combi, Daniel M. Siegel

We perform high-resolution three-dimensional general-relativistic magnetohydrodynamic simulations with neutrino transport of binary neutron star (BNS) mergers resulting in a long-lived remnant neutron star, with properties typical of galactic BNS and consistent with those inferred for the first observed BNS merger GW170817. We demonstrate self-consistently t

Dmitriy Bilyk, Damir Ferizović, Alexey Glazyrin, Ryan Matzke

This paper is devoted to spherical measures and point configurations optimizing three-point energies. Our main goal is to extend the classic optimization problems based on pairs of distances between points to the context of three-point potentials. In particular, we study three-point analogues of the sphere packing problem and the optimization problem for $p$

Niklas Rohling, Roberto E. Troncoso

Magnon spin transport in a metal-antiferromagnetic insulator-ferromagnetic insulator heterostructure is considered. The spin current is generated via the spin Seebeck effect and in the limit of clean sample where the effects of interface imperfections and lattice defects are excluded. For NiO as an antiferromagnetic insulator we have a magnetic order of anti

Nicholas I-Hsien Kuo, Louisa Jorm, Sebastiano Barbieri

This paper presents a novel approach to simulating electronic health records (EHRs) using diffusion probabilistic models (DPMs). Specifically, we demonstrate the effectiveness of DPMs in synthesising longitudinal EHRs that capture mixed-type variables, including numeric, binary, and categorical variables. To our knowledge, this represents the first use of DP

Yuki Fujimura, Takahiro Kushida, Takuya Funatomi, Yasuhiro Mukaigawa

Non-line-of-sight (NLOS) imaging is conducted to infer invisible scenes from indirect light on visible objects. The neural transient field (NeTF) was proposed for representing scenes as neural radiance fields in NLOS scenes. We propose NLOS neural implicit surface (NLOS-NeuS), which extends the NeTF to neural implicit surfaces with a signed distance function

Eason Chen

We generated 25000 conversations labeled with Big Five Personality traits using prompt programming at GPT-3. Then we train Big Five classification models with these data and evaluate them with 2500 data from generated dialogues and real conversational datasets labeled in Big Five by human annotators. The results indicated that this approach is promising for

Seonghoon Jeong, Sangho Lee, Hwejae Lee, Huy Kang Kim

Controller Area Network (CAN) is an essential networking protocol that connects multiple electronic control units (ECUs) in a vehicle. However, CAN-based in-vehicle networks (IVNs) face security risks owing to the CAN mechanisms. An adversary can sabotage a vehicle by leveraging the security risks if they can access the CAN bus. Thus, recent actions and cybe

Zijian Liu, Zhengyuan Zhou

Recently, several studies consider the stochastic optimization problem but in a heavy-tailed noise regime, i.e., the difference between the stochastic gradient and the true gradient is assumed to have a finite $p$-th moment (say being upper bounded by $\sigma^{p}$ for some $\sigma\geq0$) where $p\in(1,2]$, which not only generalizes the traditional finite va

Ruofan Chen

In this article, using the numerically exact time-evolving matrix product operators method, we study the fidelity out-of-time-order correlator (FOTOC) in the unbiased spin-boson model at zero temperature. It is found that after the initial exponential growth of FOTOC, the information of the system dynamics will adulterate into the FOTOC. This makes the FOTOC

Valentin J. M. Le Gouellec, Anaëlle J. Maury, Charles L. H. Hull, Antoine Verliat

The polarized dust emission observed in Class 0 protostellar cores at high angular resolution with ALMA has raised several concerns about the grain alignment conditions in these regions. We aim to study the role of the radiation field on the grain alignment mechanisms occurring in the interior (<1000 au) of Class 0 protostars. We produce synthetic observatio

Yujun Jiao, Mingze Miao, Zhishuai Yin, Chunyuan Lei

Accurate and robust trajectory prediction of neighboring agents is critical for autonomous vehicles traversing in complex scenes. Most methods proposed in recent years are deep learning-based due to their strength in encoding complex interactions. However, unplausible predictions are often generated since they rely heavily on past observations and cannot eff

Yilin Wang

A perfect Kagome lattice features flat bands that usually lead to strong electronic correlation effects, but how electronic correlation, in turn, stabilizes a perfect Kagome lattice has rarely been explored. Here, we study such effect in a superconducting ($T_c \sim 7.8$ K) Kagome metal LaRu$_3$Si$_2$ with a distorted Kagome plane consisting of pure Ru ions,

Sota Arakawa, Daisuke Nishiura, Mikito Furuichi

In recent years, the gravitational collapse of pebble clumps in the early Solar System has been regarded as a plausible scenario for the origin of comets. In this context, ``pebbles'' represent mm- to cm-sized dust aggregates composed of (sub)micron-sized dust particles, and the structure of km-sized comets is thought to be an agglomerate of pebbles. The con

Tanner N. Carawan, Rebecca Field, Bertrand J. Guillou, David Mehrle

We compute the homotopy Mackey functors of the $KU_G$-local equivariant sphere spectrum when $G$ is a finite $q$-group for an odd prime $q$, building on the degree zero case from arXiv:2204.03797.

Renjie Wei, Zechun Liu, Yuchen Fan, Runsheng Wang

Deep neural networks for image super-resolution (SR) have demonstrated superior performance. However, the large memory and computation consumption hinders their deployment on resource-constrained devices. Binary neural networks (BNNs), which quantize the floating point weights and activations to 1-bit can significantly reduce the cost. Although BNNs for imag

Eduardo Rhod, Behnam Ghavami, Zhenman Fang, Lesley Shannon

Many aerospace and automotive applications use FPGAs in their designs due to their low power and reconfigurability requirements. Meanwhile, such applications also pose a high standard on system reliability, which makes the early-stage reliability analysis for FPGA-based designs very critical. In this paper, we present a framework that enables fast and accura

Masahiko Miyamoto

We prove that if V is a unitary simple holomorphic vertex operator algebra of CFT type, then V is rational, that is, all N-gradable V-modules are direct sums of copies of V.

Puning Yang, Jian Liang, Jie Cao, Ran He

Out-of-distribution (OOD) detection aims to detect test samples that do not fall into any training in-distribution (ID) classes. Prior efforts focus on regularizing models with ID data only, largely underperforming counterparts that utilize auxiliary outliers. However, data safety and privacy make it infeasible to collect task-specific outliers in advance fo

Behnam Nikoobakht

The analytic derivation of the dynamic Stark shift of hydrogenic energy levels in the presence of the circularly polarized laser light is presented. We use the classical framework with considering an adiabatically damped laser+atom interaction and an approach relies on time-independent perturbation theory with a second-quantized laser+atom dipole interaction

Enduo Zhao, Murilo M. Marinho, Kanako Harada

Robotic assistance for experimental manipulation in the life sciences is expected to enable precise manipulation of valuable samples, regardless of the skill of the scientist. Experimental specimens in the life sciences are subject to individual variability and deformation, and therefore require autonomous robotic control. As an example, we are studying the

Junxing Fan, Zhanqiang Xue, Hongyang Xing, Dan Lu

Bound states in the continuum (BICs) have exhibited extraordinary properties in photonics for enhanced light-matter interactions that enable appealing applications in nonlinear optics, biosensors, and ultrafast optical switches. The most common strategy to apply BICs in a metasurface is by breaking symmetry of resonators in the uniform array that leaks the o

Sarah Pungitore, Toluwanimi Olorunnisola, Jarrod Mosier, Vignesh Subbian

Post-acute sequelae of SARS-CoV-2 (PASC) is an increasingly recognized yet incompletely understood public health concern. Several studies have examined various ways to phenotype PASC to better characterize this heterogeneous condition. However, many gaps in PASC phenotyping research exist, including a lack of the following: 1) standardized definitions for PA

Takashi Ui

In games with incomplete and ambiguous information, rational behavior depends not only on fundamental ambiguity (ambiguity about states) but also on strategic ambiguity (ambiguity about others' actions), which further induces hierarchies of ambiguous beliefs. We study the impacts of strategic ambiguity in global games and demonstrate the distinct effects of

Chaoyu Pan, Shuhan Zheng, Meimei Yang, Zhiwei Liu

We employ the Boltzmann transport model to study the charmonium regeneration with non-thermal charm quarks in relativistic heavy-ion collisions. As heavy quarks do not reach kinetic thermalization in the quark-gluon plasma (QGP), the final transverse momentum distribution of regenerated charmonium depends on the degree of charm quark kinetic thermalization.

Phong C. H. Nguyen, Joseph B. Choi, H. S. Udaykumar, Stephen Baek

Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous due to the high dimensionality of the solution space. Recently, machine learning (ML) has emerged as a promising solution that can either serve as a surrogate for, accelerate or au

John Graff, Albert Medina, Francis Lagor

Estimation of unsteady flow fields around flight vehicles may improve flow interactions and lead to enhanced vehicle performance. Although flow-field representations can be very high-dimensional, their dynamics can have low-order representations and may be estimated using a few, appropriately placed measurements. This paper presents a sensor-selection framew

Yuxuan Zhao, Enmeng Lu, Yi Zeng

At the core of bodily self-consciousness is the perception of the ownership of one's body. Recent efforts to gain a deeper understanding of the mechanisms behind the brain's encoding of the self-body have led to various attempts to develop a unified theoretical framework to explain related behavioral and neurophysiological phenomena. A central question to be

Jiashu Wu, Yang Wang, Jinpeng Wang, Hekang Wang

As the capacity of Solid-State Drives (SSDs) is constantly being optimised and boosted with gradually reduced cost, the SSD cluster is now widely deployed as part of the hybrid storage system in various scenarios such as cloud computing and big data processing. However, despite its rapid developments, the performance of the SSD cluster remains largely under-

Chanwoo Kim, Trinh T. Nguyen

A rigorous derivation of point vortex systems from kinetic equations has been a challenging open problem, due to singular layers in the inviscid limit, giving a large velocity gradient in the Boltzmann equations. In this paper, we derive the Helmholtz-Kirchhoff point-vortex system from the hydrodynamic limits of the Boltzmann equations. We construct Boltzman

Haojie Hou, Yan-Xia Ren, Renming Song

We first study the convergence of solutions of a system of F-KPP equations related to irreducible multitype branching Brownian motions with Heaviside-type initial conditions to traveling wave solutions. Then we apply this convergence result to prove that the extremal processes of irreducible multitype branching Brownian motions converges weakly to a cluster

Zizhao Hu, Mohammad Rostami

Binary concepts are empirically used by humans to generalize efficiently. And they are based on Bernoulli distribution which is the building block of information. These concepts span both low-level and high-level features such as "large vs small" and "a neuron is active or inactive". Binary concepts are ubiquitous features and can be used to transfer knowled

Nathan Braswell, Sharjeel Khan, Santosh Pande

Macros are a common part of Lisp languages, and one of their most lauded features. Much research has gone into making macros both safer and more powerful resulting in developments in multiple areas, including maintaining hygiene, and typed program staging. However, macros do suffer from various downsides, including being second-class. Particularly egregious

Minsuk Chang, Stefania Druga, Alex Fiannaca, Pedro Vergani

This paper examines the art practices, artwork, and motivations of prolific users of the latest generation of text-to-image models. Through interviews, observations, and a user survey, we present a sampling of the artistic styles and describe the developed community of practice around generative AI. We find that: 1) the text prompt and the resulting image ca

Takahiro Morimoto, Sota Kitamura, Naoto Nagaosa

We review recent developments in the research of nonlinear and nonequilibrium phenomena in solids focusing on their geometrical aspects. We start with introducing the basic concepts of geometrical phases of Bloch electrons and Floquet theory for periodically driven systems. Then we review recent attempts to engineer topological phases in nonequilibrium matte

Juan Carlos Del Águila, Tonatiuh Matos

In this work the possible geodesic completeness of an electromagnetic dipole wormhole is studied in detail. The space-time contains a curvature singularity and belongs to a class of solutions to the Einstein-Maxwell equations with a coupled scalar field. Specifically, a numerical analysis is performed to examine congruences of null geodesics that are directe

Patrick Murphy, Misha Perepelitsa, Ilya Timofeyev, Matan Lieber-Kotz

Studies in the collective motility of organisms use a range of analytical approaches to formulate continuous kinetic models of collective dynamics from rules or equations describing agent interactions. However, the derivation of these kinetic models often relies on Boltzmann's hypothesis of "molecular chaos", that correlations between individuals are short-l

Junbin Fang, You Jiang, Canjian Jiang, Zoe L. Jiang

Adversarial attacks can mislead deep learning models to make false predictions by implanting small perturbations to the original input that are imperceptible to the human eye, which poses a huge security threat to the computer vision systems based on deep learning. Physical adversarial attacks, which is more realistic, as the perturbation is introduced to th

Yanqing Wang, Jing Yang, Yulin Ye

In this paper, we are concerned with the energy and magnetic helicity conservation of weak solutions for both the electron and Hall magnetohydrodynamic equations. Various sufficient criteria to ensure the energy and magnetic helicity conservation in Onsager's critical spaces $\underline{B}^{\alpha}_{p,VMO}$ and $B^{\alpha}_{p,c(\mathbb{N})}$ in these systems

Vrinda Malhotra, Katerina Potika, Mark Stamp

Managing the threat posed by malware requires accurate detection and classification techniques. Traditional detection strategies, such as signature scanning, rely on manual analysis of malware to extract relevant features, which is labor intensive and requires expert knowledge. Function call graphs consist of a set of program functions and their inter-proced

Yizhe Li, Yu-Lin Tsai, Xuebin Ren, Chia-Mu Yu

Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model. In this work, we explore the benefits of VP in constructing compelling neural network classifiers with differential privacy (DP). We explore and integrate VP into canonical DP training meth

Heng Yang, Marco Pavone

The two-stage object pose estimation paradigm first detects semantic keypoints on the image and then estimates the 6D pose by minimizing reprojection errors. Despite performing well on standard benchmarks, existing techniques offer no provable guarantees on the quality and uncertainty of the estimation. In this paper, we inject two fundamental changes, namel

Yanxia Qian, Yongchao Zhang, Yunqing Huang, Suchuan Dong

We consider the approximation of a class of dynamic partial differential equations (PDE) of second order in time by the physics-informed neural network (PINN) approach, and provide an error analysis of PINN for the wave equation, the Sine-Gordon equation and the linear elastodynamic equation. Our analyses show that, with feed-forward neural networks having t

Dmitry Korotkin, Peter Zograf

We study the moduli space of meromorphic 1-forms on complex algebraic curves having at most simple poles with fixed nonzero residues. We interpret the Bergman tau function on this moduli space as a section of a line bundle and study its asymptotic behavior near the boundary and the locus of forms with non-simple zeros. As an application, we decompose the pro