March 2023 arXiv papers — page 94

Aidan Kaminsky, Lars Bonne, Doris Arzoumanian, Simon Coudé

Filaments are ubiquitous in the interstellar medium (ISM), yet their formation and evolution remains the topic of intense debate. In order to obtain a more comprehensive view of the 3D morphology and evolution of the Musca filament, we model the C$^{18}$O(2-1) emission along the filament crest with several large-scale velocity field structures. This indicate

Joseph Konan, Ojas Bhargave, Shikhar Agnihotri, Hojeong Lee

In this paper, we present a method for fine-tuning models trained on the Deep Noise Suppression (DNS) 2020 Challenge to improve their performance on Voice over Internet Protocol (VoIP) applications. Our approach involves adapting the DNS 2020 models to the specific acoustic characteristics of VoIP communications, which includes distortion and artifacts cause

Niccolò Zagli, Valerio Lucarini, Grigorios Pavliotis

We consider a class of nonequilibrium systems of interacting agents with pairwise interactions and quenched disorder in the dynamics featuring, in the thermodynamic limit, phase transitions. We provide conditions on the microscopic structure of interactions among the agents that lead to a dimension reduction of the system in terms of a finite number of react

Andrew Peng, Jiangpeng He, Fengqing Zhu

Food image analysis is the groundwork for image-based dietary assessment, which is the process of monitoring what kinds of food and how much energy is consumed using captured food or eating scene images. Existing deep learning-based methods learn the visual representation for downstream tasks based on human annotation of each food image. However, most food i

G. M. I. K. Galagoda, W. M. C. A. Karunarathne, R. S. Bates, K. M. H. V. P. Gangathilaka

The global pandemic situation has severely affected all countries. As a result, almost all countries had to adjust to online technologies to continue their processes. In addition, Sri Lanka is yearly spending ten billion on elections. We have examined a proper way of minimizing the cost of hosting these events online. To solve the existing problems and incre

Soufiane Belharbi, Shakeeb Murtaza, Marco Pedersoli, Ismail Ben Ayed

Leveraging spatiotemporal information in videos is critical for weakly supervised video object localization (WSVOL) tasks. However, state-of-the-art methods only rely on visual and motion cues, while discarding discriminative information, making them susceptible to inaccurate localizations. Recently, discriminative models have been explored for WSVOL tasks u

Rasoul Akhavan Mahdavi, Abdulrahman Diaa, Florian Kerschbaum

Homomorphic Encryption (HE) is a commonly used tool for building privacy-preserving applications. However, in scenarios with many clients and high-latency networks, communication costs due to large ciphertext sizes are the bottleneck. In this paper, we present a new compression technique that uses an additive homomorphic encryption scheme with small cipherte

Xing-Yue Duan, Xiong Ying, Si-Yang Leng, Jürgen Kurths

Reservoir computing (RC), a particular form of recurrent neural network, is under explosive development due to its exceptional efficacy and high performance in reconstruction or/and prediction of complex physical systems. However, the mechanism triggering such effective applications of RC is still unclear, awaiting deep and systematic exploration. Here, comb

Haimiao Mo, Shuai Ding, Siu Cheung Hui

Early screening for anxiety and appropriate interventions are essential to reduce the incidence of self-harm and suicide in patients. Due to limited medical resources, traditional methods that overly rely on physician expertise and specialized equipment cannot simultaneously meet the needs for high accuracy and model interpretability. Multimodal data can pro

Zeqiang Lai, Chenggang Yan, Ying Fu

In this paper, we present a Hybrid Spectral Denoising Transformer (HSDT) for hyperspectral image denoising. Challenges in adapting transformer for HSI arise from the capabilities to tackle existing limitations of CNN-based methods in capturing the global and local spatial-spectral correlations while maintaining efficiency and flexibility. To address these is

Yuhang Gao, Yanlong Lin, Lang Liu

The hot nucleus $^{162}\mathrm{Dy}$ is investigated using covariant density functional theory, where the shell-model-like approach treats the pairing correlation. Lee-Yang's theorem is applied to classify the pairing phase transition by analyzing the distribution of zeros of the partition function in the complex temperature plane. The distribution of zeros o

Qing Lyu, Josh Tan, Michael E. Zapadka, Janardhana Ponnatapura

The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility of using ChatGPT in experiments on using ChatGPT to translate radiology reports into plain language for patients and healthcare providers so that they are educated for improved hea

Zhongtao Fu, Xiaoyu Lei, Xubing Chen, Mohamed Ibrahim Ahmed

In view of the classical visual servoing trajectory planning method which only considers the camera trajectory, this paper proposes one homography matrix based trajectory planning method for robot uncalibrated visual servoing. Taking the robot-end-effector frame as one generic case, eigenvalue decomposition is utilized to calculate the infinite homography ma

Xingyu Chen, Yu Deng, Baoyuan Wang

Generating images with both photorealism and multiview 3D consistency is crucial for 3D-aware GANs, yet existing methods struggle to achieve them simultaneously. Improving the photorealism via CNN-based 2D super-resolution can break the strict 3D consistency, while keeping the 3D consistency by learning high-resolution 3D representations for direct rendering

Jiahong Zhang, Lihong Cao, Moning Zhang, Wenlong Fu

Backpropagation-based supervised learning has achieved great success in computer vision tasks. However, its biological plausibility is always controversial. Recently, the bio-inspired Hebbian learning rule (HLR) has received extensive attention. Self-Organizing Map (SOM) uses the competitive HLR to establish connections between neurons, obtaining visual feat

Fanglei Xue, Yifan Sun, Yi Yang

This paper investigates unsupervised representation learning for facial expression analysis. We think Unsupervised Facial Expression Representation (UFER) deserves exploration and has the potential to address some key challenges in facial expression analysis, such as scaling, annotation bias, the discrepancy between discrete labels and continuous emotions, a

Aadirupa Saha, Branislav Kveton

Most bandit algorithms assume that the reward variances or their upper bounds are known, and that they are the same for all arms. This naturally leads to suboptimal performance and higher regret due to variance overestimation. On the other hand, underestimated reward variances may lead to linear regret due to committing early to a suboptimal arm. This motiva

Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar

Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this work, we propose an exploration method that effectively encourages cooperative exploration based on the idea of sequential action-computation scheme. The high-level intuition is that to perform optimism-based exploration, agents would explore cooperative

Anthony Z. Liu, Lajanugen Logeswaran, Sungryull Sohn, Honglak Lee

Planning is an important capability of artificial agents that perform long-horizon tasks in real-world environments. In this work, we explore the use of pre-trained language models (PLMs) to reason about plan sequences from text instructions in embodied visual environments. Prior PLM based approaches for planning either assume observations are available in t

Yuxuan Li, Qibin Hou, Zhaohui Zheng, Ming-Ming Cheng

Recent research on remote sensing object detection has largely focused on improving the representation of oriented bounding boxes but has overlooked the unique prior knowledge presented in remote sensing scenarios. Such prior knowledge can be useful because tiny remote sensing objects may be mistakenly detected without referencing a sufficiently long-range c

Xiaoyue Wu, Fiona C. Meldrum, Katherine Skipper, Yushi Yang

Colloidal particles self assemble into a wide range of structures under external AC electric fields due to induced dipolar interactions [Yethiraj and Van Blaaderen Nature 421 513 (2003)]. As a result of these dipolar interactions, at low volume fraction the system is modulated between a hard-sphere like state (in the case of zero applied field) and a "string

Manuel Leal, César Lozano Huerta, Montserrat Vite

We compute the dimension of certain components of the family of smooth determinantal degree $d$ surfaces in $\mathbb{P}^3$, and show that each of them is the closure of a component of the Noether-Lefschetz locus $NL(d)$. Our computations exhibit that smooth determinantal surfaces in $\mathbb{P}^3$ of degree 4 form a divisor in $|\mathcal{O}_{\mathbb{P}^3}(4)

Junqi Qian, Paul Weng, Chenmien Tan

When applying reinforcement learning (RL) to a new problem, reward engineering is a necessary, but often difficult and error-prone task a system designer has to face. To avoid this step, we propose LR4GPM, a novel (deep) RL method that can optimize a global performance metric, which is supposed to be available as part of the problem description. LR4GPM alter

Pu Zhang, Tianhua Chen, Bin Liu

Deep learning has become the dominating approach for object detection. To achieve accurate fine-grained detection, one needs to employ a large enough model and a vast amount of data annotations. In this paper, we propose a commonsense knowledge inference module (CKIM) which leverages commonsense knowledge to assist a lightweight deep neural network base coar

Daniels Krimans, Seth Putterman

Motivated by the high energy focusing found in rapidly collapsing bubbles that is relevant to implosion processes that concentrate energy density, such as sonoluminescence, we consider a calculation of an empty cavity collapse in a compressible Euler fluid. We review and then use the method based on similarity theory that was previously used to compute the p

Arnab Bhattacharjee, Tapan K. Saha, Ashu Verma, Sukumar Mishra

An increased energy demand, and environmental pressure to accommodate higher levels of renewable energy and flexible loads like electric vehicles have led to numerous smart transformations in the modern power systems. These transformations make the cyber-physical power system highly susceptible to cyber-adversaries targeting its numerous operations. In this

Dongzhou Huang

Suppose that a random variable $X$ of interest is observed. This paper concerns "the least favorable noise" $\hat{Y}_{\epsilon}$, which maximizes the prediction error $E [X - E[X|X+Y]]^2 $ (or minimizes the variance of $E[X| X+Y]$) in the class of $Y$ with $Y$ independent of $X$ and $\mathrm{var} Y \leq \epsilon^2$. This problem was first studied by Ernst, K

Carlos Bautista, Leonardo de Lima, Ricardo D. Matheus, Aurore Savoy-Navarro

It is a common feature of vector-like extensions of the electroweak sector to have near degenerate states, such as electroweak doublets. In simplified models, it is usually assumed that these have decay widths saturated by two-body channels. As a consequence, experimental searches can be done focusing on only one of the states of the doublet. Taking as an ex

Walter Carballosa, Jessica Khera, Francisco Reyes

In this work we study the acyclic orientations of graphs. We obtain an encoding of the acyclic orientations of the complete $p$-partite graph with size of its parts $n_1,n_2,\ldots,n_p$ via a vector with $p$ symbols and length $n=n_1+n_2+\ldots+n_p$ when the parts are fixed but not the vertices in each part. We also give a recursive way to construct all acyc

Yichi Zhang, Fang-Yi Yu, Grant Schoenebeck, David Kempe

The conference peer review process involves three constituencies with different objectives: authors want their papers accepted at prestigious venues (and quickly), conferences want to present a program with many high-quality and few low-quality papers, and reviewers want to avoid being overburdened by reviews. These objectives are far from aligned, primarily

Philippe Nadeau, Vasu Tewari

Stanley's theory of $(P,\omega)$-partitions is a standard tool in combinatorics. It can be extended to allow for the presence of a restriction, that is a given maximal value for partitions at each vertex of the poset, as was shown by Assaf and Bergeron. Here we present a variation on their approach, which applies more generally. The enumerative side of the t

Erica Su, Robert E. Weiss, Kouros Nouri-Mahdavi, Andrew J. Holbrook

We model longitudinal macular thickness measurements to monitor the course of glaucoma and prevent vision loss due to disease progression. The macular thickness varies over a 6$\times$6 grid of locations on the retina with additional variability arising from the imaging process at each visit. Currently, ophthalmologists estimate slopes using repeated simple

Stefano Gogioso, Nicola Pinzani

We provide a unified operational framework for the study of causality, non-locality and contextuality, in a fully device-independent and theory-independent setting. We define causaltopes, our chosen portmanteau of "causal polytopes", for arbitrary spaces of input histories and arbitrary choices of input contexts. We show that causaltopes are obtained by slic

Guang Yang

In this article we investigate the rough paths structure of a process $X_t$ living in a fixed Wiener chaos. Specifically, we formulate various types of rough lifts of $X_t$ and study their properties. As application, we study the integrabilities of quantities related to rough differential equations driven by $X_t$.

Shuailiang Yang, Jianxi Song, Xiaoyu Wang, De-Min Li

We investigate the single transverse-spin asymmetry with a $\sin (\phi_h-\phi_S)$ modulation in the charged Kaon produced and in $\Lambda$ hyperon produced SIDIS process within the theoretical framework of transverse momentum dependent (TMD) factorization at the next-to-leading-logarithmic order. The asymmetry is contributed by the convolution of Sivers func

Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi

Large language models (LLMs) can perform complex reasoning in few- and zero-shot settings by generating intermediate chain of thought (CoT) reasoning steps. Further, each reasoning step can rely on external tools to support computation beyond the core LLM capabilities (e.g. search/running code). Prior work on CoT prompting and tool use typically requires han

Haoran Guan

For the purpose of inspecting power plants, autonomous robots can be built using reinforcement learning techniques. The method replicates the environment and employs a simple reinforcement learning (RL) algorithm. This strategy might be applied in several sectors, including the electricity generation sector. A pre-trained model with perception, planning, and

Yuda Bi

Deep learning has become a prominent computational modeling tool in the areas of computer vision and image processing in recent years. This research comprehensively analyzes the different deep-learning methods used for image-to-image translation and reconstruction in the natural and medical imaging domains. We examine the famous deep learning frameworks, suc

Philip Metzger

Economic parameters are identified for an in-space industry where the capital is made on one planet, it is transported to and teleoperated on a second planet, and the product is transported off the second planet for consumption. This framework is used to model the long-run cost of lunar propellant production to help answer whether it is commercially competit

Brandon Eskridge, Henry Krakauer, Shiwei Zhang

Molecular magnets have received significant attention because of their potential applications in quantum information and quantum computing. A delicate balance of electron correlation, spin-orbit coupling (SOC), ligand field splitting, and other effects produces a persistent magnetic moment within each molecular magnet unit. The discovery and design of molecu

Long Chen, Jingrong Wei

A class of monotone operator equations, which can be decomposed into sum of the gradient of a strongly convex function and a linear and skew-symmetric operator, is considered in this work. Based on discretization of the generalized gradient flow, gradient and skew-symmetric splitting (GSS) methods are proposed and proved to converge in linear rates. To furth

Ruoxi Sun, Minhui Xue, Gareth Tyson, Shuo Wang

The digital age has brought a world of opportunity to children. Connectivity can be a game-changer for some of the world's most marginalized children. However, while legislatures around the world have enacted regulations to protect children's online privacy, and app stores have instituted various protections, privacy in mobile apps remains a growing concern

Yanhua Xu

Flow cytometry mainly used for detecting the characteristics of a number of biochemical substances based on the expression of specific markers in cells. It is particularly useful for detecting membrane surface receptors, antigens, ions, or during DNA/RNA expression. Not only can it be employed as a biomedical research tool for recognising distinctive types o

Cornelius Adejoro, Luise Arn, Larissa Schwartz, Tom Yeh

This paper presents a new approach to engaging children in Nigeria to share their views of AI. This approach is centered on an inclusive writing contest for children in a secondary school in Abuja to write about AI to compete for prizes and share their writings with others. A preliminary analysis of the first 11 articles we received exhibits diverse gender a

Indranil Biswas, Peter O'Sullivan

We study the behaviour of principal bundles under pullback along proper surjective morphisms of either schemes over an algebraically closed field of characteristic 0 or complex analytic spaces.

Wenjin Fu, Yue Han, Jiangpeng He, Sriram Baireddy

Generative Adversarial Networks (GAN) have been widely investigated for image synthesis based on their powerful representation learning ability. In this work, we explore the StyleGAN and its application of synthetic food image generation. Despite the impressive performance of GAN for natural image generation, food images suffer from high intra-class diversit

Jian Zheng, Tianyu Dai, Jared Miller, Mario Sznaier

This paper presents a tractable framework for data-driven synthesis of robustly safe control laws. Given noisy experimental data and some priors about the structure of the system, the goal is to synthesize a state feedback law such that the trajectories of the closed loop system are guaranteed to avoid an unsafe set even in the presence of unknown but bounde

Mengge Zhang, Jie Li, Xiangke Wang

This paper systematically studies the cooperative area coverage and target tracking problem of multiple-unmanned aerial vehicles (multi-UAVs). The problem is solved by decomposing into three sub-problems: information fusion, task assignment, and multi-UAV behavior decision-making. Specifically, in the information fusion process, we use the maximum consistenc

Taosha Guo, Abed AlRahman Al Makdah, Vishaal Krishnan, Fabio Pasqualetti

In this paper we study an imitation and transfer learning setting for Linear Quadratic Gaussian (LQG) control, where (i) the system dynamics, noise statistics and cost function are unknown and expert data is provided (that is, sequences of optimal inputs and outputs) to learn the LQG controller, and (ii) multiple control tasks are performed for the same syst

Alan Chan, Herbie Bradley, Nitarshan Rajkumar

Democratization of AI means not only that people can freely use AI, but also that people can collectively decide how AI is to be used. In particular, collective decision-making power is required to redress the negative externalities from the development of increasingly advanced AI systems, including degradation of the digital commons and unemployment from au

Masatake Hirao, Hiroshi Nozaki, Koji Tasaka

In this paper, we study shells of the $D_4$ lattice with a {slight generalization} of spherical $t$-designs due to Delsarte-Goethals-Seidel, namely, the spherical design of harmonic index $T$ (spherical $T$-design for short) introduced by Delsarte-Seidel. We first observe that{, for any positive integer $m$,} the $2m$-shell of $D_4$ is an antipodal spherical

Wenqian Zhao, Qi Sun, Yang Bai, Wenbo Li

Recent years have witnessed impressive progress in super-resolution (SR) processing. However, its real-time inference requirement sets a challenge not only for the model design but also for the on-chip implementation. In this paper, we implement a full-stack SR acceleration framework on embedded GPU devices. The special dictionary learning algorithm used in

Long Zhao, Liangzhe Yuan, Boqing Gong, Yin Cui

This work focuses on training a single visual relationship detector predicting over the union of label spaces from multiple datasets. Merging labels spanning different datasets could be challenging due to inconsistent taxonomies. The issue is exacerbated in visual relationship detection when second-order visual semantics are introduced between pairs of objec

Diogo Belloni, Matthias R. Schreiber

Accreting compact objects are crucial to understand several important astrophysical phenomena such as Type Ia supernovae, gravitational waves, or X-ray and $\gamma$-ray bursts. In addition, they are natural laboratories to infer fundamental properties of stars, to investigate high-energy phenomena and accretion processes, to test theories of stellar and bina

Aron Brenner, Rahman Khorramfar, Saurabh Amin

Effective investment planning decisions are crucial to ensure cyber-physical infrastructures satisfy performance requirements over an extended time horizon. Computing these decisions often requires solving Capacity Expansion Problems (CEPs). In the context of regional-scale energy systems, these problems are prohibitively expensive to solve due to large netw

Mark Kamsma

We study the model theory of vector spaces with a bilinear form over a fixed field. For finite fields this can be, and has been, done in the classical framework of full first-order logic. For infinite fields we need different logical frameworks. First we take a category-theoretic approach, which requires very little set-up. We show that linear independence f

Tianhao Lin

Object detection has been used in a wide range of industries. For example, in autonomous driving, the task of object detection is to accurately and efficiently identify and locate a large number of predefined classes of object instances (vehicles, pedestrians, traffic signs, etc.) from videos of roads. In robotics, the industry robot needs to recognize speci

Jochen Stiasny, Spyros Chatzivasileiadis

The simulation of power system dynamics poses a computationally expensive task. Considering the growing uncertainty of generation and demand patterns, thousands of scenarios need to be continuously assessed to ensure the safety of power systems. Physics-Informed Neural Networks (PINNs) have recently emerged as a promising solution for drastically acceleratin

Vincent A. Voelz, Vijay S. Pande, Gregory R. Bowman

Simulations of biomolecules have enormous potential to inform our understanding of biology but require extremely demanding calculations. For over twenty years, the Folding@home distributed computing project has pioneered a massively parallel approach to biomolecular simulation, harnessing the resources of citizen scientists across the globe. Here, we summari

Lubashan Pathirana, Jeffrey Schenker

A discrete quantum process is represented by a sequence of quantum operations, which are completely positive maps that are not necessarily trace preserving. We consider quantum processes that are obtained by repeated iterations of a quantum operation with noise. Such ergodic quantum processes generalize independent quantum processes. An ergodic theorem descr

Zhuohan Xie, Miao Li, Trevor Cohn, Jey Han Lau

Numerous evaluation metrics have been developed for natural language generation tasks, but their effectiveness in evaluating stories is limited as they are not specifically tailored to assess intricate aspects of storytelling, such as fluency and interestingness. In this paper, we introduce DELTASCORE, a novel methodology that employs perturbation techniques

Maxim Grigoriev, Dmitry Rudinsky

It is well known that a $Q$-manifold gives rise to an $L_\infty$-algebra structure on the tangent space at a fixed point of the homological vector field. From the field theory perspective this implies that the expansion of a classical Batalin-Vilkovisky (BV) formulation around a vacuum solution can be equivalently cast into the form of an $L_\infty$-algebra.

Hiroyuki Ootomo, Hidetaka Manabe, Kenji Harada, Rio Yokota

Quantum circuit simulation provides the foundation for the development of quantum algorithms and the verification of quantum supremacy. Among the various methods for quantum circuit simulation, tensor network contraction has been increasing in popularity due to its ability to simulate a larger number of qubits. During tensor contraction, the input tensors ar

Rohit Parasnis, Seyyedali Hosseinalipour, Yun-Wei Chu, Mung Chiang

Semi-decentralized federated learning blends the conventional device to-server (D2S) interaction structure of federated model training with localized device-to-device (D2D) communications. We study this architecture over practical edge networks with multiple D2D clusters modeled as time-varying and directed communication graphs. Our investigation results in

Jiazhen Xu, Janice L. Scealy, Andrew T. A. Wood, Tao Zou

Score matching is an estimation procedure that has been developed for statistical models whose probability density function is known up to proportionality but whose normalizing constant is intractable, so that maximum likelihood is difficult or impossible to implement. To date, applications of score matching have focused more on continuous IID models. Motiva

Yitzchak Shmalo, Jonathan Jenkins, Oleksii Krupchytskyi

In this work, we present some applications of random matrix theory for the training of deep neural networks. Recently, random matrix theory (RMT) has been applied to the overfitting problem in deep learning. Specifically, it has been shown that the spectrum of the weight layers of a deep neural network (DNN) can be studied and understood using techniques fro

Lautaro Estienne, Matias Vera, Leonardo Rey Vega

Sentiment Classification is a fundamental task in the field of Natural Language Processing, and has very important academic and commercial applications. It aims to automatically predict the degree of sentiment present in a text that contains opinions and subjectivity at some level, like product and movie reviews, or tweets. This can be really difficult to ac

Sichen Li, Jochem Snuverink, Fernando Perez-Cruz, Andreas Adelmann

Unforeseen particle accelerator interruptions, also known as interlocks, lead to abrupt operational changes despite being necessary safety measures. These may result in substantial loss of beam time and perhaps even equipment damage. We propose a simple yet powerful binary classification model aiming to forecast such interruptions, in the case of the High In

Fartash Faghri, Hadi Pouransari, Sachin Mehta, Mehrdad Farajtabar

We propose Dataset Reinforcement, a strategy to improve a dataset once such that the accuracy of any model architecture trained on the reinforced dataset is improved at no additional training cost for users. We propose a Dataset Reinforcement strategy based on data augmentation and knowledge distillation. Our generic strategy is designed based on extensive a

Nicola Lorenzoni, Namgee Cho, James Lim, Dario Tamascelli

Conducting precise electronic-vibrational dynamics simulations of molecular systems poses significant challenges when dealing with realistic environments composed of numerous vibrational modes. Here, we introduce a technique for the construction of effective phonon spectral densities that capture accurately open system dynamics over a finite time interval of

Danish Iqbal, Barbora Buhnova, Emilia Cioroaica

The adoption process of innovative software-intensive technologies leverages complex trust concerns in different forms and shapes. Perceived safety plays a fundamental role in technology adoption, being especially crucial in the case of those innovative software-driven technologies characterized by a high degree of dynamism and unpredictability, like collabo

Bin Shuai, Min Hua, Yanfei Li, Shijin Shuai

Developing intelligent energy management systems with high adaptability and superiority is necessary and significant for Hybrid Electric Vehicles (HEVs). This paper proposed an ensemble learning-based scheme based on a learning automata module (LAM) to enhance vehicle energy efficiency. Two parallel base learners following two exploration-to-exploitation rat

Minhao He, Jiaqi Cai, Huiyuan Zheng, Eric Seewald

Moir\'e excitons are emergent optical excitations in 2D semiconductors with deep moir\'e superlattice potentials. While these excitations have been realized in several platforms, a system with dynamically tunable moir\'e potential to tailor the moir\'e exciton properties is yet to be realized. Here, we present a continuously tunable moir\'e potential in a mo

Sutanoy Dasgupta, Peng Zhao, Jacob Helwig, Prasenjit Ghosh

Gaussian graphical models typically assume a homogeneous structure across all subjects, which is often restrictive in applications. In this article, we propose a weighted pseudo-likelihood approach for graphical modeling which allows different subjects to have different graphical structures depending on extraneous covariates. The pseudo-likelihood approach r

Jaeseung Lim, Jongkeun Na, Nojun Kwak

Active Learning (AL) and Semi-supervised Learning are two techniques that have been studied to reduce the high cost of deep learning by using a small amount of labeled data and a large amount of unlabeled data. To improve the accuracy of models at a lower cost, we propose a method called Active Semi-supervised Learning (ASSL), which combines AL and SSL. To m

Chen Song, Chandrajit Bajaj, Qixing Huang

We present DeblurSR, a novel motion deblurring approach that converts a blurry image into a sharp video. DeblurSR utilizes event data to compensate for motion ambiguities and exploits the spiking representation to parameterize the sharp output video as a mapping from time to intensity. Our key contribution, the Spiking Representation (SR), is inspired by the

Jacob O. Tørring, Ben van Werkhoven, Filip Petrovic, Floris-Jan Willemsen

As computing system become more complex, it is becoming harder for programmers to keep their codes optimized as the hardware gets updated. Autotuners try to alleviate this by hiding as many architecture-based optimization details as possible from the user, so that the code can be used efficiently across different generations of systems. In this article we in

Vainavi Viswanath, Kaushik Shivakumar, Jainil Ajmera, Mallika Parulekar

Tracing - estimating the spatial state of - long deformable linear objects such as cables, threads, hoses, or ropes, is useful for a broad range of tasks in homes, retail, factories, construction, transportation, and healthcare. For long deformable linear objects (DLOs or simply cables) with many (over 25) crossings, we present HANDLOOM (Heterogeneous Autore

Roman Chertovskih, Nikolay Pogodaev, Maxim Staritsyn, Antonio Pedro Aguiar

Motivated by the problem of designing robust composite pulses for Bloch equations in the presence of natural perturbations, we study an abstract optimal ensemble control problem in a probabilistic setting with a general nonlinear performance criterion. The model under study addresses mean-field dynamics described by a linear continuity equation in the space

George S. Rizos, Konstantinos A. Draziotis

In the present paper, we extend previous results of an id scheme based on compact knapsack problem defined by one equation. We present a sound three-move id scheme based on compact knapsack problem defined by an integer matrix. We study this problem by providing attacks based on lattices. Furthermore, we provide the corresponding digital signature obtained b

Zhen-Xia Niu, Chao Gao

Manipulating nonlinear excitations, including solitons and vortices, is an essential topic in quantum many-body physics. A recent progress in this direction is a new protocol proposed in [Phys. Rev. Res. 2, 043256 (2020)] to produce dark solitons in a one-dimensional atomic Bose-Einstein condensate (BEC) by quenching inter-atomic interaction. Motivated by th

Yu Hin Au, Levent Tunçel

We study the lift-and-project rank of the stable set polytopes of graphs with respect to the Lov\'asz-Schrijver SDP operator $\text{LS}_+$. In particular, we focus on a search for relatively small graphs with high $\text{LS}_+$-rank (i.e., the least number of iterations of the $\text{LS}_+$ operator on the fractional stable set polytope to compute the stable

Haiguang Li, Trausti Thormundsson, Ivan Poupyrev, Nicholas Gillian

Mobile and embedded machine learning developers frequently have to compromise between two inferior on-device deployment strategies: sacrifice accuracy and aggressively shrink their models to run on dedicated low-power cores; or sacrifice battery by running larger models on more powerful compute cores such as neural processing units or the main application pr

Weibo Gong, Chirag S. Trasikar, Bradley Zylstra

Spatial signal processing algorithms often use pre-given coordinate systems to label pixel positions. These processing algorithms are thus burdened by an external reference grid, making the acquisition of relative, intrinsic features difficult. This is in contrast to animal vision and cognition: animals recognize features without an external coordinate syste

Lautaro Estienne

Mathematical models for the generation of the action potential can improve the understanding of physiological mechanisms that are consequence of the electrical activity in neurons. In such models, some equations involving empirically obtained functions of the membrane potential are usually defined. The best known of these models, the Hodgkin-Huxley model, is

Pieter M. van Staden, Peter A. Forsyth, Yuying Li

We present a parsimonious neural network approach, which does not rely on dynamic programming techniques, to solve dynamic portfolio optimization problems subject to multiple investment constraints. The number of parameters of the (potentially deep) neural network remains independent of the number of portfolio rebalancing events, and in contrast to, for exam

Sina Hafezi, Alastair H. Moore, Pierre Guiraud, Patrick A. Naylor

A two-stage multi-channel speech enhancement method is proposed which consists of a novel adaptive beamformer, Hybrid Minimum Variance Distortionless Response (MVDR), Isotropic-MVDR (Iso), and a novel multi-channel spectral Principal Components Analysis (PCA) denoising. In the first stage, the Hybrid-MVDR performs multiple MVDRs using a dictionary of pre-def

Yufan Xu, Susanne Horn, Jonathan M. Aurnou

Coupled laboratory-numerical experiments of Rayleigh-B\'enard convection (RBC) in liquid gallium subject to a vertical magnetic field are presented. The experiments are carried out in two cylindrical containers with diameter-to-height aspect ratio $\Gamma = 1.0$ and $2.0$ at varying thermal forcing (Rayleigh numbers $10^5 \lesssim Ra \lesssim 10^8$) and magn

Divya Kothandaraman, Tianyi Zhou, Ming Lin, Dinesh Manocha

We present a novel method, Aerial Diffusion, for generating aerial views from a single ground-view image using text guidance. Aerial Diffusion leverages a pretrained text-image diffusion model for prior knowledge. We address two main challenges corresponding to domain gap between the ground-view and the aerial view and the two views being far apart in the te

Yuki Shirai, Devesh K. Jha, Arvind U. Raghunathan

Generalizable manipulation requires that robots be able to interact with novel objects and environment. This requirement makes manipulation extremely challenging as a robot has to reason about complex frictional interactions with uncertainty in physical properties of the object and the environment. In this paper, we study robust optimization for planning of

Farnoosh Hashemi, Ali Behrouz, Milad Rezaei Hajidehi

Searching for local communities is an important research challenge that allows for personalized community discovery and supports advanced data analysis in various complex networks, such as the World Wide Web, social networks, and brain networks. The evolution of these networks over time has motivated several recent studies to identify local communities in te

Buddha Dev Ghosh

Let $M$ be a compact hyperbolic $3$-manifold with volume $V$. Let $L$ be a link such that $M\setminus L$ is hyperbolic. For any hyperbolic link $L$ in $M$, in this article, we establish an upper bound of the length of an $n^{th}$ shortest closed geodesic as a logarithmic function of $V$ in $M\setminus L$. Our works complement the work of Lakeland and Leining

Justin Dressel, Gregory Reznik, Lev Vaidman

Hatim Salih discovered a method for transferring a quantum state with no particles present in the transmission channel, which he named counterportation. Recently [H. Salih, Quantum Sci. Technol. 8, 025016 (2023)], he presented a feasible procedure for its implementation. The modification of the protocol by Aharonov and Vaidman, adopted by Salih, justifies th

David S. Dean, Satya N. Majumdar, Gregory Schehr

We consider the problem of leakage or effusion of an ensemble of independent stochastic processes from a region where they are initially randomly distributed. The case of Brownian motion, initially confined to the left half line with uniform density and leaking into the positive half line is an example which has been extensively studied in the literature. He

Anish Agashe

In a given geometry, the kinematics of a congruence of curves is described by a set of three quantities called expansion, rotation, and shear. The equations governing the evolution of these quantities are referred to as kinematic equations. In this paper, the kinematics of congruence of curves in a metric-affine geometry are analysed. Without assuming an und

Pedro Enrique Iturria Rivera, Marcel Chenier, Bernard Herscovici, Burak Kantarci

IEEE 802.11be -Extremely High Throughput-, commercially known as Wireless-Fidelity (Wi-Fi) 7 is the newest IEEE 802.11 amendment that comes to address the increasingly throughput hungry services such as Ultra High Definition (4K/8K) Video and Virtual/Augmented Reality (VR/AR). To do so, IEEE 802.11be presents a set of novel features that will boost the Wi-Fi

Talip Ucar

We present a framework for learning Node Embeddings from Static Subgraphs (NESS) using a graph autoencoder (GAE) in a transductive setting. NESS is based on two key ideas: i) Partitioning the training graph to multiple static, sparse subgraphs with non-overlapping edges using random edge split during data pre-processing, ii) Aggregating the node representati

Fabio J. R. Costa, Thiago G-L. Brito, Ingrid D. Barcelos, Luiz Fernando Zagonel

Single layers of transition metal dichalcogenides, such as WSe$_2$ have gathered increasing attention due to their intense electron-hole interactions, being considered promising candidates for developing novel optical applications. Within the few-layer regime, these systems become highly sensitive to the surrounding environment, enabling the possibility of u

Emma Bluemke, Tantum Collins, Ben Garfinkel, Andrew Trask

The development of privacy-enhancing technologies has made immense progress in reducing trade-offs between privacy and performance in data exchange and analysis. Similar tools for structured transparency could be useful for AI governance by offering capabilities such as external scrutiny, auditing, and source verification. It is useful to view these differen

Rohan Mohapatra, Austin Coursey, Saptarshi Sengupta

On a daily basis, data centers process huge volumes of data backed by the proliferation of inexpensive hard disks. Data stored in these disks serve a range of critical functional needs from financial, and healthcare to aerospace. As such, premature disk failure and consequent loss of data can be catastrophic. To mitigate the risk of failures, cloud storage p