March 2023 arXiv papers — page 86

Steven Landgraf, Kira Wursthorn, Markus Hillemann, Markus Ulrich

Deep neural networks lack interpretability and tend to be overconfident, which poses a serious problem in safety-critical applications like autonomous driving, medical imaging, or machine vision tasks with high demands on reliability. Quantifying the predictive uncertainty is a promising endeavour to open up the use of deep neural networks for such applicati

Mingzhou Yin, Roy S. Smith

The kernel-based method has been successfully applied in linear system identification using stable kernel designs. From a Gaussian process perspective, it automatically provides probabilistic error bounds for the identified models from the posterior covariance, which are useful in robust and stochastic control. However, the error bounds require knowledge of

Andreas Lohrer, Darpan Malik, Claudius Zelenka, Peer Kröger

Group Anomaly Detection (GAD) identifies unusual pattern in groups where individual members might not be anomalous. This task is of major importance across multiple disciplines, in which also sequences like trajectories can be considered as a group. As groups become more diverse in heterogeneity and size, detecting group anomalies becomes challenging, especi

Felix Härer

Decentralized applications rely on non-centralized technical infrastructures and coordination principles. Without trusted third parties, their execution is not controlled by entities exercising centralized coordination but is instead realized through technologies supporting distribution such as blockchains and serverless computing. Executing decentralized ap

Giulio Maria Bianco, Gaetano Marrocco

LoRa low-power wide-area network protocol has recently gained attention for deploying ad-hoc search and rescue (SaR) systems. They could be empowered by exploiting body-UAV links that enable communications between a body-worn radio and a UAV-mounted one. However, to employ UAVs effectively, knowledge of the signal's propagation in the environment is required

Francisco Castillo-Eslava, Carlos Mougan, Alejandro Romero-Reche, Steffen Staab

We examine the potential impact of Large Language Models (LLM) on the recognition of territorial sovereignty and its legitimization. We argue that while technology tools, such as Google Maps and Large Language Models (LLM) like OpenAI's ChatGPT, are often perceived as impartial and objective, this perception is flawed, as AI algorithms reflect the biases of

Bruno Chiarellotto, Nicola Mazzari, Yukihide Nakada

A conjecture, recently stated by Flach and Morin, relates the action of the monodromy on the Galois invariant part of the p-adic Beilinson-Hyodo-Kato cohomology of the generic fiber of a scheme defined over a DVR of mixed characteristic to (the cohomology of) its special fiber. We prove the conjecture in the case the special fiber, of the given arithmetic sc

E. D'Aniello, M. Maiuriello, J. B. Seoane Sepulveda

Motivated by recent investigations \cite{Costakis, Bonilla} on the notion of recurrence in linear dynamics, we deepen into the notions of recurrence and frequent recurrence in the setting of dissipative composition operators with bounded distortion, a class of linear operators which includes backward shifts. Among other results, we show that these two notion

Haifeng Yang, Xiaona Yin, Jianghui Cai, Yuqing Yang

LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope) has completed the observation of nearly 20 million celestial objects, including a class of spectra labeled `Unknown'. Besides low signal-to-noise ratio, these spectra often show some anomalous features that do not work well with current templates. In this paper, a total of 638,000 `Unknown' s

Marcos Escobar-Anel, Michel Kschonnek, Rudi Zagst

We study the expected utility portfolio optimization problem in an incomplete financial market where the risky asset dynamics depend on stochastic factors and the portfolio allocation is constrained to lie within a given convex set. We employ fundamental duality results from real constrained optimization to formally derive a dual representation of the associ

Sahel Ashhab, Tomoko Fuse, Fumiki Yoshihara, Sunmi Kim

We investigate the dynamics of a qubit-oscillator system under the influence of a linear sweep of system parameters. We consider two main cases. In the first case, we consider sweeping the parameters between the regime of a weakly correlated ground state and the regime of a strongly correlated ground state, a situation that can be viewed as a finite-duration

Jiwen Yu, Yinhuai Wang, Chen Zhao, Bernard Ghanem

Recently, conditional diffusion models have gained popularity in numerous applications due to their exceptional generation ability. However, many existing methods are training-required. They need to train a time-dependent classifier or a condition-dependent score estimator, which increases the cost of constructing conditional diffusion models and is inconven

Vincent Ouazan-Reboul, Jaime Agudo-Canalejo, Ramin Golestanian

We study analytically and numerically a model metabolic cycle composed of an arbitrary number of species of catalytically active particles. Each species converts a substrate into a product, the latter being used as the substrate by the next species in the cycle. Through a combination of catalytic activity and chemotactic mobility, the active particles develo

Yuhe Ding, Jian Liang, Jie Cao, Aihua Zheng

Existing face stylization methods always acquire the presence of the target (style) domain during the translation process, which violates privacy regulations and limits their applicability in real-world systems. To address this issue, we propose a new method called MODel-drIven Face stYlization (MODIFY), which relies on the generative model to bypass the dep

Shuai Wang, Zipei Yan, Daoan Zhang, Haining Wei

Multi-modality medical imaging is crucial in clinical treatment as it can provide complementary information for medical image segmentation. However, collecting multi-modal data in clinical is difficult due to the limitation of the scan time and other clinical situations. As such, it is clinically meaningful to develop an image segmentation paradigm to handle

Joseph Ben Geloun, Reiko Toriumi

Enhanced tensor field theories (eTFT) have dominant graphs that differ from the melonic diagrams of conventional tensor field theories. They therefore describe pertinent candidates to escape the so-called branched polymer phase, the universal geometry found for tensor models. For generic order $d$ of the tensor field, we compute the perturbative $\beta$-func

Hyuntae Kim, Hamin Chang, Hyungbo Shim

This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carried out using only the system's state/input measurements. When

Jihyun Lee, Seungyeon Seo, Yunsu Kim, Gary Geunbae Lee

We present our work on Track 2 in the Dialog System Technology Challenges 11 (DSTC11). DSTC11-Track2 aims to provide a benchmark for zero-shot, cross-domain, intent-set induction. In the absence of in-domain training dataset, robust utterance representation that can be used across domains is necessary to induce users' intentions. To achieve this, we leverage

Zixi Tuo, Huan Yang, Jianlong Fu, Yujie Dun

Existing real-world video super-resolution (VSR) methods focus on designing a general degradation pipeline for open-domain videos while ignoring data intrinsic characteristics which strongly limit their performance when applying to some specific domains (eg., animation videos). In this paper, we thoroughly explore the characteristics of animation videos and

Jianhao Jiao, Feiyi Chen, Hexiang Wei, Jin Wu

The combination of LiDARs and cameras enables a mobile robot to perceive environments with multi-modal data, becoming a key factor in achieving robust perception. Traditional frame cameras are sensitive to changing illumination conditions, motivating us to introduce novel event cameras to make LiDAR-camera fusion more complete and robust. However, to jointly

Siyu Teng, Xuemin Hu, Peng Deng, Bai Li

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This pape

Angel Felipe Magnossão de Paula, Imene Bensalem, Paolo Rosso, Wajdi Zaghouani

This paper describes our participation in the shared task of hate speech detection, which is one of the subtasks of the CERIST NLP Challenge 2022. Our experiments evaluate the performance of six transformer models and their combination using 2 ensemble approaches. The best results on the training set, in a five-fold cross validation scenario, were obtained b

K. Asnaashari, D. Bondarenko, R. V. Krems

While quantum computing algorithms have been widely applied for electronic structure calculations, applications to molecular dynamics remain scarce. Complex and varied landscapes of molecular potential energy surfaces give rise to vibrational states with a wide range of properties, making it difficult to construct a general representation of ro-vibrational s

Takuto Kawakami, Gen Tamaki, Mikito Koshino

We theoretically study magnetic ground states of doped zigzag graphene nanoribbons and the emergence of topological domain walls. Using the Hartree-Fock mean-field approach and an effective continuum model, we demonstrated that the carrier doping stabilizes a magnetic structure with alternating antiferromagnetic domains, where the doped carriers are accommod

Carolin Hannusch, Giuseppe Filippone

We give a decoding algorithm for a class of error-correcting codes, which can be used in the DHH-cryptosystem, which is a candidate for post-quantum cryptography, since it is of McEliece type. Furthermore, we implement the encryption and decryption algorithms for this cryptosystem and investigate its performance.

Xin Mao, Zhiwei Li, Shuning Dong, Jingtai Li

Muon radiography is an innovative and non-destructive technique for internal density structure imaging, based on measuring the attenuation of cosmic-ray muons after they penetrate the target. Due to the strong penetration ability of muons, the detection range of muon radiography can reach the order of hundreds of meters or even kilometers. Using a portable m

Cheick Abdoul Kadir A. Kounta, Lionel Arnaud, Bernard Kamsu-Foguem, Fana Tangara

Most of the work on chatter detection is based on laboratory machining tests, thus without the constraints of noise, the variety of situations to be managed in the industry, and the uncertainties on the parameters (sensor position, tool engagement, and sometimes even spindle rotation frequency). This work presents an approach first based on mechanical skills

Chunyi Li, May Lim, Abdelhak Bentaleb, Roger Zimmermann

In today's Internet, HTTP Adaptive Streaming (HAS) is the mainstream standard for video streaming, which switches the bitrate of the video content based on an Adaptive BitRate (ABR) algorithm. An effective Quality of Experience (QoE) assessment metric can provide crucial feedback to an ABR algorithm. However, predicting such real-time QoE on the client side

Hubert Baniecki, Bartlomiej Sobieski, Patryk Szatkowski, Przemyslaw Bombinski

Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications. However, only a few interpretable machine learning methods comply with its challenges. To facilitate a comprehensive explanatory analysis of survival models, we formally introduce time-depende

Joris De Moor, Christian Sadel, Hermann Schulz-Baldes

For a generalized Su-Schrieffer-Heeger model the energy zero is always critical and hyperbolic in the sense that all reduced transfer matrices commute and have their spectrum off the unit circle. Disorder driven topological phase transitions in this model are characterized by a vanishing Lyapunov exponent at the critical energy. It is shown that the integrat

E. V. Sokolov

Suppose that $\Gamma$ is a non-empty connected graph, $\mathfrak{G}$ is the fundamental group of a graph of groups over $\Gamma$, and $\mathcal{C}$ is a root class of groups (the last means that $\mathcal{C}$ contains non-trivial groups and is closed under taking subgroups, extensions, and Cartesian powers of a certain type). It is known that $\mathfrak{G}$

Sebastian W. Schmitt, Rama K. Vasudevan, Maurice Seifert, Albina Y. Borisevich

Reducing the dimensions of ferroelectric materials down to the nanoscale has strong implications on the ferroelectric polarization pattern and on the ability to switch the polarization. As the size of ferroelectric domains shrinks to nanometer scale, the heterogeneity of the polarization pattern becomes increasingly pronounced, enabling a large variety of po

Chaofan Ma, Yuhuan Yang, Chen Ju, Fei Zhang

Learning from a large corpus of data, pre-trained models have achieved impressive progress nowadays. As popular generative pre-training, diffusion models capture both low-level visual knowledge and high-level semantic relations. In this paper, we propose to exploit such knowledgeable diffusion models for mainstream discriminative tasks, i.e., unsupervised ob

Xiuxian Yang, Wanxiang Feng, Xiao-Ping Li, Gui-Bin Liu

Recently, Heusler ferromagnets have been found to exhibit unconventional anomalous electric, thermal, and thermoelectric transport properties. In this study, we employed first-principles density functional theory calculations to systematically investigate both intrinsic and extrinsic contributions to the anomalous Hall effect (AHE), anomalous Nernst effect (

Luca Gerolla, Martin Hairer, Xue-Mei Li

We study the large-scale dynamics of the solution to a nonlinear stochastic heat equation (SHE) in dimensions $d \geq 3$ with long-range dependence. This equation is driven by multiplicative Gaussian noise, which is white in time and coloured in space with non-integrable spatial covariance that decays at the rate of $|x|^{-\kappa}$ at infinity, where $\kappa

Tihomir Knežević, Eva Jelavić, Yuichi Yamazaki, Takeshi Ohshima

We report on boron-related defects in the low-doped n-type (nitrogen-doped) 4H-SiC semitransparent Schottky barrier diodes (SBDs) studied by minority carrier transient spectroscopy (MCTS). An unknown concentration of boron was introduced during chemical vapor deposition (CVD) crystal growth. Boron incorporation was found to lead to the appearance of at least

Ryota Mikami

To prove log-concavity of the characteristic polynomials of matroids, Adiprasito-Huh-Katz proved the K\"{a}hler package (the hard Lefschetz theorem and the Hodge-Riemann bilinear relations) for their Chow rings. Amini-Piquerez generalized it to tropical cohomology of smooth projective tropical varieties. Their proofs were combinatorial. In this paper, we est

Mojtaba Shahin, Mansooreh Zahedi, Hourieh Khalajzadeh, Ali Rezaei Nasab

Mobile software apps ("apps") are one of the prevailing digital technologies that our modern life heavily depends on. A key issue in the development of apps is how to design gender-inclusive apps. Apps that do not consider gender inclusion, diversity, and equality in their design can create barriers (e.g., excluding some of the users because of their gender)

Haoran Li, XiaoLu Li, Yihang Lin, Yanbin Hao

Video prediction is a complex time-series forecasting task with great potential in many use cases. However, traditional methods prioritize accuracy and overlook slow prediction speeds due to complex model structures, redundant information, and excessive GPU memory consumption. These methods often predict frames sequentially, making acceleration difficult and

Qingtao Liu, Yu Cui, Qi Ye, Zhengnan Sun

Robotic dexterous grasping is a challenging problem due to the high degree of freedom (DoF) and complex contacts of multi-fingered robotic hands. Existing deep reinforcement learning (DRL) based methods leverage human demonstrations to reduce sample complexity due to the high dimensional action space with dexterous grasping. However, less attention has been

Decheng An, Defang Duan, Zihan Zhang, Qiwen Jiang

Room-temperature superconductivity has been a long-standing goal for scientific progress and human development. Thermodynamic stability is a prerequisite for material synthesis and application. Here, we perform a combination of high-throughput screening and structural search and uncover two thermodynamically stable room-temperature superconductors, Fd-3m-Li2

Pravin Kumar, Tushar Kanta Naik, Neha Nanda, Mahender Singh

The virtual braid group $VB_n$, the virtual twin group $VT_n$ and the virtual triplet group $VL_n$ are extensions of the symmetric group $S_n$, which are motivated by the Alexander-Markov correspondence for virtual knot theories. The kernels of natural epimorphisms of these groups onto the symmetric group $S_n$ are the pure virtual braid group $VP_n$, the pu

Tony Lindeberg

This paper presents a theory for how geometric image transformations can be handled by a first layer of linear receptive fields, in terms of true covariance properties, which, in turn, enable geometric invariance properties at higher levels in the visual hierarchy. Specifically, we develop this theory for a generalized Gaussian derivative model for visual re

Joshua D. Scarsbrook, Mark Utting, Ryan K. L. Ko

TypeScript is a quickly evolving superset of JavaScript with active development of new features. Our paper seeks to understand how quickly these features are adopted by the developer community. Existing work in JavaScript shows the adoption of dynamic language features can be a major hindrance to static analysis. As TypeScript evolves the addition of feature

Yongwoo Lee, Minhyeok Lee, Suhwan Cho, Sangyoun Lee

Salient object detection (SOD) is a task that involves identifying and segmenting the most visually prominent object in an image. Existing solutions can accomplish this use a multi-scale feature fusion mechanism to detect the global context of an image. However, as there is no consideration of the structures in the image nor the relations between distant pix

Bingqi Shen, Shuwei Dai, Yuyin Chen, Rong Xiong

3D object detection serves as the core basis of the perception tasks in autonomous driving. Recent years have seen the rapid progress of multi-modal fusion strategies for more robust and accurate 3D object detection. However, current researches for robust fusion are all learning-based frameworks, which demand a large amount of training data and are inconveni

Trong-Thang Pham, Nhat Le, Tuong Do, Hung Nguyen

Audio-driven talking head animation is a challenging research topic with many real-world applications. Recent works have focused on creating photo-realistic 2D animation, while learning different talking or singing styles remains an open problem. In this paper, we present a new method to generate talking head animation with learnable style references. Given

Kohki Uno, Keiichi Maeda

A wind-driven model is a new framework to model observational properties of transients that are powered by continuous outflow from a central system. While it has been applied to Fast Blue Optical Transients (FBOTs), the applicability has been limited to post-peak behaviours due to the steady-state assumptions; non-steady-state physics, e.g., expanding outflo

Ahmed Boughammoura

We present a simplified computational rule for the back-propagation formulas for artificial neural networks. In this work, we provide a generic two-step rule for the back-propagation algorithm in matrix notation. Moreover, this rule incorporates both the forward and backward phases of the computations involved in the learning process. Specifically, this recu

Haozhe Wu, Jia Jia, Junliang Xing, Hongwei Xu

Audio-Driven Face Animation is an eagerly anticipated technique for applications such as VR/AR, games, and movie making. With the rapid development of 3D engines, there is an increasing demand for driving 3D faces with audio. However, currently available 3D face animation datasets are either scale-limited or quality-unsatisfied, which hampers further develop

Barbara Kaltenbacher, William Rundell

Nonlinearity parameter tomography leads to the problem of identifying a coefficient in a nonlinear wave equation (such as the Westervelt equation) modeling ultrasound propagation. In this paper we transfer this into frequency domain, where the Westervelt equation gets replaced by a coupled system of Helmholtz equations with quadratic nonlinearities. For the

Zhou Yang, Chenyu Wang, Jieke Shi, Thong Hoang

Artificial Intelligence systems, which benefit from the availability of large-scale datasets and increasing computational power, have become effective solutions to various critical tasks, such as natural language understanding, speech recognition, and image processing. The advancement of these AI systems is inseparable from open-source software (OSS). This p

Yuhao Lin, Haiming Xu, Lingqiao Liu, Jinan Zou

Autoencoding, which aims to reconstruct the input images through a bottleneck latent representation, is one of the classic feature representation learning strategies. It has been shown effective as an auxiliary task for semi-supervised learning but has become less popular as more sophisticated methods have been proposed in recent years. In this paper, we rev

Anik Kumar Paul, Arun D Mahindrakar, Rachel K Kalaimani

This letter presents an almost sure convergence of the zeroth-order mirror descent algorithm. The algorithm admits non-smooth convex functions and a biased oracle which only provides noisy function value at any desired point. We approximate the subgradient of the objective function using Nesterov's Gaussian Approximation (NGA) with certain alternations sugge

Senqiao Yang, Jiarui Wu, Jiaming Liu, Xiaoqi Li

The visual prompts have provided an efficient manner in addressing visual cross-domain problems. In previous works, Visual Domain Prompt (VDP) first introduces domain prompts to tackle the classification Test-Time Adaptation (TTA) problem by warping image-level prompts on the input and fine-tuning prompts for each target domain. However, since the image-leve

Shuai Fu, Tim Dwyer, Peter J. Stuckey, Jackson Wain

Dynamically typed programming languages are popular in education and the software industry. While presenting a low barrier to entry, they suffer from run-time type errors and longer-term problems in code quality and maintainability. Statically typed languages, while showing strength in these aspects, lack in learnability and ease of use. In particular, fixin

Ke Zou, Tian Lin, Xuedong Yuan, Haoyu Chen

Multimodality eye disease screening is crucial in ophthalmology as it integrates information from diverse sources to complement their respective performances. However, the existing methods are weak in assessing the reliability of each unimodality, and directly fusing an unreliable modality may cause screening errors. To address this issue, we introduce a nov

Kuo Wang, Lingbo Liu, Yang Liu, Guanbin Li

The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest. In addition to spatial-temporal correlations, the functionality of urban areas also plays a crucial role in traffic flow prediction. However, the exploration of regional functional attributes mainly focuses on addin

Tatsuki Odake, Hlér Kristjánsson, Akihito Soeda, Mio Murao

We present a quantum algorithm to achieve higher-order transformations of Hamiltonian dynamics. Namely, the algorithm takes as input a finite number of queries to a black-box seed Hamiltonian dynamics to simulate a desired Hamiltonian. Our algorithm efficiently simulates linear transformations of any seed Hamiltonian with a bounded energy range consisting of

Mingwei Ma, Philippe Bourges, Yvan Sidis, Alexandre Ivanov

There are few inelastic neutron scattering (INS) reports on the superconducting single crystals of FeAs-1111 system, even though it was first discovered in 2008, due to the extreme difficulty in large single crystal growth. In this paper, we have studied the low-energy spin excitations in the optimally electron-doped CaFe$_{0.88}$Co$_{0.12}$AsF single crysta

Aiham M. Rostom

To describe the pre- and post-selected quantum ensembles, a complex quantity called the weak value of an operator is used. The weak value is highly controversial due to the fact that it is not bounded by the possible eigenvalues of the corresponding operator. Nevertheless, the obtaining of the anomalous weak value is regarded as a powerful technique in the q

Darshan Gera, Badveeti Naveen Siva Kumar, Bobbili Veerendra Raj Kumar, S Balasubramanian

The fifth Affective Behavior Analysis in-the-wild (ABAW) competition has multiple challenges such as Valence-Arousal Estimation Challenge, Expression Classification Challenge, Action Unit Detection Challenge, Emotional Reaction Intensity Estimation Challenge. In this paper we have dealt only expression classification challenge using multiple approaches such

Tomoki Inoue

We study a random dynamical system such that one transformation is randomly selected from a family of transformations and then applied on each iteration. For such random dynamical systems, we consider estimates of absolutely continuous invariant measures. Since the random dynamical systems are made by complicated compositions of many deterministic maps and p

Qinghui Sun, Constantine P. Deliyannis, Aaron Steinhauer, Barbara J. Anthony-Twarog

We consider WIYN/Hydra spectra of 329 photometric candidate members of the 420-Myr-old open cluster M48, and report Lithium detections or upper limits for 234 members and likely members. The 171 single members define a number of notable Li-mass trends, some delineated even more clearly than in Hyades/Praesepe: The giants are consistent with subgiant Li dilut

Anh Duy Nguyen, Huy Hieu Pham, Huynh Thanh Trung, Quoc Viet Hung Nguyen

Due to the significant resemblance in visual appearance, pill misuse is prevalent and has become a critical issue, responsible for one-third of all deaths worldwide. Pill identification, thus, is a crucial concern needed to be investigated thoroughly. Recently, several attempts have been made to exploit deep learning to tackle the pill identification problem

R. A. Dumer, M. Godoy

The Ising model on a $restricted$ scale-free network (SFN) has been studied employing Monte Carlo simulations. This network is described by a power-law degree distribution in the form $P(k)~k^{-\alpha}$, and is called restricted, because independently of the network size, we always have fixed the maximum $k_{m}$ and a minimum $k_{0}$ degree on distribution,

Yubiao Yue, Minghua Jiang, Xinyue Zhang, Jialong Xu

Swift and accurate diagnosis for earlier-stage monkeypox (mpox) patients is crucial to avoiding its spread. However, the similarities between common skin disorders and mpox and the need for professional diagnosis unavoidably impaired the diagnosis of earlier-stage mpox patients and contributed to mpox outbreak. To address the challenge, we proposed "Super Mo

Daehan Kim, Minseok Seo, Kwanyong Park, Inkyu Shin

Mixup provides interpolated training samples and allows the model to obtain smoother decision boundaries for better generalization. The idea can be naturally applied to the domain adaptation task, where we can mix the source and target samples to obtain domain-mixed samples for better adaptation. However, the extension of the idea from classification to segm

Dongcheng Zou, Hao Peng, Xiang Huang, Renyu Yang

Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it is susceptible to low-quality and unreliable structure, which has been a norm rather than an exception in real-world graphs. Existing graph structure learning (GSL) frameworks still lack robustness and interpretability. This paper proposes a general GSL framework, SE

Zhen Zhang, Yuantao Ding, Zhirong Huang, Feng Zhou

The multiplexing capabilities of superconducting X-ray free-electron lasers (FELs) have gained much attention in recent years. The demanding requirements for photon properties from multiple undulator lines necessitate more flexible beam manipulation techniques to achieve the goal of "beam on demand". In this paper, we investigate a multiplexed configuration

Jie Wang, Zhihao Shi, Xize Liang, Defu Lian

Subgraph-wise sampling -- a promising class of mini-batch training techniques for graph neural networks (GNNs -- is critical for real-world applications. During the message passing (MP) in GNNs, subgraph-wise sampling methods discard messages outside the mini-batches in backward passes to avoid the well-known neighbor explosion problem, i.e., the exponential

Jaroslaw Kwapisz, Ioannis Roudas, Eric Fink

The use of multidimensional modulations can decrease the energy consumption of optical links. In this paper, we propose and study Mode Vector Modulation (MVM), a generalized polarization modulation scheme for transmission over multimode/multicore optical fibers or free space. Similar to Polarization Shift Keying (PolSK) and Stokes Vector Modulation (SVM), MV

Diego Mauricio Gomez-Coral, Cory Gerrity, Riccardo Munini, Philip von Doetinchem

Deuterons are the most abundant secondary cosmic ray species in the Galaxy, but their study has been severely limited due to experimental challenges. In an era with new experiments and high-precision measurements in cosmic rays, having a low-uncertainty deuteron flux in a wide energy range becomes possible. The deuteron-over-helium ratio ($d$/$^4$He) is impo

Zhaoheng Li, Xinyu Pi, Yongjoo Park

With data pipeline tools and the expressiveness of SQL, managing interdependent materialized views (MVs) are becoming increasingly easy. These MVs are updated repeatedly upon new data ingestion (e.g., daily), from which database admins can observe performance metrics (e.g., refresh time of each MV, size on disk) in a consistent way for different types of upd

Zheng Tan, Longxiu Huang, HanQin Cai, Yifei Lou

Tensor completion is an important problem in modern data analysis. In this work, we investigate a specific sampling strategy, referred to as tubal sampling. We propose two novel non-convex tensor completion frameworks that are easy to implement, named tensor $L_1$-$L_2$ (TL12) and tensor completion via CUR (TCCUR). We test the efficiency of both methods on s

Xuanyu Zhang, Bin Chen, Wenzhen Zou, Shuai Liu

Hyperspectral imaging plays a pivotal role in a wide range of applications, like remote sensing, medicine, and cytology. By acquiring 3D hyperspectral images (HSIs) via 2D sensors, the coded aperture snapshot spectral imaging (CASSI) has achieved great success due to its hardware-friendly implementation and fast imaging speed. However, for some less spectral

Koichiro Yawata, Yoshihiro Osakabe, Takuya Okuyama, Akinori Asahara

Molecular fingerprints are widely used for predicting chemical properties, and selecting appropriate fingerprints is important. We generate new fingerprints based on the assumption that a performance of prediction using a more effective fingerprint is better. We generate effective interaction fingerprints that are the product of multiple base fingerprints. I

Koichiro Yawata, Yoshihiro Osakabe, Takuya Okuyama, Akinori Asahara

This paper proposes an extension of regression trees by quadratic unconstrained binary optimization (QUBO). Regression trees are very popular prediction models that are trainable with tabular datasets, but their accuracy is insufficient because the decision rules are too simple. The proposed method extends the decision rules in decision trees to multi-dimens

Sayar Karmakar, Moumanti Podder, Souvik Roy, Soumyarup Sadhukhan

We introduce and study a model stemming from game theory for the spread of an epidemic throughout a given population. Each agent is allowed to choose an action whose value dictates to what extent they limit their social interactions, if at all. Each of them is endowed with a certain amount of immunity such that if the viral risk/exposure is more than that th

Anthony Sirico, Daniel R. Herber

Many complex engineering systems can be represented in a topological form, such as graphs. This paper utilizes a machine learning technique called Geometric Deep Learning (GDL) to aid designers with challenging, graph-centric design problems. The strategy presented here is to take the graph data and apply GDL to seek the best realizable performing solution e

Weilai Xiang, Hongyu Yang, Di Huang, Yunhong Wang

Inspired by recent advances in diffusion models, which are reminiscent of denoising autoencoders, we investigate whether they can acquire discriminative representations for classification via generative pre-training. This paper shows that the networks in diffusion models, namely denoising diffusion autoencoders (DDAE), are unified self-supervised learners: b

Quyuan Lin, Rongchang Liu, Weinan Wang

In this paper, we consider the stochastic Boussinesq equations on $\mathbb T^3$ with transport noise and rough initial data. We first prove the existence and uniqueness of the local pathwise solution with initial data in $L^p(\Omega;L^p)$ for $p>5$. By assuming additional smallness on the initial data and the noise, we establish the global existence of the p

Peiyu Xiong, Michael Tegegn, Jaskeerat Singh Sarin, Shubhraneel Pal

Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to confuse the model into making a mistake. Such examples pose a serious threat to the applicability of machine-learning-based systems, especially in life- and safety-critical domains. To address this problem, the area of adversarial robustness investigates

Juan Chen, Yingchun Zhou

Selection of covariates is crucial in the estimation of average treatment effects given observational data with high or even ultra-high dimensional pretreatment variables. Existing methods for this problem typically assume sparse linear models for both outcome and univariate treatment, and cannot handle situations with ultra-high dimensional covariates. In t

Taiki Ishiyama, Koki Ono, Tetsushi Takano, Ayaki Sunaga

We observe a weakly allowed optical transition of atomic ytterbium from the ground state to the metastable state $4f^{13}5d6s^2 \: (J=2)$ for all five bosonic and two fermionic isotopes with resolved Zeeman and hyperfine structures. This inner-shell orbital transition has been proposed as a new frequency standard as well as a quantum sensor for new physics.

Zhipeng Chang, Ruiling Ma, Wenliang Jia

The demand for pedestrian detection has created a challenging problem for various visual tasks such as image fusion. As infrared images can capture thermal radiation information, image fusion between infrared and visible images could significantly improve target detection under environmental limitations. In our project, we would approach by preprocessing our

Jingxi Li, Xurong Li, Nezih T. Yardimci, Jingtian Hu

Terahertz waves offer numerous advantages for the nondestructive detection of hidden objects/defects in materials, as they can penetrate through most optically-opaque materials. However, existing terahertz inspection systems are restricted in their throughput and accuracy (especially for detecting small features) due to their limited speed and resolution. Fu

Nabil M. Khalifa, Haithem E. Taha

The study of vortex dynamics using a variational formulation has an extensive history and a rich literature. The standard Hamiltonian function that describes the dynamics of interacting point vortices of constant strength is the Kirchhoff-Routh (KR) function. This function was not obtained from basic definitions of classical mechanics (i.e., in terms of kine

Joseph DeRose, Nickolas Kokron, Arka Banerjee, Shi-Fan Chen

We present the Aemulus $\nu$ simulations: a suite of 150 $(1.05 h^{-1}\rm Gpc)^3$ $N$-body simulations with a mass resolution of $3.51\times 10^{10} \frac{\Omega_{cb}}{0.3} ~ h^{-1} M_{\odot}$ in a $w\nu$CDM cosmological parameter space. The simulations have been explicitly designed to span a broad range in $\sigma_8$ to facilitate investigations of tension

Bowen Xue, Yifan Mao, Shaileshh Bojja Venkatakrishnan, Sreeram Kannan

Peer-to-peer (P2P) networks underlie a variety of decentralized paradigms including blockchains, distributed file storage and decentralized domain name systems. A central primitive in P2P networks is the peer selection algorithm, which decides how a node should select a fixed number of neighbors to connect with. In this paper, we consider the design of a pee

Giorgio Giannone, Faez Ahmed

Topology Optimization seeks to find the best design that satisfies a set of constraints while maximizing system performance. Traditional iterative optimization methods like SIMP can be computationally expensive and get stuck in local minima, limiting their applicability to complex or large-scale problems. Learning-based approaches have been developed to acce

Xinwei Song, Tianyi Yang, Dengji Zhao

Incentivizing the existing participants to invite new participants to join an auction, matching or cooperative game have been extensively studied recently. One common challenge to design such incentive in these games is that the invitees and inviters are competitors. To have such an incentive, we normally have to sacrifice some of the traditional properties.

Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang

As a fundamental problem in computer vision, multi-view stereo (MVS) aims at recovering the 3D geometry of a target from a set of 2D images. Recent advances in MVS have shown that it is important to perceive non-local structured information for recovering geometry in low-textured areas. In this work, we propose a Hierarchical Prior Mining for Non-local Multi

Jiaqi Xu, Xiaowei Hu, Lei Zhu, Qi Dou

Video dehazing aims to recover haze-free frames with high visibility and contrast. This paper presents a novel framework to effectively explore the physical haze priors and aggregate temporal information. Specifically, we design a memory-based physical prior guidance module to encode the prior-related features into long-range memory. Besides, we formulate a

Yifei Chen, Dapeng Chen, Ruijin Liu, Hao Li

Visual-Language Models (VLMs) have significantly advanced action video recognition. Supervised by the semantics of action labels, recent works adapt the visual branch of VLMs to learn video representations. Despite the effectiveness proved by these works, we believe that the potential of VLMs has yet to be fully harnessed. In light of this, we exploit the se

Shawn Rasheed, Jens Dietrich, Amjed Tahir

Test flakiness is a problem that affects testing and processes that rely on it. Several factors cause or influence the flakiness of test outcomes. Test execution order, randomness and concurrency are some of the more common and well-studied causes. Some studies mention code instrumentation as a factor that causes or affects test flakiness. However, evidence

Xin Li, Yixin Bao, Liping Zhang

Let $\langle m,n,p \rangle$ be the matrix multiplication tensor. The solution set of Brent equations corresponds to the tensor decompositions of $\langle m,n,p \rangle$. We study the local dimensions of solutions of the Brent equations over the field of complex numbers. The rank of Jacobian matrix of Brent equations provides an upper bound of the local dimen

Qi Wang, Wenchuan Wu, Chenhui Lin, Bin Wang

With numerous distributed energy resources (DERs) integrated into the distribution networks (DNs), the coordinated economic dispatch (C-ED) is essential for the integrated transmission and distribution grids. For large scale power grids, the centralized C-ED meets high computational burden and information privacy issues. To tackle these issues, this paper pr

Joshua Ainslie, Tao Lei, Michiel de Jong, Santiago Ontañón

Many natural language processing tasks benefit from long inputs, but processing long documents with Transformers is expensive -- not only due to quadratic attention complexity but also from applying feedforward and projection layers to every token. However, not all tokens are equally important, especially for longer documents. We propose CoLT5, a long-input

Shintaro Eijima, Masahiro Ibe, Kai Murai

The gauged $U(1)_{L_{\mu}-L_{\tau}}$ symmetry is the simplest possibility to explain the observed muon $g-2$, while being consistent with the neutrino oscillations through the seesaw mechanism. In this paper, we investigate if leptogenesis can work at the same time. At first glance, leptogenesis seems challenging because the right-handed neutrino masses are