April 2023 arXiv papers — page 105

Matcha Naga Gayathri, Sriprabha Ramanarayanan, Mohammad Al Fahim, Rahul G S

Deep cascaded architectures for magnetic resonance imaging (MRI) acceleration have shown remarkable success in providing high-quality reconstruction. However, as the number of cascades increases, the improvements in reconstruction tend to become marginal, indicating possible excess model capacity. Knowledge distillation (KD) is an emerging technique to compr

Ben Kenwright

This paper presents an uncomplicated dynamic controller for generating physically-plausible three-dimensional full-body biped character rise motions on-the-fly at run-time. Our low-dimensional controller uses fundamental reference information (e.g., center-of-mass, hands, and feet locations) to produce balanced biped get-up poses by means of a real-time phys

Wei Ju, Zheng Fang, Yiyang Gu, Zequn Liu

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected

Deepak, Arpita Chatterjee

A coherent state is defined conventionally in different ways such as a displaced vacuum state, an eigenket of annihilation operator or as an infinite dimensional Poissonian superposition of Fock states. In this work, we describe a superposition $(ta+ra^\dagger)$ of field annihilation and creation operators acting on a continuous variable coherent state $|{\a

Arthur C. Campello

This article focuses on estimating distribution elements over a high-dimensional binary hypercube from multivariate binary data. A popular approach to this problem, optimizing Walsh basis coefficients, is made more interpretable by an alternative representation as a "Fourier-Walsh" diagonalization. Allowing monotonic transformations of the resulting matrix e

P. K. Das, Arpita Chatterjee

In this paper, we investigate one-time passing of a $V$-type three-level atom through a single-mode interacting field in a cavity. We extend the idea of elementary Jaynes-Cummings model by assuming that the field vector belongs to interacting Fock space. In the process, we arrive at a state vector which will be analyzed to study the nonclassicality of the ev

Yunpeng Han, Lisai Zhang, Qingcai Chen, Zhijian Chen

Fashion vision-language pre-training models have shown efficacy for a wide range of downstream tasks. However, general vision-language pre-training models pay less attention to fine-grained domain features, while these features are important in distinguishing the specific domain tasks from general tasks. We propose a method for fine-grained fashion vision-la

Runyu Zhu, Yidong Xu, Bin Yue, Xuelei Chen

Observations are beginning to constrain the history of the epoch of reionization (EoR). Modeling the reionization process is indispensable to interpret the observations, to infer the properties of ionizing sources, and to probe the various astrophysical processes from the observational data. Here we present an improved version of the semi-numerical simulatio

Shangzhou Xia, Jianjun Zhao

Quantum entanglement plays a crucial role in quantum computing. Entangling information has important implications for understanding the behavior of quantum programs and avoiding entanglement-induced errors. Entanglement analysis is a static code analysis technique that determines which qubit may entangle with another qubit and establishes an entanglement gra

Inderjeet Singh, Kazuya Kakizaki, Toshinori Araki

In this work, we investigate the potential threat of adversarial examples to the security of face recognition systems. Although previous research has explored the adversarial risk to individual components of FRSs, our study presents an initial exploration of an adversary simultaneously fooling multiple components: the face detector and feature extractor in a

Attiano Purpura-Pontoniere, Demetri Terzopoulos, Adam Wang, Abdullah-Al-Zubaer Imran

Disease diagnosis from medical images via supervised learning is usually dependent on tedious, error-prone, and costly image labeling by medical experts. Alternatively, semi-supervised learning and self-supervised learning offer effectiveness through the acquisition of valuable insights from readily available unlabeled images. We present Semi-Supervised Rela

Michael Freitas Gustavo, Matti Hellström, Toon Verstraelen

We apply a global sensitivity method, the Hilbert-Schmidt independence criterion (HSIC), to the reparameterization of a Zn/S/H ReaxFF force field to identify the most appropriate parameters for reparameterization. Parameter selection remains a challenge in this context as high dimensional optimizations are prone to overfitting and take a long time, but selec

Ben Kenwright

This paper proposes a real-time physically-based method for simulating vehicle deformation. Our system synthesizes vehicle deformation characteristics by considering a low-dimensional coupled vehicle body technique. We simulate the motion and crumbling behavior of vehicles smashing into rigid objects. We explain and demonstrate the combination of a reduced c

Masamichi Zaizen, Hiroki Nagakura

The question of what ingredients characterize the quasi-steady state of fast neutrino-flavor conversion (FFC) is one of the long-standing riddles in neutrino oscillation. Addressing this issue is necessary for accurate modeling of neutrino transport in core-collapse supernova and binary neutron star merger. Recent numerical simulations of FFC have shown, how

A. Armatol, B. Broerman, L. Dumoulin, A. Giuliani

The first detailed study on the performance of a ZnO-based cryogenic scintillating bolometer as a detector to search for rare processes in zinc isotopes was performed. A 7.2 g ZnO low-temperature detector, containing more than 80\% of zinc in its mass, exhibits good energy resolution of baseline noise 1.0--2.7 keV FWHM at various working temperatures resulti

Jonas Ney, Bilal Hammoud, Norbert Wehn

In communication systems, Autoencoder (AE) refers to the concept of replacing parts of the transmitter and receiver by artificial neural networks (ANNs) to train the system end-to-end over a channel model. This approach aims to improve communication performance, especially for varying channel conditions, with the cost of high computational complexity for tra

Maija Kāle, Matīss Rikters

Food choice is a complex phenomenon shaped by factors such as taste, ambience, culture or weather. In this paper, we explore food-related tweeting in different weather conditions. We inspect a Latvian food tweet dataset spanning the past decade in conjunction with a weather observation dataset consisting of average temperature, precipitation, and other pheno

Alain Jungo, Lars Doorenbos, Tommaso Da Col, Maarten Beelen

Purpose: A fundamental problem in designing safe machine learning systems is identifying when samples presented to a deployed model differ from those observed at training time. Detecting so-called out-of-distribution (OoD) samples is crucial in safety-critical applications such as robotically guided retinal microsurgery, where distances between the instrumen

Gilles Dowek

In this note, we defend that the notion of algorithm as a set of execution traces is somewhat independent of the notion of abstract state machine. It can be reformulated in the more general framework of small step operational semantics.

Sangmin Ji, Satoshi Iwamoto

Improving the photon-spin conversion efficiency without polarization dependence is a major challenge in realizing quantum interfaces gate-defined quantum dots (QDs) for polarization-encoded photonic quantum network systems. Previously, we reported the design of an air-bridge bull's-eye cavity that enhances the photon absorption efficiency of an embedded gate

Dennis Gramlich, Tobias Holicki, Carsten W. Scherer, Christian Ebenbauer

In this paper, we revisit structure exploiting SDP solvers dedicated to the solution of Kalman-Yakubovic-Popov semi-definite programs (KYP-SDPs). These SDPs inherit their name from the KYP Lemma and they play a crucial role in e.g. robustness analysis, robust state feedback synthesis, and robust estimator synthesis for uncertain dynamical systems. Off-the-sh

Simon R. Eugster, Jonas Harsch

The standard in rod finite element formulations is the Bubnov-Galerkin projection method, where the test functions arise from a consistent variation of the ansatz functions. This approach becomes increasingly complex when highly nonlinear ansatz functions are chosen to approximate the rod's centerline and cross-section orientations. Using a Petrov-Galerkin p

Naoto Dainobu

Let $E$ be an elliptic curve over $\mathbb{Q}$, $p$ an odd prime number and $n$ a positive integer. In this article, we investigate the ideal class group $\mathrm{Cl}(\mathbb{Q}(E[p^n]))$ of the $p^n$-division field $\mathbb{Q}(E[p^n])$ of $E$. We introduce a certain subgroup $E(\mathbb{Q})_{\mathrm{ur},p^n}$ of $E(\mathbb{Q})$ and study the $p$-adic valuati

Victoria Bollo, Valentino González, Mauro Stefanon, Pascal A. Oesch

We present the H{\alpha} luminosity function (LF) derived from a large sample of Lyman break galaxies at z {\sim} 4.5 over the GOODS-South and North fields. This study makes use of the new, full-depth Spitzer/IRAC [3.6] and [4.5] imaging from the GOODS Re-ionization Era wide-Area Treasury from the Spitzer program. The H{\alpha} flux is derived from the offse

Nigel Adams, Adriano Augusto, Michael Davern, Marcello La Rosa

As the first phase in the Business Process Management (BPM) lifecycle, process identification addresses the problem of identifying which processes to prioritize for improvement. Process selection plays a critical role in this phase, but it is a step with known pitfalls. Decision makers rely frequently on subjective criteria, and their knowledge of the altern

Michael Krause, Christof Weiß, Meinard Müller

Many tasks in music information retrieval (MIR) involve weakly aligned data, where exact temporal correspondences are unknown. The connectionist temporal classification (CTC) loss is a standard technique to learn feature representations based on weakly aligned training data. However, CTC is limited to discrete-valued target sequences and can be difficult to

Ganesh Subramaniam, Avik De, Tee-How Loo, Yong Kheng Goh

In this exclusive study of the modified $f(Q)$ theory of gravity in the open and closed type Friedmann-Lema\^itre-Robertson-Walker (FLRW) universe model, we impose some constraints from the classical energy conditions. The viable range of parameter $\beta$ for two different $f(Q)$ models, $f(Q)=Q+\beta Q^2$ and $f(Q)=Q+\beta\sqrt{-Q}$, are analyzed in detail

Wenjie Li, Sunseng Pyon, Akiyoshi Yagi, Tong Ren

Interplay between superconductivity and charge-density wave (CDW) in 2H-NbSe2 single crystals irradiated by 3 MeV protons is studied. Both Tc and TCDW are found to decrease monotonically with the increase in irradiation dose. This behavior is different from electron-irradiated NbSe2, where TCDW is suppressed monotonically with the increase in dose, while Tc

Kaushik Srinivasan, Mickael D. Chekroun, James C. McWilliams

We parameterize sub-grid scale (SGS) fluxes in sinusoidally forced two-dimensional turbulence on the $\beta$-plane at high Reynolds numbers (Re$\sim$25000) using simple 2-layer Convolutional Neural Networks (CNN) having only O(1000)parameters, two orders of magnitude smaller than recent studies employing deeper CNNs with 8-10 layers; we obtain stable, accura

Xinyu Zeng, Yulong Hui, Jiahong Shen, Andrew Pavlo

Columnar storage is a core component of a modern data analytics system. Although many database management systems (DBMSs) have proprietary storage formats, most provide extensive support to open-source storage formats such as Parquet and ORC to facilitate cross-platform data sharing. But these formats were developed over a decade ago, in the early 2010s, for

Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen

Cross-domain recommendation (CDR) aims to leverage the correlation of users' behaviors in both the source and target domains to improve the user preference modeling in the target domain. Conventional CDR methods typically explore the dual-relations between the source and target domains' behaviors. However, this may ignore the informative mixed behaviors that

Lina Chen, Yu Chen, Zhenyu Gao, Kaiyuan Zhou

We experimentally study the dynamical modes excited by current-induced spin-orbit torque and its electrostatic gating effect in a 3-terminal planar nano-gap spin Hall nano-oscillator (SHNO) with a moderate interfacial perpendicular magnetic anisotropy (IPMA). Both quasilinear propagating spin-wave and localized "bullet" modes are achieved and controlled by v

Lars Lammers, Do Tran Van, Tom M. W. Nye, Stephan F. Huckemann

It has been observed that the sample mean of certain probability distributions in Billera-Holmes-Vogtmann (BHV) phylogenetic spaces is confined to a lower-dimensional subspace for large enough sample size. This non-standard behavior has been called stickiness and poses difficulties in statistical applications when comparing samples of sticky distributions. W

Athanasios Kehagias

We formulate and study a two-player static duel game as a nonzero-sum discounted stochastic game. Players $P_{1},P_{2}$ are standing in place and, in each turn, one or both may shoot at the other player. If $P_{n}$ shoots at $P_{m}$ ($m\neq n$), either he hits and kills him (with probability $p_{n}$) or he misses him and $P_{m}$ is unaffected (with probabili

Julien Rouzot, Julien Ferry, Marie-José Huguet

Machine Learning models are increasingly used for decision making, in particular in high-stakes applications such as credit scoring, medicine or recidivism prediction. However, there are growing concerns about these models with respect to their lack of interpretability and the undesirable biases they can generate or reproduce. While the concepts of interpret

Md Ishtyaq Mahmud, Muntasir Mamun, Ahmed Abdelgawad

Breast cancer is one of the most common and dangerous cancers in women, while it can also afflict men. Breast cancer treatment and detection are greatly aided by the use of histopathological images since they contain sufficient phenotypic data. A Deep Neural Network (DNN) is commonly employed to improve accuracy and breast cancer detection. In our research,

Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw

One of the most popular methods for reducing the complexity of assemblies of finite element models in the field of structural dynamics is component mode synthesis. A main challenge of component mode synthesis is balancing model complexity and model accuracy, because it is difficult to predict how component reduction influences assembly model accuracy. This w

Qiqi Duan, Chang Shao, Guochen Zhou, Haobin Yang

Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer black-box optimization framework, on non-separable functions. First, we reveal empirical reasons of when decomposition-based methods are preferred or not in

Mizna Ashraf, Jessy Jose, Gregory Herczeg, Min Fang

The process of accretion through circumstellar disks in young stellar objects is an integral part of star formation and the $H\alpha$ emission line is a prominent signature of accretion in low-mass stars. We present the detection and characterization of $H\alpha$ emission line sources in the central region of a distant, low-metallicity young stellar cluster

Guojun Xu, Ying Zhang

The relation between quark masses and CKM mixing is studied based on an approximate chiral $SO(2)_L\times SO(2)_R$ flavor symmetry of quark mass matrix. In mass hierarchy limit, the mass ratio effect to CKM mixing is suppressed, which separates mass hierarchy and quark flavor mixing into two independent problems. We show that CKM mixing is dominated by two l

Ramin Mehrabifard, Zeinab Kabarkouhi, Fatemeh Rezaei, Kamal Hajisharifi

In the last decades, to improve the CAP treatment efficiency, its biological effects in combination with other physical modalities have widely investigated. However, the physical insight into most of supposed synergistic effects remained elusive. In this regard, the synergetic effect of cold plasma and magnetic field has been used for different applications,

Naoko Kunugi, Kyoichi Suzuki

We discuss representations of finite groups having a common central $p$-subgroup $Z$, where $p$ is a prime number. For the principal $p$-blocks, we give a method of constructing a relative $Z$-stable equivalence of Morita type, which is a generalization of a stable equivalence of Morita type, and was introduced by Wang and Zhang in a more general setting. Th

Lanyun Zhu, Tianrun Chen, Jianxiong Yin, Simon See

Continual Semantic Segmentation (CSS) extends static semantic segmentation by incrementally introducing new classes for training. To alleviate the catastrophic forgetting issue in CSS, a memory buffer that stores a small number of samples from the previous classes is constructed for replay. However, existing methods select the memory samples either randomly

Christian Bagshaw

In recent years, there has been a lot of progress in obtaining non-trivial bounds for bilinear forms of Kloosterman sums in $\mathbb{Z}/m\mathbb{Z}$ for arbitrary integers $m$. These results have been motivated by a wide variety of applications, such as improved asymptotic formulas for moments of $L$-functions. However, there has been very little work done i

Chris Nagele, Hideyuki Umeda

GN-z11 is an unusually luminous high redshift galaxy which was recently observed to have strong nitrogen lines while at the same time lacking traditional signatures of AGN activity. These observations have been interpreted as a super-solar nitrogen abundance which is challenging to explain with standard stellar evolution and supernovae enrichment. We present

Kushin Mukherjee, Siddharth Suresh, Timothy T. Rogers

Semantic feature norms, lists of features that concepts do and do not possess, have played a central role in characterizing human conceptual knowledge, but require extensive human labor. Large language models (LLMs) offer a novel avenue for the automatic generation of such feature lists, but are prone to significant error. Here, we present a new method for c

Luoxuan Weng, Minfeng Zhu, Kam Kwai Wong, Shi Liu

Large language models (LLMs) have gained popularity in various fields for their exceptional capability of generating human-like text. Their potential misuse has raised social concerns about plagiarism in academic contexts. However, effective artificial scientific text detection is a non-trivial task due to several challenges, including 1) the lack of a clear

Sophie Wharrie, Zhiyu Yang, Andrea Ganna, Samuel Kaski

Family history is considered a risk factor for many diseases because it implicitly captures shared genetic, environmental and lifestyle factors. Finland's nationwide electronic health record (EHR) system spanning multiple generations presents new opportunities for studying a connected network of medical histories for entire families. In this work we present

Christian Bagshaw, Bryce Kerr

In recent decades, the use of ideas from Minkowski's Geometry of Numbers has gained recognition as a helpful tool in bounding the number of solutions to modular congruences with variables from short intervals. In 1941, Mahler introduced an analogue to the Geometry of Numbers in function fields over finite fields. Here, we build on Mahler's ideas and develop

Dongqi Han, Kenji Doya, Dongsheng Li, Jun Tani

How to behave efficiently and flexibly is a central problem for understanding biological agents and creating intelligent embodied AI. It has been well known that behavior can be classified as two types: reward-maximizing habitual behavior, which is fast while inflexible; and goal-directed behavior, which is flexible while slow. Conventionally, habitual and g

Shaowei Wang, Yun Peng, Jin Li, Zikai Wen

The shuffle model of differential privacy provides promising privacy-utility balances in decentralized, privacy-preserving data analysis. However, the current analyses of privacy amplification via shuffling lack both tightness and generality. To address this issue, we propose the \emph{variation-ratio reduction} as a comprehensive framework for privacy ampli

Dominikus Zielke, Stefan Briefi, Ursel Fantz

In the fusion experiment ITER powerful neutral beam injection (NBI) systems will be used. The NBI's core component is a negative hydrogen ion source, which is based on a modular concept. Eight cylindrical drivers, each having a volume of several liters, are attached to one common expansion and extraction region. Within the drivers an inductively coupled plas

Kaito Fujii

This paper investigates equilibrium computation and the price of anarchy for Bayesian games, which are the fundamental models of games with incomplete information. In normal-form games with complete information, it is known that efficiently computable no-regret dynamics converge to correlated equilibria, and the price of anarchy for correlated equilibria can

Bikramjit Das, Vicky Fasen-Hartmann

In this paper, we compute multivariate tail risk probabilities where the marginal risks are heavy-tailed and the dependence structure is a Gaussian copula. The marginal heavy-tailed risks are modeled using regular variation which leads to a few interesting consequences. First, as the threshold increases, we note that the rate of decay of probabilities of tai

F. Marin, D. Hutsemékers, I. Liodakis, R. Antonucci

The growth of supermassive black holes (SMBHs) through merging has long been predicted but its detection remains elusive. However, a promising target has been discovered in the Seyfert-1 galaxy J1430+2303. If a binary system truly lies at the center of J1430+2303, the usual symmetry expected from pole-on views in active galactic nuclei (AGNs) responsible for

Minjae Kim, Sangkook Choi, Walber Hugo Brito, Gabriel Kotliar

The iron-based superconductor FeSe$_{1-x}$Te$_{x}$ (FST) has recently gained significant attention as a host of two distinct physical phenomena: ($i$) Majorana zero modes which can serve as potential topologically protected qubits, and ($ii$) a realization of the orbital selective Mott transition (OSMT). In this Letter, we connect these two phenomena and pro

Zhongyin Xu, Yanyong Hong

In this paper, we introduce the definition of pre-Gel'fand-Dorfman algebra and present several constructions. Moreover, we show that a class of left-symmetric conformal algebras named quadratic left-symmetric conformal algebras are one to one correspondence with pre-Gel'fand-Dorfman algebras. Then we investigate the simplicities and central extensions of qua

Zhongyin Xu, Yanyong Hong

Let $R$ be a left-symmetric conformal algebra and $Q$ be a $\mathbb{C}[\partial]$-module. We introduce the notion of a unified product for left-symmetric conformal algebras and apply it to construct an object $\mathcal{H}^2_R(Q,R)$ to describe and classify all left-symmetric conformal algebra structures on the direct sum $E=R\oplus Q$ as a $\mathbb{C}[\parti

Ritu Dhaulakhandi, Bikash K. Behera, Felix J. Seo

The factorization of a large digit integer in polynomial time is a challenging computational task to decipher. The exponential growth of computation can be alleviated if the factorization problem is changed to an optimization problem with the quantum computation process with the generalized Grover's algorithm and a suitable analytic algebra. In this article,

Adithya Bhat, Akhil Bandarupalli, Manish Nagaraj, Saurabh Bagchi

Modern Byzantine Fault-Tolerant State Machine Replication (BFT-SMR) solutions focus on reducing communication complexity, improving throughput, or lowering latency. This work explores the energy efficiency of BFT-SMR protocols. First, we propose a novel SMR protocol that optimizes for the steady state, i.e., when the leader is correct. This is done by reduci

Sanghyun Kim, Deunsol Jung, Minsu Cho

Recent state-of-the-art methods for HOI detection typically build on transformer architectures with two decoder branches, one for human-object pair detection and the other for interaction classification. Such disentangled transformers, however, may suffer from insufficient context exchange between the branches and lead to a lack of context information for re

Soumik Ghosh, Subir K. Das

The kinetics of domain growth and aging in conserved order parameter systems, in the presence of short-range interaction, is widely studied. Due to technical difficulties and lack of resources, regarding computation, the dynamics is still not well established in the cases where long-range interactions are involved. Here we present related results from the Mo

Yeteng Liao, Han wang

For the first time, the repeated wear phenomenon of high-frequency power failure on the data block area in intermittent computing file system is found. A method to improve NVM wear in ICFS under high-frequency power failure scenarios is proposed.

Fabrizio Ottati, Giovanna Turvani, Marco Vacca, Guido Masera

The speed of modern digital systems is severely limited by memory latency (the ``Memory Wall'' problem). Data exchange between Logic and Memory is also responsible for a large part of the system energy consumption. Logic--In--Memory (LiM) represents an attractive solution to this problem. By performing part of the computations directly inside the memory the

Boxin Du, Lihui Liu, Jiejun Xu, Fei Wang

Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation. However, existing GNN-based models on social recommendation suffer from serious problems of generalization and oversmoothness, because of the underexplored negative sampling method and the direct implanting of the off-the-shelf GNN mo

Yuma Fujita, Masanori Nagao, Akira Miura, Daisuke Urushihara

Superconductors are influenced by high-entropy alloys (HEAs); these have been investigated in various functional materials. REOBiS$_2$ (RE = La, Ce, Pr, Nd, Sm, and Gd in different combinations) single crystals with HEAs at the RE-site were successfully grown using the flux method. The obtained crystals were plate-shaped (1 mm$^2$) with a well-developed c-pl

Joydipto Bhattacharya, Pampa Sadhukhan, Shuvam Sarkar, Vipin Kumar Singh

A combined study employing density functional theory (DFT) using the experimentally determined modulated structures and bulk-sensitive hard x-ray photoelectron spectroscopy on single-crystalline Ni$_2$MnGa is presented in this work. For the aforementioned modulated structures, all of the characteristic features in the experimental valence band (VB) are in ex

Guangyong Wei, Zhikui Duan, Shiren Li, Guangguang Yang

In recent years, a great deal of attention has been paid to the Transformer network for speech recognition tasks due to its excellent model performance. However, the Transformer network always involves heavy computation and large number of parameters, causing serious deployment problems in devices with limited computation sources or storage memory. In this p

Simon C. -C. Ho, Tetsuya Hashimoto, Tomotsugu Goto, Yu-Wei Lin

Understanding dark matter is one of the most urgent questions in modern physics. A very interesting candidate is primordial black holes (PBHs; Carr2016). For the mass ranges of $< 10^{-16} M_{\odot}$ and $> 100 M_{\odot}$, PBHs have been ruled out. However, they are still poorly constrained in the mass ranges of $10^{-16} - 100 M_{\odot}$ (Belotsky et al. 20

Yasuji Sawada, Yasukazu Daigaku, Kenji Toma

Self-replicability is the unique attribute observed in all the living organisms and the question how the life was physically initiated could be equivalent to the question how self-replicating informative polymers were formed in the abiotic material world. It has been suggested that the present DNA and proteins world was preceded by RNA world in which genetic

M. A. Chukeev, A. S. Kurdyubov, I. I. Ryzhov, V. A. Lovtcius

Exciton excited states in the quantum well are studied via their effect on the nonradiative broadening of the ground exciton resonance. Dependence of the nonradiative broadening of the ground exciton state on the photon energy of additional laser excitation was measured. Applying magnetic field up to 6 T, we could trace the formation of Landau levels and evo

Ayyoob Hamza, Hassan Habibi Gharakheili, Theophilus A. Benson, Gustavo Batista

IoT networks are increasingly becoming target of sophisticated new cyber-attacks. Anomaly-based detection methods are promising in finding new attacks, but there are certain practical challenges like false-positive alarms, hard to explain, and difficult to scale cost-effectively. The IETF recent standard called Manufacturer Usage Description (MUD) seems prom

Peichen Zhong, Bowen Deng, Tanjin He, Zhengyan Lun

Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. In this study, we present a machine-learning model (

Travis Driver, Panagiotis Tsiotras

Missions to small celestial bodies rely heavily on optical feature tracking for characterization of and relative navigation around the target body. While techniques for feature tracking based on deep learning are a promising alternative to current human-in-the-loop processes, designing deep architectures that can operate onboard spacecraft is challenging due

Ram Brustein, A. J. M. Medved, Tom Shindelman

The frozen star model provides a classical description of a regularized black hole and is based upon the idea that regularizing the singularity requires deviations from the Schwarzschild geometry which extend over horizon-sized scales, as well as maximally negative radial pressure as an equation of state. The frozen star has also been shown to be ultra-stabl

Akalanka Galappaththi, Sarah Nadi

Mining repetitive code changes from version control history is a common way of discovering unknown change patterns. Such change patterns can be used in code recommender systems or automated program repair techniques. While there are such tools and datasets exist for Java, there is little work on finding and recommending such changes in Python. In this paper,

Xinnan Dai, Caihua Shan, Jie Zheng, Xiaoxiao Li

Genes are fundamental for analyzing biological systems and many recent works proposed to utilize gene expression for various biological tasks by deep learning models. Despite their promising performance, it is hard for deep neural networks to provide biological insights for humans due to their black-box nature. Recently, some works integrated biological know

Max Resnick

Many early order flow auction designs handle the payment for orders when they execute on the chain rather than when they are won in the auction. Payments in these auctions only take place when the orders are executed, creating a free option for whoever wins the order. Bids in these auctions set the strike price of this option rather than the option premium.

Armaghan Zafar, Ian R. Manchester

In this paper, we present a structured solver based on the preconditioned conjugate gradient method (PCGM) for solving the linear quadratic (LQ) optimal control problem for $K \times N$ sub-systems connected in a two-dimensional (2D) grid structure. Our main contribution is the development of a structured preconditioner based on a fixed number of inner-outer

Xiao Li, Eric Chan, Mohsen Lesani

Byzantine quorum systems provide higher throughput than proof-of-work and incur modest energy consumption. Further, their modern incarnations incorporate personalized and heterogeneous trust. Thus, they are emerging as an appealing candidate for global financial infrastructure. However, since their quorums are not uniform across processes anymore, the proper

Yao Teng, Haisong Liu, Sheng Guo, Limin Wang

Previous object detectors make predictions based on dense grid points or numerous preset anchors. Most of these detectors are trained with one-to-many label assignment strategies. On the contrary, recent query-based object detectors depend on a sparse set of learnable queries and a series of decoder layers. The one-to-one label assignment is independently ap

Ahmed M. Mousbah, Hesham M. Abdullah, Waleed S. Mohammed, Ali M. El-Refy

Buffaloes are farm animals that contribute to food security by providing high quality meat and milk. They can better tolerate the adverse effects of global climate change on their meat and milk production. Despite their advantages, buffaloes are heavily neglected animals with fewer studies compared to other farm animals, hence, the real potential of buffaloe

Nikolai Merkel, Ruben Mayer, Tawkir Ahmed Fakir, Hans-Arno Jacobsen

For distributed graph processing on massive graphs, a graph is partitioned into multiple equally-sized parts which are distributed among machines in a compute cluster. In the last decade, many partitioning algorithms have been developed which differ from each other with respect to the partitioning quality, the run-time of the partitioning and the type of gra

Alexei Rybkin, Efim Pelinovsky, Noah Palmer

We put forward a simple but effective explicit method of recovering initial data for the nonlinear shallow water system from the reading at the shoreline. We then apply our method to the tsunami waves inverse problem.

Yuchen Hu, Chen Chen, Qiushi Zhu, Eng Siong Chng

Automatic speech recognition (ASR) has gained remarkable successes thanks to recent advances of deep learning, but it usually degrades significantly under real-world noisy conditions. Recent works introduce speech enhancement (SE) as front-end to improve speech quality, which is proved effective but may not be optimal for downstream ASR due to speech distort

Taichi Kato, Naoto Kojiguchi

We analyzed All-Sky Automated Survey for Supernovae (ASAS-SN), Asteroid Terrestrial-impact Last Alert System (ATLAS) and Transiting Exoplanet Survey Satellite (TESS) observations of CM Mic and found that this object belongs to a small group of ER UMa stars showing standstills. In addition to typical ER UMa-type cycles, the object showed standstills between 2

Yunheng Shen, Haoxiang Wang, Hairong Lv

Federated learning aims to learn a global model collaboratively while the training data belongs to different clients and is not allowed to be exchanged. However, the statistical heterogeneity challenge on non-IID data, such as class imbalance in classification, will cause client drift and significantly reduce the performance of the global model. This paper p

Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions. However, these generative models suffer from intrinsic limitations such as the instability of GANs and the restricted representation ability of VAEs. Such limitations hinder the accura

Cheng Xin, Soham Mukherjee, Shreyas N. Samaga, Tamal K. Dey

$1$-parameter persistent homology, a cornerstone in Topological Data Analysis (TDA), studies the evolution of topological features such as connected components and cycles hidden in data. It has been applied to enhance the representation power of deep learning models, such as Graph Neural Networks (GNNs). To enrich the representations of topological features,

Xiaobin Sun, Yingchao Xie

In this paper, we study the averaging principle and central limit theorem for multi-scale stochastic differential equations with state-dependent switching. To accomplish this, we first study the Poisson equation associated with a Markov chain and the regularity of its solutions. As applications of the results on the Poisson equations, we prove three averagin

Mohammadreza Armandpour, Ali Sadeghian, Huangjie Zheng, Amir Sadeghian

Although text-to-image diffusion models have made significant strides in generating images from text, they are sometimes more inclined to generate images like the data on which the model was trained rather than the provided text. This limitation has hindered their usage in both 2D and 3D applications. To address this problem, we explored the use of negative

Kyu Beom Han, Olivia G. Odenthal, Woo Jae Kim, Sung-Eui Yoon

Auxiliary features such as geometric buffers (G-buffers) and path descriptors (P-buffers) have been shown to significantly improve Monte Carlo (MC) denoising. However, recent approaches implicitly learn to exploit auxiliary features for denoising, which could lead to insufficient utilization of each type of auxiliary features. To overcome such an issue, we p

Francisco Eron, Muhammad Noman, Raphael Ricon de Oliveira, Deigo de Souza Marques

Coffee which is prepared from the grinded roasted seeds of harvested coffee cherries, is one of the most consumed beverage and traded commodity, globally. To manually monitor the coffee field regularly, and inform about plant and soil health, as well as estimate yield and harvesting time, is labor-intensive, time-consuming and error-prone. Some recent studie

Kazumasa Nomura, Paul Terwilliger

Let $\F$ denote a field, and let $V$ denote a vector space over $\F$ with finite positive dimension. A Leonard pair on $V$ is an ordered pair of diagonalizable $\F$-linear maps $A: V \to V$ and $A^* : V \to V$ that each act on an eigenbasis for the other in an irreducible tridiagonal fashion. Let $A,A^*$ denote a Leonard pair on $V$. Let $\{v_i\}_{i=0}^d$ de

Hamza Boukraichi, Nissrine Akkari, Fabien Casenave, David Ryckelynck

Convolutional neural networks are now seeing widespread use in a variety of fields, including image classification, facial and object recognition, medical imaging analysis, and many more. In addition, there are applications such as physics-informed simulators in which accurate forecasts in real time with a minimal lag are required. The present neural network

Huanhuan Li, Xuechao Zou, Yu-an Zhang, Jiangcai Zhaba

The Three-River-Source region is a highly significant natural reserve in China that harbors a plethora of botanical resources. To meet the practical requirements of botanical research and intelligent plant management, we construct a dataset for Plant detection in the Three-River-Source region (PTRS). It comprises 21 types, 6965 high-resolution images of 2160

Ganlin Yang, Guoqiang Wei, Zhizheng Zhang, Yan Lu

Most Neural Radiance Fields (NeRFs) exhibit limited generalization capabilities, which restrict their applicability in representing multiple scenes using a single model. To address this problem, existing generalizable NeRF methods simply condition the model on image features. These methods still struggle to learn precise global representations over diverse s

Hyun-Myung Chun, Jordan M. Horowitz

We study the response of chemical reaction networks driven far from equilibrium to logarithmic perturbations of reaction rates. The response of the mean number of a chemical species is observed to be quantitively limited by number fluctuations as well as the maximum thermodynamic driving force. We prove these trade-offs for linear chemical reaction networks

Soohyun Kim, Junho Kim, Taekyung Kim, Hwan Heo

In this paper, we tackle the challenging task of Panoramic Image-to-Image translation (Pano-I2I) for the first time. This task is difficult due to the geometric distortion of panoramic images and the lack of a panoramic image dataset with diverse conditions, like weather or time. To address these challenges, we propose a panoramic distortion-aware I2I model

Danwei Li, Zhengyu Zhang, Siyang Yuan, Mingze Gao

Multi-task learning (MTL) aims to enhance the performance and efficiency of machine learning models by simultaneously training them on multiple tasks. However, MTL research faces two challenges: 1) effectively modeling the relationships between tasks to enable knowledge sharing, and 2) jointly learning task-specific and shared knowledge. In this paper, we pr