July 2023 arXiv papers — page 81

Rui Zhang, Yixin Su, Bayu Distiawan Trisedya, Xiaoyan Zhao

The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-based methods have been proposed for this task. However, to our best knowledge, existing methods all require manually crafted seed alignments, which are expensive to obtain. In this p

Moyuki Yamamoto, Hodaka Kurokawa, Satoshi Fujii, Toshiharu Makino

Phonons confined in mechanical resonators can be coupled to a variety of quantum systems and are expected to be applied to hybrid quantum systems. Diamond surface acoustic wave (SAW) devices are capable of high efficiency in phonon interaction with color centers in diamond. The temperature dependence of the quality factor is crucial for inferring the governi

Bing Liu, Rui-zhi Yang, Xin-yu He, Felix Aharonian

MeV nuclear de-excitation lines serve as a unique tool to study low-energy cosmic rays (CRs), containing both spectral and elemental information of the interacting material. In this paper, we estimated the possible nuclear de-excitation lines from the young supernova remnant Cassiopeia A. Given different CR spectral shapes and interacting materials, we found

Kazuho Kobayashi, Seiya Ueno, Takehiro Higuchi

Multi-robot patrolling is the potential application for robotic systems to survey wide areas efficiently without human burdens and mistakes. However, such systems have few examples of real-world applications due to their lack of human predictability. This paper proposes an algorithm: Local Reactive (LR) for multi-robot patrolling to satisfy both needs: (i)pa

Dimitrios Giannakis, Yuka Hashimoto, Masahiro Ikeda, Isao Ishikawa

We introduce a linear operator on a Hilbert $C^*$-module for analyzing skew-product dynamical systems. The operator is defined by composition and multiplication. We show that it admits a decomposition in the Hilbert $C^*$-module, called eigenoperator decomposition, that generalizes the concept of the eigenvalue decomposition. This decomposition reconstructs

Arman Zharmagambetov, Brandon Amos, Aaron Ferber, Taoan Huang

Recent works in learning-integrated optimization have shown promise in settings where the optimization problem is only partially observed or where general-purpose optimizers perform poorly without expert tuning. By learning an optimizer $\mathbf{g}$ to tackle these challenging problems with $f$ as the objective, the optimization process can be substantially

A. M. Mukhamedzhanov

{\bf{Purpose}:} In this paper, the proton resonance width of the near-threshold resonance is calculated using two different approaches. The values of the proton width are used to calculate the low-energy $S$-factor. \\ {\bf{Method:}} First, the proton resonance width is estimated using the mirror symmetry of the resonance $ {}^{11}{\rm C}(E_{x}=8.70\, {\rm M

Rithesh Murthy, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu

In this paper, we propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. Existing AutoGPT-style techniques have inherent limitations, such as a heavy reliance on precise descriptions for decision-making, and the lack of a systematic approach to leverage try-and-fail procedures akin to traditional Reinforcement Learning

Yu Chen, Chang-Zhi Lu, Yu Lu, Tingting Zhang

Massive neutrinos are expected to affect the large-scale structure formation, including the major component of solid substances, dark matter halos. How halos are influenced by neutrinos is vital and interesting, and angular momentum (AM) as a significant feature provides a statistical perspective for this issue. Exploring halos from TianNu N-body cosmologica

Kavya Remesh, Job Chunkath

Electrocardiography (ECG) plays a significant role in diagnosing heart-related issues, it provides, accurate, fast, and dependable insights into crucial parameters like QRS complex duration, the R-R interval, and the occurrence, amplitude, and duration of P, R, and T waves. However, utilizing ECG for prolonged monitoring poses challenges as it necessitates c

Michael Gully-Santiago, Caroline V. Morley, Jessica Luna, Morgan MacLeod

Atmospheric escape shapes the fate of exoplanets, with statistical evidence for transformative mass loss imprinted across the mass-radius-insolation distribution. Here we present transit spectroscopy of the highly irradiated, low-gravity, inflated hot Saturn HAT-P-67 b. The Habitable Zone Planet Finder (HPF) spectra show a detection of up to 10% absorption d

Junichi Miyakoshi

Understanding the pattern formation in communities has been at the center of attention in various fields. Here we introduce a novel model, called an "information-particle model," which is based on the reaction-diffusion model and the distributed behavior model. The information particle drives competition or coordination among species. Therefore, a traverse o

Jennifer L. Clarke, Laurel D. Cooper, Monica F. Poelchau, Tanya Z. Berardini

Over the last several decades, there has been rapid growth in the number and scope of agricultural genetics, genomics and breeding (GGB) databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and br

Ning Xu, Li Shi, Xudong Pei, Weiyang Zhang

Electrochemical reactions represent essential processes in fundamental chemistry that foster a wide range of applications. Although most electrochemical reactions in bulk substances can be well described by the classical Marcus-Gerischer charge transfer theory, the realistic reaction character and mechanism in dimensionally confined systems remain unknown. H

Antonio Anna Mele

The Haar measure plays a vital role in quantum information, but its study often requires a deep understanding of representation theory, posing a challenge for beginners. This tutorial aims to provide a basic introduction to Haar measure tools in quantum information, utilizing only basic knowledge of linear algebra and thus aiming to make this topic more acce

Anahita Namvar, Chandra Thapa, Salil S. Kanhere

IoT device identification is the process of recognizing and verifying connected IoT devices to the network. This is an essential process for ensuring that only authorized devices can access the network, and it is necessary for network management and maintenance. In recent years, machine learning models have been used widely for automating the process of iden

Taichi Kato

SDSS J183131.63+420220.2 is an AM CVn-type cataclysmic variable. Using Asteroid Terrestrial-impact Last Alert System (ATLAS) and Zwicky Transient Facility (ZTF) data, I found that this object is actually a helium dwarf nova, which experienced a long (~6 yr) standstill (2017 to 2022). The object is currently (in 2023) in ER UMa-type state with supercycles of

Kun Meng, Xi-Long Fan, Song Li, Wen-Biao Han

The Johannsen black hole (BH) is a generic rotating BH admitting three constants of motions (energy, angular momentum, and Carter constant) and is characterized by four deviation parameters besides mass and spin, which could be a model-independent probe of the no-hair theorem. We systematically study the dynamics of null particles around Johannsen BH, reveal

Jinshi Sai, Hsi-Wei Yen, Nagayoshi Ohashi, John J. Tobin

We have observed the Class 0/I protostellar system Ced110 IRS4 at an angular resolution of $0.05''$ ($\sim$10 au) as a part of the ALMA large program; Early Planet Formation in the Embedded Disks (eDisk). The 1.3 mm dust continuum emission reveals that Ced110 IRS4 is a binary system with a projected separation of $\sim$250 au. The continuum emissions associa

Zhengjing Ma, Gang Mei

Forecasting how landslides will evolve over time or whether they will fail is a challenging task due to a variety of factors, both internal and external. Despite their considerable potential to address these challenges, deep learning techniques lack interpretability, undermining the credibility of the forecasts they produce. The recent development of transfo

Bin Chen, Jiechong Song, Jingfen Xie, Jian Zhang

By absorbing the merits of both the model- and data-driven methods, deep physics-engaged learning scheme achieves high-accuracy and interpretable image reconstruction. It has attracted growing attention and become the mainstream for inverse imaging tasks. Focusing on the image compressed sensing (CS) problem, we find the intrinsic defect of this emerging par

Tianyao Shi, Yingxuan Yang, Yunlong Cheng, Xiaofeng Gao

Multi-tenancy in public clouds may lead to co-location interference on shared resources, which possibly results in performance degradation of cloud applications. Cloud providers want to know when such events happen and how serious the degradation is, to perform interference-aware migrations and alleviate the problem. However, virtual machines (VM) in Infrast

Yasuaki Kobayashi, Kazuhiro Kurita, Kunihiro Wasa

Finding a maximum cardinality common independent set in two matroids (also known as \textsc{Matroid Intersection}) is a classical combinatorial optimization problem, which generalizes several well-known problems, such as finding a maximum bipartite matching, a maximum colorful forest, and an arborescence in directed graphs. Enumerating all maximal common ind

Mohammad Wardat, Breno Dantas Cruz, Wei Le, Hridesh Rajan

Deep Learning (DL) applications are being used to solve problems in critical domains (e.g., autonomous driving or medical diagnosis systems). Thus, developers need to debug their systems to ensure that the expected behavior is delivered. However, it is hard and expensive to debug DNNs. When the failure symptoms or unsatisfied accuracies are reported after tr

Ning Gao, Qiying Huang, Cen Li, Shi Jin

Wireless networks are vulnerable to physical layer spoofing attacks due to the wireless broadcast nature, thus, integrating communications and security (ICAS) is urgently needed for 6G endogenous security. In this letter, we propose an environment semantics enabled physical layer authentication network based on deep learning, namely EsaNet, to authenticate t

Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky

Growing concerns regarding algorithmic fairness have led to a surge in methodologies to mitigate algorithmic bias. However, such methodologies largely assume that observed labels in training data are correct. This is problematic because bias in labels is pervasive across important domains, including healthcare, hiring, and content moderation. In particular,

Taoran Sheng, Manfred Huber

Deep learning has been successfully applied to human activity recognition. However, training deep neural networks requires explicitly labeled data which is difficult to acquire. In this paper, we present a model with multiple siamese networks that are trained by using only the information about the similarity between pairs of data samples without knowing the

João Barata, Yacine Mehtar-Tani

In recent years, there has been an effort towards establishing a more complete picture for jet substructure in the presence of the quark gluon plasma. Such a program requires not only a more detailed description of medium induced effects, but also the design of novel substructure observables. Very recently, it has been noticed that Energy Energy correlators

Harkirat Singh, Daren Liu, David L. Henann

Dense mixtures of particles of varying size tend to segregate based on size during flow. Granular size-segregation plays an important role in many industrial and geophysical processes, but the development of coupled, continuum models capable of predicting the evolution of segregation dynamics and flow fields in dense granular media across different geometrie

Mengting Ai, Tianxin Wei, Yifan Chen, Zeming Guo

Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications. However, this process is known to be expensive, especially on edge devices with low computing power. While general approaches (e.g. quantization and distillation) have been widely studied to reduce the compute/memory of PLM fin

Xugui Zhou, Anqi Chen, Maxfield Kouzel, Haotian Ren

Adaptive Cruise Control (ACC) is a widely used driver assistance technology for maintaining the desired speed and safe distance to the leading vehicle. This paper evaluates the security of the deep neural network (DNN) based ACC systems under runtime stealthy perception attacks that strategically inject perturbations into camera data to cause forward collisi

Yanglin Hu, Maximilian P. E. Lock, Mischa P. Woods

We derive the predicted time dilation of delocalized atomic clocks in an optical lattice setup in the presence of a gravitational field to leading order in quantum relativistic corrections. We investigate exotic quantum states of motion whose relativistic time dilation is outside of the realm of classical general relativity, finding a regime where $^{24}\mat

Igor Kudelin, William Groman, Qing-Xin Ji, Joel Guo

Numerous modern technologies are reliant on the low-phase noise and exquisite timing stability of microwave signals. Substantial progress has been made in the field of microwave photonics, whereby low noise microwave signals are generated by the down-conversion of ultra-stable optical references using a frequency comb. Such systems, however, are constructed

Jonathan Landy, Tsvi Tlusty, YeongKyu Lee, YongSeok Jho

We introduce an RG-inspired coarse-graining for extracting the collective features of data. The key to successful coarse-graining lies in finding appropriate pairs of data sets. We coarse-grain the two closest data in a regular real-space RG in a lattice while considers the overall information loss in momentum-space RG. Here we compromise the two measures fo

Russell Miller

The absolute Galois group Gal$(\overline{\mathbb{Q}}/\mathbb{Q})$ of the field $\mathbb{Q}$ of rational numbers can be presented as a highly computable object, under the notion of type-2 Turing computation. We formalize such a presentation and use it to address several effectiveness questions about Gal$(\overline{\mathbb{Q}}/\mathbb{Q})$: the difficulty of c

Yongji Wang, Ching-Yao Lai

Deep learning techniques are increasingly applied to scientific problems, where the precision of networks is crucial. Despite being deemed as universal function approximators, neural networks, in practice, struggle to reduce the prediction errors below $O(10^{-5})$ even with large network size and extended training iterations. To address this issue, we devel

Pedro Sequeira, Melinda Gervasio

In recent years, advances in deep learning have resulted in a plethora of successes in the use of reinforcement learning (RL) to solve complex sequential decision tasks with high-dimensional inputs. However, existing systems lack the necessary mechanisms to provide humans with a holistic view of their competence, presenting an impediment to their adoption, p

Peipei Zhang, Chao Wang, Yu-Xian Li, Lixue Zhai

The transport properties of electrons in graphene $p$-$n$ junction with uniform Kekul\'e lattice distortion have been studied using the tight-binding model and the Landauer-B\"uttiker formalism combined with the nonequilibrium Green's function method. In the Kekul\'e-ordered graphene, the original $K$ and $K^{\prime}$ valleys of the pristine graphene are fol

Ruiqing Sun, Ping Jian

Multi-choice Machine Reading Comprehension (MRC) is a challenging extension of Natural Language Processing (NLP) that requires the ability to comprehend the semantics and logical relationships between entities in a given text. The MRC task has traditionally been viewed as a process of answering questions based on the given text. This single-stage approach ha

Yu-Ran Gu, Chao Bian, Chao Qian

Submodular maximization arises in many applications, and has attracted a lot of research attentions from various areas such as artificial intelligence, finance and operations research. Previous studies mainly consider only one kind of constraint, while many real-world problems often involve several constraints. In this paper, we consider the problem of submo

Siddharth Tourani, Carsten Rother, Muhammad Haris Khan, Bogdan Savchynskyy

We contribute to the sparsely populated area of unsupervised deep graph matching with application to keypoint matching in images. Contrary to the standard \emph{supervised} approach, our method does not require ground truth correspondences between keypoint pairs. Instead, it is self-supervised by enforcing consistency of matchings between images of the same

Xingyue Ma, Hongying Chen, Ri He, Zhanbo Yu

The first-principles-based effective Hamiltonian scheme provides one of the most accurate modeling technique for large-scale structures, especially for ferroelectrics. However, the parameterization of the effective Hamiltonian is complicated and can be difficult for some complex systems such as high-entropy perovskites. Here, we propose a general form of eff

Huiling Mao, Yuta Sasaki, Yuta Kobayashi, Shinji Isogami

Antiferromagnetic $\mathrm{L1_2}$-$\mathrm{Mn_3Ir}$ combines outstanding spin-transport properties with magnons in the terahertz (THz) frequency range. However, the THz radiation emitted by ultrafast spin-to-charge conversion via the inverse spin Hall effect remains unexplored. In this study, we measured the THz emission and transmission of a permalloy/(111)

Jingyao Wang, Luntian Mou, Changwen Zheng, Wen Gao

Handwriting authentication is a valuable tool used in various fields, such as fraud prevention and cultural heritage protection. However, it remains a challenging task due to the complex features, severe damage, and lack of supervision. In this paper, we propose a novel Contrastive Self-Supervised Learning framework for Robust Handwriting Authentication (CSS

Jianlan Luo, Charles Xu, Xinyang Geng, Gilbert Feng

We study the problem of learning to perform multi-stage robotic manipulation tasks, with applications to cable routing, where the robot must route a cable through a series of clips. This setting presents challenges representative of complex multi-stage robotic manipulation scenarios: handling deformable objects, closing the loop on visual perception, and han

Qi Xin, Xiao-Song Yang, Zhi-Gang Wang

In this work, we systematically investigate the singly-charmed pentaquark molecular states $D^{(*)}N$, $D^{(*)}\Xi^{(*)}$ and $D_s^{(*)}\Xi^{(*)}$ with the QCD sum rules by carrying out the operator product expansion up to the vacuum condensates of dimension 13 and taking fully account of the light-flavor $SU(3)$ breaking effects. The numerical results favor

Chaochao Chen, Xiaohua Feng, Yuyuan Li, Lingjuan Lyu

As the parameter size of Large Language Models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data. In response, existing research has attempted to make a breakthrough by incorporating Federated Learning (FL) into LLMs. Conversely, considering the outstanding performance of LLMs in task generalization, researchers

Jingyao Wang, Wenwen Qiang, Xingzhe Su, Changwen Zheng

Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks. It is commonly believed that increasing task diversity will enhance the generalization ability of meta-learning models. However, this paper challenges this view through empirical and theoretical analysis. We obtain three c

Yuan Zhang, Tyrone Fernando, Mohamed Darouach

In this paper, new characterizations for functional observability, functional detectability, and structural functional observability (SFO) are developed, and based on them, the related optimal sensor placement problems are investigated. A novel concept of modal functional observability coinciding with the notion of modal observability is proposed. This notio

Cheng-Kuang Wu, Wei-Lin Chen, Hsin-Hsi Chen

We explore the extension of chain-of-thought (CoT) prompting to medical reasoning for the task of automatic diagnosis. Motivated by doctors' underlying reasoning process, we present Diagnostic-Reasoning CoT (DR-CoT). Empirical results demonstrate that by simply prompting large language models trained only on general text corpus with two DR-CoT exemplars, the

Yaoyu Zhang, Zhongwang Zhang, Leyang Zhang, Zhiwei Bai

We propose an optimistic estimate to evaluate the best possible fitting performance of nonlinear models. It yields an optimistic sample size that quantifies the smallest possible sample size to fit/recover a target function using a nonlinear model. We estimate the optimistic sample sizes for matrix factorization models, deep models, and deep neural networks

Brent A. Wallace, Jennie Si

Continuous-time nonlinear optimal control problems hold great promise in real-world applications. After decades of development, reinforcement learning (RL) has achieved some of the greatest successes as a general nonlinear control design method. However, a recent comprehensive analysis of state-of-the-art continuous-time RL (CT-RL) methods, namely, adaptive

Zhe Huang, Ruijie Jiang, Shuchin Aeron, Michael C. Hughes

In typical medical image classification problems, labeled data is scarce while unlabeled data is more available. Semi-supervised learning and self-supervised learning are two different research directions that can improve accuracy by learning from extra unlabeled data. Recent methods from both directions have reported significant gains on traditional benchma

Dongxiao Zhao, Hussein Aluie

We generalize the `filtering spectrum' [1] to probe scales along different directions by spatial coarse-graining. This multi-dimensional filtering spectrum quantifies the spectral content of flows that are not necessarily homogeneous. From multi-dimensional spectral information, we propose a simple metric for shape anisotropy at various scales. The method is

Chunfeng Li, Rui Wang, Shuai Zhang, Yuyuan Qin

Symmetry breaking in quantum materials is of great importance and can lead to nonreciprocal charge transport. Topological insulators provide a unique platform to study nonreciprocal charge transport due to their surface states, especially quantum Hall states under external magnetic field. Here, we report the observation of nonreciprocal charge transport medi

Moslem Mahdavifar

Observation of the purely quantum mechanical effects of quasi-classical states is of utmost importance since these states are realistic sources of radiation and do not have any shortage in photon numbers. Therefore, they do not face the scalability problem as much as other single-photon sources do, which makes them much more robust against photon loss. Moreo

Weihai Zhang, Bor-Sen Chen

This paper discusses the stabilizability, weak stabilizability, exact observability and robust quadratic stabilizability of linear stochastic control systems. By means of the spectrum technique of the generalized Lyapunov operator, a necessary and sufficient condition is given for stabilizability and weak stabilizability of stochastic systems, respectively.

Xian Shi

Finite tight frames play an important role in miscellaneous areas, including quantum information theory. Here we apply a class of tight frames, equiangular tight frames, to address the problem of detecting the entanglement of bipartite states. Here we derive some entanglement criteria based on positive operator-valued measurements built from equiangular tigh

Zeen Song, Xingzhe Su, Jingyao Wang, Wenwen Qiang

In recent years, self-supervised learning (SSL) has emerged as a promising approach for extracting valuable representations from unlabeled data. One successful SSL method is contrastive learning, which aims to bring positive examples closer while pushing negative examples apart. Many current contrastive learning approaches utilize a parameterized projection

Pengcheng, Peng, Yun, Qingzhao

The increased adoption of smart contracts in many industries has made them an attractive target for cybercriminals, leading to millions of dollars in losses. Thus, deploying smart contracts with detected vulnerabilities (known to developers) are not acceptable, and fixing all the detected vulnerabilities is needed, which incurs high manual labor cost without

F. S. Lohmann, A. Schnorr-Müller, M. Trevisan, T. V. Ricci

We investigate the formation and evolution of z=0 massive compact galaxies (MCGs) in the IllustrisTNG cosmological simulation. We found that, as in observations, MCGs are mainly old (median age $\sim 10.8$ Gyr), have super-solar metallicities (median $\log Z/Z_{\odot}\sim0.35$) and are $\alpha$-enhanced (median $[\alpha/Fe]\sim0.25$). The age distribution ex

Huiyuan Chen, Chin-Chia Michael Yeh, Yujie Fan, Yan Zheng

Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering. However, GNNs tend to yield inferior performance when the distributions of training and test data are not aligned well. Also, training GNNs requires optimizing non-convex neural networks with an abundance of local and global minima, which may differ widely in their

Ziyi Yin, Rafael Orozco, Mathias Louboutin, Felix J. Herrmann

Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved

Laurens P. Stoop, Karin van der Wiel, William Zappa, Arno Haverkamp

We propose an index to quantify and analyse the impact of climatological variability on the energy system at different timescales. We define the Climatological Renewable Energy Deviation Index (CREDI) as the cumulative anomaly of a renewable resource with respect to its climate over a specific time period of interest. For this we introduce the smooth, yet ph

Wenhao Wu, Yuxin Song, Zhun Sun, Jingdong Wang

Temporal modeling plays a crucial role in understanding video content. To tackle this problem, previous studies built complicated temporal relations through time sequence thanks to the development of computationally powerful devices. In this work, we explore the potential of four simple arithmetic operations for temporal modeling. Specifically, we first capt

Edson B. Pinto-Luque

The research project aims to apply an integrated approach to natural language processing NLP to satisfaction surveys. It will focus on understanding and extracting relevant information from survey responses, analyzing feelings, and identifying recurring word patterns. NLP techniques will be used to determine emotional polarity, classify responses into positi

Stephanie Wang, Jinxing Li

The Earth's ring current is highly dynamic and is strongly influenced by the solar wind. The ring current alters the planet's magnetic field, defining geomagnetic storms. In this study, we investigate the decay timescales of ring current protons using observations from the Van Allen Probes. Since proton fluxes typically exhibit exponential decay after big st

David Burguet, Ruxi Shi

We define the topological multiplicity of an invertible topological system $(X,T)$ as the minimal number $k$ of real continuous functions $f_1,\cdots, f_k$ such that the functions $f_i\circ T^n$, $n\in\mathbb Z$, $1\leq i\leq k,$ span a dense linear vector space in the space of real continuous functions on $X$ endowed with the supremum norm. We study some pr

Sepideh Malektaji, Amin Ebrahimzadeh, Halima Elbiaze, Roch Glitho

With the explosive demands for data, content delivery networks are facing ever-increasing challenges to meet end-users quality-of-experience requirements, especially in terms of delay. Content can be migrated from surrogate servers to local caches closer to end-users to address delay challenges. Unfortunately, these local caches have limited capacities, and

H. T. Anh, N. V. Phu, N. Q. Dieu

In this paper, we introduce the class $\mathcal{E}_{m,F}(\Omega)$ and solve complex $m$-Hessian equations in the class $\mathcal{E}_{m,F}(\Omega)$. Afterthat, we study subextension in the class $\mathcal{E}_{m,F}(\Omega)$ with the weighted Hessian measure of subextension unchanged. This is an extensive version of the result in [24] in the case when the small

Arnab Adhikary, Wang Yang, Robert Raussendorf

Quantum states picked from non-trivial symmetry protected topological (SPT) phases have computational power in measurement based quantum computation. This power is uniform across SPT phases, and is unlocked by measurements that break the symmetry. Except at special points in the phase, all computational schemes known to date place these symmetry-breaking mea

Soukaina Ouledsidi Ali, Zakaria Ait Hmitti, Halima Elbiaze, Roch Glitho

Computing in the network (COIN) is a promising technology that allows processing to be carried out within network devices such as switches and network interface cards. Time sensitive application can achieve their quality of service (QoS) target by flexibly distributing the caching and computing tasks in the cloud-edge-mist continuum. This paper highlights th

Fatemeh Aghaaliakbari, Zakaria Ait Hmitti, Marsa Rayani, Manel Gherari

Applications such as holographic concerts are now emerging. However, their provisioning remains highly challenging. Requirements such as high bandwidth and ultra-low latency are still very challenging for the current network infrastructure. In-network computing (INC) is an emerging paradigm that enables the distribution of computing tasks across the network

Mohammad Sayad Haghighi

Security of Intelligent Transportation Systems (ITS) heavily depends on the security of the underlying components that create such a smart ecosystem. Adaptive Cruise Control (ACC) is embedded into most modern vehicles. In this report, we study the situations that the two vehicles involved in a cruise control scenario create. More precisely, after breaking do

Daniel Engelsman, Yair Stolero, Itzik Klein

Inertial navigation systems (INS) are widely used in almost any operational environment, including aviation, marine, and land vehicles. Inertial measurements from accelerometers and gyroscopes allow the INS to estimate position, velocity, and orientation of its host vehicle. However, as inherent sensor measurement errors propagate into the state estimates, a

Soukaina Ouledsidi Ali, Halima Elbiaze, Roch Glitho, Wessam Ajib

Microservices are a promising technology for future networks, and many research efforts have been devoted to optimally placing microservices in cloud data centers. However, microservices deployment in edge and in-network devices is more expensive than the cloud. Additionally, several works do not consider the main requirements of microservice architecture, s

Mehrad Jaloli, Marzia Cescon

This paper presents a novel multi-agent reinforcement learning (RL) approach for personalized glucose control in individuals with type 1 diabetes (T1D). The method employs a closed-loop system consisting of a blood glucose (BG) metabolic model and a multi-agent soft actor-critic RL model acting as the basal-bolus advisor. Performance evaluation is conducted

Abhijeet Alase, Kevin D. Stubbs, Barry C. Sanders, David L. Feder

Quasiparticle poisoning errors in Majorana-based qubits are not suppressed by the underlying topological properties, which undermines the usefulness of this proposed platform. This work tackles the errors originating from intrinsically excited quasiparticles by developing an erasure conversion scheme based on local quasiparticle detection. To model such meas

B. Tripathi, A. E. Fraser, P. W. Terry, E. G. Zweibel

To comprehensively understand saturation of two-dimensional ($2$D) magnetized Kelvin-Helmholtz-instability-driven turbulence, energy transfer analysis is extended from the traditional interaction between scales to include eigenmode interactions, by using the nonlinear couplings of linear eigenmodes of the ideal instability. While both kinetic and magnetic en

Keita Mikami, Shu Nakamura, Yukihide Tadano

Continuum limits of Laplace operators on general lattices are considered, and it is shown that these operators converge to elliptic operators on the Euclidean space in the sense of the generalized norm resolvent convergence. We then study operators on the hexagonal lattice, which does not apply the above general theory, but we can show its Laplace operator c

Taedong Yun

High-dimensional clinical data have become invaluable resources for genetic studies, due to their accessibility in biobank-scale datasets and the development of high performance modeling techniques especially using deep learning. Recent work has shown that low dimensional embeddings of these clinical data learned by variational autoencoders (VAE) can be used

Livia Owen, Jonathan Hoseana, Benny Yong

We study the codimension-two bifurcations exhibited by a recently-developed SIR-type mathematical model for the spread of COVID-19, as its two main parameters -- the susceptible individuals' cautiousness level and the hospitals' bed-occupancy rate -- vary over their domains. We use AUTO to generate the model's bifurcation diagrams near the relevant bifurcati

Kenji Nakahira

In category theory, the use of string diagrams is well known to aid in the intuitive understanding of certain concepts, particularly when dealing with adjunctions and monoidal categories. We show that string diagrams are also useful in exploring fundamental properties of basic concepts in category theory, such as universal properties, (co)limits, Kan extensi

Quanquan C. Liu, Vaidehi Srinivas

We formulate the predicted-updates dynamic model, one of the first beyond-worst-case models for dynamic algorithms, which generalizes a large set of well-studied dynamic models including the offline dynamic, incremental, and decremental models to the fully dynamic setting when given predictions about the update times of the elements. In the most basic form o

Patrizio Bifulco, Delio Mugnolo

We study integral kernels of strongly continuous semigroups on Lebesgue spaces over metric measure spaces. Based on semigroup smoothing properties and abstract Morrey-type inequalities, we give sufficient conditions for H\"older or Lipschitz continuity of the kernels. We apply our results to (pseudo)differential operators on domains and quantum graphs, to La

Willy Hereman, Unal Goktas

A simplified version of Hirota's method for the computation of solitary waves and solitons of nonlinear PDEs is presented. A change of dependent variable transforms the PDE into an equation that is homogeneous of degree. Solitons are then computed using a perturbation-like scheme involving linear and nonlinear operators in a finite number of steps. The metho

Shuaifeng Li, Panayotis G. Kevrekidis, Jinkyu Yang

In this study, we present a method for generating a synthetic gauge field in origami metamaterials with continuously varying geometrical parameters. By modulating the mass term in the Dirac equation linearly, we create a synthetic gauge field in the vertical direction, which allows for the quantization of Landau levels through the generated pseudomagnetic fi

Xiaojian Du, Stephan Ochsenfeld, Sören Schlichting

We present a simple approach to extract hydrodynamic sound modes and non-hydrodynamic modes in kinetic theories from response functions of the energy-momentum tensor. By comparing the response functions in four types of kinetic theories, namely the Relaxation-Time Approximation, scalar $\phi^4$ theory, SU(3) Yang-Mills theory and QCD kinetic theory, we find

HeuiChan Lim, Saumya Debray

Bug localization techniques for Just-in-Time (JIT) compilers are based on analyzing the execution behaviors of the target JIT compiler on a set of test programs generated for this purpose; characteristics of these test inputs can significantly impact the accuracy of bug localization. However, current approaches for automatic test program generation do not wo

T. Congy, G. A. El, G. Roberti, A. Tovbis

We use the spectral kinetic theory of soliton gas to investigate the likelihood of extreme events in integrable turbulence described by the one-dimensional focusing nonlinear Schr\"odinger equation (fNLSE). This is done by invoking a stochastic interpretation of the inverse scattering transform for fNLSE and analytically evaluating the kurtosis of the emergi

B. Blok, J. Mehl

We study the role of $1\rightarrow2$ and $1\rightarrow3$ processes in triple charmonium production. We see that the ratio of effective cross sections of TPS and DPS only moderately depends on charmonium transverse momenta, but the total DPS and TPS cross sections each separately may have rather strong dependence on charmonia transverse momenta in the central

Jinniao Qiu, Yang Yang

This paper is devoted to the stochastic optimal control problem of infinite-dimensional differential systems allowing for both path-dependence and measurable randomness. As opposed to the deterministic path-dependent cases studied by Bayraktar and Keller [J. Funct. Anal. 275 (2018), 2096--2161], the value function turns out to be a random field on the path s

Mustafa Yasir, John Palowitch, Anton Tsitsulin, Long Tran-Thanh

Despite a surge in interest in GNN development, homogeneity in benchmarking datasets still presents a fundamental issue to GNN research. GraphWorld is a recent solution which uses the Stochastic Block Model (SBM) to generate diverse populations of synthetic graphs for benchmarking any GNN task. Despite its success, the SBM imposed fundamental limitations on

Nosseiba Ben Salem, Younes Bennani, Joseph Karkazan, Abir Barbara

Deep learning-based applications have seen a lot of success in recent years. Text, audio, image, and video have all been explored with great success using deep learning approaches. The use of convolutional neural networks (CNN) in computer vision, in particular, has yielded reliable results. In order to achieve these results, a large amount of data is requir

James Holehouse, S. Redner

We investigate the first-passage properties of nearest-neighbor hopping on a finite interval with disordered hopping rates. We develop an approach that relies on the backward equation, in conjunction with probability generating functions, to obtain all moments, as well as the distribution of first-passage times. Our approach is simpler than previous approach

Eduardo Silva

We study random walks on the lampshuffler group $\mathrm{FSym}(H)\rtimes H$, where $H$ is a finitely generated group and $\mathrm{FSym}(H)$ is the group of finitary permutations of $H$. We show that for any step distribution $\mu$ with a finite first moment that induces a transient random walk on $H$, the permutation coordinate of the random walk almost sure

Ayan Chatterjee, Robin Walters, Giulia Menichetti, Tina Eliassi-Rad

Link prediction is a crucial task in graph machine learning with diverse applications. We explore the interplay between node attributes and graph topology and demonstrate that incorporating pre-trained node attributes improves the generalization power of link prediction models. Our proposed method, UPNA (Unsupervised Pre-training of Node Attributes), solves

Rebwar Khalid Hamad, Tarik A. Rashid

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, ne

Ted Selker

New systems employ Machine Learning to sift through large knowledge sources, creating flexible Large Language Models. These models discern context and predict sequential information in various communication forms. Generative AI, leveraging Transformers, generates textual or visual outputs mimicking human responses. It proposes one or multiple contextually fe

Ruida Zhou, Tao Liu, Min Cheng, Dileep Kalathil

We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testing environment. Previous policy-based robust RL algorithms mainly focus on the tabular setting under uncertainty sets that facilitate robust policy evaluation, but are no longer tr