October 2023 arXiv papers — page 99

Abdullah Abu Zaid, Baha Eddine Youcef Belmekki, Mohamed-Slim Alouini

In this paper, we propose the integration of tethered flying platforms in cooperative vehicular ad hoc networks (VANETs) to alleviate the problems of rapid urbanization. In this context, we study the performance of VANETs by deriving approximate outage probability and average achievable rate expressions using tools from stochastic geometry. We compare betwee

Barbara Jäger, Martin Vollmann

We present a calculation of the continuum part of the gamma-ray spectra resulting from Dark Matter annihilation in the framework of the MSSM taking into account Sommerfeld effects. Concentrating on pure wino and pure higgsino scenarios we compare our calculation to existing work and explore the numerical impact of the features not captured by previous approx

Michael Fucilla, Andrey Grabovsky, Emilie Li, Lech Szymanowski

We calculate the cross-sections of diffractive single hadron photo- or electroproduction with large $p_T$, on a nucleon or a nucleus in the shockwave formalism. We use the hybrid formalism mixing collinear factorization with high energy small-$x$ factorization with the impact factors computed at next-to-leading order accuracy. We prove the cancellation of di

Henry Lam, Zitong Wang

Stochastic gradient descent (SGD) or stochastic approximation has been widely used in model training and stochastic optimization. While there is a huge literature on analyzing its convergence, inference on the obtained solutions from SGD has only been recently studied, yet it is important due to the growing need for uncertainty quantification. We investigate

Nazmi Burak Budanur

I present a data-driven predictive modeling tool that is applicable to high-dimensional chaotic systems with unstable periodic orbits. The basic idea is using deep neural networks to learn coordinate transformations between the trajectories in the periodic orbits' neighborhoods and those of low-dimensional linear systems in a latent space. I argue that the r

Linsong Wei, Yuqi Xu, Weihua Yang, Yunxia Zhang

Among graphs with 13 edges, there are exactly three internally 4-connected graphs which are $Oct^{+}$, cube+e and $ K_{3,3} +v$. A complete characterization of all 4-connected graphs with no $Oct^{+}$-minor is given in [John Maharry, An excluded minor theorem for the octahedron plus an edge, Journal of Graph Theory 57(2) (2008) 124-130]. Let $K_{3,3}+v$ deno

Jesper Lykke Jacobsen, Rongvoram Nivesvivat, Hubert Saleur

Using methods from the conformal bootstrap, we study the properties of Noether currents in the critical $O(n)$ loop model. We confirm that they do not give rise to a Kac-Moody algebra (for $n\neq 2$), a result expected from the underlying lack of unitarity. By studying four-point functions in detail, we fully determine the current-current OPEs, and thus obta

Umme Hafsa Momy

In the world today, diabetic complications are a major factor in the disease's high mortality rate. Diabetes mellitus has been a major source of concern for decades due to its global prevalence and the resulting rising costs to individuals, governments, and healthcare systems on both a social and economic level. The complex interplay between nutrition, exerc

Huijun Shen, Guo Chen, Bojie Li, Xingtong Lin

Remote Direct Memory Access (RDMA) has been haunted by the need of pinning down memory regions. Pinning limits the memory utilization because it impedes on-demand paging and swapping. It also increases the initialization latency of large memory applications from seconds to minutes. To remove memory pining, existing approaches often require special hardware w

Junjie Wang, Yaoping Hou, Xueyi Huang

In this paper, we study the Tur\'{a}n problem for $C_{2k+1}^{-}$. Suppose that $\dot{G}$ is an unbalanced signed graph of order $n$ with $e(\dot{G})$ edges. Let $\lambda_{1} (\dot{G})$ be the largest eigenvalue of $\dot{G}$, and $C_{2k+1}^{-}$ be the set of the negative cycle with length $2k+1$($3 \le k \le \frac{n}{15}$). We prove that if $\dot{G}$ is a $C_

Zening Li, Rong-Hua Li, Meihao Liao, Fusheng Jin

Graph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks, serious privacy concerns arise when the graph data contains personal or sensitive information. To address this issue, we investigate and develop graph embedding algorithms that sat

Georges Gagneré, Cédric Plessiet

The meeting between the director and researcher Georges Gagner{\'e} and the digital artist and researcher C\'edric Plessiet at Paris 8 University through experiments crossing live performance and video game led to the development of tools from game techniques video and dedicated to theatrical experimentation on a mixed stage involving physical actors and ava

Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli

Machine learning algorithms are designed to capture complex relationships between features. In this context, the high dimensionality of data often results in poor model performance, with the risk of overfitting. Feature selection, the process of selecting a subset of relevant and non-redundant features, is, therefore, an essential step to mitigate these issu

Francisco A. Delesma, Moritz Leucke, Dorothea Golze, Patrick Rinke

Four-center two-electron Coulomb integrals routinely appear in electronic structure algorithms. The resolution-of-the-identity (RI) is a popular technique to reduce the computational cost for the numerical evaluation of these integrals in localized basis-sets codes. Recently, Duchemin and Blase proposed a separable RI scheme [J. Chem. Phys. 150, 174120 (2019

Wahei Hara, Yuki Hirano

Let $X$ be a generic quasi-symmetric representation of a connected reductive group $G$. The GIT quotient stack $\mathfrak{X}=[X^{\rm ss}(\ell)/G]$ with respect to a generic $\ell$ is a (stacky) crepant resolution of the affine quotient $X/G$, and it is derived equivalent to a noncommutative crepant resolution (=NCCR) of $X/G$. Halpern-Leistner and Sam showed

Xinyun Zhang, Weiwei Ye, Minbo Yang

In this paper, we are interested in some problems related to the following biharmonic hartree equation \begin{equation*} \Delta^{2} u=(|x|^{-\alpha}\ast |u|^{p})u^{p-1},\sp \text{in}\quad\R^N. \end{equation*} where $p=\frac{2N-\alpha}{N-4}$, $N\geq 9$ and $0<\alpha<N$. First, by using the spherical harmonic decomposition and the Funk-Heck formula of the sphe

Khoa Tran, Lam Pham, Vy-Rin Nguyen, Ho-Si-Hung Nguyen

Motor bearing fault detection (MBFD) is critical for maintaining the reliability and operational efficiency of industrial machinery. Early detection of bearing faults can prevent system failures, reduce operational downtime, and lower maintenance costs. In this paper, we propose a robust deep learning-based system for MBFD that incorporates multiple training

Di Zhang

In this work, we revisit the renormalization group equations (RGEs) of dimension-seven (dim-7) operators in the Standard Model effective field theory (SMEFT) resulting from mixing among dim-7 operators themselves by means of the background field method. Adopting a recently proposed physical basis for dim-7 operators, we achieve the explicit RGEs of all non-r

Víctor Jaramillo, Mariana Lira, Daniel Martínez-Carbajal, Darío Núñez

Solving the Einstein-Klein-Gordon-Maxwell system, we construct and analyze the properties of an electrically charged wormhole, formed from a complex, massive scalar field, with self-interaction, and endowed with an electric charge. The scalar field is minimally coupled to the gravitational and the Maxwell field. Covering regions of the value of the different

Shitong Duan, Xiaoyuan Yi, Peng Zhang, Tun Lu

Large Language Models (LLMs) have made unprecedented breakthroughs, yet their increasing integration into everyday life might raise societal risks due to generated unethical content. Despite extensive study on specific issues like bias, the intrinsic values of LLMs remain largely unexplored from a moral philosophy perspective. This work delves into ethical v

Akaki Tsunoda

The short message service (SMS) is a service for exchanging texts via mobile networks that has been developed not only as a means of text communication between subscribers but also as a means to remotely manage Internet of Things (IoT) devices. However, the originating number of an SMS can be spoofed. If IoT devices authenticate administrators based on the o

M. Singh, P. Saha, K. Kumar, D. Takhar

We report on the structural, electrical and magnetic measurements in as-grown polycrystalline samples of Pb10-xCux(PO4)6O. This compound has been recently reported to be a room temperature superconductor. Our as-grown specimen has excellent XRD matching with the original submission of Lee et al. This sample has 1.5% of Cu2S as an impurity phase. A resistive

Liping Zhang, Weitian Chen

Cardiac magnetic resonance imaging (CMR) has been widely used in clinical practice for the medical diagnosis of cardiac diseases. However, the long acquisition time hinders its development in real-time applications. Here, we propose a novel self-consistency guided multi-prior learning framework named $k$-$t$ CLAIR to exploit spatiotemporal correlations from

Shubham Kumar Nigam, Aniket Deroy, Noel Shallum, Ayush Kumar Mishra

This paper describes our submission to the SemEval-2023 for Task 6 on LegalEval: Understanding Legal Texts. Our submission concentrated on three subtasks: Legal Named Entity Recognition (L-NER) for Task-B, Legal Judgment Prediction (LJP) for Task-C1, and Court Judgment Prediction with Explanation (CJPE) for Task-C2. We conducted various experiments on these

Junkang Wu, Jiawei Chen, Jiancan Wu, Wentao Shi

This study reveals the inherent tolerance of contrastive learning (CL) towards sampling bias, wherein negative samples may encompass similar semantics (\eg labels). However, existing theories fall short in providing explanations for this phenomenon. We bridge this research gap by analyzing CL through the lens of distributionally robust optimization (DRO), yi

Yan Xiang, Zhen Zhang, Danni Chang, Lei Tu

Auditory-verbal training is essential for children with hearing challenges, and the gamification approach has become a promising direction for improving the rehabilitation experience and effect. However, the specific influence of the gamified training approach on participants at different rehabilitation stages has not been empirically studied. This paper is

Lin Wang, Wenqi Fan, Jiatong Li, Yao Ma

The rapid development of Internet technology has given rise to a vast amount of graph-structured data. Graph Neural Networks (GNNs), as an effective method for various graph mining tasks, incurs substantial computational resource costs when dealing with large-scale graph data. A data-centric manner solution is proposed to condense the large graph dataset int

Kendric Schefers

Let $\boldsymbol{Z}$ be a derived global complete intersection over $\mathbb{C}$. We compute the periodic cyclic homology of the category of ind-coherent sheaves with prescribed singular support on $\boldsymbol{Z}$ in terms of the microlocal homology, a family of chain theories living between cohomology and Borel-Moore homology. Our result is a microlocal ge

Haiquan Lu, Yong Zeng, Changsheng You, Yu Han

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising technology for the sixth-generation (6G) mobile communication networks. By significantly boosting the antenna number or size to at least an order of magnitude beyond current massive MIMO systems, XL-MIMO is expected to unprecedentedly enhance the spectral efficiency and spatial res

Daniel Romero, Tien Ngoc Ha, Peter Gerstoft

In a spoofing attack, an attacker impersonates a legitimate user to access or modify data belonging to the latter. Typical approaches for spoofing detection in the physical layer declare an attack when a change is observed in certain channel features, such as the received signal strength (RSS) measured by spatially distributed receivers. However, since chann

Sabah Al-Fedaghi

It has been stated that the notion of cause and effect is one object of study that sciences and engineering revolve around. Lately, in software engineering, diagrammatic causal inference methods (e.g., Pearl s model) have gained popularity (e.g., analyzing causes and effects of change in software requirement development). This paper concerns diagrammatical (

Tsutomu Kobayashi, Takashi Hiramatsu

Higher-order scalar-tensor theories having an instantaneous mode do not develop the Ostrogradsky instability even if a seemingly dangerous mode is present. Such theories satisfy only partially the degeneracy conditions that are usually imposed to remove the dangerous mode completely, and are dubbed as U-DHOST theories. We study weak gravitational fields sour

Yang Liu, Shi Gu

The registration of pathological images plays an important role in medical applications. Despite its significance, most researchers in this field primarily focus on the registration of normal tissue into normal tissue. The negative impact of focal tissue, such as the loss of spatial correspondence information and the abnormal distortion of tissue, are rarely

I. De Martino, R. Della Monica, D. Rubiera-Garcia

We consider the optical appearance under a thin accretion disk of a regular black hole with a central de Sitter core implementing $\mathcal{O}(l^2/r^2)$ far-corrections to the Schwarzschild black hole. We use the choice $l=0.25M$, which satisfies recently found constraints from the motion of the S2 star around Sgr A$^*$ in this model, and which leads to ther

Ge Yan, Lipeng Zhu, Rui Zhang

Intelligent reflecting surface (IRS) can bring significant performance enhancement for wireless communication systems by reconfiguring wireless channels via passive signal reflection. However, such performance improvement generally relies on the knowledge of channel state information (CSI) for IRS-associated links. Prior IRS channel estimation strategies mai

Jiayu Pan, Yin Sun, Ness B. Shroff

In this paper, we study a sampling problem where a source takes samples from a Wiener process and transmits them through a wireless channel to a remote estimator. Due to channel fading, interference, and potential collisions, the packet transmissions are unreliable and could take random time durations. Our objective is to devise an optimal causal sampling po

Raju Shrestha, Tien Ngoc Ha, Pham Q. Viet, Daniel Romero

Radio maps provide metrics such as the received signal strength at every location in a geographical region of interest. Extensive research has been carried out in this context, but it relies almost exclusively on synthetic-data experiments. Thus, the practical aspects of the radio map estimation (RME) problem as well as the performance of existing estimators

Mitsuki Morita, Masato Kikuchi, Tadachika Ozono

Although lyrics represent an essential component of music, few music information processing studies have been conducted on the characteristics of lyricists. Because these characteristics may be valuable for musical applications, such as recommendations, they warrant further study. We considered a potential method that extracts features representing the chara

Sayantan Choudhury, Kritartha Dey, Ahaskar Karde, Sudhakar Panda

We investigate the explicit role of negative local non-Gaussianity, $f_{\rm NL}$, in suppressing the abundance of primordial black holes (PBHs) in the single-field model of Galileon inflation. PBH formation requires significantly enhancing the scalar power spectrum, which greatly affects their abundance. The associated frequencies in the nHz regime are also

Viswanath KumarSkandPriya, Abdulhalim Dandoush, Gladys Diaz

Network slicing plays a crucial role in the progression of 5G and beyond, facilitating dedicated logical networks to meet diverse and specific service requirements. The principle of End-to-End (E2E) slice includes not only a service chain of physical or virtual functions for the radio and core of 5G/6G networks but also the full path to the application serve

Gyuseong Lee, Wooseok Jang, Jinhyeon Kim, Jaewoo Jung

Learning robust vision models that perform well in out-of-distribution (OOD) situations is an important task for model deployment in real-world settings. Despite extensive research in this field, many proposed methods have only shown minor performance improvements compared to the simplest empirical risk minimization (ERM) approach, which was evaluated on a b

Soma Heydari, Kayoomars Karami

Here, generation of PBHs and secondary GWs from non-canonical inflation with quartic potential have been probed. It is illustrated that, quartic potential in non-canonical setup with a generalized power-law Lagrangian density can source a consistent inflationary era with the latest observational data. Besides, we show that our model satisfies the swampland c

Ashley Fernandez, Swaraj Dube

This paper proposes MapGPT which is a novel approach that integrates the capabilities of language models, specifically large language models (LLMs), with spatial data processing techniques. This paper introduces MapGPT, which aims to bridge the gap between natural language understanding and spatial data analysis by highlighting the relevant core building blo

Rajarshi Saha, Varun Srivastava, Mert Pilanci

Matrices are exceptionally useful in various fields of study as they provide a convenient framework to organize and manipulate data in a structured manner. However, modern matrices can involve billions of elements, making their storage and processing quite demanding in terms of computational resources and memory usage. Although prohibitively large, such matr

Ankit Dulat, Amit D. Lad, C. Aparajit, Anandam Choudhary

Ultrahigh peak power femtosecond laser pulses create extreme states of matter that are currently being probed with great interest. Plasma optics have been proposed for shaping and amplifying high-power pulses, but they are subject to huge modulations and fluctuations due to the very nature of excitation at high intensities. Multidimensional characterization

Tomohito Kasahara, Daisuke Kawahara

Automatic evaluation of text generation is essential for improving the accuracy of generation tasks. In light of the current trend towards increasingly larger decoder-based language models, we investigate automatic evaluation methods based on such models for text generation. This paper compares various methods, including tuning with encoder-based models and

Jinsong Chen, Gaichao Li, John E. Hopcroft, Kun He

The emerging graph Transformers have achieved impressive performance for graph representation learning over graph neural networks (GNNs). In this work, we regard the self-attention mechanism, the core module of graph Transformers, as a two-step aggregation operation on a fully connected graph. Due to the property of generating positive attention values, the

Donghoon Jang

In dimension 4, we extend the correspondence between compact nonsingular toric varieties and regular fans to a correspondence between almost complex torus manifolds and families of multi-fans in a geometric way, where an (almost) complex torus manifold is a $2n$-dimensional compact connected (almost) complex manifold equipped with an effective action of a re

Chung-Han Hsieh, Xin-Yu Wang

This paper introduces a novel robust trading paradigm, called \textit{multi-double linear policies}, situated within a \textit{generalized} lattice market. Distinctively, our framework departs from most existing robust trading strategies, which are predominantly limited to single or paired assets and typically embed asset correlation within the trading strat

Yuxi Wei, Juntong Peng, Tong He, Chenxin Xu

To analyze multivariate time series, most previous methods assume regular subsampling of time series, where the interval between adjacent measurements and the number of samples remain unchanged. Practically, data collection systems could produce irregularly sampled time series due to sensor failures and interventions. However, existing methods designed for r

Kun Fang, Munan Zhang, Ruqi Shi, Yinan Li

Quantum computing has shown tremendous promise in addressing complex computational problems, yet its practical realization is hindered by the limited availability of qubits for computation. Recent advancements in quantum hardware have introduced mid-circuit measurements and resets, enabling the reuse of measured qubits and significantly reducing the qubit re

Shivam Saxena, Jayesh P. Pabari

The dichotomy referred to as a partition or separation of a whole into two parts and specifically, the dichotomy is very important feature of Mars between the Southern and Northern regions of Mars, and another thing that makes Mars very special that is the occurrence of Dust Devils on Mars. So, we studied and survey the dust devils occurrence on Mars in diff

Gayatri Das, S. Saha Ray

This article is concerned with an approximate analytical solution for the time fractional Kudryashov Sinelshchikov equation by using the reproducing kernel Hilbert space method. The main tools of this method are reproducing kernel theory, some important Hilbert spaces, the normal basis, orthogonalisation process, and homogenization. The effectiveness of repr

So Chigusa, Sudhakantha Girmohanta, Yuichiro Nakai, Yufei Zhang

Future muon colliders with center-of-mass energy of $\mathcal{O}(1-10)$ TeV can provide a clean high-energy environment with advantages in searches for TeV-scale axion-like particles (ALPs), pseudo-Nambu-Goldstone bosons associated with spontaneously broken global symmetries, which are widely predicted in physics beyond the Standard Model (SM). We exploit AL

Christopher Kiehl, Thanmay S. Menon, Dawson P. Hewatt, Svenja Knappe

We demonstrate how to measure in situ for heading errors of optically pumped magnetometers (OPMs) in the challenging parameter regime of compact vapor cells with imperfect optical pumping and high buffer gas pressure. For this, we utilize microwave-driven Ramsey and Rabi frequency spectroscopy (FS) to independently characterize scalar heading errors in free

Chong Zhang, Ya Guo, Yi Tu, Huan Chen

Recent advances in multimodal pre-trained models have significantly improved information extraction from visually-rich documents (VrDs), in which named entity recognition (NER) is treated as a sequence-labeling task of predicting the BIO entity tags for tokens, following the typical setting of NLP. However, BIO-tagging scheme relies on the correct order of m

Zichen Wang, Chuanhao Li, Chenyu Song, Lianghui Wang

We study the federated pure exploration problem of multi-armed bandits and linear bandits, where $M$ agents cooperatively identify the best arm via communicating with the central server. To enhance the robustness against latency and unavailability of agents that are common in practice, we propose the first federated asynchronous multi-armed bandit and linear

Mostafa Honari Latifpour, Byoung Jun Park, Yoshihisa Yamamoto, Myoung-Gyun Suh

The rapid advancements in machine learning across numerous industries have amplified the demand for extensive matrix-vector multiplication operations, thereby challenging the capacities of traditional von Neumann computing architectures. To address this, researchers are currently exploring alternatives such as in-memory computing systems to develop faster an

Guo Yao Tham, Ranjith Nair, Mile Gu

In covert target detection, Alice attempts to send optical or microwave probes to determine the presence or absence of a weakly-reflecting target embedded in thermal background radiation within a target region, while striving to remain undetected by an adversary, Willie, who is co-located with the target and collects all light that does not return to Alice.

Zu-Xing Yang, Xiao-Hua Fan, Zhi-Pan Li, Haozhao Liang

Through ensemble learning with multitasking and complex connection neural networks, we aggregated nuclear properties, including ground state charge radii, binding energies, and single-particle state information obtained from the Kohn-Sham auxiliary single-particle systems. Compared to traditional density functional theory, our model can more accurately chara

Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen

Deep generative models have shown tremendous capability in data density estimation and data generation from finite samples. While these models have shown impressive performance by learning correlations among features in the data, some fundamental shortcomings are their lack of explainability, tendency to induce spurious correlations, and poor out-of-distribu

Atsunori Ogawa, Takafumi Moriya, Naoyuki Kamo, Naohiro Tawara

We propose a new shallow fusion (SF) method to exploit an external backward language model (BLM) for end-to-end automatic speech recognition (ASR). The BLM has complementary characteristics with a forward language model (FLM), and the effectiveness of their combination has been confirmed by rescoring ASR hypotheses as post-processing. In the proposed SF, we

Harry Shomer, Yao Ma, Haitao Mao, Juanhui Li

Link prediction is a common task on graph-structured data that has seen applications in a variety of domains. Classically, hand-crafted heuristics were used for this task. Heuristic measures are chosen such that they correlate well with the underlying factors related to link formation. In recent years, a new class of methods has emerged that combines the adv

Teayong Lee, Myoungwon Jeon, Volker Bromm

Observing the first generation of stars, Population III (Pop III), is still a challenge even with the James Webb Space Telescope (JWST) due to their faintness. Instead, searching for fossil records of Pop III stars in nearby dwarf galaxies provides an alternative method for studying their physical properties. It is intriguing that a star recently discovered

Daniel Harsono, Feng Long, Paola Pinilla, Alessia A. Rota

While the most exciting explanation of the observed dust asymmetries in protoplanetary disks is the presence of protoplanets, other mechanisms can also form the dust features. This paper presents dual-wavelength Atacama Large Millimeter/submillimeter Array (ALMA) observations of a large asymmetric dusty ring around the M-type star CIDA 9A. We detect a dust a

Andrea Cappelletti

We explore the relationship between the category of MV-algebras and its full subcategories of perfect and semisimple algebras, showing that this pair of subcategories defines a pretorsion theory. We study the Galois structure associated with the reflection of semisimple MV-algebras, proving that it is admissible from the point of view of categorical Galois t

Seung-Hyun Nam, Vincent Y. F. Tan, Si-Hyeon Lee

We consider a private discrete distribution estimation problem with one-bit communication constraint. The privacy constraints are imposed with respect to the local differential privacy and the maximal leakage. The estimation error is quantified by the worst-case mean squared error. We completely characterize the first-order asymptotics of this privacy-utilit

Shahram Ghorbani, John H. L. Hansen

Accurately classifying accents and assessing accentedness in non-native speakers are both challenging tasks due to the complexity and diversity of accent and dialect variations. In this study, embeddings from advanced pre-trained language identification (LID) and speaker identification (SID) models are leveraged to improve the accuracy of accent classificati

Yingyi Ma, Zhe Liu, Ozlem Kalinli

Language models (LMs) have been commonly adopted to boost the performance of automatic speech recognition (ASR) particularly in domain adaptation tasks. Conventional way of LM training treats all the words in corpora equally, resulting in suboptimal improvements in ASR performance. In this work, we introduce a novel correction focused LM training approach wh

Tom Kimpson

RelativisticDynamics.jl is an open-source Julia package for relativistic spin-orbital dynamics in the gravitational strong-field of a Kerr spacetime. Existing codes for modelling the dynamics of spinning objects like pulsars in the strong-field regime are generally lacking, since such systems occupy an intermediate regime that is generally overlooked. At the

Anita B. Agarwal, Rohit Rajesh, Nitin Arul

Accurate and timely hyperlocal weather predictions are essential for various applications, ranging from agriculture to disaster management. In this paper, we propose a novel approach that combines hyperlocal weather prediction and anomaly detection using IoT sensor networks and advanced machine learning techniques. Our approach leverages data from multiple s

Rostand A. K. Fezeu, Jason Carpenter, Claudio Fiandrino, Eman Ramadan

Fifth Generation (5G) mobile networks mark a significant shift from previous generations of networks. By introducing a flexible design, 5G networks support highly diverse application requirements. Currently, the landscape of previous measurement studies does not shed light on 5G network configuration and the inherent implications to application performance.

P. Jamsheena, A. V. Chithra

Let $R$ be a commutative ring with unity. The essential ideal graph $\mathcal{E}_R$ of $R$ is a graph in which the vertex set comprises of set of all nonzero proper ideals of $R$ and two vertices $I$ and $K$ are adjacent if and only if $I+K$ is an essential ideal. In this paper, we discuss the structure of an induced subgraph of the essential ideal graph of

Xinyi Gao, Wentao Zhang, Junliang Yu, Yingxia Shao

Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse array of applications. However, the sheer size of large-scale graphs presents a significant challenge to real-time inference with GNNs. Although existing Scalable GNNs leverage linear propagation to preprocess the features and accelerate the training and inference procedure, these

Ziqing Zhu, Xiang Gao, Siqi Bu, Ka Wing Chan

The integration of individual microgrids (MGs) into Microgrid Clusters (MGCs) significantly improves the reliability and flexibility of energy supply, through resource sharing and ensuring backup during outages. The dispatch of MGCs is the key challenge to be tackled to ensure their secure and economic operation. Currently, there is a lack of optimization me

Yufan Huang, Mengnan Qi, Yongqiang Yao, Maoquan Wang

Software version migration and program translation are an important and costly part of the lifecycle of large codebases. Traditional machine translation relies on parallel corpora for supervised translation, which is not feasible for program translation due to a dearth of aligned data. Recent unsupervised neural machine translation techniques have overcome d

Fangwen Mu, Lin Shi, Song Wang, Zhuohao Yu

We introduce a novel framework named ClarifyGPT, which aims to enhance code generation by empowering LLMs with the ability to identify ambiguous requirements and ask targeted clarifying questions. In particular, ClarifyGPT first detects whether a given requirement is ambiguous by performing a code consistency check. If it is ambiguous, ClarifyGPT prompts an

Hao Liu, Michael Matthies, John Russo, Lorenzo Rovigatti

Sophisticated statistical mechanics approaches and human intuition have demonstrated the possibility to self-assemble complex lattices or finite size constructs, but have mostly only been successful in silico. The proposed strategies quite often fail in experiment due to unpredicted traps associated to kinetic slowing down (gelation, glass transition), as we

Tanner D. Harms, Steven L. Brunton, Beverley J. McKeon

Lagrangian Coherent Structures (LCS) are flow features which are defined to objectively characterize complex fluid behavior over a finite time regardless of the orientation of the observer. Fluidic applications of LCS include geophysical, aerodynamic, biological, and bio-inspired flows -- among others -- and can be generalized to broader classes of dynamical

Yao Yao, Qihang Lin, Tianbao Yang

This paper explores numerical methods for solving a convex differentiable semi-infinite program. We introduce a primal-dual gradient method which performs three updates iteratively: a momentum gradient ascend step to update the constraint parameters, a momentum gradient ascend step to update the dual variables, and a gradient descend step to update the prima

Wenzhe Liu, Wei Xiao, Meng Wang, Shan Yang

Audio coding is an essential module in the real-time communication system. Neural audio codecs can compress audio samples with a low bitrate due to the strong modeling and generative capabilities of deep neural networks. To address the poor high-frequency expression and high computational cost and storage consumption, we proposed an integrated framework that

P. Z. Zhao, Tianqi Chen, Sirui Liu, Jiangbin Gong

Dynamical decoupling represents an active approach towards the protection of quantum memories and quantum gates. Because dynamical decoupling operations can interfere with a system's own time evolution, the protection of quantum gates is more challenging than that of quantum states. In this work, we put forward a simple but general approach towards the reali

Leonardo A. Poveda, Juan Galvis, Eric Chung

This paper investigates an efficient exponential integrator generalized multiscale finite element method for solving a class of time-evolving partial differential equations in bounded domains. The proposed method first performs the spatial discretization of the model problem using constraint energy minimizing generalized multiscale finite element method (CEM

Huan Qing

The Grade of Membership (GoM) model, which allows subjects to belong to multiple latent classes, is a powerful tool for inferring latent classes in categorical data. However, its application is limited to categorical data with nonnegative integer responses, as it assumes that the response matrix is generated from Bernoulli or Binomial distributions, making i

Tianmu Zhu, Wei Xu

This chapter introduces the application of HCI design processes and design principles in e-government and e-democracy. We elaborate on HCI design processes and six HCI design principles in the context of e-government and e-democracy, including citizen-centered design, usability, accessibility, access to information, transaction efficiency, and security and p

Sean Kim, Eliot Yoo, Samuel Kim

Graduation and dropout rates have always been a serious consideration for educational institutions and students. High dropout rates negatively impact both the lives of individual students and institutions. To address this problem, this study examined university dropout prediction using academic, demographic, socioeconomic, and macroeconomic data types. Addit

Litong Xu, Tingting Xi

The temporal sidelobes of few-cycle pulses seriously restrict their applications in ultrafast science. We propose a unique mechanism that enables the generation of sub-two-cycle pulses with high temporal quality based on soliton self-cleaning in air-plasma channels. A robust spatiotemporal soliton could be formed from pulse self-compression by modulating the

Hiroki Kobayashi, Farzad Gholami, S. Macrae Montgomery, Masato Tanaka

Locomotive soft robots (SoRos) have gained prominence due to their adaptability. Traditional locomotive SoRo design is based on limb structures inspired by biological organisms and requires human intervention. Evolutionary robotics, designed using evolutionary algorithms (EAs), have shown potential for automatic design. However, EA-based methods face the cha

Huan Qing

The latent class model has been proposed as a powerful tool for cluster analysis of categorical data in various fields such as social, psychological, behavioral, and biological sciences. However, one important limitation of the latent class model is that it is only suitable for data with binary responses, making it fail to model real-world data with continuo

Philip Easo, Tom Hutchcroft

We prove Schramm's locality conjecture for Bernoulli bond percolation on transitive graphs: If $(G_n)_{n\geq 1}$ is a sequence of infinite vertex-transitive graphs converging locally to a vertex-transitive graph $G$ and $p_c(G_n) \neq 1$ for every $n \geq 1$ then $\lim_{n\to\infty} p_c(G_n)=p_c(G)$. Equivalently, the critical probability $p_c$ defines a cont

Sepehr Samavi, James R. Han, Florian Shkurti, Angela P. Schoellig

Robots need to predict and react to human motions to navigate through a crowd without collisions. Many existing methods decouple prediction from planning, which does not account for the interaction between robot and human motions and can lead to the robot getting stuck. We propose SICNav, a Model Predictive Control (MPC) method that jointly solves for robot

Bin Wang, Zhengyuan Liu, Nancy F. Chen

Conventional dialogue summarization methods directly generate summaries and do not consider user's specific interests. This poses challenges in cases where the users are more focused on particular topics or aspects. With the advancement of instruction-finetuned language models, we introduce instruction-tuning to dialogues to expand the capability set of dial

Varghese Kurian, Sridharakumar Narasimhan

In potential flow networks, the equilibrium flow rates are usually not proportional to the demands and flow control elements are required to regulate the flow. The control elements can broadly be classified into two types - discrete and continuous. Discrete control elements can have only two operational states: fully open or fully closed. On the other hand,

Jiajun Yan

Non-compact hyperk\"ahler spaces arise frequently in gauge theory. The 4-dimensional hyperk\"ahler ALE spaces are a special class of non-compact hyperk\"ahler spaces. They are in one-to-one correspondence with the finite subgroups of SU(2) and have interesting connections with representation theory and singularity theory, captured by the McKay Correspondence

Bohyun Kim, Hangjie Ji, Andrea L. Bertozzi, Abolfazl Sadeghpour

When a thin liquid film flows down on a vertical fiber, one can observe the complex and captivating interfacial dynamics of an unsteady flow. Such dynamics are applicable in various fluid experiments due to their high surface area-to-volume ratio. Recent studies verified that when the flow undergoes regime transitions, the magnitude of the film thickness cha

Hristo G. Chipilski

The majority of data assimilation (DA) methods in the geosciences are based on Gaussian assumptions. While these assumptions facilitate efficient algorithms, they cause analysis biases and subsequent forecast degradations. Non-parametric, particle-based DA algorithms have superior accuracy, but their application to high-dimensional models still poses operati

Haowei Wang, Jiayi Ji, Tianyu Guo, Yilong Yang

Panoptic Narrative Detection (PND) and Segmentation (PNS) are two challenging tasks that involve identifying and locating multiple targets in an image according to a long narrative description. In this paper, we propose a unified and effective framework called NICE that can jointly learn these two panoptic narrative recognition tasks. Existing visual groundi

Mikio Takezawa, Ryota Suzuki, Junichi Takahashi, Kaito Shimizu

Rare-earth (RE) atoms in solid-state materials are attractive components for photonic quantum information systems because of their coherence properties even in high-temperature environments. We have experimentally performed the single-site optical spectroscopy and optical addressing of a single RE atom in an amorphous silica optical fiber at room temperature

Kai Shao, Hao Geng, Erfu Liu, Jose L. Lado

A valley filter capable of generating a valley-polarized current is a crucial element in valleytronics, yet its implementation remains challenging. Here, we propose a valley filter made of a graphene bilayer which exhibits a 1D moir\'{e} pattern in the overlapping region of the two layers controlled by heterostrain. In the presence of a lattice modulation be

Yanghai Yu, Fang Liu

It is shown in \cite[J. Differ. Equ., (2022)]{22jde} that given initial data $u_0\in B^{s}_{p,r}$ and for some $T>0$, the solutions of the parabolic-type Keller-Segel equations converge strongly in $L^\infty_TB^{s}_{p,r}$ to the hyperbolic Keller-Segel equations as the diffusivity parameter $\epsilon$ tends to zero. In this paper, we furthermore prove this s