March 2023 arXiv papers — page 108

Rikio Ichishima, Francesc A. Muntaner-Batle

For all positive even integers $n$, graphs of order $n$ with degree sequence \begin{equation*} S_{n}:1,2,\dots,n/2,n/2,n/2+1,n/2+2,\dots,n-1 \end{equation*} naturally arose in the study of a labeling problem in \cite{IMO}. This fact motivated the authors of the aforementioned paper to study these sequences and as a result of this study they proved that there

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using initial-state radiation events from a total integrated luminosity of 11.957 fb$^{-1}$ of $e^+e^-$ collision data collected at center-of-mass energies between 3.773 and 4.258 GeV with the BESIII detector at BEPCII, the cross section for the process $e^{+}e^{-}\rightarrow\Lambda\bar{\Lambda}$ is measured in 16 $\Lambda\bar{\Lambda}$ invariant mass interv

Jihye Hwang, Kate Pattle, Harriet Parsons, Mallory Go

We present the first polarized dust emission measurements of the Horsehead Nebula, obtained using the POL-2 polarimeter on the Submillimetre Common-User Bolometer Array 2 (SCUBA-2) camera on the James Clerk Maxwell Telescope (JCMT). The Horsehead Nebula contains two sub-millimeter sources, a photodissociation region (PDR; SMM1) and a starless core (SMM2). We

Anirban Bhattacharjee, Sushant Vijayan, Sandeep K Juneja

We consider the best arm identification problem in the stochastic multi-armed bandit framework where each arm has a tiny probability of realizing large rewards while with overwhelming probability the reward is zero. A key application of this framework is in online advertising where click rates of advertisements could be a fraction of a single percent and fin

Xiaoyu Liu, Hanlin Lu, Jianbo Yuan, Xinyu Li

The attention-based Transformers have been increasingly applied to audio classification because of their global receptive field and ability to handle long-term dependency. However, the existing frameworks which are mainly extended from the Vision Transformers are not perfectly compatible with audio signals. In this paper, we introduce a Causal Audio Transfor

Xinran Liu, Xiaoqiong Liu, Ziruo Yi, Xin Zhou

Planar object tracking is a critical computer vision problem and has drawn increasing interest owing to its key roles in robotics, augmented reality, etc. Despite rapid progress, its further development, especially in the deep learning era, is largely hindered due to the lack of large-scale challenging benchmarks. Addressing this, we introduce PlanarTrack, a

Yifan Peng, Jaesong Lee, Shinji Watanabe

Transformer-based end-to-end speech recognition has achieved great success. However, the large footprint and computational overhead make it difficult to deploy these models in some real-world applications. Model compression techniques can reduce the model size and speed up inference, but the compressed model has a fixed architecture which might be suboptimal

Chaoxing Huang, Yurui Qian, Jian Hou, Baiyan Jiang

Multi-parametric mapping of MRI relaxations in liver has the potential of revealing pathological information of the liver. A self-supervised learning based multi-parametric mapping method is proposed to map T$T_{1\rho}$ and T$_2$ simultaneously, by utilising the relaxation constraint in the learning process. Data noise of different mapping tasks is utilised

Souradip Chakraborty, Kasun Weerakoon, Prithvi Poddar, Mohamed Elnoor

Reinforcement learning-based policies for continuous control robotic navigation tasks often fail to adapt to changes in the environment during real-time deployment, which may result in catastrophic failures. To address this limitation, we propose a novel approach called RE-MOVE (REquest help and MOVE on) to adapt already trained policy to real-time changes i

Mingshuai Liu, Shubo Lv, Zihan Zhang, Runduo Han

In ICASSP 2023 speech signal improvement challenge, we developed a dual-stage neural model which improves speech signal quality induced by different distortions in a stage-wise divide-and-conquer fashion. Specifically, in the first stage, the speech improvement network focuses on recovering the missing components of the spectrum, while in the second stage, o

Samuel Marks

Let $L/\mathbb{Q}_p$ be a finite extension. We introduce $L$-typical prisms, a mild generalization of prisms. Following ideas of Bhatt, Scholze, and Wu, we show that certain vector bundles, called Laurent $F$-crystals, on the $L$-typical prismatic site of a formal scheme $X$ over $\mathrm{Spf}\mathcal{O}_L$ are equivalent to $\mathcal{O}_L$-linear local syst

Chenze Dong, Khee-Gan Lee, Metin Ata, Benjamin Horowitz

We report a $z=2.30$ galaxy protocluster (COSTCO-I) in the COSMOS field, where the Lyman-$\alpha$ forest as seen in the CLAMATO IGM tomography survey does not show significant absorption. This departs from the transmission-density relationship (often dubbed the fluctuating Gunn-Peterson approximation; FGPA) usually expected to hold at this epoch, which would

Zhihao Chen, Yang Zhou, Anh Tran, Junting Zhao

Medical phrase grounding (MPG) aims to locate the most relevant region in a medical image, given a phrase query describing certain medical findings, which is an important task for medical image analysis and radiological diagnosis. However, existing visual grounding methods rely on general visual features for identifying objects in natural images and are not

Huanqing Wang, Kaixiang Zhang, Keyi Zhu, Ziyou Song

With the rapid surge in the number of on-road Electric Vehicles (EVs), the amount of spent lithium-ion (Li-ion) batteries is also expected to explosively grow. The spent battery packs contain valuable metal and materials that should be recovered, recycled, and reused. However, only less than 5% of the Li-ion batteries are currently recycled, due to a multitu

Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun

Knowledge plays a critical role in artificial intelligence. Recently, the extensive success of pre-trained language models (PLMs) has raised significant attention about how knowledge can be acquired, maintained, updated and used by language models. Despite the enormous amount of related studies, there still lacks a unified view of how knowledge circulates wi

Jaspreet Ranjit, Tianlu Wang, Baishakhi Ray, Vicente Ordonez

We introduce a framework to measure how biases change before and after fine-tuning a large scale visual recognition model for a downstream task. Deep learning models trained on increasing amounts of data are known to encode societal biases. Many computer vision systems today rely on models typically pretrained on large scale datasets. While bias mitigation t

Sai Wang, Yi Gong

Complex-variable matrix optimization problems (CMOPs) in Frobenius norm emerge in many areas of applied mathematics and engineering applications. In this letter, we focus on solving CMOPs by iterative methods. For unconstrained CMOPs, we prove that the gradient descent (GD) method is feasible in the complex domain. Further, in view of reducing the computatio

Stephan Baier, Sean B. Lynch

We improve the large sieve inequality with $k$th-power moduli, for all $k\ge 4$. Our method relates these inequalities to a restricted variant of Waring's problem. Firstly, we input a classical divisor bound on the number of representations of a positive integer as a sum of two $k$th-powers. Secondly, we input a recent and general result of Wooley on mean va

Geoffrey Bryden, Neal J. Turner, Petr Pokorny, Youngmin Seo

The solar system's Zodiacal Cloud is visible to the unaided eye, yet the origin of its constituent dust particles is not well understood, with a wide range of proposed divisions between sources in the asteroid belt and Jupiter Family comets. The amount of dust contributed by Oort Cloud comets is uncertain. Knowledge of the Zodiacal Cloud's structure and orig

Dibyendu Sardar, John L. Bohn

A six-dimensional potential energy surface is constructed for the spin-polarized triplet state of CaF-CaF by \textit{ab initio} calculations at the CCSD(T) level of theory, followed by Gaussian process interpolation. The potential is utilized to calculate the density of states for this bi alkaline-earth-halogen system where we find the value 0.038 $\mu$K$^{-

Yunjie Ji, Yan Gong, Yiping Peng, Chao Ni

As a natural language assistant, ChatGPT is capable of performing various tasks, including but not limited to article generation, code completion, and data analysis. Furthermore, ChatGPT has consistently demonstrated a remarkable level of accuracy and reliability in terms of content evaluation, exhibiting the capability of mimicking human preferences. To fur

Haibo Shen, Juyu Xiao, Yihao Luo, Xiang Cao

Neuromorphic vision sensors (event cameras) simulate biological visual perception systems and have the advantages of high temporal resolution, less data redundancy, low power consumption, and large dynamic range. Since both events and spikes are modeled from neural signals, event cameras are inherently suitable for spiking neural networks (SNNs), which are c

Tina Behnia, Ganesh Ramachandra Kini, Vala Vakilian, Christos Thrampoulidis

Various logit-adjusted parameterizations of the cross-entropy (CE) loss have been proposed as alternatives to weighted CE for training large models on label-imbalanced data far beyond the zero train error regime. The driving force behind those designs has been the theory of implicit bias, which for linear(ized) models, explains why they successfully induce b

Haonan Hu, Dazhong Rong, Jianhai Chen, Qinming He

The cold-start problem is quite challenging for existing recommendation models. Specifically, for the new items with only a few interactions, their ID embeddings are trained inadequately, leading to poor recommendation performance. Some recent studies introduce meta learning to solve the cold-start problem by generating meta embeddings for new items as their

Zhaoshuai Qi, Xiaojun Liu, Xiaolin Liu, Jiaqi Yang

As the gold standard for phase retrieval, phase-shifting algorithm (PS) has been widely used in optical interferometry, fringe projection profilometry, etc. However, capturing multiple fringe patterns in PS limits the algorithm to only a narrow range of application. To this end, a deep learning (DL) model based digital PS algorithm from only a single fringe

Zhipeng Luo, Gongjie Zhang, Changqing Zhou, Zhonghua Wu

The task of 3D single object tracking (SOT) with LiDAR point clouds is crucial for various applications, such as autonomous driving and robotics. However, existing approaches have primarily relied on appearance matching or motion modeling within only two successive frames, thereby overlooking the long-range continuous motion property of objects in 3D space.

Teppei Minoda, Shohei Saga, Tomo Takahashi, Hiroyuki Tashiro

In the most distant reaches of the Universe, the 21-cm hyperfine transition in neutral hydrogen provides one of the only available tracers of large-scale structure. A number of instruments have been working and planned to measure the 21-cm line signals, and in particular, Experiment to Detect the Global EoR Signature (EDGES) recently has reported the first d

Nabeel Gillani, Doug Beeferman, Christine Vega-Pourheydarian, Cassandra Overney

Most US school districts draw "attendance boundaries" to define catchment areas that assign students to schools near their homes, often recapitulating neighborhood demographic segregation in schools. Focusing on elementary schools, we ask: how much might we reduce school segregation by redrawing attendance boundaries? Combining parent preference data with me

Miku Yoshida, Md. Riad Kasem, Aichi Yamashita, Ken-ichi Uchida

Recently, thermal switching has been extensively studied because it is a key component for thermal management in electronic devices. Here, we show a huge magneto-thermal-switching ratio (MTSR) in pure Nb at temperatures lower than its superconducting transition temperature (Tc = 9.2 K). The MTSR increases with decreasing temperature, and MTSR of 650% was obs

Runsheng Xu, Xin Xia, Jinlong Li, Hanzhao Li

Modern perception systems of autonomous vehicles are known to be sensitive to occlusions and lack the capability of long perceiving range. It has been one of the key bottlenecks that prevents Level 5 autonomy. Recent research has demonstrated that the Vehicle-to-Vehicle (V2V) cooperative perception system has great potential to revolutionize the autonomous d

Kaiqi Zhao, Animesh Jain, Ming Zhao

Pruning is a promising approach to compress deep learning models in order to deploy them on resource-constrained edge devices. However, many existing pruning solutions are based on unstructured pruning, which yields models that cannot efficiently run on commodity hardware; and they often require users to manually explore and tune the pruning process, which i

Leila Ismail, Huned Materwala, Alain Hennebelle

COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set

Kaiqi Zhao, Yitao Chen, Ming Zhao

Knowledge Transfer (KT) achieves competitive performance and is widely used for image classification tasks in model compression and transfer learning. Existing KT works transfer the information from a large model ("teacher") to train a small model ("student") by minimizing the difference of their conditionally independent output distributions. However, these

Xiao Wang, Ying Wang, Ziwei Xuan, Guo-Jun Qi

Unsupervised learning of vision transformers seeks to pretrain an encoder via pretext tasks without labels. Among them is the Masked Image Modeling (MIM) aligned with pretraining of language transformers by predicting masked patches as a pretext task. A criterion in unsupervised pretraining is the pretext task needs to be sufficiently hard to prevent the tra

Yasutoshi Ida, Sekitoshi Kanai, Kazuki Adachi, Atsutoshi Kumagai

Regularized discrete optimal transport (OT) is a powerful tool to measure the distance between two discrete distributions that have been constructed from data samples on two different domains. While it has a wide range of applications in machine learning, in some cases the sampled data from only one of the domains will have class labels such as unsupervised

Yi Zhang, Xiaoyang Huang, Bingbing Ni, Teng Li

We develop an effective point cloud rendering pipeline for novel view synthesis, which enables high fidelity local detail reconstruction, real-time rendering and user-friendly editing. In the heart of our pipeline is an adaptive frequency modulation module called Adaptive Frequency Net (AFNet), which utilizes a hypernetwork to learn the local texture frequen

Lishuang Zhan, Yancheng Cao, Qitai Chen, Haole Guo

Touch is an important channel for human-robot interaction, while it is challenging for robots to recognize human touch accurately and make appropriate responses. In this paper, we design and implement a set of large-format distributed flexible pressure sensors on a robot dog to enable natural human-robot tactile interaction. Through a heuristic study, we sor

Hayato Shimabukuro, Kenji Hasegawa, Akira Kuchinomachi, Hidenobu Yajima

The dark age of the universe, when no luminous object had existed, ended with the birth of the first stars, galaxies, and blackholes. This epoch is called cosmic dawn. Cosmic reionization is the major transition of the intergalactic medium (IGM) in the universe driven by ionizing photons emitted from luminous objects. Although the epoch through the dark age

Sicong Cao, Xiaobing Sun, Xiaoxue Wu, Lili Bo

Java (de)serialization is prone to causing security-critical vulnerabilities that attackers can invoke existing methods (gadgets) on the application's classpath to construct a gadget chain to perform malicious behaviors. Several techniques have been proposed to statically identify suspicious gadget chains and dynamically generate injection objects for fuzzin

Hyungjun Lim, Younggwan Kim, Kiho Yeom, Eunjoo Seo

Self-supervised learning method that provides generalized speech representations has recently received increasing attention. Wav2vec 2.0 is the most famous example, showing remarkable performance in numerous downstream speech processing tasks. Despite its success, it is challenging to use it directly for wake-up word detection on mobile devices due to its ex

Jeffrey S. Ovall, Samuel E. Reynolds

Recent advancements in finite element methods allows for the implementation of mesh cells with curved edges. In the present work, we develop the tools necessary to employ multiply connected mesh cells, i.e. cells with holes, in planar domains. Our focus is efficient evaluation the $H^1$ semi-inner product and $L^2$ inner product of implicitly-defined finite

Dominik R. G. Schleicher, Bastián Reinoso, Ralf S. Klessen

Numerical simulations have shown the occurence of a scenario termed ''super-competitive accretion'', a term that describes a situation where only the central few objects grow supermassive while a larger number of stars compete for the reservoir, with significant accretion flows of $\gtrsim0.1$ M$_\odot$ yr$^{-1}$. This scenario particularly implies that the

Youxi Wu, Shuhui Cheng, Yan Li, Rongjie Lv

The three-way decisions strategy has been employed to construct network topology in a single hidden layer feedforward neural network (SFNN). However, this model has a general performance, and does not consider the process costs, since it has fixed threshold parameters. Inspired by the sequential three-way decisions (STWD), this paper proposes STWD with an SF

Zi-Xu Yang, Liang Zhang, S. N. Zhang, M. Méndez

Fast transitions between different types of power density spectra (PDS) happening over timescales of several tens of seconds are rare phenomena in black hole X-ray binaries. In this paper, we report a broadband spectral-timing analysis of the fast transitions observed in the 2021 outburst of GX 339-4 using NICER and HXMT observations. We observe transitions

Hui Li, Jinghan Jia, Shijun Liang, Yuguang Yao

Although deep learning (DL) has gained much popularity for accelerated magnetic resonance imaging (MRI), recent studies have shown that DL-based MRI reconstruction models could be oversensitive to tiny input perturbations (that are called 'adversarial perturbations'), which cause unstable, low-quality reconstructed images. This raises the question of how to

Shoyu Nagaoka, Manabu Oura

We express the weight enumerators of self-dual and doubly even (Type II for short) codes of length $24$ with a specified basis. As a consequence, we present some congruence relations among the weight enumerators.

Steven Shaw, Kanishka Tyagi, Shan Zhang

Many radar signal processing methodologies are being developed for critical road safety perception tasks. Unfortunately, these signal processing algorithms are often poorly suited to run on embedded hardware accelerators used in automobiles. Conversely, end-to-end machine learning (ML) approaches better exploit the performance gains brought by specialized ac

Neşet Özkan Tan, Alex Yuxuan Peng, Joshua Bensemann, Qiming Bao

Identifying words that impact a task's performance more than others is a challenge in natural language processing. Transformers models have recently addressed this issue by incorporating an attention mechanism that assigns greater attention (i.e., relevance) scores to some words than others. Because of the attention mechanism's high computational cost, trans

Rima Kaafarani, Leila Ismail, Oussama Zahwe

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to developers and business owners requirements. Therefore, several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred block

Sergio Giardino

We present two novel solutions of real Hilbert state quaternionic quantum mechanics ($\mathbb H$QM). Firstly, we observe that the angular momentum operator admits two different classes of physically non-equivalent free particles. As a second result, we study the Larmor precession to observe that it has a quaternionic solution where a novel phenomenological i

Haohan Wang, Liang Liu, Boshen Zhang, Jiangning Zhang

Fully supervised object detection requires training images in which all instances are annotated. This is actually impractical due to the high labor and time costs and the unavoidable missing annotations. As a result, the incomplete annotation in each image could provide misleading supervision and harm the training. Recent works on sparsely annotated object d

Anh Phuong Ngo, Christian Thomas, Ali Karimoddini, Hieu T. Nguyen

The trajectory planning problem for a swarm of multiple UAVs is known as a challenging nonconvex optimization problem, particularly due to a large number of collision avoidance constraints required for individual pairs of UAVs in the swarm. In this paper, we tackle this nonconvexity by leveraging the difference of convex function (DC) programming. We introdu

Wenqi Guo, Jeffrey Uhlmann

As online dating has become more popular in the past few years, an efficient and effective algorithm to match users is needed. In this project, we proposed a new dating matching algorithm that uses Kendall-Tau distance to measure the similarity between users based on their ranking for items in a list. (e.g., their favourite sports, music, etc.) To increase t

Yuma Torikoshi, Yasuharu Nishi, Juichi Takahashi

Deep Learning (DL) is one of the most popular research topics in machine learning and DL-driven image recognition systems have developed rapidly. Recent research has employed metamorphic testing (MT) to detect misclassified images. Most of them discuss metamorphic relations (MR), with limited attention given to which regions should be transformed. We focus o

Tomoki Kimura, Yuka Fujii, Hajime Kita, Fuminori Tsuchiya

Bodies such as planets, moons, and asteroids in our solar system are the brightest objects in the low-frequency radio astronomy at $\lesssim$ 10 GHz. The low-frequency radio emissions from our solar system bodies exhibit various observed characteristics in the spectrum, polarization, periodicity, and flux. The observed characteristics are essential probes fo

Rohan Badlani, Akshit Arora, Subhankar Ghosh, Rafael Valle

We introduce VANI, a very lightweight multi-lingual accent controllable speech synthesis system. Our model builds upon disentanglement strategies proposed in RADMMM and supports explicit control of accent, language, speaker and fine-grained $F_0$ and energy features for speech synthesis. We utilize the Indic languages dataset, released for LIMMITS 2023 as pa

Koji Mori, Takeshi G. Tsuru, Kazuhiro Nakazawa, Yoshihiro Ueda

In this multi-messenger astronomy era, all the observational probes are improving their sensitivities and overall performance. The Focusing on Relativistic universe and Cosmic Evolution (FORCE) mission, the product of a JAXA/NASA collaboration, will reach a 10 times higher sensitivity in the hard X-ray band ($E >$ 10~keV) in comparison with any previous hard

Yuansong Zhu, Yu Zhao

Diffusion models have become a powerful family of deep generative models, with record-breaking performance in many applications. This paper first gives an overview and derivation of the basic theory of diffusion models, then reviews the research results of diffusion models in the field of natural language processing, from text generation, text-driven image g

Koji Mori, Hiroshi Tomida, Hiroshi Nakajima, Takashi Okajima

Xtend is a soft X-ray imaging telescope developed for the X-Ray Imaging and Spectroscopy Mission (XRISM). XRISM is scheduled to be launched in the Japanese fiscal year 2022. Xtend consists of the Soft X-ray Imager (SXI), an X-ray CCD camera, and the X-ray Mirror Assembly (XMA), a thin-foil-nested conically approximated Wolter-I optics. The SXI uses the P-cha

Liping Li

It is well known that a regular diffusion on an interval $I$ without killing inside is uniquely determined by a canonical scale function $s$ and a canonical speed measure $m$. Note that $s$ is a strictly increasing and continuous function and $m$ is a fully supported Radon measure on $I$. In this paper we will associate a general triple $(I,s,m)$, where $s$

Rong Cong

The combined effect of electronic correlation and strong spin-orbit-coupling(SOC) can give rise to a variety of exotic quantum phases. Double perovskites provide a simple structure to study the spin-orbit-lattice entangled states. In this thesis, focusing on the 5d osmate double perovskite system, we conduct a combination of work including theoretical model

Miao Ye, Linqiang Huang, Xiaofang Deng, Yong Wang

Message transmission and message synchronization for multicontroller interdomain routing in software-defined networking (SDN) have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawback

Liping Li

A quasidiffusion is by definition a time-changed Brownian motion on certain closed subset of $\mathbb{R}$. The aim of this paper is two-fold. On one hand, we will put forward a generation of quasidiffusion, called skip-free Hunt process, by way of a pathwise setup. As an analogue of regular diffusion on an interval, a skip-free Hunt process also admits a so-

Zifeng Zhao, Feiyu Jiang, Yi Yu, Xi Chen

We consider a high-dimensional dynamic pricing problem under non-stationarity, where a firm sells products to $T$ sequentially arriving consumers that behave according to an unknown demand model with potential changes at unknown times. The demand model is assumed to be a high-dimensional generalized linear model (GLM), allowing for a feature vector in $\math

Julian Neri, Sebastian Braun

Real-time single-channel speech separation aims to unmix an audio stream captured from a single microphone that contains multiple people talking at once, environmental noise, and reverberation into multiple de-reverberated and noise-free speech tracks, each track containing only one talker. While large state-of-the-art DNNs can achieve excellent separation f

Shanbing Li, Mingxin Wang

This paper is concerned with positive solutions of boundary value problems \begin{equation*} \left\{\begin{array}{ll} {\rm div}\left(d(v)\nabla u-u\chi(v)\nabla v\right)+\lambda u-u^2 +\gamma u F(v)=0, & x \in \Omega,\\[1mm] D \Delta v+\mu v-v^2-u F(v)=0, & x \in \Omega,\\[1mm] u=v=0, & x \in \partial \Omega. \end{array}\right. \end{equation*} This is the st

Liping Li, Jiangang Ying

Quasidiffusions are, by definition, time-changed Brownian motions on certain closed subset of $\mathbb{R}$. They admit an explicit representation of Dirichlet forms in terms of so-called speed measures. The Fukushima subspace of a Dirichlet form means another regular Dichichlet form on the same state space but having a smaller Dirichlet space. In this paper

Yijun Chen, Guodong Shi

In this paper, we study network games where players are involved in information aggregation processes subject to the differential privacy requirement for players' payoff functions. We propose a Laplace linear-quadratic functional perturbation mechanism, which perturbs players' payoff functions with linear-quadratic functions whose coefficients are produced f

Mengyan Zeng, Yao Huang, Baolin Zhang, Yanmei Hao

We present a compact, long-term nearly continuous operation of a room-temperature Ca$^+$ optical clock setup towards a transportable clock, achieving an overall systematic uncertainty of $4.8\times 10^{-18}$ and an uptime rate of 97.8% over an 8-day period. The active liquid-cooling scheme is adopted, combined with the precise temperature measurement with 13

Hector Henriquez, Diana Fuentes, Francisco Suarez, Patricio Gonzalez

Lung cancer is the leading cause of cancer death in the world. Accurate determination of the EGFR (epidermal growth factor receptor) mutation status is highly relevant for the proper treatment of this patients. Purpose: The aim of this study was to predict the mutational status of the EGFR in non-small cell lung cancer patients using radiomics features extra

Yong Huang, Qinfeng Li, Qiuqi Li

In the present paper, we study the boundary concentration breaking phenomena on two thermal insulation problems considered on Lipschitz domains, based on Serrin's overdetermined results, perturbation argument and comparison of Laplacian eigenvalues with different boundary conditions. Since neither of the functionals in the two problems is $C^1$, another key

Hanyu Zhou, Yi Chang, Wending Yan, Luxin Yan

Optical flow has achieved great success under clean scenes, but suffers from restricted performance under foggy scenes. To bridge the clean-to-foggy domain gap, the existing methods typically adopt the domain adaptation to transfer the motion knowledge from clean to synthetic foggy domain. However, these methods unexpectedly neglect the synthetic-to-real dom

Grace J. Li, Jiajie Luo, Mason A. Porter

People's opinions change with time as they interact with each other. In a bounded-confidence model (BCM) of opinion dynamics, individuals (which are represented by the nodes of a network) have continuous-valued opinions and are influenced by neighboring nodes whose opinions are sufficiently similar to theirs (i.e., are within a confidence bound). In this pap

Keer Yang, Guanqun Zhang, Chuan Bi, Qiang Guan

In recent years, there have been quite a few attempts to apply intelligent techniques to financial trading, i.e., constructing automatic and intelligent trading framework based on historical stock price. Due to the unpredictable, uncertainty and volatile nature of financial market, researchers have also resorted to deep learning to construct the intelligent

Jai-chan Hwang, Hyerim Noh

Defining the electric and magnetic field vectors in curved spacetime requires a proper choice of the observer's frame four-vector. Related literature shows that this fundamental issue in physics still needs to be properly resolved. In recent literature on using electromagnetic means to detect gravitational waves, an ad hoc definition based on regarding $F_{a

Gamaliel Tellez-Sanchez, Juan Bory Reyes

In this paper we provided a classification for partitions of intervals on the hyperbolic plane. Given a partition, to be named strong, we define a notion of a hyperbolic-valued functions of bounded variation and a kind of Riemann-Stieltjes integral. A condition relating to both concepts appears to be natural for the existence of the integral, as it occurs in

Kostas Alexandridis

We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processin

Sijie Chen, Min Zhuang, Ruihuang Fang, Yun Chen

Quantum lock-in amplifier aims to extract an alternating signal within strong noise background by using quantum strategy. However, as the target signal usually has an unknown initial phase, we can't obtain the complete information of its amplitude, frequency and phase in a single lock-in measurement. Here, to overcome this challenge, we give a general protoc

Yuqi Zhou, Kaarthik Sundar, Deepjyoti Deka, Hao Zhu

Wildfires pose a significant threat to the safe and reliable operations of the electric grid. To mitigate wildfire risk, system operators resort to public safety power shutoffs, or PSPS, that shed load for a subset of customers. As wildfire risk forecasts are stochastic, such decision-making may often be sub-optimal. This paper proposes a two-stage topology

Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Nathalie Japkowicz

Anomaly detection is of paramount importance in many real-world domains, characterized by evolving behavior. Lifelong learning represents an emerging trend, answering the need for machine learning models that continuously adapt to new challenges in dynamic environments while retaining past knowledge. However, limited efforts are dedicated to building foundat

Michael V. Klibanov, Jingzhi Li, Hongyu Liu

The second order Mean Field Games system (MFGS) in a bounded domain with the lateral Cauchy data is considered. This means that both Dirichlet and Neumann boundary data for the solution the MFGS are given. Two H\"older stability estimates for two slightly diffeent cases are derived. These estimates indicate how stable the solution of the MFGS is with respect

Peng Gao, Qingzhao Zhu, Hongsheng Lu, Chuang Gan

Correspondence identification (CoID) is an essential component for collaborative perception in multi-robot systems, such as connected autonomous vehicles. The goal of CoID is to identify the correspondence of objects observed by multiple robots in their own field of view in order for robots to consistently refer to the same objects. CoID is challenging due t

Chow-Choong Ngeow, Anupam Bhardwaj, Matthew J. Graham, Brian F. Healy

SX Phoenicis (SXP) variables are short period pulsating stars that exhibit a period-luminosity (PL) relation. We derived the gri-band PL and extinction-free period-Wesenheit (PW) relations, as well as the period-color (PC) and reddening-free period-Q-index (PQ) relations for 47 SXP stars in located in 21 globular clusters using the optical light curves taken

David BÉkollÈ, Edgar-Landry Tchoundja, Arsene-Brice Zotsa-Ngoufack

In this work, we extend the theory of B\'ekoll\`e-Bonami $B_p$ weights. Here we replace the constant $p$ by a non-negative measurable function $p(\cdot),$ which is log-H\"older continuous function with lower bound $1$. We show that the Bergman projector on the unit ball of $\mathbb C^n$ is continuous on the weighted variable Lebesgue spaces $L^{p(\cdot)}(w)$

Kazumi Okuyama, Kenta Suzuki

In this paper, we study one-loop contributions in the double-scaling limit of the SYK model from the chord diagrams and Liouville type effective action. We compute and clarify the meaning of each component consisting of the one-loop corrections for the two- and time-ordered four-point functions of light operators. We also reproduce the exact expression of th

Daniel Lawson, Ahmed H. Qureshi

Recent work has shown the promise of creating generalist, transformer-based, models for language, vision, and sequential decision-making problems. To create such models, we generally require centralized training objectives, data, and compute. It is of interest if we can more flexibly create generalist policies by merging together multiple, task-specific, ind

Chen Sun, Shuo Wang

Spectrum sharing has long been considered as method to improve spectrum resource utilization. Centralized geolocation database approach has been accepted globally for commercial applications. Recently blockchain has been considered as a platform to support spectrum sharing in a distributed manner. Like other commodities, spectrum or right of spectrum usage c

Adam B. Watts, Luca Cortese, Barbara Catinella, Toby Brown

The quenching of cluster satellite galaxies is inextricably linked to the suppression of their cold interstellar medium (ISM) by environmental mechanisms. While the removal of neutral atomic hydrogen (HI) at large radii is well studied, how the environment impacts the remaining gas in the centres of galaxies, which are dominated by molecular gas, is less cle

Rodrigo P. Silva, Lara Sousa, Ivan Yu. Rybak

We study the impact of kinks on the cosmic microwave background (CMB) anisotropies generated by cosmic string networks. To do so, we extend the Unconnected Segment Model to describe the stress-energy tensor of a network of cosmic strings with kinks and implement this extension in CMBACT to compute the CMB anisotropies generated by these wiggly string network

Tae Eun Choe, Jane Wu, Xiaolin Lin, Karen Kwon

We present an algorithm to detect unseen road debris using a small set of synthetic models. Early detection of road debris is critical for safe autonomous or assisted driving, yet the development of a robust road debris detection model has not been widely discussed. There are two main challenges to building a road debris detector: first, data collection of r

João Vitorino, Tiago Dias, Tiago Fonseca, Eva Maia

It is imperative to safeguard computer applications and information systems against the growing number of cyber-attacks. Automated software testing tools can be developed to quickly analyze many lines of code and detect vulnerabilities by generating function-specific testing data. This process draws similarities to the constrained adversarial examples genera

Shih-Han Chou, James J. Little, Leonid Sigal

Existing dense or paragraph video captioning approaches rely on holistic representations of videos, possibly coupled with learned object/action representations, to condition hierarchical language decoders. However, they fundamentally lack the commonsense knowledge of the world required to reason about progression of events, causality, and even the function o

Zixuan Li, Jingtao Shi

This paper is devoted to a Stackelberg stochastic differential game for a linear mean-field type stochastic differential system with a mean-field type quadratic cost functional in finite horizon. The coefficients in the state equation and weighting matrices in the cost functional are all deterministic. Closed-loop Stackelberg equilibrium strategies are intro

Yiye Chen, Yunzhi Lin, Ruinian Xu, Patricio A. Vela

Deep neural networks are susceptible to generating overconfident yet erroneous predictions when presented with data beyond known concepts. This challenge underscores the importance of detecting out-of-distribution (OOD) samples in the open world. In this work, we propose a novel feature-space OOD detection score based on class-specific and class-agnostic inf

Anh Tran, Hojun Lim

Crystal plasticity finite element model (CPFEM) is a powerful numerical simulation in the integrated computational materials engineering (ICME) toolboxes that relates microstructures to homogenized materials properties and establishes the structure-property linkages in computational materials science. However, to establish the predictive capability, one need

Mina Rezaei, Patsy Eubanks Owens

From the urbanists' perspective, the everyday experience of young people, as an underrepresented group in the design of public spaces, includes tactics they use to challenge the strategies which rule over urban spaces. In this regard, youth led social movements are a set of collective tactics which groups of young people use to resist power structures. Socia

Prasun C. Tripathi, Mohammod N. I. Suvon, Lawrence Schobs, Shuo Zhou

Heart failure is a serious and life-threatening condition that can lead to elevated pressure in the left ventricle. Pulmonary Arterial Wedge Pressure (PAWP) is an important surrogate marker indicating high pressure in the left ventricle. PAWP is determined by Right Heart Catheterization (RHC) but it is an invasive procedure. A non-invasive method is useful i

Ping Chen, Xingpeng Zhang, Ye Li, Ju Tao

Naked eye recognition of age is usually based on comparison with the age of others. However, this idea is ignored by computer tasks because it is difficult to obtain representative contrast images of each age. Inspired by the transfer learning, we designed the Delta Age AdaIN (DAA) operation to obtain the feature difference with each age, which obtains the s

Valerie Berthé, Karma Dajani, Charlene Kalle, Ela Krawczyk

We first survey the current state of the art concerning the dynamical properties of multidimensional continued fraction algorithms defined dynamically as piecewise fractional maps and compare them with algorithms based on lattice reduction. We discuss their convergence properties and the quality of the rational approximation, and stress the interest for thes

Javier Díaz, Hiroyasu Ando, GoEun Han, Olga Malyshevskaya

Traditionally, the neuronal dynamics underlying electroencephalograms (EEG) have been understood as arising from \textit{rhythmic oscillators with varying degrees of synchronization}. This dominant metaphor employs frequency domain EEG analysis to identify the most prominent populations of neuronal current sources in terms of their frequency and spectral pow