Reviewerly/exHarmony huggingface dataset:
SSANet: normal-mode interference spectrum extraction via SSA algorithm-unrolled neural network

Authors:
raw_author_name: Wei Gao
raw_affiliation_string: College of Marine Technology, Ocean University of China, Qingdao, China
raw_author_name: Xiaolei Li
raw_affiliation_string: College of Marine Technology, Ocean University of China, Qingdao, China

Abstract: In ocean acoustic fields, extracting the normal-mode interference spectrum (NMIS) from the received sound intensity spectrum (SIS) plays an important role in waveguide-invariant estimation and underwater source ranging. However, the received SIS often has a low signal-to-noise ratio (SNR) owing to ocean ambient noise and the limitations of the received equipment. This can lead to significant performance degradation for the traditional methods of extracting NMIS at low SNR conditions. To address this issue, a new deep neural network model called SSANet is proposed to obtain NMIS based on unrolling the traditional singular spectrum analysis (SSA) algorithm. First, the steps of embedding and singular value decomposition (SVD) in SSA is achieved by the convolutional network. Second, the grouping step of the SSA is simulated using the matrix multiply weight layer, ReLU layer, point multiply weight layer and matrix multiply weight layer. Third, the diagonal averaging step was implemented using a fully connected network. Simulation results in canonical ocean waveguide environments demonstrate that SSANet outperforms other traditional methods such as Fourier transform (FT), multiple signal classification (MUSIC), and SSA in terms of root mean square error, mean absolute error, and extraction performance.
Novel iterative cubature Kalman filters under maximum correntropy criterion for the robust state estimation

Authors:
raw_author_name: Weidong Zhou
raw_affiliation_string: Integrated Navigation Technology Laboratory, Institute of Marine Navigation Technology, School of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China

Abstract: Abstract The cubature Kalman filter (CKF) based on maximum correntropy criterion (MCC) is robust under non‐Gaussian noises, but it may encounter numerical problems when there are large outliers. First, based on the MCC and weighted least square (WLS) methods, a new cost function is introduced to calculate the state and covariance updates, which can better solve numerical problems caused by large outliers. Then, a nonlinear measurement function is used, and the measurement information is updated through the latest iterative values, which can obtain more accurate results under various non‐Gaussian noises. In addition, the Gauss–Newton, Levenberg–Marquardt, and Quasi–Newton methods are used, and three iterative algorithms named GN‐IMCC‐CKF, LM‐IMCC‐CKF, and QN‐IMCC‐CKF are respectively derived for the updating steps. Compared with the existing algorithms, the simulation results of two classical models show that the proposed algorithms have better numerical stability and estimation accuracy under non‐Gaussian systems.
The Bitcoin price and Bitcoin price uncertainty: Evidence of Bitcoin price volatility

Authors:
raw_author_name: Nezir Köse
raw_affiliation_string: Department of Economics Beykent University Sarıyer Istanbul Türkiye
raw_author_name: Emre Ünal
raw_affiliation_string: Department of Economics Firat University  Merkez Elazig Türkiye; Urban Institute and Department of Civil Engineering Kyushu University Fukuoka Japan
raw_author_name: Boqiang Lin
raw_affiliation_string: School of Management China Institute for Studies in Energy Policy, Xiamen University Xiamen Fujian China

Abstract: Abstract This study examines the Bitcoin price by taking into account global factors, including the Chicago Board Options Exchange's Market Volatility Index (VIX), the US dollar index, the gold price, the oil price, and Bitcoin price volatility. The analysis is conducted using the structural vector autoregression (SVAR) model. The variance decomposition findings revealed that the influence of the VIX on the Bitcoin price was initially restricted, but progressively intensified over time. Among the indicators, Bitcoin price volatility had the highest explanatory share in both daily and weekly data analysis. The impulse response functions demonstrated a statistically significant inverse relationship between the VIX and the Bitcoin price. Furthermore, the analysis revealed that the Bitcoin price was mostly impacted by its own volatility. This implies that investing in Bitcoin requires a certain level of risk‐taking.
Measuring the similarity between shapes of buildings using graph edit distance

Authors:
raw_author_name: Houpu Li
raw_affiliation_string: Control Engineering Laboratory, Naval University of Engineering, Wuhan, People’s Republic of China
raw_author_name: Yongyang Xu
raw_affiliation_string: National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, People’s Republic of China; School of Computer Science, China University of Geosciences, Wuhan, People’s Republic of China
raw_author_name: Jiangwen Liu
raw_affiliation_string: School of Geography and Information Engineering, China University of Geosciences, Wuhan, People’s Republic of China
raw_author_name: Zhanlong Chen
raw_affiliation_string: Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences (Wuhan), Wuhan, People’s Republic of China; National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, People’s Republic of China; School of Computer Science, China University of Geosciences, Wuhan, People’s Republic of China

Abstract: Building data play a crucial role in geographic information science, and building shape similarity can be used for spatial inquiry, cartographic generalization, map updating, data quality assessment, and other spatial data mining applications. However, conventional shape representation and measurement methods use geometric and statistical measures and may not effectively capture the subtle differences among building shapes. Geospatial artificial intelligence methods often overlook nuanced differences in building contours, posing a challenge when precise fine-grained measurements are required. Furthermore, they have low interpretability. This study proposes a graph edit distance-based method for measuring the similarity between building shapes. It uses geometrics characteristics to construct a graph, matches and associates similar regional features between two building contours, and defines edit costs based on geometric and positional differences that align with the Gestalt principle of visual similarity. It converts the optimal edit path problem to the maximum weighted cluster solution problem and utilizes a heuristic approach to achieve the exact optimal solution within a moderately suitable timeframe. The experimental results demonstrated that the proposed method identifies detailed differences between building contours, providing accurate similarity measurement with high interpretability.
EPolar‐UNet: An edge‐attending polar UNet for automatic medical image segmentation with small datasets

Authors:
raw_author_name: Yuling Wang
raw_affiliation_string: Department of Interventional Ultrasound The Fifth Medical Center of Chinese PLA General Hospital Beijing China
raw_author_name: Jie Yu
raw_affiliation_string: Department of Interventional Ultrasound The Fifth Medical Center of Chinese PLA General Hospital Beijing China
raw_author_name: Lei Xu
raw_affiliation_string: Zhejiang Qiushi Institute for Mathematical Medicine Hangzhou China
raw_author_name: Xiaoqian Zhang
raw_affiliation_string: School of Mathematical Sciences Zhejiang University Hangzhou China
raw_author_name: Ping Liang
raw_affiliation_string: Department of Interventional Ultrasound The Fifth Medical Center of Chinese PLA General Hospital Beijing China
raw_author_name: Dexing Kong
raw_affiliation_string: School of Mathematical Sciences Zhejiang University Hangzhou China

Abstract: Medical image segmentation is one of the most key steps in computer-aided clinical diagnosis, geometric characterization, measurement, image registration, and so forth. Convolutional neural networks especially UNet and its variants have been successfully used in many medical image segmentation tasks. However, the results are limited by the deficiency in extracting high resolution edge information because of the design of the skip connections in UNet and the need for large available datasets.In this paper, we proposed an edge-attending polar UNet (EPolar-UNet), which was trained on the polar coordinate system instead of classic Cartesian coordinate system with an edge-attending construction in skip connection path.EPolar-UNet extracted the location information from an eight-stacked hourglass network as the pole for polar transformation and extracted the boundary cues from an edge-attending UNet, which consisted of a deconvolution layer and a subtraction operation.We evaluated the performance of EPolar-UNet across three imaging modalities for different segmentation tasks: CVC-ClinicDB dataset for polyp, ISIC-2018 dataset for skin lesion, and our private ultrasound dataset for liver tumor segmentation. Our proposed model outperformed state-of-the-art models on all three datasets and needed only 30%-60% of training data compared with the benchmark UNet model to achieve similar performances for medical image segmentation tasks.We proposed an end-to-end EPolar-UNet for automatic medical image segmentation and showed good performance on small datasets, which was critical in the field of medical image segmentation.
Deep Cryogenic Temperature CMOS Circuit and System Design for Quantum Computing Applications

Authors:
raw_author_name: Jency Rubia J
raw_affiliation_string: Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
raw_author_name: Sherin Shibi C
raw_affiliation_string: SRM Institute of Science and Technology
raw_author_name: Rosi A
raw_affiliation_string: Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
raw_author_name: Ezhil E Nithila
raw_affiliation_string: Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

Abstract: Quantum computing is a fascinating and rapidly evolving field of technology that promises to revolutionize many areas of science, engineering, and society. The fundamental unit of quantum computing is the quantum bit that can exist in two or more states concurrently, as opposed to a classical bit that can only be either 0 or 1. Any subatomic element, including atoms, electrons, and photons, can be used to implement qubits. The chosen sub-atomic elements should have quantum mechanical properties. Most commonly, photons have been used to implement qubits. Qubits can be manipulated and read by applying external fields or pulses, such as lasers, magnets, or microwaves. Quantum computers are currently suffering from various complications such as size, operating temperature, coherence problems, entanglement, etc. The realization of quantum computing, a novel paradigm that uses quantum mechanical phenomena to do computations that are not possible with classical computers, is made possible, most crucially, by the need for a quantum processor and a quantum SOC. As a result, Cryo-CMOS technology can make it possible to integrate a Quantum system on a chip. Cryo-CMOS devices are electronic circuits that operate at cryogenic temperatures, usually below 77 K (−196 °C).
Exploring the application of dual‐energy <scp>CT</scp> to discriminate sediment facies in a varved sequence

Authors:
raw_author_name: P. Francus
raw_affiliation_string: Centre Eau Terre Environnement Institut National de la Recherche Scientifique  Québec Canada; GEOTOP—Research Centre on the Dynamics of the Earth System  Québec Canada

Abstract: Abstract Dual‐energy X‐ray computed tomography consists of imaging objects using two incident X‐ray beams of different energy to distinguish the different compounds within a sample based on their density (electron density, ρ e ) and elemental composition (effective atomic number, Z eff ). The stoichiometric calibration for dual‐energy X‐ray computed tomography was already successfully implemented to identify single and homogeneous minerals easily and non‐destructively. It is here applied for the first time to a more complex and heterogeneous sample, a varved sediment core with three distinct facies. The output of dual‐energy X‐ray computed tomography was compared against elemental geochemistry obtained at the same resolution using a micro‐XRF core scanner. The three individual facies can be successfully differentiated using dual‐energy X‐ray computed tomography because their range of ρ e and Z eff values allow their discrimination. Correlations with elemental geochemistry are also discussed but are less conclusive, probably because of variations in grain size and porosity, and because these high resolution analyses were not performed at the exact same location. The paper not only eventually discusses the limitations when using dual‐energy X‐ray computed tomography on sediments but also demonstrates its potential to quantitatively study sediment cores in a non‐destructive way.
Overcoming Diverse Undesired Effects in Recommender Systems: A Deontological Approach

Authors:
raw_author_name: Paula G. Duran
raw_affiliation_string: Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
raw_author_name: Pere Gilabert
raw_affiliation_string: Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
raw_author_name: Santi Seguí
raw_affiliation_string: Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
raw_author_name: Jordi Vitrià
raw_affiliation_string: Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain

Abstract: In today’s digital landscape, recommender systems have gained ubiquity as a means of directing users towards personalized products, services, and content. However, despite their widespread adoption and a long track of research, these systems are not immune to shortcomings. A significant challenge faced by recommender systems is the presence of biases, which produces various undesirable effects, prominently the popularity bias. This bias hampers the diversity of recommended items, thus restricting users’ exposure to less popular or niche content. Furthermore, this issue is compounded when multiple stakeholders are considered, requiring the balance of multiple, potentially conflicting objectives. In this paper, we present a new approach to address a wide range of undesired consequences in recommender systems that involve various stakeholders. Instead of adopting a consequentialist perspective that aims to mitigate the repercussions of a recommendation policy, we propose a deontological approach centered around a minimal set of ethical principles. More precisely, we introduce two distinct principles aimed at avoiding overconfidence in predictions and accurately modeling the genuine interests of users. The proposed approach circumvents the need for defining a multi-objective system, which has been identified as one of the main limitations when developing complex recommenders. Through extensive experimentation, we show the efficacy of our approach in mitigating the adverse impact of the recommender from both user and item perspectives, ultimately enhancing various beyond accuracy metrics. This study underscores the significance of responsible and equitable recommendations and proposes a strategy that can be easily deployed in real-world scenarios.
Generative machine learning for detector response modeling with a conditional normalizing flow

Authors:
raw_author_name: Shuo Han
raw_affiliation_string: Physics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, U.S.A.
raw_author_name: Xiangyang Ju
raw_affiliation_string: Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, U.S.A.

Abstract: Abstract In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the Large Hadron Collider (LHC) experiments. Our objective is to develop a generative model capable of efficiently simulating detector responses for specific particle observables, focusing on the correlations between detector responses of different particles in the same event and accommodating asymmetric detector responses. We present a conditional normalizing flow model (𝒞𝒩ℱ) based on a chain of Masked Autoregressive Flows, which effectively incorporates conditional variables and models high-dimensional density distributions. We assess the performance of the 𝒞𝒩ℱ model using a simulated sample of Higgs boson decaying to diphoton events at the LHC. We create reconstruction-level observables using a smearing technique. We show that conditional normalizing flows can accurately model complex detector responses and their correlation. This method can potentially reduce the computational burden associated with generating large numbers of simulated events while ensuring that the generated events meet the requirements for data analyses. We make our code available at https://github.com/allixu/normalizing_flow_for_detector_response .
Distributed computing with the cloud

Authors:
raw_author_name: Yehuda Afek
raw_affiliation_string: School of CS, Tel Aviv University, 6997801, Tel Aviv, Israel
raw_author_name: Gal Giladi
raw_affiliation_string: School of CS, Tel Aviv University, 6997801, Tel Aviv, Israel
raw_author_name: Boaz Patt-Shamir
raw_affiliation_string: School of EE, Tel Aviv University, 6997801, Tel Aviv, Israel

Abstract: Abstract We investigate the effect of omnipresent cloud storage on distributed computing. To this end, we specify a network model with links of prescribed bandwidth that connect standard processing nodes, and, in addition, passive storage nodes. Each passive node represents a cloud storage system, such as Dropbox, Google Drive etc. We study a few tasks in this model, assuming a single cloud node connected to all other nodes, which are connected to each other arbitrarily. We give implementations for basic tasks of collaboratively writing to and reading from the cloud, and for more advanced applications such as matrix multiplication and federated learning. Our results show that utilizing node-cloud links as well as node-node links can considerably speed up computations, compared to the case where processors communicate either only through the cloud or only through the network links. We first show how to optimally read and write large files to and from the cloud in general graphs using flow techniques. We use these primitives to derive algorithms for combining , where every processor node has an input value and the task is to compute a combined value under some given associative operator. In the special but common case of “fat links,” where we assume that links between processors are bidirectional and have high bandwidth, we provide near-optimal algorithms for any commutative combining operator (such as vector addition). For the task of matrix multiplication (or other non-commutative combining operators), where the inputs are ordered, we present tight results in the simple “wheel” network, where procesing nodes are arranged in a ring, and are all connected to a single cloud node.
Full-function Pavlov associative learning photonic neural networks based on SOA and DFB-SA

Authors:
raw_author_name: Dianzhuang Zheng
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University 1 , Xian 710071, China
raw_author_name: Shuiying Xiang
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University 1 , Xian 710071, China
raw_author_name: Xingxing Guo
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University 1 , Xian 710071, China
raw_author_name: Yahui Zhang
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University 1 , Xian 710071, China
raw_author_name: Xiaojun Zhu
raw_affiliation_string: School of Information Science and Technology, Nantong University 3 , Nantong, Jiangsu 226019, China .
raw_author_name: Yuechun Shi
raw_affiliation_string: Yongjiang Laboratory 4 , No. 1792 Cihai South Road, Ningbo 315202, China
raw_author_name: Xiangfei Chen
raw_affiliation_string: Key Laboratory of Intelligent Optical Sensing and Manipulation, Ministry of Education, The National Laboratory of Solid State Micro Structures, The College of Engineering and Applied Sciences, Institute of Optical Communication Engineering, Nanjing University 5 , Nanjing 210023, China
raw_author_name: Yue Hao
raw_affiliation_string: State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University 2 , Xian 710071, China

Abstract: Pavlovian associative learning, a form of classical conditioning, has significantly impacted the development of psychology and neuroscience. However, the realization of a prototypical photonic neural network (PNN) for full-function Pavlov associative learning, encompassing both photonic synapses and photonic neurons, has not been achieved to date. In this study, we propose and experimentally demonstrate the first InP-based full-function Pavlov associative learning PNN. The PNN utilizes semiconductor optical amplifiers (SOAs) as photonic synapses and the distributed feedback laser with a saturable absorber (DFB-SA) as the photonic spiking neuron. The connection weights between neurons in the PNN can be dynamically changed based on the fast, time-varying weighting properties of the SOA. The optical output of the SOA can be directly coupled into the DFB-SA laser for nonlinear computation without additional photoelectric conversion. The results indicate that the PNN can successfully perform brain-like computing functions such as associative learning, forgetting, and pattern recall. Furthermore, we analyze the performance of PNN in terms of speed, energy consumption, bandwidth, and cascadability. A computational model of the PNN is derived based on the distributed time-domain coupled traveling wave equations. The numerical results agree well with the experimental findings. The proposed full-function Pavlovian associative learning PNN is expected to play an important role in the development of the field of photonic brain-like neuromorphic computing.