Reviewerly/exHarmony huggingface dataset:
Dynamics and quantum correlations in two independently driven Rydberg atoms with distinct laser fields

We study the population dynamics in a two-atom setup in which each atom is driven independently by different light fields, but coupling the same Rydberg state. In particular, we look at how an offset in the Rabi frequencies between two atoms influences the dynamics. We find novel features such as amplifying the Rabi frequency of one atom, together with strong Rydberg-Rydberg interactions freezes the dynamics in the second atom. We characterize the Rydberg-biased freezing phenomenon in detail, with effective Hamiltonians obtained for various limits of the system parameters. In the absence of Rabi-offset, the doubly excited state population exhibits a Lorentzian profile as a function of interaction, whereas for very small offsets it shows splitting and thus peaks. Using an effective Hamiltonian as well as the perturbation theory for weak interactions, we show that the peak arises from a competition between Rabi-offset and Rydberg-Rydberg interactions when both are sufficiently small, together with the Rydberg blockade at large interactions. The effective Hamiltonians provide us with analytical results which are in an excellent agreement with full numerical solutions. Also, we analyze the growth and the dynamics of quantum correlations such as entanglement entropy and quantum discord for the coherent dynamics. We extend our studies to the dissipative case in which the spontaneous emission from the Rydberg state is taken into account and in particular, we look at the purity and quantum discord of the steady states. To conclude, our studies reveal that the local manipulation of an atom using Rabi-offset can be an ideal tool to control the quantum correlations and in general, quantum states of the composite two-qubit systems.
Cost Fairness for Blockchain-Based Two-Party Exchange Protocols

Blockchains can guarantee fairness during the exchange of digital goods such that in a two-party exchange no one is defrauded by a malicious opponent. While several notions of fairness have been discussed in the literature, they all ignore that damage cannot only be incurred by the malicious failure of the exchange, but also by an unfair allocation of transaction costs. To address this issue we: 1. define the novel concept of cost fairness, which 2. builds on the notion of maximum cost matrices that formalize transaction costs in different combinations of benevolent and malicious behavior. 3. We show how limited notions of cost fairness can be achieved by modifying an existing exchange protocol or using a container protocol. In particular, we also provide 4. a tool that let us predict the maximum cost matrix for a specific protocol execution and, thus, gives trade exchange parties the possibility to weigh not only the value of transaction of exchanged goods but also the associated transaction costs.
Conditioned measurement fused estimate Unscented Kalman filter for underwater target tracking using acoustic signals captured by Towed array

Abstract In the paper, an algorithm named Robust Unscented Kalman filter (R-UKF) is proposed to handle the popular ocean issue called underwater passive object/target tracking in a more efficient manner than the conventional algorithms. This R-UKF algorithm using acoustic signals supplied by a Towed array is developed when two novel Techniques named as Estimate Fusion (EF) Method and Measurement Conditioning (MC) Method are applied simultaneously to the existing Unscented kalman filter (UKF). Estimate Fusion method and Measurement Conditioning methods operate on the principles of weighted averaging of measurements in space and time respectively. EF Method contributed to the improvement by believing that the collective opinion about state estimation is much better than the individual opinions. This is accomplished by relying on Multiple sensors of TA and Multiple Intermediate estimators instead of single one. On the other hand MC method contributed to the improvements by application of the soothed measurements instead of traditional ones. The soothing is possible by weighted averaging of the current and track of past sensor data. Montecarlo simulations in Matlab(R2009a) shows that, R-UKF display better performance that its base algorithm UKF. Moreover, Optimal Performance (produce low estimation errors without demanding complex processors) of R-UKF makes it even more attractive.
Fips: An OpenGL based FITS viewer

FITS (Flexible Image Transport System) is a common format for astronomical data storage. It was first standardised in the early 1980s. Even though astronomical data is now processed mostly using software, visual data inspection by a human is still important during equipment or software commissioning and while observing. We present Fips, a cross-platform FITS file viewer open source software. To the best of our knowledge, it is for the first time that the image rendering algorithms are implemented mostly on GPU (graphics processing unit). We show that it is possible to implement a fully-capable FITS viewer using OpenGL interface. We also emphasise the advantages of using GPUs for efficient image handling.
A Privacy-protection Scheme for Smart Water Grid Based on Blockchain and Machine Learning

In Smart Water Grid (SWG), smart water meters (SWMs) are installed in customers ' homes in order to provide near-real-time water consumption data. However, near-real-time data collected by SWMs can reveal the user's privacy. In this paper, we propose a user privacy protection scheme that relies on Blockchain technology and a Machine Learning algorithm called k-means++. K-means++ is used to group users into clusters and, each cluster has a private Blockchain to record its members' data. We use pseudonyms to mask users' identities and the Bloom filter is used for quick authentication. The proposed scheme is validated using simulations in python.
Independent component analysis (ICA): A statistical approach to the analysis of superimposed rock paintings

Independent Component Analysis (ICA) is a statistical technique for decomposing information from datasets into maximally independent components. ICA allows the researcher to recover two or more independent signals that appear mixed within the same dataset. This paper shows ICA to be an extremely effective method for separating different colours found in rock paintings into discrete images or components. The comparison between the results of ICA and PCA (Principal Component Analysis) shows that ICA accurately separates panels with more than one type of colour, while PCA achieves a lower degree of separation. This study also shows that in scenes with monochrome depictions, ICA tends to be slightly more effective in separating the pigments from the rock. The ICA method has been applied successfully to several rock art panels from Northern Chile, where the use of diverse types of mineral pigments is common. Two analyses conducted at the Pampa El Muerto 11 site in the Northern Chilean highlands reveal how ICA can contribute to a more compelling interpretation of more intricate panels. The comparison between the results of ICA and PCA (Principal Components Analysis) shows that ICA correctly separates panels with more than one type of pigment, while PCA achieves a lower degree of separation. This study also shows that in scenes with monochrome depictions, ICA tends to be slightly more effective in separating the pigments from the rock. ICA algorithm has been successfully in several rock panels from Northern Chile, where the use of diverse types of mineral pigments is usual. Two panels from the Pampa El Muerto site have been analysed with the technique mentioned above, informing that its application can collaborate on a more compelling interpretation of intricate panels.
MVmed: Fast Multi-Object Tracking in the Compressed Domain

We present MVmed, an algorithm for real-time online tracking of people and objects in MPEG-4 and H.264 compressed videos and integrate it into a multi-purpose tracking software for manufacturing sites. To support arbitrary video sources with no prior setup our tracker needs to be compatible with a variety of video codecs and camera settings. Existing compressed domain trackers are limited in this regard. They require a fixed interval of key frames, use only P frames and usually support only a single codec. MVmed overcomes these limitations and supports both MPEG-4 and H.264 codecs, P and B frames and arbitrary key frame intervals. On the MOT17 benchmark MVmed achieves a MOTA of 45.3 % at 42.1 Hz (266.9 Hz without detection) which is as accurate but significantly faster than the previous state of the art in compressed domain tracking. With this work we release the source code of MVmed and a Python package for motion vector extraction from video.
In-Home Emergency Detection Using an Ambient Ultra-Wideband Radar Sensor and Deep Learning

Human behavior analysis in indoor environments from ambient sensor data is one of the most challenging human-machine interaction research topics. In this paper, we present a deep learning-based novel approach for human posture recognition and emergency detection using an ultra-wideband radar sensor. The strength of this sensor is that it collects less privacy-related information than a regular camera. The sensor is installed on a mobile robot to observe a subject from a short distance. At first, raw data is collected from the sensor. Then, the data is used to train a Recurrent Neural Network (RNN) for modeling of different human activities and conditions, including normal and emergency conditions. Finally, the trained model is used for testing the model on new inputs. The experiments were performed using two datasets recorded in lab environments, and the proposed approach produced better recognition performance than the conventional ones. The proposed method can be applied in many prominent research fields (e.g., human-robot interaction) for different practical applications such as mobile robots for eldercare.
BowTie - A Deep Learning Feedforward Neural Network for Sentiment Analysis

How to model and encode the semantics of human-written text and select the type of neural network to process it are not settled issues in sentiment analysis. Accuracy and transferability are critical issues in machine learning in general. These properties are closely related to the loss estimates for the trained model. I present a computationally-efficient and accurate feedforward neural network for sentiment prediction capable of maintaining low losses. When coupled with an effective semantics model of the text, it provides highly accurate models with low losses. Experimental results on representative benchmark datasets and comparisons to other methods (DISCLAIMER: This paper is not subject to copyright in the United States. Commercial products are identified in order to adequately specify certain procedures. In no case does such identification imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the identified products are necessarily the best available for the purpose.) show the advantages of the new approach.
QoS and power network stability aware simultaneous optimization of data center revenue and expenses

Data center profits are directly impacted by electric power consumption, which in turn has critical environmental implications. Geo distributed data centers maximize profits by optimizing resource allocation and leveraging diverse power tariffs available in a deregulated market. However, a power network state apathetic profit maximization can destabilize the grid and hamper a data center's sustainability goals. In this paper, we formulate the data center profit maximization as a constrained optimization problem. We then propose a multi-level algorithm where the lower level scheme is based on evolutionary optimization. The algorithm simultaneously optimizes revenue and expenses while preserving QoS and power network stability. The proposed approach considers real-time demand-based energy price variations to orchestrate request routing and resource allocation. The approach is suitable for heterogeneous and homogeneous data center architectures. The proposed scheme is also thermal-aware and considers both computing and cooling consumption. Simulation results show that the proposed technique is effective; it achieves a higher profit over a broad range of price variations and data center utilization levels.
The need for learning bioethics and law for biology students

Although the creation in 2018 of a pair of genome-edited HIV-resistant twins in China caused widespread condemnation, we question whether the law and ethics relevant to such cases are dealt with adequately in university programmes. Biology educators are entrusted with upholding the discipline's standards, but are they doing enough to guide students towards ethical and law-abiding practices? As it seems likely that events such as the recent COVID-19 outbreak will increasingly focus public attention on issues of research integrity and biosecurity, we assessed the world's top 20 undergraduate programmes in biology published by Quacquarelli Symonds. Most of these appear to lack any interaction between biology and law/ethics and the factual training for the next generation. Suggestions are made to improve teaching and learning processes in biological education.
Legal Regulation of Cyberbullying— From a Chinese perspective

Cyberbullying has the basic characteristics of traditional bullying, but it has its own unique characteristics. This poses a challenge to the existing legal regulation. When the Internet is still in the stage of rapid development and expansion, and its impact on social rules has not yet been fully demonstrated, it is not appropriate to typologize cyberbullying. Cyberbullying can be regulated by expanding the interpretation of existing laws. When the conditions are ripe, special regulations can be enacted, which adhere to the principle that a balance should be struck between freedom of speech and regulation and between regulation by public law and judicial relief, improve the existing legal provisions on civil remedies and criminal regulation, and clarify the conditions for the application of such legal provisions.
An Improved Moving Target Detection Algorithm Based on Vibe

ViBe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">is</sub> a pixel-level detection algorithm. However, the algorithm is easy to introduce the ghost region by establishing the initial background model in the first frame of the image, and the fixed threshold for background update cannot adapt to the scene with large background changes. The above problems may cause the error detection of the moving target. This paper presents an improved algorithm to initialize the background model using multi-frame image pixels. In the detection of moving target, the adaptive threshold is set according to the distance between the current pixel value and the background model and the measured value of frame difference. Create a counter for each foreground point to quickly eliminate ghosts. Experimental results show that the improved algorithm can suppress the ghost more effectively and detect the foreground target more accurately.
Design of EPICS and Web-Based Remote Control Software of Near-Infrared Sky Brightness Monitor in Antarctica

The Near-infrared Sky Brightness Monitor (NIRBM) aims to measure the middle infrared sky background in Antarctica. The NIRBM mainly consists of an InGaAs detector, a chopper, a reflector, a cooler and a black body. The reflector can rotate to scan the sky with a field of view ranging from 0{\deg} to 180{\deg}. Electromechanical control and weak signal readout functions are accomplished by the same circuit, whose core chip is a STM32F407VG microcontroller. Considering the environment is harsh for humans in Antarctica, a multi-level remote control software system is designed and implemented. A set of EPICS IOCs are developed to control each hardware module independently via serial port communication with the STM32 microcontroller. The tornado web framework and PyEpics are introduced as a combination where PyEpics is used to monitor or change the EPICS Process Variables, functioning as a client for the EPICS framework. Tornado is responsible for the specific operation process of inter-device collaboration, and expose a set of interfaces to users to make calls. Considering the high delay and low bandwidth of the network environment, the tornado back-end is designed as a master-and-agent architecture to improve domestic user experience. The master node is deployed in Antarctic while multiple agent nodes can be deployed domestic. The master and agent nodes communicate with each other through the WebSocket protocol to exchange latest information so that bandwidth is saved. The GUI is implemented in the form of single-page application based on the Vue framework which communicates with tornado through WebSocket and AJAX requests. The web page integrates device control, data curve drawing, alarm display, auto observation and other functions together.
Multi-level adaptive neuro-fuzzy inference system-based reconstruction of 1D ISOMAP representations

This paper addresses the inverse problem of isometric feature mapping (ISOMAP) via multi-level adaptive neuro-fuzzy inference system (ML-ANFIS). ISOMAP is a conventional nonlinear dimensionality reduction (NLDR) method, which prospects for low dimensional interior structure embedded in high dimensional data space. The inverse problem of ISOMAP reconstructs the original high dimensional data from the related low dimensional ISOMAP representations and holds promising applications in data representations, generation, compression and visualization. Because the reconstruction of 1D ISOMAP representations is ill-posed and undetermined, ML-ANFIS is wielded to augment the recovery quality of general ISOMAP reconstruction algorithm. By linearly combing inputs with nonlinear weights as output, ML-ANFIS can efficiently achieve the latent nonlinear relationship between the low-performance result of general ISOMAP reconstruction algorithm and its original data. The membership functions and fuzzy rules of ML-ANFIS can be automatically learned by gradient descent method as deep learning. It is demonstrated by the experimental results that, in the situation of 1D representations, the proposed method is superior to the state-of-the-art methods, such as nearest neighbor (NN), discrete cosine transformation (DCT), sparse representations (SR) and classical ANFIS algorithms, in the reconstruction performance of video data.
Automatic eye localization for hospitalized infants and children using convolutional neural networks

Reliable localization and tracking of the eye region in the pediatric hospital environment is a significant challenge for clinical decision support and patient monitoring applications. Existing work in eye localization achieves high performance on adult datasets but performs poorly in the busy pediatric hospital environment, where face appearance varies because of age, position and the presence of medical equipment. We developed two new datasets: a training dataset using public image data from internet searches, and a test dataset using 59 recordings of patients in a pediatric intensive care unit. We trained two eye localization models, using the Faster R-CNN algorithm to fine-tune a pre-trained ResNet base network, and evaluated them using the images from the pediatric ICU. The convolutional neural network trained with a combination of adult and child data achieved an 79.7% eye localization rate, significantly higher than the model trained on adult data alone. With additional pre-processing to equalize image contrast, the localization rate rises to 84%. The results demonstrate the potential of convolutional neural networks for eye localization and tracking in a pediatric ICU setting, even when training data is limited. We obtained significant performance gains by adding task-specific images to the training dataset, highlighting the need for custom models and datasets for specialized applications like pediatric patient monitoring. The moderate size of our added training dataset shows that it is feasible to develop an internal training dataset for clinical computer vision applications, and apply it with transfer learning to fine-tune existing pre-trained models.
Image Encryption Based on Fractional-order Chen Hyperchaotic System

This paper proposes a novel image encryption algorithm with the encrypted sequence encoded based on a fractional-order hyperchaotic Chen system. Specifically, a set of system parameters is firstly generated using an 8-byte secret key. Then we create the encrypted sequence by mixing the system parameters with the hyperchaos sequence generated by the fractional-order Chen system. In the end, the encryption sequence is XOR with the image plaintext to produce the encrypted image. In order to increase the ability to resist attack, the ciphertext of the previous pixel is employed for the encryption of subsequent pixels. Numerical experiments has demonstrated the cryptographic excellency of the proposed encryption algorithm.
From a quantum theory to a classical one

We present and discuss a formal approach for describing the quantum to classical crossover based on the group-theoretic construction of generalized coherent states. The method was originally introduced by Yaffe (Rev Mod Phys 54:407, 1982) in 1982 for tackling large-N quantum field theories and has been recently used for studying open quantum systems whose environment, while becoming macroscopic, may or may not display a classical behaviour (Liuzzo-Scorpo et al. in EPL (Europhys Lett) 111(4):40008, 2015; Rossi et al. in Phys Rev A 96:032116, 2017; Foti et al. in Quantum 3:179, 2019; Coppo in Schwarzschild black holes as macroscopic quantum systems, Università degli studi di Firenze, Florence, 2019). Referring to these recent developments, in this paper we provide the essential elements of Yaffe’s approach in the framework of standard quantum mechanics, so as to clarify how the approach can be used without referring to quantum field theory. Moreover, we address the role played by a possible global symmetry in making the large-N limit of the original quantum theory to flow into a formally well-defined classical theory, and we specifically consider the quantum-to-classical crossover of angular momentum. We also give details of a paradigmatic example, namely that of N free one-dimensional spinless particles. Finally, we discuss upon the foundational requirement that any classical description should ultimately be derived from an underlying quantum theory, that, however, is not, and should never be confused with, the one obtained via some quantization procedure of the classical description itself.
Exploratory Experiments on Programming Autonomous Robots in Jadescript

This paper describes exploratory experiments to validate the possibility of programming autonomous robots using an agent-oriented programming language.Proper perception of the environment, by means of various types of sensors, and timely reaction to external events, by means of effective actuators, are essential to provide robots with a sufficient level of autonomy.The agent-oriented programming paradigm is relevant with this respect because it offers language-level abstractions to process events and to command actuators.A recent agent-oriented programming language called Jadescript is presented in this paper together with its new features specifically designed to handle events.Exploratory experiments on a simple case-study application are presented to show the validity of the proposed approach and to exemplify the use of the language to program autonomous robots.