Reviewerly/exHarmony huggingface dataset:
Q-GPU: A Recipe of Optimizations for Quantum Circuit Simulation Using GPUs

In recent years, quantum computing has undergone significant developments and has established its supremacy in many application domains. While quantum hardware is accessible to the public through the cloud environment, a robust and efficient quantum circuit simulator is necessary to investigate the constraints and foster quantum computing development, such as quantum algorithm development and quantum device architecture exploration. In this paper, we observe that most of the publicly available quantum circuit simulators (e.g., QISKit from IBM, QDK from Microsoft, and Qsim-Cirq from Google) suffer from slow simulation and poor scalability when the number of qubits increases. To this end, we systematically investigate the deficiencies in quantum circuit simulation (QCS) and propose Q-GPU, a framework that leverages GPUs with comprehensive optimizations to allow efficient and scalable QCS. Specifically, Q-GPU features i) proactive state amplitude transfer, ii) zero state amplitude pruning, iii) delayed qubit involvement, and iv) lossless nonzero state amplitude compression. Experimental results across nine representative quantum circuits indicate that Q-GPU significantly reduces the execution time of the state-of-the-art GPU-based QCS by 71.89% (3.55&#x00D7; speedup). Q-GPU also outperforms the state-of-the-art OpenMP CPU implementation, the Google Qsim-Cirq simulator, and the Microsoft QDK simulator by 1.49&#x00D7;, 2.02&#x00D7;, and 10.82&#x00D7;, respectively.
SIHRNet: a fully convolutional network for single image highlight removal with a real-world dataset

Specular highlight in images is detrimental to accuracy in object recognition tasks. The prior model-based methods for single image highlight removal (SHIR) are limited in images with large highlight regions or achromatic regions, and recent learning-based methods do not perform well due to lack of proper datasets for training either. A network for SHIR is proposed, which is trained with losses that utilize image intrinsic features and can reconstruct a smooth and natural specular-free image from a single input highlight image. Dichromatic reflection model is used to compute the pseudo specular-free image for providing complementary information for the network. A real-world dataset with highlight images and the corresponding ground-truth specular-free images is collected for network training and quantitative evaluation. The proposed network is validated by comprehensive quantitative experiments and outperforms state-of-the-art highlight removal approaches in structural similarity and peak signal-to-noise ratio. Experimental results also show that the network could improve the recognition performance in applications of computer vision. Our source code is available at https://github.com/coach-wang/SIHRNet.
Optimally ordering IDK classifiers subject to deadlines

Abstract A classifier is a software component, often based on Deep Learning, that categorizes each input provided to it into one of a fixed set of classes. An IDK classifier may additionally output “I Don’t Know” (IDK) for certain inputs. Multiple distinct IDK classifiers may be available for the same classification problem, offering different trade-offs between effectiveness, i.e. the probability of successful classification, and efficiency, i.e. execution time. Optimal offline algorithms are proposed for sequentially ordering IDK classifiers such that the expected duration to successfully classify an input is minimized, optionally subject to a hard deadline on the maximum time permitted for classification. Solutions are provided considering independent and dependent relationships between pairs of classifiers, as well as a mix of the two.
Mip-NeRF RGB-D: Depth Assisted Fast Neural Radiance Fields

Neural scene representations, such as Neural Radiance Fields (NeRF), are based on training a multilayer perceptron (MLP) using a set of color images with known poses. An increasing number of devices now produce RGB-D(color + depth) information, which has been shown to be very important for a wide range of tasks. Therefore, the aim of this paper is to investigate what improvements can be made to these promising implicit representations by incorporating depth information with the color images. In particular, the recently proposed Mip-NeRF approach, which uses conical frustums instead of rays for volume rendering, allows one to account for the varying area of a pixel with distance from the camera center. The proposed method additionally models depth uncertainty. This allows to address major limitations of NeRF-based approaches including improving the accuracy of geometry, reduced artifacts, faster training time, and shortened prediction time. Experiments are performed on well-known benchmark scenes, and comparisons show improved accuracy in scene geometry and photometric reconstruction, while reducing the training time by 3 - 5 times.
Deadline-aware Preemptive Job Scheduling in Hadoop YARN Clusters

As a popular open-source framework for big data processing, Hadoop Yarn has been widely used by large internet and e-commerce companies such as Amazon, Alibaba, and Facebook. One of the main challenges faced by Hadoop service providers is to optimize the scheduling of MapReduce jobs in order to provide desired performance levels to users and improve cluster resource utilization. In this paper, we propose a deadline-aware preemptive job scheduling strategy, named DAPS, to minimize the job deadline misses. In the proposed DAPS, the job scheduling problem is formulated as an online optimization problem, and a preemptive resource allocation algorithm is developed to search for a good job scheduling policy. We implement the proposed DAPS in Hadoop Yarn clusters and evaluate the effectiveness of the proposed DAPS through several experiments. Our proposed approach has an average job completion rate of 92.33%, which is better than capacity scheduler (62.94%), Earliest Deadline First scheduler (78.76%), and PDSonQueue (87.58%). The experimental results show that the performance of the proposed DAPS is superior to the state-of-the-art strategies applied to similar problems.
An event-triggered real-time motion planning strategy for autonomous vehicles

Motion planning is an essential part of autonomous vehicles. The planning process should respond to environmental changes in real time to ensure safety. This paper proposes an event-triggered real-time motion planning strategy to achieve a more real-time planning effect and a scenario-based planning process. The path planning process is discretized into several parts and integrated into the behavioral planning process. A hierarchical finite state machine (HFSM) based integrated motion planning process is proposed to trigger the discrete path planning parts according to environmental events. Thus, the output reference path can be obtained. Experiment and simulation results show the efficiency of our strategy.
Object Detection Algorithm Based on Lightweight YOLOv4 for UAV

With the development of artificial intelligence technology, the mission scope of UAV has gradually expanded from the traditional image acquisition of high-altitude target area to aerial photography of cities, mapping regions, forest inspection, plant protection monitoring, rescue missions, fire rescue and other aspects. In recent years, the industrial development of UAV has reduced the cost of UAV, improved the performance of camera, stability and other aspects, and more UAV has been put into real life. Among them, the research on object detection in UAV images has become a hot topic, and a large number of excellent algorithms have emerged, aiming at improving the accuracy and speed of object detection. The object detection of UAV is faced with the following problems: small detected targets in the field of vision, weak computing power of the embedded system, weak real-time detection, etc. By comparing some classical object detection algorithms, using YOLOv4 as the basic network model, the structure of the network and the algorithm of image prediction box selection are improved. Then, a lightweight YOLOv4 object detection algorithm for small target is proposed. It can not only improve the speed and accuracy of object detection for the UAV, but also apply to mobile or embedded platform of the UAV. What's more, the realization of real-time object detection greatly improves the UAV's scene understanding ability and operational efficiency.
Hindrance Detection and Avoidance in Driverless Cars Through Deep Learning Techniques

Generally, a technique to detect each shifting and static item concurrently is needed due to the fact the static items inclusive of containers can fall on the street in the front of a vehicle and they may be risky. Obstacles can be detected and avoided by using neural networks. Obstacle detection algorithms have the capability to detect the items that can be out of doors. Irregularities on the street floor that aren't affecting the riding aren't considered. In this case, the detection may be primarily based on looking for unique patterns. In the chapter, a hybrid network based on the combination of stacked auto encoder-based deep belief network is built to detect the obstacles on the road. The system achieves accuracy greater than 90% when compared with existing literature.
Ramadan effect in the cryptocurrency markets

Purpose This paper examines the effect of the holy month of Ramadan on the returns and conditional volatility of cryptocurrency markets. Design/methodology/approach The closing prices of six cryptocurrencies have been considered. The study employs different classical tests for checking if the efficiency behaviour is similar during Ramadan celebration days and non-Ramadan days. Besides, dummy variable regression technique for assessing this anomaly on returns and volatilities has been applied. Findings Although no significant effect on returns and volatility for Litecoin has been found, the results provide evidence about the existence of the Ramadan effects in cryptocurrency markets. The results of the mean equations show the existence of Ramadan effect for Ethereum, Ripple, Stellar and BinanceCoin for all considered models. Significant effect on Bitcoin returns is found with an autoregressive model of order 1. The results of conditional volatility show Ramadan effect on volatility is not detected. Originality/value First, a new contribution in the incipient study of cryptocurrency analysis. Second, a comprehensive review of recently published empirical articles about Ramadan effect on traditional assets has been carried out. Third, unlike most of the papers focussed on the study of Bitcoin, this study has been extended to six cryptocurrencies. Ramadan effect have not been analysed in cryptomarkets yet. This study come to fill this gap and analyses Ramadan effect, previously documented for traditional assets, in particular, stock index from Muslim countries, but not yet analysed in the cryptocurrency markets.
Flying-qubit control via a three-level atom with tunable waveguide couplings

The control of flying qubits is at the core of quantum networks. When the flying qubits are carried by single-photon fields, the control involves not only their logical states but also their shapes. In this paper we explore a variety of flying-qubit control problems using a three-level atom with time-varying tunable couplings to two input-output channels. It is shown that one can tune the couplings of a $\mathrm{\ensuremath{\Lambda}}$-type atom to distribute a single photon into the two channels with arbitrary shapes, or use a $\mathrm{\ensuremath{\Lambda}}$-type atom or a $V$-type atom to catch an arbitrary-shape distributed single photon. The $\mathrm{\ensuremath{\Lambda}}$-type atom can also be designed to transfer a flying qubit from one channel to the other, with both the central frequency and the photon shape being converted. With a $\mathrm{\ensuremath{\Xi}}$-type atom, one can shape a pair of correlated photons via cascaded emission. In all cases, analytical formulas are derived for the coupling functions to fulfill these control tasks. Their correlation properties and physical limitations are discussed as well. These results provide useful control protocols for high-fidelity quantum information transmission over complex quantum networks.
Marker-less motion capture system using OpenPose

Motion capture systems are widely used to measure athletic performance and as a diagnostic tool in sports medicine. Standard motion capture systems record body movement using: (1) a set of cameras to localize body segments; or (2) specialized suits in which inertial measurement units are directly attached to body segments. The major drawbacks of these systems are limited portability, affordability, and accessibility. This contribution presents a markerless motion capture system using a commercially available sports camera and the OpenPose human pose estimation algorithm. We have validated the proposed markerless system by analyzing the human biometrics during running and jumping movements.
The Relationship between Digital RMB and Digital Economy in China

By comparing the historical patterns of currency development, this paper pointed out the inevitability of the development of digital currency and the relationship between digital currency and the digital economy. With the example of China, this paper predicts the future development trend of digital currency. In the context of the rapid development of private cryptocurrency, China launched the digital currency based on serving the digital economy and committed to the globalisation of the digital renminbi (RMB) and the globalisation of the digital economy. The global economy in 2022 ushered in a period of stagnation, and China treats digital fiat currency and digital economic development as a breakthrough to pursue economic transformation and new growth. It has become one of the forefront countries, with numerous experiences that can be learned by countries around the world.
Converting bistatic radar measurements to Cartesian position for tracking

Tracking with 2-D bistatic radar measurements is a challenging problem due to the nonlinear relationship between the sine-space radar measurements and the Cartesian coordinates, especially for long distances. For 2-D bistatic radar, this nonlinearity leads to a non-elliptical measurement uncertainty region in 2-D Cartesian coordinates, similar to a crescent, that causes consistency problems for a tracking filter. A solution is suggested by developing an unbiased and statistically consistent conversion of the position measurements to Cartesian coordinates, based on second order Taylor expansion. Such an approach was successfully used for monostatic radars but considered impractical for the bistatic case due to the difficulty to derive explicit conversion expressions. The implementation includes conversion of the bistatic range (r<sub>b</sub>) and sine-space angle measurement (u) to Cartesian position coordinates and tracking with a standard linear Kalman filter using the converted measurements, now linear in the state. This method is compared to the best-known existing filter, the converted measurement sigma point Kalman filter. Results show improved performance especially in terms of tracker consistency, keeping the state estimation error covariance statistically consistent with the actual estimation errors.
Stablecoins as a new form of digital money: Emission, circulation, regulation and risk management

Stablecoins as a new form of digital money, in contrast to the traditional forms of money prevailing today, maintain their stability by being tied to basic reserves (national currencies, goods or digital assets). Basically, they circulate in developed countries (primarily in the USA, EU countries or financial centers of Asia) in order to carry out exchange transactions with digital currencies or increase trade turnover and lending volumes. Over the time, they can become not only a reliable and inclusive means of payment, which will be actively used in transactions both nationally and internationally, but also widely used as a store of value. However, the integration of stablecoins with a modern monetary and payment system can only happen if there is a proper and coordinated regulation of all monetary authorities. Based on the analysis of the economic nature, the current standards and models for regulating the emission and circulation of stablecoins, as well as the existing international practice of managing their risks, it has been concluded that it is necessary to abandon the prohibitive approach to the use of this form of digital money. Instead of banning the use of stablecoins as a means of payment and savings, the Bank of Russia should justify and propose acceptable models for regulating the issue and circulation of stablecoins. Such models should provide for clear back-up mechanisms for the issue of stablecoins and requirements for their issuers to minimize the risks of both holders and issuers of stablecoins.
Curating Player Experience Through Simulations in City Games

The use of games as a method for planning and designing cities is often associated with visualisation, from simplistic to immersive environments. They can also include complex and sophisticated models which provide an evidence base. The use of such technology as artefacts, aids, or mechanics curates the player experience in different and very often subtle ways, influencing how we engage with (simulated) urban phenomena, and, therefore, how the games can be used. In this article, we aim to explore how different aspects of technology use in city games influence the player experience and game outcomes. The article describes two games built upon the same city gaming framework, played with professionals in Rome and Haifa, respectively. Using a mixed-method, action research approach, the article examines how the high-tech, free form single-player games elicit the mental models of players (traffic controllers and planners in both cases). Questionnaires and the players’ reflections on the gameplay, models used, and outcomes have been transcribed and analysed. Observations and results point to several dimensions that are critical to the outcomes of digital city games. Agency, exploration, openness, complexity, and learning are aspects that are strongly influenced by technology and models, and in turn, determine the outcomes of the game. City games that balance these aspects unlock player expertise to better understand the game dynamics and enable their imagination to better negotiate and resolve conflicts in design and planning.
Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition

Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually annotated labels. To work well, the label model needs to contextually identify and emphasize well-performed LFs while down-weighting the under-performers. However, evaluating the LFs is challenging due to the lack of ground truths. To address this issue, we propose the sparse conditional hidden Markov model (Sparse-CHMM). Instead of predicting the entire emission matrix as other HMM-based methods, Sparse-CHMM focuses on estimating its diagonal elements, which are considered as the reliability scores of the LFs. The sparse scores are then expanded to the full-fledged emission matrix with pre-defined expansion functions. We also augment the emission with weighted XOR scores, which track the probabilities of an LF observing incorrect entities. Sparse-CHMM is optimized through unsupervised learning with a three-stage training pipeline that reduces the training difficulty and prevents the model from falling into local optima. Compared with the baselines in the Wrench benchmark, Sparse-CHMM achieves a 3.01 average F1 score improvement on five comprehensive datasets. Experiments show that each component of Sparse-CHMM is effective, and the estimated LF reliabilities strongly correlate with true LF F1 scores.
Joint controllers in large research consortia: a funnel model to distinguish controllers in the sense of the GDPR from other partners in the consortium

<ns7:p>Large European research consortia in the health sciences face challenges regarding the governance of personal data collected, generated and/or shared during their collective research. A controller in the sense of the GDPR is the entity which decides about purposes and means of the data processing. Case law of the Court of Justice of the European Union (CJEU) and Guidelines of the European Data Protection Board (EDPB) indicate that all partners in the consortium would be joint controllers. This paper summarises the case law, the Guidelines and literature on joint controllership, gives a brief account of a webinar organised on the issue by Lygature and the MLC Foundation. Participants at the webinar agreed in large majority that it would be extreme if all partners in the consortium would become joint controllers. There was less agreement how to disentangle partners who are controllers of a study from those who are not. In order to disentangle responsibilities, we propose a funnel model with consecutive steps acting as sieves in the funnel. It differentiates between two types of partners: all partners who are involved in shaping the project as a whole versus those specific partners who are more closely involved in a sub-study following from the DoA or the use of the data Platform. If the role of the partner would be comparable to that of an outside advisor, that partner would not be a data controller even though the partner is part of the consortium. We propose further nuances for the disentanglement which takes place in various steps.</ns7:p><ns7:p> Uncertainty about formal controllership under the GDPR can stifle collaboration in consortia due to concerns over (shared) responsibility and liability. Data subjects’ ability to exercise their right can also be affected by this. The funnel model proposes a way out of this conundrum.</ns7:p>
BI-RADS-Based Classification of Mammographic Soft Tissue Opacities Using a Deep Convolutional Neural Network

The aim of this study was to investigate the potential of a machine learning algorithm to classify breast cancer solely by the presence of soft tissue opacities in mammograms, independent of other morphological features, using a deep convolutional neural network (dCNN). Soft tissue opacities were classified based on their radiological appearance using the ACR BI-RADS atlas. We included 1744 mammograms from 438 patients to create 7242 icons by manual labeling. The icons were sorted into three categories: "no opacities" (BI-RADS 1), "probably benign opacities" (BI-RADS 2/3) and "suspicious opacities" (BI-RADS 4/5). A dCNN was trained (70% of data), validated (20%) and finally tested (10%). A sliding window approach was applied to create colored probability maps for visual impression. Diagnostic performance of the dCNN was compared to human readout by experienced radiologists on a "real-world" dataset. The accuracies of the models on the test dataset ranged between 73.8% and 89.8%. Compared to human readout, our dCNN achieved a higher specificity (100%, 95% CI: 85.4-100%; reader 1: 86.2%, 95% CI: 67.4-95.5%; reader 2: 79.3%, 95% CI: 59.7-91.3%), and the sensitivity (84.0%, 95% CI: 63.9-95.5%) was lower than that of human readers (reader 1:88.0%, 95% CI: 67.4-95.4%; reader 2:88.0%, 95% CI: 67.7-96.8%). In conclusion, a dCNN can be used for the automatic detection as well as the standardized and observer-independent classification of soft tissue opacities in mammograms independent of the presence of microcalcifications. Human decision making in accordance with the BI-RADS classification can be mimicked by artificial intelligence.