Miracle-dz/UniFAR-FTUs huggingface dataset:
Asynchronous Differential Evolution with Strategy Adaptation for Global Numerical Optimization

A new variant of Differential Evolution (DE) algorithm called A-SaDE algorithm is proposed. A-SaDE algorithm is based on two DE variants, Self-adaptive DE (SaDE) and Asynchronous DE (ADE) algorithms. SaDE algorithm is one of the well-known DE algorithms and shows powerful optimization performance by automatically tuning the mutation strategies as well as the control parameters. The asynchronous scheme is recently proposed, and it helps to find better solutions by increasing the greediness. We incorporated these two algorithms called A-SaDE algorithm and tested it with 13 scalable benchmark problems. The experimental results confirm that A-SaDE algorithm outperforms original SaDE algorithm, especially for unimodal functions.
Digitization of Public Services: A Systematic Literature Review

This paper presents a systematic literature review of the digitization of services carried out by the governments of several countries. The main contribution of this work is the identification of the processes and methodologies adopted by these governments to provide their services to the citizen. These results serve as inputs to guide an analysis of the initial efforts of the Brazilian Government in the construction of a digital platform for the provision of its services directed to the citizen, seeking to analyze their needs and improving the services currently provided.
End-to-End Blind Quality Assessment of Compressed Videos Using Deep Neural Networks

Blind video quality assessment (BVQA) algorithms are traditionally designed with a two-stage approach - a feature extraction stage that computes typically hand-crafted spatial and/or temporal features, and a regression stage working in the feature space that predicts the perceptual quality of the video. Unlike the traditional BVQA methods, we propose a Video Multi-task End-to-end Optimized neural Network (V-MEON) that merges the two stages into one, where the feature extractor and the regressor are jointly optimized. Our model uses a multi-task DNN framework that not only estimates the perceptual quality of the test video but also provides a probabilistic prediction of its codec type. This framework allows us to train the network with two complementary sets of labels, both of which can be obtained at low cost. The training process is composed of two steps. In the first step, early convolutional layers are pre-trained to extract spatiotemporal quality-related features with the codec classification subtask. In the second step, initialized with the pre-trained feature extractor, the whole network is jointly optimized with the two subtasks together. An additional critical step is the adoption of 3D convolutional layers, which creates novel spatiotemporal features that lead to a significant performance boost. Experimental results show that the proposed model clearly outperforms state-of-the-art BVQA methods.The source code of V-MEON is available at https://ece.uwaterloo. ca/~zduanmu/acmmm2018bvqa.
[18F]AV‐1451 binding is increased in frontotemporal dementia due to C9orf72 expansion

The PET ligand [18F]AV‐1451 was developed to bind tau pathology in Alzheimer's disease, but increased binding has been shown in both genetic tauopathies and in semantic dementia, a disease strongly associated with TDP‐43 pathology. Here we assessed [18F]AV‐1451 binding in behavioral variant frontotemporal dementia due to a hexanucleotide repeat expansion in C9orf72, characterized by TDP‐43 pathology. We show that the C9orf72 mutation increases binding in frontotemporal cortex, with a distinctive distribution of binding compared with healthy controls.
Neuropsychological function at first episode in treatment-resistant psychosis: findings from the ÆSOP-10 study

Abstract Background Neuropsychological investigations can help untangle the aetiological and phenomenological heterogeneity of schizophrenia but have scarcely been employed in the context of treatment-resistant (TR) schizophrenia. No population-based study has examined neuropsychological function in the first-episode of TR psychosis. Methods We report baseline neuropsychological findings from a longitudinal, population-based study of first-episode psychosis, which followed up cases from index admission to 10 years. At the 10-year follow up patients were classified as treatment responsive or TR after reconstructing their entire case histories. Of 145 cases with neuropsychological data at baseline, 113 were classified as treatment responsive, and 32 as TR at the 10-year follow-up. Results Compared with 257 community controls, both case groups showed baseline deficits in three composite neuropsychological scores, derived from principal component analysis: verbal intelligence and fluency, visuospatial ability and executive function, and verbal memory and learning (p values⩽0.001). Compared with treatment responders, TR cases showed deficits in verbal intelligence and fluency, both in the extended psychosis sample (t = −2.32; p = 0.022) and in the schizophrenia diagnostic subgroup (t = −2.49; p = 0.017). Similar relative deficits in the TR cases emerged in sub-/sensitivity analyses excluding patients with delayed-onset treatment resistance (p values<0.01–0.001) and those born outside the UK (p values<0.05). Conclusions Verbal intelligence and fluency are impaired in patients with TR psychosis compared with those who respond to treatment. This differential is already detectable – at a group level – at the first illness episode, supporting the conceptualisation of TR psychosis as a severe, pathogenically distinct variant, embedded in aberrant neurodevelopmental processes.
A RGB-D SLAM Algorithm Combining ORB Features and BOW

With1 the rapid development of robots and UAVs, navigation has become a very important topic. The combination of GPS+IMU is undoubtedly the standard of outdoor navigation. Indoor navigation, simultaneous localization and mapping (SLAM) is considered to be the key to autonomous navigation of robots in unknown environments. The traditional SLAM algorithm based on RGB-D camera adopts SIFT feature and needs GPU acceleration to overcome the drawback of SIFT feature extraction. In view of the above problems, a SLAM algorithm combining ORB features with closed loop detection is proposed. At the front end, using improved ORB algorithm to speed up the image feature point extraction, combined RANSAC with ICP to calculate the position and posture of the camera. In the back end, closed loop detection algorithm is proposed to eliminate the cumulative error of the robot, the pose of the camera are optimized by using graph optimization tool to obtain globally consistent camera pose and point cloud.
Force‐frequency relationship and early relaxation kinetics are preserved upon sarcoplasmic blockade in human myocardium

In this study, we investigated the quantitative and qualitative role of the sarcoplasmic reticulum (SR) in the regulation of the force‐frequency relationship (FFR). We blocked the function of SR with cyclopiazonic acid (CPA) and ryanodine and measured twitch kinetics and developed force at various stimulation frequencies in nonfailing and failing intact human right ventricular trabeculae. We found that developed forces are only slightly reduced upon SR blockade, while the positive FFR in nonfailing trabeculae and negative FFR in failing trabeculae were both preserved. The contraction kinetics (dF/dt, dF/dt/F, and time to peak), however, were significantly slower at all frequencies tested. Kinetics of first 50% of relaxation (RT50) was not affected by SR blockade. Kinetics of entire relaxation process (RT90) was overall slower at low frequencies, but not at high frequencies. From our findings, we conclude that the SR is not essential for FFR, and its role in regulation of FFR lies mostly in contraction kinetics. Unlike small rodents, human myocardium contractile function is near‐normal in absence of a functional SR with little changes in contractile force, and with preservation with the main regulation of FFR.
North Korea’s Nuclear Belligerence: The Extreeme Application of the Realist Theory

weapons,  To provide security for non-nuclear weapon states which have surrendered nuclear option,  To promote international co-operation in the peaceful uses of nuclear energy, and  To pursue negotiations in good faith towards nuclear disarmament leading to the eventual of nuclear weapons. Ilishan-Remo, Ogun State, Nigeria Abstract: The possession of nuclear weapon capability by the United States of America in 1945 set other world powers on the race to add the lethal weapon to their arsenals. Between 1945 and 1964, all the five emerging champions of the WWII possessed nuclear capability and became known as Nuclear Weapon States (NWS). A bid to curb nuclear proliferation and avert the concomitant ominous risk which it might portent to world security led to the establishment of the Nuclear Non-Proliferation Treaty (NPT). Having opted out of the NPT, North Korea embarked on a brazen development and testing of nuclear weapons in defiance of all nuclear weapon regulatory bodies. While examining the underlining factors for North Korea’s defiance, this research discovered that North Korea pursuit of nuclear weapons is absolutely in conformity with the realist proposition. However, since the belligerent attitude has made the country a pariah state and produced adverse effects on the nation’s economic growth and the citizens welfare, the research suggests that North Korea should play down on ‘hard power’ which subjects it to international odium and explore the option of‘soft power’ which states use to their own advantage Descriptive research
A Passive Heart Rate Measurement Method Using Camera

Heart rate is one of the most important Physiological characteristics related to the health. Recently, the non-contact heart rate measurement has got more attentions. However, these methods only focused on the active measurement. In this paper, we study the non-contact measurement of HR in a passive way which extends its further use into the smart home or e-health. In this system, a RGB camera is used to capture the video images about the objective. For these captured images, we strengthen the color from the captured images with an EVM algorithm, and use KCF algorithm to locate the chosen RoI. After that, the ICA algorithm is utilized to extract the effective signal, and the heart rate is obtained by an ideal band pass filter with the fast Fourier transform. Experimental results showed that our system can measure the heart rate effectively.
Learning to Detect, Associate, and Recognize Human Actions and Surrounding Scenes in Untrimmed Videos

While recognizing human actions and surrounding scenes addresses different aspects of video understanding, they have strong correlations that can be used to complement the singular information of each other. In this paper, we propose an approach for joint action and scene recognition that is formulated in an end-to-end learning framework based on temporal attention techniques and the fusion of them. By applying temporal attention modules to the generic feature network, action and scene features are extracted efficiently, and then they are composed to a single feature vector through the proposed fusion module. Our experiments on the CoVieW18 dataset show that our model is able to detect temporal attention with only weak supervision, and remarkably improves multi-task action and scene classification accuracies.
Neural Retrieval with Partially Shared Embedding Spaces

One category of neural information retrieval models tries to learn text representation in a common embedding space for both queries and documents. However, a single embedding space is not always sufficient, since queries and documents are different in terms of length, number of topics covered, etc. We argue that queries and documents should be mapped into different but overlapping embedding spaces, which is named Partially Shared Embedding Space (PSES) model in this paper. PSES consists of two embedding spaces respectively for queries and documents, and a shared embedding space capturing common features of two sources. Those three embeddings are learned by jointly obeying three constraints: a feature separation constraint, a pairwise matching constraint, and a reconstruction constraint. Experiments on standard TREC collections indicate that PSES leads to significant better performance of retrieval over traditional IR models and several neural IR models with only one embedding space.
Effects of a single long-acting muscarinic antagonist agent and a long-acting muscarinic antagonist/long-acting β2-adrenoceptor agonist combination on lung function and symptoms in untreated COPD patients in Japan

Background A large body of evidence suggests that long-acting β2-adrenoceptor agonist (LABA)/long-acting muscarinic antagonist (LAMA) combinations induce a strong synergistic bronchodilatory effect in human isolated airways. Moreover, a recent post hoc analysis demonstrated clinical synergism between LABAs and LAMAs, which induces a synergistic improvement not only in lung function but also in dyspnea in COPD patients. Aim The aim of this study is to examine the baseline factors related to improvement in lung function or clinical symptoms that results from the administration of LAMA or LAMA/LABA and to compare the differences in improvement in lung function or clinical symptoms between LAMA and LAMA/LABA. Methods Among 829 patients with COPD who were treated with LAMA or LAMA/LABA in our hospital, 112 patients (aged 40–89 years) matched the criteria. Of these 112 patients, 71 received LAMA (LAMA group) and 41 received LAMA/LABA (LAMA/LABA group) as the initial treatment. Various examination results such as lung function test values, symptom change, and frequency of exacerbations were compared between the two groups. Results Compared with the monotherapy, the combination therapy significantly improved the FEV1, inspiratory capacity (IC), and total COPD assessment test (CAT) scores. Comparing the improvement in each domain of the CAT produced by the combination therapy with that of the monotherapy, larger improvements were found for the domains of going out and sleeping. The frequency of exacerbations during the 24 weeks was significantly lower in the combination therapy group than in the LAMA monotherapy group (P=0.034). Although no relationship was found between improvement in FEV1 and any pretreatment factors in the LAMA/LABA group, the improvement in the CAT score was strongly related to the baseline CAT score, smoking index, and air trapping index (P-value <1×10−4). Conclusion In this study of clinical practice, we found that LAMA/LABA combination therapy improved the clinical symptoms of COPD and IC and that the effects of the combination therapy were consistent with those observed in previous clinical trials.
Swimming Pool Occupancy Analysis using Deep Learning on Low Quality Video

Automatically creating spatio-temporal occupancy analysis of public swimming pools is of great interest, both for administrators to optimize the use of these expensive facilities, and for users to schedule their activities outside peak hours. In this paper we apply current state-of-the-art deep learning methods within human detection on low quality swimming pool video. Furthermore, we propose a method for analyzing the spatio-temporal occupancy of a swimming pool. We show that it is possible to precisely detect swimmers in very challenging conditions by obtaining an AUC of 93.48 % from YOLOv2. An acceptable AUC of 79.29 % was obtained from Tiny-YOLO, which can be implemented on a low-cost embedded system capable of producing results in real-time on site. We expect that the performance of both networks can be improved with more training data.
Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.
STACL: Simultaneous Translation with Integrated Anticipation and Controllable Latency

Simultaneous translation, which translates sentences before they are finished, is useful in many scenarios but is notoriously difficult due to word-order differences and simultaneity requirements. We introduce a very simple yet surprisingly effective `wait-k' model trained to generate the target sentence concurrently with the source sentence, but always k words behind, for any given k. This framework seamlessly integrates anticipation and translation in a single model that involves only minor changes to the existing neural translation framework. Experiments on Chinese-to-English simultaneous translation achieve a 5-word latency with 3.4 (single-ref) BLEU points degradation in quality compared to full-sentence non-simultaneous translation. We also formulate a new latency metric that addresses deficiencies in previous ones.
Pseudo-Propositional Logic

Propositional logic is the main ingredient used to build up SAT solvers which have gradually become powerful tools to solve a variety of important and complicated problems such as planning, scheduling, and verifications. However further uses of these solvers are subject to the resulting complexity of transforming counting constraints into conjunctive normal form (CNF). This transformation leads, generally, to a substantial increase in the number of variables and clauses, due to the limitation of the expressive power of propositional logic. To overcome this drawback, this work extends the alphabet of propositional logic by including the natural numbers as a means of counting and adjusts the underlying language accordingly. The resulting representational formalism, called pseudo-propositional logic, can be viewed as a generalization of propositional logic where counting constraints are naturally formulated, and the generalized inference rules can be as easily applied and implemented as arithmetic.
Dimerization of Polycyclic Aromatic Hydrocarbon Molecules and Radicals under Flame Conditions.

This work presents a dynamic and kinetic study on the dimerization of polycyclic aromatic hydrocarbon (PAH) molecules and radicals under flame conditions using reactive force field (ReaxFF) molecular dynamics (MD) simulations. The accuracy of the ReaxFF force field is evaluated through comparing with quantum chemistry (QC) calculations of the barrier heights and species concentrations of PAHs reacting with H and OH radicals. A series of homobinary collisions between PAH molecules/radicals are performed to reveal the influence of temperature, molecular size, PAH composition, and the number of radical sites on the dynamics and kinetics of PAH dimerization. Instead of directly forming the strong covalent bonds, the majority of the binary collisions between PAH radicals are bound with weak intermolecular interactions. Effects of oxygen on PAH radical dimerization are also investigated, which indicates that the oxygenated PAH radicals are less likely to contribute to soot nucleation. In addition, the temperature, PAH characteristic, and radical site dependent collision efficiency for PAH radical-radical combinations is extracted from this study.
Dynamic Weight Based Load Balancing for Microservice Cluster

In this paper1, the coarse grained load balancing algorithm, such as round based algorithm and random algorithm, which is provided by the mainstream framework of micro service cluster, is too rough, and the current proposed load balancing algorithms of fixed weights can not achieve a better load balance in the case of more micro service requests, and then the paper proposes a new method of load balancing. A new dynamic weight load balancing algorithm. This algorithm selects the data of CPU utilization rate, disk utilization rate, memory utilization rate and server connection number which can reflect the load condition of the server as the parameters of the load evaluation. This paper also selects the changes of each parameter to calculate the dynamic weight, the number of parameters and the weight of the server to evaluate the real time of the server. The load reflects the current load of the server more comprehensively and more accurately. Compared with some current load balancing algorithms, this paper dynamically compares the current load situation of each server, and finally selects the server with the smallest load to carry on the request forward, and better realizes the load balancing. According to the experimental results, we can see that the algorithm has a larger throughput and shorter response time in the case of many requests, which shows that the dynamic weight algorithm in this paper has a certain theoretical basis and practical significance.
Local and Global Information Fusion for Top-N Recommendation in Heterogeneous Information Network

Since heterogeneous information network (HIN) is able to integrate complex information and contain rich semantics, there is a surge of HIN based recommendation in recent years. Although existing methods have achieved performance improvement to some extent, they still face the following problems: how to extensively exploit and comprehensively explore the local and global information in HIN for recommendation. To address these issues, we propose a unified model LGRec to fuse local and global information for top-N recommendation in HIN. We firstly model most informative local neighbor information for users and items respectively with a co-attention mechanism. In addition, our model learns effective relation representations between users and items to capture rich information in HIN by optimizing a multi-label classification problem. Finally, we combine the two parts into an unified model for top-N recommendation. Extensive experiments on four real-world datasets demonstrate the effectiveness of the proposed model.