Reviewerly/exHarmony huggingface dataset:
Decoherence in the three-state quantum walk

Quantum walks are dynamic systems with a wide range of applications in quantum computation and quantum simulation of analog systems, therefore it is of common interest to understand what changes from an isolated process to one embedded in an environment. In the present work, we analyze the decoherence in a three-state uni-dimensional quantum walk. The approaches taken into consideration to account for the environment effects are phase and amplitude damping Kraus operators, unitary noise on the coin space, and broken links. The results are compared with the two-state quantum walk.
Even sampling photonic-integrated interferometric array for synthetic aperture imaging

To improve the effectiveness of spatial spectrum sampling for the photonic-integrated interferometric imaging, an array forming scheme is proposed with evenly distributed interferometric baselines, which is referred to as the even sampling photonic-integrated interferometric array (ESPIA). The subaperture array of ESPIA is configured as equi-spaced concentric rings. The subaperture beams are coupled and transmitted to the photonic integrated circuit through fiber optic channels and paired into baselines by the interferometric beam combination. The characteristics of ESPIA are analyzed with the discrete modulation transfer function (D-MTF) and multi-resolution mutual information (MR-MI). The simulation results show that it can realize the even sampling coverage of spatial spectrum effectively. With the same scale of synthetic aperture and subaperture array, it can also improve the capabilities of information acquisition for the interferometric array.
Association Rule Mining Based on Hybrid Whale Optimization Algorithm

Association Rule Mining(ARM) is one of the most significant and active research areas in data mining. Recently, Whale Optimization Algorithm (WOA) has been successfully applied in the field of data mining, however, it easily falls into the local optimum. Therefore, an improved WOA based adaptive parameter strategy and Levy Flight mechanism (LWOA) is applied to mine association rules. Meanwhile, a hybrid strategy that blends two algorithms to balance the exploration and exploitation phases is put forward, that is, grey wolf optimization algorithm (GWO), artificial bee colony algorithm (ABC) and cuckoo search algorithm (CS) are devoted to improving the convergence of LWOA. The approach performs a global search and finds the association rules sets by modeling the rule mining task as a multi-objective problem that simultaneously meets support, confidence, lift, and certain factor, which is examined on multiple data sets. Experimental results verify that the proposed method has better mining performance compared to other algorithms involved in the paper.
Evaluating Hardware Memory Disaggregation under Delay and Contention

Hardware memory disaggregation is an emerging trend in datacenters that provides access to remote memory as part of a shared pool or unused memory on machines across the network. Memory disaggregation aims to improve memory utilization and scale memory-intensive applications. Current state-of-the-art prototypes have shown that hardware disaggregated memory is a reality at the rack-scale. However, the memory utilization benefits of memory disaggregation can only be fully realized at larger scales enabled by a datacenter-wide network. Introduction of a datacenter network results in new performance and reliability failures that may manifest as higher network latency. Additionally, sharing of the network introduces new points of contention between multiple applications. In this work, we characterize the impact of variable network latency and contention in an open-source hardware disaggregated memory prototype - ThymesisFlow. To support our characterization, we have developed a delay injection framework that introduces delays in remote memory access to emulate network latency. Based on the characterization results, we develop insights into how reliability and resource allocation mechanisms should evolve to support hardware memory disaggregation beyond rack-scale in datacenters.
Quantum reflection of single photons in a cold Rydberg atomic gas

We propose a scheme for realizing the quantum reflection of single photons in a cold Rydberg atomic gas via electromagnetically induced transparency, by which a deep and tunable attractive potential can be prepared by using stored gate photons. Such a scheme is promising for designing dispersion-type single-photon switches, and may be taken as a quantum device for observing the wave and particle natures of photons simultaneously.
Reducing Unnecessary Biopsies Using Digital Breast Tomosynthesis and Ultrasound in Dense and Nondense Breasts

Aim: To compare digital breast tomosynthesis (DBT) and ultrasound in women recalled for assessment after a positive screening mammogram and assess the potential for each of these tools to reduce unnecessary biopsies. Methods: This data linkage study included 538 women recalled for assessment from January 2017 to December 2019. The association between the recalled mammographic abnormalities and breast density was analysed using the chi-square independence test. Relative risks and the number of recalled cases requiring DBT and ultrasound assessment to prevent one unnecessary biopsy were compared using the McNemar test. Results: Breast density significantly influenced recall decisions (p < 0.001). Ultrasound showed greater potential to decrease unnecessary biopsies than DBT: in entirely fatty (21% vs. 5%; p = 0.04); scattered fibroglandular (23% vs. 10%; p = 0.003); heterogeneously dense (34% vs. 7%; p < 0.001) and extremely dense (39% vs. 9%; p < 0.001) breasts. The number of benign cases needing assessment to prevent one unnecessary biopsy was significantly lower with ultrasound than DBT in heterogeneously dense (1.8 vs. 7; p < 0.001) and extremely dense (1.9 vs. 5.1; p = 0.03) breasts. Conclusion: Women with dense breasts are more likely to be recalled for assessment and have a false-positive biopsy. Women with dense breasts benefit more from ultrasound assessment than from DBT.
PROMPT: Process Mining and Paravector Tensor-Based Physical Health Monitoring Framework

The provision of physical healthcare services during the isolation phase is one of the major challenges associated with the current COVID-19 pandemic. Smart healthcare services face a major challenge in the form of human behavior, which is based on human activities, complex patterns, and subjective nature. Although the advancement in portable sensors and artificial intelligence has led to unobtrusive activity recognition systems, very few studies deal with behavior tracking for addressing the problem of variability and behavior dynamics. In this regard, we propose the fusion of PRocess mining and Paravector Tensor (PROMPT)-based physical health monitoring framework that not only tracks subjective human behavior, but also deals with the intensity variations associated with inertial measurement units. Our experimental analysis of a publicly available dataset shows that the proposed method achieves 14.56% better accuracy in comparison to existing works. We also propose a generalized framework for healthcare applications using wearable sensors and the PROMPT method for its triage with physical health monitoring systems in the real world.
TEACHING CASE. GAMIFICATION IN HIGHER EDUCATION: TURNING TRAINING IN FACE-TO-FACE AND ONLINE STUDIES

The profile of university students has changed in recent years. The use of the Internet and social networks has fostered immediacy in everyday life. Added to this is the fact that in recent years the number of students known as “sisi” generation has increased, that is, students who study and work at the same time. For this student profile, not only is a master class enough, but, given the effort they make to be able to train -in person or online (e-learning)- we must, at least, take into account different techniques or tools that can help encourage participation, motivate students and facilitate the acquisition and assimilation of the knowledge exposed in the training sessions. In this line, we present a teaching case in which gamification tools applicable to education were used in order to report an experience that makes the use of this type of programs or gamification techniques to be valued and taken as a reference when teaching. in a context of higher education both in the face-to-face environment and in the online environment. In the present study, the experience of applying gamification tools or techniques, both in face-to-face studies and in online studies, has been satisfactory for the students and, as they indicate, has helped them in their learning. In line with other authors, our didactic case corroborates the results of the improvement in the understanding and learning of students in higher education.
ERNet

Brain extraction and registration are important preprocessing steps in neuroimaging data analysis, where the goal is to extract the brain regions from MRI scans (ie extraction step) and align them with a target brain image (ie registration step). Conventional research mainly focuses on developing methods for the extraction and registration tasks separately under supervised settings. The performance of these methods highly depends on the amount of training samples and visual inspections performed by experts for error correction. However, in many medical studies, collecting voxel-level labels and conducting manual quality control in high-dimensional neuroimages (eg 3D MRI) are very expensive and time-consuming. Moreover, brain extraction and registration are highly related tasks in neuroimaging data and should be solved collectively. In this paper, we study the problem of unsupervised collective extraction and registration in neuroimaging data. We propose a unified end-to-end framework, called ERNet (Extraction-Registration Network), to jointly optimize the extraction and registration tasks, allowing feedback between them. Specifically, we use a pair of multi-stage extraction and registration modules to learn the extraction mask and transformation, where the extraction network improves the extraction accuracy incrementally and the registration network successively warps the extracted image until it is well-aligned with the target image. Experiment results on real-world datasets show that our proposed method can effectively improve the performance on extraction and registration tasks in neuroimaging data.
Flash

Data plane verification can be an important technique to reduce network disruptions, and researchers have recently made significant progress in achieving fast data plane verification. However, as we apply existing data plane verification techniques to large-scale networks, two problems appear due to extremes. First, existing techniques cannot handle too-fast arrivals, which we call update storms, when a large number of data plane updates must be processed in a short time. Second, existing techniques cannot handle well too-slow arrivals, which we call long-tail update arrivals, when the updates from a number of switches take a long time to arrive.
Decomposition Methods for Solving Finite-Horizon Large MDPs

Conventional algorithms for solving Markov decision processes (MDPs) become intractable for a large finite state and action spaces. Several studies have been devoted to this issue, but most of them only treat infinite-horizon MDPs. This paper is one of the first works to deal with non-stationary finite-horizon MDPs by proposing a new decomposition approach, which consists in partitioning the problem into smaller restricted finite-horizon MDPs, each restricted MDP is solved independently, in a specific order, using the proposed hierarchical backward induction (HBI) algorithm based on the backward induction (BI) algorithm. Next, the sub-local solutions are combined to obtain a global solution. An example of racetrack problems shows the performance of the proposal decomposition technique.
Evolutionary Design of Reduced Precision Preprocessor for Levodopa-Induced Dyskinesia Classifier

The aim of this work is to design a hardware-efficient implementation of data preprocessing in the task of levodopa-induced dyskinesia classification. In this task, there are three approaches implemented and compared: 1) evolution of magnitude approximation using Cartesian genetic programming, 2) design of preprocessing unit using two-population coevolution (2P-CoEA) of cartesian programs and fitness predictors, which are small subsets of training set, and 3) a design using three-population coevolution (3P-CoEA) combining compositional coevolution of preprocessor and classifier with coevolution of fitness predictors. Experimental results show that all of the three investigated approaches are capable of producing energy-saving solutions, suitable for implementation in hardware unit, with a quality comparable to baseline software implementation. Design of approximate magnitude leads to correctly working solutions, however, more energy-demanding than other investigated approaches. 3P-CoEA is capable of designing both preprocessor and classifier compositionally while achieving smaller solutions than the design of approximate magnitude. Presented 2P-CoEA results in the smallest and the most energy-efficient solutions along with producing a solution with significantly better classification quality for one part of test data in comparison with the software implementation.
Multilingual Transformer Encoders: a Word-Level Task-Agnostic Evaluation

Some Transformer-based models can perform cross-lingual transfer learning: those models can be trained on a specific task in one language and give relatively good results on the same task in another language, despite having been pre-trained on monolingual tasks only. But, there is no consensus yet on whether those transformer-based models learn universal patterns across languages. We propose a word-level task-agnostic method to evaluate the alignment of contextualized representations built by such models. We show that our method provides more accurate translated word pairs than previous methods to evaluate word-level alignment. And our results show that some inner layers of multilingual Transformer-based models outperform other explicitly aligned representations, and even more so according to a stricter definition of multilingual alignment.
Bagging-AdaTSK: An Ensemble Fuzzy Classifier for High-Dimensional Data

Using fuzzy systems to deal with high-dimensional data is still a challenging work, even though our recently proposed adaptive Takagi-Sugeno-Kang (AdaTSK) model equipped with Ada-softmin can be effectively employed to solve high-dimensional classification problems. Facing high-dimensional data, AdaTSK is prone to overfitting phenomenon, which results in poor performance. While ensemble learning is an effective technique to help the base learners to improve the final performance and avoid overfitting. Therefore, in this paper, we propose an ensemble fuzzy classifier integrating an improved bagging strategy and AdaTSK model to handle high-dimensional classification problems, which is named as Bagging-AdaTSK. At first, an improved bagging strategy is introduced and the original dataset is split into multiple subsets containing fewer samples and features. These subsets are overlapped with each other and can cover all the samples and features to guarantee the satisfactory accuracy. Then, on each subset, an AdaTSK model is trained as a base learner. Finally, these trained AdaTSK models are aggregated together to conduct the task, which results in so-called Bagging-AdaTSK. The experimental results on high-dimensional datasets demonstrate that Bagging-AdaTSK has competitive performance.
TD classifier

To date, the identification and quantification of Technical Debt (TD) rely heavily on a few sophisticated tools that check for violations of certain predefined rules, usually through static analysis. Different tools result in divergent TD estimates calling into question the reliability of findings derived by a single tool. To alleviate this issue, we present a tool that employs machine learning on a dataset built upon the convergence of three widely-adopted TD Assessment tools to automatically assess the class-level TD for any arbitrary Java project. The proposed tool is able to classify software classes as high-TD or not, by synthesizing source code and repository activity information retrieved by employing four popular open source analyzers. The classification results are combined with proper visualization techniques, to enable the identification of classes that are more likely to be problematic. To demonstrate the proposed tool and evaluate its usefulness, a case study is conducted based on a real-world open-source software project. The proposed tool is expected to facilitate TD management activities and enable further experimentation through its use in an academic or industrial setting.
Electronic Evidence in Administrative Proceedings

The study established the role of electronic evidence in the system of administrative procedures in Ukraine and in the member states of the Council of Europe. Direct observation, comparison and analysis of the content of the documents were used. The key results of the study were the peculiarities identified from the use of electronic evidence in administrative procedures among the 47 member states of the Council of Europe; established sources of creation, origin of electronic evidence to be used in administrative procedures; the resolute attitude of the European Court of Human Rights and the Committee of Ministers of the Council of Europe towards electronic evidence in administrative proceedings. Unlike paper documents, electronic documents require special attention to their review, search and involvement in the case. It is concluded that the study of electronic evidence should be approached from the point of view of the knowledge and skills of specialists, experts and interpreters who have the appropriate license and experience. The prospects for further investigations are establishing the importance of law enforcement agencies in the field of cybersecurity in ensuring the integrity of electronic evidence used in administrative proceedings.
Robust S-Y-biLSTM object tracking method for on-road objects shoot from an unmanned aerial vehicle

being one of the most challenging tasks in computer vision dynamical object tracking problems have faced additional issues regarding the unmanned aerial vehicle. In particular, image degradation, uneven object intensity, variety in object sizes, etc. In this paper, we proposed the implementation of the S-Y-biLSTM technique for on-road object shooting from an unmanned aerial vehicle. The proposed method contains the YOLOv4eff-based network as a feature map extraction, the SSDeff network as an object detection pipeline as well a bidirectional LSTM-based network as a tracking backbone technique. The object detection technique was improved by replacing the VGG-16 with the DenseNet-S-32-1. In addition, by suggesting a feature fusion module, we have added a residual prediction model for each feature layer within the SSD network for object detection. The training and testing process of the proposed network was performed based on the dataset of on-road objects shouted at the height of 15-45 meters. The results were shown that the proposed object tracking method S-Y-biLSTM has achieved higher accuracy and robustness compared to LYOLOv4eff, DeepSort and ROLO applied to the unmanned aerial vehicle.
End‐to‐end power equipment detection and localization with RM transformer

Power equipment detection and localization is the key component of automatic inspection tasks in substations. To solve the challenges such as complex environment and lack of training data, utilization of context information is considered here. For substation scenes, object relation modelling (RM) is proven to be useful, but an end-to-end efficient framework which is suitable for non-Convolutional Neural Networks (CNN) is still missing. Therefore, an extended Transformer network with an elaborately designed RM module is proposed. As the foundation, transformer network is better than CNN in terms of context dependency construction. On top of that, an RM module is plugged to adjust the decoded feature embeddings based on their appearance, position and class information. The module is based on a graph attention neural network which uses similarity as weights of nodes. The experiments show that the proposed method has a 16.2% improvement in accuracy compared to pipeline, and even 6.4% higher than the most recent models, largely promoting the construction of intelligent substations.
Thermalization by off-shell processes: The virtues of small virtuality

We study the thermalization of a scalar field $\mathrm{\ensuremath{\Phi}}$ coupled to two other scalar fields ${\ensuremath{\chi}}_{1,2}$ that constitute a bath in thermal equilibrium. For a range of masses the $\mathrm{\ensuremath{\Phi}}$ propagator features threshold and infrared divergences, a vanishing residue at the (quasi)particle pole, and vanishing on-shell decay rates thereby preventing the equilibration of $\mathrm{\ensuremath{\Phi}}$ with the bath via on-shell processes. Inspired by the theory of quantum open systems we obtain a quantum master equation for the reduced density matrix of $\mathrm{\ensuremath{\Phi}}$ that includes the time dependence of bath correlations, yielding time dependent rates in the dynamics of relaxation and allowing virtual processes with an energy ${q}_{0}$ whose difference from the on-shell energy is of $\mathcal{O}(1/t)$ at long time $t$. These off-shell processes lead to thermalization despite vanishing $S$-matrix rates. In the case of threshold divergences we find that a thermal fixed point is approached as ${e}^{\ensuremath{-}\sqrt{t/{t}^{*}}}$ with the relaxation time ${t}^{*}$ becoming shorter at high temperature as a consequence of stimulated emission and absorption. In the infrared case, the thermal fixed point is approached as ${e}^{\ensuremath{-}\ensuremath{\gamma}(t)}$, where $\ensuremath{\gamma}(t)$ features a crossover between $\mathrm{ln}(t)$ and $t$ behavior for $t\ensuremath{\gg}1/T$. The vanishing of the residue and the crossover in relaxational dynamics in this case is strikingly reminiscent of the orthogonality catastrophe in heavy impurity systems. The results yield more general lessons on thermalization via virtual processes.