Reviewerly/exHarmony huggingface dataset:
A pretrained proximal policy optimization algorithm with reward shaping for aircraft guidance to a moving destination in three-dimensional continuous space

To enhance the performance of guiding an aircraft to a moving destination in a certain direction in three-dimensional continuous space, it is essential to develop an efficient intelligent algorithm. In this article, a pretrained proximal policy optimization (PPO) with reward shaping algorithm, which does not require an accurate model, is proposed to solve the guidance problem of manned aircraft and unmanned aerial vehicles. Continuous action reward function and position reward function are presented, by which the training speed is increased and the performance of the generated trajectory is improved. Using pretrained PPO, a new agent can be trained efficiently for a new task. A reinforcement learning framework is built, in which an agent can be trained to generate a reference trajectory or a series of guidance instructions. General simulation results show that the proposed method can significantly improve the training efficiency and trajectory performance. The carrier-based aircraft approach simulation is carried out to prove the application value of the proposed approach.
Mixed Reality Flood Visualizations: Reflections on Development and Usability of Current Systems

Interest in and use of 3D visualizations for analysis and communication of flooding risks has been increasing. At the same time, an ecosystem of 3D user interfaces has also been emerging. Together, they offer exciting potential opportunities for flood visualization. In order to understand how we turn potential into real value, we need to develop better understandings of technical workflows, capabilities of the resulting systems, their usability, and implications for practice. Starting with existing geospatial datasets, we develop single user and collaborative visualization prototypes that leverage capabilities of the state-of-the art HoloLens 2 mixed reality system. By using the 3D displays, positional tracking, spatial mapping, and hand- and eye-tracking, we seek to unpack the capabilities of these tools for meaningful spatial data practice. We reflect on the user experience, hardware performance, and usability of these tools and discuss the implications of these technologies for flood risk management, and broader spatial planning practice.
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks

Autoregressive language models pretrained on large corpora have been successful at solving downstream tasks, even with zero-shot usage. However, there is little theoretical justification for their success. This paper considers the following questions: (1) Why should learning the distribution of natural language help with downstream classification tasks? (2) Why do features learned using language modeling help solve downstream tasks with linear classifiers? For (1), we hypothesize, and verify empirically, that classification tasks of interest can be reformulated as next word prediction tasks, thus making language modeling a meaningful pretraining task. For (2), we analyze properties of the cross-entropy objective to show that ϵ-optimal language models in cross-entropy (log-perplexity) learn features that are O(ϵ)-good on natural linear classification tasks, thus demonstrating mathematically that doing well on language modeling can be beneficial for downstream tasks. We perform experiments to verify assumptions and validate theoretical results. Our theoretical insights motivate a simple alternative to the cross-entropy objective that performs well on some linear classification tasks.
Bayesian Optimisation of Large-scale Photonic Reservoir Computers

Reservoir computing is a growing paradigm for simplified training of recurrent neural networks, with a high potential for hardware implementations. Numerous experiments in optics and electronics yield comparable performance with digital state-of-the-art algorithms. Many of the most recent works in the field focus on large-scale photonic systems, with tens of thousands of physical nodes and arbitrary interconnections. While this trend significantly expands the potential applications of photonic reservoir computing, it also complicates the optimisation of the high number of hyper-parameters of the system. In this work, we propose the use of Bayesian optimisation for efficient exploration of the hyper-parameter space in a minimum number of iteration. We test this approach on a previously reported large-scale experimental system, compare it with the commonly used grid search, and report notable improvements in performance and the number of experimental iterations required to optimise the hyper-parameters. Bayesian optimisation thus has the potential to become the standard method for tuning the hyper-parameters in photonic reservoir computing.
Sum-product networks: A survey

A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent probability distributions and non-terminal nodes represent convex sums (weighted averages) and products of probability distributions.They are closely related to probabilistic graphical models, in particular to Bayesian networks with multiple context-specific independencies.Their main advantage is the possibility of building tractable models from data, i.e., models that can perform several inference tasks in time proportional to the number of edges in the graph.They are somewhat similar to neural networks and can address the same kinds of problems, such as image processing and natural language understanding.This paper offers a survey of SPNs, including their definition, the main algorithms for inference and learning from data, several applications, a brief review of software libraries, and a comparison with related models.
Hierarchical Adaptive Eddy-Capturing Approach for Modeling and Simulation of Turbulent Flows

A short review of wavelet-based adaptive methods for modeling and simulation of incompressible turbulent flows is presented. Wavelet-based computational modeling approaches of different fidelities are recast into an integrated hierarchical adaptive eddy-capturing turbulence modeling framework. The wavelet threshold filtering procedure and the associated wavelet-filtered Navier–Stokes equations are briefly discussed, along with the adaptive wavelet collocation method that is used for numerical computations. Depending on the level of wavelet thresholding, the simulation is possibly supplemented with a localized closure model. The latest advancements in spatiotemporally varying wavelet thresholding procedures along with the adaptive-anisotropic wavelet-collocation method make the development of a fully adaptive approach feasible with potential applications for complex turbulent flows.
Exploration for Platinum-Group Minerals in Till: A New Approach to the Recovery, Counting, Mineral Identification and Chemical Characterization

The discovery of new mineral deposits contributes to the sustainable mineral industrial development, which is essential to satisfy global resource demands. The exploration for new mineral resources is challenging in Canada since its vast lands are mostly covered by a thick layer of Quaternary sediments that obscure bedrock geology. In the course of the recent decades, indicator minerals recovered from till heavy mineral concentrates have been effectively used to prospect for a broad range of mineral deposits including diamond, gold, and base metals. However, these methods traditionally focus on (visual) investigation of the 0.25–2.0 mm grain-size fraction of unconsolidated sediments, whilst our observations emphasize on higher abundance, or sometimes unique occurrence of precious metal (Au, Ag, and platinum-group elements) minerals in the finer-grained fractions (&lt;0.25 mm). This study aims to present the advantages of applying a mineral detection routine initially developed for gold grains counting and characterization, to platinum-group minerals in &lt;50 µm till heavy mineral concentrates. This technique, which uses an automated scanning electron microscopy (SEM) equipped with an energy dispersive spectrometer, can provide quantitative mineralogical and semi-quantitative chemical data of heavy minerals of interest, simultaneously. This work presents the mineralogical and chemical characteristics, the grain size distribution, and the surface textures of 2664 discrete platinum-group mineral grains recovered from the processing of 5194 glacial sediment samples collected from different zones in the Canadian Shield (mostly Quebec and Ontario provinces). Fifty-eight different platinum-group mineral species have been identified to date, among which sperrylite (PtAs2) is by far the most abundant (n = 1488; 55.86%). Textural and mineral-chemical data suggest that detrital platinum-group minerals in the studied samples have been derived, at least in part, from Au-rich ore systems.
Bitcoin Theft Detection Based on Supervised Machine Learning Algorithms

Since its inception, Bitcoin has been subject to numerous thefts due to its enormous economic value. Hackers steal Bitcoin wallet keys to transfer Bitcoin from compromised users, causing huge economic losses to victims. To address the security threat of Bitcoin theft, supervised learning methods were used in this study to detect and provide warnings about Bitcoin theft events. To overcome the shortcomings of the existing work, more comprehensive features of Bitcoin transaction data were extracted, the unbalanced dataset was equalized, and five supervised methods—the k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and multi-layer perceptron (MLP) techniques—as well as three unsupervised methods—the local outlier factor (LOF), one-class support vector machine (OCSVM), and Mahalanobis distance-based approach (MDB)—were used for detection. The best performer among these algorithms was the RF algorithm, which achieved recall, precision, and F1 values of 95.9%. The experimental results showed that the designed features are more effective than the currently used ones. The results of the supervised methods were significantly better than those of the unsupervised methods, and the results of the supervised methods could be further improved after equalizing the training set.
Engineering spin-spin interactions with optical tweezers in trapped ions

We propose a new method for generating programmable interactions in one- and two-dimensional trapped-ion quantum simulators. Here we consider the use of optical tweezers to engineer the sound-wave spectrum of trapped ion crystals. We show that this approach allows us to tune the interactions and connectivity of the ion qubits beyond the power-law interactions accessible in current setups. We demonstrate the experimental feasibility of our proposal using realistic tweezer settings and experimentally relevant trap parameters to generate the optimal tweezer patterns to create target spin-spin interaction patterns in both one- and two-dimensional crystals. Our approach will advance quantum simulation in trapped-ion platforms as it allows them to realize a broader family of quantum spin Hamiltonians.
On the User-Centric Comparative Remote Evaluation of Interactive Video Search Systems

In the research of video retrieval systems, comparative assessments during dedicated retrieval competitions provide priceless insights into the performance of individual systems. The scope and depth of such evaluations are unfortunately hard to improve, due to the limitations by the set-up costs, logistics, and organization complexity of large events. We show that this easily impairs the statistical significance of the collected results, and the reproducibility of the competition outcomes. In this article, we present a methodology for remote comparative evaluations of content-based video retrieval systems and demonstrate that such evaluations scale-up to sizes that reliably produce statistically robust results, and propose additional measures that increase the replicability of the experiment. The proposed remote evaluation methodology forms a major contribution toward open science in interactive retrieval benchmarks. At the same time, the detailed evaluation reports form an interesting source of new observations about many subtle, previously inaccessible aspects of video retrieval.
Deep multimodal learning for cross-modal retrieval: One model for all tasks

Abstract We investigate the effectiveness of a successful model in Visual-Question-Answering (VQA) problems as the core component in a cross-modal retrieval system that can accept images or text as queries, in order to retrieve relevant data from a multimodal document collection. To this end, we adapt the VQA model for deep multimodal learning to combine visual and textual representations for information search, and we call this model “Deep Multimodal Embeddings (DME)”. Instead of training the model to answer questions, we supervise DME to classify semantic topics/concepts previously identified in the document collection of interest. In contrast to previous approaches, we found that this model can handle any multimodal query with a single architecture while producing improved or competitive results in all retrieval tasks. We evaluate the model performance with 3 widely known databases for cross-modal retrieval tasks: Wikipedia Retrieval Database, Pascal Sentences, and MIR-Flickr-25k. The results show that the DME model learns effective multimodal representations, resulting in strongly improved retrieval performance, specifically in the top results of the ranked list, which are the most important to users in the most-common scenarios of information retrieval. Our work represents a new baseline for a wide set of different methodologies for cross-modal retrieval.
Learning safe neural network controllers with barrier certificates

Abstract We provide a new approach to synthesize controllers for nonlinear continuous dynamical systems with control against safety properties. The controllers are based on neural networks (NNs). To certify the safety property we utilize barrier functions, which are represented by NNs as well. We train the controller-NN and barrier-NN simultaneously, achieving a verification-in-the-loop synthesis. We provide a prototype tool nncontroller with a number of case studies. The experiment results confirm the feasibility and efficacy of our approach.
Orthros: A Low-Latency PRF

We present Orthros, a 128-bit block pseudorandom function. It is designed with primary focus on latency of fully unrolled circuits. For this purpose, we adopt a parallel structure comprising two keyed permutations. The round function of each permutation is similar to Midori, a low-energy block cipher, however we thoroughly revise it to reduce latency, and introduce different rounds to significantly improve cryptographic strength in a small number of rounds. We provide a comprehensive, dedicated security analysis. For hardware implementation, Orthros achieves the lowest latency among the state-of-the-art low-latency primitives. For example, using the STM 90nm library, Orthros achieves a minimum latency of around 2.4 ns, while other constructions like PRINCE, Midori-128 and QARMA9-128- σ0 achieve 2.56 ns, 4.10 ns, 4.38 ns respectively.
Test and application of the geochemical lithogene on weathering profiles developed over granitic and basaltic rocks in China

Geochemical gene is a new technique presented recently and its property needs to be tested. Two weathering profiles developed over granitic and basaltic rocks were sampled and analyzed in Dunhua area in Northeast China with a cool temperate monsoon climate. Based on their major and trace elemental compositions, the geochemical lithogene numbered as LG01 was coded and samples' gene similarities were calculated relative to their parent rocks and top soils respectively. According to the similar gene criterion of ≥80% on gene similarity, the geochemical lithogene LG01 shows not only good heredity (on similar gene from parent rock to top soil) but also good inheritance (on similar gene from top soil to parent rock) in each weathering profile. That is to say, the geochemical lithogene LG01 illustrates good stability during weathering. According to the composition classification method based on the acidic similarity (a gene similarity relative to the acidic rock with the LG01 gene of 10202020202), the weathered products of the profile developed over granite are acidic-like composition (with acidic similarity of ≥80%) and those formed on basalt are basic-like composition (with acidic similarity of ≤20%). These indicate the validity of the composition classification method based on the geochemical lithogene LG01. In order to test the property and application of LG01 in a wider range, four newly reported weathering profiles (a granitic profile and a basaltic profile located in a warm temperate monsoon climate zone, another granitic profile in a subtropical monsoon climate zone, and another basaltic profile in a tropical monsoon climate zone) were also discussed. Their results verify the conclusion that the geochemical lithogene LG01 not only illustrates good stability among samples due to rock weathering but also is a potential tool on classification of rocks and their weathered products.
Moving Object Detection Technology of Line Dancing Based on Machine Vision

In this paper, line dancing's moving object detection technology based on machine vision is studied to improve object detection. For this purpose, the improved frame difference for the background modeling technique is combined with the target detection algorithm. The moving target is extracted, and the postmorphological processing is carried out to make the target detection more accurate. Based on this, the tracking target is determined on the time axis of the moving target tracking stage, the position of the target in each frame is found, and the most similar target is found in each frame of the video sequence. The association relationship is established to determine a moving object template or feature. Through certain measurement criteria, the mean-shift algorithm is used to search the optimal candidate target in the image frame and carry out the corresponding matching to realize moving objects' tracking. This method can detect the moving targets of line dancing in various areas through the experimental analysis, which will not be affected by the position or distance, and always has a more accurate detection effect.
Interference at the single-photon level based on silica photonics robust against channel disturbance

Quantum key distribution (QKD) provides a solution for communication of unconditional security. However, the quantum channel disturbance in the field severely increases the quantum bit-error rate, degrading the performance of a QKD system. Here we present a setup comprising silica planar light wave circuits (PLCs), which is robust against the channel polarization disturbance. Our PLCs are based on the asymmetric Mach–Zehnder interferometer (AMZI), integrated with a tunable power splitter and thermo-optic phase modulators. The polarization characteristics of the AMZI PLC are investigated by a novel pulse self-interfering method to determine the operation temperature of implementing polarization insensitivity. Over a 20 km fiber channel with 30 Hz polarization scrambling, our time-bin phase-encoding QKD setup is characterized with an interference fringe visibility of 98.72%. The extinction ratio for the phase states is kept between 18 and 21 dB for 6 h without active phase correction.
DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

In this paper, we propose a robust face recognition model called DeepWTPCA-L 1 using WTPCA-L 1 features and a CNN-LSTM architecture.First, WTPCA-L 1 algorithm, composed of Three-level decomposition of discrete wavelet transform followed by PCA-L 1 algorithm, is exploited to extract face features.Then, the extracted features are used as inputs of the proposed CNN-LSTM architecture.To evaluate the robustness of the proposed approach, several face recognition datasets have been used.In addition, the proposed method is trained on noisy images using Gaussian, and Salt & Pepper noise added to the facial images of each dataset.The results of the experiment indicate that the proposed model achieves high recognition performance on three well-known standard face databases.When compared to state-of-the-art methods, the proposed model achieves a better face recognition rate.
Data augmentation in a hybrid approach for aspect-based sentiment analysis

Data augmentation is a way to increase the diversity of available data by applying constrained transformations on the original data. This strategy has been widely used in image classification but has to the best of our knowledge not yet been used in aspect-based sentiment analysis (ABSA). ABSA is a text analysis technique that determines aspects and their associated sentiment in opinionated text. In this paper, we investigate the effect of data augmentation on a state-of-the-art hybrid approach for aspect-based sentiment analysis (HAABSA). We apply modified versions of easy data augmentation (EDA), backtranslation, and word mixup. We evaluate the proposed techniques on the SemEval 2015 and SemEval 2016 datasets. The best result is obtained with the adjusted version of EDA, which yields a 0.5 percentage point improvement on the SemEval 2016 dataset and 1 percentage point increase on the SemEval 2015 dataset compared to the original HAABSA model.
Multi‐scale pedestrian detection based on self‐attention and adaptively spatial feature fusion

Pedestrian detection is a classic problem in computer vision, which has an essential impact on the safety of urban autonomous driving. Although significant improvement has been made in pedestrian detection recently, small-scale pedestrian detection is still challenging. To effectively tackle this issue, a multi-scale pedestrian detector based on self-attention mechanism and adaptive spatial feature fusion is proposed in this paper. In order to better extract global information, the spatial attention mechanism asymmetric pyramid non-local block (APNB) module is applied. To achieve scale-invariance detection, multiple detection branches are designed, which include a high-resolution detection branch and a low-resolution detection branch. In integrating multi-scale features, the adaptively spatial feature fusion (ASFF) method is employed, which can solve the problem of feature inconsistency across different scales. Experimental results show that the proposed method obtains competitive performance on Caltech and CityPersons datasets.
The Relevance of Financial Inclusion on Sustainable Economic Growth in Sub-Saharan African Nations

The primary concern of this examination is to systematically survey the importance of inclusive access to finance on the growth in terms of the economy in 48 sub-Saharan African (SSA) sovereign states with periodicity from 1995 to 2017. This study reports the results using both static and dynamic estimation techniques. For consistency, the baseline finding of the study estimation is based on the Generalized Method of Moments (GMM) system GMM. This article finds that there is a complimentary association between the present degree of inclusiveness of finance and economic advancement in SSA. The suggestion deduced in this examination is that programs with the plan of comprehensive financing ought to be custom fitted to the agricultural segment of the economy to encourage more economic opportunities for development in a sustainable manner.