Reviewerly/exHarmony huggingface dataset:
Cross-Modality Person Re-Identification Based on Dual-Path Multi-Branch Network

Person re-identification is an important surveillance task of searching and identifying pedestrian across different images or video frames. Despite a significant progress has been made in person re-identification based on RGB image sensors, few work focus on the person re-identification between RGB and infrared images, which is a challenging cross-modality problem and has been widely encountered in a dark environment or at night. In addition to the challenges for the same identity associated with variations in camera viewpoints and person poses, there is a non-negligible shift across different sensor modalities since the visual characteristics from RGB and infrared images are heterogeneous. In this paper, we propose a novel end-to-end dual-path multi-branch network for RGB-infrared cross-modality person re-identification, which introduces the multi-branch deep network architecture. The experimental results obtained with SYSU-MM01 datasets indicate that the proposed method can successfully transfer descriptive visual characteristic between RGB and infrared sensor modality. It can significantly outperform state-of-the-art conventional methods and convolutional neural network methods.
How does the offense-defense balance scale?

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Reversible GANs for Memory-Efficient Image-To-Image Translation

The pix2pix and CycleGAN losses have vastly improved the qualitative and quantitative visual quality of results in image-to-image translation tasks. We extend this framework by exploring approximately invertible architectures which are well suited to these losses. These architectures are approximately invertible by design and thus partially satisfy cycle-consistency before training even begins. Furthermore, since invertible architectures have constant memory complexity in depth, these models can be built arbitrarily deep. We are able to demonstrate superior quantitative output on the Cityscapes and Maps datasets at near constant memory budget.
Automated detection of altered mental status in emergency department clinical notes: a deep learning approach

Machine learning has been used extensively in clinical text classification tasks. Deep learning approaches using word embeddings have been recently gaining momentum in biomedical applications. In an effort to automate the identification of altered mental status (AMS) in emergency department provider notes for the purpose of decision support, we compare the performance of classic bag-of-words-based machine learning classifiers and novel deep learning approaches.We used a case-control study design to extract an adequate number of clinical notes with AMS and non-AMS based on ICD codes. The notes were parsed to extract the history of present illness, which was used as the clinical text for the classifiers. The notes were manually labeled by clinicians. As a baseline for comparison, we tested several traditional bag-of-words based classifiers. We then tested several deep learning models using a convolutional neural network architecture with three different types of word embeddings, a pre-trained word2vec model and two models without pre-training but with different word embedding dimensions.We evaluated the models on 1130 labeled notes from the emergency department. The deep learning models had the best overall performance with an area under the ROC curve of 98.5% and an accuracy of 94.5%. Pre-training word embeddings on the unlabeled corpus reduced training iterations and had performance that was statistically no different than the other deep learning models.This supervised deep learning approach performs exceedingly well for the detection of AMS symptoms in clinical text in our environment. Further work is needed for the generalizability of these findings, including evaluation of these models in other types of clinical notes and other environments. The results seem promising for the ultimate use of these types of classifiers in combination with other information derived from the electronic health records as input for clinical decision support.
Big data clustering with varied density based on MapReduce

The DBSCAN algorithm is a prevalent method of density-based clustering algorithms, the most important feature of which is the ability to detect arbitrary shapes and varied clusters and noise data. Nevertheless, this algorithm faces a number of challenges, including failure to find clusters of varied densities. On the other hand, with the rapid development of the information age, plenty of data are produced every day, such that a single machine alone cannot process this volume of data; hence, new technologies are required to store and extract information from this volume of data. A large volume of data that is beyond the capabilities of existing software is called Big data. In this paper, we have attempted to introduce a new algorithm for clustering big data with varied density using a Hadoop platform running MapReduce. The main idea of this research is the use of local density to find each point's density. This strategy can avoid the situation of connecting clusters with varying densities. The proposed algorithm is implemented and compared with other algorithms using the MapReduce paradigm and shows the best varying density clustering capability and scalability.
Developing of the new models in solar radiation estimation with curve fitting based on moving least-squares approximation

Recently, serious progress has been made in solar energy applications in developed and developing countries. Solar radiation on a horizontal surface is the basic parameter required for the design of solar energy systems and for evaluating the system performance. Therefore, solar radiation the exact determination of the amount of land in different latitudes on the earth's surface is of great importance in many solar energy applications. In this study, Angstorm coefficients were determined by Moving Least Squares Approximation (MLSA). Three different models were obtained by using the moving least squares method. In this study, new empirical models were developed for determining the monthly average daily global solar radiation on a horizontal surface for Antalya. The developed models were compared with the models in the literature by using different error analysis methods. The statistical compatibility of the investigated models was tested and the model closest to the measurements was determined. Although, this study concluded that the suggested methods are applicable to estimate the monthly average daily diffuse radiation on a horizontal surface for selected region, it has been observed that the performance of these models varies according to years and the error analysis test used. If the results are generally evaluated the developed linear model showed the best performance to estimate the global solar radiation on a horizontal surface for Antalya. Also, among the models used in the literature, the Togrul model showed the best performance for selected region.
Hybrid Quantum Computing with Conditional Beam Splitter Gate in Trapped Ion System

The hybrid approach to quantum computation simultaneously utilizes both discrete and continuous variables which offers the advantage of higher density encoding and processing powers for the same physical resources. Trapped ions, with discrete internal states and motional modes which can be described by continuous variables in an infinite dimensional Hilbert space, offer a natural platform for this approach. A nonlinear gate for universal quantum computing can be implemented with the conditional beam splitter Hamiltonian $|e\rangle \langle e| ( a^{\dagger} b + a b^{\dagger})$ that swaps the quantum states of two motional modes, depending on the ion's internal state. We realize such a gate and demonstrate its applications for quantum state overlap measurements, single-shot parity measurement, and generation of NOON states.
Unbiased FIR Filtering for Time-Stamped Discretely Delayed and Missing Data

The unbiased finite impulse response (UFIR) filtering approach is developed for discrete-time state-space models with time-stamped discretely delayed and missing data. The model with k-step-lags in observations is transformed to have no latency and expanded on a finite horizon of N most recent data points. It is shown that the optimal horizon for the UFIR filter is practically k-invariant, unlike the tuning factor of the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter. Higher robustness of the UFIR filter against the Kalman and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filters is justified theoretically in uncertain environments with discretely delayed and missing data. Experimental verification is provided based on GPS-based tracking of a moving vehicle to demonstrate a good agreement with the theory.
Video Super-Resolution Based on 3D-CNNS with Consideration of Scene Change

In video super-resolution, the spatio-temporal coherence between, and among the frames must be exploited appropriately for the accurate prediction of the high resolution frames. Although 2D-CNNs are powerful in modelling images, 3D-CNNs are more suitable for spatio-temporal feature extraction as they can preserve the temporal information. To this end, we propose an effective 3D-CNN for video super-resolution that does not require motion alignment as preprocessing. The proposed 3DSRnet maintains the temporal depth of spatio-temporal feature maps to maximally capture the temporally nonlinear characteristics between low and high resolution frames, and adopts residual learning in conjunction with the sub-pixel outputs. It outperforms the state-of-the-art method with average 0.45 dB and 0.36 dB higher in PSNR, for scale 3 and 4, in the Vidset4 benchmark. Our 3DSRnet first deals with the performance drop due to scene change, which is important in practice but has not been previously considered.
Quantum Image Watermarking Algorithm Based on Haar Wavelet Transform

In this paper, a novel frequency domain quantum watermarking scheme is proposed based on the Flexible Representation of Quantum Images, which can embed a 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n1</sup> × 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n1</sup> binary watermark image into a 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> × 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> grayscale carrier image. The quantum Haar wavelet transform is developed and used to decompose quantum images. The diagonal detail coefficients of the carrier image are obtained from the image decomposition. Then, according to the watermark image information, the diagonal wavelet coefficients are either unchanged or slightly modified. Since all of the used quantum operations are invertible, extraction of the watermark image is performed in a straightforward manner by reversing the watermarking embedding process. Finally, the proposed quantum image watermarking scheme is simulated on a classical computer and evaluated under different carrier and watermark images. The simulation results and performance analyses indicate the high performance of the presented watermarking scheme in terms of the similarity between the watermarked and carrier images.
Comparison of ReLU and linear saturated activation functions in neural network for universal approximation

Activation functions used in hidden layers directly affect the possibilities for describing nonlinear systems using a feedforward neural network. Furthermore, linear based activation functions are less computationally demanding than their nonlinear alternatives. In addition, feedforward neural networks with linear based activation functions can be advantageously used for control of nonlinear systems, as shown in previous authors' publications. This paper aims to compare two types of linear based functions - symmetric linear saturated function and the rectifier linear unit (ReLU) function as activation functions of the feedforward neural network used for a nonlinear system approximation. Topologies with one hidden layer and the combination of defined quantities of hidden layer neurons in the feedforward neural network are used. Strict criteria are applied for the conditions of the experiments; specifically, the Levenberg-Marquardt algorithm is applied as a training algorithm and the Nguyen-Widrow algorithm is used for the weights and biases initialization. Three benchmark systems are then selected as nonlinear plants for approximation, which should serve as a repeatable source of data for testing. The training data are acquired by the computation of the output as a reaction to a specified colored input signal. The comparison is based on the convergence speed of the training for a fixed value of the error function, and also on the performance over a constant number of epochs. At the end of the experiments, only small differences between the performance of both applied activation functions are observed. Although the symmetric linear saturated activation function provides the lesser median of the final error function value across the all tested numbers of neurons in topologies, the ReLU function seems to be also capable of use as the activation function for nonlinear system modeling.
Machine learning application to automatically classify heavy minerals in river sand by using SEM/EDS data

Heavy minerals are generally trace components of sand or sandstone. Fast and accurate heavy mineral classification has become a necessity. Energy Dispersive X-ray Spectrometers (EDS) integrated with Scanning Electron Microscopy (SEM) were used to obtain rapid heavy mineral elemental compositions. However, mineral identification is challenging since there are wide ranges of spectral datasets for natural minerals. This study aimed to find a reliable, machine learning classifier for identifying various heavy minerals based on EDS data. After selecting 22 distinct heavy minerals from modern river sands, we obtained their elemental data by SEM/EDS. The elemental data from a total of 3067 mineral grains were collected under various instrumental conditions. We compared the classification performance of four classifiers (Decision Tree, Random Forest, Support Vector Machine, Bayesian Network). Our results indicated that machine learning methods, especially Random Forest, can be used as the most effective classifier for heavy mineral classification.
Image Inpainting For Random Areas Using Dense Context Features

Deep convolutional neural networks (DCNN) have demonstrated their potential to generate reasonable results in image inpainting. Some existing method uses convolution to generate surrounding features, then passes features by fully connected layers, and finally predicts missing regions. Although the final result is semantically reasonable, some blurred situations generated because the standard convolution is used, which conditioned on the effective pixels and the substitute values in the masked holes. In this paper, we introduce dense blocks for the U-Net architecture, which can alleviate the problem of gradient disappearance, while also reducing the number of parameters. The most important is that it can enhance the transfer of features and make more efficient use of them. Partial convolution is used to solve the problem of artifacts such as color differences and blurring. Experiments on the place365 dataset demonstrate our approach can generate more detailed and semantically reasonable results in random area image inpainting.
On-Premises Serverless Computing for Event-Driven Data Processing Applications

The advent of open-source serverless computing frameworks has introduced the ability to bring the Functions-as-a-Service (FaaS) paradigm for applications to be executed on-premises. In particular, data-driven scientific applications can benefit from these frameworks with the ability to trigger scalable computation in response to incoming workloads of files to be processed. This paper introduces an open-source framework to achieve on-premises serverless computing for event-driven data processing applications that features: i) the automated provisioning of an elastic Kubernetes cluster that can grow and shrink, in terms of the number of nodes, on multi-Clouds; ii) the automated deployment of a FaaS framework together with a data storage back-end that triggers events upon file uploads; iii) a service that provides a REST API to orchestrate the creation of such functions and iv) a graphical user interface that provides a unified entry point to interact with the aforementioned services. Together, this provides a framework to deploy a computing platform to create highly-parallel event-driven file-processing serverless applications that execute on customized runtime environments provided by Docker containers that run on an elastic Kubernetes cluster. The usefulness of this framework is exemplified by means of the execution of a data-driven workflow for optimised object detection on video. The workflow is tested under three different workloads which process ten, a hundred and a thousand functions. The results show that the presented architecture is able to process such workloads taking advantage of its elasticity to make a sensible usage of the resources.
Overview of PAN 2019: Bots and Gender Profiling, Celebrity Profiling, Cross-Domain Authorship Attribution and Style Change Detection

We briefly report on the four shared tasks organized as part of the PAN 2019 evaluation lab on digital text forensics and authorship analysis. Each task is introduced, motivated, and the results obtained are presented. Altogether, the four tasks attracted 373 registrations, yielding 72 successful submissions. This, and the fact that we continue to invite the submission of software rather than its run output using the TIRA experimentation platform, demarcates a good start into the second decade of PAN evaluations labs.
2.5D Convolution for RGB-D Semantic Segmentation

Convolutional neural networks (CNN) have achieved great success in RGB semantic segmentation. RGB-D images provide additional depth information, which can improve segmentation performance. To take full advantages of the 3D geometry relations provided by RGB-D images, in this paper, we propose 2.5D convolution, which mimics one 3D convolution kernel by several masked 2D convolution kernels. Our 2.5D convolution can effectively process spatial relations between pixels in a manner similar to 3D convolution while still sampling pixels on 2D plane, and thus saves computational cost. And it can be seamlessly incorporated into pretrained CNNs. Experiments on two challenging RGB-D semantic segmentation benchmarks NYUDv2 and SUN-RGBD validate the effectiveness of our approach.
Body Sensor Network-Based Robust Gait Analysis: Toward Clinical and at Home Use

Gait analysis has become an important tool for diagnosing disease and evaluating disease progression. Currently gait analysis was mainly conducted by experienced physicians and relies on medical observation or complex medical equipment; hence, the application has been limited. This research aims to build an ambulatory gait analysis system based on the emerging body sensor networks. A calibration method for a magnetometer was proposed to deal with ubiquitous a magnetic disturbance. A proportional integral controller-based complementary filter and error correction of gait parameters have been defined with a multi-level data fusion algorithm. Preliminary gait analysis trials were conducted on both healthy subjects and patients with abnormal gait. Experimental results indicated that the proposed scheme has significant advantages in terms of test accuracy and efficiency, as well as operation complexity compared with conventional gait analysis approaches. Accordingly, measurement of gait abnormality may provide a wealth of information regarding neuromotor status and the characterization of some neurological disorders. It is concluded that the proposed gait analysis system has great potential as an auxiliary for medical rehabilitation.
LP-BNN: Ultra-low-Latency BNN Inference with Layer Parallelism

High inference latency seriously limits the deployment of DNNs in real-time domains such as autonomous driving, robotic control, and many others. To address this emerging challenge, researchers have proposed approximate DNNs with reduced precision, e.g., Binarized Neural Networks (BNNs). While BNNs can be built to have little loss in accuracy, latency reduction still has much room for improvement. In this paper, we propose a single-FPGA-based BNN accelerator that achieves microsecond-level ultra-low-latency inference of ImageNet, LP-BNN. We obtain this performance via several design optimizations. First, we optimize the network structure by removing Batch Normalization (BN) functions which leads to significant latency in BNNs without any loss on accuracy. Second, we propose a parameterized architecture which is based on layer parallelism and supports nearly perfect load balancing for various types of BNNs. Third, we fuse all the convolution layers and the first fully connected layer. We process them in parallel through fine-grained inter-layer pipelining. With our proposed accelerator, the inference of binarized AlexNet, VGGNet, and ResNet are completed within 21.5us, 335us, and 67.8us respectively, with no loss in accuracy as compared with other BNN implementations.
Cooperative sensor fusion in centralized sensor networks using Cauchy–Schwarz divergence

This paper presents a new solution for statistical fusion of multi-sensor information acquired from different fields of view, in a centralized sensor network. The focus is on applications that involve tracking unknown number of objects with time-varying states. Our solution is a track-to-track fusion method in which the information contents of posteriors are combined. Existing information-theoretic solutions for track-to-track fusion in sensor networks are commonly devised based on minimizing the average information divergence from the local posteriors to the fused one. A common approach is to use Generalized Covariance Intersection rule for sensor fusion. This approach works best when all the sensors detect the same object(s), and performs poorly when fields-of-view are different. We suggest Cauchy–Schwarz divergence to be used for measuring information divergence. We demonstrate that employing Cauchy–Schwarz divergence leads to fusion rules that are generally more tolerant to imperfect consensus. We show that the proposed fusion rule for multiple Poisson posteriors is the weighted arithmetic mean of the Poisson densities. Furthermore, we derive the fusion rule for labeled multi Bernoulli filter by approximating the labeled multi Bernoulli density to its first order moment. Numerical experiments show the superior performance of our solution compared to Kullback–Leibler averaging method.