Reviewerly/exHarmony huggingface dataset:
Context-aware seeds for read mapping

Most modern seed-and-extend NGS read mappers employ a seeding scheme that requires extracting t non-overlapping seeds in each read in order to find all valid mappings under an edit distance threshold of t. As t grows, this seeding scheme forces mappers to use more and shorter seeds, which increases the seed hits (seed frequencies) and therefore reduces the efficiency of mappers.We propose a novel seeding framework, context-aware seeds (CAS). CAS guarantees finding all valid mappings but uses fewer (and longer) seeds, which reduces seed frequencies and increases efficiency of mappers. CAS achieves this improvement by attaching a confidence radius to each seed in the reference. We prove that all valid mappings can be found if the sum of confidence radii of seeds are greater than t. CAS generalizes the existing pigeonhole-principle-based seeding scheme in which this confidence radius is implicitly always 1. Moreover, we design an efficient algorithm that constructs the confidence radius database in linear time. We experiment CAS with E. coli genome and show that CAS significantly reduces seed frequencies when compared with the state-of-the-art pigeonhole-principle-based seeding algorithm, the Optimal Seed Solver.https://github.com/Kingsford-Group/CAS_code.
One-way LOCC indistinguishable lattice states via operator structures

Lattice states are a class of quantum states that naturally generalize the fundamental set of Bell states. We apply recent results from quantum error correction and from one-way local operations and classical communication (LOCC) theory that are built on the structure theory of operator systems and operator algebras, to develop a technique for the construction of relatively small sets of lattice states not distinguishable by one-way LOCC schemes. We also present examples, show the construction extends to generalized Pauli states, and compare the construction to other recent work.
Implementing FAIR data for people and machines: Impacts and implications - results of a research data community workshop

The Implementing FAIR Data for People and Machines: Impacts and Implications workshop was organized by the Board on Research Data and Information of the National Academies of Sciences, Engineering, and Medicine (NASEM), the CENDI Federal Information Managers Group, the Research Data Alliance (RDA), and the National Federation of Advanced Information Services (NFAIS), and held at NASEM’s Keck Center in Washington, DC on September 11, 2019. The goals of the Implementing FAIR Data workshop were to discuss the current status of FAIR data implementation, share what is being done to encourage scientists to share data in machine-readable formats, and examine the implications of FAIR data implementation for people and machines. FAIR data policies, tools, and measures of FAIR data compliance were considered from multiple perspectives. Marcia McNutt, President of the National Academy of Sciences (NAS), offered opening remarks, and the keynote address was presented by Barend Mons, Professor of Bioinformatics at Leiden University Medical Center and President of the International Science Council’s Committee on Data (CODATA). Three panel discussions addressed (1) the perspectives of scientists and administrators from U.S. federal agencies, (2) case studies on the implementation of FAIR data practices, and (3) principles and methods of measuring FAIR data compliance. The automation of scientific workflows was discussed by Stuart Feldman, Chief Scientist of Schmidt Futures, a philanthropic organization devoted to investing in research, technology, and science. The workshop closed with highlights and takeaways from each session as summarized by the moderators, followed by general questions.
Self-Attentive Associative Memory

Heretofore, neural networks with external memory are restricted to single memory with lossy representations of memory interactions. A rich representation of relationships between memory pieces urges a high-order and segregated relational memory. In this paper, we propose to separate the storage of individual experiences (item memory) and their occurring relationships (relational memory). The idea is implemented through a novel Self-attentive Associative Memory (SAM) operator. Found upon outer product, SAM forms a set of associative memories that represent the hypothetical high-order relationships between arbitrary pairs of memory elements, through which a relational memory is constructed from an item memory. The two memories are wired into a single sequential model capable of both memorization and relational reasoning. We achieve competitive results with our proposed two-memory model in a diversity of machine learning tasks, from challenging synthetic problems to practical testbeds such as geometry, graph, reinforcement learning, and question answering.
Ultrasound Medical Image Denoising Using Threshold Based Wavelet Transformation Method

Ultrasound imaging is most commonly used and secure medical diagnostic approach its low cost, noninvasive nature of real time image construction. However because of signal dependant noise existence, ultrasound imaging gets degraded: A baseline phase in image processing is removing diverse noise types from image. Noise sources of image generally occur during storage, transmission and image acquisition. Image denoising is an issue determined in diverse computer vision and image processing crisis. There are diverse prevailing approaches to denoise image. The significant property of finest denoising of an image model has to remove noise while preserving edges. For wavelet thresholding paradigm, Fisz transformation method is brought-in in the current work. Furthermore, it has problem with poor denoising performance and hence the quality of image is minimized considerably. In this paper, Improved Threshold based Wavelet Transformation Method (ITWTM) is suggested to rectify above mentioned crisis, to enhance the better denoising images, the soft threshold method is introduced. To enhance visual quality of noisy image, it simply changes coefficients with help of softthresholding method. Speckle noise, additive noise, Gaussian noise and multiplicative noise factors affect the ultrasound images, which minimize the image quality and effects the human interpretation. So, ITWTM helps to minimize the noise rate considerably for the provided US image. The experimental result confirms that the proposed ITWTM provides better performance with respect to higher PSNR, SSIM and lower MSE, execution time rather than the previous Fisz transformation and DWT methods.
Teaching Container-Based DevOps Practices

We present the design of a online course that focuses on container-based virtualization as part of the DevOps toolchain. In addition, we outline the professional background of participants taking the course, and describe how this affects perceived previous knowledge of DevOps. We found out that the self-evaluated conceptual understanding of DevOps topics is nearly equal regardless of the participants professional identity (e.g., student or developer). However, there are significant differences in how much participants have used tools like Docker before. We conclude that there is a clear need for lifelong learning among software engineering professionals as (future) developers often struggle in operations related skills such as command line or networking.
Symmetric Regularization based BERT for Pair-wise Semantic Reasoning

The ability of semantic reasoning over the sentence pair is essential for many natural language understanding tasks, e.g., natural language inference and machine reading comprehension. A recent significant improvement in these tasks comes from BERT. As reported, the next sentence prediction (NSP) in BERT is of great significance for downstream problems with sentence-pair input. Despite its effectiveness, NSP still lacks the essential signal to distinguish between entailment and shallow correlation. To remedy this, we propose to augment the NSP task to a multi-class categorization task, which includes previous sentence prediction (PSP). This task encourages the model to learn the subtle semantics, thereby improves the ability of semantic understanding. Furthermore, by using a smoothing technique, the scopes of NSP and PSP are expanded into a broader range which includes close but nonsuccessive sentences. This simple method yields remarkable improvement against vanilla BERT. Our method consistently improves the performance on the NLI and MRC benchmarks by a large margin, including the challenging HANS dataset.
An Optimized Framework for Plane-Probing Algorithms

A plane-probing algorithm computes the normal vector of a digital plane from a starting point and a predicate “Is a point $${x}$$ in the digital plane?”. This predicate is used to probe the digital plane as locally as possible and decide on the fly the next points to consider. However, several existing plane-probing algorithms return the correct normal vector only for some specific starting points and an approximation otherwise, e.g., the H- and R-algorithm proposed in Lachaud et al. (J Math Imaging Vis 59(1):23–39, 2017). In this paper, we present a general framework for these plane-probing algorithms that provides a way of retrieving the correct normal vector from any starting point, while keeping their main features. There are $$O(\omega \log \omega )$$ calls to the predicate in the worst-case scenario, where $$\omega $$ is the thickness of the underlying digital plane, but far fewer calls are experimentally observed on average. In the context of digital surface analysis, the resulting algorithm is expected to be of great interest for normal estimation and shape reconstruction.
Boosting binary masks for multi-domain learning through affine transformations

In this work, we present a new, algorithm for multi-domain learning. Given a pretrained architecture and a set of visual domains received sequentially, the goal of multi-domain learning is to produce a single model performing a task in all the domains together. Recent works showed how we can address this problem by masking the internal weights of a given original conv-net through learned binary variables. In this work, we provide a general formulation of binary mask based models for multi-domain learning by affine transformations of the original network parameters. Our formulation obtains significantly higher levels of adaptation to new domains, achieving performances comparable to domain-specific models while requiring slightly more than 1 bit per network parameter per additional domain. Experiments on two popular benchmarks showcase the power of our approach, achieving performances close to state-of-the-art methods on the Visual Decathlon Challenge.
Kernel Machines That Adapt To Gpus For Effective Large Batch Training.

Modern machine learning models are typically trained using Stochastic Gradient Descent (SGD) on massively parallel computing resources such as GPUs. Increasing mini-batch size is a simple and direct way to utilize the parallel computing capacity. For small batch an increase in batch size results in the proportional reduction in the training time, a phenomenon known as linear scaling. However, increasing batch size beyond a certain value leads to no further improvement in training time. In this paper we develop the first analytical framework that extends linear scaling to match the parallel computing capacity of a resource. The framework is designed for a class of classical kernel machines. It automatically modifies a standard kernel machine to output a mathematically equivalent prediction function, yet allowing for extended linear scaling, i.e., higher effective parallelization and faster training time on given hardware. The resulting algorithms are accurate, principled and very fast. For example, using a single Titan Xp GPU, training on ImageNet with $1.3\times 10^6$ data points and $1000$ labels takes under an hour, while smaller datasets, such as MNIST, take seconds. As the parameters are chosen analytically, based on the theoretical bounds, little tuning beyond selecting the kernel and the kernel parameter is needed, further facilitating the practical use of these methods.
A Study of Text Summarization Techniques for Generating Meeting Minutes

A lot of research has been conducted all over the world in the domain of automatic text summarization and more specifically using machine learning techniques. Many state of the art prototypes partially solve this problem so we decided to use some of them to build a tool for automatic generation of meeting minutes. In fact, this was not an easy work and this paper presents various experiments that we did using Deep Learning, GANs and Transformers to achieve this goal as well as dead ends we have encountered during this study. We think providing such a feedback may be useful to other researchers who would like to undertake the same type of work to allow them to know where to go and where not to go.
Controlled bidirectional quantum secure direct communication protocol based on Grover’s algorithm

A novel controlled bidirectional quantum secure direct communication (CBQSDC) protocol based on Grover’s algorithm is proposed in our paper, which is on the basis of investigating the properties of it. To avoid information leakage, the strategy of adding control-not gate is employed in our protocol. The proposed protocol uses Bell state as quantum resource to transmit information in a dual-quantum-channel. Through the proposed measures, we can not only prevent information leakage, but also improve the efficiency of transmission. As can be shown from the security analysis, the Grover’s algorithm has great application prospects in the research of CBQSDC.
Supervised and Unsupervised Detections for Multiple Object Tracking in Traffic Scenes: A Comparative Study

In this paper, we propose a multiple object tracker, called MF-Tracker, that integrates multiple classical features (spatial distances and colours) and modern features (detection labels and re-identification features) in its tracking framework. Since our tracker can work with detections coming either from unsupervised and supervised object detectors, we also investigated the impact of supervised and unsupervised detection inputs in our method and for tracking road users in general. We also compared our results with existing methods that were applied on the UA-Detrac and the UrbanTracker datasets. Results show that our proposed method is performing very well in both datasets with different inputs (MOTA ranging from 0.3491 to 0.5805 for unsupervised inputs on the UrbanTracker dataset and an average MOTA of 0.7638 for supervised inputs on the UA Detrac dataset) under different circumstances. A well-trained supervised object detector can give better results in challenging scenarios. However, in simpler scenarios, if good training data is not available, unsupervised method can perform well and can be a good alternative.
Applications of machine learning in acute care research

Abstract Artificial intelligence has been successfully applied to numerous health care and non‐health care‐related applications and its use in emergency medicine has been expanding. Among its advantages are its speed in decision making and the opportunity for rapid, actionable deduction from unstructured data with that increases with access to larger volumes of data. Artificial intelligence algorithms are currently being applied to enable faster prognosis and diagnosis of diseases and to improve patient outcomes. 1,2 Despite the successful application of artificial intelligence, it is still fraught with limitations and “unknowns” pertaining to the fact that a model's accuracy is dependent on the amount of information available for training the model, and the understanding of the complexity presented by current artificial intelligence and machine learning algorithms is often limited in many individuals outside of those involved in the field. This paper reviews the applications of artificial intelligence and machine learning to acute care research and highlights commonly used machine learning techniques, limitations, and potential future applications.
Study on Real-Time Face Recognition and Tracking for Criminal Revealing

Chaitanya, A. Krishna Kartheek, C. H. Nandan, DurgeshFace recognition describes a surface framework, which is capable of processing image and detection. The proposed paper demonstrates three contributions: the first is to introduce the image representation, known as an integral image, the second application of Ada Boost learning algorithm, and the third is the cascaded framework. This includes observation, bio-metrics and video coding. Here, the primary objective is to implement a real-time system using a field-programmable gate array (FPGA) to track and detect human expression. The expression recognition involves colour-shaped coating separation and image purifying. Moreover, it involves different types of search engines which are based on computer vision. A computer controls Pan Tilt Zoom (PTZ) cameras in CCTV for surveillance systems for the face. The multi-view face tracking on a mobile platform has three methods (local binary patterns) and enhancing procedure. These face detectors are adaptable, extended for multi-opinion face recognition over the revolution ability and face matrix partition scheme is proposed to accelerate the face tracking process.
Blockchain: From Technology to Marketplaces

The articles in this special section provide a glimpse of the diverse research challenges in adopting blockchain technology into mainstream applications. The four articles focus on the following core issues: scalability, transparency versus privacy, standardization, ecosystem, and integration.
Feature sensitivity criterion-based sampling strategy from the Optimization based on Phylogram Analysis (Fs-OPA) and Cox regression applied to mental disorder datasets

Digital datasets in several health care facilities, as hospitals and prehospital services, accumulated data from thousands of patients for more than a decade. In general, there is no local team with enough experts with the required different skills capable of analyzing them in entirety. The integration of those abilities usually demands a relatively long-period and is cost. Considering that scenario, this paper proposes a new Feature Sensitivity technique that can automatically deal with a large dataset. It uses a criterion-based sampling strategy from the Optimization based on Phylogram Analysis. Called FS-opa, the new approach seems proper for dealing with any types of raw data from health centers and manipulate their entire datasets. Besides, FS-opa can find the principal features for the construction of inference models without depending on expert knowledge of the problem domain. The selected features can be combined with usual statistical or machine learning methods to perform predictions. The new method can mine entire datasets from scratch. FS-opa was evaluated using a relatively large dataset from electronic health records of mental disorder prehospital services in Brazil. Cox's approach was integrated to FS-opa to generate survival analysis models related to the length of stay (LOS) in hospitals, assuming that it is a relevant aspect that can benefit estimates of the efficiency of hospitals and the quality of patient treatments. Since FS-opa can work with raw datasets, no knowledge from the problem domain was used to obtain the preliminary prediction models found. Results show that FS-opa succeeded in performing a feature sensitivity analysis using only the raw data available. In this way, FS-opa can find the principal features without bias of an inference model, since the proposed method does not use it. Moreover, the experiments show that FS-opa can provide models with a useful trade-off according to their representativeness and parsimony. It can benefit further analyses by experts since they can focus on aspects that benefit problem modeling.
Augmenting a De-identification System for Swedish Clinical Text Using Open Resources and Deep Learning

Electronic patient records are produced in abundance every day and there is a demand to use them for research or management purposes. The records, however, contain information in the free text that ...
On the time-independent Hamiltonian in real-time and imaginary-time quantum annealing*

We present the analog analogue of Grover’s problem as an example of the time-independent Hamiltonian for applying the speed limit of the imaginary-time Schrödinger equation derived by Okuyama and Ohzeki and the new class of energy-time uncertainty relation proposed by Kieu. It is found that the computational time of the imaginary-time quantum annealing of this Grover search can be exponentially small, while the counterpart of the quantum evolution driven by the real-time Schrödinger equation could only provide square root speedup, compared with classic search. The present results are consistent with the cases of the time-dependent quantum evolution of the natural Grover problem in previous works. We once again emphasize that the logarithm and square root algorithmic performances are generic in imaginary-time quantum annealing and quantum evolution driven by real-time Schrödinger equation, respectively. Also, we provide evidences to search deep reasons why the imaginary-time quantum annealing can lead to exponential speedup and the real-time quantum annealing can make square root speedup.
Advanced Virtual Apparel Try Using Augmented Reality (AVATAR)

The new trends in fashion and newly designed outfits motivate customers for shopping. Constraints of place and time have been suppressed by rapid growth in online stores. Predicting the appropriate size and quality remains an unsolved problem. Offline shopping is another option that is preferred by a majority of customers. The trial of the same apparel by multiple customers may lead to various skin diseases and degrades the quality of apparel. The privacy of customers can be violated due to spy cameras in trial rooms. Advanced Virtual Apparel Try using Augmented Reality (AVATAR) proposes a multi-sensor body scanner combined with social media technologies. This revolutionizes the classic desktop paradigm into the direction of intuitive, “natural interaction” where people interface with the technological world through hand gestures, speech, and body language. AVATAR consists of an IoT mirror acting as a virtual trial room. The system is benefited by the web camera and TensorFlow which gathers real-time coordinates of the customer’s body. The OpenCV library scales the 3D model of the apparel following the coordinates. Hand gestures are used to interact with the system. Face color recognition using machine learning drives the recommendation system to suggest suitable apparel colors to customers. Besides, a web interface facilitates sharing the real-time experience of the customers trying the 3D apparel model for quick feedback.