Miracle-dz/UniFAR-FTUs huggingface dataset:
Next‐generation technologies and data analytical approaches for epigenomics

Epigenetics refers to the collection of heritable features that modulate the genome–environment interaction without being encoded in the actual DNA sequence. While being mitotically and sometimes even meiotically transmitted, epigenetic traits often demonstrate extensive flexibility. This allows cells to acquire diverse gene expression patterns during differentiation, but also to adapt to a changing environment. However, epigenetic alterations are not always beneficial to the organism, as they are, for example, frequently identified in human diseases such as cancer. Accurate and cost‐efficient genome‐scale profiling of epigenetic features is thus of major importance to pinpoint these “epimutations,” for example, to monitor the epigenetic impact of environmental exposure. Over the last decade, the field of epigenetics has been revolutionized by several innovative “epigenomics” technologies exactly addressing this need. In this review, we discuss and compare widely used next‐generation methods to assess DNA methylation and hydroxymethylation, noncoding RNA expression, histone modifications, and nucleosome positioning. Although recent methods are typically based on “second‐generation” sequencing, we also pay attention to still commonly used array‐ and PCR‐based methods, and look forward to the additional advantages of single‐molecule sequencing. As the current bottleneck in epigenomics research is the analysis rather than generation of data, the basic difficulties and problem‐solving strategies regarding data preprocessing and statistical analysis are introduced for the different technologies. Finally, we also consider the complications associated with epigenomic studies of species with yet unsequenced genomes and possible solutions. Environ. Mol. Mutagen. 55:155–170, 2014. © 2013 Wiley Periodicals, Inc.
Combination of CNN-GRU Model to Recognize Characters of a License Plate number without Segmentation

Recognition is a genre of manipulation of digitized image automation for discovering the number plate details from a given image. Due to various factors, it is difficult to achieve great recognition results for the license plate. In general, human beings can easily read characters in license plate, but the machine cannot do until it is trained to do so. Now a day’s vehicles are increasing day by day, to note down every vehicle plate number manually is difficult. To avoid that, optical character recognition (OCR) technology is used which extracts the license plate directly. In this paper, CNN (convolution neural network) –GRU (gated recurrent unit) model is developed.CNN is used for feature extraction and GRU is used for sequencing without using any segmentation methods. Finally, the character is recognized by utilizing a model design which is prepared on the dataset by GRU unit. A deep learning technique increases performance than traditional approaches like template matching. The testing precision of the proposed framework is 100% and training accuracy is 90%.
Computing a Near-Maximum Independent Set in Dynamic Graphs

As a fundamental NP-hard problem in graph theory, the maximum independent set (MIS) has attracted a lot of efforts to improve the time efficiency. However, most graphs in real scenarios are usually changing over time. But the previous studies take the stationary graphs as input, the computation of MIS in dynamic graphs receives little attention. Since computing the exact MIS is intractable, we compute the high-quality (large-size) independent set for dynamic graphs in this paper, where 4 graph updating operations are allowed: adding or deleting a vertex or an edge. Based on two state-of-the-art reduction rules that are designed for static graphs, we propose a novel scheme, i.e., dependency graph based independent set computation, which can support computing the high-quality independent set on the basis of the previous result rather than calculating from scratch. Moreover, a dynamic searching strategy is devised to improve time efficiency. In order to make it more useful in practical applications, we devise an effective yet efficient method to deal with the batch update. To confirm the effectiveness and efficiency of the proposed methods, we conduct extensive experiments over both real and synthetic datasets.
Dependence of plasma sheet energy fluxes and currents on solar wind‐magnetosphere coupling

[1] The epsilon solar wind-magnetosphere coupling function was used to estimate the rate at which energy from the solar wind was being dissipated in the inner magnetosphere and ionosphere. The Geotail CPI ion and electron data sets showed that the average percentage of this energy that was carried earthward by energetic particles at |z| < 2.5 RE in the central plasma sheet increased from approximately zero at x = −28 RE to 25% at x = −10 RE when epsilon was high. Much of this increase was attributed to the adiabatic energization of particles as flux tubes collapsed following reconnection events. The Geotail magnetometer and CPI ion data sets also were used to estimate the average electric fields and currents. These parameters provided an alternative viewpoint from which to examine particle energization. The parallel volume current density exhibited a stronger dependence on epsilon than did the perpendicular sheet current density. During disturbed times, a maximum of the parallel current density at a fixed x, y location existed at a distance of ∼3 RE from the neutral sheet. A strong dawn-dusk asymmetry was seen in the long-term averaged region 1 field-aligned currents. The ratio of upgoing dusk side to downgoing dawn side currents at the same x was ∼1.7 throughout the portion of the plasma sheet that could be studied, and was seen during both quiet and disturbed conditions. Previous papers showed that most of this excess upgoing dusk side parallel current was closed through connections to a dusk side downgoing region 0 current system.
Studies on Different CNN Algorithms for Face Skin Disease Classification Based on Clinical Images

Skin problems not only injure physical health but also induce psychological problems, especially for patients whose faces have been damaged or even disfigured. Using smart devices, most of the people are able to obtain convenient clinical images of their face skin condition. On the other hand, the convolutional neural networks (CNNs) have achieved near or even better performance than human beings in the imaging field. Therefore, this paper studied different CNN algorithms for face skin disease classification based on the clinical images. First, from Xiangya–Derm, which is, to the best of our knowledge, China’s largest clinical image dataset of skin diseases, we established a dataset that contains 2656 face images belonging to six common skin diseases [seborrheic keratosis (SK), actinic keratosis (AK), rosacea (ROS), lupus erythematosus (LE), basal cell carcinoma (BCC), and squamous cell carcinoma (SCC)]. We performed studies using five mainstream network algorithms to classify these diseases in the dataset and compared the results. Then, we performed studies using an independent dataset of the same disease types, but from other body parts, to perform transfer learning on our models. Comparing the performances, the models that used transfer learning achieved a higher average precision and recall for almost all structures. In the test dataset, which included 388 facial images, the best model achieved 92.9%, 89.2%, and 84.3% recalls for the LE, BCC, and SK, respectively, and the mean recall and precision reached 77.0% and 70.8%.
Cyber-Physical System of Intelligent Diagnosis of Generator Winding Insulation

The article proves the urgency of developing a cyber-physical technical condition diagnosis system for highvoltage generating equipment. The most vulnerable part of such equipment is the winding insulation. The most simple and affordable method for determining this fault is the analysis of the electrodischarge activity (EDA). This method allows determining the insulation defect at the initial stage of development. It does not allow for a critical failure. One of the main problems of generating equipment diagnostics is the considerable distance of its elements from each other. At the same time, an important task is the exchange of information on the state of the equipment elements. To solve this problem, a cyber-physical prediction diagnosis system is proposed. The diagnosis method for generating equipment windings is proposed. This method allows determining the technical condition of the generators insulation during its operation without complicated measuring devices using artificial intelligence. The effectiveness of this method for turbogenerator diagnosing is shown.
A Review on Role of Bloom Filter on DNA Assembly

The advancement of DNA assembly techniques has greatly boosted up the bioinformatics research and discovery. More precisely, DNA assembly has achieved tremendous popularity due to the ability to decode the hidden information in the DNA. DNA assembly is the process of finding the correct sequence of the nucleotide bases in DNA. The key challenges are a) size of the genomic data and, b) time to process the genomic data. Apparently, genomic data are voluminous consisting of many repeated fragments. The huge sized genomic data makes the DNA assembling a time consuming process. To address the space and time complexity, bloom filter is deployed in DNA assembling. Moreover, bloom filter plays a vital role in DNA processing to deal with the repeated data of DNA. A bloom filter is a probabilistic data structure for membership filter. Bloom filter uses a tiny amount of memory size to store information on the genomic data. However, DNA assembling is a very memory-intensive process. The whole process consists of many stages. In every stage, the repeated data need to be taken care of. Hence, bloom filter is deployed in every stage for its implementation. This paper presents the impact of bloom filter in DNA assembling process. It also gives a precise explanation on every aspect of the DNA assembling process. The focus of this paper is to review the techniques that implemented bloom filter.
Low Temperature and Fast Growth of Polycrystalline MoS2 Films using Low Temperature Sublimation Sources

With the advent of the Internet of Things (IoT), applications such as flexible electronics, optoelectronics, self and low power electronics, and neuromorphic electronics are emerging rapidly, two dimensional (2D) layered materials such as molybdenum disulfide (MoS2) have shown great potential and are called to have a major role in these areas. However, low cost and high yield synthesis of 2D materials is critical towards their commercial implementation. Here, we present a chemical vapor deposition (CVD) process for the synthesis of MoS2 films using novel precursors with low sublimations point, which enables a fast and low temperature growth of crystalline 2D MoS2 films greatly reducing the process cost. Raman spectroscopy, photoluminescence (PL) and X-ray photoelectron spectroscopy (XPS), scanning tunneling microscope (STM) and electrical transport are used to determine the crystallinity and elucidate the compositional, optical and electrical characteristics of the CVD grown MoS2 for its use in future electronic applications.
A Robust Framework for Abnormal Human Action Recognition Using $\boldsymbol{\mathcal{R}}$ -Transform and Zernike Moments in Depth Videos

The aim of the algorithm is to detect the abnormal actions that are more prone to elderly people in order to make them more independent and improve their quality of life. The framework is structured to construct a robust feature vector by computing R-transform and Zernike moments on average energy silhouette images (AESIs). The AESIs are generated by the integral sum of the segmented silhouettes obtained from the Microsoft’s Kinect sensor v1. The proposed feature descriptor possesses scale-, translation-, and rotation-invariant properties that are also less sensitive to noise and minimizes data redundancy. It enhances the proposed algorithm’s robustness and makes the classification process more efficient. The proposed work is validated on a novel abnormal human action (AbHA) dataset and three publically available 3D datasets—UR fall detection dataset, Kinect Activity Recognition dataset, and multi-view NUCLA dataset. The proposed framework exhibits superior results from other state-of-the-art methods in terms of average recognition accuracy (ARA). The experimental results demonstrate 96.5%, 96.64%, 95.9%, and 86.4% ARA on the UR fall detection dataset, the KARD dataset, the AbHA dataset, and the multi-view NUCLA dataset, respectively.
Real-Time Control of Microgrid via Multi-Agent Approach

The aim of the paper is to introduce a new method for electrical microgrid real-time control design by means of a multi-agent approach disposal. A microgrid of the most general structure and content is represented in the form of Multi-Agent System (MAS) through proposed agents formalization. It allows for the possibility to apply multi-agent presentation for the tasks of control and management. The paper discusses an attempt to synthesize a MAS and to define its structure and functionality in order to optimize the real-time operation of a microgrid by optimal scheduling of its distributed generation and storage, managing demand and supply. The economic and environmental factors, as well as standardized electric power reliability indices were accepted as optimization criteria indices. The given example demonstrates the architecture's design of the MAS as well as real-time optimal control design which schedules different types of power generation (operating mode) in a coordinated manner and, in accordance with the power demand, consumption rate and availability of the resources, ensures stable and reliable power supply.
GRIT: Consistent Distributed Transactions Across Polyglot Microservices with Multiple Databases

The popular microservice architecture for applications brings new challenges for consistent distributed transactions across multiple microservices. These microservices may be implemented in different languages, and access multiple underlying databases. Consistent distributed transactions are a real requirement but are very hard to achieve with existing technologies in these environments. In this demo we present GRIT: a system that resolves this challenge by cleverly leveraging deterministic database technologies and optimistic concurrency control protocol(OCC). A transaction is optimistically executed with its read-set and write-set captured during the execution phase. Then at the commit time, conflict checking is performed and a global commit decision is made. A logically committed transaction is persisted into logs first, and then asynchronously applied to the physical databases deterministically. GRIT is able to achieve consistent, high throughput and serializable distributed transactions for any applications invoking microservices. The demonstration offers a walk-through of how GRIT can easily support distributed transactions across multiple microservices and databases.
Robotic Artificial Muscles: Current Progress and Future Perspectives

Robotic artificial muscles are a subset of artificial muscles that are capable of producing biologically inspired motions useful for robot systems, i.e., large power-to-weight ratios, inherent compliance, and large range of motions. These actuators, ranging from shape memory alloys to dielectric elastomers, are increasingly popular for biomimetic robots as they may operate without using complex linkage designs or other cumbersome mechanisms. Recent achievements in fabrication, modeling, and control methods have significantly contributed to their potential utilization in a wide range of applications. However, no survey paper has gone into depth regarding considerations pertaining to their selection, design, and usage in generating biomimetic motions. In this paper, we discuss important characteristics and considerations in the selection, design, and implementation of various prominent and unique robotic artificial muscles for biomimetic robots, and provide perspectives on next-generation muscle-powered robots.
HuMAn: Human Movement Analytics via WiFi Probes

In this paper, we describe our approach that enables spatial human analytics using WiFi probes. Our approach works as follows: we first set up our beacons in the area. Then, we use our novel localization method, which operates in a smart-phone, to broadcasts probe requests in a high frequency. Our beacons collect these probe requests and relate them to locations, as estimated from our localization method in the smartphone. The combined WiFi probes with locations are then used to generate a radio map and enable tracking even of devices that are not equipped with our smartphone application.
Top-m Clustering with a Noisy Oracle

In this paper, we analyse the problem of top-$m$ clustering with access to a noisy oracle. We consider a model where there are $n$ nodes, belonging to $k$ clusters. We have access to an oracle which when queried with a pair of nodes, returns a binary answer indicating whether they belong to the same cluster or not, but with a probability of error p. Our goal is to identify the top-m clusters in terms of size, using the noisy answers from the oracle. This setting was recently studied in [9], which provides an iterative algorithm for the case of complete clustering, i.e., $m=k$. We identify conditions (on the relative sizes of clusters) under which the first $m$ stages of the algorithm would recover the top $m$ clusters. We also analyze the query complexity of the algorithm and provide an upper bound which is a function of the number of recovered clusters $m$ and the sizes of the top clusters.
Two-transistor Voltage-Measurement-Based Test Structure for Fast Extraction of MOS Mismatch Design Parameters

This paper proposes a new test structure and a measurement method for measuring MOS transistors mismatches. The structure is based on the combination of two stacked MOS transistors and the measurement methodology relies on two single DC voltage measurements. The method allows to extract separately $\sigma(\triangle V_{TH})$ and $\sigma(\Delta\beta/\beta)$ and enables fast extraction of design MOS mismatch parameters such as $A_{V_{TH}}$ and $A_{\beta}$ with less than 2% error. The simple data post-processing algorithm results in an increase of $\pmb{30\mathrm{x}}$ in the measurement speed with data correlation coefficient not less than 0.94 $(R^{2}\geq \pmb{0.94})$. Rapid address decoding and bias configuration have been used in order to select each device in a two-dimensional (2D) DUT matrix of FETs. The experimental data presented herein for analysis is taken from measurements in a prototype fabricated in 65nm CMOS bulk process.
Cooperative spectrum sensing technique for identifying illegal FM broadcast radio stations using an energy detector and a peaks detector

A spectrum sensing system for detecting illegal radio broadcast emissions is proposed using a centralized cooperative spectrum sensing technique based on a cognitive radio network architecture. Two possible decision making solutions are proposed. The first one consists of an energy detector is commonly used in cognitive radio systems, which is adapted to work under a changing threshold. This method takes into account the changing conditions of the illegal emissions. The second one is a peak detector used for finding carrier frequencies. Both solutions were tested for the FM radio broadcasting comparing their performance in terms of the receiver operating characteristic curve.
Commutative reversible data hiding and encryption

This work proposes a novel scheme of commutative reversible data hiding and encryption. In encryption part, the gray values of two neighboring pixels are masked by same pseudo-random bits. In data-hiding part, the additional data are embedded into various bit planes with a reversible manner, and a parameter optimization method based on a capacity–distortion criterion is used to ensure a good performance. Because the data space used for accommodating the additional data is not affected by the encryption operation, the data embedded in plain/encrypted domain can be extracted from encrypted/plain domain, and the way of insertion/extraction of additional data in plain domain is same as that in encrypted domain. Furthermore, the original image can be recovered without any error from an image containing additional data. Copyright © 2013 John Wiley & Sons, Ltd.
Construction Of Topological Graphic Passwords By Hanzi-gpws

Graphical passwords are familiar with people in nowadays, such as 2-dimension code, face-authentication, finger-print authentication, and so on. In which 2-dimension code is widely used in everywhere of the world. In this paper, we design a new-type of graphical password, called Hanzi-gpw, which is the abbreviation of Hanzi graphical password, it is differ from the existing graphical passwords, since Hanzi-gpws are saved in computer by algebraic matrix. We realize the transformation between text-based passwords (TB-paws) from Hanzi-gpws. Several methods for generating TB-paws from Hanzi-gpws are introduced; these methods are based on topological structures and graph coloring/labelings. To use of Chinese characters to design passwords may appeal to most Chinese people. By the way, we find new graph labelling in which some mathematical conjecture are produced.
Size and Shape in Biology

Arguments based on elastic stability and flexure, as opposed to the more conventional ones based on yield strength, require that living organisms adopt forms whereby lengths increase as the ⅔ power of diameter. The somatic dimensions of several species of animals and of a wide variety of trees fit this rule well. It is a simple matter to show that energy metabolism during maximal sustained work depends on body cross-sectional area, not total body surface area as proposed by Rubner (1) and many after him. This result and the result requiring animal proportions to change with size amount to a derivation of Kleiber's law, a statement only empirical until now, correlating the metabolically related variables with body weight raised to the � power. In the present model, biological frequencies are predicted to go inversely as body weight to the � power, and total body surface areas should correlate with body weight to the ⅝ power. All predictions of the proposed model are tested by comparison with existing data, and the fit is considered satisfactory. In The Fire of Life, Kleiber (5) wrote "When the concepts concerned with the relation of body size and metabolic rate are clarified, . then compartive physiology of metabolism will be of great help in solving one of the most intricate and interesting problems in biology, namely the regulation of the rate of cell metabolism. " Although Hill (23) realized that "the essential point about a large animal is that its structure should be capable of bearing its own weight and this leaves less play for other factors," he was forced to use an oversimplified "geometric similarity" hypothesis in his important work on animal locomotion and muscular dynamics. It is my hope that the model proposed here promises useful answers in comparisons of living things on both the microscopic and the gross scale, as part of the growing science of form, which asks precisely how organisms are diverse and yet again how they are alike.