Miracle-dz/UniFAR-FTUs huggingface dataset:
Layered Approximation for Deep Neural Networks

Artificial Intelligence has created immense hype in the last decade and the credit for the same goes to the groundbreaking breakthrough named “Deep Learning” or “Deep Neural Network”. Although “Artificial Neural Network”, the foundation of Deep Learning as a concept has been prevalent since 1958 but the actual implementation for solving real business use cases has only been possible over the last decade. Deep Neural Networks has demonstrated significant results in the fields of computer vision, speech recognition, and machine translation, and outperformed human brain in many instances. Artificial Neural Network, as it is inspired from human biological neural superstructure has few structural similarities but not possible in terms of how the human brain or biological neural network works because we have still limited information on its functioning. Nevertheless, Deep Neural Network paved the way for many possibilities and currently, it is the most promising technology that we have in the field of Artificial Intelligence.
A Novel Application of Levenshtein Distance for Assessment of High-Level Motor Planning Underlying Performance During Learning of Complex Motor Sequences

Few studies have examined high-level motor plans underlying cognitive-motor performance during practice of complex action sequences. These investigations have assessed performance through fairly simple metrics without examining how practice affects the structures of action sequences. By adapting the Levenshtein distance (LD) method to the motor domain, we propose a computational approach to accurately capture performance dynamics during practice of action sequences. Practice performance dynamics were assessed by computing the LD based on the number of insertions, deletions, and substitutions of actions needed to transform any sequence into a reference sequence (having a minimal number of actions to complete the task). Also, combining LD-based performance with mental workload metrics allowed assessment of cognitive-motor efficiency dynamics. This approach was tested on the Tower of Hanoi task. The findings revealed that throughout practice this method could capture: i) action sequence performance improvements as indexed by a reduced LD (decrease of insertions and substitutions), ii) structural modifications of the high-level plans, iii) an attenuation of mental workload, and iv) enhanced cognitive-motor efficiency. This effort complements prior work examining the practice of complex action sequences in healthy adults and has potential for probing cognitive-motor impairment in clinical populations as well as the development/assessment of cognitive robotic controllers.
A Study to Evaluate the Utility of Using Opportunistic Screening for Hypertension in Primary Care Settings Using a Two Phase Study Design

Opportunistic screening may serve as an effective tool for estimating the burden of a disease. In case of diseases like hypertension, a simple non-invasive modality (BP measurement) is used in quantifying the burden. The study examined the utility of developing opportunistic screening as a modality for hypertension in a primary care setting. It was conducted in two phases. Phase 1 estimated of burden of hypertension in a tertiary care centre in a rural area of North West India. 424 attendants accompanying patients suffering from episodic illnesses of 2-3 days in outpatient departments of the hospital were invited to participate and 400 (94.3% response rate) were screened for blood pressure (BP), height and weight. The second phase of the study comprised of comparative assessment of studies across India in rural setting. As per the JNC VII criteria for classifying hypertension 40.5% (162/400) were pre hypertensive, 15% (60/400) were in Stage 1 hypertension and 6.5% (26/400) in Stage 2 hypertension, with a total of 21.5% hypertensive subjects. Prevalence of hypertension in various studies conducted across India in rural settings give a range from 5-40% with a mean prevalence of 25.63%. Hence, opportunistic screening can be used as a useful tool to screen for early detection of hypertension and also to raise awareness about it in a primary care setting.
Digital Image Watermarking Using Optimization and Encryption

In this paper, with the technique of chaotic encryption-based blind digital image watermarking, we introduce a new grey wolf optimization. Discrete cosine transform (DCT) is employed before embedding the watermark within the host image. The image at host is split into 8x8 which is a non-overlapping blocks before employing DCT application, and the watermark bit is included through modifying the differences between the neighboring blocks DCT coefficients. Then, grey wolf optimization algorithm (GWO) is employed. Arnold transform is used along with the chaotic encryption to provide additional watermark security. The presented model is applied to a collection of benchmark images and the results validated the performance in terms of PSNR. The presented method attains maximum PSNR value on entirely given test images.
Cross-Domain Deep Visual Feature Generation for Mandarin Audio–Visual Speech Recognition

There has been a long term interest in using visual information to improve automatic speech recognition (ASR) system performance. Both audio and visual information are required in conventional audio visual speech recognition (AVSR) systems. This limits their wider applications when visual modality is not present. To this end, one possible solution is to use acoustic-to-visual (A2V) inversion techniques to generate visual features. Previous research in this direction used synthetic acoustic-articulatory parallel data in inversion model training. The acoustic mismatch between the audio-visual (AV) parallel data and target data was not considered. In addition, the target language to apply these technologies has been focused on English. In this article, a real 3D Audio-Visual Mandarin Continuous Speech (3DAV-MCS) corpus was used to train deep neural network based A2V inversion models. Cross-domain adaptation of the inversion models allows suitable visual features to be generated from acoustic data of mismatched domains. The proposed cross-domain deep visual feature generation techniques were evaluated on two state-of-the-art Mandarin speech recognition tasks: DAPRA GALE broadcast transcription and BOLT conversational telephone speech recognition. The AVSR systems constructed using the cross-domain generated visual features consistently outperformed the baseline convolutional neural network (CNN) ASR systems by up to 3.3% absolute (9.1% relative) character error rate (CER) reductions after both speaker adaptive training and sequence discriminative training were performed.
Methods and test equipment of technological information sensors

The main aim of research is establishing dependence between a distance from inductive and capacitive technological information sensors to the acting element and imprecision of its operating in track mode. A track mode is understood as the passage of the acting element past the sensor. It is shown that with a decrease in the distance, the sensor imprecision will increase twice. This circumstance should be taken into account while designing automation systems.
Preventive Nanomedicine: Nanomedicine for dendritic cell-targeted immunotherapy

2010 ¦ Volume 3 ¦ Issue 1 25 Active immunotherapies against tumors and viruses have to induce functional memory CTLs that requires the activation of naïve T cells by dendritic cells (DC). Sipuleucel-T, the first cancer immunotherapeutic product expected to create new treatment paradigm, employs transplantation of ex vivo cultured DCs. To improve the clinical benefits and reduce the costs of ex vivo therapies, in vivo DC loading nanomedicine technologies are under development. These DC-based nanomedicine products must overcome the complex problems of receptor targeting and uptake of the antigen, maturation and migration of DCs to the lymphoid organs, antigen processing and presentation to naïve T cells. The combination of two new technologies, antigen nanoformulation and topical DermaPrep administration, has shown to target antigens in vivo to lymph nodes by a subset of epidermal DC leading to the induction of functional memory T cells. This DC-targeting nanomedicine platform technology has been developed for the treatment of infectious diseases and cancers, including HIV/AIDS.
Prototype of a rail wear measuring system using digital image processing

The rail is one of the most important components of railways. It serves directly as a running track for the rolling stock in this transportation system, suffering wear because of this. Most of the manual equipment for measuring these problems does not allow fast data acquisition, requiring point-to-point measurements, besides it usually has a high price. As an alternative to collecting data efficiently and detecting wear on rails, it was proposed to build an equipment prototype for discrete image acquisition in a row and, through the processing of these images using computational techniques, to extract the desired measure of the wear. It was developed a mobile rail track cart for capturing images of the rail with the profile demarked by a laser beam. All the photos of the databank were processed in laboratory using the implemented processing routine, being possible to extract the values of the vertical wear measurement and compare it with the measurements made with a template tool. The results showed a maximum absolute mean error of 0,80 millimeters (comparing the average of the measurements with the measurement of the manual template) and maximum standard deviation of 0,9794 mm for vertical wear, in addition to the values of absolute mean error of 0,43 mm and maximum standard deviation among the experiments of 0,6046 mm for lateral wear. It was possible to verify that the built prototype and the processing algorithm were able to acquire the data efficiently and portray the wear.
Data Driven QoE-QoS Association Modeling of Conversational Video

In recent years, the variety and volume of multimedia services have increased exponentially. Like most of other multimedia services, the conversational video service has stringent quality of service and experience requirements. In order to better support users QoE (Quality of Experience) and allocate network resources more effectively, this paper focuses on the QoS (Quality of Service)-QoE association modeling of conversational video flows. Through statistical analysis of the network video flow data, statistical distribution association between QoE and some network layer QoS parameters of the conversational video service is studied by use of the ITU-T P.1201. 1 model, and relevant distribution functions are fitted. From the perspective of user’s experience, the QoS parameter recommendation indices are given for each MOS (Mean Opinion Score) value interval, and a QoE connected QoS model is established. Through the verification and comparison with an existing work, the rationality of the recommended QoS indices is demonstrated, which can help the network operators strike a balance between saving network resources and satisfying user’s quality of experience.
CUCUMIS TRIGONUS ROXB: A REVIEW

Cucumis trigonus Roxb. (Bitter guard) is a tendrillar herb which belongs to Cucurbitaceae family. It is used for various ailments in Indian traditional system of medicine. The dried fruit is consider as indigestible and is used as thermogenic, anthelmintic, febrifuge, expectorant, liver tonic, stomachic, appetizer, purgative and as intellect promoting. It is also used to treat leprosy, fever, jaundice, diabetes, cough, bronchitis, anaemia, constipation and other abdominal disorders. The highest extractive values of Cucumis trigonus Roxb. is found with water and lowest value in Petroleum ether. The seeds contains unsaturated lipids as their major constituents, thus act as a coolant and astringent in nature. The fruit pulp of Cucumis trigonus is bitter, acrid, thermogenic, anthelmintic, liver tonic, cardio tonic, appetizer, expectorant and intellect promoting.Cucumis trigonus Roxb.contains Cucurbitacin as one the main chemical constituents. Due to Flavonoids, Tannins, Alkaloids and Saponins; Cucumis trigonus Roxb.have anti-oxidant activity.
Leech Therapy in Digital Replantation/Revascularization: A Review of 760 Consecutive Digits

dIScloSureS: The authors have no financial disclosures. This work was supported by the Department of Defense Multidisciplinary University Research Initiative (MURI) program (#W911NF0610218), the National Institutes of Health T32 Training Grant (#GM0008616-11), and the Plastic Surgery Educational Foundation Pilot Research Grant (#174964). Leech Therapy in digital replantation/ revascularization: A review of 760 Consecutive digits
A Retrospective Respiratory Gating System Based on Epipolar Consistency Conditions

: Motion artifacts of in vivo imaging, due to rapid respiration rate and respiration displacements of the mice while free-breathing, is a major challenge in micro computed tomography(micro-CT). The respiratory gating is often served for either projective images acquisition or per projection qualification, so as to eliminate the artifacts brought by in vivo motion. In this paper, we propose a novel respiratory gating method, which firstly divides one rotation cycle into a number of segments, and extracts the respiratory signal from the projective image series of current segment by the value of the epipolar consistency conditions (ECC), then in terms of the measured average respiratory period, sets next segment’s start-up time and rotation speed of the gantry for respiratory phase synchronization, and so on so forth. The gating procedure is through the whole projections of three cycles, only one among three projections at each angle is qualified by their phase value and is retained for future use for tomographic image reconstruction. In practical experiment, the ECC based gating method and the conventional hardware gating method are employed on micro CT imaging of C57BL/6 mice respectively. The result shows that, compared with the hardware based one, the proposed method not only achieve much better consistency in the projection images, but also suppresses the streak artifacts more effectively on the different parts like the breast, abdomen and head of in vivo mice.
Future Generation Computer Systems

Conditional Restricted Boltzmann Machine (CRBM) is a promising candidate for a multidimensional system modeling that can learn a probability distribution over a set of data. It is a specific type of an artificial neural network with one input (visible) and one output (hidden) layer. Recently published works demonstrate that CRBM is a suitable mechanism for modeling multidimensional time series such as human motion, workload characterization, city traffic analysis. The process of learning and inference of these systems relies on linear algebra functions like matrix–matrix multiplication, and for higher data sets, they are very compute-intensive. In this paper, we present a configurable framework for CRBM based workloads for arbitrary large models. We show how to accelerate the learning process of CRBM with FPGAs and OpenCL, and we conduct an extensive scalability study for different model sizes and system configurations. We show significant improvement in performance/Watt for large models and batch sizes (from 1.51x up to 5.71x depending on the host configuration) when we use FPGA and OpenCL for the acceleration, and limited benefits for small models comparing to the state-of-the-art CPU solution. ©
A study on approximate solutions and accuracy of parallel algorithms for solving system of ODEs

The purpose of this paper is, to study the numerical computation of system of ordinary differential equations of first order with initial value problems and all steps of parallel algorithms of R.K. methods are tested in MATLAB (2009a) [2]. In this part, we use four classical RK2 order methods to introduce the basic ideas associated with initial value problems for solving system of ODEs of two or more equations. At the end we make a comparison between these numerical methods for approximate solutions and numerical errors. Numerical examples are given to illustrate the computational accuracy and robustness of these parallel algorithms.
True-Average Current-Mode Control of DC-DC Power Converters: Analysis, Design, and Characterization

Saini, Dalvir K. , Ph.D., Department of Electrical Engineering, Wright State University, 2018. True-Average Current-Mode Control of DC-DC Power Converters: Analysis, Design, and Characterization. Energy efficient, wide-bandwidth, and well-regulated dc-dc power converters are in great demand in today’s emerging technologies in areas such as medical, communication, aerospace, and automotive industries. In addition to design and selection of the converter components, a robust closed-loop modeling is very essential for reliable power-electronic systems. Two closed-loop control techniques for power converters exist: voltage-mode control and current-mode control. The principles of voltage-mode control have been explored in great depths by researchers over the last two decades. However, the dynamic modeling of current-mode controlled dc-dc power converters has many uncharted areas that needs careful attention. Two main methods exist under the category of current-mode control: peak current-mode control and average current-mode control. Both of these control strategies are very attractive in applications that require fast control speeds, improved voltage regulation, and improved power supply noise rejection ratio. In recent technological advancements, where high noise immunity and tight regulation are desired, the average current-mode control has proven to be a superior choice, when compared to other control techniques for power converters. In this dissertation, a complete systematic theoretical framework for analysis, design, characterization, and measurements of the dc-dc converters with average current-mode control is introduced. To overcome the drawbacks of the traditional average current-mode control method, a new “true-average” current-mode control technique is proposed. The new technique is implemented on the basic converter topologies namely, buck, boost, and buck-boost. The dynamic small-signal models of iii the converter power-stages are developed using the circuit-averaging technique. The inner-current loop of the power converters is designed and their frequency-domain, time-domain, and pole-zero domain characteristics are exploited. Subsequently, the outer-voltage loop is designed in the presence of current-controlled power stage and the overall converter performance is evaluated against dynamically-varying operating conditions. A laboratory prototype of a buck-boost converter for 12 V to 5 V at 25 W operating at 200 kHz was designed, built, and measured. The theoretically predicted results were validated both through simulations and experiments. The techniques to measure the small-signal open-loop and closed-loop transfer functions are also provided. Excellent agreement between the theoretical and experimental results were observed.
Researching of the deep neural network for amber gemstone classification

This project is based on the researching of the deep neural network for the classification of amber gemstone seen as a great opportunity to expand the range of application of the well known deep learning, which have been used more and more in different fields. First, it is explained the theoretical concepts about machine learning and deep learning, besides a little comparison between them in order to for the reader to make the thesis more understandable. It is also introduced the different machine learning classifiers and the different methods of deep learning which have been used during the presented work. After having the basic concepts it will be explained the different methods that have been studied throughout the thesis to find the best possible accuracy in the training, validation and testing of the data set. Finally, it will be showcased the different results obtained followed by a short explanation per each of them. In this work the main technique that will be used for the researching is the method of transfer learning. Due to the lack of huge amounts of database regarding the amber stones, this technique can be a lot profitable to achieve the best accuracy. Nevertheless other methods like training from scratch will be tested; in which it will be used YOLO (You Only Looks Once). There is plenty of developed architectures that can be used for transfer learning, but only AlexNet will be used, since is one of the most used. The main tool for performing all the training and testing is MATLAB, which is quite suitable for processing images and with the latest version now it is able to run deep neural network with ease.
Diabetes mellitus : Advances of research and medical care. V. Pathology in diabetes mellitus.

糖尿病の病因あるいは合併症病変として古典的に記されてきた病理学的所見の解釈が,分子生物学的研究の進歩に伴い変わってきている. また,多くの糖尿病動物モデルを用いた研究成果もヒト糖尿病との病理変化との対応なしには,意義付けが困難なことも多い. 本稿では糖尿病の成因論から糖尿病での膵島変化について,合併症病変については腎症,神経症を中心に,病理学的立場から見た病変の意義を動物モデルでの研究成果を加えて概説する.
COMPARISON OF CYTOKINE IN SALIVA FROM ORAL CANCER PATIENTS AND NORMAL PERSON

本研究は口腔癌・前癌病変の診断において，非侵襲的・簡便性・反復性という条件を満たす一般診療所でも行うことができるスクリーニング検査法に，唾液中の口腔癌に特異なバイオマーカーを応用することを最終目的としている。 口腔癌患者19名（男性9名，女性10名 平均年齢60. 9歳），健常者20名（男性15名，女性5名 平均年齢32歳）の全唾液を試料としてELISA法によりIL-1β，-6，-8を測定したところ，口腔癌患者のIL-6が健常者と比較して有意差をもって高値を示した。 また，健常者の全唾液からmRNAを抽出する試みを行ったところ，RNAの抽出が可能でありこれを試料としてRT-PCRにより3種のhousekeeping geneを検出することができた。 このことから全唾液を検体とした口腔癌ならびに前癌病変の簡易スクリーニング検査への応用が期待された。
Short-term welfare implications of capture-mark-recapture trapping of medium-sized mammals: the brushtail possum ( Trichosurus vulpecula ) as a case study

Capture-mark-recapture (CMR) repeated at intervals is a commonly used method of population estimation of mammals. Most guidelines for the use of wild mammals recommend adequate measures to ensure a captured animal has food and water available, as needed, until it is released. Small mammals are often highlighted as needing special care, but recommendations are either less clear or absent for mediumand large-sized mammals. We therefore examined the short-term welfare implications of CMR trapping of medium-sized mammals, using the marsupial, brushtail possum (Trichosurus vulpecula) as a case study. Possums were caught in cage traps baited with circa 30 g of food and set for three consecutive days at monthly intervals. Timing devices on the traps measured when traps were sprung. Possums were confined in traps for, on average, 89% of each night they were trapped, plus a further 2–6 h between sunrise and the actual time of release from the traps. They lost 3–8% of their bodyweight on average, depending on the number of captures in a trapping session. A recently developed framework for humaneness assessment, based on five welfare domains, gave an overall welfare impact of mild-moderate for cage trapping of possums. The short-term solutions to these welfare impacts would be, at a minimum, to provide trapped possums with adequate additional food and water, and minimise their time spent in the traps. Guidelines for the use of wild animals in research and management could also be more prescriptive regarding the provision of food and water in studies of medium-sized mammals that involve repeated capture.