Miracle-dz/UniFAR-FTUs huggingface dataset:
A 0.4-V 0.93-nW/kHz Relaxation Oscillator Exploiting Comparator Temperature-Dependent Delay to Achieve 94-ppm/°C Stability

This paper presents the analysis and design of a relaxation oscillator that counteracts the complementary-to-absolute-temperature (CTAT) property of the comparator delay with the proportional-to-absolute-temperature (PTAT) property of the <italic>RC</italic> core to realize temperature-stabilized operation. By using a feedback bias network to linearize the comparator CTAT delay, thus improving the overall temperature stability by 20<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>, this technique enables a comparator with ~20<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> less bandwidth and an overall oscillator with ~5<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> lower power than conventional approaches. In a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> silicon on insulator CMOS process, this design consumes 1.14 nW from a 0.4-V supply operating at 1.22 kHz, with a temperature coefficient (TC) as low as 40 ppm/°C (<inline-formula> <tex-math notation="LaTeX">$\mu = 94$ </tex-math></inline-formula> ppm/°C for <inline-formula> <tex-math notation="LaTeX">$n = 5$ </tex-math></inline-formula>) achieving state-of-the-art efficiency (0.93 nW/kHz) for kilohertz-range relaxation oscillators.
Performance Analysis of Entanglement-based Competition Resolution in Distributed Systems

In the field of technology the competition for common resources has been with us for the last couple of decades. As systems become more complex newer and more efficient methods are needed to manage and possibly decrease the overhead caused by the competing entities. Advance in other fields like quantum computing can provide new methods to construct such algorithms. In this paper a full performance analysis is given on how a distributed systems resource access control mechanism performs with the proposed quantum-entanglement based algorithm.
Optimizing Group-By and Aggregation using GPU-CPU Co-Processing

textabstractWhile GPU query processing is a well-studied area, real adoption is limited in practice as typically GPU execution is only signicantly faster than CPU execution if the data resides in GPU memory, which limits scalability to small data scenarios where performance tends to be less critical. Another problem is that not all query code (e.g. UDFs) will realistically be able to run on GPUs. We therefore investigate CPU-GPU co-processing, where both the CPU and GPU are involved in evaluating the query in scenarios where the data does not t in the GPU memory. As we wish to deeply explore opportunities for optimizing execution speed, we narrow our focus further to a specic well-studied OLAP scenario, amenable to such co-processing, in the form of the TPC-H benchmark Query 1. For this query, and at large scale factors, we are able to improve performance signicantly over the state{of{the{art for GPU implementations; we present competitive performance of a GPU versus a state{of{the{art multi-core CPU baseline a novelty for data exceeding GPU memory size; and nally, we show that co-processing does provide signicant additional speedup over any of the processors individually. We achieve this performance improvement by utilizing parallelism-friendly compression to alleviate the PCIe transfer bottleneck, query-compilation-like fusion of the processing operations, and a simple yet eective scheduling mechanism. We hope that some of these features can inspire future work on GPU-focused and heterogeneous analytic DBMSes.
MicroRNA-99a-5p in circulating immune cells as a potential biomarker for the early diagnosis of ischemic stroke

OBJECTIVES: We have previously shown the neuroprotective function of microRNA-99a-5p in experimental stroke. Here, we explore its diagnostic potential for acute stroke patients. MATERIALS AND METHODS: MiR-99a-5p levels in circulating from acute stroke patients and control were measured by real-time polymerase chain reaction. Pearson's correlation and receiver operator characteristic (ROC) curves were used to analyze clinical significance of miR-99a-5p and its sensitivity and specificity for stroke diagnosis. RESULTS: We demonstrated that miR-99a-5p expression was upregulated in neutrophils of both ischemic stroke and hemorrhage patients, while was only increased in the lymphocytes of hemorrhage patients. ROC analysis revealed that the miR-99a-5p level in neutrophils and lymphocytes had a moderate diagnostic value for stroke. Moreover, a positive correlation existed between plasma miR-99a-5p levels and neutrophil numbers or neutrophil/lymphocyte ratio. Meanwhile, miR-99a-5p levels in neutrophils were negatively correlated with thrombin time, while positively correlated with D-dimer and urea levels. Lymphocytic miR-99a-5p levels were positively correlated with platelet mean volume and distribution width. CONCLUSION: This study demonstrated that miR-99a-5p levels in circulating immune cells might facilitate the diagnosis of ischemic stroke.
Mammogram Classification in Transform Domain

Recently, breast cancer is major reason of cancer deaths among women. When cells in the breast tissue grow rapidly and gets divided without control breast cancer takes place which leads to formation of a mass or lump called as tumour. Here, the proposed method details comprehensive study and incorporation of image processing techniques to detect and classify tumours in terms of their accuracy. The proposed pre-processing technique removes all the unwanted labels present in an image to find region of interest (ROI). The various transformation methods such as Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), and Radon Transform are applied to the ROI. Later, Gray-Level Co-Occurrence Matrix (GLCM) features are obtained. Lastly, the classification accuracy of detected abnormality is being found out using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers. The recommended method is verified using Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) publicly available dataset. From the implementation, it has been inferred that out of three proposed methods a highest of 93.89% accuracy is achieved using combination of DFT and SVM classifier for DDSM database whereas for MIAS it returns 80% accuracy.
psichomics: graphical application for alternative splicing quantification and analysis

Alternative pre-mRNA splicing generates functionally distinct transcripts from the same gene and is involved in the control of multiple cellular processes, with its dysregulation being associated with a variety of pathologies. The advent of next-generation sequencing has enabled global studies of alternative splicing in different physiological and disease contexts. However, current bioinformatics tools for alternative splicing analysis from RNA-seq data are not user-friendly, disregard available exon-exon junction quantification or have limited downstream analysis features. To overcome such limitations, we have developed psichomics, an R package with an intuitive graphical interface for alternative splicing quantification and downstream dimensionality reduction, differential splicing and gene expression and survival analyses based on The Cancer Genome Atlas, the Genotype-Tissue Expression project and user-provided data. These integrative analyses can also incorporate clinical and molecular sample-associated features. We successfully used psichomics to reveal alternative splicing signatures specific to stage I breast cancer and associated novel putative prognostic factors.
Design of Safe Wireless Power Transfer Systems for Electric Vehicles

Wireless charging of electric vehicles is convenient but in order to make it safe the exposure of humans to electromagnetic fields must be below acceptable limits. We have designed a prototype system that transmits 3 kW with an efficiency of 85% where the magnetic fields around and inside the vehicle are below the EU council recommendation of $6.25\mu\text{T}$ at 85 kHz.
Representative Selection on a Hypersphere

Finding representative examples is important for pattern discovery and data analytics. In this letter, we propose a novel formulation for representative selection via center reconstruction on a hypersphere, which makes the selection not affect the center information of given data, thus, the overall data distribution can also be easily maintained by those selected representatives. We adopt the proximal gradient strategy and the fast iterative shrinkage-thresholding algorithm to solve the problem. Compared with most existing methods with cubic time complexity in the number of samples, our method is considerably more efficient, with time complexity reduced to being quadratic. Our formulation has only one parameter. We analyze the behavior of this parameter and analyze its bound theoretically. Experiments on synthesis and real-world datasets validate the effectiveness and efficiency of our method and demonstrate its robustness to noise compared with the state-of-the-art methods.
Streamingrec: a framework for benchmarking stream-based news recommenders

News is one of the earliest application domains of recommender systems, and recommending items from a virtually endless stream of news is still a relevant problem today. News recommendation is different from other application domains in a variety of ways, e.g., because new items constantly become available for recommendation. To be effective, news recommenders therefore have to continuously consider the latest items in the incoming stream of news in their recommendation models. However, today's public software libraries for algorithm benchmarking mostly do not consider these particularities of the domain. As a result, authors often rely on proprietary protocols, which hampers the comparability of the obtained results. In this paper, we present StreamingRec as a framework for evaluating streaming-based news recommenders in a replicable way. The open-source framework implements a replay-based evaluation protocol that allows algorithms to update the underlying models in real-time when new events are recorded and new articles are available for recommendation. Furthermore, a variety of baseline algorithms for session-based recommendation are part of StreamingRec. For these, we also report a number of performance results for two datasets, which confirm the importance of immediate model updates.
Performance Analysis of Cryptographic Algorithms for Cloud Security

Cloud generally relates to storing, processing and managing of data over a network. The cloud provides mainly three services, storage as a service, platform as a service and software as a service using internet, based on the user's demand. As it became a vital platform, many companies are building their infrastructure. This has increased the corresponding vulnerabilities and threats as these services are often provided by third parties. So, security of the information in the cloud is a noteworthy issue for the cloud client. On the other hand, Performance of the security-based algorithms is another major concern. So, the work plan is to analyze the efficiency of these algorithms, based on the encryption time, decryption time, encryption throughput and memory utilized for encryption by the algorithms for different sizes of file formats like text, image and video. Further, eliminating the privacy concerns of data stored in cloud by enhancing the security using encryption algorithms like DES, 3DES, AES and BLOWFISH.
Oral adenosine 5′-triphosphate supplementation improved hemodynamic and autonomic parameters after exercise in hypertensive women

The aim of this study was to verify the autonomic modulation and blood pressure after adenosine-5’-triphosphate (ATP) supplementation associated to acute aerobic exercise in hypertensive women. Eleven hypertensive women (age, 61.8±5.0 years) completed a randomized, double blind trial: ATP supplement condition (ATP=400 mg) or placebo. After 30 min of supplementation or placebo intake, the subjects performed 30 min of aerobic exercise (70%–75% of maximum heart rate). The autonomic modulation was assessed by heart rate variability during rest and recovery (postexercise until 30 min of recovery), the square root of the mean squared difference between adjacent RR intervals (RMSSD), standard deviation of successive values (SDNN), low frequency (LF) and high frequency (HF) were measured. The blood pressure (systolic blood pressure [SBP] and diastolic blood pressure, mmHg) were recorded at rest, immediately postexercise, post-10, post-20, and post-30 min after exercise. For RMSSD, there was statistically significant difference during recovery, with higher RMSSD for ATP compared to placebo (rest=16.4±8.5 vs. placebo=11.6±4.0; ATP=18.5±9.7 msec; P=0.020). When analyzing the delta (recovery minus rest), the RMSSD (ATP=2.1± 7.2 msec vs. placebo=−4.7±7.5 msec; P=0.009), LF (ATP=−19.8±122.7 vs. placebo=−94.1±200.2 msec2; P=0.02), and SDNN (ATP=−2.8±12.2 msec vs. placebo=−10.6±10.5 msec; P=0.010) were higher for ATP than placebo. Furthermore, there was a greater postexercise hypotension at 20 min for ATP (SBP: ATP=−13.2±8.4 mmHg vs. placebo=−6.1±9.9 mmHg; P=0.006). Acute ATP supplementation promoted greater postexercise hypotension for systolic blood pressure and induced faster recovery of heart rate variability in hypertensive women.
Automatic Engagement Prediction with GAP Feature

In this paper, we propose an automatic engagement prediction method for the Engagement in the Wild sub-challenge of EmotiW 2018. We first design a novel Gaze-AU-Pose (GAP) feature taking into account the information of gaze, action units and head pose of a subject. The GAP feature is then used for the subsequent engagement level prediction. To efficiently predict the engagement level for a long-time video, we divide the long-time video into multiple overlapped video clips and extract GAP feature for each clip. A deep model consisting of a Gated Recurrent Unit (GRU) layer and a fully connected layer is used as the engagement predictor. Finally, a mean pooling layer is applied to the per-clip estimation to get the final engagement level of the whole video. Experimental results on the validation set and test set show the effectiveness of the proposed approach. In particular, our approach achieves a promising result with an MSE of 0.0724 on the test set of Engagement Prediction Challenge of EmotiW 2018.t with an MSE of 0.072391 on the test set of Engagement Prediction Challenge of EmotiW 2018.
Seafarers’ Quality of Life: Organizational Culture, Self-Efficacy, and Perceived Fatigue

Using the Culture-Work-Health model, this study investigates the factors influencing the quality of life of seafarers. This study conducted a survey of 320 seafarers who have lived and worked on a ship for more than six months. This self-administered questionnaire included questions on organizational culture and support, self-efficacy, perceived fatigue, as well as the quality of work life. Organizational culture and self-efficacy were identified as factors affecting the quality of work life, while organizational support was found to have an indirect effect through self-efficacy and perceived fatigue. The final model accounts for 63.1% of the variance in seafarers’ quality of life. As such, this study shows that self-efficacy is important for the quality of life of seafarers, having both direct and indirect effects. Moreover, organizational support may prove to be the primary intervention point for relieving perceived fatigue and enhancing self-efficacy, thus improving the quality of work life.
Analyzing Skin Pigment Discoloration on Cheeks by Using Image Processing

Melanin is the pigment that discerns the color of human skin. The special cells produce melanin in the skin. If these cells are damaged or unhealthy, skin discoloration is visible. Skin pigment discoloration on cheeks is a hazardous fact as a symptom of human skin disease with a possibility of losing natural beauty. The extracted information of the skin discoloration can work as a guide to diagnosis the disease. In this research, different imaging techniques like watershed method, edge detection and morphological operations are used to analyze and extract the information of cheek’s discoloration lesion by measuring the pixel number of lesion on skin. The image analyzing results are visually examined by the skin specialist and are observed to be highly accurate. The visual results are presented in the description and the accuracy of mathematical analysis is 94.88 percent.
Convolutional Neural Network for Categorization of Lung Tissue Patterns in Interstitial Lung Diseases

In computer aided diagnosis (CAD), automatic tissue categorization is very crucial part. Deep learning methods provide excellent results in the area where medicinal image analysis is required. Here, we have propose and develop a framework in which Convolutional Neural Network (CNN) will be used for tissue categorization of Interstitial Lung Diseases. The planned framework made up of 5 convolutional layers and 2 fully connected layers. The last layer provide different outputs of the tissue patterns such as healthy, Honeycombing, Ground Glass Opacity (GGO), Reticulation etc. We used a dataset of 100 HRCT scan which are collected from different radiology centers for training and evaluation of the system. In future we can use a three- dimensional images of the CT scans and also we can integrate this system into a computer aided diagnosis (CAD) system which will assist radiologist for better diagnosis.
Long-Range Traversability Awareness and Low-Lying Obstacle Negotiation with RealSense for the Visually Impaired

The application of RGB-D sensors provides a revolutionary force in the research field of computer vision, where traversable area detection and obstacle avoidance are the fundamental topics to aid visually impaired people. However, the detection using RGB-D sensors is limited by the sparse depth map and the narrow field of view, which hampers longer traversability awareness and lower obstacle negotiation. This paper proposes an effective approach to combine a pair of wearable smart glasses and a waist-worn pathfinder to assist the visually impaired. Traversable lines are generated to feedback the visually impaired through real-time stereo sound after real-world coordinates adjustment with a RealSense R200 (RGB-D sensor) and an IMU integrated in the pair of smart glasses. Low-lying obstacles are detected by using another RGB-D sensor of RealSense, the RS410, as the waist-worn pathfinder to provide early warning. Indoor and outdoor detection results as well as a field test demonstrate the usefulness and effectiveness of our approach.
Cutting-edge evidence of adjuvant treatments for gastric cancer

ABSTRACT Introduction: In recent decades, adjuvant therapy has secured a firm position in the treatment of gastric cancer by improving patient prognosis. Currently, standard therapy varies between East Asia, Europe and North America due to heterogeneities of the tumor, race, medical environment and/or surgical procedure. Although adjuvant strategies, proved effective in each region and implemented to clinical practice, certain patient populations with advanced gastric cancer show recurrence and a fatal prognosis. The development of a universal adjuvant therapy with a high efficacy and acceptable adverse events or with less toxicity and non-inferiority seems to have become urgent and imperative. Areas covered: In this review, we aimed to summarize the current knowledge regarding adjuvant therapies for gastric cancer, including chemotherapy, chemoradiotherapy and molecular targeted therapy, based on clinical trials and to introduce the pivotal ongoing phase III trials. Expert commentary: The efficacy of adjuvant therapy to prevent recurrence remains insufficient although several trials have shown a significant benefit in patients with localized advanced gastric cancer. The development of more effective and universal multimodal adjuvant therapy is required.
Networking named content

Network use has evolved to be dominated by content distribution and retrieval, while networking technology still speaks only of connections between hosts. Accessing content and services requires mapping from the what that users care about to the network's where. We present Content-Centric Networking (CCN) which treats content as a primitive - decoupling location from identity, security and access, and retrieving content by name. Using new approaches to routing named content, derived heavily from IP, we can simultaneously achieve scalability, security and performance. We implemented our architecture's basic features and demonstrate resilience and performance with secure file downloads and VoIP calls.
Mathematical Modeling and Control of QNET Rotary Inverted Pendulum in MATLAB and Real Time Implementation in Lab View Using ELVIS

The objective of this project is the design and comparison of the various controllers for QNET (Quanser NI ELVIS Trainer) Rotary Inverted Pendulum (RIP) Board 2.0 for NI ELVIS. The RIP system is a multivariable mode with highly nonlinear behavior. It is used in the design of highly complex applications like automatic aircraft landing system, and humanoid robot stabilization. In this work, the RIP model is mathematically derived using motion equation of Euler-Lagrange. For controlling the system, PP (pole placement) and LQR(Linear Quadratic Regulator) controllers are designed and implemented in MATLAB for the balancing of the pendulum arm in up-right position. A comparative study was made for analyzing the performance of the controllers in terms of settling time and overshoot range, etc. Based on designed parameters using MATLAB simulation, the control ofreal time Rotary inverted pendulum arm is implemented in LabVIEW platform interfaced with NI ELVIS. From the experimental results of MATLAB simulation and LabVIEWinterfacing, the LQR controller was better than the PP controller in controlling the pendulum arm in upright position.
Reduced Activity of Geranylgeranyl Diphosphate Synthase Mutant Is Involved in Bisphosphonate-Induced Atypical Fractures

Bisphosphonates are widely used for treating osteoporosis, a common disorder in which bone strength is reduced, increasing the risk for fractures. Rarely, bisphosphonates can paradoxically lead to atypical fractures occurring spontaneously or with trivial trauma. Recently, a novel missense mutation (D188Y) in the GGPS1 gene, encoding for geranylgeranyl diphosphate synthase (GGPPS), was associated with bisphosphonate-induced atypical fractures. However, the molecular basis for GGPPS involvement in this devastating condition remains elusive. Here, we show that while maintaining an overall unperturbed global enzyme structure, the D188Y mutation leads to ∼4-fold catalytic activity decrease. Furthermore, GGPPS-D188Y is unable to support cross-species complementation, highlighting the functional significance of the reduced catalytic activity observed in vitro. We next determined the crystal structure of apo-GGPPS-D188Y, revealing that while Y188 does not alter the protein fold, its bulky side chain sterically interferes with substrate binding. In agreement, we show that GGPPS-D188Y exhibits ∼3-fold reduction in the binding affinity of zoledronate, a commonly used bisphosphonate. However, inhibition of the mutated enzyme by zoledronate, in pharmacologically relevant concentrations, is maintained. Finally, we determined the crystal structure of zoledronate-bound GGPPS-D188Y, revealing large ligand-induced binding pocket rearrangements, revising the previous model for GGPPS-bisphosphonate interactions. In conclusion, we propose that among heterozygotes residual GGPPS activity is sufficient to support physiologic cellular function, concealing any pathologic phenotype. However, under bisphosphonate treatment, GGPPS activity is reduced below a crucial threshold for osteoclast function, leading to impaired bone remodeling and increased susceptibility to atypical fractures.