Miracle-dz/UniFAR-FTUs huggingface dataset:
A portable and low-cost 12-lead ECG device for sustainable remote healthcare

The diagnosis of cardiac patients is commonly done using Electrocardiogram (ECG). But in the frequently used 3- Lead ECG device, all the cardiac diseases are not covered in the diagnosis. This paper illustrates an approach towards developing a 12-Lead ECG system. The existing 12-Lead ECG systems are costlier and require a large setup size. Also, these machines are not that user-friendly to be operated in rural areas. The proposed system is compact which makes it portable and shows effective results in ambulatory systems. This manifesto presents a customized approach towards designing an ECG amplifier, and Digital data converting and storing unit, enabling the domain of ‘Internet of Things’ which makes it a very effective mode of Telemedicine. The received digital data is directly uploaded to an online database. To minimize the cost, reduction in hardware has been suggested without compromising on the quality of signal captured. Use of basic embedded and open source tools made the system simple and easy in terms of handling. This study has fostered a successful working model which helps in acquiring 12 ECG outputs altogether for a thorough diagnosis of cardiac patients.
TALMUD: transfer learning for multiple domains

Most collaborative Recommender Systems (RS) operate in a single domain (such as movies, books, etc.) and are capable of providing recommendations based on historical usage data which is collected in the specific domain only. Cross-domain recommenders address the sparsity problem by using Machine Learning (ML) techniques to transfer knowledge from a dense domain into a sparse target domain. In this paper we propose a transfer learning technique that extracts knowledge from multiple domains containing rich data (e.g., movies and music) and generates recommendations for a sparse target domain (e.g., games). Our method learns the relatedness between the different source domains and the target domain, without requiring overlapping users between domains. The model integrates the appropriate amount of knowledge from each domain in order to enrich the target domain data. Experiments with several datasets reveal that, using multiple sources and the relatedness between domains improves accuracy of results.
Quantum programming: A review

In this paper, we discuss an actual state of the quantum programming. At present, we don't how know to solve some algorithmic problems, mainly the processing of complex information, using the current computer architecture in real time. The quantum approaches promise new opportunities in the processing of complex information. This paper is divided to three parts. The first part of paper is focused on the theoretical basis of quantum computer. The second part presents current state in this area. The third part shows experiments on a quantum computer. The experiments are performed on IBM Q platform.
A ±45° dual-polarized equilateral triangular dielectric resonator antenna

A ±45° dual-polarized dielectric resonator antenna operating at 5.5 GHz is presented. The slant polarization is achieved by edge feeding the equilateral triangular dielectric resonator through a conformal strip. The proposed antenna exhibits an impedance bandwidth of 12.04% (5.23–5.90 GHz) for both the ports and the port-to-port isolation is better than 15 dB throughout the bandwidth. The antenna radiates along the broadside direction and the difference between the co-polar and cross polar field is 15 dB in both the planes. The diversity performance of the antenna is found to be good with envelope correlation coefficient below 0.014. The proposed antenna is suitable for WLAN applications.
Open Source Tools for the Information Theoretic Analysis of Neural Data

The recent and rapid development of open source software tools for the analysis of neurophysiological datasets consisting of simultaneous multiple recordings of spikes, field potentials and other neural signals holds the promise for a significant advance in the standardization, transparency, quality, reproducibility and variety of techniques used to analyze neurophysiological data and for the integration of information obtained at different spatial and temporal scales. In this review we focus on recent advances in open source toolboxes for the information theoretic analysis of neural responses. We also present examples of their use to investigate the role of spike timing precision, correlations across neurons, and field potential fluctuations in the encoding of sensory information. These information toolboxes, available both in MATLAB and Python programming environments, hold the potential to enlarge the domain of application of information theory to neuroscience and to lead to new discoveries about how neurons encode and transmit information.
The Modelverse: A tool for Multi-Paradigm Modelling and simulation

Multi-Paradigm Modelling (MPM) has been proposed to tackle the complexities found in Cyber-Physical Systems. MPM advocates the explicit modelling of all pertinent parts and aspects of complex systems. It adresses and integrates three orthogonal dimensions: multi-abstraction modelling, concerned with the (refinement, generalization, …) relationships between models; multi-formalism modelling, concerned with the (multi-view, multi-component, …) coupling of and transformation between models described in different formalisms; explicitly modelling the often complex, concurrent workflows. Current modelling, analysis and simulation tools support only isolated parts of MPM. The core methods and techniques enabling MPM are modelling language engineering, model operations (such as transformation and simulation), and workflow modelling. This paper delves into each enabler, presenting its relation to MPM and how it is supported in our prototype tool: the Modelverse. An automotive power window example is used to illustrate the Modelverse's capabilities. All aspects are explicitly modelled and enacted with a Formalism Transformation Graph + Process Model (FTG+PM).
Dietary calcium, vitamin D, and the risk of colorectal cancer.

BACKGROUND Calcium and vitamin D have a potential protective effect against colorectal cancer. OBJECTIVE We investigated the association of dietary intake of calcium and vitamin D with the risk of colorectal cancer in a large prospective cohort study of middle-aged Japanese men and women. DESIGN A total of 74 639 subjects (35 194 men and 39 445 women) who participated in the Japan Public Health Center-based Prospective Study were followed from 1995-1999 to the end of 2004, during which time 761 cases of colorectal cancer (464 men, 297 women) were newly identified. Dietary intake of nutrients was calculated with the use of a 138-item self-administered food-frequency questionnaire. RESULTS After adjusting for potential confounding factors, the multivariate hazard ratio in the highest quintile of dietary calcium intake compared with the lowest was 0.71 (95% CI: 0.52, 0.98) among men. The association appeared to decrease considerably among subjects in the second quintile without a clear further dose-response relation (P for trend: 0.09). No association was seen among women. No statistically significant association with dietary vitamin D intake was seen in either men or women, although men in the highest dietary intake group of both nutrients had a lower risk than did men in the lowest group. CONCLUSIONS These findings indicate a potential decrease in the risk of colorectal cancer with higher dietary intake of calcium among middle-aged Japanese men, who have a relatively low dietary intake of calcium. Although vitamin D and colorectal cancer risk were not associated, potential effect modification between calcium and vitamin D on the risk of colorectal cancer was indicated.
Predicting Frailty Condition in Elderly Using Multidimensional Socioclinical Databases

Smart cities face the challenge of combining sustainable national welfare with high living standards. In the last decades, life expectancy increased globally, leading to various age-related issues in almost all developed countries. Frailty affects elderly who are experiencing daily life limitations due to cognitive and functional impairments and represents a remarkable burden for national health systems. In this paper, we proposed two different predictive models for frailty by exploiting 12 socioclinical databases. Emergency hospitalization or all-cause mortality within a year were used as surrogates of frailty. The first model was able to assign a frailty risk score to each subject older than 65 years old, identifying five different classes for tailor made interventions. The second prediction model assigned a worsening risk score to each subject in the first nonfrail class, namely the probability to move in a higher frailty class within the year. We conducted a retrospective cohort study based on the whole elderly population of the Municipality of Bologna, Italy. We created a baseline cohort of 95 368 subjects for the frailty risk model and a baseline cohort of 58 789 subjects for the worsening risk model, respectively. To evaluate the predictive ability of our models through calibration and discrimination estimates, we used, respectively, a six-year and a four-year observation period. Good discriminatory power and calibration were obtained, demonstrating a good predictive ability of the models.
Forming incoherent twin boundaries: a new way for nanograin growth under cyclic loading

ABSTRACT Obvious grain growth was observed in regions around the fatigue cracks in the Au thin films under cyclic loading. Dominant generation of twin boundaries (TBs) and low-angle grain boundaries was observed in the coarsened grains. The experimental evidence shows that the newly formed TBs are mainly incoherent TBs, rather than the classical coherent TBs. GRAPHICAL ABSTRACT IMPACT STATEMENT A number of newly formed twin boundaries (TBs) are observed to be mainly incoherent TBs rather than the classical coherent TBs in the coarsened grains of the fatigued Au film.
Making Bertha Cooperate–Team AnnieWAY’s Entry to the 2016 Grand Cooperative Driving Challenge

This paper presents the concepts and methods utilized by Team AnnieWAY for the 2016 Grand Cooperative Driving Challenge. The paper introduces the automated vehicle BerthaOne. The vehicle, even though being based on the Bertha platform, distinguishes itself from its siblings by its software modules and algorithms. We, therefore, describe its system architecture and algorithms for perception, cooperation and motion planning. In Particular, we present a motion planner that plans different maneuvers flexibly by augmenting the cost function with situation specific cost terms. We subsequently describe the requirements of the 2016 GCDC and evaluate our performance during the competition.
Bandwidth-aware DRAM page migration for heterogeneous mobile memory systems

Bandwidth demands for mobile consumer electronics devices, including smartphones and tablets, have been continuously increasing in recent years. A heterogeneous memory system (HMS) integrating different types of mobile DRAM devices, such as LPDDR4 and WideIO2, is an attractive solution to scale DRAM bandwidth beyond the maximum bandwidth of a single device type. One of the key challenges in HMS design is how to identify hot (cold) pages and migrate them to the faster (slower) devices at a low cost. This becomes more difficult as more devices like LPDDR4X adopt dynamic voltage and frequency scaling (DVFS) for DRAM I/O to reduce power consumption. At a low I/O voltage and frequency, a significant portion of DRAM bandwidth can be consumed by page migration traffic to slow down user applications. Thus, this paper proposes a novel page migration technique towards bandwidth-aware memory management (BAMM) for an HMS composed of both fast WideIO2 and slow LPDDR4 devices. BAMM periodically measures the bandwidth consumption for the LPDDR4 channel and adjusts both the maximum cap for migration bandwidth and DVFS level to keep the migration overhead low. Our evaluation using a detailed cycle-level simulator demonstrates that BAMM improves the energy efficiency of the memory system by 6.3% on average (and up to 11.6%) with minimal performance overhead over the baseline HMS with no dynamic page migration.
Early Hospital Readmissions after an Acute Exacerbation of Chronic Obstructive Pulmonary Disease in the Nationwide Readmissions Database

&NA; Rationale: Understanding the causes and factors related to readmission for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) within a nationwide database including all payers and ages can provide valuable input for the development of generalizable readmission reduction strategies. Objectives: To determine the rates, causes, and predictors for early (3‐, 7‐, and 30‐d) readmission in patients hospitalized with AECOPD in the United States using the Nationwide Readmission Database after the initiation of the Hospital Readmissions Reduction Program, but before its expansion to COPD. Methods: We conducted an analysis of the Nationwide Readmission Database from 2013 to 2014. Index admissions and readmissions for an AECOPD were defined consistent with Hospital Readmissions Reduction Program guidelines. We investigated the percentage of 30‐day readmissions occurring each day after discharge and the most common readmission diagnoses at different time periods after hospitalization. The relationship between predictors (categorized as patient, clinical, and hospital factors) and early readmission were evaluated using a hierarchical two‐level logistic model. To examine covariate effects on early‐day readmission, predictors for 3‐, 7‐, and 30‐day readmissions were modeled separately. Results: There were 202,300 30‐day readmissions after 1,055,830 index AECOPD admissions, a rate of 19.2%. The highest readmission rates (4.2‐5.5%) were within the first 72 hours of discharge, and 58% of readmissions were within the first 15 days. Respiratory‐based diseases were the most common reasons for readmission (52.4%), and COPD was the most common diagnosis (28.4%). Readmission diagnoses were similar at different time periods after discharge. Early readmission was associated with patient (Medicaid payer status, lower household income, and higher comorbidity burden) and clinical factors (longer length of stay and discharge to a skilled nursing facility). Predictors did not vary substantially by time of readmission after discharge within the 30‐day window. Conclusions: Thirty‐day readmissions after an AECOPD remain a major healthcare burden, and are characterized by a similar spectrum of readmission diagnoses. Predictors associated with readmission include both patient and clinical factors. Development of a COPD‐specific risk stratification algorithm based on these factors may be necessary to better predict patients with AECOPD at high risk of early readmission.
Performance analysis of an efficient technique for ordering programs into multiple processors architectures

During these years, applications such as telecommunications, signal processing, digital communication with advanced features (Multi-antenna, equalization…) witness a rapid evaluation accompanied with an increase of user exigencies in terms of latency, power of computation… To satisfy these requirements, researchers work hard to adduce an efficient scheduling for hardware/software applications; where hardware is composed of multiple processors and software is represented by models of computation, synchronous dataflow (SDF) graph for instance. In this paper, we present a performance analysis of our proposed approach that calculates the best number of processors required for the scheduling process. Our proposed algorithm was evaluated by using heuristics for the scheduling process. We compared the results obtained from different scenarios. Then, we showed that the combination of our proposed algorithm with heuristics gives the best schedule in terms of speed up, efficiency and number of processors.
The Relationship Between Drivers’ Cognitive Fatigue and Speed Variability During Monotonous Daytime Driving

A lack of task workload can lead to drivers’ cognitive fatigue and vigilance decrement during a prolonged drive. This study examined the effects of speed variability on driving fatigue in a monotonous drive. Twenty-one participants participated in a 60-min simulated driving task. All participants’ cognitive fatigue was assessed using psychological and physiological measurements. Results showed that among all participants, variability of vehicle speed was negatively correlated with sleepiness and hypo-vigilance during the driving task. Further, drivers in the large variability group reported less sleepiness, less fatigue, and more vigilance than those in the small variability group did during the driving task. These drivers also presented a smaller electroencephalogram spectral index (𝜃+α)/β during the task, where 𝜃, α, and β are the power spectra of three different frequency bands: theta (𝜃, 4∼8 Hz), alpha (α, 8∼13 Hz), and beta (β, 13∼30 Hz). Our findings suggested that the larger variability of speed within the speed limit may have a deterrent effect on drivers’ cognitive fatigue.
A Parallel Pairwise Alignment with Pruning for Large Genomic Sequences

Pairwise sequence alignment is a common and fundamental task in Computational Biology, which constitutes the basis for many Bioinformatics applications. In the post-genomic era, there is an increasing demand to align long DNA sequences to discover their functions. In this paper, we propose a parallel pairwise alignment algorithm for large genomic sequences by recursively dividing the whole genomic sequences into small pieces, with an effective pruning strategy to reduce search and computation space. We implemented rigorous tests on a 4-core computer using real genomic sequences and artificially generated sequences. The results show that our implementation can achieve speedup 10.64 with 99.75% accuracy compared to the sequential algorithm. As far as we know, this is the first time that MBP (mega base-pairs) sequences are globally aligned with an affine gap penalty.
DeepSigns : A Generic Watermarking Framework for Protecting the Ownership of Deep Learning Models

Deep Learning (DL) models have caused a paradigm shift in our ability to comprehend raw data in various important fields, ranging from intelligence warfare and healthcare to autonomous transportation and automated manufacturing. A practical concern, in the rush to adopt DL models as a service, is protecting the models against Intellectual Property (IP) infringement. The DL models are commonly built by allocating significant computational resources that process vast amounts of proprietary training data. The resulting models are therefore considered to be the IP of the model builder and need to be protected to preserve the owner's competitive advantage. This paper proposes DeepSigns, a novel end-to-end IP protection framework that enables insertion of coherent digital watermarks in contemporary DL models. DeepSigns, for the first time, introduces a generic watermarking methodology that can be used for protecting DL owner's IP rights in both white-box and black-box settings, where the adversary may or may not have the knowledge of the model internals. The suggested methodology is based on embedding the owner's signature (watermark) in the probability density function (pdf) of the data abstraction obtained in different layers of a DL model. DeepSigns can demonstrably withstand various removal and transformation attacks, including model compression, model fine-tuning, and watermark overwriting. Proof-of-concept evaluations on MNIST, and CIFAR10 datasets, as well as a wide variety of neural network architectures including Wide Residual Networks, Convolution Neural Networks, and Multi-Layer Perceptrons corroborate DeepSigns' effectiveness and applicability.
A Characterization of Interventional Distributions in Semi-Markovian Causal Models

We offer a complete characterization of the set of distributions that could be induced by local interventions on variables governed by a causal Bayesian network of unknown structure, in which some of the variables remain unmeasured. We show that such distributions are constrained by a simply formulated set of inequalities, from which bounds can be derived on causal effects that are not directly measured in randomized experiments.
FPGA Implementation of an Improved Watchdog Timer for Safety-Critical Applications

Embedded systems that are employed in safety-critical applications require highest reliability. External watchdog timers are used in such systems to automatically handle and recover from operation time related failures. Most of the available external watchdog timers use additional circuitry to adjust their timeout periods and provide only limited features in terms of their functionality. This paper describes the architecture and design of an improved configurable watchdog timer that can be employed in safety-critical applications. Several fault detection mechanisms are built into the watchdog, which adds to its robustness. The functionality and operations are rather general and it can be used to monitor the operations of any processor based real-time system. This paper also discusses the implementation of the proposed watchdog timer in a Field Programmable Gate Array (FPGA). This allows the design to be easily adaptable to different applications, while reducing the overall system cost. The effectiveness of the proposed watchdog timer to detect and respond to faults is first studied by analysing the simulation results. The design is validated in a real-time hardware by injecting faults through the software while the processor is executing, and conclusions are drawn.
Time-Lock Puzzles in the Random Oracle

A time-lock puzzle is a mechanism for sending messages “to the future”. The sender publishes a puzzle whose solution is the message to be sent, thus hiding it until enough time has elapsed for the puzzle to be solved. For timelock puzzles to be useful, generating a puzzle should take less time than solving it. Since adversaries may have access to many more computers than honest solvers, massively parallel solvers should not be able to produce a solution much faster than serial ones. To date, we know of only one mechanism that is believed to satisfy these properties: the one proposed by Rivest, Shamir and Wagner (1996), who originally introduced the notion of time-lock puzzles. Their puzzle is based on the serial nature of exponentiation and the hardness of factoring, and is therefore vulnerable to advances in factoring techniques (as well as to quantum attacks). In this work, we study the possibility of constructing time-lock puzzles in the random-oracle model. Our main result is negative, ruling out time-lock puzzles that require more parallel time to solve than the total work required to generate a puzzle. In particular, this should rule out black-box constructions of such timelock puzzles from one-way permutations and collision-resistant hash-functions. On the positive side, we construct a time-lock puzzle with a linear gap in parallel time: a new puzzle can be generated with one round of n parallel queries to the random oracle, but n rounds of serial queries are required to solve it (even for massively parallel adversaries).