Miracle-dz/UniFAR-FTUs huggingface dataset:
TileNET: Scalable Architecture for High-Throughput Ternary Convolution Neural Networks Using FPGAs

Convolution Neural Networks (CNNs) are becoming increasing popular in Advanced driver assistance systems (ADAS) and Autonomated driving (AD) for camera perception enabling multiple applications like object detection, lane detection and semantic segmentation. Ever increasing need for high resolution multiple cameras around car necessitates a huge-throughput in the order of about few 10's of TeraMACs per second (TMACS) along with high accuracy of detection. Existing implementations do not scale, with performance ranging only in the order of a few Giga operations per second. This paper, proposes a novel tiled architecture for CNNs that uses only ternarized weights, while input and output features are kept full precision resulting in minimal loss of accuracy. The proposed solution is implemented on Virtex-7 FPGA resulting in throughput of 13.76 TOPS. The post-implementation power simulation for AlexNet consumes 16 W, orders of magnitude lower than exist in GPUs.
Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning

Blended learning combines online digital resources with traditional classroom activities and enables students to attain higher learning performance through well-defined interactive strategies involving online and traditional learning activities. Learning analytics is a conceptual framework and as a part of our Precision education used to analyze and predict students’ performance and provide timely interventions based on student learning profiles. This study applied learning analytics and educational big data approaches for the early prediction of students’ final academic performance in a blended Calculus course. Real data with 21 variables were collected from the proposed course, consisting of video-viewing behaviors, out-of-class practice behaviors, homework and quiz scores, and after-school tutoring. This study applied principal component regression to predict students’ final academic performance. The experimental results show that students’ final academic performance could be predicted when only one-third of the semester had elapsed. In addition, we identified seven critical factors that affect students’ academic performance, consisting of four online factors and three traditional factors. The results showed that the blended data set combining online and traditional critical factors had the highest predictive performance.
The Treatment of Sleep Disorders in Parkinson’s Disease: From Research to Clinical Practice

Sleep disorders (SDs) are one of the most frequent non-motor symptoms of Parkinson’s disease (PD), usually increasing in frequency over the course of the disease and disability progression. SDs include nocturnal and diurnal manifestations such as insomnia, REM sleep behavior disorder, and excessive daytime sleepiness. The causes of SDs in PD are numerous, including the neurodegeneration process itself, which can disrupt the networks regulating the sleep–wake cycle and deplete a large number of cerebral amines possibly playing a role in the initiation and maintenance of sleep. Despite the significant prevalence of SDs in PD patients, few clinical trials on SDs treatment have been conducted. Our aim is to critically review the principal therapeutic options for the most common SDs in PD. The appropriate diagnosis and treatment of SDs in PD can lead to the consolidation of nocturnal sleep, the enhancement of daytime alertness, and the amelioration of the quality of life of the patients.
Human Papillomavirus and Carcinogenesis in the Upper Aero-Digestive Tract

The evaluation of causality for these infectious agents as human carcinogens is difficult given their ubiquitous nature, the substantial length of time between infection and the cancer event, the nature of cofactors, and the rarity of malignancy among those infected. Thus, a central problem for the epidemiologist is to define the natural history of infection and to identify those factors that are related to the development of cancer. Hence, informative biomarkers of the agent (such as viral load), of the host (such as abnormal antibody pattern), and of other oncogenic exposures (such as tobacco use) are required for understanding the viral-human interactions and for developing interventions [2].
Computational Accountability in MAS Organizations with ADOPT

This work studies how the notion of accountability can play a key role in the design and realization of distributed systems that are open and that involve autonomous agents that should harmonize their own goals with the organizational goals. The socio–technical systems that support the work inside human companies and organizations are examples of such systems. The approach that is proposed in order to pursue this purpose is set in the context of multiagent systems organizations, and relies on an explicit specification of relationships among the involved agents for capturing who is accountable to whom and for what. Such accountability relationships are created along with the agents’ operations and interactions in a shared environment. In order to guarantee accountability as a design property of the system, a specific interaction protocol is suggested. Properties of this protocol are verified, and a case study is provided consisting of an actual implementation. Finally, we discuss the impact on real-world application domains and trace possible evolutions of the proposal.
Perhaps quality of life should not be used as a trigger for discussing resuscitation and advance care plans

As a subjective measure, quality of life should not be used as a trigger for discussing resuscitation, goals of care or advanced care plans. Limitations and challenges in measurement and assessment of quality of life have been recognised. It is a fluid construct and subject to changes and response shifts. Surrogate quality of life estimations may misrepresent individual worth and self‐perceived quality of life. Disagreement over quality of life statements may cause harm.
A Systematic Approach to Determining the Weights of the Capacitors in the DAC of a Non-binary Redundant SAR ADCs

The use of non-binary weighted DACs with redundant DAC capacitor weights has been shown to provide significant speed improvements in SAR ADCs. The non-binary SAR algorithm has an in-built correction mechanism against bit decision errors caused by incomplete DAC settling by providing overlap ranges in the search space and allowing alternative bit decision paths for approximating a given input voltage. In this paper we propose to use overlap ranges not only for speed-improvement but also for providing immunity to noise in the least-significant bit decisions thereby improving ADC robustness in noisy environments. Furthermore, we propose a systematic approach to the design of the redundant SAR ADC by providing an algorithm for determining the weights of the capacitors in the DAC to obtain the required DAC settling time improvement as well as noise immunity in the last few bit decisions as compared to a binary weighted SAR ADC. A 10-bit SAR ADC designed using the above weight distribution algorithm shows a 4X improvement in bit cycle-time (200ps) and a 5.5 dB improvement in noise immunity over the binary SAR ADC using 180 nm CMOS technology.
Low-complexity greedy algorithm in compressed sensing for the adapted decoding of ECGs

The balanced weighted orthogonal matching pursuit (bWOMP) algorithm for recovering signals in compressed sensing (CS) based system is presented as a specialized recovering tool for Electrocardiograph (ECG) signals. Being based on the standard OMP approach, bWOMP is a lightweight reconstruction algorithm both in terms of complexity and memory footprint. Furthermore, the concept of weighting is introduced in the algorithm by exploring a prior knowledge on ECG signals. Experimental results show a performance increase of about 10 dB with respect to the standard OMP approach, and also an increase with respect to the decoding approaches considered as the state-of-the-art. In this case the gain could be as high as 4 dB with respect to the best of currently known decoding approaches.
MAPK/p38 regulation of cytoskeleton rearrangement accelerates induction of macrophage activation by TLR4, but not TLR3

Toll-like receptor 3 (TLR3) and TLR4 utilize adaptor proteins to activate mitogen-activated protein kinase (MAPK), resulting in the acute but transient inflammatory response aimed at the clearance of pathogens. In the present study, it was demonstrated that macrophage activation by lipopolysaccharide (LPS) or poly(I:C), leading to changes in cell morphology, differed significantly between the mouse macrophage cell line RAW264.7 and mouse primary peritoneal macrophages. Moreover, the expression of α- and β-tubulin was markedly decreased following LPS stimulation. By contrast, α- and β-tubulin expression were only mildly increased following poly(I:C) treatment. However, the expression of β-actin and GAPDH was not significantly affected. Furthermore, it was verified that vincristine pretreatment abrogated the cytoskeleton rearrangement and decreased the synthesis and secretion of proinflammatory cytokines and migration of macrophages caused by LPS. Finally, it was observed that the MAPK/p38 signaling pathway regulating cytoskeleton rearrangement may participate in LPS-induced macrophage cytokine production and migration. Overall, the findings of the present study indicated that MAPK/p38 regulation of the cytoskeleton, particularly tubulin proteins, plays an important role in LPS-induced inflammatory responses via alleviating the synthesis and secretion of proinflammatory cytokines and inhibiting the migration of macrophages.
Towards automated generation of Java methods: A way of automated reuse-based programming

Automatic programming has been researched for a long time. A variety of methodologies have been proposed. However, they have limited applicability, or they can generate only a few lines of code. In this research, the authors are trying to generate source code of Java methods based on their specifications. In this paper, we propose a reuse-based code generation technique with method signature and test cases. First, our technique searches existing Java methods whose signature are the same as the one input by a user. Then, our technique reworks each of them by using test cases input by the user. Methods passing all the test cases are given to the user. At this moment, the authors have implemented a naive prototype and conducted experiments with four open source software. In total, our technique succeeded to generate 18 Java methods. In this paper, we also introduce some actual examples of generated Java methods and some ideas to enhance our technique.
A comparative study on dyeing, tenacity and colour fastness characteristics of dyed and modified silk fibres

Dyeing behaviour, tenacity and colour fastness characteristics of degummed, AA-, MMA- and EMA-grafted silk fibres with A052, AV7 and AR114 were investigated. Dye absorption was greatly affected by the parameters variables, such as dye concentration, sodium sulphate and acetic acid as electrolyte concentration, dyeing time and dyeing temperature, and depends on the dye and fibre characteristics. The dye absorption of A052, AV7 and AR114 dyes by AA-, MMA- and EMA-grafted silk fibres at optimized condition was 5-12% higher than that of degummed silk fibre. Due to the dyeing treatment both the modified and unmodified fibres lost tenacity to about 5% and the tenacity of modified dyed silk fibre was higher in both heat and sunlight effect. The colour fastness characteristics of dyed modified silk fibre was higher in sunlight, soap wash, some acid and alkalis than that of dyed degummed silk fibre.
GNSS Transpolar Earth Reflectometry exploriNg System (G-TERN): Mission Concept

The global navigation satellite system (GNSS) Transpolar Earth Reflectometry exploriNg system (G-TERN) was proposed in response to ESA’s Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists. The primary objective of the mission is to quantify at high spatio-temporal resolution crucial characteristics, processes and interactions between sea ice, and other Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The objective is articulated through three key questions. 1) In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere, and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation, and melt)? 2) What are the impacts of extreme events and feedback mechanisms on sea ice evolution? 3) What are the effects of the cryosphere behaviors, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events? To contribute answering these questions, G-TERN will measure key parameters of the sea ice, the oceans, and the atmosphere with frequent and dense coverage over polar areas, becoming a “dynamic mapper” of the ice conditions, the ice production, and the loss in multiple time and space scales, and surrounding environment. Over polar areas, the G-TERN will measure sea ice surface elevation (<10 cm precision), roughness, and polarimetry aspects at 30-km resolution and 3-days full coverage. G-TERN will implement the interferometric GNSS reflectometry concept, from a single satellite in near-polar orbit with capability for 12 simultaneous observations. Unlike currently orbiting GNSS reflectometry missions, the G-TERN uses the full GNSS available bandwidth to improve its ranging measurements. The lifetime would be 2025–2030 or optimally 2025–2035, covering key stages of the transition toward a nearly ice-free Arctic Ocean in summer. This paper describes the mission objectives, it reviews its measurement techniques, summarizes the suggested implementation, and finally, it estimates the expected performance.
A vision for a ‘microbcentric’ future

Microbes are the most abundant lifeforms on the planet and perform functions critical for all other life to exist. Environmental ‘omic’ technologies provide the capacity to discover the ‘what, how and why’ of indigenous species. However, in order to accurately interpret this data, sound conceptual frameworks are required. Here I argue that our understanding of microbes will advance much more effectively if we adopt a microbcentric, and not anthropocentric view of the world.
Quality Changes of N-3 PUFAs Enriched and Conventional Eggs under Different Home Storage Conditions with Wireless Sensor Network

Omega-3 polyunsaturated fatty acids (n-3 PUFAs) enriched eggs are popular for their enrichment of PUFAs, but their freshness and quality are prone to decline with time, particularly if storage conditions are not well maintained. Home storage is the last but a neglected important tach in eggs supply chain, but few studies are available on the egg storage in the home stage. This study aimed to evaluate the internal quality change of both n-3 PUFAs enriched and conventional eggs by simulating eggs stored at consumers’ home in refrigeration and under room temperature. The egg quality indices (Haugh unit, yolk index, weight, and albumen pH) were adopted and analyzed for both storage conditions. Wireless sensor network (WSN) was used for real-time monitoring of the temperature and humidity during storage. The results showed that temperature, humidity and time of storage all have an influence on the freshness of both n-3 PUFAs enriched and conventional eggs and there is no significant difference happened to n-3 PUFAs enriched eggs and conventional eggs. Refrigeration can decelerate the quality deterioration of both n-3 PUFAs enriched and conventional eggs and consumers should be educated about how to maintain the internal quality of eggs during home storage.
IDE-independent program comprehension tools via source file overwriting

Traditionally, we have two possibilities to design tools for program comprehension and analysis. The first option is to create a standalone program, independent of any source code editor. This way, the act of source code editing is separated from the act of viewing the code analysis results. The second option is to create a plugin for a specific IDE (integrated development environment) — in this case, a separate version must be created for each IDE. We propose an approach where information about source code elements is written directly into source files as annotations or special comments. Before committing to a version control system, the annotations are removed from the source code to avoid code pollution. We briefly evaluate the approach and delineate its limitations.
An Auxiliary Variable Method for Markov Chain Monte Carlo Algorithms in High Dimension

In this paper, we are interested in Bayesian inverse problems where either the data fidelity term or the prior distribution is Gaussian or driven from a hierarchical Gaussian model. Generally, Markov chain Monte Carlo (MCMC) algorithms allow us to generate sets of samples that are employed to infer some relevant parameters of the underlying distributions. However, when the parameter space is high-dimensional, the performance of stochastic sampling algorithms is very sensitive to existing dependencies between parameters. In particular, this problem arises when one aims to sample from a high-dimensional Gaussian distribution whose covariance matrix does not present a simple structure. Another challenge is the design of Metropolis–Hastings proposals that make use of information about the local geometry of the target density in order to speed up the convergence and improve mixing properties in the parameter space, while not being too computationally expensive. These two contexts are mainly related to the presence of two heterogeneous sources of dependencies stemming either from the prior or the likelihood in the sense that the related covariance matrices cannot be diagonalized in the same basis. In this work, we address these two issues. Our contribution consists of adding auxiliary variables to the model in order to dissociate the two sources of dependencies. In the new augmented space, only one source of correlation remains directly related to the target parameters, the other sources of correlations being captured by the auxiliary variables. Experiments are conducted on two practical image restoration problems—namely the recovery of multichannel blurred images embedded in Gaussian noise and the recovery of signal corrupted by a mixed Gaussian noise. Experimental results indicate that adding the proposed auxiliary variables makes the sampling problem simpler since the new conditional distribution no longer contains highly heterogeneous correlations. Thus, the computational cost of each iteration of the Gibbs sampler is significantly reduced while ensuring good mixing properties.
High Detail Terrain Models and Multiresolution Path Finding Algorithms for Border Guard Constructive Simulator. A Study of Effective Movement Algorithms in High Resolution Simulation Environment

This paper presents an overview of models and algorithms for executing elementary movement tasks within constructive simulation tool SymSG Border Tactics. SymSG Border Tactics is a distributed simulation software dedicated for tactical training including high-resolution law enforcement and opponent modelling as well as their interaction design. Required level of details within the models (single officer) enforced high detail terrain and behavioural models. Large scale of GIS data required for the simulator to perform adequate exercise execution has been a challenge. The novelty of the presented solution is hybrid approach which involves a discrete squarebased Due to required resolution of terrain models (cells 10x10m) for larger scenarios up to 50x20km an efficient terrain representation model had to be designed followed by multicriteria path finding algorithms. The paper presents implementation details, architectural concepts and experimental analysis of proposed solutions deployed in TRL IX simulation environment for Polish Border Guard training. Proposed solution provide multi-resolution and hybrid capabilities for terrain representation and simulation objects movement supplemented by high detail behavioural models, reflecting authorities and opponents actions as well as interactions.
Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses

Background Diabetic retinopathy (DR) is one of the leading causes of blindness in the United States and world-wide. DR is a silent disease that may go unnoticed until it is too late for effective treatment. Therefore, early detection could improve the chances of therapeutic interventions that would alleviate its effects. Methodology Graded fundus photography and systemic data from 3443 ACCORD-Eye Study participants were used to estimate Random Forest (RF) and logistic regression classifiers. We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. RF measures of variable importance are used to detect factors that affect classification performance. Principal Findings Both types of data were informative when discriminating participants with or without DR. RF based models produced much higher classification accuracy than those based on logistic regression. Combining both types of data did not increase accuracy but did increase statistical discrimination of healthy participants who subsequently did or did not have DR events during four years of follow-up. RF variable importance criteria revealed that microaneurysms counts in both eyes seemed to play the most important role in discrimination among the graded fundus variables, while the number of medicines and diabetes duration were the most relevant among the systemic variables. Conclusions and Significance We have introduced RF methods to DR classification analyses based on fundus photography data. In addition, we propose an approach to DR risk assessment based on metrics derived from graded fundus photography and systemic data. Our results suggest that RF methods could be a valuable tool to diagnose DR diagnosis and evaluate its progression.
Femorofemoral Crossover Bypass Graft Has Excellent Patency When Performed with EVAR for AAA with UIOD

Objectives To compare the durability of the crossover femorofemoral bypass graft (CFFBG) in combination with aorto-uni-iliac stent graft (AUIS) for abdominal aortic aneurysm with the durability of CFFBG used in the treatment of unilateral iliac occlusive disease (UIOD). Methods We analyzed the clinical records of 69 patients who underwent CFFBG from 1992 until 2010. Group I consisted of 34 patients who received CFFBGs in combination with AUIS. Group II consisted of 35 patients treated with CFFBG for UIOD. The mean period of follow up was 2.7 years. Outcomes analyzed included primary graft patency, secondary graft patency, and postoperative morbidity and mortality. Results There was one death in each group. Wound infection complicated 11.4% of CFFBGs performed as a sole procedure for UIOD and 5.8% of cases in combination with AUIS (P = 0.673). Primary graft patency was 96.5% and 96.5% at 2 and 5 years in group I, compared with 76.6% and 53.7% in group II (P = 0.046, 0.009). Secondary graft patency at 5 years was 100% and 92.9% for groups I and II, respectively. No variables independently influenced primary graft patency. Patients in group I experienced complications that could be linked to the bypass graft in 20.5% of cases, after long-term follow-up. Conclusions The CFFBG possesses superior long-term durability and patency when implemented in combination with aorto-uni-iliac stent grafts and does not seem to compromise the endpoint success of endovascular treatment.