Miracle-dz/UniFAR-FTUs huggingface dataset:
Configuration of alarm wireless sensor element

The paper addresses morphological (modular) design of a wireless sensor element as composition of the sensor element configuration from its component alternatives. A hierarchical four-level and-or tree model is used for the wireless sensor element. The Bottom-Up solving process is based on Hierarchical Morphological Multicriteria Design (HMMD): (i) multicriteria selection of alternatives for system parts, (ii) composing the selected alternatives into a resultant combination (while taking into account ordinal quality of the alternatives above and their compatibility). An illustrative numerical example describes the design process for a fire alarm wireless sensor element.
Socioeconomics Real-time Macroeconomic Data and Uncertainty

Most macroeconomic data is continuously revised as additional information becomes available. We suggest that revisions of data is an increasingly important source of uncertainty about the state of the economy and offer an alternative channel of uncertainty data uncertainty. This paper adds on the uncertainty literature and focuses on data uncertainty, which originates in the revision structure of data. We find that apart from the general and economic policy uncertainty, the data uncertainty has been rising throughout the past decade in the US and Euro area. To the best of our knowledge, this is the first study of Euro area data uncertainty. Our analysis shows that there is a positive lagged effect of economic policy uncertainty on data uncertainty for both regions. Our finding corresponds to the recent literature on increased macroeconomic and economic policy uncertainty during and after the "Great Recession”.
The effect of social adversity on the fatigue syndrome, psychiatric disorders and physical recovery, following glandular fever

Synopsis Two hundred and fifty patients attending primary care with glandular fever or an upper respiratory tract infection were studied prospectively up to 6 months after onset. Of these patients 228 were interviewed with the Life Events and Difficulties Schedule and the Schedule for Affective Disorders and Schizophrenia, giving Research Diagnostic Criteria for psychiatric disorders. The experience of severe social adversity (provoking agents) had a significant association with psychiatric disorder at 2 months (odds ratio = 5·3) and 6 months (odds ratio = 5·8) after onset of infection. This association was especially significant for depressive illness (odds ratio = 9·1 at 2 months and 11·9 at 6 months). In contrast, social adversity had little association with the development of the post-infectious fatigue syndrome, or delayed physical recovery. Social adversity may be an important maintaining factor for psychiatric disorders, especially depressive illness, following acute infections.
Current approaches to the treatment of Parkinson’s disease

Enormous progress has been made in the treatment of Parkinson’s disease (PD). As a result of advances in experimental therapeutics, many promising therapies for PD are emerging. Levodopa remains the most potent drug for controlling PD symptoms, yet is associated with significant complications such as the “wearing off” effect, levodopa-induced dyskinesias and other motor complications. Catechol-o-methyl-transferase inhibitors, dopamine agonists and nondopaminergic therapy are alternative modalities in the management of PD and may be used concomitantly with levodopa or one another. The neurosurgical treatment, focusing on deep brain stimulation, is reviewed briefly. Although this review has attempted to highlight the most recent advances in the treatment of PD, it is important to note that new treatments are not necessarily better than the established conventional therapy and that the treatment options must be individualized and tailored to the needs of each individual patient.
Coronary Calcification and Its Relation to Extracoronary Atherosclerosis in Asymptomatic Hypercholesterolemic Men

BackgroundThe prevalence of coronary calcifications and extracoronary plaques was studied in patients with asymptomatic hypercholesterolemia. Methods and ResultsUltrafast computed tomography for coronary calcification (presence or absence: calcium score) and echographic assessment of carotid, aortic, and femoral plaques were performed in 111 hypercholesterolemic men: 65% had coronary calcification, 72% had extracoronary plaque. The two lesions were associated as: 1) compared with subjects without coronary calcification, those with calcification had a higher prevalence of aortic (p < 0.05) and femoral (p < 0.01) plaque and of two diseased sites (p < 0.05); 2) the prevalence of coronary calcification was higher in the presence than in the absence of aortic (p < 0.05) or femoral (p < 0.01) plaque and higher in two (p < 0.01) and three diseased (p < 0.05) sites than in no diseased site; 3) the calcium score was higher in the presence than in the absence of carotid (p < 0.05), aortic (p < 0.05), or femoral (p < 0.001) plaque, higher in two (p < 0.001) and three diseased (p < 0.05) sites than in no diseased sites, and higher in two (p < 0.01) than in one diseased site; and 4) the calcium score correlated with femoral plaque (p < 0.001). Overall, the presence of two or three diseased extracoronary sites versus no or one diseased site showed a power of 78% for predicting coronary calcification. Coronary calcium score correlated with age (p < 0.01) and triglycerides (p < 0.05). ConclusionsThe close relation between coronary calcium and extracoronary plaques suggests that echography of extracoronary vessels could aid in the screening of coronary atherosclerosis in high-risk, asymptomatic individuals.
Bootstrapping Face Detection with Hard Negative Examples

Recently significant performance improvement in face detection was made possible by deeply trained convolutional networks. In this report, a novel approach for training state-of-the-art face detector is described. The key is to exploit the idea of hard negative mining and iteratively update the Faster R-CNN based face detector with the hard negatives harvested from a large set of background examples. We demonstrate that our face detector outperforms state-of-the-art detectors on the FDDB dataset, which is the de facto standard for evaluating face detection algorithms.
High—Performance Solar‐Blind Deep Ultraviolet Photodetector Based on Individual Single‐Crystalline Zn2GeO4 Nanowire

Solar‐blind deep ultraviolet (DUV) photodetectors have been a hot topic in recent years because of their wide commercial and military applications. A wide bandgap (4.68 eV) of ternary oxide Zn2GeO4 makes it an ideal material for the solar‐blind DUV detection. Unfortunately, the sensing performance of previously reported photodetectors based on Zn2GeO4 nanowires has been unsatisfactory for practical applications, because they suffer from long response and decay times, low responsivity, and quantum efficiency. Here, high‐performance solar‐blind DUV photodetectors are developed based on individual single‐crystalline Zn2GeO4 nanowires. The transport mechanism is discussed in the frame of the small polaron theory. In situ electrical characterization of individual Zn2GeO4 nanowires reveals a high gain under high energy electron beam. The devices demonstrate outstanding solar‐blind light sensing performances: a responsivity of 5.11 × 103 A W−1, external quantum efficiency of 2.45 × 106%, detectivity of ≈2.91 × 1011 Jones, τrise ≈ 10 ms, and τdecay ≈ 13 ms, which are superior to all reported Zn2GeO4 and other ternary oxide nanowire photodetectors. These results render the Zn2GeO4 nanowires particularly valuable for optoelectronic devices.
Evaluating the performance of various segmentation techniques in industrial radiographs

Abstract At present, image processing concepts are widely used in different fields, such as remote sensing, communication, medical imaging, forensics and industrial inspection. Image segmentation is one of the key processes in image processing key stages. Segmentation is a process of extracting various features of the image which can be merged or split to build the object of interest, on which image analysis and interpretation can be performed. Many researchers have proposed various segmentation algorithms to extract the region of interest from an image in various domains. Each segmentation algorithm has its own pros and cons based on the nature of the image and its quality. Especially, extracting a region of interest from a gray scale image is incredibly complex compared to colour images. This paper attempts to perform a study of various widely used segmentation techniques in gray scale images, mostly in industrial radiographic images that would help the process of defects detection in non-destructive testing.
Attentional modulation of perceptual stabilization

Perceptual priming is generally regarded as a passive and automatic process, as it is obtained even without awareness of the prime. Recent studies have introduced a more active form of perceptual priming in which priming for a subsequent ambiguous stimulus is triggered by the subjective percept, that is, interpretation of a previous ambiguous stimulus. This phenomenon known as stabilization does not require a conscious effort to actively maintain one perceptual interpretation. In this study, we show that distraction of attention, during and even after the prime presentation, interferes with the build-up of perceptual memory for stabilization. This implies that despite the apparent automaticity, stabilization involves an active attentional process for encoding and retention. The disruption during the encoding can be attributed to the reduction in sensory signals for the prime. However, the disruption during the retention suggests that the implicit memory trace of the prime necessitates the attentional resource to fully develop. The active nature of the build-up of perceptual memory for stabilization is consistent with the idea that perceptual memory increases its strength gradually over a few seconds. These findings suggest that seemingly automatic and effortless cognitive processes can compete with online perceptual processing for common attentional resources.
A Survey on Trapping Sets and Stopping Sets

LDPC codes are used in many applications, however, their error correcting capabilities are limited by the presence of stopping sets and trapping sets. Trapping sets and stopping sets occur when specific low-wiehgt error patterns cause a decoder to fail. Trapping sets were first discovered with investigation of the error floor of the Margulis code. Possible solutions are constructions which avoid creating trapping sets, such as progressive edge growth (PEG), or methods which remove trapping sets from existing constructions, such as graph covers. This survey examines trapping sets and stopping sets in LDPC codes over channels such as BSC, BEC and AWGNC.
A Robust Instance-based Video Search Method by Re-ranking for Multiple Visual Codebooks

With the development of Internet and computer technology, the digital videos increase explosively, so how to get the interesting video clips from the massive video dataset quickly and efficiently has become an urgent problem in the field of information retrieval (IR). In this paper, a new Instance-based video search (INS) engine is proposed to solve the urgent problem. Firstly, video key-frames are extracted and grouped from queries and video dataset. Secondly, robust low-level visual features are extracted and projected to multiple visual codebooks. Finally, after similarity computing and feature fusion, a re-ranking scheme is implemented to vote the final search results. The proposed framework was evaluated at TRECVID 2011 on instance search task(INS), and achieved the 2nd place among 47 participants around the world, which indicated the effectiveness of our system.
TagSNP Selection Based on Pairwise LD Criteria and Power Analysis in Association Studies

TagSNP selection is an important step in designing case control association studies. Among selection methods that have proliferated, the ones based on pairwise LD measurement are attractive for the purpose of designing association studies. The goal is to minimize the number of markers selected for genotyping in a particular platform and therefore reduce genotyping cost while simultaneously representing information provided by all other markers. Depending on the platform, it is also important to select sets that are robust against occasional genotyping failure. An array of methods has been proposed to effectively select these tagSNPs using various criteria. In this study, we extend the algorithms used in FESTA, a computer program we previously developed for picking tagSNPs using r2 criteria. We applied FESTA to the HapMap whole chromosome data in two different populations, and we also performed a power analysis for case-control association studies using simulated data. FESTA chooses 294322 tagSNPs in the autosomes in the CEPH samples. The YORUBA samples require 61.5% more tagSNPs than the CEPH samples. The power study showed that limiting ourselves to only tagSNPs, instead of choosing all SNPs in the interval for an association study, results in a power loss of only about 5-10%.
On Sum-Rate Maximization Approach to Network Beamforming and Power Allocation for Asynchronous Single-Carrier Two-Way Relay Networks

We study the sum-rate maximization problem, under a total power budget, for asynchronous single-carrier bi-directional relay networks, consisting of two transceivers and multiple amplify-and-forward relays. When different transceiver-relay links cause significantly different propagation delays in the signal they convey, the end-to-end channel is not amenable to a frequency-flat modeling; rather, a multi-path channel model is appropriate. Such a multi-path channel model results in inter-symbol-interference at the transceivers. Aiming to maximize the sum-rate of this channel over the relay weights and transceivers’ powers, we rigorously prove that such a sum-rate maximization problem leads to a relay selection scheme, where only those relays, which contribute to one of the taps of the end-to-end channel impulse response (CIR), are turned on. Indeed, we prove that the optimal end-to-end CIR has only one non-zero tap, rendering the end-to-end channel frequency-flat. Our proof shows that the mean-squared-error (MSE) optimal joint post-channel equalization, network beamforming, and power allocation scheme is sum-rate-optimal. The equivalence of MSE-optimal and sum-rate-optimal solutions is interesting, as MSE minimization promotes end-to-end reliability, while sum-rate maximization advocates for multiplexing gain. These approaches often pull the design of communication systems in different directions. For the aforementioned scenario, these approaches are identical as we prove.
Novel wave digital equivalents of passive elements

The wave digital equivalent of passive elements using wave theory and bilinear transform is formulated in yesteryear, but the bilinear transform produces large distortion in the mid and high frequency range. In this paper, a novel wave digital equivalents of analog passive elements are developed. Instead of bilinear transform, two different transforms are used for analog to digital (A/D) conversion in order to overcome the limitation of the aforementioned transform. The comparison of transforms along with the resultant wave digital equivalents of passive elements using these transforms and wave theory is introduced. In this paper, it is also considered that practically, analog elements are not ideal, but leaky. So, wave digital equivalents of leaky analog elements are also proposed.
Notice of Violation of IEEE Publication PrinciplesOn the Analysis of Queues with Long Range Dependent Traffic: An Extended Maximum Entropy Approach

An extended maximum entropy (EME) framework is proposed for capturing long tail behaviour of queue lengths in broadband heterogeneous networks. The work is based on the maximisation of Tsaliis parametric entropy function for non-extensive systems subject to appropriate mean value constraints. Novel closed form expressions and asymptotic power laws are characterised for the state probabilities of single server queues with (or, without) finite capacity and self-similar/long-range dependent traffic. The EME approach employs, by analogy, as a mean queue length constraint, a heuristic generalisation of a formula suggested by Norros in the context of a simple storage model with infinite capacity and fractional Brownian motion (FBM) process as an input traffic process. Consequently, a relationship is postulated between the non-extensivity parameter, q, of Tsallis entropy and the Hurst self-similarity measure, H. Moreover, efficient numerical procedures are devised, based on the Newton-Raphson method, to compute the Lagrange's undetermined multipliers and, thus, the EME queue length distributions and associated performance metrics. Typical numerical tests are included to assess the credibility of the EME solutions and the adverse impact of self-similar traffic on queue performance.
Language technologies and the evolution of the semantic web

The availability of huge amounts of semantic markup on the Web promises to enable a quantum leap in the level of support available to Web users for locating, aggregating, sharing, interpreting and customizing information. While we cannot claim that a large scale Semantic Web already exists, a number of applications have been produced, which generate and exploit semantic markup, to provide advanced search and querying functionalities, and to allow the visualization and management of heterogeneous, distributed data. While these tools provide evidence of the feasibility and tremendous potential value of the enterprise, they all suffer from major limitations, to do primarily with the limited degree of scale and heterogeneity of the semantic data they use. Nevertheless, we argue that we are at a key point in the brief history of the Semantic Web and that the very latest demonstrators already give us a glimpse of what future applications will look like. In this paper, we describe the already visible effects of these changes by analyzing the evolution of Semantic Web tools from smart databases towards applications that harness collective intelligence. We also point out that language technology plays an important role in making this evolution sustainable and we highlight the need for improved support, especially in the area of large-scale linguistic resources.
Teaching an Advanced Design, Team-Oriented Software Project Course

Students learn about design principles and "best practices" in many courses. However, small scale assignments do not give enough opportunity for students to appreciate the value of software design principles or even to learn how to apply principles in practice. To fill the gap between theoretical and experiential knowledge, we introduced a team-based project course focused on design and implementation phases of the software development lifecycle. We teach design principles and team work in problem-based way, through architectural concepts and iterative development process. The product students build must meet stated quality requirements in terms of reliability, reusability and documentation. We trust this kind of the course is essential in curricula as it allows students better absorb knowledge learned in other software engineering courses. Such course also plays a role in better preparing students for industrial work. We describe a teaching method, course infrastructure and lessons learned over three years of teaching of our course. Based on experiences, we postulate and motivate the need for teaching at least two project courses in undergraduate curricula, one dealing with design and process issues, and the other focused on unstable requirements
Effect of compacting pressure on shape retention during supersolidus liquid phase sintering of Cu base alloys

The compacting pressure is one of the key parameters which affects sintering phenomena, such as fragmentation, rearrangement and densification. However, the type of alloy also has a similar significance which should be considered. To demonstrate the effect of the mentioned parameters, two types of copper base alloys, i.e. Cu–9Sn–8Pb and Cu–28Zn, were investigated in the present study. Prealloyed powders were compacted at 100 and 400 MPa, respectively, and sintered in the range of 890–970°C for 20 min. In situ images of the samples were taken at various sintering conditions, and the shapes were recorded. For both materials, in case of compaction at lower pressure, the gravity-induced distortion known as ‘elephant foot’ phenomenon was not observed, and the specimens show shape retention during sintering even at higher temperatures, while samples compacted at 400 MPa showed different responses to the sintering conditions: A-shaped distortion (elephant foot) occurred in the bronze alloy, while in the brass alloy O-shaped distortion was observed.
Time-varying process model for intelligent prediction of strain and temperature in micro-turning process

Time-varying process models for micro-machining processes are important as they aid in control of machining parameters. In this research, a state-space-based process model for the temperature and strain generated near the cutting edge of the tool tip is identified using system identification approach. Fiber Bragg grating sensors were placed rigidly near the cutting edge of the tool tip in a micro-turning setup. Subsequently, micro-turning operations were carried out on aluminum and mild steel. The computer numerically controlled program was such that the machining parameters (feed velocity, depth of cut and RPM) change with machining time. The time-varying machining parameters act as inputs to the model, and the dynamic values of strain and temperature serve as model output. A state-space model was generated using the experimental data. Subsequently, a Kalman filter was used to intelligently predict the values of strain and temperature at the cutting edge of tool tip in advance using the model parameters identified by state-space modeling. Experimental results confirm that the time-varying model and the Kalman filter proposed in this research are effective in predicting the strain and temperature in advance with high accuracy. The maximum error in prediction of temperature was 0.4 °C, whereas for strain prediction, the maximum error was 0. 3µ∈.
Meiotic analysis of the hybrids between cultivated and synthetic tetraploid groundnuts

Groundnut is susceptible to a range of diseases and pests owing to its narrow genetic base. Closely related wild relatives of groundnut are diploid, and crossability between diploid wild relatives and tetraploid cultivated groundnut is difficult. Amphidiploid and autotetraploid groundnuts, which are tetraploids (also called synthetic groundnut), were developed at ICRISAT. Crossability between cultivated tetraploid groundnut and synthetic tetraploid groundnut was a straightforward process. Crosses between cultivated and synthetic tetraploid groundnuts yielded mature seeds. The resultant F1 hybrids were cytologically analysed to study the relationship between the chromosomes/genomes of the parents i.e. between Arachis hypogaea cultivars and synthetic tetraploid groundnut. Meiotic study showed that there was good chromosome pairing between the parental species resulting in high pollen fertility. Thus, development of synthetic groundnut is a feasible way of utilizing important wild species gene pool of Arachis, closely related to cultivated groundnut, for the improvement to the crop.