Miracle-dz/UniFAR-FTUs huggingface dataset:
An all‐hardware implementation of the subpixel refinement stage in SIFT algorithm

This paper proposes an all‐hardware architecture to perform the subpixel refinement operation in the scale invariant transform algorithm. Although the literature describes several hardware implementations of this algorithm, due to its complexity, most of them are based on simplifications of it. These implementations normally exclude the subpixel refinement stage, which, however, is an essential process to obtain accurate results in image matching applications.The architecture has been described in very high–speed integrated circuit hardware description language at register transfer level and synthesized on a Xilinx Zynq 7020 device. The latency of the proposed architecture to generate a refinement operation is 211 clock cycles, and the throughput achieved exploiting pipeline techniques is 64 cycles. The architecture uses fixed‐point data representation and has been tested with images from known databases, yielding very good performance compared with the floating‐point software implementation of the algorithm.
A developmental arrest? Interruption and identity in adolescent chronic pain

Introduction: Although the pediatric pain literature has explored the role of developmental factors in young children's acute pain, relatively less is known about specific developmental challenges in adolescents with chronic pain. Objectives: To meet this knowledge gap, this study sought to adopt an idiographic phenomenological approach to examine how adolescents make sense of their own development in the context of living with chronic pain. Methods: Semistructured interviews were conducted with ten adolescents (12–17 years; 7 females) recruited from a tertiary care pain treatment programme. Interview data were transcribed verbatim and analysed using Interpretative Phenomenological Analysis. Results: Study findings identified 2 themes: “An externally imposed lens on identity” and “Paradoxes of developmental progress. ” The first theme highlighted an understanding of how adolescent identity is perceived. Some adolescents perceived identity as distinct from pain, whereas others perceived identity as part of their chronic pain condition. This theme also detailed how identity was negotiated by adolescents and others through engagement with valued activities. The second theme represented an understanding of how chronic pain disrupts and alters adolescent developmental trajectories at an individual level, suggesting possibilities of enhanced and delayed trajectories. Enhanced trajectories were associated with increased management of emotionally difficult situations and resulted in mastery of complex interpersonal skills. Conclusion: Findings provided a nuanced understanding of developmental progress in the context of adolescent chronic pain and suggested challenges with drawing normative comparisons. Future research could extend findings by adopting a longitudinal approach to studying adolescent development and eliciting accounts from broader social groups.
Temporal correlation model-based transmission power control in wireless body area network

Researchers have encountered many challenges in developing communication system in wireless body area networks (WBANs). These challenges include the dynamic characteristics of WBAN channels, limited energy resources, and strict requirements, such as high reliability and low latency. To achieve highly reliable and energy-efficient communication in the WBAN, temporal correlation model-based transmission power control (TCM-TPC) is proposed. In this method, the channel condition is firstly determined based on a long-term mean channel gain and the last known channel gain using a temporal correlation model. The channel is estimated as a conditional distribution of channel gains. After that, the transmit output power is selected from the estimated channel to satisfy a pre-defined outage probability. Performance of the proposed TCM-TPC method was evaluated on a network simulator for a walking scenario. The evaluation results showed that the TCM-TPC had up to 1.48% of packet loss, while other TPC methods had up to 6.86% of packet loss. Furthermore, for a high data rate application, the TCM-TPC showed the lowest energy consumption for all sensor nodes.
In‐pixel analog memories for a pixel‐based background subtraction algorithm on CMOS vision sensors

This paper addresses the most suitable in‐pixel analog memory bank for running a hardware‐oriented approach of the well‐known Pixel‐Based Adaptive Segmenter algorithm on CMOS vision sensors. The high number of memory accesses, typically up to 200 times, along with the long time elapsed before an analog memory in a pixel is updated, around 5 seconds at 30 fps, constrains the memory topology. This work assesses the impact of nominal and process nonidealities of the 3 main analog memory topologies, namely, open‐loop, closed‐loop, and integrator architectures for background subtraction over the CDNET14 database. This is the first step towards the implementation of a CMOS vision chip with per‐pixel processing to run the Pixel‐Based Adaptive Segmenter.
Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates

Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomisation of events between subjects. We consider the lack of validity found with onesample permutation (sign ipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all datasets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (P = 0.01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.
Cognitive Neural Network Driving DoF-Scalable Limbs in Time-Evolving Situations

Object handling and manipulation are vital skills for humans and autonomous humanoid robots. The fundamental bases of how our brain solves such tasks remain largely unknown. Here we develop a novel approach that addresses the problem of limb movements in time-evolving situations at an abstract cognitive level. We exploit the concept of generalized cognitive maps constructed in the so-called handspace by a neural network simulating a wave simultaneously exploring different subject actions, independently on the number of objects in the workspace. We show that the approach is scalable to limbs with minimalistic and redundant numbers of degrees of freedom (DOF). It also allows biasing the effort of reaching a target among different DOF.
Dynamic Start-up Accelerating Chracterisitic of the Flexible Robotic Manipulator Based on Virtual Prototype Experiment

To accurately obtain the overall dynamic characteristics of the flexible robotic manipulator (FRM) in the whole operating process, the dynamic modeling and vibration characteristic of the FRM in the star-up accelerating process are investigated in this paper. Regarding the process of achieving a uniform motion involves a start-up accelerating process and a uniform running process, the initial conditions of the vibration responses are determined and considered in the dynamic modeling, which have considerable influence on the dynamic properties of FRMs and are ignored and defined as zero in the existent literatures however. The Hamilton's principle is introduced to establish the dynamic model of the FRM. Based on the established dynamic model, the vibration responses of the FRM with varied velocities are obtained and analyzed. Through virtual prototype experiment, the proposed dynamic model of the FRM is verified. The results obtained in this paper are significant for accurately evaluating the overall dynamic characteristics in the whole operating process.
Industrial Requirements Classification for Redundancy and Inconsistency Detection in SEMIOS

Requirements are usually "hand-written" and suffers from several problems like redundancy and inconsistency. The problems of redundancy and inconsistency between requirements or sets of requirements impact negatively the success of final products. Manually processing these issues requires too much time and it is very costly. The main contribution of this paper is the use of k-means algorithm for a redundancy and inconsistency detection in a new context, which is Requirements Engineering context. Also, we introduce a filtering approach to eliminate "noisy" requirements and a preprocessing step based on the Natural Language Processing (NLP) technique to see the impact of this latter on the k-means results. We use Part-Of-Speech (POS) tagging and noun chunking to detect technical business terms associated to the requirements documents that we analyze. We experiment this approach on real industrial datasets. The results show the efficiency of the k-means clustering algorithm, especially with the filtering and preprocessing steps. Our approach is using the software SEMIOS and will be integrated as a new functionality.
Fuzzy Logic-Based Data Analytics on Predicting the Effect of Hurricanes on the Stock Market

In the current era of big data, high volumes of a wide variety of valuable data of different veracity are generated or collected at a high velocity. A rich source of these big data is the stock market. Since the inception of the stock market, people have been trying to "beat" it for the purpose of monetary gain. A stock market is an exchange where people trade shares of companies, also called stocks. The purpose of the exchange is to make it easy to match buyers and sellers together to make transactions. The usual goal of someone participating in the stock market it to generate profit through the buying and selling of stocks. The main way people accomplish this is by buying a stock, waiting anywhere from seconds to decades, and then hopefully selling for more than they bought it for. This is where the common term "buy low, sell high" comes from. There are many factors (e.g., hurricanes) that may affect the stock price. In this paper, we present a computational intelligent tool that applies fuzzy logic-based data analytics to predict the effect of hurricanes on the stock market.
Examination of Hospital Workers’ Emotional Responses to an Infectious Disease Outbreak: Lessons From the 2015 MERS Co-V Outbreak in South Korea

ABSTRACT Hospital workers are critical for a successful response to an infectious disease outbreak and for preventing disease transmission to the community. Therefore, hospital crisis management should implement efforts to improve hospital workers’ preparedness in responding to public health emergencies caused by infectious diseases. Traditionally, preparedness and skill of hospital workers have been emphasized, but awareness of the importance of the emotional mindset of hospital workers in dealing with disease outbreaks has only recently increased; therefore, empirical approaches to examining emotional responses of hospital workers has been limited. This study analyzed qualitative data of the 2015 Middle East Respiratory Syndrome outbreak in South Korea. In particular, negative emotions and stress experienced by hospital workers who treated patients were characterized, as were the events that triggered such experiences. These events were categorized into four themes (eg, Mistake, Missing, Delay Due to Communication Failure). Identifying events that trigger negative emotions in hospital workers has important implications for hospitals’ management guidance in relation to an infectious disease outbreak. (Disaster Med Public Health Preparedness. 2019;13:504-510)
Building effective multilevel HIV prevention partnerships with Black men who have sex with men: experience from HPTN 073, a pre‐exposure prophylaxis study in three US cities

Building effective multilevel HIV prevention partnerships with Black men who have sex with men: experience from HPTN 073, a pre-exposure prophylaxis study in three US cities Darrell P Wheeler, Jonathan Lucas , Leo Wilton, LaRon E Nelson, Christopher Hucks-Ortiz, C Chauncey Watson, Craig Hutchinson, Kenneth H Mayer, Irene Kuo, Manya Magnus, Geetha Beauchamp, Steven Shoptaw, Lynda Marie Emel, Ying Q Chen, Lisa Hightow-Weidman and Sheldon D Fields
Real-Time On-Board Recognition of Continuous Locomotion Modes for Amputees With Robotic Transtibial Prostheses

Human intent recognition is important to the control of robotic prosthesis. In this paper, we propose a multi-level real-time on-board system to recognize continuous locomotion modes. A cascaded classification strategy is designed for the recognition of six steady locomotion modes and 10 transitions. On-board signals of the robotic prosthesis include two inertial measurement units and one load cell. Three transtibial amputees are recruited in the experiments. The prediction decision time of the real-time on-board cascaded classification system is about 3.3 ms, which is enough short compared with the sliding window increment 10 ms. It is easy to recognize the standing and ambulation in the first-level classification with a 99.86% accuracy by quadratic discriminant analysis (QDA) classifier. In the second-level classification, threshold method is adopted to divide one stride into swing and stance phases. In swing phase, five steady modes are recognized with a total accuracy of 96.40%. In stance phase, all these five steady modes are recognized with a total accuracy of 91.21%. The average recognition accuracy of the three subjects is 93.21% by QDA classifier. Besides, for transitions, the proposed system could recognize all transitions rightly. The designed system is feasible and effective to realize real-time on-board recognition of continuous locomotion modes, which is promising for the further control of the prosthesis.
TORC1‐signalling is down‐regulated in Saccharomyces cerevisiae hsp30Δ cells by SNF1‐dependent mechanisms

Hsp30 is a plasma membrane localized heat shock protein in Saccharomyces cerevisiae whose expression is induced by numerous environmental stressors. Elucidation of its mechanism of action has remained elusive primarily because hsp30Δ cells do not show a strong phenotype. To identify cellular functions associated with Hsp30, we thus compared the transcriptome of BY4741hsp30Δ with that of its wild type counterpart. Our studies indicate down‐regulation of the target of rapamycin complex 1 (TORC1)‐dependent gene‐expression programme in hsp30Δ cells. We further show that TORC1‐signalling through its effectors (Sch9 and Tap42) was down‐regulated in the deletion strain. Specifically, (a) phosphorylation levels of Sch9 were lower and nuclear exclusion of Rim15 (Sch9‐downstream function) was overridden in hsp30Δ cells, (b) membrane association of Tor1 and Tap42 was lower in hsp30Δ cells, and (c) Tap42‐downstream functions were abrogated in the deletion strain. Furthermore, transcription factors Rtg1, Rtg3, Gat1, and Gln3 were localized in the nucleus of the hsp30Δ as observed upon inactivation of TORC1. Studies aimed at determining how TORC1‐signalling is down‐regulated in hsp30Δ cells indicated that total reducing sugar levels were lower and ADP:ATP ratio was higher in hsp30Δ cells —conditions known to activate the Snf1 kinase and consequently to the inactivation of TORC1. We thus determined if TORC1‐signalling could be restored in hsp30Δ cells upon the deletion of SNF1. Sch9 phosphorylation levels (TORC1‐signalling) was restored to wild type levels in hsp30Δsnf1Δ cells. TORC1‐signalling is thus down‐regulated in hsp30Δ cells by SNF1‐dependent mechanisms. A probable role for Hsp30 is discussed.
A case report and literature review

Rationale: Tracheobronchial benign tumors are uncommon; particularly, bronchial pleomorphic adenoma is one of the rarest benign tumors that develop in bronchus (only 7 reported cases, among which only 4 cases of pleomorphic adenoma were seen arising from right main bronchus). Patientconcerns: In this report, a 38-year-old woman suffered from progressive shortness of breath for 5 years due to right main bronchial pleomorphic adenoma. Diagnoses: The patient was diagnosed as right main bronchial pleomorphic adenoma based on chest computed tomography enhanced scan, bronchoscopy, and histological examination. Interventions:An electrosurgical snare was performed to resect the neoplasm and several APC were administered at the sites of the resection to provide hemostasis and further coagulate for the residual neoplasm. Outcomes: The patient was free of symptoms and the lumen of right main bronchus was clear during the follow-up period for 10 months without any procedure-related complications. Lessons:Bronchial pleomorphic adenoma is extremely rare, however, we should take it into consideration if a patient suffered from shortness of breath without an exact cause. Abbreviations: CT = computed tomography; APC = argon plasma coagulation; NBI = narrow band imaging.
Demographic and Clinical Features of Pediatric Uveitis in Israel

ABSTRACT Purpose: To report the epidemiology, etiology, ocular characteristics, treatment and visual outcome of pediatric uveitis in Israel. Methods: Retrospective study from two tertiary uveitis centers. Results: Included were 107 patients (182 eyes), 55% females. Mean age at diagnosis 8.8 years. Uveitis was predominantly anterior, idiopathic, bilateral, and chronic. Systemic associations were seen in 36% of patients of which the most common disease was juvenile idiopathic arthritis. Infectious uveitis accounted for 37% of posterior uveitis cases of which toxoplasmosis was the most common cause. Anterior segment complications were commonly observed at presentation (41%); the most predominant were posterior synechiae, cataract, and band keratopathy. The most common posterior segment complications were papillitis, epiretinal membrane, and macular atrophy/scar. Ninety-three percent of eyes had visual acuity >20/40 at last follow-up. Conclusion: The pattern of pediatric uveitis in Israel is similar to that in the western world. Visual outcome was good in most eyes.
A view of High Dimensional, Large-Scale and Big Data Fuzzy Rule based Regression and Control Systems

This work deals with the methodologies employed in the specialized literature on fuzzy regression and control systems to perform the learning process under conditions of high dimensionality and/or large-scale sets of examples. First, we look at algorithmic approaches in the last decade, where this specific area has experienced its bigger growth, and then at new resources nowadays based on distributed programming with MapReduce. We conclude trying to identify the challenges and future guidelines.
Multi-source Subnetwork-level Transfer in CNNs Using Filter-Trees

Convolutional Neural Networks (CNNs) are very effective for many pattern recognition tasks. However, training deep CNNs needs extensive computation and large training data. In this paper we propose Bank of Filter-Trees (BFT) as a transfer learning mechanism for improving efficiency of learning CNNs. A filter-tree corresponding to a filter in $k^{th}$ convolutional layer of a CNN is a subnetwork consisting of the filter along with all its connections to filters in all preceding layers. An ensemble of such filter-trees created from many CNNs learnt on different but related tasks, forms the BFT. To learn a new CNN, we sample from the BFT to select a set of filter trees. This fixes the first few layers of the target net and only the remaining network would be learnt using training data of new task. Through simulations we demonstrate the effectiveness of this idea of BFT. This method constitutes a novel transfer learning technique where transfer is at a subnetwork level; transfer can be effected from multiple source networks, the number of weights to be learnt is same as a single CNN; and, with no finetuning of the transferred weights, the performance achieved is quite good. In all our experiments the number of filter trees sampled is kept same as the number of filters in the $k^{th}$ layer of the new CNN. This is not a limitation it is just to keep the number of filters freshly learnt in the subsequent layers equal to a single CNN for a fair comparison.
Collaborative Multi-View Attributed Networks Mining

Graph clustering techniques are very useful for detecting densely connected groups in large graphs. Many existing graph clustering methods mainly focus on the topological structure, but ignore the vertex properties. Existing graph clustering methods have been recently extended to deal with nodes attribute. In this paper we propose a new method which uses the nodes attributes information along with the topological structure of the network in the clustering process. In order to use the information about the attributes nodes, the collaborative clustering can be employed in the model. The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple sites by applying different clustering algorithms and therefore improve the final clustering result. The purpose of this article is to introduce a new attributed collaborative multi-view networks based on community detection in networks and topological collaborative learning. The idea consists in modifying databases by adding virtual points which convey clustering information, to change the position of centers of the clustering solution. Experimental results demonstrate the effectiveness of the proposed method through comparisons with the state-of-the-art graph clustering methods on synthetic and real datasets.