Miracle-dz/UniFAR-FTUs huggingface dataset:
In Reply to: “Effect of Piecemeal vs En Bloc Approaches to the Lateral Temporal Bone on Survival Outcomes”

We read with great interest the article by Muelleman et al concerning piecemeal resection versus lateral temporal bone resection (LTBR) of T1-T3 squamous cell carcinoma (SCC). The authors state that piecemeal resection might be required in cases of low-lying tegmen or laterally placed vascular structures. In the >250 LTBRs performed by our group, these 2 anatomic constraints are exceedingly rare (<5%). Anatomic constraints to standard en bloc LTBR are addressed with careful surgical technique. Low-lying tegmen is handled by following the middle fossa dura medially and anteriorly until the temporomandibular joint capsule is reached. In cases where this bone is <2 mm thick, the superior bony canal is drilled away, but the remaining canal is still intact, allowing en bloc resection. The high-riding jugular bulb and the laterally placed carotid artery present their own challenges. The high-riding jugular bulb is always medial to the facial nerve, and the facial nerve is generally preserved with en bloc LTBR. Thus, staying lateral to the facial nerve and working medial to the annulus allows the surgeon to surpass the highriding jugular. The laterally placed carotid is slightly more difficult to handle and is why osteotomes are not used. Intraoperatively, the lateral carotid canal is identified in the middle ear after the hypotympanic air cells have been removed. The surgeon can then follow the carotid canal and drill between it and the annulus to complete the inferior canal cut. The authors do not adequately describe their patient population. It is unclear if all cases were primary SCC of the ear canal or if they included external ear SCC, periauricular SCC, or metastatic SCC to the parotid gland that secondarily involved the ear canal. The authors omit other confounders between the groups, such as age, bone invasion, perineural invasion, and lymph node metastases. The reason for piecemeal resection is not included. From an oncologic viewpoint, the follow-up time in this series is inadequate (median, 11 months; 40% with <4 months), given that the mean time to recurrence for SCC of the ear canal is 13 months but can be as long as 3 years. In fact, one is unable to determine if there is a simple difference in follow-up time between the groups. This article has too many deficiencies to make any conclusions regarding oncologic safety of such an approach.
Advanced Hybrid Beamforming Design for MIMO Systems

One of the fundamental problems for hybrid beamformer design in MIMO systems is that, in Singular-Value-Decomposition (SVD) based beamforming, the analog beamformer is limited to constant modulus. In order to solve this issue, an algorithm is proposed in this paper. Specifically, the parallel data streams are expressed mathematically, and then the math expression of the analog beamformer is achieved through maximizing the power of desired data streams while minimizing the power of interference. Then, the digital part of the beamformer is calculated based on a channel equivalent process, i.e., considering the phase response vector of the derived analog beamformers as part of the channel observed by the digital beamformers. Simulations are performed on the proposed algorithm and compared to a recent state-of-the-art technique. The results show that the proposed one outperforms in terms of sphere of application (i.e., different antenna array sizes and channel sparsities).
Joint segmentation and registration for 4D lung CT images based on Markov random field

The study of segmentation and registration of lung volume in medical images has been an active area with the emergence and development of 4D CT (Computed Tomography) medical imaging techniques. Precise image segmentation and registration methods are becoming more and more important in computer-aided diagnosis and treatment. Despite the significant progress which has been made in the medical image segmentation and registration, lung segmentation and registration is still a challenging task. In this paper, a joint segmentation and registration method for 4D lung CT images is proposed, which extends a general simultaneous segmentation and registration framework based on MRF (Markov Random Field) and utilizes the segmentation results of one frame as an atlas for the initialization step. Furthermore, a stochastic sampling approach is introduced for the computation of registration similarity measurement. The proposed method is evaluated on a public lung CT data set and the experimental results show its improved performance compared with the conventional methods.
Analysis of Scratch Project with Process Data

Scratch is a visual programming language which has been widely used in the K-12 education. The data generated during the process of student programming can truly reflect behaviors of students during their creation. Analysis of these process data can help us understand the thinking process of students thus does further researches on the students' logical thinking ability. To this end, this paper first designs a novel method of collecting process data using Scratch platform. Then a data analysis method for the complicated process data is proposed. Finally, by comparing with the static data, we verify the correctness of the process data and make analysis from the boys and girls.
Evaluation of Heterogeneous Multicore Cluster Architectures Designed for Mobile Computing

Heterogenous multicore architectures are becoming ubiquitous in high-end smartphones. In this paper, we present a study of two modern heterogeneous multicoure architectures based on real measurements. One architecture implements the well-established ARM big. LITTLE 2-cluster approach while the second one takes a leap forward and extends the concept to 3 clusters. In concrete, we analyze the performance of those platforms running parallel workloads. Furthermore, we study the energy and power consumption of the 3-cluster architecture and the effects of high clock frequency on SoC temperature.
Design of Smart Mess Application Using Ubiquitous Computing

In this era of technology advancement, everything requires monitoring and controlling. Our project focuses on making a smart mess using ubiquitous computing which includes making an application to keep track of food storage, mess monitoring for maintaining the quality of food and the entire backend like management for workers, gas supply, temperature sensors, and so on. Different sensors are used for sensing the conditions of the food. Considering all the external conditions which will affect the quality of food and use a database for storage of values sensed in real time. Mess Manager gets notified via messages whenever there is a shortage of food or in case of an emergency to take the necessary action.
Design and Assessment of a Collaborative 3D Interaction Technique for Handheld Augmented Reality

We present the design of a handheld-based interface for collaborative manipulations of 3D objects in mobile augmented reality. Our approach combines touch gestures and device movements for fast and precise control of 7-DOF transformations. Moreover, the interface creates a shared medium where several users can interact through their point-of-view and simultaneously manipulate 3D virtual augmentations. We evaluated our collaborative solution in two parts. First, we assessed our interface in single user mode, comparing the user task performance in three conditions: touch gestures, device movements and hybrid. Then, we conducted a study with 30 participants to understand and classify the strategies that arise while working in pairs, when partners are free to make their task organization. Furthermore, we investigated the effectiveness of simultaneous manipulations compared with the individual approach.
Loop Gain Adaptation for Optimum Jitter Tolerance in Digital CDRs

A loop gain adaptation technique is proposed, which optimizes the jitter tolerance (JTOL) of a 28 Gb/s phase interpolator (PI)-based clock and data recovery (CDR) circuit implemented in 28 nm CMOS. The technique increases the CDR’s loop gain to suppress the most jitter while monitoring the autocorrelation function of the bang-bang phase detector (BB-PD) output to prevent the CDR from becoming too underdamped. The proposed technique requires no knowledge of the CDR’s loop latency or input jitter characteristics.
The Resting Potential and K+ Currents in Primary Human Articular Chondrocytes

Human transplant programs provide significant opportunities for detailed in vitro assessments of physiological properties of selected tissues and cell types. We present a semi-quantitative study of the fundamental electrophysiological/biophysical characteristics of human chondrocytes, focused on K+ transport mechanisms, and their ability to regulate to the resting membrane potential, Em. Patch clamp studies on these enzymatically isolated human chondrocytes reveal consistent expression of at least three functionally distinct K+ currents, as well as transient receptor potential (TRP) currents. The small size of these cells and their exceptionally low current densities present significant technical challenges for electrophysiological recordings. These limitations have been addressed by parallel development of a mathematical model of these K+ and TRP channel ion transfer mechanisms in an attempt to reveal their contributions to Em. In combination, these experimental results and simulations yield new insights into: (i) the ionic basis for Em and its expected range of values; (ii) modulation of Em by the unique articular joint extracellular milieu; (iii) some aspects of TRP channel mediated depolarization-secretion coupling; (iv) some of the essential biophysical principles that regulate K+ channel function in “chondrons. ” The chondron denotes the chondrocyte and its immediate extracellular compartment. The presence of discrete localized surface charges and associated zeta potentials at the chondrocyte surface are regulated by cell metabolism and can modulate interactions of chondrocytes with the extracellular matrix. Semi-quantitative analysis of these factors in chondrocyte/chondron function may yield insights into progressive osteoarthritis.
Enhancing Mobile User Performance Through Data Caching Over Edge Computing Wireless Networks

A novel scheme is proposed for mobile edge networks to improve data content access. In mobile edge networks, data contents can be cached in radio resource heads (RRHs) and users’ equipments (UEs). Caching data contents in UEs leverages data sharing among UEs instead of sending data requests to the remote content resources. However, UEs are equipped with limited cache capabilities, therefore, only limited contents can be cached in each UE. In this paper, our focus is caching data contents in mobile UEs for maximizing content sharing among them. We formulate a local popularity based caching scheme as a Knapsack problem. The caching gap for each content is calculated and only beneficial contents are cached that can provide enhancement of data offloading. Unlike previous works, we consider a dynamic network topology. For obtaining realistic contact and inter-contact patterns of UEs, we devised real human mobility data traces. Simulation results demonstrate that our proposed caching mechanism can significantly improve data offloading in mobile network with users’ intermittent connectivity.
Quality Inspection of Remote Radio Unit (RRU) Power Port Using IBVS

For the quality inspection task of RRU, it is necessary to insert the testing probes into its power and network ports. In this paper, the problem of alignment of robot's end effector with the power port of remote radio unit (RRU) in 4 degrees of freedom (DoF) has been solved. An image based visual servo (IBVS) controller has been designed to perform the alignment task by using the visual features of the power port in image plane. Decoupled features have been selected which eliminate the need to use image jacobian during the control design. This not only reduces the computational cost but also removes the hassle of dealing with image jacobian singularities during the visual servo. The findings have been validated both by performing simulations and designing an experiment which uses an industrial manipulator.
Novel Dynamic Model of a Photovoltaic Module

This paper presents a novel dynamic model of a photovoltaic (PV) module. The two-diode model of a PV module is upgraded with the diodes' parasitic diffusion and junction capacitances, which are, in turn, modeled as variable parameters. The experimental I-V static characteristics were measured for the monocrystalline PV module, at three different stages of a day. The measured characteristics were compared with the commonly used single-diode and two-diode models of the PV module. The simulation model of the proposed PV model was built in the MATLAB-Simulink environment. Influence of the solar irradiation and the temperature on the parasitic capacitances is shown. Also, dynamic behavior of the PV module is analyzed in simulations.
Face Recognition Using Diagonal Two-Dimensional Linear Discriminant Analysis and Wavelet Transform

Linear discriminant analysis (LDA) is one of the most popular subspace techniques widely used in face recognition (FR) to extract low dimensional features that discriminate a set of facial images belong to a certain classes. In LDA-based FR, 2D images modeled as 1D image vectors. The high dimensionality and singularity problem are two important drawbacks of such modeling. An alternative version of LDA has been proposed to resolve these drawbacks named two-dimensional linear discriminant analysis (2D-LDA) which directly operates on 2D facial image matrices. 2D-LDA significantly lowered the computational cost of LDA, and straightforward in features extraction, hence, possess high discriminatively and accuracy, as well as it completely alleviated singularity problem. Further, subspace-based FR methods have been implemented in wavelet domain motivated by the fact that mapping images from pixels space to that of wavelets can provide enhanced features carrying the most informative information of the images in reduced dimensionality. Likewise other 2D subspace methods, 2D-LDA only reflects the correlation between image rows when evaluating the scatter matrices. An improvement has been made to these methods via correlating the information between image rows and that of columns by using diagonal facial images. Accordance to aforementioned observations, a FR method is proposed in this paper in which diagonal 2D-LDA performed in wavelet subspace aiming to implicitly combine the beneficial properties of 2D-LDA, wavelet transform (WT) and diagonal image in one framework to achieve improved recognition performance. The competitively of proposed method is demonstrated by experimental results on standard face dataset.
Rapid and Accurate Identification of Candida albicans and Candida dubliniensis by Real-Time PCR and Melting Curve Analysis

Objective: Candida albicans and Candida dubliniensis are germ tube-positive pathogenic yeast species. Accurate identification of these two species is warranted since C. albicans is a highly pathogenic species while C. dubliniensis exhibits increased adherence to buccal epithelial cells, reduced susceptibility to azoles and resistance to flucytosine. We have developed a duplex real-time PCR assay for rapid detection and differentiation between clinical C. albicans and C. dubliniensis isolates. Materials and Methods: A duplex real-time PCR assay was developed by using two species-specific primer pairs and SYBR Green dye to differentiate C. albicans and C. dubliniensis isolates via melting curve analysis of real-time PCR amplicons. Amplification products were also analyzed by agarose gel electrophoresis to confirm real-time PCR results. Results: Melting temperatures (Tm) for reference strains of C. albicans and C. dubliniensis were 86.55 and 82.75°C, respectively. No amplicon was obtained with DNA from reference strains of 8 other common Candida spp. When real-time PCR was applied on 226 clinical isolates previously identified by the Vitek 2 system and/or PCR sequencing of rDNA, Tm values for C. albicans (n = 113) and C. dubliniensis (n = 98) were 86.68 ± 0.529 and 82.616 ± 0.535°C, respectively. The results were confirmed by agarose gel electrophoresis. No amplicon was obtained from 15 isolates belonging to 9 other Candida spp. Conclusions: The real-time PCR assay described here does not require prior identification of clinical yeast isolates as C. albicans/C. dubliniensis by germ tube formation and accurately reports results within 2 h. Detection of amplicons by agarose gel electrophoresis is also suitable for resource-poor settings devoid of real-time PCR facilities.
Localization of Multiple Simultaneously Active Speakers in an Acoustic Sensor Network

This paper addresses the localization of an unknown number of acoustic sources in an enclosure. We extend a well established algorithm for localization of acoustic sources, which is based on the Expectation Maximization (EM) algorithm for clustering of phase differences by a Gaussian mixture model. Supporting a more appropriate probabilistic model for spherical data such as direction of arrival or phase differences, the von Mises distribution is used to derive a localization algorithm for multiple simultaneously active sources. Experiments with simulated room impulse responses confirm the superiority of the proposed algorithm to the existing method in terms of localization performance.
Efficacy and Safety of Oral TDF-Based Pre-exposure Prophylaxis for Men Who Have Sex With Men: A Systematic Review and Meta-Analysis

Background: Pre-exposure prophylaxis (PrEP) is used as an HIV prevention method by people at substantial risk of HIV infection. This systematic review and meta-analysis evaluates current clinical evidence for use of oral TDF-based PrEP among men who have sex with men. Methods: A comprehensive literature search in PubMed, web of science, Google Scholar and ClinicalTrials.gov was performed. A random-effects meta-analysis was conducted using the event rate (ER) for estimation of the incidence of HIV and grade 3 or 4 adverse events (AE) among PrEP arm and using risk ratio (RR) for comparison of incidence of HIV and grade 3 or 4 AE between PrEP recipients and PrEP non-users. Blood-based adherence levels were also divided into three categories with reference to previous meta-analysis. Subgroup meta-analysis was also performed to evaluate whether blood-based adherence levels moderated the effect of TDF-based PrEP on HIV incidence. Narrative review was used due to inconsistent measurements of risk behavior and drug resistance. This review is registered on the PROSPERO database (CRD42017077965). Results: Fourteen studies were included in the review. Oral TDF-based PrEP significantly reduced HIV incidence with minimum drug resistance and tolerable safety risks (HIV incidence, ER = 1.1%, 95% CI 0.6–2.0%, p < 0.001, RR = 0.244, 95% CI 0.111–0.537, p < 0.001 and grade 3 or 4 AEs, ER = 13.0%, 95% CI 9.9–16.9%, p < 0.001, RR = 1.059, 95% CI 0.824–1.362, p = 0.653). Oral TDF-based PrEP was more effective in reducing HIV incidence with high levels of blood-based PrEP adherence (ER, 0.4%) compared to moderate adherence (2.9%; p < 0.001). Most studies found no association between PrEP use and self-reported sexual behavior. Conclusion: Oral TDF-based PrEP is an effective intervention to prevent against HIV infection among MSM. Well-designed implementation science studies that integrate sociobehavioral and biomedical interventions are needed to identify optimal PrEP delivery models in different populations to translate biomedical efficacy into real-world efficacy.
BrainNets: Human Emotion Recognition Using an Internet of Brian Things Platform

Human wearable helmet is a useful tool for monitoring the status of miners in the mining industry. However, there is little research regarding human emotion recognition in an extreme environment. In this paper, an emotional state evoked paradigm is designed to identify the brain area where the emotion feature is most evident. Next, the correct electrode position is determined for the collection of the negative emotion by the electroencephalograph (EEG) based on the international 10–20 system of electrode placement. And then, a fusion algorithm of the anxiety level is proposed to evaluate the person’s mental state using the θ, α, and β rhythms of an EEG. Experiments demonstrate that the position Fp2 is the best electrode position for obtaining the anxiety level parameter. The most visible EEG changes appear within the first two seconds following stimulation. The amplitudes of the θ rhythm increase most significantly in the negative emotional state.
Deep learning and low rank dictionary model for mHealth data classification

In the context of mobile Health (mHealth) applications, data are prone to several sources of contamination which would lead to false interpretation and misleading classification results. In this paper, a robust deep learning approach with low rank model is proposed to classify mHealth vital signs. Further-more, we propose using the Schatten-p norm instead of the classic nuclear norm since it has shown better recovery performance for several applications. We conduct a comprehensive study where we compare our method to the state-of-art methods and evaluate its performance with respect to the key system parameters. Our findings show indeed that combining deep network with dictionary learning model is effective for vital signs classification even in presence of 50% corruption with 8% improvement over the closest performance.
Recent Advance of Hybrid Energy Storage Systems for Electrified Vehicles

A hybrid energy storage system (HESS) that combines batteries and ultracapacitors (UCs) presents unique electric energy storage capability over traditional Energy Storage Systems (ESS) made of pure batteries or UCs. As a critical powertrain component of an electrified vehicle (EV), the performance and life of the ESS dominate the performance and life-cycle cost of the pure electric vehicle (PEV) and plug-in hybrid electric vehicle (PHEV) due to the large size of their ESS. Different from traditional power density and energy density considerations, the use of battery and UC HESS today is more geared toward the use of UCs to take over the high frequency, dynamic charge and discharge to ensure quick system response and to extend battery life by reducing its frequent charge and discharge. In this paper, the recent advance of HESS and relevant technologies have been reviewed. The state-of-the-art of battery ESS and modeling method, considering its performance degradation under different use patterns are first presented. Methods for modeling the UC and DC/DC converter in the HESS, along with various HESS architectures, are also overviewed. Energy management methods of HESS are then reviewed according to recent literature to derive appropriate energy split strategies between the batteries and UCs. Finally, various HESS-based applications from public transportation to construction machinery are discussed to illustrate the benefits of HESS.
A Formal Semantics of the MULTI-ML Language

In the context of high performance computing, it is important to avoid indeterminism and dead-locks. Multi-ML is a functional parallel programming language "à la ML", designed to program hierarchical architectures in a structured way. It is based of the Multi-BSP bridging model. To ensure that a program "cannot go wrong", we first need to define how a program "goes". To do so, we propose a formal operational semantics of the Multi-ML language to ensure the properties of the Multi-BSP model. We first describe a core-language and then introduce the big step's semantics evaluation rules. Then, we propose a set of evaluation rules that describe the behaviour of the Multi-ML language. The memory model is also precisely defined, as the Multi-BSP model deals with multiple level of nested memories.