Miracle-dz/UniFAR-FTUs huggingface dataset:
2^B3^C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks

In this paper, we tackle the classification of gender in facial images with deep learning. Our convolutional neural networks (CNN) use the VGG-16 architecture [1] and are pretrained on ImageNet for image classification. Our proposed method (2^B3^C) first detects the face in the facial image, increases the margin of a detected face by 50%, cropping the face with two boxes three crop schemes (Left, Middle, and Right crop) and extracts the CNN predictions on the cropped schemes. The CNNs of our method is fine-tuned on the Adience and LFW with gender annotations. We show the effectiveness of our method by achieving 90.8% classification on Adience and achieving competitive 95.3% classification accuracy on LFW dataset. In addition, to check the true ability of our method, our gender classification system has a frame rate of 7-10 fps (frames per seconds) on a GPU considering real-time scenarios.
A Comprehensive Multimorbidity Index for Predicting Mortality in Intensive Care Unit Patients.

BACKGROUND Accurate prediction of mortality for patients admitted to the intensive care units (ICUs) is an important component of medical care. However, little is known about the role of multimorbidity in predicting end of life for high-risk and vulnerable patients. OBJECTIVE The aim of the study was to derive and validate a multimorbidity risk model in an attempt to predict all-cause mortality at 6 and 12 months posthospital discharge. METHODS This is a retrospective, observational, clinical cohort study. Data were collected on 442,692 ICU patients who received care through the Veterans Administration between January 2003 and December 2013. The primary outcome was all-cause mortality at 6 and 12 months posthospital discharge. We divided the data into derivation (80%) and validation (20%) sets. Using multivariable logistic regression models, we compared prognostic models based on age, principal diagnosis groups, physiological markers, immunosuppressants, comorbidity categories, and a newly developed multimorbidity index (MMI) based on 5695 comorbidities. The cross-validated area under the receiver operating characteristic curve (AUC) was used to report the accuracy of predicting all-cause mortality at 6 and 12 months of hospital discharge. RESULTS The average age of patients was 68.87 years (standard deviation = 12.1), 95.9% were males, 44.9% were widowed, divorced, or separated. The relative order of accuracy in predicting mortality was the MMI (AUC = 0.84, CI = 0.83-0.84), VA Inpatient Evaluation Center index (AUC = 0.80, CI = 0.79-0.81), principal diagnosis groups (AUC = 0.74, CI = 0.73-0.74), comorbidities (AUC = 0.69, CI = 0.68-0.70), physiological markers (AUC = 0.65, CI = 0.64-0.65), age (AUC = 0.60, CI = 0.60-0.61),and immunosuppressant use (AUC = 0.59, CI = 0.58-0.59). CONCLUSIONS The MMI improved the accuracy of predicting short- and long-term all-cause mortality for ICU patients. Further prospective studies are needed to validate the index in different clinical settings and test generalizability of results in patients outside the VA system of care.
VOLUMETRIC FOREST CHANGE DETECTION THROUGH VHR SATELLITE IMAGERY

Abstract. Quick and economical ways of detecting of planimetric and volumetric changes of forest areas are in high demand. A research platform, called FORSAT (A satellite processing platform for high resolution forest assessment), was developed for the extraction of 3D geometric information from VHR (very-high resolution) imagery from satellite optical sensors and automatic change detection. This 3D forest information solution was developed during a Eurostars project. FORSAT includes two main units. The first one is dedicated to the geometric and radiometric processing of satellite optical imagery and 2D/3D information extraction. This includes: image radiometric pre-processing, image and ground point measurement, improvement of geometric sensor orientation, quasiepipolar image generation for stereo measurements, digital surface model (DSM) extraction by using a precise and robust image matching approach specially designed for VHR satellite imagery, generation of orthoimages, and 3D measurements in single images using mono-plotting and in stereo images as well as triplets. FORSAT supports most of the VHR optically imagery commonly used for civil applications: IKONOS, OrbView – 3, SPOT – 5 HRS, SPOT – 5 HRG, QuickBird, GeoEye-1, WorldView-1/2, Pleiades 1A/1B, SPOT 6/7, and sensors of similar type to be expected in the future. The second unit of FORSAT is dedicated to 3D surface comparison for change detection. It allows users to import digital elevation models (DEMs), align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes between epochs. To this end our 3D surface matching method LS3D is being used. FORSAT is a single source and flexible forest information solution with a very competitive price/quality ratio, allowing expert and non-expert remote sensing users to monitor forests in three and four dimensions from VHR optical imagery for many forest information needs. The capacity and benefits of FORSAT have been tested in six case studies located in Austria, Cyprus, Spain, Switzerland and Turkey, using optical data from different sensors and with the purpose to monitor forest with different geometric characteristics. The validation run on Cyprus dataset is reported and commented.
To investigate classical scheduling schemes with power management in IaaS cloud environment for HPC workloads

This paper addresses the well-known virtual machine (VM) scheduling problem in infrastructure as a service (IaaS) cloud environments to reduce the operational cost of the system together satisfying quality of service (QoS) constraints. We provide the study to investigate classical scheduling schemes incorporated with power management techniques i.e., the dynamic voltage and frequency scaling (DVFS) in Private IaaS cloud environment for high performance computing (HPC) workloads. Specifically, one first served-based policy i.e., first come first serve (FCFS), two size-based policies i.e., smallest job first (SmJF) and shortest job first (ShJF), and one backfilling policy i.e., aggressive backfilling first (AgBF), are considered to investigate the environment. The highlighted the strengths and weaknesses of the VM scheduling schemes in this paper can help to choose the most appropriate scheme in the environment.
Coordinated voltage control strategy for hybrid multi-infeed HVDC systems

A coordinated secondary voltage control (SVC) strategy for hybrid multi-infeed HVDC systems is proposed in this paper. A quadratic programming model is built for the coordinated SVC strategy, which is solved periodically to obtain adjustments of various reactive power sources. The voltage reference values of pilot buses can be achieved through the reactive power adjustments. Simulation results of a test system verify the effectiveness of the proposed SVC strategy.
Effects of Subthalamic Nucleus Deep Brain Stimulation on Objective Sleep Outcomes in Parkinson's Disease

Sleep dysfunction is a common and disabling nonmotor symptom in Parkinson's disease (PD). DBS of the STN improves motor symptoms and subjective sleep in PD, but alternative stimulation parameters to optimize sleep have not been explored. We hypothesized that low‐frequency STN DBS would improve objective sleep more than conventional settings.
The Proximal Augmented Lagrangian Method for Nonsmooth Composite Optimization

We study a class of optimization problems in which the objective function is given by the sum of a differentiable but possibly nonconvex component and a nondifferentiable convex regularization term. We introduce an auxiliary variable to separate the objective function components and utilize the Moreau envelope of the regularization term to derive the proximal augmented Lagrangian—a continuously differentiable function obtained by constraining the augmented Lagrangian to the manifold that corresponds to the explicit minimization over the variable in the nonsmooth term. The continuous differentiability of this function with respect to both primal and dual variables allows us to leverage the method of multipliers (MM) to compute optimal primal-dual pairs by solving a sequence of differentiable problems. The MM algorithm is applicable to a broader class of problems than proximal gradient methods and it has stronger convergence guarantees and a more refined step-size update rules than the alternating direction method of multipliers (ADMM). These features make it an attractive option for solving structured optimal control problems. We also develop an algorithm based on the primal-descent dual-ascent gradient method and prove global (exponential) asymptotic stability when the differentiable component of the objective function is (strongly) convex and the regularization term is convex. Finally, we identify classes of problems for which the primal-dual gradient flow dynamics are convenient for distributed implementation and compare/contrast our framework to the existing approaches.
Tensile Strength of a Ti/Thermoplastic ABS Matrix CFRTP Joint Connected by Surface Activated Carbon Fiber Cross-Weave Irradiated by Electron Beam

A new method with extremely large friction force taking advantage of the high interface area of carbon ber (CF: 6 μm-diameter) cross weave cloth, surface activated by homogeneous low voltage electron beam irradiation (HLEBI) on the ABS half-length prior to dipping in ABS resin to enhance the ability of dif cult to adhere thermoplastic with CF has been suggested for a joint (Ti/EBCF/ABS) of carbon ber reinforced thermoplastic ABS polymer and titanium (Ti). The joint was strengthened by the HLEBI over that without HLEBI. Experimental results showed ultimate tensile strength (σb) of the (Ti/EBCF/ABS) was 18.2 MPa: 2.1 and 9.1 and 4.2 times higher than that: without HLEBI (Ti/CF/ABS) (8.64 MPa); with glue (Ti/Glue/ABS) (2.00 MPa); and without glue (Ti/ABS) (4.32 MPa), respectively. Since the cross sectional area of CF impregnated ABS portion was about 1/12 that of the entire ABS wrapped CFRTP, the corrected σb (σb) value of (Ti/EBCF/CFRTP) (139 MPa) could be estimated and was 2.5 times higher than σb of (Ti/CF/CFRTP) (56.5 MPa). X-ray diffraction (XRD) and wavelength dispersive X-ray spectroscopy (WDS) analysis showed titanium carbide TiC was not detected. A “strain hardening” model was constructed to predict deformation mechanisms in the (Ti/ABS) and (Ti/EBCF/ABS) joints. The new joint method of applying HLEBI to the carbon ber cloth remarkably enhanced the safety level of lightweight material with high resistance to fracture for airplanes and automobiles over that without HLEBI. [doi:10.2320/matertrans.M2016017]
Potential Applications of Nanoparticles

Nanoparticles (NP) are defined as particles with a diameter smaller than 100 nm, are increasingly used in different applications, including drug carrier s ystems and to pass organ barriers such as the blood -brain barrier. Because of their unique properties Nanocry stals (quantum dots) and other nanoparticles (gold colloids, nanobars, dendrimers and nanoshells) have been receiving a lot of attention for potential use in Thera peutics, Bioengineering and therapeutics drug discovery. In this review potential use of these Nanocrystals and Nanoparticles in various important areas has been d iscussed. Special properties of these nanoparticles may offer new advancement in drug discovery.
Genetic comparison of two color morphs of northern snakehead (Channa argus) and Chnannidae family

Abstract Relationships of two species of Channa argus (northern snakeheads) named “Bicolor type” and “White type”, as well as other six Channa species were investigated based on their partial sequence of mitochondrial DNA 16S rRNA genes in the present study. For the Channa family, the average genetic distance was 0.0863 with the inter-species genetic distance ranged from 0.0173 to 0.1384, and the average intra-species genetic distance in the genus channa was estimated as 0.00273 (range: 0.0019 to 0.0038). For the two C. argus species, the mean pair-wise genetic distance between “Bicolor type” and “White type” northern snakehead was estimated as 0.00218, which was within the intra-species genetic distance interval for Channa species, indicating that they belonged to the same species at molecular level. Moreover, these snakeheads can be divided into two distinct groups via 16S rRNA, which might be more accurate for Channa classification than traditional method based on the absence or presence of the pelvic fins of the fish.
Ethanolamine Catabolism in Pseudomonas aeruginosa PAO1 Is Regulated by the Enhancer-Binding Protein EatR (PA4021) and the Alternative Sigma Factor RpoN

ABSTRACT Although genes encoding enzymes and proteins related to ethanolamine catabolism are widely distributed in the genomes of Pseudomonas spp. , ethanolamine catabolism has received little attention among this metabolically versatile group of bacteria. In an attempt to shed light on this subject, this study focused on defining the key regulatory factors that govern the expression of the central ethanolamine catabolic pathway in Pseudomonas aeruginosa PAO1. This pathway is encoded by the PA4022-eat-eutBC operon and consists of a transport protein (Eat), an ethanolamine-ammonia lyase (EutBC), and an acetaldehyde dehydrogenase (PA4022). EutBC is an essential enzyme in ethanolamine catabolism because it hydrolyzes this amino alcohol into ammonia and acetaldehyde. The acetaldehyde intermediate is then converted into acetate in a reaction catalyzed by acetaldehyde dehydrogenase. Using a combination of growth analyses and β-galactosidase fusions, the enhancer-binding protein PA4021 and the sigma factor RpoN were shown to be positive regulators of the PA4022-eat-eutBC operon in P. aeruginosa PAO1. PA4021 and RpoN were required for growth on ethanolamine, and both of these regulatory proteins were essential for induction of the PA4022-eat-eutBC operon. Unexpectedly, the results indicate that acetaldehyde (and not ethanolamine) serves as the inducer molecule that is sensed by PA4021 and leads to the transcriptional activation of the PA4022-eat-eutBC operon. Due to its regulatory role in ethanolamine catabolism, PA4021 was given the name EatR. Both EatR and its target genes are conserved in several other Pseudomonas spp. , suggesting that these bacteria share a mechanism for regulating ethanolamine catabolism. IMPORTANCE The results of this study provide a basis for understanding ethanolamine catabolism and its regulation in Pseudomonas aeruginosa PAO1. Interestingly, expression of the ethanolamine-catabolic genes in this bacterium was found to be under the control of a positive-feedback regulatory loop in a manner dependent on the transcriptional regulator PA4021, the sigma factor RpoN, and the metabolite acetaldehyde. Previously characterized regulators of ethanolamine catabolism are known to sense and respond directly to ethanolamine. In contrast, PA4021 (EatR) appears to monitor the intracellular levels of free acetaldehyde and responds through transcriptional activation of the ethanolamine-catabolic genes. This regulatory mechanism is unique and represents an alternative strategy used by bacteria to govern the acquisition of ethanolamine from their surroundings.
Towards lightweight satisfiability solvers for self-verification

Solvers for Boolean satisfiability (SAT solvers) are essential for various hardware and software verification tasks such as equivalence checking, property checking, coverage analysis, etc. Nevertheless, despite the fact that very powerful solvers have been developed in the recent decades, this progress often still cannot cope with the exponentially increasing complexity of those verification tasks. As a consequence, researchers and engineers are investigating complementarily different verification approaches which require changes in the core methods as well. Self-verification is such a promising approach where e.g. SAT solvers have to be executed on the system itself. This comes with hardware restrictions such as limited memory and motivates lightweight SAT solvers. This work provides a case study towards the development of such solvers. To this end, we consider several core techniques of SAT solvers (such as clause learning, Boolean constraint propagation, etc.) and discuss as well as evaluate how they contribute to both, the run-time performance but also the required memory requirements. The findings from this case study provide a basis for the development of dedicated, i.e. lightweight, SAT solvers to be used in self-verification solutions.
Off-policy Monte Carlo agents with variable behaviour policies

This paper looks at the convergence property of off-policy Monte Carlo agents with variable behaviour policies. It presents results about convergence and lack of convergence. Even if the agent generates every possible episode history infinitely often, the algorithm can fail to converge on the correct Q-values. On the other hand, it can converge on the correct Q-values under certain conditions. For instance, if, during the n-th episode, the agent has an independent probability of 1/ log(n) of following the original policy at any given state, then it will converge on the right Q-values for that policy.
Modeling the Peano fluidic muscle and the effects of its material properties on its static and dynamic behavior

The promise of wearable assistive robotics cannot be realized without the development of actuators that mimic the behavior and form of biological muscles. Planar fluidic muscles known as Peano muscles or pouch motors have the potential to provide the high force and compliance of McKibben pneumatic artificial muscles with the low threshold pressure of pleated pneumatic artificial muscles. Yet they do so in a soft and slim form that can be discreetly distributed over the human body. This work is an investigation into the empirical modeling of the Peano muscle, the effect of its material on its performance, and its capabilities and limitations. We discovered that the Peano muscle could provide responsive and discreet actuation of soft and rigid bodies requiring strains between 15% and 30%. Ideally, they are made of non-viscoelastic materials with high tensile and low bending stiffnesses. While Sarosi et al’s empirical model accurately captures its static behavior with an root mean square error of 10.2 N, their dynamic model overestimates oscillation frequency and damping. We propose that the Peano muscle be modeled by a parallel ideal contractile unit and viscoelastic element, both in series with another viscoelastic element.
Bottlenecks Identification in Software Development Process: A Quali-Quantitative Approach

This paper presents the quali-quantitative results of a study on the identification of bottlenecks in the software development process. The research was developed in an environment learning of Software Engineering and had the collaboration of students of the Department of Computer Science (DCC) of the Federal University of Rio de Janeiro (UFRJ). The main objective of the research is to verify the existence of productive bottlenecks in Software Development Process (SDP), typify them and if possible to promote treatments to solve the impacts in the process and in the software product. For this, three experimental rounds were carried out and three different domains were applied. Each round had deferential teams working in the same domain. The experiments also involved the Unified Process (UP) to guiding the SDP and the Theory of Constraints (TOC) to identify and treat the restrictive elements found in the productive process. As results, the work presents a set of qualitative constraints subdivided into two groups: (1) Behavioral Constraints (BC), and (2) Technical Constraints (TC). The identification of these qualitative constraints allowed quantitative bottlenecks to be detected. The research also showed that bottlenecks are associated with certain tasks performed in the software development environment within a given domain. In general, these tasks are consuming resources, such as effort, time and human resources. This fact leads to the software product to a low quality due to the lack of completeness and correctness of the artifacts delivered to the end of the productive process.
Multiorgan structures detection using deep convolutional neural networks

Many automatic image analysis algorithms in medical imaging require a good initialization to work properly. A similar problem occurs in many imaging-based clinical workflows, which depend on anatomical landmarks. The localization of anatomic structures based on a defined context provides with a solution to that problem, which turns out to be more challenging in medical imaging where labeled images are difficult to obtain. We propose a two-stage process to detect and regress 2D bounding boxes of predefined anatomical structures based on a 2D surrounding context. First, we use a deep convolutional neural network (DCNN) architecture to detect the optimal slice where an anatomical structure is present, based on relevant landmark features. After this detection, we employ a similar architecture to perform a 2D regression with the aim of proposing a bounding box where the structure is encompassed. We trained and tested our system for 57 anatomical structures defined in axial, sagittal and coronal planes with a dataset of 504 labeled Computed Tomography (CT) scans. We compared our method with a well-known object detection algorithm (Viola Jones) and with the inter-rater error for two human experts. Despite the relatively small number of scans and the exhaustive number of structures analyzed, our method obtained promising and consistent results, which proves our architecture very generalizable to other anatomical structures.
Diagnostic validity across racial and ethnic groups in the assessment of adolescent DSM‐IV disorders

We examine differential validity of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM‐IV) diagnoses assessed by the fully‐structured Composite International Diagnostic Interview Version 3.0 (CIDI) among Latino, non‐Latino Black, and non‐Latino White adolescents in comparison to gold standard diagnoses derived from the Schedule for Affective Disorders and Schizophrenia for School‐age Children (K‐SADS). Results are based on the National Comorbidity Survey Replication Adolescent Supplement, a national US survey of adolescent mental health. Clinicians re‐interviewed 347 adolescent/parent dyads with the K‐SADS. Sensitivity and/or specificity of CIDI diagnoses varied significantly by ethnicity/race for four of ten disorders. Modifications to algorithms sometimes reduced bias in prevalence estimates, but at the cost of reducing individual‐level concordance. These findings document the importance of assessing fully‐structured diagnostic instruments for differential accuracy in ethnic/racial subgroups. Copyright © 2012 John Wiley & Sons, Ltd.
A 7.5‐μW 0.08‐mm2 single‐ended SC delta‐sigma ADC for acoustic sensor applications

This study presents an ultra‐low‐power, small‐size, 1‐bit, single‐ended, and switched‐capacitor (SC) delta‐sigma analog‐to‐digital converter (ADC) for wireless acoustic sensor nodes. This wireless sensor node has a delta‐sigma ADC that converts the sensed signal to a digital signal for convenient data processing and emphasizes the features of small size and low‐power consumption. The chip area of the delta‐sigma ADC is dominated by the capacitor; therefore, a novel common‐mode (CM) controlling technique with only transistors is proposed. This ADC achieves an extremely small size of 0.08 mm2 in a 130‐nm CMOS process. The conventional operational transconductance amplifiers (OTAs) are replaced by inverters in the weak inversion region to achieve high power efficiency. At 4‐MHz sampling frequency and 0.7‐V power supply voltage, the delta‐sigma ADC achieves a 55.8‐dB signal‐to‐noise‐plus‐distortion ratio (SNDR) and a 298‐fJ/step figure‐of‐merit (FOM) in a signal bandwidth of 25 kHz, while consuming only 7.5 μW of power. Copyright © 2015 John Wiley & Sons, Ltd.