Miracle-dz/UniFAR-FTUs huggingface dataset:
Exemplar extraction using spatio-temporal hierarchical agglomerative clustering for face recognition in video

Many recent works have attempted to improve object recognition by exploiting temporal dynamics, an intrinsic property of video sequences. In this paper, a new spatio-temporal hierarchical agglomerative clustering (STHAC) method is proposed for automatic extraction of face exemplars for face recognition in video sequences. Two variants of STHAC are presented — a global variety that unifies spatial and temporal distances between points, and a local variety that introduces perturbation of distances based on a local spatio-temporal neighborhood criterion. Faces that are nearest to the cluster means are chosen as exemplars for the testing stage, where subjects in the test video sequences are recognized using a probabilistic-based classifier. Extensive evaluation on a face video database demonstrates the effectiveness of our proposed method, and the significance of incorporating temporal information for exemplar extraction.
Detection technique for stator inter-turn faults in BLDC motors based on third harmonic components of line currents

This study proposes a technique for detecting stator inter-turn faults in brushless dc motors using the third harmonic components of line currents. The faults are attributed to insulation degradation between adjacent coils in a phase and, they cause distortions in the magnetic fields, which affect the harmonic components in line currents. To analyze the harmonic components under the asymmetric magnetic fields in a faulty motor, a system matrix is developed. As a result, the third harmonic components in line currents are noticeably generated in comparison to other harmonics, and the fault can be detected by determining whether the third harmonics exist. The proposed technique is verified by experiment under different operating conditions.
Attractor design and prediction-based adaption for a robot waltz dancer in physical human-robot interaction

Physical human-robot interaction between a human leader and a robot follower in waltz is studied in this paper. The dancers' body dynamics in single-support phase are modeled as inverted pendulums. On the robot side, an ankle torque control method is proposed and applied. The control law forms a time-dependent vector field, which makes the nominal orbit of the robot to be an attractor. To physically interact with human, the human leader's state is estimated from range image data by using an extended Kalman filter. Parameters of the robot's orbit are then adjusted according to the leader's estimated and predicted state. The proposed method is verified by simulation results.
Heterogeneous UAV swarm system for target search in adversarial environment

Unmanned Aerial Systems (UAS) have great potential to aid in search and situation assessment. Here, we present a UAV swarm system performing target search in adversarial environment. It utilizes a Ant-Colony (ACO) and Artificial Potential Function (APF) based decentralised target search strategy. ACO meta-heuristic forms the higher level guidance algorithm and APF provide global and local path planning. Uncertainty maps are used to represent probable target locations. The algorithm is scalable and is shown to be robust to agent loss. Its distributed nature makes it ideal for applications in large scale search operations. Trajectory estimates are factored into prioritization resulting into better target selection and faster search.
Sulfoconjugation and glucuronidation of salicylamide in isolated rat hepatocytes.

Sulfoconjugation and glucuronidation of salicylamide by isolated hepatocytes were examined with various concentrations of salicylamide and sodium sulfate. The ratio of sulfate to glucuronide formed changed markedly, depending on the concentrations of salicylamide and inorganic sulfate in the medium. The apparent Km value of sulfoconjugation for salicylamide was 0.006 mM, while its Vmax value varied depending on the concentration of inorganic sulfate (e.g., 2.1 and 0.5 nmoles/min/10(6) cells at 1.2 and 0.5 mM inorganic sulfate, respectively). The Km and Vmax values of glucuronidation for salicylamide were 0.19 mM and 1.28 nmoles/min/10(6) cells, respectively, in the absence of sodium sulfate. Glucuronidation was suppressed in the presence of inorganic sulfate. The suppression could be attributable to the competitive consumption of salicylamide by sulfotransferase. Additional in vivo experiments revealed that an extra amount of salicylamide markedly lowered the blood inorganic sulfate levels of rats. The significance of the finding is discussed in conjunction with the variation of the Vmax value of sulfoconjugation with the inorganic sulfate concentration.
Friction Force and Stresses Analysis for Contact of Assembled Details

The present work deals with the computation of surface stresses and deformation in the presence of friction. The evaluation of the elastic-plastic contact is analyzed revealing three distinct stages that range from fully elastic through elastic-plastic to fully plastic contact interface. Several factors of sliding friction model are discussed: surface roughness, mechanical properties and contact load and areas that have strong effect on the friction force. The critical interference that marks the transition from elastic to elastic- plastic and plastic deformation is found out and its connection with plasticity index. A finite element program for determination contact analysis of the assembled details and due to details of deformation that arose a normal and tangencial stress is used.
The potential benefit of scaling up malaria prevention to reduce low birth weight in Africa

Low birth weight (LBW) is one of the most important risk factors for neonatal mortality and morbidity, childhood stunting, reduced cognitive development, and chronic diseases later in life. In sub-Saharan Africa, where the burden of LBW is very high, Plasmodium falciparum malaria is one of the major causes, contributing to an estimated 20% of LBW cases [1]. A new report suggests that substantial gains in reducing LBW in sub-Saharan Africa could be achieved by scaling up a key malaria intervention known as intermittent preventive treatment in pregnancy (IPTp) [2]. In areas with stable P. falciparum malaria transmission, IPTp is a key strategy for reducing the burden of malaria in pregnancy and subsequent LBW; other important strategies are widespread deployment of long-lasting insecticide-treated bed nets (ITNs) and access to prompt diagnosis and effective treatment. IPTp involves the administration of curative doses of sulphadoxine-pyrimethamine (SP) to pregnant women attending antenatal clinics (ANC) without testing for malaria infection. IPTp has shown substantial benefit in reducing LBW and severe maternal anaemia (pooled risk ratio from multiple trials was 0.8 and 0.6, respectively) [3]. Its benefits are believed to stem from the clearance of any infection that may be present when the drug is given and a short-lived prophylactic effect against subsequent P. falciparum infection because of the drugs’ long half-lives (4–9 days for sulphadoxine and ~3 days for pyrimethamine [4]. Since 2004, WHO has recommended IPTp for all pregnant women in areas of stable malaria transmission in addition to the use of ITNs [5]. The initial recommendation was at least two separate doses, and monthly doses in pregnancy are now recommended where possible. Despite this, IPTp coverage in at-risk populations in sub-Saharan Africa is very low in many regions; overall, an estimated 24% of women currently receive at least two doses [6]. Furthermore, coverage with ITNs during pregnancy is similarly low at an estimated 35% in at-risk populations [2]. Of additional concern is the declining efficacy of IPTp with SP due to increasing resistance in many regions in Africa (particularly resistance caused by six specific mutations) [7].
Leveraging Weak Semantic Relevance for Complex Video Event Classification

Existing video event classification approaches suffer from limited human-labeled semantic annotations. Weak semantic annotations can be harvested from Web-knowledge without involving any human interaction. However such weak annotations are noisy, thus can not be effectively utilized without distinguishing its reliability. In this paper, we propose a novel approach to automatically maximize the utility of weak semantic annotations (formalized as the semantic relevance of video shots to the target event) to facilitate video event classification. A novel attention model is designed to determine the attention scores of video shots, where the weak semantic relevance is considered as atten-tional guidance. Specifically, our model jointly optimizes two objectives at different levels. The first one is the classification loss corresponding to video-level groundtruth labels, and the second is the shot-level relevance loss corresponding to weak semantic relevance. We use a long short-term memory (LSTM) layer to capture the temporal information carried by the shots of a video. In each timestep, the LSTM employs the attention model to weight the current shot under the guidance of its weak semantic relevance to the event of interest. Thus, we can automatically exploit weak semantic relevance to assist video event classification. Extensive experiments have been conducted on three complex large-scale video event datasets i.e., MEDTest14, ActivityNet and FCVID. Our approach achieves the state-of-the-art classification performance on all three datasets. The significant performance improvement upon the conventional attention model also demonstrates the effectiveness of our model.
A Skill-based library for retargetable embedded analog cores

This paper describes the automatic generation and reusability of physical layouts of analog and mixed-signal blocks based on high-functionality pCells that are fully independent of technologies. The high-functionality pCell library presently contains over 42 pCells and is fully compliant with 7 different sets of technology design rules from 5 different foundries. Practical examples employed in industrial projects are illus-
Synthesis of monodisperse CeO2–ZrO2 particles exhibiting cyclic superelasticity over hundreds of cycles

Nano- and microscale CeO2-ZrO2 (CZ) shape memory ceramics are promising materials for smart micro-electro-mechanical systems (MEMS), sensing, actuation and energy damping applications, but the processing science for scalable production of such small volume ceramics has not yet been established. Herein, we report a modified sol-gel method to synthesize highly monodisperse spherical CZ particles with diameters in the range of ~0.8-3.0 μm. Synchrotron X-ray micro-diffraction (μSXRD) confirmed that most of the particles are single crystal after annealing at 1450°C. Having a monocrystalline structure and a small specimen length scale, the particles exhibit significantly enhanced shape memory and superelasticity properties with up to ~4.7% of compression being completely recoverable. Highly reproducible superelasticity through over five hundred strain cycles, with dissipated energy up to ~40 MJ/m3 per cycle, is achieved in the CZ particles containing 16 mol% ceria. This cycling capability is enhanced by ten times compared with our first demonstration using micropillars (only 50 cycles in Lai et.al, Science, 2013, 341, 1505). Furthermore, the effects of cycling and testing temperature (in 25-400°C) on superelasticity have been investigated. This article is protected by copyright. All rights reserved.
ElastMan: elasticity manager for elastic key-value stores in the cloud

The increasing spread of elastic Cloud services, together with the pay-as-you-go pricing model of Cloud computing, has led to the need of an elasticity controller. The controller automatically resizes an elastic service in response to changes in workload, in order to meet Service Level Objectives (SLOs) at a reduced cost. However, variable performance of Cloud Virtual Machines and nonlinearities in Cloud services, such as the diminishing reward of adding a service instance with increasing the scale, complicates the controller design. We present the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores. ElastMan combines feedforward and feedback control. Feedforward control is used to respond to spikes in the workload by quickly resizing the service to meet SLOs at a minimal cost. Feedback control is used to correct modeling errors and to handle diurnal workload. To address nonlinearities, our design of ElastMan leverages the near-linear scalability of elastic Cloud services in order to build a scale-independent model of the service. We have implemented and evaluated ElastMan using the Voldemort key-value store running in an OpenStack Cloud environment. Our evaluation shows the feasibility and effectiveness of our approach to automation of Cloud service elasticity.
Mammary Cancer Bone Metastasis Follow-up Using Multimodal Small-Animal MR and PET Imaging

Despite tremendous progress in the management of breast cancer, the survival rate of this disease is still correlated with the development of metastases—most notably, those of the bone. Diagnosis of bone metastasis requires a combination of multiple imaging modalities. MR imaging remains the best modality for soft-tissue visualization, allowing for the distinction between benign and malignant lesions in many cases. On the other hand, PET imaging is frequently more specific at detecting bone metastasis by measuring the accumulation of radiotracers, such as 18F-sodium fluoride (18F-NaF) and 18F-FDG. Thus, the main purpose of this study was to longitudinally monitor bone tumor progression using PET/MR image coregistration to improve noninvasive imaging–assisted diagnoses. Methods: After surgical implantation of mammary MRMT-1 cells in a rat femur, we performed minimally invasive imaging procedures at different time points throughout tumor development. The procedure consisted of sequential coregistered MR and PET image acquisition, using gadolinium-diethylenetriaminepentaacetic acid (DTPA) as a contrast agent for MR imaging and 18F-FDG, 11C-methionine, and 18F-NaF as molecular tracers for PET imaging. The animals were then euthanized, and complementary radiologic (micro-CT scans) and histologic analyses were performed. Results: In this preclinical study, we demonstrated that coregistered MR and PET images provide helpful information in a rat mammary–derived bone cancer model. First, MR imaging provided a high-definition anatomic resolution that made the localization of bone resorption and tumor extension detectable between days 9 and 18 after the injection of cancer cells in the medullary channel of the femur. Indeed, the calculation of mean standardized uptake value (SUVmean) and maximal SUV (SUVmax) in bone and soft-tissue regions, as defined from the gadolinium-DTPA contrast-enhanced MR images, showed 18F-NaF uptake modifications and increased 18F-FDG or 11C-methionine uptake in the bone and surrounding soft tissues. 18F-FDG and 11C-methionine were compared in terms of the magnitude of change in their uptake and variability. We observed that 11C-methionine SUVmean variations in the tumor were more important than those of 18F-FDG. We also found fewer interindividual variations using SUVmean as a quantitative parameter than SUVmax. Conclusion: This preclinical evaluation demonstrated that a PET/MR image coregistration protocol provided a powerful tool to evaluate bone tumor progression in a rat model of bone metastasis and that this protocol could be translated to improve the clinical outcome for metastatic breast cancer management.
Tool and Phase recognition using contextual CNN features

A transfer learning method for generating features suitable for surgical tools and phase recognition from the ImageNet classification features [1] is proposed here. In addition, methods are developed for generating contextual features and combining them with time series analysis for final classification using multi-class random forest. The proposed pipeline is tested over the training and testing datasets of M2CAI16 challenges: tool and phase detection. Encouraging results are obtained by leave-one-out cross validation evaluation on the training dataset.
A Survey on Opportunistic Routing in Wireless Communication Networks

The great advances made in the wireless technology have enabled the deployment of wireless communication networks in some of the harshest environments such as volcanoes, hurricane-affected regions, and underground mines. In such challenging environments suffering from the lack of infrastructure, traditional routing is not efficient and sometimes not even feasible. Moreover, the exponential growth of the number of wireless connected devices has created the need for a new routing paradigm that could benefit from the potentials offered by these heterogeneous wireless devices. Hence, in order to overcome the traditional routing limitations, and to increase the capacity of current dynamic heterogeneous wireless networks, the opportunistic routing paradigm has been proposed and developed in recent research works. Motivated by the great interest that has been attributed to this new paradigm within the last decade, we provide a comprehensive survey of the existing literature related to opportunistic routing. We first study the main design building blocks of opportunistic routing. Then, we provide a taxonomy for opportunistic routing proposals, based on their routing objectives as well as the optimization tools and approaches used in the routing design. Hence, five opportunistic routing classes are defined and studied in this paper, namely, geographic opportunistic routing, link-state-aware opportunistic routing, probabilistic opportunistic routing, optimization-based opportunistic routing, and cross-layer opportunistic routing. We also review the main protocols proposed in the literature for each class. Finally, we identify and discuss the main future research directions related to the opportunistic routing design, optimization, and deployment.
Mapping a Locus for Familial Thoracic Aortic Aneurysms and Dissections (TAAD2) to 3p24–25

Background—Familial thoracic aortic aneurysms and dissections (TAAD) occur as part of known syndromes such as Marfan syndrome but can also be inherited in families in an autosomal dominant manner as an isolated condition. Previous studies have mapped genes causing nonsyndromic familial TAAD to 5q13–15 (TAAD1) and 11q23.2-q24 (FAA1). Further genetic heterogeneity for the condition was evident by the presence of TAAD in some families not linked to these known loci. Methods and Results—A 4-generation family with dominant mode of inheritance of TAAD was studied. Affected status was determined by dilation of the ascending aorta, surgical repair of an aneurysm or dissection, or death as the result of aortic dissection. None of the family members evaluated met the diagnostic criteria for Marfan syndrome. After exclusion of known loci for familial TAAD, a genome-wide scan was carried out to map the defective gene causing the disease in the family. A locus was mapped to a 25-cM region on 3p24–25 with a maximum multipoint logarithm of the odds score of 4.28. Conclusions—A third locus for nonsyndromic TAAD was mapped to 3p24–25 and termed the TAAD2 locus. This locus overlaps a previously mapped second locus for Marfan syndrome, termed the MFS2 locus. Future characterization of the TAAD2 gene will determine if TAAD2 is allelic to MFS2. In addition, identification of the TAAD2 gene will improve the presymptomatic diagnosis of individuals with this life-threatening genetic syndrome and provide information concerning the pathogenesis of the disease.
Efficient Testing of Electronic Devices

Testing electronic devices in order to assure the quality of individual products can be time-consuming and expensive. To speed up this process without compromising on reliability, testing strategies have been developed at NIST. They are based on the extraction of lowdimensional linear error models, using a priori knowledge of the structure of the device or data from an exhaustively tested training and validation set. Production testing then is performed only on a small subset of all possible test points which must be chosen optimally. This paper describes statistical and computational aspects of these procedures and reports on the theoretical background, heuristics, algorithms, and practical experiences. 1 The Need for EfficientTesting Testing is a critical step for assuring the quality of electronic devices. For complicated devices, the cost of testing is quite significant and may exceed 20 % of the sale price. Efficient yet reliable testing strategies that assure the quality of every single device can therefore result in substantial savings. For many device types, the number of test points is vastly larger than the number of parameters that are expected to determine the device behavior. For example, a 13-bit AID converter has 213 = 8192 possible test points, but it can be described with only a few dozen parameters. Efficient testing strategies try to identify error patterns that occur for a certain device type and build a mathematical model for it. For a given new device, the parameters for these patterns are then determined from measurements at a well-chosen reduced set of test 1Electronics and Electrical Engineering Laboratory, Technology Administration, u.s. Department of Commerce. Official contribution of the National Institute of Standards and Technologyj not subject to copyright in the United States. Electricity Division* . National Institute of Standards and Technology' Gaithersburg, MD 20899 ' ~:, ;
The benefit and cost of winner-picking : Redistribution vs . Incentives ∗

A multi-divisional firm can engage in ”winner-picking” to redistribute scarce funds efficiently across divisions. But there is a conflict between rewarding winners (investing) and producing resources with which to reward winners (incentives). Managers in winning divisions are tempted to free-ride on resources produced by managers in loosing divisions whose incentives to produce resources, anticipating their loss, are also weakened. Corporate headquarter’s investment and incentive policies are therefore inextricably linked and have to be treated as jointly endogenous. The analysis links corporate strategy, compensation and the value of diversification to the characteristics of multi-divisional firms. JEL-Classification: G31, G34, L23
The study the effect of polymer and surfactant concentration on characteristics of nanoparticle formulations

The Aim of the present work was to study the effect of polymer and surfactant concentration on characteristics of nanoparticle formulations. Nanoparticles were prepared by solvent evaporation method. The prepared nanoparticles were characterized by particle size, entrapment efficiency, drug loading. Compatibility study of drug and polymer was carried out by Fourier transform infrared spectroscopy (FTIR). The results of this study showed that the particle size, entrapment efficiency and drug loading were 295.5nm, 79.61% and 36.78% respectively. The drug release from optimized batch was found to be 74.98 % in 10 hrs. In this study Glipizide loaded PLA nanoparticles were prepared successfully and showed promising sustained drug release profile.