Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheduling Latency-Sensitive Applications in Edge Computing

Edge computing is an emerging technology that aims to include latency-sensitive and data-intensive applications such as mobile or IoT services, into the cloud ecosystem by placing computational resources at the edge of the network. Close proximity to producers and consumers of data brings significant benefits in latency and bandwidth. However, edge resources are, by definition, limited in comparison to cloud counterparts, thus, a trade-off exists between deploying a service closest to its users and avoiding resource overload. We propose a score-based edge service scheduling algorithm that evaluates both network and computational capabilities of edge nodes and outputs the maximum scoring mapping between services and resources. Our extensive simulation based on a live video streaming service, demonstrates significant improvements in both network delay and service time. Additionally, we compare edge computing technology with the state-of-the-art cloud computing and content delivery network solutions within the context of latency-sensitive and data-intensive applications. Our results show that edge computing enhanced with suggested scheduling algorithm is a viable solution for achieving high quality of service and responsivity in deploying such applications.
Traffic sign recognition based on kernel sparse representation

This paper proposes a novel approach based on scale invariant feature transform (SIFT) and kernel sparse representation for traffic sign recognition in complex traffic scenes. This module consists of several steps. In the first stage, SIFT is introduced for feature extraction from samples and test targets, respectively. The features are mapping to the kernel space. In the second stage, we construct an over-complete dictionary based on kernel sparse representation. Finally, traffic objects are recognized by computing sparseness and reconstruction residuals in the dictionary. Experiment results show that the proposed approach enhances the class discriminant ability using traffic features with higher recognition preciseness and robustness in complex traffic scenes compared with SVM, SRC.
Wide-Scale Adoption of Photovoltaic Energy: Grid Code Modifications Are Explored in the Distribution Grid

Current grid standards largely require that low-power (e.g., several kilowatts) single-phase photovoltaic (PV) systems operate at unity power factor (PF) with maximum power point tracking (MPPT), and disconnect from the grid under grid faults by means of islanding detection. However, in the case of wide-scale penetration of single-phase PV systems in the distributed grid, disconnection under grid faults can contribute to 1) voltage flickers, 2) power outages, and 3) system instability. This article explores grid code modifications for a wide-scale adoption of PV systems in the distribution grid. In addition, based on the fact that Italy and Japan have recently undertaken a major review of standards for PV power conversion systems connected to low-voltage networks, the importance of low voltage ride-through (LVRT) for single-phase PV power systems under grid faults is considered, along with three reactive power injection strategies. Simulations are presented for a PV power system with a LVRT capability and ancillary services. An example of a full-bridge single-phase grid connected system is tested experimentally to demonstrate the potential benefits. Additionally, grid codes for advanced PV systems with the discussed features are summarized.
Effects on human motor strategies of physical interaction with a force-controlled wrist rehabilitation robot

In this work we analyze the effects of a force control scheme, implemented on a robot for wrist rehabilitation (InMotion3 by Interactive Motion Inc. [1]) to reduce the perceived mechanical impedance, on human motor strategies during pointing tasks. Previous studies showed that mechanical impedance of the robot interferes with natural motor strategies [2]. Three healthy subjects performed pointing tasks in three different operative ways: i) with a lightweight hand-held device (capable of measuring wrist rotations without introducing negligible loading effects); ii) with the InMotion3 controlled using the default control with the impedance parameters set to zero; iii) with the same robot, controlled using a direct force control scheme, which reduces the perceived mechanical impedance. In order to study the effects of the force control on the intrinsic kinematic constraints, we assessed wrist rotations with the same protocol we already used to study wrist motor strategies during redundant pointing tasks. The thickness of Donders surfaces (see [3] and [4] for details) indicates how much a soft constraint (such as Donders Law) applies to wrist kinematics during pointing tasks. The effects of force control can be primarily found in the variability of the fitted surfaces as well as in their thickness values. In particular, thickness values, which for the uncontrolled robot are one order of magnitude smaller than the physiological values (suggesting that the constraint has a mechanical origin rather than a neural one), return back into the physiological range in the case of the force-controlled robot.
Latent Maximum Margin Clustering

We present a maximum margin framework that clusters data using latent variables. Using latent representations enables our framework to model unobserved information embedded in the data. We implement our idea by large margin learning, and develop an alternating descent algorithm to effectively solve the resultant non-convex optimization problem. We instantiate our latent maximum margin clustering framework with tag-based video clustering tasks, where each video is represented by a latent tag model describing the presence or absence of video tags. Experimental results obtained on three standard datasets show that the proposed method outperforms non-latent maximum margin clustering as well as conventional clustering approaches.
Learning Slow Features with Reservoir Networks for Biologically-inspired Robot Localization

This work proposes a hierarchical biologicallyinspired architecture for learning sensor-based spatial representations of a robot environment in an unsupervised way. The learning is based on the fact that high-level concepts, such as the robot position, which vary in a slower timescale, can be found in a fast-varying input signal, like distance sensors. It is also assumed that the input is low-dimensional, providing limited information from the environment. The proposed architecture is composed of three layers, where the first layer, called the reservoir, is a fixed randomly generated recurrent neural network, which projects the input into a high-dimensional, dynamic space. The second layer is trained with Slow Feature Analysis (SFA), generating instantaneous slowly-varying signals from the reservoir states. Using Independent Component Analysis (ICA), the third layer implements sparse coding on the SFA output. The architecture, called RC-SFA, benefits from the short-term memory of the reservoir and the unsupervised learning mechanisms of SFA and ICA. We show that, using a limited number of noisy short-range distance sensors, mobile robots are able to learn to self-localize in simulation as well as in real environments. It is not only the current sensor reading which is needed for predicting the robot position, but also a history of the input stream. We compare the RC-SFA model with a time-delayed model using only SFA and ICA, and show that the reservoir is essential for the temporal processing of the the input stream. Results also show that the SFA and ICA layers show activation patterns which resemble, respectively, the firing of grid cells and hippocampal place cells found in the brain of rodents.
Learning human-robot handovers through π-STAM: Policy improvement with spatio-temporal affordance maps

Human-robot handovers are characterized by high uncertainty and poor structure of the problem that make them difficult tasks. While machine learning methods have shown promising results, their application to problems with large state dimensionality, such as in the case of humanoid robots, is still limited. Additionally, by using these methods and during the interaction with the human operator, no guarantees can be obtained on the correct interpretation of spatial constraints (e.g., from social rules). In this paper, we present Policy Improvement with Spatio-Temporal Affordance Maps - π-STAM, a novel iterative algorithm to learn spatial affordances and generate robot behaviors. Our goal consists in generating a policy that adapts to the unknown action semantics by using affordances. In this way, while learning to perform a human-robot handover task, we can (1) efficiently generate good policies with few training episodes, and (2) easily encode action semantics and, if available, enforce prior knowledge in it. We experimentally validate our approach both in simulation and on a real NAO robot whose task consists in taking an object from the hands of a human. The obtained results show that our algorithm obtains a good policy while reducing the computational load and time duration of the learning process.
Schumpeter's Creative Destruction: A Review of the Evidence

Thoughtful scholars from Adam Smith to Jared Diamond, have asked the same life-and-death question: why do some societies succeed, and others fail, in producing the goods that make life long, healthy and prosperous? Smith's answer was basically that when societies adopt the rules of market capitalism, their economies grow,:and when they do not adopt the rules of market capitnlism, their economies do not. Since Smith, other economists have developed more formal models of economic growth. The classic "Solow growth model" emphasized the investment of capital. Romer's "New Growth Theory," includes knowledge as a variable. What is mainly missing from both the new and the old growth theories is a useful discussion of incentives and entrepreneurship. The theories are not just academic abstractions. Policymakers, wanting to improve the world, sometimes act in part on the basis of the
Volume Illustration of Muscle from Diffusion Tensor Images

Medical illustration has demonstrated its effectiveness to depict salient anatomical features while hiding the irrelevant details. Current solutions are ineffective for visualizing fibrous structures such as muscle, because typical datasets (CT or MRI) do not contain directional details. In this paper, we introduce a new muscle illustration approach that leverages diffusion tensor imaging (DTI) data and example-based texture synthesis techniques. Beginning with a volumetric diffusion tensor image, we reformulate it into a scalar field and an auxiliary guidance vector field to represent the structure and orientation of a muscle bundle. A muscle mask derived from the input diffusion tensor image is used to classify the muscle structure. The guidance vector field is further refined to remove noise and clarify structure. To simulate the internal appearance of the muscle, we propose a new two-dimensional example based solid texture synthesis algorithm that builds a solid texture constrained by the guidance vector field. Illustrating the constructed scalar field and solid texture efficiently highlights the global appearance of the muscle as well as the local shape and structure of the muscle fibers in an illustrative fashion. We have applied the proposed approach to five example datasets (four pig hearts and a pig leg), demonstrating plausible illustration and expressiveness.
Estimation of right ventricular volume, quantitative assessment of wall motion and trabeculae mass in arrhythmogenic right ventricular dysplasia

The aim of this study was to gain a wide perspective of the arrhythmogenic right ventricular dysplasia (ARVD) by developing algorithms for Cardiac Magnetic Resonance Imaging. We developed a semi-automatic procedure to assess the Right Ventricle (RV) volumes and to quantify RV wall motion; moreover, with the increased visible details in a single MR image, a manual method to evaluate the trabeculae mass was performed. All the algorithms used were based on the level set theory which allows detecting both endocardial and wall surfaces, as well as the black parts characterizing the trabeculae. 6 normal subjects and 6 subjects with ARVD have been investigated. Our method and the standard manual method for volume estimation were significantly correlated (y=0.92×+6.56), (r=0.92 p&#60;0.001). Wall Motion results showed a significant reduction of RV segmental function in patients with ARVD, Inferior Wall was the most involved with more than 80% reduction (p&#60;0.001) compared with normal subjects, while RV outflow tract (RVOT) was the least involved with less than 50% reduction (p&#60;0.001) compared to normal subjects. A repeatability test was executed on trabeculae mass assessment, which showed a high intra observer correlation, in fact the results were significant at 95% of the cases.
Improved rat genome gene prediction by integration of ESTs with RNA-Seq information

MOTIVATION RNA-Seq (also called whole-transcriptome sequencing) is an emerging technology that uses the capabilities of next-generation sequencing to detect and quantify entire transcripts. One of its important applications is the improvement of existing genome annotations. RNA-Seq provides rapid, comprehensive and cost-effective tools for the discovery of novel genes and transcripts compared with expressed sequence tag (EST), which is instrumental in gene discovery and gene sequence determination. The rat is widely used as a laboratory disease model, but has a less well-annotated genome as compared with humans and mice. In this study, we incorporated deep RNA-Seq data from three rat tissues-bone marrow, brain and kidney-with EST data to improve the annotation of the rat genome. RESULTS Our analysis identified 32 197 transcripts, including 13 461 known transcripts, 13 934 novel isoforms and 4802 new genes, which almost doubled the numbers of transcripts in the current public rat genome database (rn5). Comparisons of our predicted protein-coding gene sets with those in public datasets suggest that RNA-Seq significantly improves genome annotation and identifies novel genes and isoforms in the rat. Importantly, the large majority of novel genes and isoforms are supported by direct evidence of RNA-Seq experiments. These predicted genes were integrated into the Rat Genome Database (RGD) and can serve as an important resource for functional studies in the research community. AVAILABILITY AND IMPLEMENTATION The predicted genes are available at http://rgd.mcw. edu.
Flexible learning of k-dependence Bayesian network classifiers

In this paper we present an extension to the classical k-dependence Bayesian network classifier algorithm. The original method intends to work for the whole continuum of Bayesian classifiers, from naïve Bayes to unrestricted networks. In our experience, it performs well for low values of k. However, the algorithm tends to degrade in more complex spaces, as it greedily tries to add k dependencies to all feature nodes of the resulting net. We try to overcome this limitation by seeking for optimal values of k on a feature per feature basis. At the same time, we look for the best feature ordering. That is, we try to estimate the joint probability distribution of optimal feature orderings and individual number of dependencies. We feel that this preserves the essence of the original algorithm, while providing notable performance improvements.
Multi-key Searchable Encryption without Random Oracle

Multi-Key Searchable Encryption (MKSE) is a new application scenario of searchable encryption, in which any user can search over all encrypted documents stored in untrusted server by submitting only one trapdoor. Firstly, we describe a general model of multi-key searchable encryption and introduce its system model and attack model. Secondly, we define its formulized model and the corresponding security with no ideal Hash function required. Furthermore, we construct a feasible and efficient MKSE scheme without random oracle. Security analysis shows that our scheme is secure.
Radarsat-2 Backscattering for the Modeling of Biophysical Parameters of Regenerating Mangrove Forests

The aim of this study is to understand the relationship between radar backscattering (σ°, β° and γ) of a multi-polarized Radarsat-2 C-band image with the structural attributes of regenerating mangrove vegetation located at the mouth of the Amazon River. CBH (circumference at breast height), height and species data were collected to characterize vegetation structure and above-ground biomass (AGB) at 17 plots with a total of 3090 measured individuals. Significant relationships between the linear σ° in VH (vertical transmit, horizontal receive) cross-polarization produced r2 values of 0.63 for the average height, 0.53 for the DBH, 0.46 for the basal area (BA) and 0.52 for the AGB. Using co-polarized HH (horizontal transmit, horizontal receive) and VV (vertical transmit, vertical receive), r2 values increased to 0.81, 0.79, 0.67 and 0.79, respectively. Vegetation attribute maps of average canopy height, DBH and AGB were generated for the study area. We conclude that multi-polarized Radarsat-2 images were adequate for characterization of vegetation attributes in areas of mangrove regeneration.
When the (Hindu) nation exiles its queers

L'A. decrit la situation des gays et lesbiennes dans l'Inde post-coloniale. Il souligne que le mouvement nationaliste hindou Rashtriya Swayamsevak Sangh (RSS) defend une perspective politique resolument homophobe, valorisant l'heterosexualite et stigmatisant, a des fins xenophobes, l'homosexualite musulmane. Le gay musulamn est envisage, dans le discours du RSS, comme un individu qui a developpe une hyper-masculinite et une hyper-sexualite perverse qui le confine au rang de quasi animal. Pour les nationalistes du RSS, l'homosexualite est le fruit du materialisme occidental. Elle est le signe de la degradation de la nation indienne. Elle resulte de l'influence musulmane et des ideaux defendus par le Parti du Congres. Selon l'A. , les violences perpetrees a l'encontre des femmes musulmanes visent a «compenser» et a «neutraliser» l'hyper-sexualite musulmane. Les dirigeants du RSS, dans leur choix de vie, au travers de la valorisation du celibat et d'une certaine feminite, s'efforcent d'apparaitre comme des individus au service de la nation. L'A. souligne que cette conception ne coincide nullement avec une valorisation de l'heterosexualite et de la masculinite virile privilegiee dans leur discours politique
Optimizing post activation potentiation for explosive activities in competitive sports

Abstract Post activation potentiation (PAP) has shown improved performance during movements requiring large muscular power output following contractions under near maximal load conditions. PAP can be described as an acute enhancement of performance or an enhancement of factors determining an explosive sports activity following a preload stimulus. In practice, PAP has been achieved by complex training, which involves a combination of a heavy loaded exercise followed by a biomechanically similar explosive activity, best if specific for a particular sport discipline. The main objective of this study was to investigate the effects of PAP on performance in explosive motor activities specific for basketball, luge and athletics throws. The novel approach to the experiments included individualized recovery time (IRT) between the conditioning exercise and the explosive activity. Additionally, the research groups were homogenous and included only competitive athletes of similar age and training experience. Thirty one well trained athletes from 3 different sport disciplines participated in the study. All athletes performed a heavy loaded conditioning activity (80-130%1RM) followed by a biomechanically similar explosive exercise, during which power (W) or the rate of power development (W/s/kg) was evaluated. The results of our experiment confirmed the effectiveness of PAP with well-trained athlets during explosive motor activities such as jumping, throwing and pushing. Additionally, our research showed that eccentric supramaximal intensities (130% 1RM) can be effective in eliciting PAP in strength trained athletes. Our experiments also showed that the IRT should be individualized because athletes differ in the strength level, training experience and muscle fiber structure. In the three experiments conducted with basketball players, track and field athletes and luge athletes, the optimal IRT equaled 6 min. This justifies the need to individualize the volume and intensity of the CA, and especially the IRT, between the CA and the explosive activity.
Stability Enhancement and Speed Control of DFOIM Using Hybrid PID Plus Fuzzy Logic Controller

Due to many advantages of (DFOIM), it finds vast applicability in diverse fields. But the only problem with them is that the desired and instantaneous speed control under the load torque disturbances is a bit difficult and complex. In this paper, the best solution to the above problem is proposed by implementing fuzzy logic (FLC) scheme based controller to tune PID controller. Here it is further proposed that implementing Ziegler-Nichols (Z-N) method for designing the PID controller is adapted with FLC. The FLC tuned PID controller will give superior control of speed and also provides sophisticated stability enhancement to DFOIM. The proposed Z-N method utilizes the acceleration error and speed error commands to train the FLC, where the errors are PID controller outputs. In Simulink-MATLAB environment, a Dynamic model for the proposed fuzzy PID control based DFOIM is designed and it is simulated. The results thus obtained in the simulation of Z-N based PID trained FLC scheme will gives superior control over the speed and enhanced stability constraints during load torque disturbances.
Functional knock down of VCAM1 in mice mediated by endoplasmatic reticulum retained intrabodies

Functional knockdowns mediated by endoplasmatic reticulum-retained antibodies (ER intrabodies) are a promising tool for research because they allow functional interference on the protein level. We demonstrate for the first time that ER intrabodies can induce a knock-down phenotype in mice. Surface VCAM1 was suppressed in bone marrow of heterozygous and homozygous ER intrabody mice (iER-VCAM1 mice). iER-VCAM1 mice did not have a lethal phenotype, in contrast to the constitutive knockout of VCAM1, but adult mice exhibited physiological effects in the form of aberrant distribution of immature B-cells in blood and bone marrow. The capability to regulate knock-down strength may spark a new approach for the functional study of membrane and plasma proteins, which may especially be valuable for generating mouse models that more closely resemble disease states than classic knockouts do.
Reproducibility of kinematics indexes of upper arm reaching movement in robot assisted therapy

One of the most appealing features of robot-assisted-therapy (RMT) consists in the possibility to quantify the performances of the rehabilitation task proposed to the patient. The analysis of kinematics indexes, providing quantitative information about movement patterns that is not captured using clinical measures, could be used as reliable outcome measures in clinical upper limb rehabilitative settings. Few papers have actually addressed this issue and aim of the study is the assessment of the reproducibility of kinematics indexes of upper arm horizontal reaching movements in normal subjects during RMT. We studied two repeated sets of 60 reaching movements of 25° amplitude at 20-30 and 40°/s target velocity in 5 healthy subjects. Reaching movements were automatically detected and quantified by symmetry, skewness, kurtosis and smoothness indexes. Results showed agreements' limits of kurtosis and smoothness narrower than other kinematics indexes, with Bland-Altman plots rapidly increasing their scatter with higher values. This suggesting to limit their evaluation only to the average of a sequence of medium velocity reaching movements of about 30°/s.