Miracle-dz/UniFAR-FTUs huggingface dataset:
The Recruitment of Shifting and Inhibition in On-line Science and Mathematics Tasks

Prior research has investigated the recruitment of inhibition in the use of science/mathematics concepts in tasks that require the rejection of a conflicting, nonscientific initial concept. The present research examines if inhibition is the only EF skill recruited in such tasks and investigates whether shifting is also involved. It also investigates whether inhibition and/or shifting are recruited in tasks in which the use of science/mathematics concepts does not require the rejection of an initial concept, or which require only the use of initial concepts. One hundred and thirty-three third- and fifth-grade children participated in two inhibition and shifting tasks and two science and mathematics conceptual understanding and conceptual change (CU&C) tasks. All the tasks were on-line, and performance was measured in accuracy and RTs. The CU&C tasks involved the use of initial concepts and of science/mathematics concepts which required conceptual changes for their initial formation. Only in one of the tasks the use of the science/mathematics concepts required the concurrent rejection of an initial concept. The results confirmed that in this task inhibition was recruited and also showed that the speed of shifting was a significant predictor of performance. Shifting was a significant predictor of performance in all the tasks, regardless of whether they involved science/mathematics or initial concepts. It is argued that shifting is likely to be recruited in complex tasks that require multiple comparisons of stimuli and the entertainment of different perspectives. Inhibition seems to be a more selective cognitive skill likely to be recruited when the use of science/mathematics concepts requires the rejection of a conflicting initial concept.
An Efficient Acyclic Contact Planner for Multiped Robots

We present a contact planner for complex legged locomotion tasks: standing up, climbing stairs using a handrail, crossing rubble, and getting out of a car. The need for such a planner was shown at the DARPA Robotics Challenge, where such behaviors could not be demonstrated (except for egress). Current planners suffer from their prohibitive algorithmic complexity because they deploy a tree of robot configurations projected in contact with the environment. We tackle this issue by introducing a reduction property: the reachability condition. This condition defines a geometric approximation of the contact manifold, which is of low dimension, presents a Cartesian topology, and can be efficiently sampled and explored. The hard contact planning problem can then be decomposed into two subproblems: first, we plan a path for the root without considering the whole-body configuration, using a sampling-based algorithm; then, we generate a discrete sequence of whole-body configurations in static equilibrium along this path, using a deterministic contact-selection algorithm. The reduction breaks the algorithm complexity encountered in previous works, resulting in the first interactive implementation of a contact planner (open source). While no contact planner has yet been proposed with theoretical completeness, we empirically show the interest of our framework: in a few seconds, with high success rates, we generate complex contact plans for various scenarios and two robots: HRP-2 and HyQ. These plans are validated in dynamic simulations or on the real HRP-2 robot.
Delta-encoder: an effective sample synthesis method for few-shot object recognition

Learning to classify new categories based on just one or a few examples is a long-standing challenge in modern computer vision. In this work, we proposes a simple yet effective method for few-shot (and one-shot) object recognition. Our approach is based on a modified auto-encoder, denoted Delta-encoder, that learns to synthesize new samples for an unseen category just by seeing few examples from it. The synthesized samples are then used to train a classifier. The proposed approach learns to both extract transferable intra-class deformations, or "deltas", between same-class pairs of training examples, and to apply those deltas to the few provided examples of a novel class (unseen during training) in order to efficiently synthesize samples from that new class. The proposed method improves over the state-of-the-art in one-shot object-recognition and compares favorably in the few-shot case. Upon acceptance code will be made available.
A Longitudinal Study Examining the Change in Functional Independence Over Time in Elderly Individuals With a Functioning Kidney Transplant

Background: Functional disability is defined as the need for assistance with self-care tasks. Objective: To document changes in functional status over time among older prevalent renal transplant recipients. Design: Single center, prospective, follow-up study. Setting: Single center, tertiary care transplant center. Patients: Patients, with a functioning kidney transplant, aged 65 years or older who underwent assessment of functional status approximately 12 months previously. Measurements: Validated tools used included Barthel Index, the Lawton-Brody Scale of Instrumental Activities of Daily Living, the Timed Up and Go test, the Veterans Specific Activity Questionnaire, the Mini-Cog, and dynamometer handgrip strength. Methods: Outpatient assessment by a trained observer. Results: Of the 82 patients previously studied, 64 (78%) patients participated in the follow-up study (mean age 70.5 ± 4.4 years, 58% male, 55% diabetic). Among those completing functional status measures, 32 (50%) had functional disability at baseline. Over the 1-year period, 11 (17%) of these patients experienced progressive functional decline, 6 (9%) exhibited no change, and 15 (23%) had functional recovery. Eleven patients (17%) initially independent, developed new-onset disability. One of the strongest predictors of progressive functional decline was having 1 or more falls in the previous year. Limitations: Assessments were performed only on 2 occasions separated by approximately 1 year. Conclusions: Fluctuations in disability states are common among older adults living with renal transplants. Episodes of functional disability may place individuals at higher risk of persistent and/or progressive disability.
Aggregation of multi-armed bandits learning algorithms for opportunistic spectrum access

Multi-armed bandit algorithms have been recently studied and evaluated for Cognitive Radio (CR), especially in the context of Opportunistic Spectrum Access (OSA). Several solutions have been explored based on various models, but it is hard to exactly predict which could be the best for real-world conditions at every instants. Hence, expert aggregation algorithms can be useful to select on the run the best algorithm for a specific situation. Aggregation algorithms, such as Exp4 dating back from 2002, have never been used for OSA learning, and we show that it appears empirically sub-efficient when applied to simple stochastic problems. In this article, we present an improved variant, called Aggregator. For synthetic OSA problems modeled as Multi-Armed Bandit (MAB) problems, simulation results are presented to demonstrate its empirical efficiency. We combine classical algorithms, such as Thompson sampling, Upper-Confidence Bounds algorithms (UCB and variants), and Bayesian or Kullback-Leibler UCB. Our algorithm offers good performance compared to state-of-the-art algorithms (Exp4, CORRAL or LearnExp), and appears as a robust approach to select on the run the best algorithm for any stochastic MAB problem, being more realistic to real-world radio settings than any tuning-based approach.
The effect of vehicle route uncertainty in green roadside communication

This paper addresses the problem of scheduling transmission requests in vehicular networks so that long-term road side unit (RSU) energy costs are minimized. We demonstrate that knowledge of vehicular routes greatly improves the energy service costs and request drop ratio of RSU transmission. At the same time, simple and fast prediction algorithms can recover a significant portion of the loss incurred by a lack of vehicle route knowledge. The proposed algorithms use recent historical traffic data and simple calculations, such as Bayesian estimates, to predict the next few routing decisions by a vehicle, in order to load balance the scheduling of its requests over the RSU network. Our simulation results show that, while the common assumption in the literature of knowing the vehicle routes is indeed crucial for achieving good performance, simple algorithms can be used in cases where vehicle routes are not known ahead of time, in order to achieve comparable costs and loss ratios.
Transcriptome Analysis Provides Insight into the Molecular Mechanisms Underlying gametophyte factor 2-Mediated Cross-Incompatibility in Maize

In maize (Zea mays L.), unilateral cross-incompatibility (UCI) is controlled by Gametophyte factors (Ga), including Ga1, Ga2, and Tcb1; however, the molecular mechanisms underpinning this process remain unexplored. Here, we report the pollination phenotype of an inbred line, 511L, which carries a near-dominant Ga2-S allele. We performed a high-throughput RNA sequencing (RNA-Seq) analysis of the compatible and incompatible crosses between 511L and B73, to identify the transcriptomic differences associated with Ga2-mediated UCI. An in vivo kinetics analysis revealed that the growth of non-self pollen tubes was blocked at the early stages after pollination in 511L, maintaining the UCI barrier in Ga2. In total, 25,759 genes were expressed, of which, 2063 differentially expressed genes (DEGs) were induced by pollination (G_GG, G_GB, B_BB, B_BG). A gene ontology (GO) enrichment analysis revealed that these genes were specifically enriched in functions involved in cell wall strength and pectic product modification. Moreover, 1839, 4382, and 5041 genes were detected to differentially express under same pollination treatments, including B_G, BG_GG, and BB_GB, respectively. A total of 1467 DEGs were constitutively expressed between the two inbred lines following pollination treatments, which were enriched in metabolic processes, flavonoid biosynthesis, cysteine biosynthesis, and vacuole functions. Furthermore, we confirmed 14 DEGs related to cell wall modification and stress by qRT-PCR, which might be involved in Ga2-S-mediated UCI. Our results provide a comprehensive foundation for the molecular mechanisms involved in silks of UCI mediated by Ga2-S.
Analysis of shape alignment using Euclidean and Manhattan distance metrics

Shape alignment using Euclidean distance metric is an important tool in pattern recognition and medical imaging applications due to its computational efficiency. However, Euclidian distance metric is not very robust and is sensitive to the outliers. This paper describes the use of Euclidean and Manhattan distance metric to analyze robustness and resistivity of the shape with respect to their orientation, position and size in the 2D plane. The simulated data set illustrate the properties of different distance metric for the robust shape alignment process.
Toll-Like Receptor 4 Triggering Promotes Cytosolic Routing of DC-SIGN-Targeted Antigens for Presentation on MHC Class I

DC-SIGN is an antigen uptake receptor expressed on dendritic cells (DCs) with specificity for glycans present on a broad variety of pathogens and is capable of directing its cargo to MHC-I and MHC-II pathways for the induction of CD8+ and CD4+ T cell responses, respectively. Therefore, DC-SIGN is a very promising target for the delivery of antigen for anti-cancer vaccination. Although the endocytic route leading to MHC-II presentation is characterized to a large extent, the mechanisms controlling DC-SIGN targeted cross-presentation of exogenous peptides on MHC-I, are not completely resolved yet. In this paper, we used imaging flow cytometry and antigen-specific CD8+ T cells to investigate the intracellular fate of DC-SIGN and its cargo in human DCs. Our data demonstrates that immature DCs and toll-like receptor 4 (TLR4) stimulated DCs had similar internalization capacity and were both able to cross-present antigen targeted via DC-SIGN. Interestingly, simultaneous triggering of TLR4 and DC-SIGN on DCs resulted in the translocation of cargo to the cytosol, leading to proteasome-dependent processing and increased CD8+ T cell activation. Understanding the dynamics of DC-SIGN-mediated uptake and processing is essential for the design of optimal DC-SIGN-targeting vaccination strategies aimed at enhancing CD8+ T cell responses.
Learning from outside the viability kernel: Why we should build robots that can fall with grace

Despite impressive results using reinforcement learning to solve complex problems from scratch, in robotics this has still been largely limited to model-based learning with very informative reward functions. One of the major challenges is that the reward landscape often has large patches with no gradient, making it difficult to sample gradients effectively. We show here that the robot state-initialization can have a more important effect on the reward landscape than is generally expected. In particular, we show the counter-intuitive benefit of including initializations that are unviable, in other words initializing in states that are doomed to fail.
Energy-Efficient Admission of Delay-Sensitive Tasks for Mobile Edge Computing

Task admission is critical to delay-sensitive applications in mobile edge computing, but is technically challenging due to its combinatorial mixed nature and consequently limited scalability. We propose an asymptotically optimal task admission approach which is able to guarantee task delays and achieve <inline-formula> <tex-math notation="LaTeX">$(1-\epsilon)$ </tex-math></inline-formula>-approximation of the computationally prohibitive maximum energy saving at a time-complexity linearly scaling with devices. <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> is linear to the quantization interval of energy. The key idea is to transform the mixed integer programming of task admission to an integer programming (IP) problem with the optimal substructure by pre-admitting resource-restrained devices. Another important aspect is a new quantized dynamic programming algorithm which we develop to exploit the optimal substructure and solve the IP. The quantization interval of energy is optimized to achieve an <inline-formula> <tex-math notation="LaTeX">$[\mathcal {O}(\epsilon),\mathcal {O}(1/\epsilon)]$ </tex-math></inline-formula>-tradeoff between the optimality loss and time complexity of the algorithm. Simulations show that our approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming.
FADS Polymorphism, Omega-3 Fatty Acids and Diabetes Risk: A Systematic Review

The role of n-3 long chain polyunsaturated fatty acids (LC n-3 PUFA) in reducing the risk of type 2 diabetes (T2DM) is not well established. The synthesis of LC n-3 PUFA requires fatty acid desaturase enzymes, which are encoded by the FADS gene. It is unclear if FADS polymorphism and dietary fatty acid intake can influence plasma or erythrocyte membrane fatty acid profile and thereby the risk of T2DM. Thus, the aim of this systematic review was to assess the current evidence for an effect of FADS polymorphism on T2DM risk and understand its associations with serum/erythrocyte and dietary LC n-3 PUFA. A systematic search was performed using PubMed, Embase, Cochrane and Scopus databases. A total of five studies met the inclusion criteria and were included in the present review. This review identified that FADS polymorphism may alter plasma fatty acid composition and play a protective role in the development of T2DM. Serum and erythrocyte LC n-3 PUFA levels were not associated with risk of T2DM, while dietary intake of LC n-3 PUFA was associated with lower risk of T2DM in one study only. The effect of LC n-3 PUFA consumption on associations between FADS polymorphism and T2DM warrants further investigation.
EMPD: An Efficient Membrane Potential Driven Supervised Learning Algorithm for Spiking Neurons

The brain-inspired spiking neurons, considered as the third generation of artificial neurons, are more biologically plausible and computationally powerful than traditional artificial neurons. One of the fundamental research in spiking neurons is to transform streams of incoming spikes into precisely timed spikes. Due to the inherent complexity of processing spike sequences, the formulation of efficient supervised learning algorithm is difficult and remains an important problem in the research area. This paper presents an efficient membrane potential driven (EMPD) supervised learning method capable of training neurons to generate desired sequences of spikes. The learning rule of EMPD is composed of two processes: 1) at desired output times, the gradient descent method is implemented to minimize the error function defined as the difference between the membrane potential and the firing threshold and 2) at undesired output time, synaptic weights are adjusted to make the membrane potential below the threshold. For efficiency, at undesired output times, EMPD calculates the membrane potential and makes a comparison with firing threshold only at some special time points when the neuron is most likely to cross the firing threshold. Experimental results show that the proposed EMPD approach has higher learning efficiency and accuracy over the existing learning algorithms.
Early Treatment of Primary HIV Infection Is Associated with Decreased Mortality.

The aim of this study was to understand factors associated with increased mortality in a cohort of primary HIV infection (PHI) in New South Wales (NSW) over three decades. Six hundred and two patients with PHI were enrolled from 1984 to 2009. Probabilistic data linkage was performed to NSW Registry of births deaths and marriages and Australian Bureau of Statistics mortality database. Mortality was measured by crude death rate. Pre highly active antiretroviral therapy (pre-HAART) era was defined as before January 1, 1997. A Cox proportional hazard model was used to identify factors associated with death. One hundred and thirty-eight deaths occurred during 6,223 person years (PY) follow-up. Overall crude death rate was 2.2 per 100 PY (95% confidence interval [CI], 1.9-2.6), 3.6 (95% CI, 3.1-4.3)in pre-HAART era and 0.20 (95% CI, 0.08-0.47) in post-HAART era. AIDS was the most frequent cause of death (52%, 72/138), all occurring in the pre-HAART era. Of non-AIDS deaths, the leading known cause was non-AIDS cancer 8% (11/138) followed by suicide 4% (6/138). On multivariate analysis, estimated date of infection in pre-HAART era and time to commencement of ART greater than 1 year post diagnosis were more likely to be associated with death (p < .05). Mortality in PHI has decreased significantly in the post-HAART era. Non-AIDS deaths due to malignancy and suicide are emerging as leading causes in this population in the post-HAART era. Time to starting ART greater than 1 year was associated with increased mortality.
Energy efficiency in hybrid beamforming large-scale mmwave multiuser MIMO with spatial modulation

The problem of radio resource allocation for global energy efficiency (GEE) maximization in mmWaves large-scale multiple-input multiple-output (MIMO) systems using hybrid-beamforming with spatial modulation is addressed. The theoretical properties of the optimization problem at hand are analyzed and two provably convergent optimization algorithms with affordable complexity are proposed. The former achieves the global optimum, while the latter trades off optimality with a lower computational complexity. Nevertheless, numerical results show that both algorithms attain global optimality in practical scenarios.
A Silicon Photonic Transceiver and Hybrid Tunable Laser for 64 Gbaud Coherent Communication

We demonstrate an all-silicon-photonic coherent link, including a hybrid tunable laser with <100kHz linewidth, >45dB SMSR, and <−135dB/Hz RIN as light source for a BGA-packaged 64Gbaud coherent transceiver with direct edge-coupling to 10μm-MFD fiber.
Full Solution Indexing for Top-K Web Service Composition

Automated service composition fulfills complex tasks by combining different existing web services together. Unfortunately, optimizing service composition is still a challenging area that needs to be addressed. In this article, we propose a novel relational database approach for automated service composition. All possible service combinations are generated beforehand and stored in a relational database. When a user request comes, our system composes SQL queries to search in the database and return the best Quality of Service (QoS) solutions. We test the performance of the proposed system with a web service challenge data set. Our experimental results demonstrate that this system can always find top-K valid solutions to satisfy user's functional and non-functional requirements.
Acceleration of image classification with Caffe framework using FPGA

Caffe is a deep learning framework, originally developed at UC Berkeley and widely used in large-scale industrial applications such as vision, speech, and multimedia. It supports many different types of deep learning architectures such as CNNs (convolutional neural networks) geared towards image classification and image recognition. In this paper we develop a platform for the efficient deployment and acceleration of Caffe framework on embedded systems that are based on the Zynq SoC. The most computational intensive part of image classification is the processing of the convolution layers of the deep learning algorithms and more specifically the GEMM (general matrix multiplication) function calls. In the proposed framework, a hardware accelerator has been implemented, validated and optimized using Xilinx SDSoC Development Environment to perform the GEMM function. The accelerator that was developed achieves up to 98x speed-up compared with the simple ARM CPU implementation. The results showed that the mapping of Caffe on the FPGA-based Zynq takes advantage of the low-power, customizable and programmable fabric and ultimately reduces time and power consumption of image classification.
Replication-Based Fault-Tolerance for Large-Scale Graph Processing

The increasing algorithmic complexity and dataset sizes necessitate the use of networked machines for many graph-parallel algorithms, which also makes fault tolerance a must due to the increasing scale of machines. Unfortunately, existing large-scale graph-parallel systems usually adopt a distributed checkpoint mechanism for fault tolerance, which incurs not only notable performance overhead but also lengthy recovery time. This paper observes that the vertex replicas created for distributed graph computation can be naturally extended for fast in-memory recovery of graph states. This paper describes Imitator, a new fault tolerance mechanism, which supports cheap maintenance of vertex states by replicating them to their replicas during normal message exchanges, and provides fast in-memory reconstruction of failed vertices from replicas in other machines. Imitator has been implemented on Cyclops with edge-cut and PowerLyra with vertex-cut. Evaluation on a 50-node EC-2 like cluster shows that Imitator incurs an average of 1.37 and 2.32 percent performance overhead (ranging from −0.6 to 3.7 percent) for Cyclops and PowerLyra respectively, and can recover from failures of more than one million of vertices with less than 3.4 seconds.