A comparison of clutching movements of freely adjusted and imposed pedal configurations for identifying discomfort assessment criteria

This paper focuses on the effects of the free pedal position adjustment on clutching movements of the left lower limb as well as on the perceived discomfort. Six automotive clutch pedal configurations were tested by 20 subjects (5 young females, 5 young males, 5 older females, 5 older males) using a multi-adjustable experimental mock-up. Results showed that the pedal position was adjusted to ensure a good starting pedal position allowing a less flexed ankle and avoiding unnecessary leg displacement from the foot rest to the position at start depression. Pedal position adjustment seemed not motivated by reducing joint torque though discomfort ratings were found significantly correlated with knee and ankle torques at the end of depression. The present work also illustrates that the less-constrained motion concept is helpful for a better understanding of people preference and useful for identifying motion-related biomechanical parameters to be considered for defining assessment criteria.     

Probabilistic and nondeterministic aspects of anonymity

The concept of anonymity comes into play in a wide range of situations, varying from voting and anonymous donations to postings on bulletin boards and sending emails. The protocols for ensuring anonymity often use random mechanisms which can be described probabilistically, while the agents’ behavior may be totally unpredictable, irregular, and hence expressible only nondeterministically. Formal definitions of the concept of anonymity have been investigated in the past either in a totally nondeterministic framework, or in a purely probabilistic one. In this paper, we investigate a notion of anonymity which combines both probability and nondeterminism, and which is suitable for describing the most general situation in which the protocol and the users can have both probabilistic and nondeterministic behavior. We also investigate the properties of the definition for the particular cases of purely nondeterministic users and purely probabilistic users. We formulate the notions of anonymity in terms of probabilistic automata, and we describe protocols and users as processes in the probabilistic π-calculus, whose semantics is again based on probabilistic automata. Throughout the paper, we illustrate our ideas by using the example of the dining cryptographers.     

Vectorized algorithms for spiking neural network simulation

High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages.     

Exact simulation of integrate-and-fire models with exponential currents

Neural networks can be simulated exactly using event-driven strategies, in which the algorithm advances directly from one spike to the next spike. It applies to neuron models for which we have (1) an explicit expression for the evolution of the state variables between spikes and (2) an explicit test on the state variables that predicts whether and when a spike will be emitted. In a previous work, we proposed a method that allows exact simulation of an integrate-and-fire model with exponential conductances, with the constraint of a single synaptic time constant. In this note, we propose a method, based on polynomial root finding, that applies to integrate-and-fire models with exponential currents, with possibly many different synaptic time constants. Models can include biexponential synaptic currents and spike-triggered adaptation currents.     

Comparing dynamics of fluency and inter-limb coordination in climbing activities using multi-scale Jensen–Shannon embedding and clustering

This paper reports the results of two studies carried out in a controlled environment aiming to understand relationships between movement patterns of coordination that emerge during climbing and performance outcomes. It involves a recent method of nonlinear dimensionality reduction, multi-scale Jensen–Shannon neighbor embedding (Lee et al., 2015), which has been applied to recordings of movement sensors in order to visualize coordination patterns adapted by climbers. Initial clustering at the climb scale provides details linking behavioral patterns with climbing fluency/smoothness (i.e., the performance outcome). Further clustering on shorter time intervals, where individual actions within a climb are analyzed, enables more detailed exploratory data analysis of behavior. Results suggest that the nature of individual learning curves (the global, trial-to-trial performance) corresponded to certain behavioral patterns (the within trial motor behavior). We highlight and discuss three distinctive learning curves and their corresponding relationship to behavioral pattern emergence, namely: no improvement and a lack of new motor behavior emergence; sudden improvement and the emergence of new motor behaviors; and gradual improvement and a lack of new motor behavior emergence.     

Large-scale user modeling with recurrent neural networks for music discovery on multiple time scales

The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has the disadvantage that it relies on explicit ratings, which are often unavailable, and generally disregards the temporal nature of music consumption. On the other hand, item co-occurrence algorithms, such as the recently introduced word2vec-based recommenders, are typically left without an effective user representation. In this paper, we present a new approach to model users through recurrent neural networks by sequentially processing consumed items, represented by any type of embeddings and other context features. This way we obtain semantically rich user representations, which capture a user’s musical taste over time. Our experimental analysis on large-scale user data shows that our model can be used to predict future songs a user will likely listen to, both in the short and long term.

     

Microstructural investigation of quartz submitted to ultra-short shock loading

A high-energy pulsed laser was used to induce very short (2 ns) pressure pulses in quartz single crystals. The microstructure of recovered specimens was characterized by optical microscopy, scanning electron microscopy and transmission electron microscopy. Whatever the peak pressures (20–90 GPa), the shocked materials showed no shock defects (amorphous lamellae, Brazil twins, etc.). The microstructure was dominated by fracturing. The present study thus suggests that for very short pulse durations, quartz can be loaded at pressures well above the Hugoniot elastic limit without undergoing solid-state amorphization. The behaviour of quartz is purely elastic-brittle.     

Volumetric Vector-Based Representation for Indirect Illumination Caching

This paper introduces a caching technique based on a volumetric representation that captures low-frequency indirect illumination. This structure is intended for efficient storage and manipulation of illumination. It is based on a 3D grid that stores a fixed set of irradiance vectors. During preprocessing, this representation can be built using almost any existing global illumination software. During rendering, the indirect illumination within a voxel is interpolated from its associated irradiance vectors, and is used as additional local light sources. Compared with other techniques, the 3D vector-based representation of our technique offers increased robustness against local geometric variations of a scene. We thus demonstrate that it may be employed as an efficient and high-quality caching data structure for bidirectional rendering techniques such as particle tracing or photon mapping.     

Reliability of spike timing is a general property of spiking model neurons

The responses of neurons to time-varying injected currents are reproducible on a trial-by-trial basis in vitro, but when a constant current is injected, small variances in interspike intervals across trials add up, eventually leading to a high variance in spike timing. It is unclear whether this difference is due to the nature of the input currents or the intrinsic properties of the neurons. Neuron responses can fail to be reproducible in two ways: dynamical noise can accumulate over time and lead to a desynchronization over trials, or several stable responses can exist, depending on the initial condition. Here we show, through simulations and theoretical considerations, that for a general class of spiking neuron models, which includes, in particular, the leaky integrate-and-fire model as well as nonlinear spiking models, aperiodic currents, contrary to periodic currents, induce reproducible responses, which are stable under noise, change in initial conditions and deterministic perturbations of the input. We provide a theoretical explanation for aperiodic currents that cross the threshold.