Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient simulation scheme for spiking neural networks

Nearly all neuronal information processing and inter¬neuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of "event-driven" simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we design, implement and evaluate critically an event-driven algorithm (EDLUT) that uses pre-calculated lookup tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly-connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend upon spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.
MULTI-OBJECTIVE OPTIMISATION TO SIMULTANEOUSLY ADDRESS ENERGY HUB SIZING AND SCHEDULING USING A LINEAR FORMULATION

Multi-energy systems are expected to play an important role in future global energy mix. Effective design and operation of such systems is crucial for the realisation of the benefits they promise. This paper details an optimisation problem dealing with the simultaneous element selection, sizing and optimal operation of an energy hub using multiple objectives and a linear formulation. The approach is applied on a UK commercial building as a case study. INTRODUCTION The ‘energy hub’ concept, introduced by Geidl and Andersson (2007), is a framework to model the interactions between multiple energy carriers, energy converters and storage technologies. There are two types of problems where the energy hub is applicable: the first one is the optimal dispatch of energy between demand and supply or between different energy hubs. The second type is the design optimisation. The latter could be further subdivided into problems dealing with the optimal layout of elements inside an energy hub out of a series of options (Geidl and Andersson, 2007) and problems dealing with the optimal sizing of all the components that have been selected for an energy hub (Sheikhi et al, 2012). The aim of this study is to combine all the aforementioned approaches into a single methodology that will simultaneously consider which elements should be place in the energy hub, what their capacity should be and how they should be operated.
The VeriFast program verifier

This note describes a separation-logic-based approach for the specification and verification of safety properties of pointer-manipulating imperative programs. The programmer may declare inductive datatypes and primitive recursive functions for specification. Verification proceeds by symbolic execution using an abstract representation of memory as a separation logic assertion. Folding or unfolding abstract predicate assertions is performed through explicit ghost statements. Lemma functions enable inductive proofs of memory representation equivalences and facts about the primitive recursive functions. An SMT solver is used to solve queries over data values; an algorithm is described that prevents non-termination of the SMT solver while enabling reduction of any ground term. Since no significant search is performed by either the verifier or the SMT solver, verification time is predictable and low. The VeriFast Program Verifier Bart Jacobs ∗ Frank Piessens Department of Computer Science, Katholieke Universiteit Leuven, Belgium {bart.jacobs,frank.piessens}@cs.kuleuven. be
Using a Learning Management System to Personalise Learning for Primary School Students.

This paper reports on one aspect of a descriptive multiple-case study which set out to explore the role of a learning management system (LMS) in personalising learning for students from the perspective of three teachers in one primary school in New Zealand. The intention was to provide insight into the role the LMS could play in classrooms when personalising learning. The research project involved gathering multiple sources of data from interviews and observations, and documentary information from the LMS. The findings suggest that the LMS has the potential to be a key part of a primary classroom environment when it is built on components that personalise learning. For the teachers in this project, one salient component of personalised learning involved ensuring learning was based on assessment for learning pedagogy and the use of the LMS as a tool to support learning. The findings highlighted the interconnected nature of personalised learning pedagogy, the LMS and classroom practice.
Distributed Data Mining Approach with Cloud Computing Architecture

The rapid development of TOT(Internet of Things) proposed new requirements to data mining system,since the traditional distributed data mining system has been unable to meet the data mining process of TOT.Based on the untrusted node of the cloud computing presents a new distributed data mining model that can achieve seamless access to distributed data mining cloud computing system to meet the needs of TOT.
Deterioration rate estimation of high speed train bogie unit based on copula function

To evaluate the mechanical wear degree of key component of high-speed train bogie, an approach using copula function to evaluate damping degeneration ratio of yaw damper is proposed in the paper. Vibration signals of a certain high-speed train are obtained under different degeneration ratios. The vibration signal is decomposed by wavelet packet filters first. The marginal distribution function of the signal is fitted by generalized Gaussian distribution. The joint probability distribution between degenerate signal and the normal signal is computed by Gaussian Copula function. The mean of joint probability density function is extracted as the feature and the degeneration ration of target signal is evaluated. The vibration signals of different parameter degeneration ratio of high-speed train bogie yaw damper are used to do experiments using the proposed method and compared with the real degeneration ratio. Experiment results shows the error is within negative 3 percentages to 3 percentages at the speed of 200km/h, which verifies the effectiveness of the proposed degeneration ratio evaluation method.
Extraction of arabic nested noun compounds based on a hybrid method of linguistic approach and statistical methods

Arabic noun compound are phrases that consist of two or more nouns. The key characteristic behind noun compounds lies on its frequent occurrences within the text. Therefore, extracting these noun compounds is essential for several domains of research such as Information Retrieval, Sentiment Analysis and Question Answering. Many research efforts have been proposed in terms of extracting Arabic noun compounds using linguistic approaches, statistical measures or a combination of both. Most of the existing methods have concentrated on the extraction of bi-gram or tri-gram noun compound. However, extracting 4-gram or 5gram noun compound or nested noun compound is challenging due to the difficulty of selecting an appropriate method with effective results. Multiple features have a significant impact on the effectiveness of extracting noun compound such as contextual information, unit-hood and term-hood. Thus, there is still room for improvement in terms of enhancing the effectiveness of nested noun compound extraction. Therefore, this study proposed a combination of linguistic approach and statistical measures in order to enhance the extraction of nested noun compound. Several preprocessing steps are involved including transformation, normalization, tokenization, and stemming. The linguistic approach that have been used in this study is Part-of-Speech tagging. In addition, a new linguistic pattern for named entities has been utilized using a list of Arabic named entities in order to enhance the linguistic approach in terms of noun compound recognition. The proposed statistical measures consists of NC-value, NTC-value and NLCvalue. The experimental results have demonstrated that NLC-value has outperformed NTC-value and NCvalue regarding to nested noun compound extraction by achieving 83%, 76%, 72% and 65% for bigram, trigram, 4-gram and 5-gram respectively.
Improved tone modeling by exploiting articulatory features for Mandarin speech recognition

Articulatory features,which represent the articulatory information,can help prosodic features to improve the performance of tone recognition.In this paper,a set of 19 pronunciation categories was given according to the pronunciation characteristics of initials and finals.Besides,19 articulatory tandem features,which are the posteriors of speech signal belonging to the 19 pronunciation categories,were obtained by hierarchical multilayer perceptron classifiers.Then these articulatory tandem features,as well as prosodic features,were used for tone modeling.Tone recognition experiments of three kinds of tone models indicate that about 5% absolute increase of accuracy can be achieved when using both articulatory features and prosodic features.When the proposed tone model is integrated into LVSCR(Large Vocabulary Continuous Speech Recognition) system,the character error rate is reduced significantly.
A simulator for Spark scheduler

Spark is a tool that is used for analyzing large amounts of data in parallel. Performing experiments on Spark is both time consuming and expensive. Furthermore, an application can be launched on Spark with several different configurations. Therefore, a simulator is required to simulate the functionality of Spark. This report presents a project conducted to create one such simulator. A design of the Spark simulator was developed, along with a working prototype. The simulator allows the user to create a representation of physical machines, components of spark and simulates the scheduling of applications on Spark. The simulator also supports several configurations. The project report also lists suggestions for future improvements.
Generic approach for graph-based description of dynamically reconfigurable architectures

Architectural adaptation is studied for handling adaptation in autonomic distributed systems. It is achieved by implementing a model-based approach for managing reconfiguration of dynamic architectures. Describing such architectures includes defining rules for describing both architectural styles and theirs reconfiguration mechanisms. Within this research context, the work presented in this paper is conducted using formal specification based on graphs and graph rewriting appropriately for tackling architectural adaptation problems. A graph-based general approach for describing architectures and handling their dynamic reconfiguration is introduced. Our approach is illustrated in the context of a distributed hierarchical application. The formal models that allow the generation of a graph grammar for dynamic architecture description and the automatic definition of transformation rules for achieving intern self-protecting during the adaptation are elaborated.
A bi-level design and operation optimization process applied to an energy centre

This paper presents the methods and results of a bi-level optimization process used to select and size the components of an energy centre as well as to determine their optimal operation. The optimization process used a single-objective optimization of operational control nested within a two-objective optimization of design parameters that minimized capital costs and carbon emissions. Different cases were examined in which the ‘credit’ assigned for the export of excess electricity to the grid was varied. Results are presented including the overall trade-off front, trends amongst design variables, operational schedules for a particular solution, and aggregated parameters like total energy supplied by each device. Constraints on roof area were critical at the design-level, and complex interactions between demand and supply were identified at the operational level. Comparison of the two cases highlights the impact of the export of excess power to the grid.
A Survey on Various Data Dissemination Approaches in Vehicular Ad Hoc Network

Objectives : The main objective of this work is to survey various techniques used in the field of data dissemination in Vehicular Ad hoc NETwork (VANET). Methods : There are different approaches used to improve data dissemination in vehicular ad-hoc network. Such approaches are context-aware information dissemination, Space-time network coding, geo-based Named data networking forwarding strategy, Route-based data pre-fetch model and etc. Findings : VANET has become more popular in the areas of vehicular transportation systems. Data dissemination which is the base of communication plays a major role in VANET system. Various data dissemination technologies were developed for avoiding the issues such as data routing, vehicle mobility and network security. This paper provides brief explanation about several data dissemination techniques and compares their performance based on the output parameters, merits and demerits. Comparison result shows that, the prefetching based data dissemination technique outperforms than other techniques. Application/Improvements: the result of this work shows that prefetching based data dissemination technique provides better result than the other dissemination techniques.
Synthèse de contraintes de conception à partir de réseaux de Petri temporels paramétrés

RESUME. Un systeme temps reel est decompose en composants appeles modules. L’objectif est de garantir des contraintes temporelles projetees sur ces modules sous la forme de contraintes parametrees. Pour cela un ensemble de contraintes de conception est calcule. Le modele sur lequel est base cette approche est celui des reseaux de Petri temporels parametres, traduits en formules de Logique Lineaire.
Autonomous rendezvous and docking technologies: status and prospects

In general, autonomous rendezvous and docking requires that two spacecraft start at a remote distance (i.e., out of sight of each other), come together into a common orbit, rendezvous, dock, and control the new combined spacecraft in both orbit and attitude. Doing this requires developing and testing a variety of new technologies including absolute and relative autonomous navigation, autonomous rendezvous and docking hardware and software (both sensors and actuators), and autonomous control of a "new" spacecraft with different mass and inertia properties than either of the two original spacecraft. While these are very workable technologies, they do require a significant change in mindset -- turning over control of thrusters and other actuators to an on-board computer. While there is substantial potential for cost savings, risk reduction, and new mission modes by use of these technologies, there is a very strong reticence to allowing operational spacecraft to control their own destiny, particularly in firing thrusters. This paper summarizes work at Microcosm and elsewhere in each of the above technologies. Autonomous navigation and absolute orbit control have been demonstrated on orbit. In conjunction with Michigan Aerospace, autonomous rendezvous and docking hardware and algorithms have been demonstrated in parabolic flights and zero-g simulations. Approaches have been proposed for more precise and robust autonomous navigation and autonomous on-orbit estimation of combined mass and inertia properties, leading to efficient orbit and attitude control of the combined spacecraft. Many of these technologies can be tested at low cost in parabolic flights, suborbital flights, and evaluation of data from existing or planned missions. Thus, a "coordinated attack" on the complete problem of fully autonomous rendezvous and docking is both feasible and potentially very low cost.
Allocation-oriented algorithm design with application to gpu computing

The wide data-parallelism of GPU processor design facilitates the execution of many concurrent, fine-grained tasks with unprecedented performance and efficiency. However, contemporary opinion regards GPU architecture as being poorly suited for many parallelizations that impose dynamic dependences between processing elements. This dissertation specifically attends to problems whose efficient solutions require cooperative allocation, i.e., their dependences stem from dynamic data placement within shared structures. The contribution of this thesis is a set of design patterns and reusable, tunable software primitives that foster the construction of cooperative, allocation-oriented parallelizations that are significantly faster, more efficient, more flexible, and more performance-portable than the state of the art. Whereas concurrent CPU programs typically leverage fine-grained atomic operations for coordinating data placement, we demonstrate the advantages of parallel prefix sum as a high performance, bulk-synchronous alternative for cooperative allocation. We construct very efficient algorithms for global and local prefix sum by employing flexible granularity coarsening techniques for properly balancing serial and parallel workloads. The resulting efficiency gains permit kernel fusion techniques whereby application-specific logic can be incorporated within our prefix sum constructs with little or no overhead. To demonstrate the viability of our methods, we construct cooperative GPU implementations for a variety of parallel list-processing primitives including reduction, prefix scan, duplicate removal, histogram, and reduce-by-key. We evaluate their performance across a wide spectrum of problem sizes, types, and target architectures. To achieve performance-portability, we present a policy-based autotuning design idiom in which we leave implementation decisions having opaque performance consequences unbound within the program text. Finally, we showcase high performance implementations for two archetypal problems in computer science: sorting and breadth-first search (BFS). We achieve excellent performance for both genres, demonstrating multiple factors of speedup over prior parallelizations for GPU and conventional multi-core platforms.
A 21-mW 8-b 125-MSample/s ADC in 0.09-mm2 0.13-μm CMOS

This paper presents an 8-b two-step subranging analog-to-digital (ADC) using interpolation, averaging, offset compensation, and pipelining techniques to accomplish an effective number of bits of 7.6 b at 125 MSample/s. The 0.13-μm CMOS ADC occupies 0.09 mm 2 and consumes 21 mW.
Algorithms for stochastic Galerkin projections: Solvers, basis adaptation and multiscale modeling and reduction

This dissertation focuses on facilitating the analysis of probabilistic models for physical systems. To that end, novel contributions are made to various aspects of the problem, namely, 1) development of efficient algorithms for solving stochastic system of equations governing the physical problem, 2) stochastic basis adaptation methods to compute the solution in reduced stochastic dimensional space and 3) stochastic upscaling methods to find coarse-scale models from the fine-scale stochastic solutions. In particular, algorithms are developed for stochastic systems that are governed by partial differential equations (PDEs) with random coefficients. Polynomial chaos-based stochastic Galerkin and stochastic collocation methods are employed for solving these equations. Solvers and preconditioners based on Gauss-Seidel and Jacobi algorithms are explored for solving system of linear equations arising from stochastic Galerkin discretization of PDEs with random input data. Gauss-Seidel and Jacobi algorithms are formulated such that the existing software is leveraged in the computational effort. These algorithms are also used to develop preconditioners to Krylov iterative methods. These solvers and preconditioners are tested by solving a steady state diffusion equation and a steady state advection-diffusion equation. Upon discretization, the former PDE results in a symmetric positive definite matrix on left-hand-side, whereas the latter results in a non-symmetric positive definite matrix. The stochastic systems face significant computational challenge due the curse of dimensionality as the solution often lives in very high dimensional space. This challenge is addressed in the present work by recognizing the low dimensional structure of many quantities of interest (QoI) even in problems that have been embedded, via parameterization, in very high-dimensional settings. A new method for the characterization of subspaces associated with low-dimensional QoI is presented here. The probability
Dynamic scheduling in grid systems

Grid computing is a relatively new distributed computing paradigm that is rapidly gaining importance. It offers a solution to the increasing demand of extensive computational and storage power, without requiring extraordinary investments in hardware infrastructure. However, in many cases the use of grids as such is insufficient without further optimizations, such as scheduling mechanisms for efficient assignment of tasks to available resources. This PhD research is dedicated to dynamic scheduling, which differs from static scheduling in that it allows for re-evaluation of already taken assignment decisions during task execution. Dynamically scheduled systems are more flexible, fault-tolerant and QoS-oriented, but also more complex and demanding than their static equivalents. Generic network-aware scheduling algorithms will be developed for grid systems with dynamically changing resource and task properties. Moreover, calibration and evaluation of the developed methods will be performed in a simulation environment and in several existing grid systems. Keywords— Grid computing, optimization, dynamic scheduling, QoS (Quality of Service), simulation, software development
Reliable stiffness reflection for telesurgery

At present, surgical master-slave systems lack any kind of force feedback. In many cases especially perception of the stiffness of tissue plays an essential role for diagnosis or intervention. This paper investigates methods to realize a reliable stiffness perception through a master-slave system. Two controllers, the classical Direct Force Feedback controller (DFF) and the Stiffness Reflecting Controller (SRC) are compared and experiments with linear springs and biological soft tissue are presented. Both controllers are non-passive, thus the stability of interactions with arbitrary passive environments cannot be guaranteed. Therefore, a pragmatic method is presented to avoid instability of the master-slave system during hard contacts, without compromising the quality of stiffness reflection for interactions with soft environments. The method consists of an observer which detects interactions with hard objects. Upon detection of a hard contact the force feedback is switched off and a rigid virtual wall is activated at the master site. This method guarantees the perception of the hard contact by the operator. The experiments demonstrate that the system remains stable for both soft and hard environments.