Miracle-dz/UniFAR-FTUs huggingface dataset:
A modified adaptive exponential integrate and fire neuron model for circuit implementation of spiking neural networks

Nowadays neuroscience is progressing to higher levels which have made it possible to have a better understanding of the brain behavior. In this scheme, spiking neural network has a great potential and have attracted much research interests. In this direction, one problem in simulations and implementations is speed and simplicity. In other hand, neuron models as building blocks of the neuronal systems have a vital role. One of the recently developed neuron models is called “Adaptive Exponential Integrate and Fire”. This model is a two dimensional system that can produce rich firing pattern. This paper proposes simplified models based on the Adaptive Exponential Integrate and Fire model. This modification simplifies the hardware implementation, increases speed and demonstrates similar dynamic behavior. These models can be used for both of analog and digital implementations. This paper can be a step in the neural network simulation and implementation as large as the brain scale.
Workplace Job Satisfaction in Britain: Evidence from Linked Employer-Employee Data

This paper examines the determinants of job satisfaction in Britain using nationally representative linked employer-employee data (WERS2004) and alternative econometric techniques. It uses eight facets of job satisfaction for the purpose. As well as underscoring the importance of accounting for unobserved workplace heterogeneity, the paper is able to highlight some new findings that relate to differential effects of dependent children and other dependents, type of employment contract and gaps between employees' skill and skills requirements of their job. Working long hours is found to be positively associated with intrinsic aspect of jobs. Public sector employment is positively associated with all facets of job satisfaction except satisfaction with pay.
A Qualitative Description of the Effect of Single Queues on Bin Counts

A large number of research articles are dedicated to queuing theory and queuing systems. Most of these articles employ a continuous representation of network traffic, in the form of timestamps or interarrival times. In this, there is a contradiction with more recent traffic models capable of capturing the multi-fractal nature of network traffic e.g. the Conservative Cascade model. These models often represent packet streams in a discrete way by calculating the bin count vector. Directly describing the effect of queuing systems on the variance-time behavior of this discrete representation of traffic is relatively unexplored terrain. This paper presents and analyzes some qualitative results on the altering of a bin count vector when passing it through a basic queuing system. The scenario is also extended with the influence of correlated background traffic.
A model for generating Arabic text from semantic representation

Text Generation is a challenging task in Natural Language Processing (NLP). Its purpose is to generate grammatically correct text from machine representation source such as a knowledge base. One of the most recent semantic representation is Rich Semantic Graph (RSG). It exploits not only the semantic representation techniques but also the Language structure and writing styles. Our work is a part of an ongoing research to create an abstractive summary for a single input document in the Arabic Language. The abstractive summary is generated through three modules; converting the input Arabic text into an RSG, then performing Graph Reduction, and finally generating the summarized text from the reduced graph. This is achieved with the aid of a domain Ontology. In this paper, we are illustrating the architecture of the third module, which works on generating Arabic text from RSG using Ontology.
Near real-time electric load approximation in low voltage cables of smart grids with models@run.time

Micro-generations and future grid usages, such as charging of electric cars, raises major challenges to monitor the electric load in low-voltage cables. Due to the highly interconnected nature, real-time measurements are problematic, both economically and technically. This entails an overload risk in electricity networks when cables must be disconnected for maintenance reasons or are accidentally damaged. Therefore, it is of great interest for electricity grid providers to anticipate the load in networks and quicker detect failures. However, computing the electric load in cables requires computational intensive power flow calculations and live consumption measurements. Today's view of the grid is usually based on on-field documentation of cables, fuses, and measurements by technicians and therefore often outdated. Thus, the electric load is usually only simulated in case of major topology variations. However, live measurements of smart meters provide new opportunities. In this paper we present a novel approach for a near-time electric load approximation by deriving in live the current electric topology and cable loads from smart meter data. We leverage the models@run.time paradigm to combine live measurements with topology characteristics of the grid. Our approach enables to approximate the load in cables, not only for the current grid topology, but also to simulate topology changes for maintenance purposes. We showed that this allows a near real-time approximation while remaining very accurate (average deviation of 1.89% compared to offline power-flow calculation tools). Developed with a grid operator, this approach will be integrated in a monitoring and warning system and as an embeddable solution for on-field simulation.
Adaptive finite volume method for distributed non-smooth parameter identification

In this paper we develop a finite volume adaptive grid refinement method for the solution of distributed parameter estimation problems with almost discontinuous coefficients. We discuss discretization on locally refined grids, as well as optimization and refinement criteria. An OcTree data structure is utilized. We show that local refinement can significantly reduce the computational effort of solving the problem, and that the resulting reconstructions can significantly improve resolution, even for noisy data and diffusive forward problems.
Ambient occlusion fields

We present a novel real-time technique for computing inter-object ambient occlusion. For each occluding object, we precompute a field in the surrounding space that encodes an approximation of the occlusion caused by the object. This volumetric information is then used at run-time in a fragment program for quickly determining the shadow cast on the receiving objects. According to our results, both the computational and storage requirements are low enough for the technique to be directly applicable to computer games running on the current graphics hardware.
Getting good at small talk: student-directed learning of social conversation skills

Abstract For students with autism spectrum disorders (ASD), learning social skills is an intricate but essential task to facilitate social inclusion and participation. Limited research is available on social skills interventions for adolescents with ASD and intellectual disabilities (ID). This article presents a case study that illustrates how the Self-Determined Learning Model of Instruction was used to improve self-chosen social conversation skills in a Norwegian 14-year old student with ASD and mild ID. This case study illustrates that, also for students with ASD and ID, giving them the opportunity to set personally relevant goals may improve their autonomous motivation, thereby increasing the likelihood of goal attainment.
More efficient oblivious transfer and extensions for faster secure computation

Protocols for secure computation enable parties to compute a joint function on their private inputs without revealing anything but the result. A foundation for secure computation is oblivious transfer (OT), which traditionally requires expensive public key cryptography. A more efficient way to perform many OTs is to extend a small number of base OTs using OT extensions based on symmetric cryptography. In this work we present optimizations and efficient implementations of OT and OT extensions in the semi-honest model. We propose a novel OT protocol with security in the standard model and improve OT extensions with respect to communication complexity, computation complexity, and scalability. We also provide specific optimizations of OT extensions that are tailored to the secure computation protocols of Yao and Goldreich-Micali-Wigderson and reduce the communication complexity even further. We experimentally verify the efficiency gains of our protocols and optimizations. By applying our implementation to current secure computation frameworks, we can securely compute a Levenshtein distance circuit with 1.29 billion AND gates at a rate of 1.2 million AND gates per second. Moreover, we demonstrate the importance of correctly implementing OT within secure computation protocols by presenting an attack on the FastGC framework.
IFIND: A PEER-TO-PEER APPLICATION FOR REAL-TIME LOCATION MONITORING ON THE MIT CAMPUS

Understanding and quantifying human flows in the built environment has been a longstanding question for researchers across a multitude of disciplines. The difficulty in addressing this question often arises during the data collecting phase. We propose a method of collecting real-time, georeferenced user data that can address the privacy issues that have hindered previous research projects. Our system is based on the social networking application iFIND, a location-based service that enables peer location exchange. iFIND relies on a device-centric positioning system and an authorization server. To collect data for analysis, users may anonymously authorize a designated server as another ‘peer’ who may store limited logs of user activity. Data sent through this service is timestamped and geocoded, but anonymous. This represents an improvement over both the usage of raw logs, which lack flexibility in format, and existing qualitative methods, which generate smaller datasets that lack in spatial and temporal precision.
Review of Parametric Radio channel prediction schemes for MIMO system

To ensure reliable communication and improve performance of a wireless communication system, it is required for the transmitter and receiver to have fair knowledge about the channel state information (CSI). Analysis of the basic channel prediction schemes like Parametric Radio channel model, Autoregressive model based prediction and Bandlimited basis expansion is done in this paper. Subspace based channel estimation techniques used by the parametric radio channel model for channel estimation is discussed in detail and a comparison of the techniques is also given in this paper work.
Linear Mimo Receivers vs. Tree Search Detection: A Performance Comparison Overview

Next generation wireless systems use a combination of large system bandwidths and multiple antennas (MIMO) to deliver very high data rate services. The efficient demodulation of MIMO signals at the receiver can be a challenging task. In this paper, we study the gains achievable by using capacity-approaching tree search MIMO detection algorithms, for differently parameterized OFDM system setups and channel scenarios. Our results indicate that the high amount of diversity typically available in broadband MIMO-OFDM systems together with the use of low rate channel coding allows achieving very good performance using even simple linear MMSE detection. In iterative receiver approaches, the combination of initial linear MMSE detection and subsequent soft interference cancellation performs within 1 - 2 dB of the respective performance bound. The complexity involved in tree search detection techniques provides benefits mainly in very low-diversity environments, or if high rate channel coding is used together with higher order modulation
Building a free, general-domain paraphrase database for Japanese

Previous works have used parallel corpora and alignment techniques from phrase-based statistical machine translation to extract and generate paraphrases. In Japanese, paraphrases for a number of paraphrase categories or domains have been extracted by this method. However, most of these resources focus on a particular phenomenon in Japanese, and there are still no Japanese paraphrase resources that cover all varieties of phrases from several domains, and are freely available. In addition, because Japanese and English vary in grammar and word ordering, we perform syntax-based preprocessing to reduce this mismatch and extract paraphrases similar in quality to those extracted using more similar language pairs. The data used in creating the Japanese paraphrases is either in the public domain, or available under the Creative Commons license, and spans a variety of genres for wide coverage.
Applications of MIMO techniques to sensing of cardiopulmonary activity

Remote monitoring of cardiopulmonary activity based on Doppler shifts in radio signals shows promise in medical and security applications, however the problems of motion artifacts and presence of multiple subjects limit the usefulness of this technique. By applying MIMO signal processing, it is possible to overcome limitations of current systems and isolate signals from multiple sources.
Designed nonlocal pseudopotentials for enhanced transferability

A pseudopotential generation method is presented which significantly improves transferability. The method exploits flexibility contained in the separable Kleinman-Bylander form of the nonlocal pseudopotential @Phys. Rev. Lett. 48, 1425 ~1982! #. By adjusting the functional form of the local potential, we are able to improve the agreement with all-electron calculations. Results are presented for the H, Si, Ca, Zr, and Pb atomic pseudopotentials. Configuration testing, logarithmic derivatives, chemical hardness, and structural tests all confirm the accuracy of these pseudopotentials. @S0163-1829~99! 14715-5#
Design, construction and modelling of a low cost miniature UAV using machine vision

The price barrier for the implementation of UAVs commercially is substantial, limiting growth in this area. This paper presents results from a project aimed at developing light-weight and low-cost systems for the on-board control of a small scale commercially available helicopter, targeted at the low-end of the market. This would allow UAVs to be used by relatively untrained operators. The control system consists of two major streams, a vision system for high-level control and a low-level attitude control system. The vision system has been developed using a combination of a blackfun processor and an inexpensive cmos camera to implement colour segmentation algorithms. The attitude control system is developed using Fleck wireless sensor nodes developed at CSIRO. These inexpensive nodes perform sensing and control of the helicopter for data acquisition purposes. Flight data has been used to identify and verify a dynamic model of the helicopter.
Noise cancelling in wideband CMOS LNAs

A noise-cancelling technique in a wideband LNA achieves low noise figure (NF) and source impedance matching without global feedback. The 0.25 μm LNA provides <2.4 dB NF from 0.01-2 GHz, total voltage gain is 13.7 dB, -3 dB bandwidth is 0.01-1.6 GHz, S/sub 12/ is <-36 dB, and S/sub 11/ is <-10 dB. IIP2 is 12 dBm, and IIP3 is 0 dBm drawing 14 mA at 2.5 V.
Specific Binding of the Pathogenic Prion Isoform: Development and Characterization of a Humanized Single-Chain Variable Antibody Fragment

Murine monoclonal antibody V5B2 which specifically recognizes the pathogenic form of the prion protein represents a potentially valuable tool in diagnostics or therapy of prion diseases. As murine antibodies elicit immune response in human, only modified forms can be used for therapeutic applications. We humanized a single-chain V5B2 antibody using variable domain resurfacing approach guided by computer modelling. Design based on sequence alignments and computer modelling resulted in a humanized version bearing 13 mutations compared to initial murine scFv. The humanized scFv was expressed in a dedicated bacterial system and purified by metal-affinity chromatography. Unaltered binding affinity to the original antigen was demonstrated by ELISA and maintained binding specificity was proved by Western blotting and immunohistochemistry. Since monoclonal antibodies against prion protein can antagonize prion propagation, humanized scFv specific for the pathogenic form of the prion protein might become a potential therapeutic reagent.
I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e. , whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e. , discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient.
A Novel Public Key Encryption Scheme for Large Image

In this paper, a novel public key encryption scheme for large image is presented based on elliptic curve. As a public key encryption scheme, it does not need to exchange and distribute secret keys. Based on elliptic curve discrete logarithm problem (ECDLP), the scheme has high security. The simulation results show that the presented scheme is computationally less complex than ECC (Elliptic Curve Cryptography) and suitable for large image encryption.
Classifier Building by Reduction of an Ensemble of Decision Trees to a Set of Rules

The paper presents a new approach for building classifiers by transforming an ensemble of classifiers into a single rule set. The proposed method improves generalization abilities of an ensemble with additional reduction of its complexity. It is dedicated to committees of decision trees and bases on transformation of a set of trees into a set of rules with a new, well-suited, weighed voting algorithm. The paper also presents experiments showing the properties and effectiveness of proposed method and direction of further research.
Reducing the Foreground Aperture Problem in Mixture of Gaussians Based Motion Detection

Separating the moving image parts from the static background is an important phase in video surveillance applications. The method based on mixture of Gaussians (MOG) is an often used and robust approach to learn the background automatically and adaptively. Known MOG methods often suffer from the phenomena called the foreground aperture problem, when parts of large moving homogenous regions become part of the background instead of being selected as moving pixels. This article introduces a new method to eliminate this problem.
Cross Talk between Metabotropic and Ionotropic Glutamate Receptor-Mediated Signaling in Parallel Fiber-Induced Inositol 1,4,5-Trisphosphate Production in Cerebellar Purkinje Cells

In many excitatory glutamatergic synapses, both ionotropic glutamate receptors (iGluRs) and metabotropic glutamate receptors (mGluRs) are closely distributed on the postsynaptic membrane. However, the functional significance of the close distribution of the two types of glutamate receptors has not been fully clarified. In this study, we examined the functional interaction between iGluR and mGluR at parallel fiber (PF)→ Purkinje cell synapses in the generation of inositol 1,4,5-trisphosphate (IP3), a key second messenger that regulates many important cellular functions. We visualized local IP3 dynamics in Purkinje cells using the green fluorescent protein-tagged pleckstrin homology domain (GFP-PHD) as a fluorescent IP3 probe. Purkinje cells were transduced with Sindbis virus encoding GFP-PHD and imaged with a two-photon laser scanning microscope. Translocation of GFP-PHD from the plasma membrane to the cytoplasm attributable to an increase in IP3 concentration was observed on PF stimulation in fine dendrites of Purkinje cells. Surprisingly, this PF-induced IP3 production was blocked not only by the group I mGluR antagonist but also by the AMPA receptor (AMPAR) antagonist. The PF-induced IP3 production was blocked by either the inhibition of G-protein activation by GDP-βS or intracellular Ca2+ buffering by BAPTA. These results show that IP3 production is mediated cooperatively by group I mGluR and AMPAR through G-protein activation and Ca2+ influx at PF→ Purkinje cell synapses, identifying the robust cross talk between iGluR and mGluR for the generation of IP3 signals.
Transforming Cyclic Circuits Into Acyclic Equivalents

Designers and high-level synthesis tools can introduce unwanted cycles in digital circuits, and for certain combinational functions, cyclic circuits that are stable and do not hold state are the smallest or most natural representations. Cyclic combinational circuits have well-defined functional behavior yet wreak havoc with most logic synthesis and timing tools, which require combinational logic to be acyclic. As such, some sort of cycle-removal step is necessary to handle these circuits with existing tools. We present a two-stage algorithm for transforming a combinational cyclic circuit into an equivalent acyclic circuit. The first part quickly and exactly characterizes all combinational behavior of a cyclic circuit. It starts by applying input patterns to each input and examining the boundary between gates whose outputs are and are not defined to find additional input patterns that make the circuit behave combinationally. It produces sets of assignments to inputs that together cover all combinational behavior. This can be used to report errors, as an optimization aid, or to restructure the circuit into an acyclic equivalent. The second stage of our algorithm does this restructuring by creating an acyclic circuit fragment from each of these assignments and assembles these fragments into an acyclic circuit that reproduces all the combinational behavior of the original cyclic circuit. Experiments show that our algorithm runs in seconds on real-life cyclic circuits, making it useful in practice.