Miracle-dz/UniFAR-FTUs huggingface dataset:
The Motivational Modulation of Proactive Control in a Modified Version of the Ax-continuous Performance Task: Evidence From Cue-based and Prime-based Preparation

Performance-contingent reward has repeatedly been shown to increase proactive control in simple response-priming paradigms like the AX continuous performance task (AX-CPT). Here, we aim to investigate whether this well-documented proactive shift under reward conditions is restricted to mere response preparation. To this end, the standard AX-CPT was modified in that the cue no longer allowed for response preparation only for rule preparation. In the first experiment, we showed that a sustained proactive shift under reward conditions as compared to a neutral control can still be found. Moreover, in a second experiment, the utility of the cueing information was reduced and turned into a priming information. This priming information was only useful but no longer necessary for task execution. The results of this second experiment show that reward expectation nevertheless promotes the usage of context information—here based on rule frequency and additional prime information. Taken together, the findings show that (1) the well-documented effect of increased proactive control under reward conditions is not restricted to mere response priming and that (2) the interaction of motivation and cognition does not depend on the distinction between cue and prime. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
Discriminative Features for Bird Species Classification

Bird species classification has received more and more attention in the field of computer vision, for its promising applications in biology and environmental studies. Although methods derived from basic-level classification are introduced to bird species classification, most of them couldn't get a satisfied result due to the absence of discriminative features and quantization errors. In this paper, we introduce discriminative features for bird species classification based on parts of birds. We first crop and align the images, obtaining some patches specifying the parts of a bird. The patches are collected, forming some codebooks to learn the intermediate-level features using sparse coding algorithm. We then learn a model which characterize the discrimination of each part of every species of birds. Finally, the learned features combined with the model are concatenated to form the final representation for training and classification. We show the effectiveness of the discriminative features on the CUB-200-2011 dataset.
Extraction of Hard Exudates using Functional Link Artificial Neural Networks

One of the major causes of vision loss is Diabetic Retinopathy (DR). Presence of Hard Exudates (HE) in retinal images is one of the prominent and most reliable symptoms of Diabetic Retinopathy. Thus, it is essential to clinically examine for HEs to perform an early diagnosis and monitoring of DR. In this paper, a classification-based approach using Functional Link Artificial Neural Network (FLANN) classifier to extract HEs in a retinal fundus image is illustrated. Luminosity Contrast Normalization pre-processing step was employed. Classification performances were compared between Multi-Layered Perceptron (MLP), Radial Basis Function (RBF) and FLANN classifiers. Better classification performance was observed for FLANN classifier. GUI package with Region of Interest (ROI) selection tool was developed.
Voice over IEEE 802.11b Capacity

In this paper, we study the VoIP capacity of IEEE 802.11b (DCF), i.e., the maximum number of simultaneous voice calls that can take place in a WLAN cell. This capacity is highly dependent on the chosen codec and on the distance from the access point (AP) to the users. Thus several codecs are studied, namely G711, GSM-EFR, and G723.1. All users are assumed to be at a fixed distance from the AP. The quality of voice calls is evaluated thanks to the E-model. Simulation results provide the following results: G711 allows up to 5 simultaneous calls, GSM-EFR up to 12, and G723.1 up to 18 for a voice quality greater than R=70 and a network delay of 20ms.
The Regular-Language Semantics of First-Order Idealized Algol

We explain how recent developments in game semantics can be applied to reasoning about equivalence of terms in a non-trivial fragment of Idealized Algol (IA) by expressing sets of complete plays as regular languages. Being derived directly from the fully abstract game semantics for IA, our model inherits its good theoretical properties; in fact, for first order IA taken as a stand-alone language the regular language model is fully abstract. The method is algorithmic and formal, which makes it suitable for automation. We show how reasoning is carried out using a meta-language of extended regular expressions, a language for which equivalence is decidable.
A 12 mW ADC delta-sigma modulator with 80 dB of dynamic range integrated in a single-chip Bluetooth transceiver

A 12 mW switched-capacitor (SC) multi-bit ADC delta-sigma modulator for baseband demodulation integrated in a single-chip Bluetooth radio-modem transceiver achieves 77 dB of SINAD and 80 dB of dynamic range over a 500 kHz bandwidth with a 32 MHz sample-rate. The 1 mm/sup 2/ circuit is implemented in a 0.35 /spl mu/m BiCMOS SOI process and operates from a 2.7 V supply.
An improved recurrent neural network language model with context vector features

Recurrent neural network language models have solved the problems of data sparseness and dimensionality disaster which exist in traditional N-gram models. RNNLMs have recently demonstrated state-of-the-art performance in speech recognition, machine translation and other tasks. In this paper, we improve the model performance by providing contextual word vectors in association with RNNLMs. This method can reinforce the ability of learning long-distance information using vectors training from Skip-gram model. The experimental results show that the proposed method can improve the perplexity performance significantly on Penn Treebank data. And we further apply the models to speech recognition task on the Wall Street Journal corpora, where we achieve obvious improvements in word-error-rate.
Synthesis of restricted complexity controllers for l 2 parametric stability margin maximization

In this paper the problem of restricted complexity stability margin maximization (RCSMM) for single-input single-output (SISO) plants affected by rank one real perturbations is considered. This problem amounts to maximizing the l2 parametric stability margin over an assigned class of restricted complexity controllers, which are described by rational transfer functions of fixed order with coefficients depending affinely on some free parameters. It is shown that the RCSMM problem, which in general admits local maxima, can be approached by means of convex optimization methods. Specifically, a lower bound of the stability margin, whose maximization can be accomplished via Linear Matrix Inequality (LMI) techniques, is developed and employed to devise an iterative procedure. Several examples are presented to illustrate the main features of the proposed approach.
A Neural Network Model for Explaining the Asymmetries between Linguistic Production and Linguistic Comprehension

Several kinds of empirical evidence point to the existence of an asymmetry between linguistic production and linguistic comprehension: in general, understanding words seems to be easier than producing them. In this contribution we propose a neural model of the relationships between the semantic and the lexical systems. Our model explains the asymmetry between language comprehension and production as an effect of the difference between the dimensions of the brain areas which process semantic and lexical information. In fact, the model’s performance in lexical recall is worse than the performance in semantic recall due to the fact that the semantic network is constituted of more computational units (neurons) then the lexical network.
A novel analytical characterization for short-term plasticity parameters in spiking neural networks

Short-term plasticity (STP) is a phenomenon that widely occurs in the neocortex with implications for learning and memory. Based on a widely used STP model, we develop an analytical characterization of the STP parameter space to determine the nature of each synapse (facilitating, depressing, or both) in a spiking neural network based on presynaptic firing rate and the corresponding STP parameters. We demonstrate consistency with previous work by leveraging the power of our characterization to replicate the functional volumes that are integral for the previous network stabilization results. We then use our characterization to predict the precise transitional point from the facilitating regime to the depressing regime in a simulated synapse, suggesting in vitro experiments to verify the underlying STP model. We conclude the work by integrating our characterization into a framework for finding suitable STP parameters for self-sustaining random, asynchronous activity in a prescribed recurrent spiking neural network. The systematic process resulting from our analytical characterization improves the success rate of finding the requisite parameters for such networks by three orders of magnitude over a random search.
Random CDMA in the multiple cell uplink environment: the effect of fading on various receivers

A simple multi-cell Rayleigh fading uplink communication model is suggested and analyzed for optimally coded randomly spread DS-CDMA with multiuser detection. The model adheres to Wyner's (1994) infinite linear cell-array setting, according to which only adjacent-cell interference is present, and characterized by a single parameter 0/spl les//spl alpha//spl les/1. The discussion is confined to asymptotic analysis where both the number of users per cell and the processing gain go to infinity, while their ratio goes to some finite constant. The spectral efficiency of various multiuser detection strategies is evaluated assuming single cell-site processing, and equal transmit powers for all users in all cells. Comparative results demonstrate how performance is affected by the introduction of inter-cell interference (with and without fading), and what is the penalty associated with the randomly spread coded DS-CDMA strategy.
A slice of hygiene: justification and consequence in the persuasiveness of prescriptive and proscriptive signs

Abstract Studies concerning sign effectiveness highlight the use of justifications and information about consequences of non-compliance. Assuming that the most persuasive messages contain both types of information, we compared the effectiveness of eight different signs (that encouraged supermarket shoppers to hygienically handle bread). The independent variables were (a) sign content (justification vs. consequence vs. justification and consequence vs. neutral) and (b) form of request (prescriptive vs. proscriptive). After assessing the subjective level of the signs’ persuasiveness through a survey (N = 240), we observed people’s compliance in a natural experiment (N = 1.440). Signs containing both a justification and information about consequences achieved the highest rating and actual compliance.
Parallel Style-Aware Image Cloning for Artworks

We present style-aware image cloning, a novel image editing approach for artworks, which allows users to seamlessly insert any photorealistic or artificial objects into an artwork to create a new image that shares the same artistic style with the original artwork. To this end, a real-time image transfer algorithm is developed to stylize the cloned object according to a distance metric based on the artistic styles and semantic information. Several interactive functions, such as layering, shadowing, semantic labeling, and direction field editing, are provided to enhance the harmonization of the composite image. Extensive experimental results demonstrate the effectiveness of our method.
MAC-BC duality with linear-feedback schemes

We show that for the multi-antenna Gaussian MAC and BC with perfect feedback, the largest achievable regions with linear-feedback schemes (called linear-feedback capacity regions) coincide when the same total input-power constraint is imposed on both channels and when the MAC channel matrices are the transposes of the BC channel matrices. In the two-user case, when either transmitters or receiver are single-antenna, the capacity region for the Gaussian MAC is known and the capacity-achieving scheme is a linear-feedback scheme. With our results we can thus determine the linear-feedback capacity region of the two-user Gaussian BC when either transmitter or receivers are single-antenna. For these cases we can also identify the linear-feedback schemes that achieve the linear-feedback capacity. Our results also extend to a partial-feedback setup where only a subset of the feedback links are present.
A radix-10 digit recurrence division unit with a constant digit selection function

Decimal floating point operations are important for applications that cannot tolerate errors from conversions between binary and decimal formats, for instance, scientific, commercial, and financial applications. In this work we present a radix-10 digit recurrence division algorithm that decomposes the quotient digits into three parts and requires only the computation of five and two times the divisor. Moreover, the divisor's multiples are selected without multiplexers and the digit selection functions are independent of the divisor's value and do not require a lookup table. The algorithm has been synthesized and verified on a Xilinx Virtex-5 FPGA and implementation results are given.
Background Subtraction via Superpixel-Based Online Matrix Decomposition with Structured Foreground Constraints

Background subtraction process plays a very essential role for various computer vision tasks. The process becomes more critical when the input scene contains variation of pixels such as swaying trees, rippling of water, illumination variations, etc. Recent methods of matrix decomposition into low-rank (e.g., corresponds to the background) and sparse (e.g., constitutes the moving objects) components such as Robust Principal Component Analysis (RPCA), have been shown to be very efficient framework for background subtraction. However, when the size of the input data grows and due to the lack of sparsity-constraints, these methods cannot cope with the real-time challenges and always show a weak performance due to the erroneous foreground regions. In order to address the above mentioned issues, this paper presents a superpixel-based matrix decomposition method together with maximum norm (max-norm) regularizations and structured sparsity constraints. The low-rank component estimated from each homogeneous region is more perfect, reliable, and efficient, since each superpixel provides different characteristics with a reduced value of rank. Online max-norm based matrix decomposition is employed on each segmented superpixel to separate the low rank and initial outliers support. And then, the structured sparsity constraints such as the generalized fussed lasso (GFL) are adopted for exploiting structural information continuously as the foreground pixels are both spatially connected and sparse. We propose an online single unified optimization framework for detecting foreground and learning the background model simultaneously. Rigorous experimental evaluations on challenging datasets demonstrate the superior performance of the proposed scheme in terms of both accuracy and computational time.
Consumption of red meat, white meat and processed meat in Irish adults in relation to dietary quality

The aim of the present study was to examine the association of red meat, white meat and processed meat consumption in Irish adults with dietary quality. A cross-sectional study of subjects, randomly selected using the electoral register, estimated habitual food intakes using a 7 d food diary in a nationally representative sample of 662 men and 717 women (not pregnant or lactating) aged 18–64 years. Consumers were classified into thirds, based on the distribution of mean daily intakes for red meat, white meat and processed meat. The mean intakes of red meat, white meat and processed meat were 51, 33 and 26 g/d respectively, and men consumed significantly more (P<0·001) than women for all meat types. In men, red meat consumption was associated with lower (P<0·001) prevalence of inadequacy for Zn, riboflavin and vitamin C intakes. Increasing processed meat intake was associated with a lower (P<0·01) level of compliance with dietary recommendations for fat, carbohydrate and fibre in men. Increasing processed meat consumption was associated with lower (P<0·01) wholemeal bread, vegetables, fruit and fish intakes in men and women. Managerial occupations were associated with lower processed meat intakes. It is important to distinguish between meat groups, as there was a large variation between the dietary quality in consumers of red meat, white meat and processed meat. Processed meat consumption is negatively associated with dietary quality and might therefore be a dietary indicator of poor dietary quality. This has important implications in nutritional epidemiological studies and for the development of food-based dietary guidelines.
Performance of a multi-agent simulation on a distributed blackboard system

Research into multi-agent systems has increased over several years, leading to demand for multi-agent system simulators to investigate the interaction between different agents and their environment. The more complex and intelligent the agents become, the more difficult it is to simulate them. Distributing the simulation across different processors would improve its performance. This paper describes the implementation and the performance of a multiagent test-bed called TileWorld on a distributed blackboard system, DARBS. The performance of the TileWorld simulation on a distributed blackboard system running on single processor and on multi-processors is also investigated.
CARNEGIE MELLON Department of Electrical and Computer Engineering ~ Iteration Mapping : Loop Software Pipelining on an XIMD

The multiple instruction streams, low synchronization cost and synchronous nature of the XIMD (variable instruction stream, multiple data stream) architecture create an opportunity for a new architecture-compiler interface. As an extension to the VLIW (Very Long Instruction Word) architecture, the XIMD can exploit all VLIW scheduling techniques but these do not take full advantage of the unique features of the XIMD. A new loop scheduling method for the XIMD, Iteration Mapping, is proposed that can exceed the performance of VLIW loop scheduling techniques on an XIMD. The medium-grained parallelism between loop iterations has been selected as the target for the first XIMD compiler implementation. This paper discusses how the relationship between the characteristics of loops and the architecture affect scheduling, presents the Iteration Mapping scheduling technique, presents performance data for loops scheduled using this new technique and compares it with recent results on software pipelining for the VLIW, and demonstrates the applicability of the technique for loop-intensive code. The weighted harmonic mean of the speedup for 4 functional units on the first fourteen Livermore Loops is currently 3.5, and the weighted harmonic mean of the speedup for selected loops that Iteration Mapping is particularly well suited for on 16 functional units is 14.5, more than 90% of linear speedup. The concepts behind Iteration Mapping are clean and understandable, leading to a compiler that is straight-forward, fast, and easy to implement.
Improving the parsing of French coordination through annotation standards and targeted features

In the present study we explore various methods for improving the transition-based parsing of coordinated structures in French. Features targeting syntactic parallelism in coordinated structures are used as additional features when training the statistical model, but also as an efficient means to find and correct annotation errors in training corpora. In terms of annotation, we compare four different annotations for coordinated structures, demonstrate the importance of globally unambiguous annotation for punctuation, and discuss the decision process of a transition-based parser for coordination, explaining why certain annotations consistently out-perform others. We compare the gains provided by different annotation standards, by targeted features, and by using a wider beam. Our best configuration gives a 37.28% reduction in the coordination error rate, when compared to the baseline SPMRL test corpus for French after manual corrections.
Method engineering process patterns

Method Engineering (ME) is based on the notion that no two software development projects are identical, and hence, the same development process is never executed twice. The need therefore arises for custom-built software development methodologies that are tailored to fit the specific project situation at hand, thus giving rise to the various Situational Method Engineering (SME) approaches currently in use. A process pattern is a description of a recurring development process fragment that can be used as a building block for engineering development processes. Software development process patterns are extensively used in SME, mainly as process components assembled together to form bespoke methodologies; however, the SME field itself has not been scrutinized as to the methodology development process fragments frequently encountered. Situational method engineering knowledge captured in the form of SME process patterns is not only useful for building and improving SME processes, but it also facilitates knowledge transfer among method engineers. We propose a set of SME process patterns obtained through studying existing method engineering approaches. The set of patterns has been used for developing http://www2.sheridanprinting. com/acm/isec/isec.cfm isec a generic pattern-based framework for SME. The framework can be used for developing SME processes according to the specific requirements of method engineering projects. The resulting process can then be enacted to yield custom-built, project-specific software development methodologies.
An Enhanced Sensor Scheduling Protocol for Wireless Sensor Networks

An important design challenge in wireless sensor network application development is to provide appropriate middleware service protocols to control the energy consumption according to specific application scenarios. In common application scenarios such as in surveillance systems, it is usually necessary to extend the system monitoring area as large as possible to cover the maximal area. The two issues of power conservation and maximizing the coverage area have to be considered together with both the sensors' communication connectivity and their power management strategy. In this work, we proposed an enhanced sensor scheduling protocol to address the application scenario of typical surveillance systems. Our protocol takes into consideration of both power conservation and coverage ratio to search for the balance between the different requirements. We proposed both centralized and de-centralized versions of sensor scheduling protocol, and compared the performance of different protocols using several metrics. The results provide evidence of the advantages of the proposed protocol compared with existing sensor scheduling protocols.