Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling Spiking Neural Activities of Brain with Object Oriented Petri nets

We propose object-oriented Petri nets for effective modelling of spiking concept of neurons. Brain is the central part of nervous system and neurons are one-membrane cells, which work concurrently to process information from sensory organs. Modelling neurons provides precise and exact ways of expression, which allow us to go beyond the insights that intuitive or commonsense reasoning alone, can yield. As the neural activities are modular and concurrent in nature we need models like Object Oriented Petri nets that are most suitable for modelling these activities. Petri nets (2) offer one means to perform some of the modelling, analysis and design of such a system in terms of its interacting components, concurrent processes and complex logical relationships. Further more analysis methods are needed for the evaluation of the produced neural networks in terms of its performance. The neural models created capture the spiking nature of the neurons and retain the essentials of the behavior to be modeled, while trying to simplify the description. The structural and behavioural properties of neuron were studied using neural Petri Nets (3). The neural net architecture was described with Timed Neural Petri nets (4) that considered various chemical changes in the neurons. However modelling the neural system by an ordinary Petri net was usually organized by analyzing the logic structure, i.e. process-oriented. Object oriented modeling changes this viewpoint. The main idea of object-oriented techniques is breaking down a system into individual objects; each with its own specified externally observable behavior. The actual details of the components are hidden from view of other designers who use the components. We find the activity of each neuron is identical and can be viewed as objects. Message transfer is used to describe the communication between objects. This paper focuses upon the description of a simplified spiking neural model using Object Oriented Petri nets.
A comparison of common spatial patterns with complex band power features in a four-class BCI experiment

We report on the offline analysis of four-class brain-computer interface (BCI) data recordings. Although the analysis is done within defined time windows (cue-based BCI), our goal is to work toward an approach which classifies on-going electroencephalogram (EEG) signals without the use of such windows (un-cued BCI). To that end, we provide some elements of that analysis related to timing issues that will become important as we pursue this goal in the future. A new set of features called complex band power (CBP) features which make explicit use of phase are introduced and are shown to produce good results. As reference methods we used traditional band power features and the method of common spatial patterns. We consider also for the first time in the context of a four-class problem the issue of variability of the features over time and how much data is required to give good classification results. We do this in a practical way where training data precedes testing data in time.
Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements

We study the following problem: recover a low-rank matrix from phaseless (magnitude-only) linear projections of each of its columns. In analogy with Robust PCA, we refer to this problem as "Phaseless PCA". It finds important applications in phaseless dynamic imaging, e.g., Fourier ptychographic imaging of live biological specimens. This work introduces the first provably correct solution, Alternating Minimization for Low-Rank Phase Retrieval (AltMinLowRaP), for this problem. Our guarantee for AltMinLowRaP shows that it can recover an $n \times q$ matrix of rank $r$ to $\epsilon$ accuracy using only about $nr^4 \log(1/\epsilon)$ measurements, as long as the matrix condition number is bounded by a numerical constant. This sample complexity is only $r^3$ times worse than the order-optimal value of $\max(n,q) r$. The only other assumption needed for our result is incoherence of the right singular vectors of the matrix. We demonstrate the practical advantage of AltMinLowRaP over existing work via extensive simulation, and some real-data, experiments. We also briefly study the dynamic extension of the above problem.
Research on Modified Artificial Bee Colony Clustering Algorithm

In order to overcome the disadvantages of the KMeans Clustering algorithm, such as the poor global search ability, being sensitive to initial cluster centric, as well as the vulnerable to trap in local optima and the slow convergence velocity in later period of the original Artificial Bee Colony (ABC) algorithm, a Modified ABC algorithm was proposed. Modified Artificial Bee Colony algorithm combined with K-means Clustering algorithm, named it as MABC-K-means algorithm, to establish Hybrid algorithm for solving framework. Through extensive testing, the MABC-K-means algorithm can improve cluster performance effectively. Finally, according to optimization solution strategy, instantiate Customer Relationship Management issue in the process of instantiating framework.
The Importance of Attribute Interactions in Conjoint Choice Design and Modeling

Within the context of choice experimental designs, most authors have proposed designs for the multinomial logit model under the assumption that only the main effects matter. Very little attention has been paid to designs for the attribute interaction models. In this paper, we present Bayesian D-optimal interaction-effects designs for the multinomial logit models and compare their predictive performances with those of main-effects designs. We show that in situations where a researcher is not sure whether or not the attribute interaction effects are present, incorporating interaction effects into both design stage and model estimation stage is most robust against misspecification of the underlying model for making precise predictions.
Matching points in poor edge information images

Pattern matching and image patches correspondence have been described in several papers. However, in most cases the results are obtained using images with high level of detail, in other words, images with useful edge information. This paper describes a method to find correspondences between images with very poor edge information -for instance a painting with a cloudless sky- and its application to reflectographic images mosaicing. Unlike other pattern matching techniques, the proposed method solves the issue of low levels of detail or lack of good information, both needed for the determination of the correspondences used by common likeness measure (cross-norm correlation), features correspondence (Harris, SUSAN) and object recognition methods. Thus, intensity-augmented ordinal measure method is used: a window-based noise-robust method to calculate matching points. This method is improved with our Point Structure Selection (PSS) procedure to select good correspondences and reject false positives.
Multijunction Fault-Tolerance Architecture for Nanoscale Crossbar Memories

Nanoscale elements are fabricated using bottom-up processes, and as such are prone to high levels of defects. Therefore, fault-tolerance is crucial for the realization of practical nanoscale devices. In this paper, we investigate a fault-tolerance scheme that utilizes redundancies in the rows and columns of a nanoscale crossbar molecular switch memory array. In particular, we explore the performance tradeoffs of time delay, power, and reliability for different amounts of redundancies. The results indicate an increase in fault-tolerance with small increases in delay and area utility.
Breast Cancer Prevalence and Mortality among Hispanic Subgroups in the United States, 2009–2013

Background. This paper presents data on breast cancer prevalence and mortality among US Hispanics and Hispanic subgroups, including Cuban, Mexican, Puerto Rican, Central American, and South American. Methods. Five-year average annual female breast cancer prevalence and mortality rates for 2009–2013 were examined using data from the National Health Interview Survey (prevalence) and the National Center for Health Statistics and the American Community Survey (mortality rates). Results. Overall breast cancer prevalence among US Hispanic women was 1.03%. Although the estimates varied slightly by Hispanic subgroup, these differences were not statistically significant. The breast cancer mortality rate for Hispanics overall was 17.71 per 100,000 women. Higher rates were observed among Cubans (17.89), Mexicans (18.78), and Puerto Ricans (19.04), and a lower rate was observed among Central and South Americans (10.15). With the exception of the rate for Cubans, all Hispanic subgroup rates were statistically significantly different from the overall Hispanic rate. Additionally, all Hispanic subgroups rates were statistically significantly higher than the Central and South American rate. Conclusion. The data reveal significant differences in mortality across Hispanic subgroups. These data enable public health officials to develop targeted interventions to help lower breast cancer mortality among the highest risk populations.
Midlife Caregiving & Employment: an Analysis of Adjustments in Work Hours and Informal Care for Female Employees in Europe

This study examines eldercare in private households and the employment behaviour of female caregivers in Europe. Based on the first three waves of the European Community Household Panel we estimate probit-models to analyse the probability of caregiving and we use a simplified difference-in-difference approach to explain the correlation between changes in caregiving behaviour and changes in working hours. We restrict our sample to middle-aged women in 12 EU-countries. In order to control for country-effects we include country dummies in our models. In addition, we run separate estimations for northern European countries on the one hand and southern European countries on the other hand. We find a significant negative association between starting or increasing informal caregiving and the change in weekly work hours. No such association emerges for women terminating a caregiving spell or reducing care hours.
Molecular characterization of an acetylcholinesterase implicated in the regulation of glucose scavenging by the parasite Schistosoma

Acetylcholinesterase (AChE) present on the surface of the trematode blood fluke Schistosoma has been implicated in the regulation of glucose scavenging from the host blood. Determination of the molecular structure and functional characteristics of this molecule is a crucial first step in understanding the novel function for AChE and in evaluating the potential of schistosome AChE as a target of new parasite control methods. We have determined the primary structure of acetylcholinesterase from Schistosoma haematobium. Immunolocalization studies confirmed that the enzyme was present on the parasite surface as well as in the muscle. The derived amino acid sequence possesses features common to acetylcholinesterases: the catalytic triad, six cysteines that form three intramolecular disulphide bonds, and aromatic residues lining the catalytic gorge. An unusual feature is that the fully processed native enzyme exists as a glycoinositol phospholipid (GPI)‐anchored dimer, but the sequence of the C‐terminus does not conform to the current consensus for GPI modification. The enzyme expressed in Xenopus oocytes showed conventional substrate specificity and sensitivity to established inhibitors of AChE, although it is relatively insensitive to the peripheral site inhibitor propidium iodide. Distinctions between host and parasite AChEs will allow the rational design of schistosome‐specific drugs and vaccines.
New inequalities for finite and infinite group problems from approximate lifting

In this paper, we derive new families of facet‐defining inequalities for the finite group problem and extreme inequalities for the infinite group problem using approximate lifting. The new valid inequalities for the finite group problem include two‐ and three‐slope facet‐defining inequalities as well as the first family of four‐slope facet‐defining inequalities. The new valid inequalities for the infinite group problem include families of two‐ and three‐slope extreme inequalities. These new inequalities not only illustrate the diversity of strong inequalities for the finite and infinite group problems, but also provide a large variety of new cutting planes for solving integer and mixed‐integer programming problems. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008
Approximate compressor based multiplier design methodology for error-resilient digital signal processing

Multiplier is a fundamental component for digital signal processing (DSP) applications and takes up the most part of the resource utilization, namely power and area. Approximate circuitry architectures have been studied as innovative paradigm for reducing resource utilization for DSP systems. In this paper, the 4:2 compressor based approximate multiplier architecture which uses both truncation and approximation of compressor is studied. A greedy selection algorithm is then proposed to identify the Pareto frontier to give the optimal accuracy-power tradeoff. A finite impulse response (FIR) filter is used as an assessment. The architecture proposed in this paper has achieved up to 21.03% and 27.72% saving on power and area for FIR filter case compared to conventional multiplier designs with a decrease of 0.3dB in output SNR.
Seasonality, Rather than Nutrient Addition or Vegetation Types, Influenced Short-Term Temperature Sensitivity of Soil Organic Carbon Decomposition

The response of microbial respiration from soil organic carbon (SOC) decomposition to environmental changes plays a key role in predicting future trends of atmospheric CO2 concentration. However, it remains uncertain whether there is a universal trend in the response of microbial respiration to increased temperature and nutrient addition among different vegetation types. In this study, soils were sampled in spring, summer, autumn and winter from five dominant vegetation types, including pine, larch and birch forest, shrubland, and grassland, in the Saihanba area of northern China. Soil samples from each season were incubated at 1, 10, and 20°C for 5 to 7 days. Nitrogen (N; 0.035 mM as NH4NO3) and phosphorus (P; 0.03 mM as P2O5) were added to soil samples, and the responses of soil microbial respiration to increased temperature and nutrient addition were determined. We found a universal trend that soil microbial respiration increased with increased temperature regardless of sampling season or vegetation type. The temperature sensitivity (indicated by Q10, the increase in respiration rate with a 10°C increase in temperature) of microbial respiration was higher in spring and autumn than in summer and winter, irrespective of vegetation type. The Q10 was significantly positively correlated with microbial biomass and the fungal: bacterial ratio. Microbial respiration (or Q10) did not significantly respond to N or P addition. Our results suggest that short-term nutrient input might not change the SOC decomposition rate or its temperature sensitivity, whereas increased temperature might significantly enhance SOC decomposition in spring and autumn, compared with winter and summer.
Real Options Pricing and Organizations: The Contingent Risks of Extended Theoretical Domains

As options models move from financial markets to corporate decision making, it is fitting to question the simple domain extension of option pricing theory by considering behavioral decision-making biases. We suggest, however, that, given the negative evolutionary consequences of ignoring optionlike investments, organizations invent heuristic rules to counter these biases. We propose the idea of a domain translation that shows how the basic insight of option pricing can be preserved through evolving complementary organizational rules.
Rethinking Privacy In social Networks: A Case Study Of Beacon

Popular online social network sites (SNS) such as Facebook and Bebo are technological platforms that are posing questions about personal privacy. This paper contributes to our understanding of the nature and form of online privacy by critically analysing the issues surrounding the failed launch of Facebook’s advertising tool Beacon. Beacon is an interesting case study because it highlighted the complexity of information ownership in an online social network. Qualitative data was gathered from 29 weblogs (blogs) representing user opinions published between 6 November 2007(when Beacon was launched) and 28 February 2008 (when commentary had dwindled). A thematic analysis of the blogs suggest that concerns such as commercialism, terms of service (TOS), lack of user control, lack of user awareness and data protection are factors that influence user perceptions of information ownership as a subset of online privacy.
Slice based metrics for estimating cohesion

Previous work has shown that a relationship exists between cohesion and the slice profile of a program. The authors explore the use of several metrics based on such slice profiles to give a quantitative estimate of the level of cohesion in a module. Example modules are used to analyze the behavior of the metric values as the modules are modified such that the cohesion is changed. The sensitivity of the metrics to the various types of changes is discussed. <<ETX>>
The FOMC in 1980: a year of reserve targeting

N October 6, 1979, the Federal Reserve announced the beginning of a new approach to the implementation of monetary policy it would attempt to achieve better control of the growth of the monetary aggregates by “placing greater emphasis in day-to-day operations on the supply of hank reserves and less emphasis on confining short-term fluctnations in the Federal funds rate. ”’ A reason for adopting such a strategy was to “assure better control over the expansion of money anti bank credit. ”L The 1980 calendar year was the first full year of nmnetarv policy under the new procedure of reserve targeting.
Exploiting sensorimotor stochasticity for learning control of variable impedance actuators

Novel anthropomorphic robotic systems increasingly employ variable impedance actuation in order to achieve robustness to uncertainty, superior agility and efficiency that are hallmarks of biological systems. Controlling and modulating impedance profiles such that it is optimally tuned to the controlled plant is crucial to realise these benefits. In this work, we propose a methodology to generate optimal control commands for variable impedance actuators under a prescribed trade-off of task accuracy and energy cost. In contrast to classical optimal control methods that typically require an accurate analytical plant dynamics model, we employ a supervised learning paradigm to acquire both the process dynamics as well as the stochastic properties. This enables us to prescribe an optimal impedance and command profile (i) tuned to the hard-to-model stochastic characteristics of a plant and (ii) adapt to the systematic changes such as a change in load.
Stochastic Vector Quantisers

In this paper a stochastic generalisation of the standard Linde-Buzo-Gray (LBG) approach to vector quantiser (VQ) design is presented, in which the encoder is implemented as the sampling of a vector of code indices from a probability distribution derived from the input vector, and the decoder is implemented as a superposition of reconstruction vectors, and the stochastic VQ is optimised using a minimum mean Euclidean reconstruction distortion criterion, as in the LBG case. Numerical simulations are used to demonstrate how this leads to self-organisation of the stochastic VQ, where different stochastically sampled code indices become associated with different input subspaces. This property may be used to automate the process of splitting high-dimensional input vectors into low-dimensional blocks before encoding them.
Reconstruction from image correspondences

A single picture provides a largely incomplete representation of the scene one is looking at. Usually it reproduces only a limited spatial portion of the scene according to the standpoint and the viewing angle, besides it contains only instantaneous information. Thus very little can be understood on the geometrical structure of the scene, the position and orientation of the observer with respect to it remaining also hard to guess. When multiple views, taken from different positions in space and time, observe the same scene, then a much deeper knowledge is potentially achievable. Understanding inter-views relations enables construction of a collective representation by fusing the information contained in every single image. Visual reconstruction methods confront with the formidable, and still unanswered, challenge of delivering a comprehensive representation of structure, motion and appearance of a scene from visual information. Multi-view visual reconstruction deals with the inference of relations among multiple views and the exploitation of revealed connections to attain the best possible representation. This thesis investigates novel methods and applications in the field of visual reconstruction from multiple views. Three main threads of research have been pursued: dense geometric reconstruction, camera pose reconstruction, sparse geometric reconstruction of deformable surfaces. Dense geometric reconstruction aims at delivering the appearance of a scene at every single point. The construction of a large panoramic image from a set of traditional pictures has been extensively studied in the context of image mosaicing techniques. An original algorithm for sequential registration suitable for real-time applications has been conceived. The integration of the algorithm into a visual surveillance system has lead to robust and efficient motion detection with Pan-Tilt-Zoom cameras. Moreover, an evaluation methodology for quantitatively assessing and comparing image mosaicing algorithms has been devised and made available to the community. Camera pose reconstruction deals with the recovery of the camera trajectory across an image sequence. A novel mosaic-based pose reconstruction algorithm has been conceived that exploit image-mosaics and traditional pose estimation algorithms to deliver more accurate estimates. An innovative markerless vision-based human-machine interface has also been proposed, so as to allow a user to interact with a gaming applications by moving a hand held consumer grade camera in unstructured environments. Finally, sparse geometric reconstruction refers to the computation of the coarse geometry of an object at few preset points. In this thesis, an innovative shape reconstruction algorithm for deformable objects has been designed. A cooperation with the Solar Impulse project allowed to deploy the algorithm in a very challenging real-world scenario, i.e. the accurate measurements of airplane wings deformations.
Transformation Based Chinese Entity Detection and Tracking

This paper proposes a unified Transformation Based Learning (TBL, Brill, 1995) framework for Chinese Entity Detection and Tracking (EDT). It consists of two sub models: a mention detection model and an entity tracking/coreference model. The first sub-model is used to adapt existing Chinese word segmentation and Named Entity (NE) recognition results to a specific EDT standard to find all the mentions. The second sub-model is used to find the coreference relation between the mentions. In addition, a feedback technique is proposed to further improve the performance of the system. We evaluated our methods on the Automatic Content Extraction (ACE, NIST, 2003) Chinese EDT corpus. Results show that it outperforms the baseline, and achieves comparable performance with the stateof-the-art methods.