Miracle-dz/UniFAR-FTUs huggingface dataset:
Micro-Foundations of Urban Agglomeration Economies

This handbook chapter studies the theoretical micro-foundations of urban agglomeration economies. We distinguish three types of micro-foundations, based on sharing, matching, and learning mechanisms. For each of these three categories, we develop one or more core models in detail and discuss the literature in relation to those models. This allows us to give a precise characterisation of some of the main theoretical underpinnings of urban agglomeration economies, to discuss modelling issues that arise when working with these tools, and to compare different sources of agglomeration economies in terms of the aggregate urban outcomes they produce as well as in terms of their normative implications.
Bayesian Network Structure Learning Based on Rough Inclusion

A kind of Bayesian network structure learning approach based on rough inclusion is put forward. First of all, the idea of the apriori algorithm is applied to mine frequent attribute sets through restraining support. Then, inclusion theory of rough set is used for mining cause and effect associated rules that determine arcs and their direction between Bayesian network variables. At one time, mining algorithm of associated rules and Bayesian network structure learning approach are presented. Finally, It shows rationality and validity of the approach by analyzing the applied procedure of example.
Neural network-based tunable microwave filter design for re-configurable biomedical hardware

A knowledge-based method for the design of tunable microwave filters for biomedical applications is described. The method uses a backpropagation neural network (BNN) for mapping RF filter responses to filter resistor, capacitor and inductor values. The BNN acts as an efficient circuit reconfiguration tool which when supplied with a set of filter responses calculates the required component values with high accuracy. To demonstrate the efficacy of the approach, a microwave high pass filter was tuned from 7GHz to 10GHz in steps of 0.2GHz. Filter hardware is reconfigured during tuning using varactor diodes, MOS resistors and tunable micro-inductors.
Fusing Near-Infrared Reflectance Spectrum and Dynamic Model to Estimate Vegetation Structural Parameters

Many efforts have been made to fuse reflectance spectrum signals and dynamic model to estimate land surface parameters from remotely sensed data more accurately as well as capture the continuous features of land surface parameters. This paper proposed a Dynamic Bayesian Networks (DBNs) data fusion algorithm to retrieve vegetation structural parameters by integrating multi-source information, such as time series observations of multi-angle spectral reflectance in near-infrared region, canopy radiative transfer model as well as the crop dynamic growth model. Using this method, leaf area index (LAI) of winter wheat was estimated using field measurement dataset. Estimated results show that the correlation coefficients between estimated and reference value is 0.965 and root-mean square error (RMSE) is 0.344. Since DBNs data fusion is a dynamic iteration algorithm, the results also confirmed the rule that the parameter state estimated from DBNs are the implicit information of observation data updated with crop growth model.
A simple tree pattern matching algorithm for code generator

This paper describes a simple tree pattern matching algorithm for the code generator of compilers. The intermediate code (Register Transfer Language) is matched with the tree-rewriting rules of the instruction description which describes the target architecture to generate the assembly code. The hashing function is used in our system to transform a tree pattern matching problem into a simple number comparison. Compared with GNU C compiler (gcc), the tree pattern matching time can be reduced by 69% and the compiler time by 6%, and the space of the instruction descriptions can be reduced by 4.10 times on DLX and 2.14 on SPARC. The size of table, which is necessary for the code generator, is quite small in our method.
Reduced Adult Hippocampal Neurogenesis and Working Memory Deficits in the Dgcr8-Deficient Mouse Model of 22q11.2 Deletion-Associated Schizophrenia Can Be Rescued by IGF2

DiGeorge syndrome chromosomal region 8 (Dgcr8), a candidate gene for 22q11.2 deletion-associated schizophrenia, encodes an essential component for microRNA (miRNA) biosynthesis that plays a pivotal role in hippocampal learning and memory. Adult neurogenesis is known to be important in hippocampus-dependent memory, but the role and molecular mechanisms of adult neurogenesis in schizophrenia remain unclear. Here, we show that Dgcr8 heterozygosity in mice leads to reduced cell proliferation and neurogenesis in adult hippocampus, as well as impaired hippocampus-dependent learning. Several schizophrenia-associated genes were downregulated in the hippocampus of Dgcr8+/− mice, and one of them, insulin-like growth factor 2 (Igf2), rescued the proliferation of adult neural stem cells both in vitro and in vivo. Furthermore, IGF2 improved the spatial working memory deficits in Dgcr8+/− mice. These data suggest that defective adult neurogenesis contributes to the cognitive impairment observed in 22q11.2 deletion-associated schizophrenia and could be rectified by IGF2.
In Search of Somatic Precursors of Spontaneous Insight

A considerable number of behavioral and neuroscientific studies on insight problem solving have revealed behavioral and neural correlates of the dynamic insight process; however, somatic correlates, particularly somatic precursors of creative insight, remain undetermined. To characterize the somatic precursor of spontaneous insight, 22 healthy volunteers were recruited to solve the compound remote associate (CRA) task in which a problem can be solved by either an insight or an analytic strategy. The participants’ peripheral nervous activities, particularly electrodermal and cardiovascular responses, were continuously monitored and separately measured. The results revealed a greater skin conductance magnitude for insight trials than for non-insight trials in the 4-s time span prior to problem solutions and two marginally significant correlations between pre-solution heart rate variability (HRV) and the solution time of insight trials. Our findings provide the first direct evidence that spontaneous insight in problem solving is a somatically peculiar process that is distinct from the stepwise process of analytic problem solving and can be represented by a special somatic precursor, which is a stronger pre-solution electrodermal activity and a correlation between problem solution time and certain HRV indicators such as the root mean square successive difference (RMSSD).
3EG J2027+3429: Another blazar behind the Galactic Plane

We report on the association of an X-ray source (WGA J2025.1+3342), serendipitously found with BeppoSAX in two separate observations, with the unidentified EGRET source 3EG J2027+3429. The source is detected from 1 keV up to about 100 keV, has a flat ($\Gamma=0.6{-}1.5$) spectrum and is highly variable both in intensity and shape. The data indicate marginal evidence for an iron line in the source rest frame. The overall X-ray luminosity is ${\sim}4 \times10^{45}$ erg s -1 , typical of a quasar. The X-ray source is coincident in radio wavelengths with a bright object characterized by a flat spectrum over the band 0.3-10 GHz while in the optical it is identified with a quasar at redshift 0.22. All available data indicate a SED compatible with a low frequency peaked or red blazar type object. This identification is interesting because this is the second blazar found behind the galactic plane in the direction of the Cygnus region.
Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles

We propose a hierarchical pipeline for skull-stripping and segmentation of anatomical structures of interest from T1-weighted images of the human brain. The pipeline is constructed based on a two-level Bayesian parameter estimation algorithm called multi-atlas likelihood fusion (MALF). In MALF, estimation of the parameter of interest is performed via maximum a posteriori estimation using the expectation-maximization (EM) algorithm. The likelihoods of multiple atlases are fused in the E-step while the optimal estimator, a single maximizer of the fused likelihoods, is then obtained in the M-step. There are two stages in the proposed pipeline; first the input T1-weighted image is automatically skull-stripped via a fast MALF, then internal brain structures of interest are automatically extracted using a regular MALF. We assess the performance of each of the two modules in the pipeline based on two sets of images with markedly different anatomical and photometric contrasts; 3T MPRAGE scans of pediatric subjects with developmental disorders vs. 1.5T SPGR scans of elderly subjects with dementia. Evaluation is performed quantitatively using the Dice overlap as well as qualitatively via visual inspections. As a result, we demonstrate subject-level differences in the performance of the proposed pipeline, which may be accounted for by age, diagnosis, or the imaging parameters (particularly the field strength). For the subcortical and ventricular structures of the two datasets, the hierarchical pipeline is capable of producing automated segmentations with Dice overlaps ranging from 0.8 to 0.964 when compared with the gold standard. Comparisons with other representative segmentation algorithms are presented, relative to which the proposed hierarchical pipeline demonstrates comparative or superior accuracy.
An EHW architecture for real-time GPS attitude determination based on parallel genetic algorithm

The paper describes a parallel genetic algorithm for the VLSI implementation of real-time GPS attitude determination systems. The genetic algorithm is based on a fine-grained model and utilises AFM (Ambiguity Function Method) for GPS attitude determination. The paper describes various implementation choices for the genetic algorithm in order to achieve both functionality and practical performance constraints such as speed, compactness and scalability. Simulation results using GPS carrier phase experimental data show that, in addition to low hardware complexity, our final genetic algorithm architecture achieves a linear speed-up with the number of processors utilised in the target VLSI chip.
INFORMATION SECURITY EFFECTIVENESS: A RESEARCH FRAMEWORK

Information security has taken on increasing importance as the size and complexity of IT issues continues to grow. Research literature in information security suggests that clarity in policies, systems auditing and clear deterrence practices enhance organizational information security effectiveness. In this paper we analyze research framework defining how the three constructs: security policies, deterrence practices and systems auditing impact information security effectiveness. A survey was conducted to collect data, the results of which suggest that there is a significant relationship between security policies and systems audit with security effectiveness.
Combinatorial advantages due to joint resource allocation in uplink CoMP systems

We explore the opportunities in cellular system design and operation offered by the joint resource allocation of the space/frequency division multiple access (SDMA/FDMA) in uplink cooperative multi-point (CoMP) systems. First, we analyze the underlying combinatorial structure of the allocation problem in terms of user set partitions and permutations. Due to the exponential complexity we propose a suboptimal greedy algorithm that allows to allocate large user populations with moderate computational complexity. Finally we provide numerical results and analyze the implications w.r. t. to system deployment, optimization of spectral/energy efficiency and fairness aspects.
Response Normalization in the Superficial Layers of the Superior Colliculus as a Possible Mechanism for Saccadic Averaging

How does the brain decide where to look? Neuronal networks within the superior colliculus (SC) encode locations of intended eye movements. When faced with multiple targets, the relative activities of neuronal populations compete for the selection of a saccade. However, the computational principles underlying saccadic choices remain poorly understood. We used voltage imaging of slices of rat SC to record circuit dynamics of population responses to single- and dual-site electrical stimulation to begin to reveal some of the principles of how populations of neurons interact. Stimulation of two distant sites simultaneously within the SC produced two distinct peaks of activity, whereas stimulation of two nearby sites simultaneously exhibited a single, merged peak centered between the two sites. The distances required to produce merged peaks of activity corresponded to target separations that evoked averaging saccades in humans performing a corresponding dual target task. The merged activity was well accounted for by a linear weighed summation and a divisive normalization of the responses evoked by the single-site stimulations. Interestingly, the merging of activity occurred within the superficial SC, suggesting a novel pathway for saccadic eye movement choice.
Learning Skill Templates for Parameterized Tasks

We consider the problem of learning skill templates for a parameterized reinforcement learning problem class T. That is, we assume that a task, i.e., an instance of the problem class, is defined by a task parameter vector τ ∈ T ⊆ R n and an associated interpretation. Likewise, a skill is considered as a parameterized policy with parameter vector θ ∈ R m. A parameterized skill [1] is a mapping Θ from task vector τ to a skill vector θ τ , i.e., Θ : τ → θ τ. Let J(θ, τ) be the expected return of the skill parametrized by θ in task τ ; the goal of parameterized skill learning is to find a mapping Θ * such that Θ * = arg max Θ P (τ)J(Θ(τ), τ)dτ , where P (τ) is the task distribution. Because the parametrized skill Θ will typically not predict the optimal θ * τ = arg max θ J(θ, τ), it is desirable to not only learn a point-estimate of θ * τ but also to give a measure of uncertainty of this prediction. We propose to learn a so-called skill template Ψ = (Θ, Ω), which contains a function Ω : τ → Σ τ with Σ τ ∈ R m×m that provides this uncertainty. Σ τ can be interpreted as the covariance of a Gaussian distribution over the skill's parameter space. Thus, a skill template Ψ can be seen as a mapping from a task to a Gaussian distribution over the skill parameter space, with Θ predicting the distribution's mean and Ω predicting the distribution's covariance. Skill templates are learned based on a set of skill weights that have been learned for specific task instances. Let E = {(τ i , θ τ i)|i = 1,. , K} be a training set consisting of experience collected in K tasks with J(θ τ i , τ i) ≈ J(θ * τ i , τ i). Learning the parameterized skill Θ can be considered as a regression problem, trained with the pairs in E. While da Silva et al. [1] used Support Vector Regression for this regression task, we use Gaussian Process Regression (GPR) since it naturally provides an uncertainty along with each prediction. Different ways of learning Ω from E are imaginable; in this abstract, we only consider the case of diagonal Σ τ with Σ τ either being a …
Quasi-linear-time substring searching by q-gram distance

The q-gram distance dq(x, y) between two strings x and y is a string similarity measure correlated with a famous string distance: the edit distance. In addition, it can be computed much faster, in linear (O(|x|+|y|)) time, than the edit distance in quadratic (O(|x||y|)) time, where | · | denotes the string length. However, it does not mean that we can find all substrings of a text t similar to a pattern p in linear time. In this paper we will propose a searching algorithm achieving quasi-linear (O(|t| log |p| + |p|)) time by the q-gram distance.
Non-detrimental Web application security scanning

The World Wide Web has become a sophisticated platform capable of delivering a broad range of applications. However, its rapid growth has resulted in numerous security problems that current technologies cannot address. Researchers from both academic and private sector are devoting a considerable amount of resources to the development of Web application security scanners (i.e., automated software testing platforms for Web application security auditing) with some success. However, little is known about their potential side effects. It is possible for an auditing process to induce permanent changes in an application's state. Due to this potential, we have so far avoided large-scale empirical evaluations of our Web Application Vulnerability and Error Scanner (WAVES). we introduce a testing methodology that allows for harmless auditing, define three testing modes - heavy, relaxed, and safe modes, and report our results from two experiments. In the first, we compared the coverage and side effects of the three scanning modes using 5 real-world Web applications chosen from the 38 found vulnerable in a previous static verification effort. In the second, we used the relaxed mode to conduct a 48-hour test involving 1120 random Web sites, of which 55 were found to be vulnerable.
Service Learning For Social Change: Raising Social Consciousness Among Sport Management Students

Recently, scholars of sport management have called for more research aimed at understanding how sport can be leveraged for social change. This interest has contributed to a burgeoning paradigm of sport management research and practice developed around using sport as a catalyst for broader human and community development. In order for sport practitioners to successfully develop, implement, and sustain these programs, integration of development-based theory and concepts are needed in sport management curricula. Service learning is one pedagogical approach for achieving this objective, and is well suited for promoting social change practices among students. This study assesses how participation in a sport-for-development (SFD) service learning project impacted the social consciousness and critical perspectives of sport management students. Results suggest the experience raised student’s awareness of community issues, developed a more holistic perspective on the role of service, and influenced their future ca.
User-Controlled Standardisation of Health Care Practices

The need to access information across different levels of the health care system has prompted attention to standardising electronic patient record (EPR) systems. However, this has proven extremely difficult to achieve. One reason is that much structuring of the EPR content translates into the need to standardise related routines and practices. In this paper, we demonstrate that the Norwegian national performance standard “cancellations of surgery” is adapted locally in ways that promote many different understandings and thus applications of the standard. We have identified differing interests in the performance standards relating to the various organisational levels of users. Using the emerging international openEHR framework as a backdrop, we discuss the effects of offering users the technical means to control standardisation processes given the different interests
Antibacterial Effects of Cissus welwitschii and Triumfetta welwitschii Extracts against Escherichia coli and Bacillus cereus

Antibiotic resistance has increased sharply, while the pace for the development of new antimicrobials has slowed down. Plants provide an alternative source for new drugs. This study aimed to screen extracts from Cissus welwitschii and Triumfetta welwitschii for antibacterial activity against Escherichia coli and Bacillus cereus. The tests conducted included a susceptibility determination test, analysis of the effect of T. welwitschii on cell wall integrity, and transport across the membrane. It was found that the T. welwitschii methanol extracts were more effective than the water extracts and had the lowest minimum inhibitory concentration and minimum bactericidal concentration at 0.125 mg/mL and 0.5 mg/mL, respectively, against E. coli and B. cereus. The C. welwitschii extract caused the most drug accumulation in E. coli. In B. cereus, no significant drug accumulation was observed. Nucleic acid leakage in B. cereus and E. coli and protein leakage in E. coli were observed after exposure to the T. welwitschii extract. The extracts from T. welwitschii had greater antibacterial activity than the extracts from C. welwitschii. T. welwitschii may be a potential source of lead compounds for that could be developed into antibacterial agents.
Born to be Wild: Using Communities of Practice as a Tool for Knowledge Management

This paper looks at what happens when Communities of Practice are used as a tool for Knowledge Management. The original concept of a Community of Practice appears to have very little in common with the knowledge sharing communities found in Knowledge Management, which are based on a revised view of 'cultivated' communities. We examine the risks and benefits of cultivating Communities of Practice rather than leaving them 'in the wild'. The paper presents the findings from two years of research in a small microelectronics firm to provide some insights into the wild vs domesticated dichotomy and discusses the implications of attempting to tame Communities of Practice in this way.
CSLM - a modular open-source continuous space language modeling toolkit

Language models play a very important role in many natural language processing applications, in particular large vocabulary speech recognition and statistical machine translation. For a long time, back-off n-gram language models were considered to be the state-of-art when large amounts of training data are available. Recently, so called continuous space methods or neural network language models have shown to systematically outperform these models and they are getting increasingly popular. This article describes an open-source toolkit that implements these models in a very efficient way, including support for GPU cards. The modular architecture makes it very easy to work with different data formats and to support various alternative models. Using data selection, resampling techniques and a highly optimized code, training on more than five billions words takes less than 24 hours. The resulting models achieve reductions in the perplexity of almost 20%. This toolkit has been very successfully applied to various languages for large vocabulary speech recognition and statistical machine translation. By making available this toolkit we hope that many more researchers will be able to work on this very promising technique, and by these means, quickly advance the field.
Three machine learning approaches in the epitope prediction

B-cell epitopes are important in both fundamental biological research and the clinical application. Here we get through three most widely used machine learning approaches (Naïve Bayesian Classifier, Support Vector Machine and the Artificial Neural Network) in the epitope prediction of both continuous and discontinuous types. As the prediction for conformational epitopes are still new in the epitope prediction, feature selection is especially analyzed. Some comparisons and the discussion about the advantages and disadvantages are made about the three methods.