Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibitory Interneurons Decorrelate Excitatory Cells to Drive Sparse Code Formation in a Spiking Model of V1

Sparse coding models of natural scenes can account for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the highly kurtotic firing rates of V1 neurons. Current spiking network models of pattern learning and sparse coding require direct inhibitory connections between the excitatory simple cells, in conflict with the physiological distinction between excitatory (glutamatergic) and inhibitory (GABAergic) neurons (Dale's Law). At the same time, the computational role of inhibitory neurons in cortical microcircuit function has yet to be fully explained. Here we show that adding a separate population of inhibitory neurons to a spiking model of V1 provides conformance to Dale's Law, proposes a computational role for at least one class of interneurons, and accounts for certain observed physiological properties in V1. When trained on natural images, this excitatory–inhibitory spiking circuit learns a sparse code with Gabor-like RFs as found in V1 using only local synaptic plasticity rules. The inhibitory neurons enable sparse code formation by suppressing predictable spikes, which actively decorrelates the excitatory population. The model predicts that only a small number of inhibitory cells is required relative to excitatory cells and that excitatory and inhibitory input should be correlated, in agreement with experimental findings in visual cortex. We also introduce a novel local learning rule that measures stimulus-dependent correlations between neurons to support “explaining away” mechanisms in neural coding.
On the Convergence Rate of Multi-Block ADMM

The alternating direction method of multipliers (ADMM) is widely used in solving structured convex optimization problems. Despite of its success in practice, the convergence properties of the standard ADMM for minimizing the sum of N (N 3) convex functions with N block variables linked by linear constraints, have remained unclear for a very long time. In this paper, we present convergence and convergence rate results for the standard ADMM applied to solve N-block (N 3) convex minimization problem, under the condition that one of these functions is convex (not necessarily strongly convex) and the other N 1 functions are strongly convex. Specically, in that case the ADMM is proven to converge with rate O(1=t) in a certain ergodic sense, and o(1=t) in non-ergodic sense, where t denotes the number of iterations.
Non-smooth 3D Modeling of a Snake Robot with Frictional Unilateral Constraints

A non-smooth 3D mathematical model of the dynamics of a snake robot (without wheels) is developed in this paper. The model is based on the framework of non-smooth dynamics and convex analysis which allows us to easily incorporate unilateral contact forces and friction forces based on Coulomb's law of dry friction. Impact and stick-slip transitions are modeled as instantaneous. A numerical integrator on impulse- velocity level, the time-stepping method, is used for simulation, which helps avoid an explicit switch between equations of motion during simulation. Numerical results are presented for a snake robot with 26 degrees of freedom and 22 possible contact points along its body. Simulation results of the snake motion pattern 'lateral undulation' are shown.
Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques

Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semi-arid regions. An integrated approach using remote sensing in addition to various statistical methods has shown success for developing soil salinity prediction models. The aim of this study was to develop statistical regression models based on remotely sensed indicators to predict and map spatial variation in soil salinity in the Al Hassa oasis. Different spectral indices were calculated from original bands of IKONOS images. Statistical correlation between field measurements of Electrical Conductivity (EC), spectral indices and IKONOS original bands showed that the Salinity Index (SI) and red band (band 3) had the highest correlation with EC. Combining these two remotely sensed variables into one model yielded the best fit with R 2 = 0.65. The results revealed that the high performance of this combined model is attributed to: (i) the spatial resolution of the images; (ii) the great potential of the enhanced images, derived from SI, by enhancing and delineating the spatial variation of soil salinity; and (iii) the superiority of band 3 in retrieving soil salinity features and patterns, which was explained by the high reflectance of the smooth and bright surface crust and the low reflectance of the coarse dark puffy crust. Soil salinity maps generated using the selected model showed that strongly saline soils (>16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors. The results demonstrate that modelling and mapping spatial variation in soil salinity based on
Why Do Firms Imitate Each Other

Scholars from diverse disciplines have proposed numerous theories of business imitation. We organize these theories into two broad categories: (1) information-based theories, where firms follow others that are perceived as having superior information, and (2) rivalry-based theories, where firms imitate others to maintain competitive parity or limit rivalry. We describe conditions under which each type of imitation is most likely and offer guidance on identifying imitation in practice. Amplification effects and other performance implications of imitation are also addressed.
Distribution O F Heavy Metals in Antarctic Marine Ecosystem

Heavy metal concentrations in Antarctic surface seawaters and some marine organisms were determined, and their distributions and bioaccumulations were discussed in comparison with other marine ecosystems. Locational difference of the heavy metal concentrations in Antarctic seawater was negligible, and their concentrations were in the order of Zn Fe> Mn> Cu>Pb Ni> Cd> Hg. When compared to the western North Pacific, Japan Sea and Bering Sea, the concentrations of Fe, Zn, and Hg in Antarctic seawater were relatively low, which provides evidence that atmospheric transport of these metals from continents, either from natural or anthropogenic sources to the Antarctic water is much less. The Cd level was relatively high compared with those in the western North Pacific and Japan Sea waters, which might be due to the upwelling of Cd-enriched deep waters to the surface. In general, the heavy metal concentrations in Antarctic marine organisms were affected by the metal concentrations in the seawater. Also, it was found that bioaccumulations of metals in Antarctic marine ecosystem were characterized by a high accumulation of Cd and low accumulation of Hg, based on the short food chain with krill as key species, and also by a low accumulation of Fe in fishes as an adaptation to the cold water condition.
Are earnings sensitivity measures risk-relevant? The case of oil price risk for the petroleum refining industry

In this study we use a sample of petroleum refining firms to examine whether earnings sensitivity measures are risk-relevant. Because actual earnings sensitivity measures as per the SEC’s new market risk disclosure rules are either unavailable, incomplete or cross-sectionally uncomparable at the present time, we construct earnings sensitivity measures from extant information from historical financial statements. Our findings indicate that such simulated earnings sensitivity measures are positively associated with contemporaneous stock market determined oil exposures (oil betas). We also find that simulated earnings sensitivity measures are useful in predicting future oil betas after controlling for past oil betas and a number of alternate risk proxies. Our evidence suggests that earnings sensitivity measures provide risk-relevant information.
Child Participation: Overcoming Disparity between New Zealand’s Family Court and Out-of-court Dispute Resolution Processes

This article considers children’s right to participate in the context of private law disputes concerning their post-separation, day-to-day care and contact arrangements. In New Zealand the approach to ascertaining children’s views has been both long-standing and systematic for contested proceedings within the Family Court (via children’s legal representatives and judicial meetings with children). However, major reform of the family justice system in 2014 shifted the emphasis to new out-of-court processes for resolving post-separation parenting arrangements. The reforms were disappointingly silent on the issue of children’s participation in the new Family Dispute Resolution services, particularly mediation. A disparity has thus arisen between opportunities for children’s engagement in New Zealand’s in-court and out-of-court dispute resolution processes. Research evidence and international developments in Australia and England and Wales are reviewed for the guidance they can offer in remedying this in New Zealand and elsewhere.
Statistics of galaxy warps in the HDF North and South

We present a statistical study of the presence of galaxy warps in the Hubble deep fields. Among a complete sample of 45 edge-on galaxies above a diameter of 1. "3, we find 5 galaxies to be certainly warped and 6 galaxies as good candidates. In addition, 4 galaxies reveal a characteristic U-warp. Compared to statistical studies of local warps, and taking into account the strong bias against observing the outer parts of galaxies at high redshift, these numbers point towards a very high frequency of warps at z similar to 1: almost all galaxy discs might be warped. Furthermore, the amplitude of warps are stronger than for local warps. This is easily interpreted in terms of higher galaxy interactions and matter accretion in the past. This result supports these two mechanisms as the best candidates for the origin of early warps. The mean observed axis ratio of our sample of edge-on galaxies is significantly larger in the high-z sample than is found for samples of local spiral galaxies. This might be due to disk thickening due to more frequent galaxy interactions.
Backbone colorings along stars and matchings in split graphs: their span is close to the chromatic number

We continue the study on backbone colorings, a variation on classical vertex colorings that was introduced at WG2003. Given a graph $G = (V,E)$ and a spanning subgraph $H$ of $G$ (the backbone of $G$), a $\gamma$-backbone coloring for $G$ and $H$ is a proper vertex coloring $V\to \{1,2,\ldots\}$ of $G$ in which the colors assigned to adjacent vertices in $H$ differ by at least $\gamma$. The algorithmic and combinatorial properties of backbone colorings have been studied for various types of backbones in a number of papers. The main outcome of earlier studies is that the minimum number $\ell$ of colors, for which such colorings $V\to \{1,2,\ldots, \ell\}$ exist, in the worst case is a factor times the chromatic number (for path, tree, matching and star backbones). We show here that for split graphs and matching or star backbones, $\ell$ is at most a small additive constant (depending on $\gamma$) higher than the chromatic number. Our proofs combine algorithmic and combinatorial arguments. We also indicate other graph classes for which our results imply better upper bounds on $\ell$ than the previously known bounds.
Extensions to Higher-Dimensions of an Unconditionally Stable ADE Scheme for the Convection-Diffusion Equation

An unconditionally stable alternating direction explicit scheme (ADE) to solve the one-dimensional unsteady convection-diffusion equation was developed by J. Xie, Z. Lin and J. Zhou in [6]. Aside from being explicit and unconditionally stable, the method is straightforward to implement. In this paper we show extensions of this scheme to higher-dimensions of the convection-diffusion equation subject to Dirichlet boundary conditions. By expressing the equation with a local series expansion over a rectangular grid, a linear system of symbolic equations is obtained which is tedious to solve for manually and we addressed this challenge using symbolic computation. The solutions obtained are explicit closed-form formulas which are then used to iteratively solve the unsteady convectiondiffusion equation by traversing the discrete grid in an alternating direction fashion. Finally, extensions to higher dimensions can be easily deduced from the 2D formulas. We conclude the paper with numerical simulation results for diffusion and convection-diffusion problems compared to analytical solutions showing the performance of the method and its numerical stability.
A configurable IP core for inverse quantized discrete cosine and integer transforms with arbitrary accuracy

An extended version of low-complexity IP Core for image/video transformations based on the CORDIC architecture is presented. This IP core is able to perform quantized 8×8 IDCT and quantized 8×8/4×4 H.264-inverse integer transforms on a configurable architecture by using only shift and add operations. Furthermore, the number for CORDIC iterations and compensation steps can be adjusted, which enables a trade-off between video compression quality in PSNR and computational complexity.
Six Centuries of British Economic Growth: A Time-Series Perspective

This paper provides a time-series analysis of recent annual estimates of real GDP and industrial output covering 1270 to 1913. We show that growth can be regarded as a segmented trend stationary process. On this basis, we find that trend growth of real GDP per person was zero prior to the 1660s but then experienced two significant accelerations, pre- and post-industrial revolution. We also find that the hallmark of the industrial revolution is a substantial increase in the trend rate of growth of industrial output rather than being an episode of difference stationary growth.
Supporting Giftedness in Families: A Resources Perspective

There is ample evidence that families are important in supporting the development of giftedness in children. Although there has been a great deal of research addressing individual and school factors in promoting giftedness, the role of parents and caregivers is comparatively underresearched, particularly in Australia. This study investigated the ways in which parents supported their children’s development, drawing on the educational and learning capital framework within the Actiotope Model of Giftedness. A qualitative design was adopted and semistructured interviews were conducted with 32 parents and caregivers. The data demonstrated that parents draw on all 10 educational and learning resources in creating favorable environments to support their children’s development.
Huntington's Disease: An Immune Perspective

Huntington's disease (HD) is a progressive neurodegenerative disorder that is caused by abnormal expansion of CAG trinucleotide repeats. Neuroinflammation is a typical feature of most neurodegenerative diseases that leads to an array of pathological changes within the affected areas in the brain. The neurodegeneration in HD is also caused by aberrant immune response in the presence of aggregated mutant huntingtin protein. The effects of immune activation in HD nervous system are a relatively unexplored area of research. This paper summarises immunological features associated with development and progression of HD.
Mode-seeking on graphs via random walks

Mode-seeking has been widely used as a powerful data analysis technique for clustering and filtering in a metric feature space. We introduce a versatile and efficient mode-seeking method for “graph” representation where general embedding of relational data is possible beyond metric spaces. Exploiting the global structure of the graph by random walks, our method intrinsically combines mode-seeking with ranking on the graph, and performs robust analysis by seeking high-ranked authoritative data and suppressing low-ranked noise and outliers. This enables mode-seeking to be applied to a large class of challenging real-world problems involving graph representation which frequently arises in computer vision. We demonstrate our method on various synthetic experiments and real applications dealing with noisy and complex data such as scene summarization and object-based image matching.
Sand Flux Simulations at a Small Scale over a Heterogeneous Mesquite Area of the Northern Chihuahuan Desert

Abstract Within areas of the Chihuahuan Desert dominated by honey mesquite bushes (Prosopis glandulosa), soil erosion causes open eroded patches and the formation of large coppice dunes. The airflow patterns around the dunes and through the open areas are correlated with sand flux and erosion. This study uses wind velocity simulations from the Quick Urban and Industrial Complex (QUIC) model in combination with a sand flux parameterization to simulate sand fluxes for each of eight storms occurring in the springs of 2003 and 2004. Total sand fluxes based on the sum of all the sand collectors located within the study domain were usually within 50% of the measured values for each of the storms, with simulations for individual sand collectors also often within 50% of the measured values. Simulated fluxes based on two different sand flux parameterizations were generally within 10% of each other, differing substantially only when the sand flux was low (near the threshold velocity). Good agreement between the fie.
Association Analysis of Noncoding Variants in Neuroligins 3 and 4X Genes with Autism Spectrum Disorder in an Italian Cohort

Since involved in synaptic transmission and located on X-chromosome, neuroligins 3 and 4X have been studied as good positional and functional candidate genes for autism spectrum disorder pathogenesis, although contradictory results have been reported. Here, we performed a case-control study to assess the association between noncoding genetic variants in NLGN3 and NLGN4X genes and autism, in an Italian cohort of 202 autistic children analyzed by high-resolution melting. The results were first compared with data from 379 European healthy controls (1000 Genomes Project) and then with those from 1061 Italian controls genotyped by Illumina single nucleotide polymorphism (SNP) array 1M-duo. Statistical evaluations were performed using Plink v1.07, with the Omnibus multiple loci approach. According to both the European and the Italian control groups, a 6-marker haplotype on NLGN4X (rs6638575(G), rs3810688(T), rs3810687(G), rs3810686(C), rs5916269(G), rs1882260(T)) was associated with autism (odd ratio = 3.58, p-value = 2.58 × 10−6 for the European controls; odds ratio = 2.42, p-value = 6.33 × 10−3 for the Italian controls). Furthermore, several haplotype blocks at 5-, 4-, 3-, and 2-, including the first 5, 4, 3, and 2 SNPs, respectively, showed a similar association with autism. We provide evidence that noncoding polymorphisms on NLGN4X may be associated to autism, suggesting the key role of NLGN4X in autism pathophysiology and in its male prevalence.
Video saliency detection in the compressed domain

Saliency detection is widely used to extract the regions of interest in images. Many saliency detection models have been proposed for videos in the uncompressed domain. However, videos are always stored in the compressed domain such as MPEG2, H.264, MPEG4 Visual, etc. In this study, we propose a video saliency detection model based on feature contrast in the compressed domain. Four features of luminance, color, texture and motion are extracted from DCT coefficients and motion vectors in the video bitstream. The static saliency map of video frames is calculated based on the luminance, color and texture features, while the motion saliency map for video frames is computed by motion feature. The final saliency map for video frames is obtained through combining the static saliency map and motion saliency map. Experimental results show good performance of the proposed video saliency detection model in the compressed domain.
Evaluating and Comparing the Effects of Group Contingencies on Mathematics Accuracy in a First-Grade Classroom: Class Average Criteria Versus Unknown Small-Group Average Criteria

Abstract An adapted alternating treatments design was used to evaluate and compare the effects of two group contingency interventions on mathematics assignment accuracy in an intact first-grade classroom. Both an interdependent contingency with class-average criteria (16 students) and a dependent contingency with criteria based on the average of a smaller, unknown, randomly selected group of students (4 students) were applied. For both contingencies, rewards and criteria were randomly selected and unknown to students. Results showed immediate, sustained, and meaningful improvements in mathematics assignment accuracy (from a class average of 64% to a class average above 83%) across both contingencies, with little differences between the two interventions. Social validity data suggest that the two teachers and the majority of the students preferred the small-group contingency. Discussion focuses on applied implications of the current results and directions for future research, including investigating side effects and idiosyncratic effects.
Fixed points of demicontinuous nearly Lipschitzian mappings in Banach spaces

We introduce the classes of nearly contraction mappings and nearly asymp- totically nonexpansive mappings. The class of nearly contraction mappings includes the class of contraction mappings, but the class of nearly asymptotically nonexpansive mappings contains the class of asymptotically nonexpansive mappings and is contained in the class of mappings of asymptotically nonexpansive type. We study the existence of fixed points and the structure of fixed point sets of mappings of these classes in Ba- nach spaces. Our results improve various celebrated results of fixed point theory in the context of demicontinuity.
Fast Kernel Sparse Representation Approaches for Classification

Sparse representation involves two relevant procedures - sparse coding and dictionary learning. Learning a dictionary from data provides a concise knowledge representation. Learning a dictionary in a higher feature space might allow a better representation of a signal. However, it is usually computationally expensive to learn a dictionary if the numbers of training data and(or) dimensions are very large using existing algorithms. In this paper, we propose a kernel dictionary learning framework for three models. We reveal that the optimization has dimension-free and parallel properties. We devise fast active-set algorithms for this framework. We investigated their performance on classification. Experimental results show that our kernel sparse representation approaches can obtain better accuracy than their linear counterparts. Furthermore, our active-set algorithms are faster than the existing interior-point and proximal algorithms.