Miracle-dz/UniFAR-FTUs huggingface dataset:
Random clique codes

A new family of associative memories based on sparse neural networks has been recently introduced. These memories achieve excellent performance thanks to the use of error-correcting coding principles. In this work, we introduce a new family of codes termed clique codes. These codes are based on the cliques in balanced n-partite graphs describing associative memories. In particular, we study an ensemble of random clique codes, and prove that such ensemble contains asymptotically good codes. Furthermore, these codes can be efficiently decoded using the neural networks based associative memories with limited complexity and memory consumption.
Statistical Mechanics Beyond the Hopfield Model: Solvable Problems in Neural Network Theory

We present four 'case study' examples of solvable problems in the theory of recurrent neural networks, which are relevant to our understanding of information processing in the brain, but which are also interesting from a purely statistical mechanical point of view, even at the level of simple models (which helps in stimulating interdisciplinary work). The examples concern issues in network dynamics, network connectivity, spike timing and synaptic plasticity.
KUIDL at the NTCIR-11 IMine Task

KUIDL team participated in the Subtopic Mining subtask of the NTCIR-11 IMine task. This paper describes our approach to generating two-level hierarchical subtopics by using Web document structures. The formal run result shows that our approach achieved the best performance in terms of H-measure in the English Subtopic Mining subtask.
H ARDWARE SOFTWARE CO-SIMULATION FOR TRAFFIC LOAD COMPUTATION USING MATLAB SIMULINK MODEL

Due to increase in number of vehicles, Traffic is a major problem faced in urban areas throughout the world. This document presents a newly developed Matlab Simulink model to compute traffic load for real time traffic signal control. Signal processing, video and image processing and Xilinx Blockset have been extensively used for traffic load computation. The approach used is Edge detection operation, wherein, Edges are extracted to identify the number of vehicles. The developed model computes the results with greater degrees of accuracy and is capable of being used to set the green signal duration so as to release the traffic dynamically on traffic junctions. Xilinx System Generator (XSG) provides Simulink Blockset for several hardware operations that could be implemented on various Xilinx Field programmable gate arrays (FPGAs). The method described in this paper involves object feature identification and detection. Xilinx System Generator provides some blocks to transform data provided from the software side of the simulation environment to the hardware side. In our case it is MATLAB Simulink to System Generator blocks. This is an important concept to understand in the design process using Xilinx System Generator. The Xilinx System Generator, embedded in MATLAB Simulink is used to program the model and then test on the FPGA board using the properties of hardware co-simulation tools.
A Multilevel Model of Postmenopausal Breast Cancer Incidence

Background: Breast cancer has a complex etiology that includes genetic, biologic, behavioral, environmental, and social factors. Etiologic factors are frequently studied in isolation with adjustment for confounding, mediating, and moderating effects of other factors. A complex systems model approach may present a more comprehensive picture of the multifactorial etiology of breast cancer. Methods: We took a transdisciplinary approach with experts from relevant fields to develop a conceptual model of the etiology of postmenopausal breast cancer. The model incorporated evidence of both the strength of association and the quality of the evidence. We operationalized this conceptual model through a mathematical simulation model with a subset of variables, namely, age, race/ethnicity, age at menarche, age at first birth, age at menopause, obesity, alcohol consumption, income, tobacco use, use of hormone therapy (HT), and BRCA1/2 genotype. Results: In simulating incidence for California in 2000, the separate impact of individual variables was modest, but reduction in HT, increase in the age at menarche, and to a lesser extent reduction in excess BMI >30 kg/m2 were more substantial. Conclusions: Complex systems models can yield new insights on the etiologic factors involved in postmenopausal breast cancer. Modification of factors at a population level may only modestly affect risk estimates, while still having an important impact on the absolute number of women affected. Impact: This novel effort highlighted the complexity of breast cancer etiology, revealed areas of challenge in the methodology of developing complex systems models, and suggested additional areas for further study. Cancer Epidemiol Biomarkers Prev; 23(10); 2078–92. ©2014 AACR.
Fast computation of look-ahead unscented Rao-Blackwellised Particle Filters

In this paper, we explore a methodology for fast computation of the look-ahead unscented Rao-Blackwellised Particle Filtering (Fast la-URBPF) algorithm. We show that the complexity of the existing la-URBPF algorithm can be substantially reduced by restricting the unscented Kalman filtering prediction and updating step to only a representative particle of a group of particles having the same discrete state or mode. Not only can Fast la-URBPF achieve equal or much higher performance than the existing unscented Kalman filtering based algorithms, but simulation results also show that its time usage is substantially lower than those algorithms. The real data test shows its superior estimation accuracy as compared to the standard particle filtering algorithm.
Topic dependent language modelling for spoken term detection

This paper investigates the effect of topic dependent language models (TDLM) on phonetic spoken term detection (STD) using dynamic match lattice spotting (DMLS). Phonetic STD consists of two steps: indexing and search. The accuracy of indexing audio segments into phone sequences using phone recognition methods directly affects the accuracy of the final STD system. If the topic of a document in known, recognizing the spoken words and indexing them to an intermediate representation is an easier task and consequently, detecting a search word in it will be more accurate and robust. In this paper, we propose the use of TDLMs in the indexing stage to improve the accuracy of STD in situations where the topic of the audio document is known in advance. It is shown that using TDLMs instead of the traditional general language model (GLM) improves STD performance according to figure of merit (FOM) criteria.
Integral and theta formulae for solutions of sl N Knizhnik-Zamolodchikov equation at level zero

The solutions of the sl_N Knizhnik-Zamolodchikov(KZ) equations at level 0 are studied. We present the integral formula which is obtained as a quasi-classical limit of the integral formula for form factors of the SU(N) invariant Thirring model due to F. Smirnov. A proof is given that those integrals satisfy sl_N KZ equation of level 0. The relation of the integral formulae with the chiral Szego kernel is clarified. As a consequence the integral formula with the special choice of cycles is rewritten in terms of the Riemann theta functions associated with the Z_N curve. This formula gives a generalization of Smirnov's formula for sl_2.
Wideband algorithms versus narrowband algorithms for adaptive filtering in the DFT domain

Adaptive filtering in the DFT domain is popular for its computational efficiency and its attractive convergence properties resulting from the applicability of the FFT and separate adaptation of individual DFT bins. Narrowband algorithms assume a complete decoupling of different frequency bins, which corresponds to assuming a circulant structure for the input data matrix. Wideband designs account for the difference between the actual Toplitz structure and the circulant structure by introducing additional constraints. In this contribution, we show that a wideband approach with rigorous implementation of appropriate constraints leads to highly efficient algorithms with excellent convergence properties. As examples we consider multichannel acoustic echo cancellation (MC-AEC) and blind source separation (BSS) of convolutive mixtures.
Capital flows & current account deficits in the 1990s: why did Latin America & East Asian countries respond differently?

The return of private capital to highly indebted less-developed countries (LDCs) in the late 1980s was accompanied by widening current account deficits in the recipient countries, which were primarily attributed to a consumption boom in Latin America and an investment surge in East Asia. Interpreting the return as an increase in the external debt ceiling, the maximum amount that can be borrowed, this paper analyzes and compares the different response of the two regions using the conceptual framework of a borrowing-constrained agent. According to it, an increase in the debt ceiling can reduce precautionary savings, and induce higher demand (and widening current account deficits) even when the borrowing constraint does not bind. Moreover, the increase in demand should be decreasing in the original ceiling. The results from a panel of fourteen Latin American and a panel of eight East Asian countries are consistent with the framework. They also indicate that the different response of the two regions can, to a large extent, be explained by Latin America's lower ceiling prior to the return. Further, the results identify several developments that have not received sufficient attention in the literature and, among other policy implications, suggest that controls on capital inflows may not be effective in preventing a current account deterioration.
Cluster Structure Inference Based on Clustering Stability with Applications to Microarray Data Analysis

This paper focuses on the stability-based approach for estimating the number of clusters in microarray data. The cluster stability approach amounts to performing clustering successively over random subsets of the available data and evaluating an index which expresses the similarity of the successive partitions obtained. We present a method for automatically estimating by starting from the distribution of the similarity index. We investigate how the selection of the hierarchical clustering (HC) method, respectively, the similarity index, influences the estimation accuracy. The paper introduces a new similarity index based on a partition distance. The performance of the new index and that of other well-known indices are experimentally evaluated by comparing the "true" data partition with the partition obtained at each level of an HC tree. A case study is conducted with a publicly available Leukemia dataset.
A complete 12CO 2-1 map of M51 with HERA: I. Radial averages of CO, HI, and radio continuum

The mechanisms governing the star formation rate in spiral galaxies are not yet clear. The nearby, almost face-on, and interacting galaxy M51 offers an excellent opportunity to study at high spatial resolutions the local star formation laws. In this first paper, we investigate the correlation of H2, HI, and total gas surface densities with the star forming activity, derived from the radio continuum (RC), along radial averages out to radii of 12kpc. We have created a complete map of M51 in 12CO 2-1 at a resolution of 450kpc using HERA at the IRAM-30m telescope. These data are combined with maps of HI and the radio-continuum at 20cm wavelength. The latter is used to estimate the star formation rate (SFR), thus allowing to study the star formation efficiency and the local Schmidt law. The velocity dispersion from CO is used to study the critical surface density and the gravitational stability of the disk. The critical gas velocity dispersions needed to stabilize the gas against gravitational collapse in the differentially rotating disk of M51 using the Toomre criterion, vary with radius between 1.7 and 6.8 km/s. Observed radially averaged dispersions derived from the CO data vary between 28 km/s in the center and 8 km/s at radii of 7 to 9 kpc. They exceed the critical dispersions by factors Q_gas of 1 to 5. We speculate that the gravitational potential of stars leads to a critically stable disk.
Do Wages Rise when Corporate Taxes Fall? - Evidence from Germany’s Tax Reform 2000

We contribute to the empirical literature on the effective incidence of corporate income taxation by using the German Business Tax Reform of the year 2000 (GBTR 2000) as a natural experiment. Its effect on wages in the manufacturing sector is identified by means of a difference-in-differences analysis that uses French firms as comparison group. We provide evidence that GBTR 2000 led to a significant and sizeable wage effect. For 2001, the first year after GBTR 2000 took effect, we estimate a short-run effect that implies a wage increase of 7.9 percent. Due to the dynamic nature of the empirical model used, the incidence effect grows gradually over time during the evaluation period.
A Novel Brain Neurovascular Unit Model with Neurons, Astrocytes and Microvascular Endothelial Cells of Rat

A novel triple cell neurovascular unit (NVU) model co-culturing with neurons, brain microvascular endothelial cells (BMECs) and astrocytes was established in this study for investigating the cerebral diseases and screening the candidates of therapeutic drug. We have first performed the cell identification and morphological characterization, analyzed the specific protein expression and determined the blood-brain barrier (BBB) function of the co-culture model under normal condition. Then, we further determined the BBB function, inflammation, cell injury and the variation of neuroprotective factor in this model after anoxia-reoxygenation. The results suggest that this model exhibited a better BBB function and significantly increased expression of P-glycoprotein (Pg-P) and ZO-1 compared with BMECs only or co-culture with astrocytes or neurons. After anoxia-reoxygenation, the pathological changes of this model were basically resemblance to the pathological changes of brain cells and BBB in vivo. And nimodipine, an antagonist of calcium, could reverse those changes as well. According to our observations, we deduce that this triple cell co-culture model exhibits the basic structure, function and cell-cell interaction of NVU, which may offer a more proper in vitro system of NVU for the further investigation of cerebral diseases and drug screening.
A Different Pattern of Production and Scavenging of Reactive Oxygen Species in Halophytic Eutrema salsugineum (Thellungiella salsuginea) Plants in Comparison to Arabidopsis thaliana and Its Relation to Salt Stress Signaling

Isolated thylakoids from halophytic Eutrema salsugineum (Thellungiella salsuginea) produces more H2O2 in comparison to glycophytic Arabidopsis thaliana. The first objective of this study was to verify whether this feature is relevant also to the intact chloroplasts and leaves. Enhanced H2O2 levels in chloroplasts and leaves of E. salsugineum were positively verified with several methods (electron microscopy, staining with Amplex Red and with diaminobenzidine). This effect was associated with a decreased ratio of O2•–/H2O2 in E. salsugineum in comparison to A. thaliana as detected by electron paramagnetic resonance method. As a next step, we tested how this specific ROS signature of halophytic species affects the antioxidant status and down-stream components of ROS signaling. Comparison of enzymatic antioxidants revealed a decreased activity of ascorbate peroxidase (APX), enhanced activity of glutathione peroxidase, and the presence of thylakoid-bound forms of iron superoxide dismutase (FeSOD) and APX in E. salsugineum. These cues were, however, independent from application of salt stress. The typical H2O2-dependent cellular responses, namely the levels of glucosinolates and stress-related hormones were determined. The total glucosinolate content in E. salsugineum water-treated leaves was higher than in A. thaliana and increased after salinity treatment. Treatment with salinity up-regulated all of tested stress hormones, their precursors and catabolites [abscisic acid (ABA), dihydrophaseic acid, phaseic acid, 1-aminocyclopropane-1-carboxylic acid, salicylic acid, jasmonic acid, cis-(+)-12-oxo-phytodienoic acid and jasmonoyl-L-isoleucine] in A. thaliana, whereas in E. salsugineum only a stimulation in ethylene synthesis and ABA catabolism was noted. Obtained results suggest that constitutively enhanced H2O2 generation in chloroplasts of E. salsugineum might be a crucial component of stress-prepardeness of this halophytic species. It shapes a very efficient antioxidant protection (in which glucosinolates might play a specific role) and a fine tuning of hormonal signaling to suppress the cell death program directed by jasmonate pathway.
A Survey on Tangka Image Inpainting

The ancient Tangka image with reflecting Tibet art has been broken by the nature and human with different degrees The part-shed Tangka image is researched as a type of special breaking. After discussing the process of image inpainting and Tangka image inpainting, we give some direction of the development of Tangka image restoration.
Negative Potentials Across Biological Membranes Promote Fusion by Class II and Class III Viral Proteins

Fusion of virions pseudotyped with a class II, SFV E1 or VEEV E, or a class III protein, VSV G is promoted by negative potentials and hindered by positive potentials across the target cell. Hemifusion is independent of polarity. Reversion of hemifused membranes into two distinct ones is responsible for voltage-dependence and inhibition of fusion.
Building Usage Profiles Using Deep Neural Nets

To improve software quality, one needs to build test scenarios resembling the usage of a software product in the field. This task is rendered challenging when a product's customer base is large and diverse. In this scenario, existing profiling approaches, such as operational profiling, are difficult to apply. In this work, we considerpublicly available video tutorials of a product to profile usage. Our goal is to construct an automatic approach to extract information about user actions from instructional videos. To achieve this goal, we use a Deep Convolutional Neural Network (DCNN) to recognize user actions. Our pilot study shows that a DCNN trained to recognize user actions in video can classify five different actions in a collection of 236 publicly available Microsoft Word tutorial videos (published on YouTube). In our empirical evaluation we report a mean average precision of 94.42% across all actions. This study demonstrates the efficacy of DCNN-based methods for extracting software usage information from videos. Moreover, this approach may aid in other software engineering activities that require information about customer usage of a product.
On the Returns to Training in Portugal

This paper investigates the earnings effects of training in the Portuguese labour market. We use the Portuguese Labour Force Survey to classify training according to multiple criteria, including providing institution, purpose, duration, and content of the training activity. First, we establish some stylised facts about the extent and determinants of different types of training. We find that there are major differences in training participation across groups, with elder, low educated workers participating substantially less. Second, we measure the wage effects of training. We find that in Portugal returns to training are large and significant. The estimated coefficients are about 12% in the case of men and 37% in the case of women. We show that discriminating between gender, education level, experience, the public and the private sector, and industrial activity reveals important differences across categories of workers. Workers with low qualifications and long professional experience earn larger returns. On average, women receive larger returns than men, though they are subject to greater variation across education and experience groups. The average effect of training is similar in the private sector and in the public sector. Experience in the private sector and education in the public sector are key determinants of the returns to training. Further, training to improve current skills and training in a firm attract largest returns. Third, the paper investigates whether and to what extent training participation affects the probability of entering and leaving unemployment. We find that being trained does not affect significantly the transition probabilities.
Distributed and Scalable PCA in the Cloud

Principal Component Analysis (PCA) is a popular technique with many applications. Recent randomized PCA algorithms scale to large datasets but face a bottleneck when the number of features is also large. We propose to mitigate this issue using a composition of structured and unstructured randomness within a randomized PCA algorithm. Initial experiments using a large graph dataset from Twitter show promising results. We demonstrate the scalability of our algorithm by implementing it both on Hadoop, and a more flexible platform named REEF.
Analysis of adherence to peak flow monitoring when recording of data is electronic

Peak flow monitoring is widely recommended in international asthma guidelines. However, suspicions about the accuracy of conventional pen and paper records were confirmed when studies with electronic spirometers showed poor adherence and falsification of data.1 There seems to be a prevailing nihilistic attitude to peak flow monitoring, largely based on the perception that satisfactory adherence cannot be achieved. We aimed to measure long term adherence to electronic peak flow monitoring when participants were aware that data were being stored and used to guide treatment. We obtained data from a 72 week randomised study comparing two starting doses of budesonide in patients aged 18-75 with poorly controlled asthma. The design and outcomes of the study are reported elsewhere.2 The study incorporated two novel features: twice daily monitoring with electronic diary spirometers (MicroMedical DiaryCard; MicroMedical, Rochester, UK) and titration of dose of budesonide (weeks 17-72) by using a clinical algorithm based on peak flow and diary data. …
Multiscale Decompositions and Optimization

In this paper, the following type Tikhonov regularization problem will be systematically studied: [(u_t,v_t):=\argmin_{u+v=f} {|v|_X+t|u|_Y},] where $Y$ is a smooth space such as a $\BV$ space or a Sobolev space and $X$ is the pace in which we measure distortion. Examples of the above problem occur in denoising in image processing, in numerically treating inverse problems, and in the sparse recovery problem of compressed sensing. It is also at the heart of interpolation of linear operators by the real method of interpolation. We shall characterize of the minimizing pair $(u_t,v_t)$ for $(X,Y)=(L_2(\Omega),\BV(\Omega))$ as a primary example and generalize Yves Meyer's result in [11] and Antonin Chambolle's result in [6]. After that, the following multiscale decomposition scheme will be studied: [u_{k+1}:=\argmin_{u\in \BV(\Omega)\cap L_2(\Omega)} {1/2|f-u|^2_{L_2}+t_{k}|u-u_k|_{\BV}},] where $u_0=0$ and $\Omega$ is a bounded Lipschitz domain in $\R^d$. This method was introduced by Eitan Tadmor et al. and we will improve the $L_2$ convergence result in \cite{Tadmor}. Other pairs such as $(X,Y)=(L_p,W^{1}(L_\tau))$ and $(X,Y)=(\ell_2,\ell_p)$ will also be mentioned. In the end, the numerical implementation for $(X,Y)=(L_2(\Omega),\BV(\Omega))$ and the corresponding convergence results will be given.