Miracle-dz/UniFAR-FTUs huggingface dataset:
Method to Approximate Initial Values for Training Lineal Neural Networks

The present paper proposes a method to calculate a set of proposed initial values for the weight matrix and the bias vector of a neural network prior to training. The method described here applies for linear neural networks with one hidden layer, and a known proportional relationship between inputs and outputs. The algorithm and the calculations are intended to be simple, to facilitate automation in small processors The method normalizes values in a tri-level form, finds the relationships on the maximum and minimum values for all combinations of inputs and outputs, averages these results and builds the weight matrix and bias vector from these results. The end result is a set of initial values prior to training, intended to have a start point for training closer to the end result. Overall result is less training time.
Typechecking Linear Data: Quantum Computation in Haskell

This paper demonstrates how to use type classes to get Haskell’s type inference engine to infer the types of an embedded language for quantum computation. The embedded language, a variant of Selinger & Valiron’s quantum lambda calculus (QLC), uses linear types to express the non-duplicability of quantum data, along with subtyping to allow convenient mixing of classical data with quantum data. The embedding makes sophisticated use of Haskell’s type classes to do logic programming at the type level.
Using BIST circuitry to measure DRV of large SRAM arrays

This paper presents a new technique to measure the data retention voltage (DRV) of large SRAM arrays in the presence of process variations. The main aim of the proposed technique is to ensure that the SRAM array operates at the minimum energy point. In the proposed procedure, the DRV is measured by implementing a small test circuit on-chip consisting of a modified form of the memory Built-In Self Test (BIST) unit, a DC-DC converter and a Test Control Unit (TCU). The circuit was developed in 90nm technology and simulated using HSPICE. Previously proposed statistical techniques determine the DRV at around 150mV, whereas the proposed technique showed that the SRAM array under test could retain its data at voltages as low as 80mV which would result in significant power savings.
Current Sensorless Power Factor Correction based on Digital Current Rebuilding

A new digital control technique for power factor correction is presented. The main novelty of the method is that there is no current sensor. Instead, the input current is digitally rebuilt, using the estimated input current for the current loop. Apart from that, the ADCs used for the acquisition of the input and output voltages have been designed ad-hoc. Taking advantage of the slow dynamic behavior of these voltages, almost completely digital ADCs have been designed, leaving only a comparator and an RC filter in the analog part. The final objective is obtaining a low cost digital controller which can be easily integrated in an ASIC along with the controller of paralleled and subsequent power sections.
Fault Dictionary Compaction By Output Sequence Removal

Fault dictionary compaction has been accomplished in the past by removing responses on individual output pins for specific test vectors. In contrast to the previous work, we present techniques for eliminating entire sequences of outputs and for efficiently storing the remaining output sequences. Experimental results on the ISCAS 85 and ISCAS 89 benchmark circuits show that the sizes of dictionaries proposed are substantially smaller than the full fault dictionary, while the dictionaries retain most or all of the diagnostic capability of the full fault dictionary.
Solution of Nonlinear Stokes Equations Discretized By High-Order Finite Elements on Nonconforming and Anisotropic Meshes, with Application to Ice Sheet Dynamics

Motivated by the need for efficient and accurate simulation of the dynamics of the polar ice sheets, we design high-order finite element discretizations and scalable solvers for the solution of nonlinear incompressible Stokes equations. We focus on power-law, shear thinning rheologies used in modeling ice dynamics and other geophysical flows. We use nonconforming hexahedral meshes and the conforming inf-sup stable finite element velocity-pressure pairings $\mathbb{Q}_k\times \mathbb{Q}^\text{disc}_{k-2}$ or $\mathbb{Q}_k \times \mathbb{P}^\text{disc}_{k-1}$. To solve the nonlinear equations, we propose a Newton-Krylov method with a block upper triangular preconditioner for the linearized Stokes systems. The diagonal blocks of this preconditioner are sparse approximations of the (1,1)-block and of its Schur complement. The (1,1)-block is approximated using linear finite elements based on the nodes of the high-order discretization, and the application of its inverse is approximated using algebraic multigrid with an incomplete factorization smoother. This preconditioner is designed to be efficient on anisotropic meshes, which are necessary to match the high aspect ratio domains typical for ice sheets. We develop and make available extensions to two libraries---a hybrid meshing scheme for the p4est parallel AMR library, and a modified smoothed aggregation scheme for PETSc---to improve their support for solving PDEs in high aspect ratio domains. In a numerical study, we find that our solver yields fast convergence that is independent of the element aspect ratio, the occurrence of nonconforming interfaces, and of mesh refinement, and that depends only weakly on the polynomial finite element order. We simulate the ice flow in a realistic description of the Antarctic ice sheet derived from field data, and study the parallel scalability of our solver for problems with up to 383M unknowns.
Non-insulin-dependent Diabetes Mellitus (niddm) Association of Diabetes Mellitus and Dementia: the Rotterdam Study

is a well-known risk factor for cardiovascular disease and stroke [1–3]. Apart from the obvious lesions caused by strokes, also subtle neurochemical, electro-physiologic and structural changes have been found in brains of patients with NIDDM [3–5]. Neuropsy-chologic test profiles of NIDDM patients, compared to those of healthy or hospital control subjects, showed decreased cognitive functions [6–9]. Several authors suggested that, analogous to its association with cerebral infarction, NIDDM is related to vascu-lar dementia [10, 11]. However, the extent to which NIDDM is associated with other dementia subtypes, in particular Alzheimer's disease (AD), is unclear [12–15]. We examined the association between NIDDM and prevalent dementia in a large population-based study. Summary Dementia and non-insulin-dependent diabetes mellitus (NIDDM) are highly prevalent disorders in the elderly. Diabetes has repeatedly been reported to affect cognition, but its relation with de-mentia is uncertain. We therefore studied the association between diabetes and dementia in the Rotter-dam Study, a large population-based study in the elderly. Of 6330 participants, aged 55 to 99 years old, complete information on diabetes and presence of dementia was available. Diabetes was diagnosed as use of anti-diabetes medication or random or post-load serum glucose over 11 mmol/l. Dementia was diagnosed through a stepped approach, including a sensitive screening of all participants and a comprehensive diagnostic work-up. Diabetes was present in 724 (11.4 %) subjects. Of the 265 dementia patients 59 (22.3 %) had diabetes. Multiple logistic regression analyses, adjusting for age and sex differences, revealed a positive association between diabetes and dementia (odds ratio: 1.3, 95 % confidence interval: 1.0–1.9). In particular, strong associations were found between dementia and diabetes treated with insulin (odds ratio: 3.2, 95 % confidence interval: 1.4–7.5). The relation was strongest with vascular dementia, but was also observed with Alzheimer's disease. These associations were independent of educational attainment, smoking, body mass index, atherosclero-sis, blood pressure and antihypertensive drug treatment , and could not be explained by clinical cerebral infarctions. The results suggest that NIDDM is associated with dementia. Alzheimer's disease may be more frequent in elderly diabetic patients treated with insulin.
Off-grid DOA estimation using temporal block sparse Bayesian inference

By considering the off-grid distance in the sparse reconstruction model, off-grid direction-of-arrival (DOA) estimation can achieve better performance. Most existing off-grid algorithms consider that the snapshots of each source are independent with each other. This contradicts with the realworld scenario, where sources often have temporal structures. To address this issue, we present a new off-grid DOA estimation method, which brings the temporal structures into the off-grid model and a temporal block sparse Bayesian inference is derived. In comparison with the off-grid block sparse Bayesian inference method, the proposed approach achieves higher estimation accuracy for off-grid source directions in low SNR situations. Numerical simulations demonstrate the preferable performance of our method.
Stock Market Analysis and Prediction

Abstract: Stock price Analysis & Prediction is a one of the sought after and popular topic throughout the last decade. By using machine learning and deep learning (RNN & LSTM) methods to make stock price prediction using real time data. While using Deep learning functions to predict and analyze stock prices are becoming more prevalent in these recent days. Its observed and assumed that machine learning as well as deep learning methods with RNN and LSTM could produce accurate results in stock price prediction. That is why we would like to try our own methods for this project. We have used a number of stocks from the S&P 500 namely as inputs and target. The data will contain open, high, and low, close, adjacent close and volume as its 6 variables. The adjacent close of each stock will become the target, and rest of the variable for a particular stock as well as all the other variables of other respective stocks will become as inputs. Index Terms: RNN: Recurrent Neural Network, S&P 500: Standard and Poor’s 500, LSTM Long short-term memory, DSP: Digital Signal Processing.
Change-point Analysis: a Powerful New Tool for Detecting Changes

Change-point analysis is a powerful new tool for determining whether a change has taken place. It is capable of detecting subtle changes missed by control charts. Further, it better characterizes the changes detected by providing confidence levels and confidence intervals. When collecting online data, a change-point analysis is not a replacement for control charting. But, because a change-point analysis can provide further information, the two methods can be used in a complementary fashion. When analyzing historical data, especially when dealing with large data sets, change-point analysis is preferable to control charting. A change-point analysis is more powerful, better characterizes the changes, controls the overall error rate, is robust to outliers, is more flexible and is simpler to use. This article describes how to perform a change-point analysis and demonstrates its capabilities through a number of examples.
The Discovery of Communication

What kind of motivation drives child language development? This article presents a computational model and a robotic experiment to articulate the hypothesis that children discover communication as a result of exploring and playing with their environment. The considered robotic agent is intrinsically motivated towards situations in which it optimally progresses in learning. To experience optimal learning progress, it must avoid situations already familiar but also situations where nothing can be learned. The robot is placed in an environment in which both communicating and non-communicating objects are present. As a consequence of its intrinsic motivation, the robot explores this environment in an organized manner focusing first on noncommunicative activities and then discovering the learning potential of certain types of interactive behavior. In this experiment, the agent ends up being interested by communication through vocal interactions without having a specific drive for communication.
2 Learning Classifier Systems for Hyperspectral Images Processing

In this article, two learning classifier system based classification techniques are described to classify remote sensing images. Usually, these images contain voluminous, complex, and sometimes erroneous and noisy data. The first approach implements ICU, an evolutionary rule discovery system, generating simple and robust rules. The second approach applies the real-valued accuracy-based classification system XCSR. The two algorithms are detailed and validated on hyperspectral data. The comparison of the system includes differences in evolution accuracy and parameter refinement.
When Model Driven Engineering meets virtual reality: Feedback from application to the Collaviz framework

Despite the increasing use of 3D Collaborative Virtual Environments (3D CVE), their development is still a cumbersome task. The various concerns to consider (distributed system, 3D graphics, etc.) complexify the development as well as the evolution of CVEs. Software engineering recently proposed methods and tools to ease the development process of complex software systems. Among them, Model-Driven Engineering (MDE) considers models as first-class entities. A model is an abstraction of a specific aspect of the system under study for a specific purpose. MDE thus breaks down a complex system into as many models for different purposes, such as: generating code from models; building domain specific programming/modeling languages (DSL); generating tools such as graphical or textual editors. In this paper we leverage MDE for developing 3D CVEs. We show how the Collaviz framework took benefits from a DSL we built. The benefits are multiple: 3D CVE designers can focus on the behavior of their virtual objects without bothering with distributed and graphics features; configuring the content of 3D CVEs and their deployment on various software and hardware platforms can be automated through code generation. We detail the development process we propose and the experiments we conducted on Collaviz.
A new edge-based complexity measure for intra-rate estimation in H.264/AVC

An efficient I-frame R-Q model is still needed by the rate control in the latest H.264/AVC video coding standard. Some recent works introduced the gradient-based image complexity measure into the frame-layer intra rate estimation in H.264/AVC, but the issue at the macroblock layer was not discussed. We propose a new edge-based complexity measure for this bit rate estimation problem at both the frame-layer and the macroblock-layer. The LOG operator is used in the edge extraction and an adaptive threshold selection method is proposed. The experiments show that the proposed edge-based measure has a good linear relation with the intra frame bit rate.
Putting Giving into Context : Examining the Effects of Religiosity , Trust , and World Society on Charitable Activity in 35 Countries

Why do people give? Social scientists have long been interested in the motives behind individual charitable giving. However, few studies have looked at the effects of social context on charitable activity, particularly in a cross-national context. This chapter uses multilevel modeling techniques to examine the effects of national context on membership in charitable and humanitarian organizations in 35 countries. I find that charitable membership is shaped by connections to world society at the country level and cosmopolitanism at the individual level. Conventional measures of income, social capital, and religiosity also hold up. Lastly, I do not find that charitable membership is a response to recent natural disasters or a reduced welfare state. The analysis has implications for our understanding of charitable activity and its roots in an increasingly expanding global humanitarian order.
Robust contour description and measurement using distance on the Grassmann manifold

We propose robust affine-invariant contour descriptor and measure for shape matching under nonlinear deformations. The descriptor is formed by orthonormal configuration matrix of local contour. The geodesic distance on Grassmann manifold is used to measure similarities of shapes under locally affine transformations, which can approximate complex deformations like articulations. A rigorous perturbation analysis proves that condition numbers of configuration matrices are critical for robustness. Then a method to improve matching stability using the condition numbers is deduced. Commonly used contour matchers, e.g., dynamical programming and others, are all applicable to the descriptor to obtain satisfied matching. Experimental evaluations are given using both synthetic and real-world images.
A Thread-wise Strategy for Incremental Crawling of Web Forums

We study in this paper the problem of incremental crawling of web forums, which is a very fundamental yet challenging step in many web applications. Traditional approaches mainly focus on scheduling the revisiting strategy of each individual page. However, simply assigning different weights for different individual pages are usually inefficient in crawling forum sites because of different characteristics between forum sites and general websites. Instead of treating each individual page independently, we propose a thread-wise strategy by taking into account thread-level statistics, for example, the number of replies and the frequency of replies, to estimate the activity trend of each thread. To extract such statistical information, we develop a simple yet very robust approach to extracting the timestamp of each post in a discussion thread. We also employ a regression model to predict the time of the next post for each thread. Based on this model, we developed a highly efficient crawler which is 2.6 times faster than state-of-the-art methods in terms of fetching new generated content, and meanwhile can still ensure a high coverage ratio. Experimental results show encouraging performance of Coverage, Bandwidth utilization, and Age for our approach on various forums.
Augmented Miniature Prototyping Toolkit for UX in Interactive Space

It is challenging to envision the user experience (UX) within an interactive and ubiquitous computing space in the early phases of a design process due to the large scale and complex interactivity among multiple people and agents. To address the limitations of existing tools, we present miniStudio, a prototyping toolkit for interactive and ubiquitous computing environments larger than human scale. It consists of software and hardware kits for constructing miniature physical spaces and testing dynamic interactions in them through a combination of paper models and Lego. It also supports prototyping for interactivity using projection-based augmented reality with invisible markers and a pro-cam system in instantly created miniature spaces. We applied Adobe Photoshop as an interactivity authoring environment since it is one of the most popular design tools. From the preliminary evaluation with junior designers, miniStudio showed the potential to support the iterative exploration of interactive spaces in the early phases of the design process.
Comparison of Bayesian Clustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods’ effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance.
Modernizing PHCpack through phcpy

PHCpack is a large software package for solving systems of polynomial equations. The executable phc is menu driven and file oriented. This paper describes the development of phcpy, a Python interface to PHCpack. Instead of navigating through menus, users of phcpy solve systems in the Python shell or via scripts. Persistent objects replace intermediate files.
Learning to Ride a Bicycle using Iterated Phantom Induction

We build upon our work on iterated phan. -. wm IDQUC1O10D anQ Ule worK 01 naDQltZ1V anQ Alstrem OD -applying reinforcemeni learning to the challenging control task of learning to ride a bicycle. Last year phantom induction was demonstrated on a nonlinear but non-dynamica1 task. The bicycle domain is a dynamical system whose next state is a function of the current state and control inputs. Randl,v and AlstrfIIm demonstrated this task was learnable using Sarsa(~) with shaping. Our approach integrates domain knowledge into the learning process in a flexible way. By taking advantage of what a domain theory expert can easily express, our . system learns to ride. in a few dozen examples rather than the thousands required by the system of Randl,v and AIstr,m. We also show that the two inputs, shifting the rider's center of mass and deflecting the handlebars, are not needed simultaneously for learning. Our system learns to ride using either input alone. By extending phantom induction into the space of dynamical systems we demonstrate the applicability of our learning technique to an important and difficult class of problems.
Influence of composite period and date of observation on phenological metrics extracted from MODIS data

Deriving date-specific phenometrics from different multi-day MODIS composite data can potentially yield very different results. The objectives of this study were therefore to quantify the differences between (i) using 8-day vs. 16-day MODIS data and (ii) using the date-flag in the 8-day MOD09 data when estimating key phenological metrics for Skukuza, South Africa. After applying Savitsky-Golay filtering, date specific phenometrics were extracted using the threshold (10% of amplitude) and delayed moving average (DMA) methods respectively. The phenometric dates calculated using the 8-day vs. 16-day composite data differed by up to 30 days, as in the case of onset of greenness decrease. Consistent differences were also found between the results of the DMA and threshold methods as they potentially identify different phonological events. The phenometrics extracted are therefore highly influenced by the MODIS input data and methods used and thus trends in such phenometrics should be interpreted with caution.