I know the name "Scilab" for a long time (http://www.scilab.org/en). For me, it is a tool for numerical analysis. It seemed not interesting in the context of the statistical data processing and data mining. Recently a mathematician colleague spoke to me about this tool. He was surprised about the low visibility of Scilab within the data mining community, knowing that it proposes functionalities which are quite similar to those of R software. I confess that I did not know Scilab from this perspective. I decided to study Scilab by setting a basic goal: is it possible to perform simply a predictive analysis process with Scilab? Namely: loading a data file (learning sample), building a predictive model, obtaining a description of its characteristics, loading a test sample, applying the model on this second set of data, building the confusion matrix and calculating the test error rate.
VALERIE (Slightly angry and becoming more so) If you’d had to have physiotherapy which involved being thumped on the back every day of your life, had chronic diarrhoea and other digestive problems, had one chest infection after another knowing that the next one might very well carry you off, been in and out of hospital more times than you could count, had to take antibiotics, digestive tablets and goodness knows what other medication and knew that you were unlikely ever to have children and even more unlikely to reach the age of 40. (Each time she pauses for breath CLAIRE interrupts, but isn’t quick enough) If you’d been waiting for three years for a heart and lung transplant that was your last hope, you might not be the chirpy, happy, life and soul of the party type either.
It is not triv ial to measure the “properness” of Twitter followees for specific YouTube videos. The challenge lies in two-fo ld: (1) The level of “properness” is not necessarily proportional to the number of followers (# follower). While a popular followee with a large #fo llo wer will guarantee a huge audiences, what video p ro motion cares is the number of “eﬀective” audiences, who are likely to show interest to the video and with higher probability to take subsequent consuming actions like watch, reshare, etc. A close analogy to advertising can be made, where the fo llo wee is viewed as advertising media, whose bid price is decided by #follower. Twitter fo llowee identification is analogous to advertising med ia selection 4, with goal to achieve the maximu m coverage and exposures in a target audience with the minimu m cost. (2) Based on the above discussion, whether a Twitter followee is proper for the promotion task is actually decided by the interest his/her follo wers show to the YouTube videos. However, we only know the followers’ activities on Twitter, based on what only the demographics or interest-s on the general level can be inferred. While, the YouTube videos are known to distribute more on specific semantic level. The discrepancy in topic granularity and aﬃliated platform makes it impractical to directly evaluate Twitter followers’ interest to YouTube videos, let alone the costly co mputation in evaluating each fo llo wer and the subsequent aggregation.
Note that k (integer) and ct (string) are inputs and mk,nk and lk (integers) are outputs of GetRhsVar. This function defines the type (ct) of input variable numbered k, i.e. the k th input variable in the calling sequence of the Scilab function. The pair mk,nk gives the dimensions (number of rows and columns) of variable numbered k if it is a matrix. If it is a chain mk*nk is its length. lk is the adress of variable numbered k in Scilab internal stack. The type of variable number k, ct, should be set to "d", "r", "i" ,"z" or "c" which stands for double, float (real), integer, double complex or character respectively. The interface should call function GetRhsVar for each of the rhs variables of the Scilab function with k=1, k=2,..., k=Rhs. Note that if the Scilab argument doesn’t match the requested type then Scilab enters an error function and returns from the interface function.
We take here two polynomials used for the Global Positioning System (GPS) and forming a preferred pair by the Gold criteria : f x 1 ( ) = + 1 x 3 + x 10 and f x t ( ) = + 1 x 2 + x 3 + x 6 + x 8 + x 9 + x 10 ; each one in Fibonacci configuration or SSRG. As it has been done for the MSRG, the conceptual model is shown in Figure 9. Figure 10 illustrates the designed model under Xcos. We denote G10 the output sequence of length 2 10 − = 1 1023 . 6.1. Correlation of Generated Sequences
In the ISO/IEC 9899:TC3 C Committee Draft , the section G.5.1 ”Multi- plicative operators”, the authors present a _Cdivd function which implements the complex division. Their implementation only scales the denominator c 2 + d 2 . This scaling is based on a power of 2, which avoid extra rounding. Only in the case of an IEEE exceptions, the algorithm recompute the division, taking into account for Nans and Infinities. According to the authors, this solves the main overflow and underflow problem. The code does not defend against overflow and underflow in the calculation of the numerator. According to Kahan  (in the Appendix ”Over/Un- derflow Undermines Complex Number Division in Java”), this code is due to Jim Thomas and Fred Tydeman.
level topic label terms did not exist in the corpus, they would select only names of people and terms that matched Wikipedia article titles from the corpus. Based on these selected corpus terms, they selected additional external terms via four methods: 1) querying Google based on the corpus terms and selecting the most frequent terms in returned snippets, 2) similar to Castanet they queried noun hypernym hierarchies from WordNet, 3) selecting Wikipedia titles of the most frequently linked to and from pages of the corpus term pages, 4) they merged terms based on Wikipedia redirects and anchor tags (e.g., "Hillary Clinton" and "Hillary R. Clinton" both merge based on their mutual redirect to the "Hillary Rodham Clinton" Wikipedia article page). Using the selected corpus term and external term lists they selected a single smaller list of topics based on the assumption that high level topic terms are infrequent in the corpus list but frequent in the external list. They selected terms that occurred more frequently in the external corpus using a difference in term frequency as well as a difference in term ranking. They applied Log Likelihood Ratio to double check that the resulting selected terms are in fact statistically significant in the external term list compared to the selected corpus term list. However, they do not maximize the utility of this method and attempt to use this statistical measure to find terms that may have been excluded by their initial selection process. Hierarchies of the resulting topic term list are then generated via the subsumption process. They conclude their research with the most extensive user study to date by leveraging the power of Amazon Mechanical Turk 1 . However,
The band structure of materials does play a very important role for their application purpose. A number of theories and models are there to give the insight of the band structure of the materials. All these models have got their own limitations respond differently to different structures. The materials we will be talking in this report here are semiconductors. The tight binding model is a very strong and interesting approach to solve the electronic band structure. Since the time, it got invented and developed, it has achieved a lot of attention of the researchers all around the world. In solid state physics the band structure calculations are done in the reciprocal space that give rise to real space band picture. The interesting part of the tight binding model is that it picks out band picture of the whole material just by probing at one K point and its nearest neighbours. Well, one can go further, second nearest neighbour and so on, if more accuracy is required in the result. The model has got some limitations also as every model has got. It excludes some interactions and does not take into account every interaction present there in the problem. So, one need to find the interaction parameters of the dominant interaction. The tight binding model has got similarities with the model given by Slater and Koster. Some people do say that the tight binding
The Raspberry Pi platform originally intended for educational purposes has become famous immediately after its introduction in 2012. The next-generation Raspberry Pi 2 (RPi2 hereinafter) was released in 2015 when almost six million Raspberry Pi of the first generation have been already sold. The second generation is based on the Broadcom BCM2836 system on a chip with a quad-core ARM Cortex-A7 processor and a Video Core IV dual-core GPU with 1 GB of RAM.
Vinter & Perruchet seem to suggest, in addition, that there may be special features of conscious thought and reflection that enable us to do more than simply re-shape the space for learning. Such features would include, for example, the use of “processes which rely on the specific power of conscious thought.” Once again, however, we fear a chicken and egg scenario. Our goal is to understand how biological agents can come to wield the knowledge to which such powers may be applied. We do concede, however, that certain aspects of very high level thought look to lie beyond the scope of our treatment. Thus Vinter & Perruchet (also Dartnall) mention the human ability not just to know a recoding function but to know that we know it. Such knowledge is not of the world so much as of the ways in which we know the world. This certainly does seem like an important ability though the extent to which it figures in daily problem solving is perhaps open to doubt. Whether such top level, meta-reflective capacities merely represent the culmination of a cascade of processes of type-1 learning and re-deployment of achieved representation (as we sus- pect) or rely on the operation of some wholly different faculty (perhaps tied up with conscious thought) is an important topic for further research. It is, of course, very likely that different neurological structures play a role in type-1 learning and type-2 re-coding (see, e.g., Dominey’s comments on the role of the frontostriatal system in type-1 learning). But this is consistent with our claim that the combination of these strategies is effectively all that nature can provide.
have grown a lot and already been a package in almost numerical software available today. This paper presents a tutorial of techniques for solving heat equation numerically by solving the boundary condition problem for ODEs and its application in hot water bath treatment of tropical fruit using lateral method of lines. The routine used is available in the SCILAB named bvode. The tutorial about how to use and information about bvode can be seen in . There is one method to solve the evolution equation numerically, known as method of lines ([2,3]). This method is done by doing space discretization using finite difference  or collocation ([2,4]), in order to obtain an initial value problem for an ODE system. In commercial PSE MATLAB, there is pdepe routine to solve spatial 1-dimensional parabolic equation. This routine based on  that use collocation method for space discretization. Another approach to solve evolution PDEs is using time discretization strategy first, and then the boundary value problem for ODEs can be obtained for each time step. It means the boundary value problem for ODEs must be solved sequentially. This technique is often known as the lateral method of lines, and also known as Rothe method. In , there is review about the lateral method of lines. This paper is written as one of tutorial of the numerical software used to solve PDEs in general. Another purpose of this paper is to support the Indonesian Goes Open Source (IGOS) in the field of Scientific Computing, considering the SCILAB is free software. Furthermore, this paper can be the first step in the procurement of alternative software that can be used, in addition to commercial software MATLAB that is quite popular with its pdepe routine. In this paper, the heat equation will be reviewed first. Then, time discretization will be briefly shown, so the boundary condition problem form for ODEs can be obtained for each discrete time step. Thus, this form can be converted so that it can be solved by using bvode in SCILAB. After the form has been converted, then the implementation for hot water bath treatment of tropical fruit using lateral method of lines will be shown using SCILAB.
There are several kinds of real-time add-ons for free open source Linux in the field of real-time automation by PCs. One of the most widely known is RTAI (Real-Time Application Interface). RTL (Real time Linux) has developed in two ways: (1) Well supported Commercial version called RT-Linux PRO (2) Non-Commercial version called RT-Linux . Free is based on RTL PRO. The free version of RTL may be considered as an independent product because the programing environment is quite different  . An alternative is to obtain a real-time kernel and add it to a commercial RT-Linux installation by ourselves. It is possible to develop real time control loop in open source mathematical and simulation software SCILAB and Simulink tool SCICOS via COMEDI add- ons for RTL. This environment allows to quickly crating real-time controller for real plants by generating and computing the full control application directly from the Scicos scheme  .
Speech is a complicated signal produced as a result of several transformations occurring at several different levels: semantic, linguistic, articulatory, and acoustic. Differences in these transformations are reflected in the differences in the acoustic properties of the speech signal. Besides there are speaker related differences which are a result of a combination of anatomical differences inherent in the vocal tract and the learned speaking habits of different individuals. In speaker recognition, all these differences are taken into account and used to discriminate between speakers .
easily be seen in Figure 12-14. Where the value of DC-link resistor decreases, the value of input current dramatically increases with more ripple. The increase in the value of input current damages the devices used in the system. During this condition, the inverter output voltage also decreases. When the value of DC-link resistance is kept constant and load resistance is large nearly opened-circuit, input current value is very small and DC-link voltage changes little. This is due to the existence of DC-link resistor. It protects the sudden increase in DC link voltage when large load resistance changes. The ripple of input current and DC-link voltage are lesser than in simulation-3 as compared with simulation-1. The DC-link voltage fluctuation is more obvious in loaded condition. This is because of power transferred of inverter. The ripple frequency of DC-link voltage fluctuation is twice the output voltage frequency of inverter.
for asymmetric catalysis (Pu, 1998), as a host for molecular recognition and enantiomer separation (Reeder et al., 1994), and also as an intermediate for the synthesis of chiral mate- rials (Zhang & Schuster, 1994). Many compounds of binaph- thol have been studied previously (Periasamy et al. , 1997; Dobashi et al., 1998; Lee et al., 1999; Du et al., 2002; Chan- drasekhar et al., 2003; Cheung et al., 2003). Recently, the title compound, (I), has been obtained and its structure (Fig. 1) is discussed here.
Our vision has children and young people at its heart. Our work contributes to preventing children and young people becoming offenders or victims of crime, and to mitigating the impact of crime on families, communities and victims. Our statutory powers, outlined in Table 1 below, enable us to lead the youth justice system, as well as to oversee and monitor its operation.
All banks incorporated in New Zealand, with more than $1 billion in deposits, are required to prepare their IT systems for the scheme. Other registered banks may opt in. Most of the locally incorporated banks operating in New Zealand are required to prepare themselves for OBR.