Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability and case studies of linear continuous-time systems under deep belief network controllers

Hybrid feedback control systems are constructed after replacing the continuous controllers with neural-network controllers (in term of neural algorithms). Here, the continuous controllers are originally designed by using the control theory in the linear time-invariant (LTI) continuous-time setting, while the neural algorithms approximate the input/output behavior of the continuous controllers through deep learning. Then, stability problem of the hybrid feedback system is formulated as that of a corresponding LTI feedback system subject to nonlinear perturbation in term of learning error. Stability conditions in bounded-input/bounded-output (BIBO) are proved. In general, when stability robustness is concerned, the suggested stability conditions show that the learning error should be as small as possible. To illustrate the theoretical results, a continuous-time PID controller being replaced with a deep belief network (DBN) controller in a DC-motor system is stimulated. That is, the input/output time responses of the PID controller are used as the training data and target data, respectively, for creating the DBN controller. This yields an DC-motor feedback system based on DBN-driving control strategy. Numerical simulations and comparisons of the DC-motor system under the PID controller and under the suggested DBN one validate the expected performances and high efficacy.
Distinguish protein decoys by Using a scoring function based on a new AMBER force field, short molecular dynamics simulations, and the generalized born solvent model

Recent works have shown the ability of physics‐based potentials (e.g., CHARMM and OPLS‐AA) and energy minimization to differentiate the native protein structures from large ensemble of non‐native structures. In this study, we extended previous work by other authors and developed an energy scoring function using a new set of AMBER parameters (also recently developed in our laboratory) in conjunction with molecular dynamics and the Generalized Born solvent model. We evaluated the performance of our new scoring function by examining its ability to distinguish between the native and decoy protein structures. Here we present a systematic comparison of our results with those obtained with use of other physics‐based potentials by previous authors. A total of 7 decoy sets, 117 protein sequences, and more than 41,000 structures were evaluated. The results of our study showed that our new scoring function represents a significant improvement over previously published physics‐based scoring functions. Proteins 2004. © 2004 Wiley‐Liss, Inc.
Corporate Credit Rating based on Bankruptcy Probability Using AdaBoost Algorithm-based Support Vector Machine

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.
Reconstructing rooms using photon echoes: A plane based model and reconstruction algorithm for looking around the corner

Can we reconstruct the entire internal shape of a room if all we can directly observe is a small portion of one internal wall, presumably through a window in the room? While conventional wisdom may indicate that this is not possible, motivated by recent work on ‘looking around corners’, we show that one can exploit light echoes to reconstruct the internal shape of hidden rooms. Existing techniques for looking around the corner using transient images model the hidden volume using voxels and try to explain the captured transient response as the sum of the transient responses obtained from individual voxels. Such a technique inherently suffers from challenges with regards to low signal to background ratios (SBR) and has difficulty scaling to larger volumes. In contrast, in this paper, we argue for using a plane-based model for the hidden surfaces. We demonstrate that such a plane-based model results in much higher SBR while simultaneously being amenable to larger spatial scales. We build an experimental prototype composed of a pulsed laser source and a single-photon avalanche detector (SPAD) that can achieve a time resolution of about 30ps and demonstrate high-fidelity reconstructions both of individual planes in a hidden volume and for reconstructing entire polygonal rooms composed of multiple planar walls.
Face recognition using modified deep learning neural network

Deep learning methods, especially convolutional neural networks have achieved significant success in the area of computer vision including the difficult face recognition problems. Training of deep models shows exceptional performance with large datasets, but they are not suitable for learning from few samples. This paper proposes a modified deep learning neural network to learn face representation from a smaller dataset. The proposed network is composed of a set of elaborately designed CNNs, RELUs and fully connected layers. The training dataset is augmented with synthetically generated samples by applying Gaussian and Poisson noise to each sample of the training set, thus doubling the size of the training set. We experimentally demonstrate that the augmented training dataset actually improves the generalization power of CNNs. The network is trained using the standard AT&T face database. Using the proposed approach for limited training data, substantial improvement in recognition rate is achieved.
Whistler mode wave growth and propagation in the prenoon magnetosphere

[1] Pitch angle scattering of electrons can limit the stably trapped particle flux in the magnetosphere and precipitate energetic electrons into the ionosphere. Whistler mode waves generated by a temperature anisotropy can mediate this pitch angle scattering over a wide range of radial distances and latitudes, but in order to correctly predict the phase space diffusion, it is important to characterize the whistler mode wave distributions that result from the instability. We use previously published observations of number density, pitch angle anisotropy, and phase space density to model the plasma in the quiet prenoon magnetosphere (defined as periods when AE < 100 nT). We investigate the global propagation and growth of whistler mode waves by studying millions of growing raypaths and demonstrate that the wave distribution at any one location is a superposition of many waves at different points along their trajectories and with different histories. We show that for observed electron plasma properties, very few raypaths undergo magnetospheric reflection; most rays grow and decay within 30 degrees of the magnetic equator. The frequency range of the wave distribution at large L can be adequately described by the solutions of the local dispersion relation, but the range of wave normal angle is different. The wave distribution is asymmetric with respect to the wave normal angle. The numerical results suggest that it is important to determine the variation of magnetospheric parameters as a function of latitude, as well as local time and L-shell.
Biodiversity and human-pathogenicity of Phialophora verrucosa and relatives in Chaetothyriales

Phialophora as defined by its type species P. verrucosa is a genus of Chaetothyriales, and a member of the group known as ‘black yeasts and relatives’. Phialophora verrucosa has been reported from mutilating human infections such as chromoblastomycosis, disseminated phaeohyphomycosis and mycetoma, while morphologically similar fungi are rather commonly isolated from the environment. Phenotypes are insufficient for correct species identification, and molecular data have revealed significant genetic variation within the complex of species currently identified as P. verrucosa or P. americana. Multilocus analysis of 118 strains revealed the existence of five reproductively isolated species apparently having different infectious potentials. Strains of the sexual morph Capronia semiimmersa cluster within P. americana. The newly defined taxa differ markedly in their predilection for the human host.
Agricultural financing, output and macroeconomic growth

This paper examined the effect of agricultural financing on agricultural output and macroeconomic output in the Nigerian economy. The error correction results from the analysis of time series data provided empirical evidence that there is a positive relationship between agricultural credit and agricultural output. Agricultural credit also had a positive effect on real GDP growth over the period of the study. However, credit default by borrowers is a serious menace which reduces the confidence of lenders. The Nigerian legal system should ensure a better credit enforcement mechanism and effective management of interest rates by monetary authorities. In addition, credit facilities and subsidies that would trickle down to individual farmers and farmers' cooperative groups will provide the needed funds to enhance agricultural output as well as economic output in Nigeria.
Strategic Priorities, Company Performance and Attitudes Towards Management Accounting Techniques: an Empirical Study

The study investigates whether there is an association between different combinations of emphasis on generic strategies (product differentiation and cost efficiency) and perceived usefulness of management accounting techniques. Previous research has found that cost leadership is associated with traditional accounting techniques and product differentiation with a variety of modern management accounting approaches. The present study focuses on the possible existence of a strategy that mixes these generic strategies. The empirical results suggest that (a) there is no difference in the attitudes towards the usefulness of traditional management accounting techniques between companies that adhere either to a single strategy or a mixed strategy; (b) there is no difference in the attitudes towards modern and traditional techniques between companies that adhere to a single strategy, whether this is product differentiation or cost efficiency, and c) companies that favour a mixed strategy seem to have a more positive attitude towards modern techniques than companies adhering to a single strategy.
The influence of employees as active consumers of Human Resource Management

This study explores human resource management (HRM) consumption behavior that employees engage in while making use of provided HRM services. It is also explored whether the potential value of HRM for employees can be generated via this HRM consumption behavior. The study found seven types of HRM consumption behavior. These can be categorized by need pursuing behaviors that consist of competence, relatedness and autonomy driven HRM consumption behavior. Also a tendency is found for HRM consumption that is compliance oriented. The resulting value of HRM for employees, consisting of human capital, social capital and organizational capital, is found to have a tendency to be influenced by the specific types of the observed HRM consumption behavior.
Tetramethylammonium hydrogen terephthalate

The asymmetric unit of the title salt, C4H12N+·C8H5O4 −, contains one half of a tetramethylammonium cation and one half of a hydrogen terephthalate monoanion. The N atom of the ammonium cation lies on a twofold rotation axis and the centre of mass of the terephthalate anion is on a centre of inversion. In the crystal, the centrosymmetric terephthalate ions are linked by a very short symmetric O—H⋯O hydrogen bond [O⋯O = 2.4610 (19) Å] into a one-dimensional polymeric chain along [1-12]. The tetramethylammonium cations and terephthalate anions are then connected through a pair of bifurcated acceptor C—H⋯O hydrogen bonds, generating a three-dimensional supramolecular network. The carboxylate groups at both ends of the terephthalate anion are charge-shared with an equal probability of 0.5.
A Family of Suboptimum Detectors for Coherent Multiuser Communications

Consideration is given to a class of suboptimum detectors for data transmitted asynchronously by K users employing direct-sequence spread-spectrum multiple access (DS/SSMA) on the additive white Gaussian noise (AWGN) channel. The general structure of these detectors consists of a bank of matched filters, a linear transformation that operates on the matched-filter outputs, and a set of threshold devices. The linear transformations are chosen to minimize either a mean-squared-error or a weighted-squared-error performance criterion. Each detector can be implemented using a tapped delay line. The number of computations performed per detected bit is linear in K in each case, and the resulting detectors are thus much simpler than the optimum detector. Under typical operating conditions, these detectors will perform much better than the conventional receiver and often nearly as well as the optimum detector. >
A framework for system level verification: the systemc case

Recent advances in hardware design has enabled integration of a complete yet complex systems on a single chip (called System-on-a-Chip: SoC). It is conceivable that the role of traditional Register Transfer level (RTL) languages will diminish to an extent akin to assembly level languages in software design. Therefore, new design languages or so-called System Level Languages (SLL) have emerged. Verification techniques for SOC designs also need to change with this trend. Combining classical verification techniques, such as simulation, with several other formal techniques, into a single approach has been gaining attention in SoC verification. Classical simulation based verification techniques when used with SystemC face several problems related to the object-oriented aspect of SystemClibrary and due to the complexity of its simulation environment. In this talk, we present our proposed methodology to verify SoC designs modeled in SystemC. To this end, we introduce a hybrid approach combining static code analysis, model checking and assertion based verification. We also propose to augment the approach by a test generation module in order to improve the coverage metrics in comparison to the classical simulation approach (mainly based on random test generation).
Decentralized Traffic Aware Scheduling for multi-hop Low power Lossy Networks in the Internet of Things

The emerging IEEE802.15. 4e standard and IETF RPL routing protocol are core to the organization of multi-hop Low-power and Lossy Networks. They provide key functionalities useful for a really viable Internet of Things. However, several open issues still remain and require research efforts to be solved. Among others, the design of effective scheduling schemes in such systems is one of the major problems; in fact, there are no specifications about how schedules should be realized. Trying to fill this gap, this paper presents a new Decentralized Traffic-Aware Scheduling algorithm, which is able to construct optimum multi-hop schedules in a distributed fashion. Its effectiveness has been proved by using simulation results and comparing it with a centralized scheme. The reported performance results encourage the use of the developed scheduling technique, since it allows a very efficient queue management, and thus it minimizes packet discards due to buffer overflows, while at the same time minimizing the network duty cycle.
Fabrication of nano-structured substrates for surface enhanced Raman spectroscopy

In this work an oxidation reduction cycle (ORC) technique for fabricating polycrystalline gold foils as effective SERS substrates was systematically optimised. The potential of two other electrochemical techniques, (1) a potential step sequence (ORC-step) and (2) a one step electrochemical method which has been developed for fabrication of nano-porous gold films (NPGF) was demonstrated as an alternative to ORC.
Classification of symbolic data using fuzzy set theory

Proposes a new algorithm to carry out classification of symbolic data using fuzzy set theory without any a priori assumption. The aim is to show how to apply fuzzy concepts to symbolic data. The new algorithm involves two stages. In the first stage, the number of classes present in the data is found using a cluster indicator, and in the second stage, fuzzy descriptions on symbolic data have been developed. The proposed work is new in the sense that no research work has previously been reported on the application of fuzzy concepts to symbolic data classification. The results of the proposed algorithm are compared with other symbolic clustering techniques.
A Distributed Approach to Improving EPC Controller Performance

A software-defined network paradigm has been recently proposed as a solution to tackle several issues in legacy networking, specifically, in cellular networks. In cellular networks such as LTE, SDN has been incorporated into different parts, especially into the Evolved Packet Core (EPC). The current SDN-based centralized EPC controller architecture for the cellular network displays weaknesses on handover efficiency and scalability. In this paper, we present a distributed approach for implementing EPC controller architecture that utilizes functional entities to handle handover procedures asynchronously in order to improve system efficiency and scalability. Moreover, we carried out a comparative analysis of current centralized EPC controller architecture and our proposed distributed EPC controller architecture. The simulation results quantify the advantages of the distributed EPC controller architecture over centralized EPC controller architecture in terms of handover latency and average throughput per user. We demonstrate that the proposed architecture offers better performance on handover and data transmission in general.
Bitopic Membrane Topology of the Stable Signal Peptide in the Tripartite Junín Virus GP-C Envelope Glycoprotein Complex

ABSTRACT The stable signal peptide (SSP) of the GP-C envelope glycoprotein of the Junín arenavirus plays a critical role in trafficking of the GP-C complex to the cell surface and in its membrane fusion activity. SSP therefore may function on both sides of the lipid membrane. In this study, we have investigated the membrane topology of SSP by confocal microscopy of cells treated with the detergent digitonin to selectively permeabilize the plasma membrane. By using an affinity tag to mark the termini of SSP in the properly assembled GP-C complex, we find that both the N and C termini reside in the cytosol. Thus, SSP adopts a bitopic topology in which the C terminus is translocated from the lumen of the endoplasmic reticulum to the cytoplasm. This model is supported by (i) the presence of two conserved hydrophobic regions in SSP (hφ1 and hφ2) and (ii) our previous demonstration that lysine-33 in the ectodomain loop is essential for pH-dependent membrane fusion. Moreover, we demonstrate that the introduction of a charged side chain or single amino acid deletion in the membrane-spanning hφ2 region significantly diminishes SSP association in the GP-C complex and abolishes membrane fusion activity. Taken together, our results suggest that bitopic membrane insertion of SSP is centrally important in the assembly and function of the tripartite GP-C complex.
Strategic Focus of CSR towards Sustainability: A Sri Lankan Perspective

Corporate Social Responsibility (CSR) being one of the fastest changing areas of management today has few facets among scholars in the world, one school of thoughts believe CSR should be used mainly as a social endeavor and the others think it should be linked to the corporate strategy for financial performance. In this context, this paper aims to observe how strategic approach of CSR initiatives leads to sustainability specially an environment where economic wounds are been settling and new opportunities are been evolving after a 30 year long civil unrest in a society. The strategic antecedents recognized in this model are resource mobilization, market development and customer orientation and the study attempts to identify the core strategic focus of those in building CSR initiatives for sustainability. Accordingly, total of 360 shareholders/investors and managers among 40 corporates who were involved in CSR activities during 05 years after a long civil war in Sri Lanka was sampled. It has found that implementing CSR initiatives strategically aiming both resource mobilization and market development tend to support sustainability of those initiatives but aiming customer orientation has less impact otherwise. In conclusion, it is advisable for corporates to approach CSR initiatives strategically gaining a return to both the society and for them and also it is important that governments too support those initiatives in rebuilding process. Keywords: strategic CSR, sustainability, rebuilding, resource mobilization and market development.
Implementation and evaluation of local breakout method for IP-based femtocell networks

Recently, femtocells, which cover a cell area of several tens of meters, have been attracting considerable attention in the field of mobile data communications. In the standard method, when a femtocell for data communication is connected through a public network such as a broadband access network or the Internet, an IPSec tunnel is established between the femtocell and the security gateway located in the mobile core network and all the data are exchanged over that tunnel for secure communication. Therefore, when access terminals communicate with home servers or the Internet using femtocells, communication data are sent back after transferring them to the mobile core network through the security gateway. As a result, the routing path becomes redundant and the load on the mobile core network also becomes high. In this paper, we propose local breakout methods which terminate the IP layer and set a flexible routing table on the femtocell based on the operator's policy. The proposed methods are then evaluated using 1xEV-DO femtocell product.