Miracle-dz/UniFAR-FTUs huggingface dataset:
Rheological Studies of PMMA–PVC Based Polymer Blend Electrolytes with LiTFSI as Doping Salt

In this research, two systems are studied. In the first system, the ratio of poly (methyl methacrylate) (PMMA) and poly (vinyl chloride) (PVC) is varied, whereas in the second system, the composition of PMMA–PVC polymer blends is varied with dopant salt, lithium bis (trifluoromethanesulfonyl) imide (LiTFSI) with a fixed ratio of 70 wt% of PMMA to 30 wt% of PVC. Oscillation tests such as amplitude sweep and frequency sweep are discussed in order to study the viscoelastic properties of samples. Elastic properties are much higher than viscous properties within the range in the amplitude sweep and oscillatory shear sweep studies. The crossover of and is absent. Linear viscoelastic (LVE) range was further determined in order to perform the frequency sweep. However, the absence of viscous behavior in the frequency sweep indicates the solid-like characteristic within the frequency regime. The viscosity of all samples is found to decrease as shear rate increases.
Demo Abstract : Testbed-Independent Experiment Specification and Execution using the COTEFE Platform

The demo illustrates the capabilities of the CONET Testbed Federation (COTEFE), focusing on the support for testbed-independent specification and execution of experiments. It leverages a prototypical implementation of the platform and its two core programming interfaces that follow the REST architectural style. The Testbed Abstraction API, that exposes the services of the individual testbeds thorough a uniform Testing-asa-Service (TaaS) abstraction, has been implemented and deployed on top of the TWIST testbed. The Testbed Federation API, that leverages these abstractions to build higher-level testbedindependent services, has been implemented and deployed using the Google App Engine infrastructure.
From Categorical Logic to Facebook Engineering

I chart a line of development from category-theoretic models of programs and logics to automatic program verification/analysis techniques that are in deployment at Facebook. Our journey takes in a number of concepts from the computer science logician's toolkit -- including categorical logic and model theory, denotational semantics, the Curry-Howard isomorphism, sub structural logic, Hoare Logic and Separation Logic, abstract interpretation, compositional program analysis, the frame problem, and abductive inference.
Usefulness of C-Reactive Protein as a Marker for Prediction of Future Coronary Events in the Asian Indian Population: Indian Atherosclerosis Research Study

Inflammation plays a pivotal role in all stages of atherosclerosis. Numerous inflammatory, lipid, and cytokines markers have been associated with coronary artery disease (CAD) risk but data directly comparing their predictive value are limited. Studies were carried to elucidate the role of high-sensitivity C-reactive protein (hsCRP), other inflammatory as well as lipid markers and their associations. Among 1021 subjects, comprising 774 CAD affected members from Indian Atherosclerosis Research Study (IARS), plasma hsCRP levels showed strong correlation with inflammatory markers, namely, IL6 (r = .373; P = <.0001), sPLA2 (r = .544; P = <.0001) as also with fibrinogen (r = .579; P = <.0001). Levels of hsCRP were higher among subjects affected by CAD who suffered a repeat coronary event as compared to those who remained event free and subjects in the top quartile of hsCRP (>3.58 mg/L) were found to have a fourfold higher risk. In conclusion, hsCRP appears to be an independent predictor of recurrent CAD events in Asian Indian population.
Antimalarial Activity of Cocos nucifera Husk Fibre: Further Studies

In this study, the antimalarial and toxicity potentials of husk fibre extracts of five Nigerian varieties of Cocos nucifera were evaluated in vitro. The only active extract fraction, West African Tall (WAT) ethyl acetate extract fraction, was then evaluated for its phytochemical constituents, antimalarial and toxicity potentials at varying doses (31.25–500 mg/kg body weight) using various organ function indices. The results revealed that WAT ethyl acetate extract fraction (WATEAEF) contained alkaloids, tannins, and flavonoids and was active against Plasmodium falciparum W2 strain maintained in continuous culture, with a selectivity index of 30.3. The same extract fraction was active in vivo against Plasmodium berghei NK65, causing more than 50% reduction in parasitaemia on days 4 and 6 after inoculation at various doses administered. WATEAEF did not significantly alter (P > 0.05) function indices of the liver and cardiovascular system at all doses administered but significantly increased (P < 0.05) plasma creatinine concentration at 250 and 500 mg/Kg body weight compared to controls. The results of this study suggest that WATEAEF possesses antimalarial activity and may not adversely affect normal liver function nor predispose subjects to cardiovascular diseases but may impair normal kidney function at higher doses. Further studies are underway to isolate the active principles.
Low Energy Quantum System Simulation

A numerical method for solving Schrodinger's equation based upon a Baker-Campbell-Hausdorff (BCH) expansion of the time evolution operator is presented herein. The technique manifestly preserves wavefunction norm, and it can be applied to problems in any number of spatial dimensions. We also identify a particular dimensionless ratio of potential to kinetic energies as a key coupling constant. This coupling establishes characteristic length and time scales for a large class of low energy quantum states, and it guides the choice of step sizes in numerical work. Using the BCH method in conjunction with an imaginary time rotation, we compute low energy eigenstates for several quantum systems coupled to non-trivial background potentials. The approach is subsequently applied to the study of 1D propagating wave packets and 2D bound state time development. Failures of classical expectations uncovered by simulations of these simple systems help develop quantum intuition. Finally, we investigate the response of a Superconducting Quantum Interference Device (SQUID) to a time dependent potential. We discuss how to engineer the potential's energy and time scales so that the SQUID acts as a quantum NOT gate. The notional simulation we present for this gate provides useful insight into the design of one candidate building block for a quantum computer.
Making the most of context-awareness in brain-computer interfaces

In order for brain-computer interfaces (BCIs) to be used reliably for extended periods of time, they must be able to adapt to the users evolving needs. This adaptation should not only be a function of the environmental (external) context, but should also consider the internal context, such as cognitive states and brain signal reliability. In this work, we propose three different shared control frameworks that have been used for BCI applications: contextual fusion, contextual gating, and contextual regulation. We review recently published results in the light of these three context-awareness frameworks. Then, we discuss important issues to consider when designing a shared controller for BCI.
Precise Feature Based Time Scales and Frequency Decorrelation Lead to a Sparse Auditory Code

Sparse redundancy reducing codes have been proposed as efficient strategies for representing sensory stimuli. A prevailing hypothesis suggests that sensory representations shift from dense redundant codes in the periphery to selective sparse codes in cortex. We propose an alternative framework where sparseness and redundancy depend on sensory integration time scales and demonstrate that the central nucleus of the inferior colliculus (ICC) of cats encodes sound features by precise sparse spike trains. Direct comparisons with auditory cortical neurons demonstrate that ICC responses were sparse and uncorrelated as long as the spike train time scales were matched to the sensory integration time scales relevant to ICC neurons. Intriguingly, correlated spiking in the ICC was substantially lower than predicted by linear or nonlinear models and strictly observed for neurons with best frequencies within a “critical band,” the hallmark of perceptual frequency resolution in mammals. This is consistent with a sparse asynchronous code throughout much of the ICC and a complementary correlation code within a critical band that may allow grouping of perceptually relevant cues.
Data Warehousing Implementation and Outsourcing Challenges: An Action Research Project with Solectron

Since the 1980s, Solectron evolved as a contract provider of manufacturing, distribution, product design, and full-service supply chain integration to many Fortune 500 companies in the telecommunications, electronics, and computer industries. In order to achieve this set of capabilities, Solectron established a tightly coupled set of collaborative relationships with supply chain participants– including both suppliers and customers. This action research study explores Solectron’s data warehousing implementation and outsourcing experiences to support the organization’s strategic direction. We uncover the absence of new set of critical preimplementation performance metrics for managers and researchers to consider under conditions of outsourcing; multiple, simultaneous projects; and lack of IT internal expertise. Thus, our research offers hypotheses that can be further tested by future research.
Data-based Manifold Reconstruction via Tangent Bundle Manifold Learning

The goal of Manifold Learning (ML) is to find a description of low-dimensional structure of an unknown q-dimensional manifold embedded in high-dimensional ambient Euclidean space R p , q < p, from their finite samples. There are a variety of formulations of the problem. The methods of Manifold Approximation (MA) reconstruct (estimate) the manifold but don’t find a low-dimensional parameterization on the manifold. The most of Manifold Embedding (ME) methods find a low-dimensional parameterization but don’t reconstruct the manifold from a sample. In the paper, the ML is considered as Tangent Bundle Manifold Learning (TBML) problem in which the manifold, its tangent spaces and lowdimensional representation accurately reconstructed from a sample. A new geometrically motivated method for the TBML solution is presented, it also gives a new solution for the MA and ME.
Evolution strategies applied to the problem of line profile decomposition in QSO spectra

We describe the decomposition of QSO absorption line ensembles by applying an evolutionary forward modelling technique. The modelling is optimized using an evolution strategy (ES) based on a novel concept of completely derandomized self-adaption. The algorithm is described in detail. Its global optimization performance in decomposing a series of simulated test spectra is compared to that of classical deterministic algorithms. Our comparison demonstrates that the ES is a highly competitive algorithm capable of calculating the optimal decomposition without requiring any particular initialization.
Poverty, Courses Taken and MCAS Test Scores in Mathematics and Science.

Mathematics the American April 10-14, The aim of this study was to look at the Massachusetts Comprehensive Assessment System (MCAS) data for 10th graders from 1998 and investigate whether these differences were significant when using the mathematics and science achievement scores. In addition, the variables of socioeconomic status, gender, and types of courses taken in mathematics and science were used in the analyses. Three hypotheses were tested. One was that the differences between African Americans and Whites in mathematics and science scores are related to types of courses taken previously. It was also expected that males would have higher mathematics scores than females and that students with low income would be more likely to have low test scores in science and mathematics. The regression analyses demonstrated that previous courses taken in mathematics and science were strong predictors of MCAS achievement scores. The other predictors, race, socioeconomic status, and gender, explained very little of the variance in achievement scores in mathematics and science. These results have strong implications for public policy issues in Massachusetts. (Contains 4 figures and 34 references.) (Author/SLD) Reproductions supplied by EDRS are the best that can be made from the original document. U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) ClErrhis document has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. 1 PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
Lifetime success and interactions of farm salmon invading a native population

Farm Atlantic salmon escape and invade rivers throughout the North Atlantic annually, which has generated growing concern about their impacts on native salmon populations. A large–scale experiment was therefore undertaken in order to quantify the lifetime success and interactions of farm salmon invading a Norwegian river. Sexually mature farm and native salmon were genetically screened, radio tagged and released into the River Imsa where no other salmon had been allowed to ascend. The farm fishes were competitively and reproductively inferior, achieving less than one–third the breeding success of the native fishes. Moreover, this inferiority was sex biased, being more pronounced in farm males than females, resulting in the principal route of gene flow involving native males mating with farm females. There were also indications of selection against farm genotypes during early survival but not thereafter. However, evidence of resource competition and competitive displacement existed as the productivity of the native population was depressed by more than 30%. Ultimately, the lifetime reproductive success (adult to adult) of the farm fishes was 16% that of the native salmon. Our results indicate that such annual invasions have the potential for impacting on population productivity, disrupting local adaptations and reducing the genetic diversity of wild salmon populations.
Evaluation of prognostic factors among patients with chronic graft-versus-host disease

Background Chronic graft-versus-host disease (cGVHD) is a major complication after allogeneic stem cell transplantation with an adverse effect on both mortality and morbidity. In 2005, the National Institute of Health proposed new criteria for diagnosis and classification of chronic graft-versus-host disease for clinical trials. New sub-categories were recognized such as late onset acute graft-versus-host disease and overlap syndrome. Design and Methods We evaluated the prognostic impact of the new sub-categories as well as the clinical scoring system proposed by the National Institute of Health in a retrospective, multicenter study of 820 patients undergoing allogeneic stem cell transplantation between 2000 and 2006 at 3 different institutions. Patients were retrospectively categorized according to the National Institute of Health criteria from patients’ medical histories. Results As far as the new sub-categories are concerned, in univariate analysis diagnosis of overlap syndrome adversely affected the outcome. Also, the number of organs involved for a cut-off value of 4 significantly influenced both cGVHD related mortality and survival. In multivariate analysis, in addition to NIH score, platelet count and performance score at the time of cGVHD diagnosis, plus gut involvement, significantly influenced outcome. These 3 variables allowed us to develop a simple score system which identifies 4 subgroups of patients with 84%, 64%, 43% and 0% overall survival at five years after cGVHD diagnosis (score 0: HR=15.96 (95% CI: 6.85–37.17), P<0.001; score 1: HR=5.47 (95% CI: 2.6–11.5), P<0.001; score 2: HR=2.8 (95% CI: 1.32–5.93), P=0.007). Conclusions In summary, we have identified a powerful and simple tool to discriminate different subgroups of patients in terms of chronic graft-versus-host disease related mortality and survival.
Brain-inspired genetic complementary learning for stock market prediction

Traditional technical analysis for stock market prediction is error-prone, especially for multiyear trend prediction. Hence, computational intelligence provides an attractive alternative. Among the plethora of methods, statistics and artificial neural network are the most popular. However, they are black boxes that are not interpretable. Genetic complementary learning (GCL) fuzzy neural network is therefore proposed. GCL is a brain-inspired learning algorithm that is a confluence of genetic algorithm (GA) and hippocampal complementary learning. Since GA has the potential of finding optimal solution, and complementary learning is one of the mechanisms underlying human recognition, GCL may offer superior performance. The experimental results have demonstrated that GCL is a competent stock market prediction system.
A Classical Catch/Throw Calculus with Tag Abstractions and its Strong Normalizability

ions and its Strong Normalizability Yukiyoshi Kameyama and Masahiko Sato Department of Information Science, Kyoto University fkameyama,masahikog@kuis.kyoto-u.ac. jp Abstract. The catch and throw constructs in Common Lisp provides a means to implement non-local exits. Nakano proposed a calculus L c=t which has inference rules for the catch and throw constructs, and whose types correspond to the intuitionistic propositional logic. He introduced the tag-abstraction/application mechanism into L c=t , which is useful to approximately represent the dynamic behavior of tags. This paper examines the calculus LK c=t , a classicalized version of L c=t . In LK c=t , we can write many programming examples which are not expressible in L c=t , moreover, algorithmic contents can be extracted from classical proofs in LK c=t . We also prove several interesting properties of LK c=t including the strong normalizability. We point out that, if we naively apply the well-known reducibility method, the tag abstraction/application mechanism is problematic. By introducing a missing elimination rule, we can successfully prove the strong normalizability of LK c=t . The catch and throw constructs in Common Lisp provides a means to implement non-local exits. Nakano proposed a calculus L c=t which has inference rules for the catch and throw constructs, and whose types correspond to the intuitionistic propositional logic. He introduced the tag-abstraction/application mechanism into L c=t , which is useful to approximately represent the dynamic behavior of tags. This paper examines the calculus LK c=t , a classicalized version of L c=t . In LK c=t , we can write many programming examples which are not expressible in L c=t , moreover, algorithmic contents can be extracted from classical proofs in LK c=t . We also prove several interesting properties of LK c=t including the strong normalizability. We point out that, if we naively apply the well-known reducibility method, the tag abstraction/application mechanism is problematic. By introducing a missing elimination rule, we can successfully prove the strong normalizability of LK c=t .
Multilingual e-mail classification using Bayesian filtering and language translation

E-mail SPAM is continuously growing threat to its users, E-mail Service Providers (ESPs) and Internet Service Providers (ISPs) as it consumes user's mailboxes, bandwidth, and time by flooding the system with unwanted and unsolicited messages. It can contain unsafe content such as virus programs, phishing frauds, and other malicious code that can be used to hatch varied types of attacks. Several techniques and tools including anti-spam filters are employed to filter out spam e-mails at servers and clients. This paper reviews methods and techniques used to filter spam e-mails currently employed at major e-mail service providers and evaluates their performance to filter non-English language e-mail messages. It proposes a technique to build a translation module that can be used to augment current spam filters to enable them to filter spam from non-English language e-mail messages. It permits the spam filter to train itself through training data set in chosen language and tune its parameters with every incoming message. The implementation of the technique through a translation module and experiments using a publicly available e-mail data corpus have successfully validated the correctness and working of the proposed technique.
Performance Analysis of Location Estimation Algorithm in ZigBee Networks Using Received Signal Strength

This paper presents a maximum likelihood estimator (MLE) for the ZigBee networks. We propose a deployment of cluster-tree topology in the ZigBee networks and derive the MLE under the log-normal models for the Received Signal Strength (RSS) measurements. The ZiLA algorithm is also proposed to apply the MLE to ZigBee networks. To validate the RSS measurement model, we have conducted exhaustive experiments. Testbed experiments are also conducted to validate the effectiveness of ZiLA algorithm.
Multilevel Linear Dimensionality Reduction using Hypergraphs for Data Analysis

Classical algorithms used for dimension reduction can be time-consuming when the data set is large. In this paper we consider a method based on hypergraph coarsening to find a smaller set of data representing a given data set, prior to performing the projection into the low-dimensional space. The cost of the dimensionality reduction process is reduced because of this hypergraph-based pre-processing step. In a hypergraph model, each data item is represented as a vertex and related data items are connected by a hyperedge, which is simply a subset of vertices. To coarsen the data, we use a method that merges pairs of vertices. In the multilevel framework, the coarsening is recursively repeated until a coarsened data set of a certain size is reached. Then we project the coarsened data into a lower dimensional space, using a known linear dimensionality reduction method. The same linear mapping from the coarsened data is then applied to the original data set for projecting data into lowdimensional space. As an application of this idea, we consider text mining. Experimental results indicate that the multilevel hypergraph technique proposed in this paper offer a very appealing cost to quality ratio.
Machine learning for predicting astigmatism in patients with keratoconus after intracorneal ring implantation

This work proposes a new approach based on Machine Learning to predict astigmatism in patients with kera-toconus (KC) after ring implantation. KC is a non-inflamatory, progressive thinning disorder of the cornea, resulting in a protusion, myopia and irregular astigmatism. The intracorneal ring implantation surgery has become a suitable technique to deal with keratoconus without the need of a corneal transplant. Two machine learning (ML) classifiers based on artificial neural network and a decision tree were used in this work. Artificial neural networks performed better than decision trees, achieving an absolute mean error lower than 2 diopters in a validation data set. An analysis of the most relevant features was also carried out.
Robust Fuzzy Congestion Control of TCP/AQM Router via Perturbed Takagi-Sugeno Fuzzy Models

Because the number of transmission control protocol sessions usually varies from time to time, a perturbed Takagi-Sugeno fuzzy model is used in this paper to represent the dynamic transmission control protocol network systems. According to the proposed perturbed Takagi-Sugeno fuzzy model, a robust fuzzy controller design approach is investigated to achieve the congestion avoidance for the transmission control protocol network systems. In order to accomplish the above mission, some sufficient conditions are derived based on the Lyapunov stability theory. By solving these sufficient stability conditions, an active queue management router can be obtained via the proposed robust fuzzy congestion control technique.