Miracle-dz/UniFAR-FTUs huggingface dataset:
6 Artificial Evolution

There are only two ways we know of to make extremely complicated things. One is by engineering, and the other is evolution. And of the two, evolution will make the more complex. Artificial evolution is a classic topic of artificial life. Researchers in artificial evolution typically pursue one of the following goals: They are interested in understanding the principles of biological evolution; in the spirit of the synthetic methodology, the understanding of biological phenomena can be greatly enhanced by trying to mimic or model aspects of the biological system under examination. The other main goal is to use methods from artificial evolution as optimization tools, or more generally as design methods. In many areas, evolutionary algorithms have proved highly useful, especially for " hard " problems. In quite a number of areas they have produced solutions that humans could not easily have derived. Humans have their inescapable biases because they have grown up in a particular environments and societies. Evolutionary methods can be seen as ways to overcome these biases because computers only have those biases that have been explicitly programmed into them. A beautiful illustration of this point is the " tubing problem " that was described by Rechenberg in the 1960s. It is illustrated in Figure 6.1. Figure 6.1: Rechenberg's " tubing problem ". (a) the standard solution, and (b) the optimal solution. The fuel flows into the tube from the left. The fuel should leave the tube (top right) such that the resistance of the fluid is minimized. The question is how the two tubes should be connected, i.e. what is the shape of
Semi-supervised Ensemble Learning Using Label Propagation

Ensemble learning has been widely used in data mining and pattern recognition. However, when the number of labeled data samples is very small, it is difficult to train a base classifier for ensemble learning, therefore, it is necessary to utilize an abundance of unlabeled data effectively. In most semi-supervised ensemble methods, the label prediction of unlabeled data and their use as pseudo-label data are common processes. However, the low accuracy of the label prediction of unlabeled data limits the ability to obtain improved ensemble members. In this paper, we propose effective ensemble learning methods for semi-supervised classification that combine label propagation and ensemble learning. We show that accurate ensemble members can be constructed using class labels predicted by a label propagation method, and unlabeled data samples are fully utilized for diverse ensemble member construction. Extensive experimental results demonstrate the performance of the proposed methods.
Mathematical Modeling and Analysis Subsurface Imaging with Support Vector Machines

A typical subsurface environment is heterogeneous, consists of multiple materials, and is often insufficiently characterized by data. The ability to delineate geologic facies and to estimate their properties from sparse data is essential for modeling physical and biochemical processes occurring in the subsurface. Geostatistics has become an invaluable tool for estimating such properties at points in a computational domain where data are not available, as well as for quantifying the corresponding uncertainty.
Network Modeling and Control of Physical Systems , DISC Theory of Port-Hamiltonian systems Chapter 1 : Port-Hamiltonian formulation of network models ; the lumped-parameter case

It is shown how port-based modeling of lumped-parameter complex physical systems (multi-body systems, electrical circuits, electromechanical systems, .) naturally leads to a geometrically defined class of systems, called port-Hamiltonian systems. These are Hamiltonian systems defined with respect to a power-conserving geometric structure capturing the basic interconnection laws, and a Hamiltonian function given by the total stored energy. The structural properties of port-Hamiltonian systems are discussed, in particular the existence of Casimir functions and its implications for stability.
Submitted to Operations Research Market Clearing Mechanisms under Uncertainty

Electricity markets face a substantial amount of uncertainty. While traditionally this uncertainty has been due to varying demand, with the integration of larger proportions of volatile renewable energy, added uncertainty from generation must also be faced. Conventional electricity market designs cope with uncertainty by running two markets: a market that is cleared ahead of time, followed by a real-time balancing market to reconcile actual realizations of demand and available generation. In such mechanisms, the initial clearing process does not take into account the distribution of outcomes in the balancing market. Recently an alternative so-called stochastic settlement market has been proposed (see e.g. Pritchard et al. (2010) and Bouffard et al. (2005)). In such a market, the ISO clears both stages in one single settlement market. In this paper we consider simplified models for two types of market clearing mechanisms. First a market clearing mechanism utilized in New Zealand, whereby firms offer in advance and are notified of a clearing quantity and price guide based on an estimate of demand. Then in real time firms are dispatched according to realized demand and prices are determined. We refer to this as NZTS. Secondly we consider a simplified stochastic programming market clearing mechanism. We demonstrate that under the assumption of symmetry, our simplified stochastic programming market clearing is equivalent to a two period single settlement (TS) system that takes count of deviation penalties in the second stage. These however differ from NZTS. We prove that this stochastic settlement models results in better social welfare than the NZTS under the assumption of symmetry. Our models are targeted towards analyzing imperfectly competitive markets. We will construct Nash equilibria of the resulting games for the introduced market clearing mechanisms and compare them under the assumptions of symmetry and in an asymmetric example.
Design of a Remote Controlled Caching Proxy System - "Architecture, Algorithm and Implementation"

A caching proxy server acts as an invisible intermediary between browsing clients and the internet servers. In the case of a Web cache, cacheable objects are always separate and are always read in their entity with no pre-fetching. In the present study we present a novel design for a system to remote control an array of proxy system. The administrator can monitor and configure the caching array system from any normal client computational facility. The current system emphasizes on the fact that the administrator can change the configuration of the system with no need either to start the system or alter the clients' statuses. This is achieved by implementing the concurrency control system under the system hierarchy. The primarily local testing of the system shows promising results to get implemented on large scale of enterprise systems.
Analysis of instance hardness in machine learning using item response theory

The analysis of instance hardness (or difficulty) can portray valuable insights about the capabilities of machine learning techniques and the demands of benchmark datasets. On the one hand, if we know that a machine learning technique behaves better than another for hard instances, we can prefer that technique whenever we expect a context with more difficult instances. On the other hand, we can evaluate a classifier by the most difficult instances it is able to solve, according to some notion of correctness. This methodology is very similar to the one used in item response theory (IRT), where the ability or proficiency of an individual (e.g., a student) is measured according to the difficulty of the items (e.g., questions) he or she is able to solve. In the current work, we propose to model instance hardness using IRT, which provides a series of tools to characterise instances, including both difficulty and discriminating power. With this characterisation, model assessment in machine learning can also be rethought, by choosing instances that are more discriminative during model evaluation and selection. In this paper, we develop a case study in which instance hardness is measured by fitting the responses of Random Forests with different number of trees. The case study reveals several insights about different levels of discrimination among instances, the adequate number of trees in RF and anomalous situations that were related to noisy instances. Additionally, we explored the idea of producing a model characteristic curve for each classifier, which provides the probability of correct responses given the instance hardness levels. Such curves can be used to select and reuse models for different distributions and levels of instance hardness in a problem.
DIFFERENT METHODS OF ANALYZING MULTIPLE SAMPLES REPEATED MEASURES DATA

Three methods of analysis viz. Standard ANOVA, Repeated Measures ANOVA and Linear Mixed Model were used to analyze two sets of data due to Cole and Grizzle (1966) and Crowder and Hand (1990) by using SAS 9.2. Four groups of dogs (dataset 1) were found to differ significantly with respect to blood histamine levels under all the three methods of analysis except linear mixed approach under H-F covariance structure, whereas different groups of pigs (dataset 2) were found to differ insignificantly with respect to body weights under all the abovesaid methods. On the basis of the values of AIC, AICC and BIC unstructured covariance structure was found best.
Anti-Inflammatory Effect of Methylpenicinoline from a Marine Isolate of Penicillium sp. (SF-5995): Inhibition of NF-κB and MAPK Pathways in Lipopolysaccharide-Induced RAW264.7 Macrophages and BV2 Microglia

In the course of a search for anti-inflammatory metabolites from marine-derived fungi, methylpenicinoline (1) was isolated from a marine isolate of Penicillin sp. Compound 1 inhibited lipopolysaccharide (LPS)-stimulated nitric oxide (NO) production by suppressing the expression of inducible NO synthase (iNOS) in RAW264.7 macrophages and BV2 microglia. It also attenuated prostaglandin E2 (PGE2) production by suppressing cyclooxygenase-2 (COX-2) expression in a concentration-dependent manner (from 10 μM to 80 μM) without affecting cell viability. In addition, compound 1 reduced the production of the pro-inflammatory cytokine interleukin-1β (IL-1β). In a further study designed to elucidate the mechanism of its anti-inflammatory effects, compound 1 was shown to block nuclear factor-kappa B (NF-κB) activation in LPS-induced RAW264.7 macrophages and BV2 microglia by inhibiting the phosphorylation of inhibitor kappa B-α (IκB-α), thereby suppressing the nuclear translocation of NF-κB dimers, namely p50 and p65, that are known to be crucial molecules associated with iNOS and COX-2 expression. In addition, compound 1 inhibited the activation of mitogen-activated protein kinase (MAPK) pathways. Taken together, the results suggest that compound 1 might be a valuable therapeutic agent for the treatment of anti-inflammatory and anti-neuroinflammatory diseases.
Image Segmentation Based on Ant Colony Optimization and K-Means Clustering

According to the characteristics of the ant colony optimization and the K-means clustering, a method for the image segmentation based on the ant colony optimization and the K-means clustering was proposed in this paper. Firstly, the basic principle of the two algorithms were introduced. Secondly, their characteristics on the image segmentation were analyzed. Finally the improved algorithm was proposed, this algorithm can effectively overcome shortages which are the slow rate of the ant colony optimization and the K-means clustering dependent on the initial clustering centers. Experimental results proved that the improved algorithm was an effective method for the image segmentation in the practical application, which could segment the object accurately.
Interferon-α Improves Phosphoantigen-Induced Vγ9Vδ2 T-Cells Interferon-γ Production during Chronic HCV Infection

In chronic HCV infection, treatment failure and defective host immune response highly demand improved therapy strategies. Vγ9Vδ2 T-cells may inhibit HCV replication in vitro through IFN-γ release after Phosphoantigen (PhAg) stimulation. The aim of our work was to analyze Vγ9Vδ2 T-cell functionality during chronic HCV infection, studying the role of IFN-α on their function capability. IFN-γ production by Vγ9Vδ2 T-cells was analyzed in vitro in 24 HCV-infected patients and 35 healthy donors (HD) after PhAg stimulation with or without IFN-α. The effect of in vivo PhAg/IFN-α administration on plasma IFN-γ levels was analyzed in M. fascicularis monkeys. A quantitative analysis of IFN-γ mRNA level and stability in Vγ9Vδ2 T-cells was also evaluated. During chronic HCV infection, Vγ9Vδ2 T-cells showed an effector/activated phenotype and were significantly impaired in IFN-γ production. Interestingly, IFN-α was able to improve their IFN-γ response to PhAg both in vitro in HD and HCV-infected patients, and in vivo in Macaca fascicularis primates. Finally, IFN-α increased IFN-γ-mRNA transcription and stability in PhAg-activated Vγ9Vδ2 T-cells. Altogether our results show a functional impairment of Vγ9Vδ2 T-cells during chronic HCV infection that can be partially restored by using IFN-α. A study aimed to evaluate the antiviral impact of PhAg/IFN-α combination may provide new insight in designing possible combined strategies to improve HCV infection treatment outcome.
Improved HPLC method for the analysis of chlorophylls and carotenoids from marine phytoplankton

Using a ternary gradlent s}istem, over 50 carotenoids, chlorophylls and their denvatives were separated from marine phytoplankton. Only 2 palrs of carotenoid pigments (19'-butanoyloxy-fucoxanthin and siphonaxanthin, and 19'-hexanoyloxyfucoxanthin and 9'-cis-neoxanthln) and 3 chlorophylls (chlorophylls c , , c2 and Mg 2.4 divinyl pheoporphyrin a5 monomethyl ester [Mg2,4D]) were not resolved. Pigment chromatograms are presented for 12 uniaigal cultures from 10 algal classes important in the marine environment: Alnphidinlum cal-terae Hulbert (Dinophyceae), Chroomonas salina (Wislouch) Butcher (Cryptophyceae), Dunallella tertrolecta Butcher (Chlorophyceae); Emil~anla huxleyi (Lohmann) Hay et Mohler and Pavlova lutheri (Droop) Green (Prymnesiophyceae); Euglena gracllis Klebs (Euglenophyceae); Micl-omonas pusllla (Butcher) Manton et Parke and Pycnococcus provasohi Guillard (Praslnophyceae); Pelagococcus subv~ridis Norris (Chrysophyceae); Phaeodactylum tncornutum Bohlln (Baclllanophyceae); Porphyndlum cruentum (Bory) Drew et Ross (Rhodophyceae), and Synechococcus sp (Cyanophyceae). A chromatogram IS also given of a complex mixture of over 50 algal pigments such as might be found in a phytoplankton field sample This method is useful for analysis of phytoplankton plgments in seawater samples and other Instances where separations of complex pigment mixtures are required
The Art of Conversation Is Coordination

When two people discuss something they can see in front of them, what is the relationship between their eye movements? We recorded the gaze of pairs of subjects engaged in live, spontaneous dialogue. Cross-recurrence analysis revealed a coupling between the eye movements of the two conversants. In the first study, we found their eye movements were coupled across several seconds. In the second, we found that this coupling increased if they both heard the same background information prior to their conversation. These results provide a direct quantification of joint attention during unscripted conversation and show that it is influenced by knowledge in the common ground.
Leishmania disease development depends on the presence of apoptotic promastigotes in the virulent inoculum

The obligate intracellular pathogen Leishmania major survives and multiplies in professional phagocytes. The evasion strategy to circumvent killing by host phagocytes and establish a productive infection is poorly understood. Here we report that the virulent inoculum of Leishmania promastigotes contains a high ratio of annexin A5-binding apoptotic parasites. This subpopulation of parasites is characterized by a round body shape, a swollen kinetoplast, nuclear condensation, and a lack of multiplication and represents dying or already dead parasites. After depleting the apoptotic parasites from a virulent population, Leishmania do not survive in phagocytes in vitro and lose their disease-inducing ability in vivo. TGF-β induced by apoptotic parasites is likely to mediate the silencing of phagocytes and lead to survival of infectious Leishmania populations. The data demonstrate that apoptotic promastigotes, in an altruistic way, enable the intracellular survival of the viable parasites.
Structural Reorganisation and Potential Toxicity of Oligomeric Species Formed during the Assembly of Amyloid Fibrils

Increasing evidence indicates that oligomeric protein assemblies may represent the molecular species responsible for cytotoxicity in a range of neurological disorders including Alzheimer and Parkinson diseases. We use all-atom computer simulations to reveal that the process of oligomerization can be divided into two steps. The first is characterised by a hydrophobic coalescence resulting in the formation of molten oligomers in which hydrophobic residues are sequestered away from the solvent. In the second step, the oligomers undergo a process of reorganisation driven by interchain hydrogen bonding interactions that induce the formation of β sheet rich assemblies in which hydrophobic groups can become exposed. Our results show that the process of aggregation into either ordered or amorphous species is largely determined by a competition between the hydrophobicity of the amino acid sequence and the tendency of polypeptide chains to form arrays of hydrogen bonds. We discuss how the increase in solvent-exposed hydrophobic surface resulting from such a competition offers an explanation for recent observations concerning the cytotoxicity of oligomeric species formed prior to mature amyloid fibrils.
Common functional principal component models for mortality forecasting

We explore models for forecasting groups of functional time series data that exploit common features in the data. Our models involve fitting common (or partially common) functional principal component models and forecasting the coefficients using univariate time series methods. We illustrate our approach by forecasting age-specific mortality rates for males and females in Australia. 4.1 Functional time series models We are interested in forecasting groups of functional time series data. For example, the annual age-specific mortality rates for a country can be considered a functional time series, {f1(x), f2(x), . , fT (x)}, observed over years 1, . , T , where x denotes age. When we observe mortality rates for subsets of the population (e.g., split by sex, or by race, or by geographical region), we have grouped functional time series. Methods for forecasting functional time series data have been developed by [9, 8, 3, 13, 6]. A related but different problem involves forecasting a segmented continuous time series [1]. While our methods have application to this second problem, we will not consider it in this paper. The most common approach to the problem of forecasting a functional time series is to use a principal components decomposition [12] and then to use univariate time series models to forecast each of the principal component scores. Rob J Hyndman Monash University, Australia, e-mail: rob.hyndman@monash.edu Farah Yasmeen University of Karachi, Pakistan, e-mail: riazfarah@yahoo.com
Unmixing fMRI with independent component analysis

Independent component analysis (ICA) is a statistical method used to discover hidden factors (sources or features) from a set of measurements or observed data such that the sources are maximally independent. Typically, it assumes a generative model where observations are assumed to be linear mixtures of independent sources and works with higher-order statistics to achieve independence. ICA has recently demonstrated considerable promise in characterizing functional magnetic resonance imaging (fMRI) data, primarily due to its intuitive nature and ability for flexible characterization of the brain function. In this article, ICA is introduced and its application to fMRI data analysis is reviewed.
Some applications of verifiable computation to biometric verification

Spurred by the advent of cloud computing, the domain of verifiable computations has known significant progress in recent years. Verifiable computation techniques enable a client to safely outsource its computations to a remote server. This server performs the calculations and generates a proof asserting their correctness. The client thereafter simply checks the proof to convince itself of the correctness of the output. In this paper, we study how recent advances in cryptographic techniques in this very domain can be applied to biometric verification.
Spike timing dependent plasticity (STDP) can ameliorate process variations in neuromorphic VLSI

A transient-detecting very large scale integration (VLSI) pixel is described, suitable for use in a visual-processing, depth-recovery algorithm based upon spike timing. A small array of pixels is coupled to an adaptive system, based upon spike timing dependent plasticity (STDP), that aims to reduce the effect of VLSI process variations on the algorithm's performance. Results from 0.35 /spl mu/m CMOS temporal differentiating pixels and STDP circuits show that the system is capable of adapting to substantially reduce the effects of process variations without interrupting the algorithm's natural processes. The concept is generic to all spike timing driven processing algorithms in a VLSI.
The New Merit Aid.

Since the early Nineties, over a dozen states have established broad-based merit aid programs. The typical program waives tuition and fees at public colleges and universities in one’s home state. Unlike traditional, elite merit programs such as the National Merit Scholarship, the new merit aid requires relatively modest academic credentials and annually funds hundreds of thousands of students. This paper examines how merit aid programs in seven states have affected an array of schooling decisions, paying particular attention to how the effects have varied by race and ethnicity. I find that the new programs typically increase the attendance probability of college-age youth by five to seven percentage points. The merit programs also shift students toward four-year schools and away from two-year schools. Surprisingly, the Georgia HOPE Scholarship, which widened racial gaps in college attendance (Dynarski, 2000) is found to be atypical in its distributional impact. The other states’ merit aid programs have actually closed racial and ethnic gaps in attendance. I attribute the Georgia program’s unique distributional effect to its relatively stringent academic requirements and a recently-eliminated provision that channeled the most generous scholarships to higher-income students.
Mining for and from the Semantic Web 2004 (SWM 2004)

Although vast amounts of textual data are freely available, many NLP algorithms exploit only a minute percentage. In this paper, we study the challenges of working at the terascale and survey reasons why researchers are not fully utilizing available resources. As a case study, we present a terascale algorithm for mining is-a relations that achieves better performance as compared to a state-of-the-art linguistically-rich method.