Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiscale Model Selection for High-Frequency Financial Data of a Large Tick Stock by Means of the Jensen-Shannon Metric

Modeling financial time series at different time scales is still an open challenge. The choice of a suitable indicator quantifying the distance between the model and the data is therefore of fundamental importance for selecting models. In this paper, we propose a multiscale model selection method based on the Jensen–Shannon distance in order to select the model that is able to better reproduce the distribution of price changes at different time scales. Specifically, we consider the problem of modeling the ultra high frequency dynamics of an asset with a large tick-to-price ratio. We study the price process at different time scales and compute the Jensen–Shannon distance between the original dataset and different models, showing that the coupling between spread and returns is important to model return distribution at different time scales of observation, ranging from the scale of single transactions to the daily time scale.
PLS and SVD based penalized logistic regression for cancer classification using microarray data

Accurate cancer prediction is important for treatment of cancers. The combination of two dimension reduction methods, partial least squares (PLS) and singular value decomposition (SVD), with the penalized logistic regression (PLR) has created powerful classifiers for cancer prediction using microarray data. Comparing with support vector machine (SVM) on seven publicly available cancer datasets, the new algorithms can achieve very good performance and run much faster. They also have the advantage that the probabilities of predictions can be directly given. PLS based PLR is also combined with recursive feature elimination (RFE) to select a 16-gene subset for acute leukemia cancer classification. The testing error on this subset of genes is empirically zero.
Reflecting Human Knowledge of Place and Route-Choice Behavior Using Big Data

Geotagged photos have been applied by many researchers to explore the spatial extent of places. This paper addresses an important challenge of using geotagged Flickr photos to delineate the spatial extent of a vague place, which is defined as a place without a clearly defined boundary. We argue that the variation of location popularity has a great impact on the estimation of such vague spatial extent of a place. We propose an approach to model the representativeness of each geotagged photo point based on its location popularity. A modified kernel density estimation method incorporating the photo representativeness is developed and tested with eight places, which cover urban vs. non-urban areas, with vs. without an official boundary cases, and at various spatial scales of state, city and district levels. Our results indicate major improvements of the proposed representativeness-weighted kernel density estimation method over the traditional kernel density estimation method in estimating the spatial extent of vague places.
DELIGNIFICATION OF PALM FIBER BY MICROWAVE ASSISTED CHEMICAL PRETREATMENT FOR IMPROVING ENERGY EFFICIENCY

Fossils fuels are main source of energy. However, their depletion has caused widespread concern. In recent years, utilization of agricultural wastes for energy has received a lot of attention. Lignocellulosic biomass is an abundantly available agricultural waste. It is a candidate for economic and environmental friendly source of biofuel production. However, high lignin content of the biomass reduces its digestibility and energy recovery. Suitable pretreatment of biomass can degrade the lignin content and enhance its conversion to biofuel. This work studied the effects of various microwave assisted chemical pretreatments on delignification of palm fiber to improve energy recovery. A total of 12 solutions using four chemicals, namely, sulphuric acid, sodium hydroxide, hydrogen peroxide, and sodium carbonate with 3 different concentrations i.e. 2%, 3.5%, and 5% were prepared. 1 g of palm fiber was pretreated in 10 ml of each solution, for 48 h. The solution along with the biomass was then heated by microwave irradiation for 5 min. The results showed that the pretreatments performed were effective in reducing lignin content and increasing cellulose content of the samples. Sodium hydroxide and hydrogen peroxide were most suitable for delignification of palm fiber. Treatment with these chemicals reduced the lignin content and changed the C/N ratio to the optimum range. This increased cellulose content enhances energy potential to produce biofuel by conversion to ethanol or biogas. (Keywords: Chemical pretreatment, Microwave pretreatment, Palm fibre, Lignocellulosic Biomass, Biomass Waste, Waste to energy)
ISSUES ON ROBUST ADAPTIVE FEEDBACK CONTROL

We discuss recent progress in the field of robust adaptive control with special emphasis on methodologies that use multiple-model architectures. We emphasize that the selection of the number of models, estimators and compensators in such architectures must be based on precise definition of the robust performance requirements. We illustrate some of the concepts and outstanding issues for a new methodology that blends robust nonadaptive mixed µ-synthesis and stochastic hypothesis-testing concepts leading to the so-called RMMAC architecture. A numerical example is used to illustrate the RMMAC design methodology. Copyright c ° 2005 IFAC
Of mice and models: improved animal models for biomedical research.

The ability to engineer the mouse genome has profoundly transformed biomedical research. During the last decade, conventional transgenic and gene knockout technologies have become invaluable experimental tools for modeling genetic disorders, assigning functions to genes, evaluating drugs and toxins, and by and large helping to answer fundamental questions in basic and applied research. In addition, the growing demand for more sophisticated murine models has also become increasingly evident. Good state-of-principle knowledge about the enormous potential of second-generation conditional mouse technology will be beneficial for any researcher interested in using these experimental tools. In this review we will focus on practice, pivotal principles, and progress in the rapidly expanding area of conditional mouse technology. The review will also present an internet compilation of available tetracycline-inducible mouse models as tools for biomedical research (http://www.zmg.uni-mainz.de/tetmouse/).
Designing Hybrid User Interfaces with Power vs. Reality Tradeoffs

In this paper we would like to discuss the process of designing hybrid user interfaces with explicit power vs. reality tradeoff decisions. We further propose to ground these tradeoffs in a firm understanding of embodied practice. To support this, we describe a design study which was grounded in explicit tradeoff decisions derived from an observational study.
N‐terminal domain of the V‐ATPase a2‐subunit displays integral membrane protein properties

V‐ATPase is a multisubunit membrane complex that functions as nanomotor coupling ATP hydrolysis with proton translocation across biological membranes. Recently, we uncovered details of the mechanism of interaction between the N‐terminal tail of the V‐ATPase a2‐subunit isoform (a2N1–402) and ARNO, a GTP/GDP exchange factor for Arf‐family small GTPases. Here, we describe the development of two methods for preparation of the a2N1–402 recombinant protein in milligram quantities sufficient for further biochemical, biophysical, and structural studies. We found two alternative amphiphilic chemicals that were required for protein stability and solubility during purification: (i) non‐detergent sulfobetaine NDSB‐256 and (ii) zwitterionic detergent FOS‐CHOLINE®12 (FC‐12). Moreover, the other factors including mild alkaline pH, the presence of reducing agents and the absence of salt were beneficial for stabilization and solubilization of the protein. A preparation of a2N1–402 in NDSB‐256 was successfully used in pull‐down and BIAcore™ protein–protein interaction experiments with ARNO, whereas the purity and quality of the second preparation in FC‐12 was validated by size‐exclusion chromatography and CD spectroscopy. Surprisingly, the detergent requirement for stabilization and solubilization of a2N1–402 and its cosedimentation with liposomes were different from peripheral domains of other transmembrane proteins. Thus, our data suggest that in contrast to current models, so called “cytosolic” tail of the a2‐subunit might actually be embedded into and/or closely associated with membrane phospholipids even in the absence of any obvious predicted transmembrane segments. We propose that a2N1–402 should be categorized as an integral monotopic domain of the a2‐subunit isoform of the V‐ATPase.
Principal component analysis for clustering gene expression data

MOTIVATION There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expression data. Other classical techniques, such as principal component analysis (PCA), have also been applied to analyze gene expression data. Using different data analysis techniques and different clustering algorithms to analyze the same data set can lead to very different conclusions. Our goal is to study the effectiveness of principal components (PCs) in capturing cluster structure. Specifically, using both real and synthetic gene expression data sets, we compared the quality of clusters obtained from the original data to the quality of clusters obtained after projecting onto subsets of the principal component axes. RESULTS Our empirical study showed that clustering with the PCs instead of the original variables does not necessarily improve, and often degrades, cluster quality. In particular, the first few PCs (which contain most of the variation in the data) do not necessarily capture most of the cluster structure. We also showed that clustering with PCs has different impact on different algorithms and different similarity metrics. Overall, we would not recommend PCA before clustering except in special circumstances.
Crossmodal error correction of continuous handwriting recognition by speech

In recognition-based user interface, users' satisfaction is determined not only by recognition accuracy but also by effort to correct recognition errors. In this paper, we introduce a crossmodal error correction technique, which allows users to correct errors of Chinese handwriting recognition by speech. The focus of the paper is a multimodal fusion algorithm supporting the crossmodal error correction. By fusing handwriting and speech recognition, the algorithm can correct errors in both character extraction and recognition of handwriting. The experimental result indicates that the algorithm is effective and efficient. Moreover, the evaluation also shows the correction technique can help users to correct errors in handwriting recognition more efficiently than the other two error correction techniques.
Specific Regulation of Procoagulant Activity on Monocytes

Hypercoagulability of blood, monocytic infiltration, and changes in pericellular and extracellular matrix glycosaminoglycans (GAGs) are observed in atherosclerosis, inflammation, and neoplasia. In the present studies, monocyte procoagulants and different GAGs including chondroitin sulfate (CS) A, CSB, CSC, CSD, CSE, and heparan sulfate, were tested either in clotting assays with whole plasma or in chromogenic assays with purified coagulation proteases. Procoagulant activity in plasma was inhibited by three of the seven GAGs, including heparan sulfate, CSE, and CSB. In contrast, activity of purified coagulation protease was inhibited only by CSE, and the inhibition was observed with intrinsic (factor VIIIa/IXa) but not extrinsic (tissue factor/factor VII) components. Reciprocal titration experiments with enzyme and substrate and Scatchard type analyses were consistent with concentration-dependent inhibitory interactions between CSE and sites on both factor VIIIa and IXa. On purified phospholipids, CSE concentration resulting in half-maximal inhibition (K) was 5 ng/ml for interaction with factor IXa and >500 ng/ml for interaction with factor VIIIa. The K values were lower for reactions on purified lipid than for reactions on monocyte surfaces and for reactions on resting than on endotoxin-stimulated monocytes. Experiments with CSE oligosaccharides of defined size indicated that the smallest CSE fragment capable of inhibitory activity was composed of 12-18 monosaccharide units. Collectively, these results indicate that factor X-activating reactions are inhibited by GAGs expressed on monocyte membranes. Inhibition is specific with respect to the structure of both the GAG and the activating protease. Lack of inhibition by added CSA, CSB, and CSC in contrast to CSE strongly suggests a direct role of 4,6-di-O-sulfated N-acetylgalactosamine GAG structures in the inhibition of intrinsic pathway protease. These findings also suggest potential pharmacologic use of CSE as specific anticoagulant in the management of prothrombotic states mediated by intrinsic pathway coagulation reactions.
Immunization Route Dictates Cross-Priming Efficiency and Impacts the Optimal Timing of Adjuvant Delivery

Delivery of cell-associated antigen represents an important strategy for vaccination. While many experimental models have been developed in order to define the critical parameters for efficient cross-priming, few have utilized quantitative methods that permit the study of the endogenous repertoire. Comparing different strategies of immunization, we report that local delivery of cell-associated antigen results in delayed T cell cross-priming due to the increased time required for antigen capture and presentation. In comparison, delivery of disseminated antigen resulted in rapid T cell priming. Surprisingly, local injection of cell-associated antigen, while slower, resulted in the differentiation of a more robust, polyfunctional, effector response. We also evaluated the combination of cell-associated antigen with poly I:C delivery and observed an immunization route-specific effect regarding the optimal timing of innate immune stimulation. These studies highlight the importance of considering the timing and persistence of antigen presentation, and suggest that intradermal injection with delayed adjuvant delivery is the optimal strategy for achieving CD8+ T cell cross-priming.
The role of small GTPases in neuronal morphogenesis and polarity

The highly dynamic remodeling and cross talk of the microtubule and actin cytoskeleton support neuronal morphogenesis. Small RhoGTPases family members have emerged as crucial regulators of cytoskeletal dynamics. In this review we will comprehensively analyze findings that support the participation of RhoA, Rac, Cdc42, and TC10 in different neuronal morphogenetic events ranging from migration to synaptic plasticity. We will specifically address the contribution of these GTPases to support neuronal polarity and axonal elongation. © 2012 Wiley Periodicals, Inc
The Limited Effectiveness of Neural Networks for Simple Question Answering on Knowledge Graphs

Simple factoid question answering (QA) is a task, where the questions can be answered by looking up a single fact in the knowledge base (KB). However, this QA task is difficult, since retrieving a single supporting fact involves searching many alternatives given a query expressed in natural language. We use a retrieval-based approach to QA. We decompose the problem into four sub-problems: entity detection, entity linking, relation prediction, and evidence integration. Entity detection and linking rely on detecting the entities in a question and linking them to the candidate entities in the KB. Relation prediction classifies a question as one of the relation types in the KB. Finally, evidence integration combines scores from entity linking and relation prediction to predict an (entity, relation) pair that answers the question. Most of the research community has explored complex neural network architectures for this task without establishing baselines to compare the results with ‘non-neural-network’ approaches. We explore several different models for entity detection and relation prediction; a few different scoring functions for entity linking and evidence integration. Our findings show that deep learning does help for the QA task, but not as much as the research community has portrayed it to be. We also present two simple yet very competitive baselines: one based on a simple neural network architecture and one that does not use any neural networks.
Experiments on automatic language identification for philippine languages using acoustic Gaussian Mixture Models

A Philippine LID system has not been previously created because of the limited amount of recorded speech data. This research initiates the LID research using the Philippine Language Database (PLD) collected by the Digital Signal Processing Laboratory of the University of the Philippines Diliman (DSP-UPD). Mel Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), Shifted Delta Cepstra (SDC) and Linear Predictive Cepstral Coefficients (LPCC) features are extracted from the speech segments. Gaussian Mixture Model (GMM) using Expectation Maximization (EM) and Universal Background Model (UBM) approach is used to model the acoustic characteristics of the language. Maximum a Posteriori (MAP) probability is then used to determine the language of a speech utterance based on the language GMMs. PLP using a 16 Mixture GMM-EM has been found to produce the best performance among the four feature vectors in discriminating the languages.
Neuroevolution of an automobile crash warning system

Many serious automobile accidents could be avoided if drivers were warned of impending crashes before they occurred. In this paper, a vehicle warning system is evolved to predict such crashes in the RARS driving simulator. The NeuroEvolution of Augmenting Topologies (NEAT) method is first used to evolve a neural network driver that can autonomously navigate a track without crashing. The network is subsequently impaired, resulting in a driver that occasionally makes mistakes and crashes. Using this impaired driver, a crash predictor is evolved that can predict how far in the future a crash is going to occur, information that can be used to generate an appropriate warning level. The main result is that NEAT can successfully evolve a warning system that takes into account the recent history of inputs and outputs, and therefore makes few errors. Experiments were also run to compare training offline from previously collected data with training online in the simulator. While both methods result in successful warning systems, offline training is both faster and more accurate. Thus, the results in this paper set the stage for developing crash predictors that are both accurate and able to adapt online, which may someday save lives in real vehicles.
Atmospheric Radiation and TGFs: Unexplained Radiation in our Skies

There is a significant correlation between atmospheric electrification via thunderstorms and the occurrence of large emissions of x-ray and gamma ray radiation known as Terrestrial Gamma Ray Flashes (TGFs). Some physical phenomenon may be explained by either the RREA or Thermal Runaway models, but the scientific community as a whole is still largely at work on the theoretical frameworks.
GViSOM for Multivariate Mixed Data Projection and Structure Visualization

Data mining has become a popular technology in analyzing complex data. Clustering is one of the data mining core techniques. In this paper, we propose a new visualized clustering approach, namely generalized visualization-induced self-organizing map (GViSOM), to cluster mixed, numeric and categorical, data. Our model integrates the ideas from SOM, GSOM, and ViSOM, and overcomes their shortcomings, including projection distortion on the maps and the incapability of handling mixed data. GViSOM can directly handle mixed data and preserves the topological structure of the original data as faithfully as possible. Experimental results of a synthetic and a real datasets demonstrate that GViSOM is able to cluster mixed data and better reveals the cluster structure than SOM, GSOM and ViSOM.
Graph-based methods for the automatic annotation and retrieval of art prints

The analysis of images taken from cultural heritage artifacts is an emerging area of research in the field of information retrieval. Current methodologies are focused on the analysis of digital images of paintings for the tasks of forgery detection and style recognition. In this paper, we introduce a graph-based method for the automatic annotation and retrieval of digital images of art prints. Such method can help art historians analyze printed art works using an annotated database of digital images of art prints. The main challenge lies in the fact that art prints generally have limited visual information. The results show that our approach produces better results in a weakly annotated database of art prints in terms of annotation and retrieval performance compared to state-of-the-art approaches based on bag of visual words.
Long‐term modelling of wind speeds using six different heuristic artificial intelligence approaches

Wind speed is an essential component that needs to be determined accurately, especially over long‐term periods for various engineering and scientific purposes including renewable energy productions, structural building sustainability and others. In this study, six different heuristic methods: multi‐layer perceptron artificial neural networks, (ANN), adaptive neuro‐fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), generalized regression neural networks (GRNN), gene expression programming (GEP) and multivariate adaptive regression spline (MARS) are developed to model monthly wind speeds using meteorological input information. The atmospheric pressure, temperature, relative humidity and rainfall values are obtained from Jolfa and Tabriz meteorological stations, Iran, and are used to build the proposed predictive models. Different statistical indicators are computed to evaluate and comprehensively assess the performance of the six heuristic methods. Over the testing phase, the ANFIS‐GP and GRNN models are seen to exhibit the highest predictive performance for the Jolfa and Tabriz stations, respectively. That is, the maximum coefficient of determination are found to be 0.874, 0.858, 0.850, 0.849, 0.847 and 0.826, for the GRNN, ANFIS‐GP, ANFIS‐SC, ANN, GEP and MARS models, respectively, for Jolfa station, respectively, revealing the superiority of GRNN over the five counterpart models. The results show the generalization capability of the tested heuristic artificial intelligence techniques for both study stations, and therefore could be explored for windspeed prediction and various decisions made in regards to climate change studies.
Arrest Attrition with Analytics

Industries such as Banking and Finance, Healthcare, Hospitality and Technology are currently experiencing high attrition rates. You, as a HR manger or senior management executive, may know some apparent causes for attrition in your company. And you also know the high costs of attrition and the negative impact it has on customer facing scenarios. But how often have you felt that if you had known earlier you could have prevented one good staffer leaving? How many of us are reactive about attrition and do damage control after a separation notice has been mailed?