Miracle-dz/UniFAR-FTUs huggingface dataset:
On Context&#8212;Tree Prediction of Individual Sequences

Motivated by the evident success of context-tree based methods in lossless data compression, we explore, in this paper, methods of the same spirit in universal prediction of individual sequences. By context-tree prediction, we refer to a family of prediction schemes, where at each time instant t, after having observed all outcomes of the data sequence x1,. ,xt-1, but not yet xt, the prediction is based on a "context" (or a state) that consists of the k most recent past outcomes xt-k,. ,xt-1, where the choice of k may depend on the contents of a possibly longer, though limited, portion of the observed past, xt-k(max),. ,xt-1. This is different from the study reported in Feder et al. (1992), where general finite-state predictors as well as "Markov" (finite-memory) predictors of fixed order, where studied in the regime of individual sequences. Another important difference between this study and Feder et al. is the asymptotic regime. While in Feder et al., the resources of the predictor (i.e., the number of states or the memory size) were kept fixed regardless of the length N of the data sequence, here we investigate situations where the number of contexts, or states, is allowed to grow concurrently with N. We are primarily interested in the following fundamental question: What is the critical growth rate of the number of contexts, below which the performance of the best context-tree predictor is still universally achievable, but above which it is not? We show that this critical growth rate is linear in N. In particular, we propose a universal context-tree algorithm that essentially achieves optimum performance as long as the growth rate is sublinear, and show that, on the other hand, this is impossible in the linear case.
Design and evaluation of parallel hashing over large-scale data

High-performance analytical data processing systems often run on servers with large amounts of memory. A common data structure used in such environment is the hash tables. This paper focuses on investigating efficient parallel hash algorithms for processing large-scale data. Currently, hash tables on distributed architectures are accessed one key at a time by local or remote threads while shared-memory approaches focus on accessing a single table with multiple threads. A relatively straightforward “bulk-operation” approach seems to have been neglected by researchers. In this work, using such a method, we propose a high-level parallel hashing framework, Structured Parallel Hashing, targeting efficiently processing massive data on distributed memory. We present a theoretical analysis of the proposed method and describe the design of our hashing implementations. The evaluation reveals a very interesting result - the proposed straightforward method can vastly outperform distributed hashing methods and can even offer performance comparable with approaches based on shared memory supercomputers which use specialized hardware predicates. Moreover, we characterize the performance of our hash implementations through extensive experiments, thereby allowing system developers to make a more informed choice for their high-performance applications.
Dynamics in the creation and depreciation of knowledge, and the returns to research:

Econometric studies of the effects of research on productivity have typically imposed arbitrary restrictions on the length and shape of the R&D lag profile. These restrictions are likely to have biased up both the measured effects of R&D on productivity and the estimated rates of return to research. This paper argues that the useful stock of public knowledge depreciates, if at all, only gradually, and we use this notion to develop a new model, which we test using data on aggregate U.S. agriculture. We reject the conventional specification in favor of a more flexible, dynamic, alternative model, in which the impact of R&D on productivity lasts much longer than in previous studies. Consequently, the real, marginal rate of return to public agricultural R&D in the United States is estimated to be less than 10 percent per annum, much smaller than the typical rates of return reported in scores of previous studies, based on conceptually flawed and inappropriately restrictive dynamic specifications. We show that conventional approaches using the same data would have resulted in a much greater (biased) estimate of the rate of return to research.
Design and Demonstration of an Opto-Electronic Neural Network Using Fixed Planar Holographic Interconnects,

Abstract Implementation of an opto-electronic Hopfield style associative memory neural network is discussedwith emphasis on the construction of an experimental system employing binary amplitude holograms. 1. Introduction A key element of most neural network systems is the massive number of weighted interconnections used totie relatively simple processing nodes together in a useful architecture. The inherent parallelism and interconnec-tion capability of optics make it a likely candidate for the implementation of the neural network interconnectionprocess. While there are several optical technologies worth exploring, we are looking at the capabilities and limi-tations of using fixed planar holographic interconnects in a neural network system and have implemented an initialtest system using planar holograms and opto-electronic nodes. 2. System All neural network systems consist of nodes (simple non-linear elements crudely imitating biologicalneurons) and weighted interconnections (synapses) between nodes. The basic system we have looked at employsoptical interconnects and electronic nodes in a feedback architecture. A prototype is shown in Figure 1.
Cobalt Chloride Attenuates Oxidative Stress and Inflammation through NF-κB Inhibition in Human Renal Proximal Tubular Epithelial Cells

We evaluated the effect of cobalt chloride (CoCl2) on TNF-α and IFN-γ-induced-inflammation and reactive oxygen species (ROS) in renal tubular epithelial cells (HK-2 cells). We treated HK-2 cells with CoCl2 before the administration of TNF-α/IFN-γ. To regulate hemeoxygenase-1 (HO-1) expression, the cells were treated CoCl2 or HO-1 siRNA. CoCl2 reduced the generation of ROS induced by TNF-α/IFN-γ. TNF-α/IFN-γ-treated-cells showed an increase in the nuclear translocation of phosphorylated NF-κBp65 protein, the DNA-binding activity of NF-κBp50 and NF-κB transcriptional activity and a decrease in IκBα protein expression. These changes were restored by CoCl2. We noted an intense increase in monocyte chemoattractant protein-1 (MCP-1) and regulated on activation normal T cell expressed and secreted (RANTES) production in TNF-α/IFN-γ-treated cells. We demonstrated that this effect was mediated through NF-κB signaling because an NF-κB inhibitor significantly reduced MCP-1 and RANTES production. CoCl2 effectively reduced MCP-1 and RANTES production. The expression of HO-1 was increased by CoCl2 and decreased by HO-1 siRNA. However, knockdown of HO-1 by RNA interference did not affect MCP-1 or RANTES production. We suggest that CoCl2 has a protective effect on TNF-α/IFN-γ-induced inflammation through the inhibition of NF-κB and ROS in HK-2 cells. However, CoCl2 appears to act in an HO-1-independent manner. Graphical Abstract
Mobile Banking in India – Issues & Challenges

— Technology plays an important role in banking sector. Banking is one of the largest financial institutions constantly explores the opportunity of technology enabled services to provide better customer experience and convenience. Mobile phone is a common technology device that became part of every individual in the information era. Mobile Banking is an emerging alternate channel for providing banking services. India is the second largest telecom market in the world, which is having high potential for expanding banking services using mobile. However, mobile banking has not become the choice of millions of people. The main objective of this study is to identify the mindset and analyse the security issues in Mobile banking among the banking customers in India.
Perception of the risk of flooding: the case of the 1995 flood in Norway

Abstract Flood risk has usually been studied in natural sciences and engineering. Floods, being natural phenomena, represent a hazard only with respect to human society. Therefore “the human response component” is no less important in flood risk assessment than those components studied traditionally. This paper presents the results of a study of the perception of flood risk among decision makers and the general public in a flood-affected area of Norway. A poll investigation addressing the public and an expert panel addressing the decision makers were used for data collection. The information obtained was analysed focusing on: (a) the perception of risk for life and health; (b) economic and environmental loss by the two groups; (c) the links for communicating the flood risk message; and (d) appropriate flood mitigation measures in particular situations and the ability of the decision makers to visualize their effects and costs. The results show that the perception of flood hazard by the general public is not realistic and that the flood risk message should be communicated better. The transparency of the different decisions made during a flood situation and their effects on the degree of risk need to be improved. The knowledge gained can be used for developing a flood assessment policy based on participatory principles.
Unsupervised Tissue Classification in Medical Images using Edge-Adaptive Clustering

AbstructA novel algorithm is proposed for performing unsupervised tissue classification in medical images by combining conventional clustering techniques with edge-adaptive segmentation techniques. Based on the fuzzy C-means algorithm, the algorithm computes a smooth segmentation while simultaneously estimating an edge field. Unlike most tissue classification algorithms that incorporate a smoothness constraint, the edge field estimation prevents the algorithm from smoothing across tissue boundaries, thereby producing robust yet accurate results. The algorithm is formulated as the minimization of an objective function that includes penalty terms to ensure that both the segmentation and edge field are relatively smooth. To compute the edge field, a difference equation with spatially varying coefficients is solved using an efficient multigrid algorithm. Some preliminary results applying the method to synthetic and magnetic resonance images are presented.
Discovering strong skyline points in high dimensional spaces

Current interests in skyline computation arise due to their relation to preference queries. Since it is guaraneed that a skyline point will not lose out in all dimensions when compared to any other point in the data set, this means that for each skyline point, there exists a set of weight assignments to the dimensions such that the point will become the top user preference.We believe that the usefulness of skyline points is not limited to such application and can be extended to data analysis and knowledge discovery as well. However, since the skyline of high dimensional datasets (which are common in data analysis applications) can contain too many points, various means must be developed to filter off the less interesting skyline points in high dimensions. In this paper, we will propose algorithms to find a set of interesting skyline points called strong skyline points. Extensive experiments show that our proposal is both effective and efficient.
Prevalence and significance of left ventricular outflow gradient during dobutamine echocardiography.

AIMS This study investigated the clinical and physiological significance of the dynamic left ventricle outflow gradient observed in some patients during dobutamine stress echocardiography. METHODS Three hundred and ninety-four consecutive patients completed dobutamine stress echocardiography using Doppler echocardiography to assess the presence of myocardial ischaemia and left ventricular outflow gradient. The prevalence of left ventricular outflow gradient was evaluated and correlated with echocardiographic and clinical findings. Fifteen patients with left ventricular outflow gradient during dobutamine infusion underwent exercise echocardiography for appearance of left ventricular outflow gradient. RESULTS Sixty-nine of 394 (17.5%) patients developed a left ventricular outflow gradient of more than 36 mmHg. In nine of them (13%) the anterior mitral valve leaflet had a systolic anterior motion. In 60 of the 69 patients (87%) there was a dynamic obstruction at the level of the papillary muscles. The mean intracavitary gradient was 75.4 (range 36-175) mmHg. There was no correlation between the presence or absence of a dobutamine stress echocardiography-induced left ventricle outflow gradient and chest pain or shortness of breath. In patients who developed a left ventricular outflow gradient ischaemic wall motion abnormalities occurred at a significantly lower frequency during dobutamine stress echocardiography (2.9 vs 16.4% P<0.001). None of the 15 patients who underwent exercise echocardiography developed significant left ventricular outflow gradient. CONCLUSION Left ventricular outflow gradient occurs occasionally during dobutamine stress echocardiography examination. Its presence is of no physiological or clinical significance.
Neural substrate for higher-order learning in an insect: Mushroom bodies are necessary for configural discriminations

Significance Mushroom bodies are prominent structures of the insect brain that have been associated with the storage and retrieval of elemental forms of memory. We took advantage of the unique cognitive capabilities of honey bees to uncover a previously unidentified function for these structures. Because the honey bee is the only insect in which higher-order, configural learning has been demonstrated, we asked whether the mushroom bodies of this insect are necessary for this task. Disrupting reversibly mushroom body function upon configural conditioning, we show that these structures are required for the acquisition of configural, but not of elemental, learning. This functionality resembles that of the vertebrate brain, where different brain regions are recruited for learning forms of different complexity. Learning theories distinguish elemental from configural learning based on their different complexity. Although the former relies on simple and unambiguous links between the learned events, the latter deals with ambiguous discriminations in which conjunctive representations of events are learned as being different from their elements. In mammals, configural learning is mediated by brain areas that are either dispensable or partially involved in elemental learning. We studied whether the insect brain follows the same principles and addressed this question in the honey bee, the only insect in which configural learning has been demonstrated. We used a combination of conditioning protocols, disruption of neural activity, and optophysiological recording of olfactory circuits in the bee brain to determine whether mushroom bodies (MBs), brain structures that are essential for memory storage and retrieval, are equally necessary for configural and elemental olfactory learning. We show that bees with anesthetized MBs distinguish odors and learn elemental olfactory discriminations but not configural ones, such as positive and negative patterning. Inhibition of GABAergic signaling in the MB calyces, but not in the lobes, impairs patterning discrimination, thus suggesting a requirement of GABAergic feedback neurons from the lobes to the calyces for nonelemental learning. These results uncover a previously unidentified role for MBs besides memory storage and retrieval: namely, their implication in the acquisition of ambiguous discrimination problems. Thus, in insects as in mammals, specific brain regions are recruited when the ambiguity of learning tasks increases, a fact that reveals similarities in the neural processes underlying the elucidation of ambiguous tasks across species.
Mechanical and walking optimization of a hexapod robot using PSO

In this paper a novel solution is introduced for determining the dimensions and construction of a walking robot using an optimization method. During our previous research and development several hexapod walking robots have been built. The latest model called Szabad(ka) II is a complex, servo motor driven, multiprocessor device made of aluminum and steel. Prior to its construction sophisticated modeling was carried out, with special attention to the smallest physical, mechanical and electrical parts. With this model the functionality of the robot could be checked before the manufacturing process. For the time being, modeling was used only for verifying the construction, but as the next step it will be used for defining the construction and dimensions. Manually this is practically impossible, therefore the process requires automatization. The definition of the optimal parameters can be solved with optimization methods. For the task the Particle Swarm Optimization (PSO) was chosen.
Out of Step, Out of Office: Electoral Accountability and House Members' Voting

Does a typical House member need to worry about the electoral ramifications of his roll-call decisions? We investigate the relationship between incumbents' electoral performance and roll-call support for their party—controlling for district ideology, challenger quality, and campaign spending, among other factors—through a series of tests of the 1956–1996 elections. The tests produce three key findings indicating that members are indeed accountable for their legislative voting. First, in each election, an incumbent receives a lower vote share the more he supports his party. Second, this effect is comparable in size to that of other widely recognized electoral determinants. Third, a member's probability of retaining office decreases as he offers increased support for his party, and this relationship holds for not only marginal, but also safe members.
Isolation and Characterization of Cellulase producing bacteria from Soil

Enzymes are delicate protein molecules necessary for life. Cellulose is the most abundant biomass on the earth (Venkata et al., 2013) Plant biomass contains cellulose as the major component. Cellulose accounts for 50% of the dry weight of plant biomass and approximately 50% of the dry weight of secondary sources of biomass such as agricultural wastes (Haruta et al., 2003). Presently huge amount of agricultural and industrial cellulosic wastes have been accumulating in environment. Cellulose has attracted worldwide attention as a renewable resource that can be converted into biobased products and bioenergy (Xing-hua et al., 2009). Celluloses are observed as the most important renewable resource for bioconversion. It has been become the economic interest to develop an effective method to hydrolyze the cellulosic biomass (Saraswati et al., 2012).
Title : Accuracy of Genomic Selection Methods in a Standard Dataset of Loblolly Pine ( Pinus taeda L . )

Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype, and expressed late in the life-cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) Ridge Regression – Best Linear Unbiased Prediction (RRBLUP), (ii) Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO are presented. In addition, a modified RR-BLUP (RR-BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development and diseaseresistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4,853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits Bayes Cπ, Bayesian LASSO and RR-BLUB B had higher predictive ability than RRBLUP and Bayes A. Fusiform rust is controlled by few genes of large effect. A limitation of RR-BLUP is the assumption of equal contribution of all markers to the observed variation. The genotypic and phenotypic data used in this study is publically available for comparative analysis of genomic selection prediction models.
Simultaneous localization and separation of biomedical signals by tensor factorization

In this paper, we introduce mathematical models based on multi-way data construction and analysis with a goal of simultaneously separating and localizing the sources in the brain by analysis of scalp electroencephalogram (EEG) data. we address the problem of EEG source separation and localization through a 3-way tensor analysis. We represent multi-channel EEG data using a third-order tensor with modes: space (channels), time samples and number of segments. Then we demonstrate that multi-way analysis techniques, in particular PARAFAC2, can successfully separate and localize disjoint sources within the brain. Also we used this method for separation of maternal and fetal ECG signals.
Unified Modeling of User Activities on Social Networking Sites

Social networking sites like Facebook and Twitter are teeming with users and the content posted by them. Several activities like friendship/followership, authoring, commenting on, liking, resharing/retweeting posts typically occur on these sites. In this paper, we make an attempt at the uniﬁed modeling of various such activities on social networking sites. We propose a novel joint latent factor model, Latent User Preference Model (LUPM), which combines the predictive power of multiple dyadic relations and text content using block and topic models coupled through a common latent representation for the users and posts. We further experiment with real world Twitter and Facebook datasets in order to understand the empirical signiﬁcance of such uniﬁed modeling.
SURVEY ON MOTIVATION FOR TREATMENT IN 95 HEROIN DEPENDENT PATIENTS

Objective:To survey the motivation for treatment in heroin dependent patients,understand the difference of motivation between participants from voluntary treatment and labor camp and analyze the differences between participants with higher motivations and those with lower motivations so as to provide scientific data for making effective rehabilitation strategy for heroin dependence.Methods:Stages of Change Readiness and Treatment Eagerness Scale(SOCRATES)was used to assess the motivation of 48 heroin dependent cases in voluntary treatment and 47 cases in a labor camp.Results:The participants' total scores were 76.7±s 8.6. The total scores,and recognition,action scores from voluntary treatment participants were higher than those from the labor camp participants(78.6±s 7.4 vs 74.5±s 9.4,P0.05;30.2±s 3.8 vs 27.4±s 4.6,P0.01;34.8±s 3.2 vs 33.2±s 5.2,P0.05). Those with a longer history of heroin abuse had higher motivation.Conclusion:The participants' total scores are low.Heroin dependent patients in labor camp have even lower motivation than those in voluntary treatment.Improving the motivation of heroin dependent patients is essential,and it seems more important for those in labor camps.
On the Minimal Martingale Measure and the Foellmer- Schweizer Decomposition

We provide three characterizations of the minimal martingale measure P associated to a given d- dimensional semimartingale X. In each case, P is shown to be the unique solution of an optimization problem where one minimizes a certain functional over a suitable class of signed local martingale measures for X. Furthermore, we extend a result of Ansel and Stricker on the Foellmer-Schweizer decomposition to the case where X is continuous, but multidimensional.