Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimization of BDDs by Evolutionary Algorithms

We present two approaches for minimization of ordered Binary Decision Diagrams (BDDs) based on Evolutionary Algorithms (EAs). We consider completely and incompletely speciied Boolean Functions (BFs). The method for completely speciied functions uses principles of genetic algorithms and combines them with problem speciic reordering heuristics to determine a good variable ordering. To obtain eecient algorithms only small populations are considered. The second approach uses EAs for assigning Don't Cares (DCs) of incompletely speciied functions such that the resulting BDD size for a xed variable ordering is minimized. The algorithm is very fast, since the genetic operators are based on synthesis operations. Experimental results are given to show the ee-ciency of our approach. For several benchmark circuits the previously best known sizes (obtained by dynamic reordering and simulated annealing) could be improved.
Orbital In-spiral into a Massive Black Hole in a Galactic Center

A massive black hole (MBH) in a galactic center drives a flow of stars into nearly radial orbits to replace those it destroyed. Stars whose orbits cross the event horizon rS or the tidal disruption radius rt are promptly destroyed in an orbital period P. Stars with orbital periapse rp slightly larger than the sink radius q ≡ max(rS, rt) may slowly spiral in as a result of dissipative interactions with the MBH, e.g., gravitational wave emission, tidal heating, or accretion disk drag, with observable consequences and implications for the MBH growth rate. Unlike prompt destruction, the in-spiral time is typically ≫P. This time is limited by the same scattering process that initially deflected the star into its eccentric orbit, since it can deflect it again to a wider orbit where dissipation is inefficient. The ratio between slow and prompt event rates is therefore much smaller than that implied by the ratio of cross sections, ~rp/q, and so only prompt disruption contributes significantly to the mass of the MBH. Conversely, most stars that scatter off the MBH survive the extreme tidal interaction ("tidal scattering"). We derive general expressions for the in-spiral event rate and the mean number of in-spiraling stars, and we show that the survival probability of tidally scattered stars is ~1 and that the number of tidally heated stars ("squeezars") and gravity-wave-emitting stars in the galactic center is ~0.1-1.
Heights and Quadratic Forms: Cassels’ Theorem and its Generalizations

In this survey paper, we discuss the classical Cassels' theorem on existence of small-height zeros of quadratic forms over Q and its many extensions, to different fields and rings, as well as to more general situations, such as existence of totally isotropic small-height subspaces. We also discuss related recent results on effective structural theorems for quadratic spaces, as well as Cassels'-type theorems for small-height zeros of quadratic forms with additional conditions. We conclude with a selection of open problems.
Robust content-dependent high-fidelity watermark for tracking in digital cinema

Forensic digital watermarking is a promising tool in the fight against piracy of copyrighted motion imagery content, but to be effective it must be (1) imperceptibly embedded in high-definition motion picture source, (2) reliably retrieved, even from degraded copies as might result from camcorder capture and subsequent very-low-bitrate compression and distribution on the Internet, and (3) secure against unauthorized removal. No existing watermarking technology has yet to meet these three simultaneous requirements of fidelity, robustness, and security. We describe here a forensic watermarking approach that meets all three requirements. It is based on the inherent robustness and imperceptibility of very low spatiotemporal frequency watermark carriers, and on a watermark placement technique that renders jamming attacks too costly in picture quality, even if the attacker has complete knowledge of the embedding algorithm. The algorithm has been tested on HD Cinemascope source material exhibited in a digital cinema viewing room. The watermark is imperceptible, yet recoverable after exhibition capture with camcorders, and after the introduction of other distortions such as low-pass filtering, noise addition, geometric shifts, and the manipulation of brightness and contrast.
Sentiment analysis for Arabizi text

This paper has used supervised learning to assign sentiment or polarity labels to tweets written in Arabizi. Arabizi is a form of writing Arabic text which relies on using Latin letters rather than Arabic letters. This form of writing is common with the Arab youth. A rule-based converter was designed and applied on the tweets to convert them from Arabizi to Arabic. Subsequently, the resultant tweets were annotated with their respective sentiment labels using crowdsourcing. This ArabiziDataset consists of 3206 tweets. Results obtained by this work reveal that SVM accuracies are higher than Naive Bayes accuracies. Secondly, removal of stopwords and mapping emoticons to their corresponding words did not greatly improve the accuracies for Arabizi data. Thirdly, eliminating neutral tweets at early stage in the classification improves Precision for both Naive Bayes and SVM. However, Recall values fluctuated, sometimes they got improved; on other times they did not improve.
Anticarcinogenic effect and modification of cytochrome P450 2E1 by dietary garlic powder in diethylnitrosamine-initiated rat hepatocarcinogenesis.

The purpose of this study was to determine the effects of dietary garlic powder on diethylnitrosamine (DEN)- induced hepatocarcinogenesis and cytochrome P450 (CYP) enzymes in weaning male Sprague-Dawley rats by using the medium-term bioassay system of Ito et al. The rats were fed diets that contained 0, 0.5, 2.0 or 5.0% garlic powder for 8 weeks, beginning the diets with the intraperitoneal (i.p.) injection of DEN. The areas of placental glutathione S-transferase (GST-P) positive foci, an effective marker for DEN-initiated lesions, were significantly decreased in the rats that were fed garlic powder diets; the numbers were significantly decreased only in the 2.0 and 5.0% garlic-powder diets. The p-Nitrophenol hydroxylase (PNPH) activities and protein levels of CYP 2E1 in the hepatic microsomes of the rats that were fed the 2.0 and 5.0% garlic powder diet were much lower than those of the basal-diet groups. Pentoxyresorufin O-dealkylase (PROD) activity and CYP 2B1 protein level were not influenced by the garlic-powder diets and carcinogen treatment. Therefore, the suppression of CYP 2E1 by garlic in the diet might influence the formation of preneoplastic foci during hepatocarcinogenesis in rats that are initiated with DEN.
Erim Report Series Research in Management Competition and Market Dynamics on the Russian Deposits Market Bibliographic Data and Classifications

In the early transition era in Russia entry barriers for commercial banks were about absent. It resulted in the mushrooming of hundreds of small, poorly-endowed and inexperienced banks. In this paper we address the question whether the claimed benefits of low entry barriers – competition and market dynamics – have resulted. We use a sample of commercial saving banks for the 1994-97 period. We conclude that there were important mobility barriers and that the removal of entry barriers did not lead to intensified competition.
Descartes and the gut: “I'm pink therefore I am”

See articles on pages 209 and 214 In 1637 Rene Descartes wrote “The soul by which I am, what I am, is entirely distinct from my body and even if the body were not, the soul would not cease to be what it is”. 1 Descartes was thus reflecting on the longstanding conundrum of relationships between body and soul which have continued to this day and are equally mirrored in our views on diseases of the gastrointestinal tract. The fathers of gastroenterology clearly recognised the relationships between the brain and gut. In his classic studies of the control of gastric secretion conducted on his subject Tom with a permanent gastric fistula,2 Stuart Wolf found that emotional state affected secretion and that mucosal blood flow, measured by a simple thermistor, altered in parallel.3 Similar relationships between rectal mucosal blood flow and psychological state were also reported by Almy.4 In his now classic “hoax” experiment, he induced anxiety in a “volunteer” by pretending to identify an abnormality during sigmoidoscopy. This induced a marked change in colonic motor activity and a change in mucosal colour. In the last half century however the onset of an era of objectivity has influenced how we view gastrointestinal symptoms. Quite rightly …
An Automated Advanced Clustering Algorithm for Text Classification

A major characteristic of text document classification problem is extremely high dimensionality of text data. In this paper we present four new algorithms for feature/word selection for the purpose of text classification. We use sequential forward selection methods based on improved mutual information criterion functions. The performance of the proposed evaluation functions compared to the information gain which evaluate features individually is discussed. We present experimental results using naive Bayes classifier based on multinomial model, linear support vector machine and k-nearest neighbor classifiers on the Reuters data set. Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. In this paper, we propose a fuzzy similarity-based self-constructing algorithm for feature clustering. The words in the feature vector of a document set are grouped into clusters, based on similarity test. Words that are similar to each other are grouped into the same cluster. Each cluster is characterized by a membership function with statistical mean and deviation. When all the words have been fed in, a desired number of clusters are formed automatically. We then have one extracted feature for each cluster. The extracted feature, corresponding to a cluster, is a weighted combination of the words contained in the cluster. By this algorithm, the derived membership functions match closely with and describe properly the real distribution of the training data. Besides, the user need not specify the number of extracted features in advance, and trial-and-error for determining the appropriate number of extracted features can then be avoided. The goal of text document classification is to assign automatically a new document into one or more predefined classes based on its contents. An increasing number of statistical classification methods and machine learning algorithms have been explored to build automatically a classifier by learning from previously labelled documents including naive Bayes, k-nearest neighbor, support vector machines, neural network, decision trees, logistic regression. The overview of discusses the main approaches to text classification. We propose a fuzzy similarity-based self-constructing feature clustering algorithm, which is an incremental feature clustering approach to reduce the number of features for the text classification task. The words in the feature vector of a document set are represented as distributions and processed one after another. Words that are similar to each other are grouped into the same cluster. Each cluster is characterized by a membership function with statistical mean and deviation. If a word is not …
Value and Events Approaches to Accounting: An Experimental Evaluation

This paper presents the results of an empirical test of the value/events hypothesis first presented by Sorter. The paper places the value and events approaches to information generation in an overall information systems framework, by simultaneously examining the accounting approaches and the psychological type of the decision maker. The experimental results indicate that Sorter's events approach has considerable merit when coupled to the psychological type of the decision maker.
Lateral Vibration of Footbridges under Crowd-Loading: Continuous Crowd Modeling Approach

In this paper, a simple 1D crowd model is proposed, which aim is to properly describe the crowd-flow phenomena occurring when pedestrians walk on a flexible footbridge. The crowd is assumed to behave like a continuous compressible fluid and the pedestrian flow is modeled in a 1-D framework using the (total) mass (of pedestrians) conservation equation. This crowd model is then coupled with a simple model for the dynamical behavior of the footbridge and an optimized modeling of synchronization effects is performed. Numerical simulations are presented to show some preliminary results.
The Long-Run Risks Model and Aggregate Asset Prices: An Empirical Assessment

The long-run risks model of asset prices explains stock price variation as a response to persistent fluctuations in the mean and volatility of aggregate consumption growth, by a representative agent with a high elasticity of intertemporal substitution. This paper documents several empirical difficulties for the model as calibrated by Bansal and Yaron (BY, 2004) and Bansal, Kiku, and Yaron (BKY, 2007a). BY's calibration counterfactually implies that long-run consumption and dividend growth should be highly persistent and predictable from stock prices. BKY's calibration does better in this respect by greatly increasing the persistence of volatility fluctuations and their impact on stock prices. This calibration fits the predictive power of stock prices for future consumption volatility, but implies much greater predictive power of stock prices for future stock return volatility than is found in the data. Neither calibration can explain why movements in real interest rates do not generate strong predictable movements in consumption growth. Finally, the long-run risks model implies extremely low yields and negative term premia on inflation-indexed bonds.
microRNA‐379 couples glucocorticoid hormones to dysfunctional lipid homeostasis

In mammals, glucocorticoids (GCs) and their intracellular receptor, the glucocorticoid receptor (GR), represent critical checkpoints in the endocrine control of energy homeostasis. Indeed, aberrant GC action is linked to severe metabolic stress conditions as seen in Cushing's syndrome, GC therapy and certain components of the Metabolic Syndrome, including obesity and insulin resistance. Here, we identify the hepatic induction of the mammalian conserved microRNA (miR)‐379/410 genomic cluster as a key component of GC/GR‐driven metabolic dysfunction. Particularly, miR‐379 was up‐regulated in mouse models of hyperglucocorticoidemia and obesity as well as human liver in a GC/GR‐dependent manner. Hepatocyte‐specific silencing of miR‐379 substantially reduced circulating very‐low‐density lipoprotein (VLDL)‐associated triglyceride (TG) levels in healthy mice and normalized aberrant lipid profiles in metabolically challenged animals, mediated through miR‐379 effects on key receptors in hepatic TG re‐uptake. As hepatic miR‐379 levels were also correlated with GC and TG levels in human obese patients, the identification of a GC/GR‐controlled miRNA cluster not only defines a novel layer of hormone‐dependent metabolic control but also paves the way to alternative miRNA‐based therapeutic approaches in metabolic dysfunction.
The CMU Pose, Illumination, and Expression Database

In the Fall of 2000, we collected a database of more than 40,000 facial images of 68 people. Using the Carnegie Mellon University 3D Room, we imaged each person across 13 different poses, under 43 different illumination conditions, and with four different expressions. We call this the CMU pose, illumination, and expression (PIE) database. We describe the imaging hardware, the collection procedure, the organization of the images, several possible uses, and how to obtain the database.
Barriers to Inquiry-Based Instruction: A Longitudinal Study of History Teachers

While there has been an increased focus on inquiry-based learning in teacher preparation programs, little is known about the influences of these programs on teacher development over time. Using activity theory as its theoretical framework, the researcher employed a longitudinal interpretative case study design to examine the development of three history teachers’ conceptual and practical tools from their methods course through their fifth year or exit from the classroom. The findings revealed that, while the teachers had inquiry-aligned beliefs and developed inquiry-related conceptual tools, a lack of practical tools and support during teacher preparation and within their eventual communities of practice had a major impact on their ability to frequently implement inquiry in their classrooms. Recommendations are offered for teacher preparation and inservice professional development programs.
Discretization-Optimization Methods for Nonlinear Elliptic Optimal Control Problems with State Constraints

We consider an optimal control problem described by a second order elliptic boundary value problem, jointly nonlinear in the state and control, with control and state constraints, where the state constraints and cost functionals involve also the state gradient. Since this problem may have no classical solutions, it is also formulated in the relaxed form. The classical problem is discretized by using a finite element method for state approximation, while the controls are approximated by elementwise constant, or linear, or multilinear, controls. Various necessary conditions for optimality are given for the classical and the relaxed problem, in the continuous and the discrete case. We then study the behavior in the limit of discrete optimality, and of discrete extremality and admissibility. Next, we apply a penalized gradient projection method to each discrete problem, and also a progressively refining version of this method to the continuous classical problem. We prove that accumulation points of sequences generated by the first method are extremal for the discrete problem, and that strong classical (resp. relaxed) accumulation points of sequences of discrete controls generated by the second method are admissible and weakly extremal classical (resp. relaxed) for the continuous classical (resp. relaxed) problem. Finally, numerical examples are given.
Is participatory social learning a performance driver for Chinese smallholder farmers

This paper aims to test the effect of smallholder farmers' participatory social learning on their gain of performance in a village of southwest China. By exploring a panel structure survey data collected in the village, we identify the social learning effect using a Spatial Autoregressive (SAR) model. Particularly, we calculate the technical efficiency and environmental efficiency from a SFA model and use them as dependent variables of the model. Moreover, we investigate the social learning of different technologies, i.e., conventional and organic farming, by separating the estimations. Our identification results suggest that the effect of social learning is weak due to the technological heterogeneity in the general case, whilst it is significantly positive for organized organic farming. However, it appears that farmers learn to improve their economic performance (i.e., maximize yield) rather than environmental performance (i.e., minimize environmentally detrimental input). These results reveal a critical limitation of social learning, and demand more environmental orientation in the agricultural extension service, which is expected to guide smallholder farmers and foster their environmental performance for sustainable agricultural development.
Characterization of the equivalence of robustification and regularization in linear, median, and matrix regression

The notion of developing statistical methods in machine learning which are robust to adversarial perturbations in the underlying data has been the subject of increasing interest in recent years. A common feature of this work is that the adversarial robustification often corresponds exactly to regularization methods which appear as a loss function plus a penalty. In this paper we deepen and extend the understanding of the connection between robustification and regularization (as achieved by penalization) in regression problems. Specifically, (a) in the context of linear regression, we characterize precisely under which conditions on the model of uncertainty used and on the loss function penalties robustification and regularization are equivalent, and (b) we extend the characterization of robustification and regularization to matrix regression problems (matrix completion and Principal Component Analysis).
A domain decomposition approach to implementing fault slip in finite‐element models of quasi‐static and dynamic crustal deformation

We employ a domain decomposition approach with Lagrange multipliers to implement fault slip in a finite‐element code, PyLith, for use in both quasi‐static and dynamic crustal deformation applications. This integrated approach to solving both quasi‐static and dynamic simulations leverages common finite‐element data structures and implementations of various boundary conditions, discretization schemes, and bulk and fault rheologies. We have developed a custom preconditioner for the Lagrange multiplier portion of the system of equations that provides excellent scalability with problem size compared to conventional additive Schwarz methods. We demonstrate application of this approach using benchmarks for both quasi‐static viscoelastic deformation and dynamic spontaneous rupture propagation that verify the numerical implementation in PyLith.
Customer Retention : The Resistance to Change Perspective

Many academics and practitioners have reiterated the importance of online customer retention in order to ensure long-term profitability. For this reason, a number of studies have identified various means and ends of developing customer retention with a greater emphasis on creating customer loyalty. However, retaining customers, especially in Internet, is very difficult because of the low search cost and low switching cost. This study offers a new conceptual framework for retaining online customers based on theoretical foundations from status quo bias theory. Particularly, this study considers online customer retention from the perspective of customer resistance to change. The empirical study of an Internet bookstore, conducted through an online survey reveals that trust, relative attractiveness and switching costs together influence customer resistance to change. This study mainly contributes by identifying another approach for retaining online customers, i.e. by creating resistance to change.
Nonlinear prediction of chaotic time series using support vector machines

A novel method for regression has been recently proposed by Vapnik et al. (1995, 1996). The technique, called support vector machine (SVM), is very well founded from the mathematical point of view and seems to provide a new insight in function approximation. We implemented the SVM and tested it on a database of chaotic time series previously used to compare the performances of different approximation techniques, including polynomial and rational approximation, local polynomial techniques, radial basis functions, and neural networks. The SVM performs better than the other approaches. We also study, for a particular time series, the variability in performance with respect to the few free parameters of SVM.