Miracle-dz/UniFAR-FTUs huggingface dataset:
A General Model of Information Sharing in Oligopoly

Under which circumstances do oligopolists have an incentive to share private information about a stochastic demand or stochastic costs? We present a general model which includes virtually all models of the existing literature on information sharing as special cases. The analysis reveals that in contrast to the apparent inconclusivenss of previous results some simple principles determining the incentives to share information can be obtained. Most existing results are generalized and some interpretations are corrected, leading to a single general theory of the topic.
Análisis de sentimientos multilingüe en la Web 2.0

Nuestra propuesta consiste en un sistema de análisis de sentimientos h́ıbrido, que consiste una aproximación h́ıbrida, ya que utiliza un léxico de palabras etiquetadas según su polaridad, además de aprendizaje automático. El léxico se genera de manera automática a partir de un corpus etiquetado, y se asigna a cada término del texto una puntuación para cada polaridad. El aprendizaje automático se encarga de combinar las puntuaciones de cada término del texto para decidir la polaridad de ese texto. En nuestro trabajo nos centraremos en la elección de los términos, en la forma de puntuarlos, y en la forma de combinarlos para determinar la polaridad de un texto.
Individual policy preferences for vocational versus academic education: Microlevel evidence for the case of Switzerland

This paper uses an original dataset from a survey conducted in Switzerland in 2007 to explore the dynamics of education policy preferences. This issue has largely been neglected in that most studies on welfare state attitudes do not look at preferences for education. We argue that education policy preferences vary along two dimensions: the distribution of resources across different sectors of the education system (that is, vocational training versus academic education) and the level of investment in education both from public and private sources. With regard to the former, the findings suggest that individual educational experience matters most, that is, individuals prefer to concentrate resources on those educational sectors that are closest to their own educational background. With regard to the latter, we find that affiliation to partisan ideologies matters much more than other variables. Proponents of the left demand more investment both from the state as well as from the private sector and oppose individual tuition fees.
A novel feedback scheme for reducing standby power of flyback converter

A novel feedback network and the related circuit design for isolated flyback converters are presented. The proposed feedback topology generates a feedback signal which is in opposite phase with the conventional one to achieve low power consumption under light/no-load conditions. For implementing the proposed scheme, a PWM controller and a secondary-side integrated circuit are designed using the SPICE model for VIS 0.5-μm 5-V/40-V high-voltage CMOS process. Simulations reveal that a 12-V/18-W flyback converter adopting this proposed feedback technique saves a power of 30 mW under no-load condition and improves the system efficiency by 1% under 10%-load condition.
WinHAP: An Efficient Haplotype Phasing Algorithm Based on Scalable Sliding Windows

Haplotype phasing represents an essential step in studying the association of genomic polymorphisms with complex genetic diseases, and in determining targets for drug designing. In recent years, huge amounts of genotype data are produced from the rapidly evolving high-throughput sequencing technologies, and the data volume challenges the community with more efficient haplotype phasing algorithms, in the senses of both running time and overall accuracy. 2SNP is one of the fastest haplotype phasing algorithms with comparable low error rates with the other algorithms. The most time-consuming step of 2SNP is the construction of a maximum spanning tree (MST) among all the heterozygous SNP pairs. We simplified this step by replacing the MST with the initial haplotypes of adjacent heterozygous SNP pairs. The multi-SNP haplotypes were estimated within a sliding window along the chromosomes. The comparative studies on four different-scale genotype datasets suggest that our algorithm WinHAP outperforms 2SNP and most of the other haplotype phasing algorithms in terms of both running speeds and overall accuracies. To facilitate the WinHAP’s application in more practical biological datasets, we released the software for free at: http://staff.ustc. edu.cn/~xuyun/winhap/index.htm.
Phylogenetic analyses of some extremely halophilic archaea isolated from Dead Sea water, determined on the basis of their 16S rRNA sequences

Twenty-two extremely halophilic aerobic archaeal strains were isolated from enrichments prepared from Dead Sea water samples collected 57 years ago. The isolates were phenotypically clustered into five different groups, and a representative from each group was chosen for further study. Almost the entire sequences of the 16S rRNA genes of these representatives, and of Haloarcula hispanica ATCC 33960, were determined to establish their phylogenetic positions. The sequences of these strains were compared to previously published sequences of 27 reference halophilic archaea (members of the family Halobacteriaceae) and two other archaea, Methanobacterium formicicum DSM 1312 and Methanospirillum hungatei DSM 864. Phylogenetic analysis using approximately 1,400 base comparisons of 16S rRNA-encoding gene sequences demonstrated that the five isolates clustered closely to species belonging to three different genera--Haloferax, Halobacterium, and Haloarcula. Strains E1 and E8 were closely related and identified as members of the species Haloferax volcanii, and strain E12 was closely related and identified as a member of the species Halobacterium salinarum. However, strains E2 and E11 clustered in the Haloarcula branch with Haloarcula hispanica as the closest relative at 98.9 and 98.8% similarity, respectively. Strains E2 and E11 could represent two new species of the genus Haloarcula. However, because strains of these two new species were isolated from a single source, they will not be named until additional strains are isolated from other sources and fully characterized.
Bounded Optimal Exploration in MDP

Within the framework of probably approximately correct Markov decision processes (PAC-MDP), much theoretical work has focused on methods to attain near optimality after a relatively long period of learning and exploration. However, practical concerns require the attainment of satisfactory behavior within a short period of time. In this paper, we relax the PAC-MDP conditions to reconcile theoretically driven exploration methods and practical needs. We propose simple algorithms for discrete and continuous state spaces, and illustrate the benefits of our proposed relaxation via theoretical analyses and numerical examples. Our algorithms also maintain anytime error bounds and average loss bounds. Our approach accommodates both Bayesian and non-Bayesian methods.
Geographical distributions of spiny pocket mice in South America: insights from predictive models

Aim Predictive models of species’ distributions use occurrence records and environmental data to produce a model of the species’ requirements and a map of its potential distribution. To determine regions of suitable environmental conditions and assess biogeographical questions regarding their ranges, we modelled the potential geographical distributions of two spiny pocket mice (Rodentia: Heteromyidae) in northwestern South America.
HowtogetaChineseName(Entity): Segmentation and Combination Issues

When building a Chinese named entity recognition system, one must deal with certain language-specific issues such as whether the model should be based on characters or words. While there is no unique answer to this question, we discuss in detail advantages and disadvantages of each model, identify problems in segmentation and suggest possible solutions, presenting our observations, analysis, and experimental results. The second topic of this paper is classifier combination. We present and describe four classifiers for Chinese named entity recognition and describe various methods for combining their outputs. The results demonstrate that classifier combination is an effective technique of improving system performance: experiments over a large annotated corpus of fine-grained entity types exhibit a 10% relative reduction in F-measure error.
Measuring Social Tension from Income Class Segregation

We develop an index that effectively measures the level of social tension generated by income class segregation. We adopt the basic concepts of between-group difference (or alienation) and within-group similarity (or identification) from the income [bi]polarization literature; but we allow for asymmetric degrees of between-group antagonism in the alienation function, and construct a more effective identification function using both the relative degree of within-group clustering and the group size. To facilitate statistical inference, we derive the asymptotic distribution of the proposed measure using results from U-statistics. As the new measure is general enough to include existing income polarization indices as well as the Gini index as special cases, the asymptotic result can be readily applied to these popular indices. Evidence from the Panel Study of Income Dynamics data suggests that, while the level of social tension shows an upward trend over the sample period of 1981 to 2005, government’s taxes and transfers have been effective in reducing the level of social tension significantly.
A Low-Power Low-Voltage CMOS Thyristor Based Delay Element

A new low power low voltage CMOS thyristor delay element is proposed in this paper. The delay range of the proposed circuit is extended by reducing charge sharing problem. The delay element has less voltage and temperature sensitivity and consumes less power. The circuit is implemented in 130nm technology and simulation result shows that the delay range is from 180ps to 9ns which is very high compared to other architectures of CMOS thyristor delay element. The power consumption is in the range of 24µW to 243µW. This circuit also has low voltage and temperature sensitivity compared to previous circuits. A digitally controlled current source is implemented to generate control current in the range of 1µA to 32µA. A cascode current mirror is used to couple the control current to the delay element circuit.
Investigation of crystalline structure of plasticized poly (lactic acid)/Banana nanofibers composites

Polylactic acid (PLA) is a promising biodegradable candidate to replace synthetic commodity plastics in many applications. However, this polymer shows high brittleness, slow rate and lower degree of crystallization. The addition of plasticizing agents can enhance the toughness, but its effects on the crystallization behavior remain inconclusive. Therefore, this research is aiming to cast light on this area. Using differential scanning calorimetry (DSC) at a 2°C/min cooling rate, extruded neat PLA samples showed lower degree of crystallinity and thermal stability. This material shows cold crystallization upon heating and does recrystallize prior melting. These results indicate a clear instability in the crystalline state are confirmed by the crystallographic results by the X-ray diffractions (XRD) pattern and atomic force microscopic imagery. The addition of around 20 wt% of glycerol triacetate (GTA) with 1wt% of banana nanofibers (BNF) almost doubled the crystallinity. This modification is believed to occur through a dilution mechanism in order to increase crystallization rate yielding a more stable crystalline structure as shown by the XRD. However, the dynamic mechanical thermal analysis (DMTA) showed a 30 to 50% reduction in the room temperature storage modulus (stiffness) is in plasticized samples when compared to neat 100% PLA. Although these results shows the possibility to enhance the crystallization through a combination of plasticizing and nanoreinforcing effects, further studies is still needed to optimize the material formulation in order to find the best ratios to secure both a good crystallization and mechanical properties. This will definitively result in a new material that can be used for current and futuristic applications.
A brief introduction to the analysis and design of Networked Control Systems

Networked control has emerged in recent years as a new and exciting area in systems science. The topic has many potential applications in diverse areas ranging from control of microrobots to biological and economic systems. The supporting theory is very rich and combines aspects of control, signal processing, telecommunications and information theory. In this paper, we will give a brief overview of recent developments in networked control with an emphasis on our contributions. We also point to several open problems in this emerging area.
An Intelligent System for Content Generation

Generation graphical content from a single template image file in art manner is an important but difficult task for artificial neural networks, mostly due to the huge difference between classification existing data and producing new data. Nevertheless artificial intelligent systems capable of generating new content are important scientific task, because their working principles are close to the human thinking processes. Here we introduce an artificial system based on a Deep Neural Network that creates images, audio or 3D-content. In this work, we propose an content generative system for producing content by using pre-trained Deep Neural Network. This is made possible mainly two technical innovations. First, we propose to use different pre-trained neural networks, so that generative system can use optimized network parameters to produce new images. Content generative system and core Deep Neural Network are weakly bound components and we can obtain different system output by core replacement. Second, proposed artificial system can be used not only for image generation, but also for producing audio content and generation 3D-models with target style.
Entropy‐based information gain approaches to detect and to characterize gene‐gene and gene‐environment interactions/correlations of complex diseases

For complex diseases, the relationship between genotypes, environment factors, and phenotype is usually complex and nonlinear. Our understanding of the genetic architecture of diseases has considerably increased over the last years. However, both conceptually and methodologically, detecting gene‐gene and gene‐environment interactions remains a challenge, despite the existence of a number of efficient methods. One method that offers great promises but has not yet been widely applied to genomic data is the entropy‐based approach of information theory. In this article, we first develop entropy‐based test statistics to identify two‐way and higher order gene‐gene and gene‐environment interactions. We then apply these methods to a bladder cancer data set and thereby test their power and identify strengths and weaknesses. For two‐way interactions, we propose an information gain (IG) approach based on mutual information. For three‐ways and higher order interactions, an interaction IG approach is used. In both cases, we develop one‐dimensional test statistics to analyze sparse data. Compared to the naive chi‐square test, the test statistics we develop have similar or higher power and is robust. Applying it to the bladder cancer data set allowed to investigate the complex interactions between DNA repair gene single nucleotide polymorphisms, smoking status, and bladder cancer susceptibility. Although not yet widely applied, entropy‐based approaches appear as a useful tool for detecting gene‐gene and gene‐environment interactions. The test statistics we develop add to a growing body methodologies that will gradually shed light on the complex architecture of common diseases. Genet. Epidemiol. 35:706–721, 2011. © 2011 Wiley Periodicals, Inc.
Self-adaptive source separation. II. Comparison of the direct, feedback, and mixed linear network

For pt.I see ibid., vol.45, p.918-26 (1997). Macchi and Moreau (1997) investigated stability and convergence of a new direct linear adaptive neural network intended for separating independent sources when it is controlled by the well-known Herault-Jutten algorithm. In this second part, we study the corresponding feedback adaptive network. For two globally sub-Gaussian sources, the network achieves quasi-convergence in the mean square sense toward a separating state. A novel mixed adaptive direct/feedback network that is free of implementation constraints is investigated from the points of view of stability and convergence and compared with the direct and feedback networks. The three networks have the same (low) complexity. The mixed one achieves the best trade-off between convergence speed and steady-state separation performance, independently of the specific mixture.
Generating web applications from process models

The business process has gained a lot of importance for design and development of software in general and web applications in particular. Moreover, a shift from individual and separate application development to customization of pre-engineered solutions promotes significant reductions in time-to-market and maintenance effort. Applying the concept of process-based software product lines to webdevelopment promises to enable the average business user to generate ready--to--run web applications from comprehensive business process models. This case-study attempts to validate the concepts of Process Family Engineering in a concrete application domain by developing a software generator for process-based web applications for e-business.
From devices to people: attribution of search activity in multi-user settings

Online services rely on unique identifiers of machines to tailor offerings to their users. An implicit assumption is made that each machine identifier maps to an individual. However, shared ma-chines are common, leading to interwoven search histories and noisy signals for applications such as personalized search and ad-vertising. We present methods for attributing search activity to individual searchers. Using ground truth data for a sample of almost four million U.S. Web searchers-containing both machine identifiers and person identifiers-we show that over half of the machine identifiers comprise the queries of multiple people. We characterize variations in features of topic, time, and other aspects such as the complexity of the information sought per the number of searchers on a machine, and show significant differences in all measures. Based on these insights, we develop models to accurately estimate when multiple people contribute to the logs ascribed to a single machine identifier. We also develop models to cluster search behavior on a machine, allowing us to attribute historical data accurately and automatically assign new search activity to the correct searcher. The findings have implications for the design of applications such as personalized search and advertising that rely heavily on machine identifiers to custom-tailor their services.
Sliding mode control design for uncertain delay systems with partial actuator degradation

This article is concerned with the design of sliding mode control for uncertain state-delayed systems with partial actuator degradation. In the systems under consideration, there are parameter uncertainties in their state matrices and possible variations in the output of the actuators. In this work, a model of actuator faults is adopted such that both normal operation and partial actuator degradation are covered. A sufficient condition is derived such that the resultant sliding mode control system is asymptotically stable despite partial actuator degradation. Furthermore, it is shown that when a couple of linear matrix inequalities are feasible, the design of the sliding mode controller can be easily obtained via convex optimisation.
Urokinase plasminogen activator receptor: Prognostic biomarker for endometrial cancer

Endometrial adenocarcinoma is the most common gynecologic malignancy in the United States. However, reliable diagnostic or prognostic tumor markers have not been identified for endometrial cancer. In this study, we examined whether urokinase plasminogen activator receptor (UPAR), a glycosyl-phosphatidylinositol-linked membrane protein, is a candidate diagnostic or prognostic marker for patients with cancer of the endometrium. Sixty-five surgically excised, formalin-fixed endometrial tissue specimens were accessioned through the Department of Pathology Registry at the University of California, Los Angeles, and analyzed for UPAR expression by using immunohistochemical techniques. A retrospective review was also performed to determine stage and histopathologic grade of disease, recurrence, and mortality. No expression of UPAR protein was present in seven patients with benign neoplasia of the endometrium. UPAR protein expression highly correlated with stage of disease (ungrouped Spearman correlation = 0.625, P < 0.0001): 40% of patients with stage I, 66% of patients with stage II, 100% of patients with stage III, and 85% with stage IV demonstrated the highest level of UPAR expression. Moreover, high UPAR expression positively correlated with grade of disease (ungrouped Spearman correlation = 0.71, P < 0.0001): 29% of grade 1 specimens, 57% of grade 2, and over 90% of specimens with grade 3, the majority representing uterine papillary serous carcinoma and mixed malignant mesodermal tumor. Finally, UPAR protein expression also positively correlated with rate of recurrence and mortality in patients with adenocarcinoma of the endometrium (ungrouped P = 0.034). Our data suggest that UPAR is a useful prognostic marker for biologically aggressive forms of endometrial cancer.
A General Regression Framework for Learning String-to-String Mappings

The problem of learning a mapping from strings to strings arises in many areas of text and speech processing. As an example, an important component of speech recognition or speech synthesis systems is a pronunciation model, which provides the possible phonemic transcriptions of a word, or a sequence of words. An accurate pronunciation model is crucial for the overall quality of such systems. Another typical task in natural language processing is part-of-speech tagging, which consists of assigning a part-of-speech tag, e.g., noun, verb, preposition, determiner, to each word of a sentence. Similarly, parsing can be viewed as a string-to-string mapping where the target alphabet contains additional symbols such as parentheses to equivalently represent the tree structure. The problem of learning string-to-string mappings may seem similar to that of regression estimation where the task consists of learning a real-valued mapping. But, a key aspect of string-to-string mappings is that the target values, in this case strings, have some structure that can be exploited in learning. In particular, a similarity measure between target values can use the decomposition of the strings
Syntactic characteristics and similarities of Japanese authors' writing styles: A kernel-based approach

The subtree kernel and the information tree kernel proposed here measure the syntactic similarity of sentences. For two syntactic trees, these kernels are defined, respectively, as the total number of common subtrees in the syntactic trees and the total information content contained in their common subtrees, where the information content of a common subtree is calculated using its probability. Analyses using the kernels enable us to capture the syntactic characteristics and similarities of five great Japanese authors' writing styles.