Miracle-dz/UniFAR-FTUs huggingface dataset:
MODULAR FORMS AND TOPOLOGY

We want to discuss various applications of modular forms in topology. The starting point is elliptic genus and its generalizations. The main techniques are the Atiyah-Singer index theorem, the Atiyah-Bott-Segal-Singer Lefschetz fixed point formula, Kac-Moody Lie algebras, modular forms and theta-functions. Just as the representations theory of classical Lie groups has close connections with the Atiyah-Singer index formula as exposed in [A1], the representation theory of loop groups plays very important role in our study. One of the most important new features of loop group representations is the modular invariance of the Kac-Weyl character formula, which allows us to derive many interesting new results and to unify many important old results in topology. In this paper we will develope along this line. We hope that the other features of loop group representations, such as fusion rules and tensor category structure may also be applied to topology. See the discussions in §3. The contents of this paper is organized in the following way. In §1 we introduce elliptic genus by combining index theory and the representation theory of loop groups. The relation of the classical index theory with representation theory of classical Lie groups was discussed in [A1]. Here one finds that just replacing the classical Lie groups by their corresponding loop groups, we get the complete theory of elliptic genus, or more generally the index theory on loop space. Especially the Dirac operator and the Witten genus of loop space are derived more convincingly in this way. Then all of the other well-known properties of elliptic genus, such as functional equations, characterizations by rigidity and fibrations, can be easily obtained. Here we only pick some less well-known results to discuss, for example the expressions of the parameters in elliptic genera in terms of theta-functions. Different from the classical Lie group case, a new feature in our situation appears, the modular invariance which, by combining with index theory, is applied to obtain many new topological results. This is the content of §2. Most results in this section are special cases of more general results. For simplicity we only give the main ideas of the proofs. We make some geometric constructions in §3 to understand elliptic cohomology. The construction in §3.3 is motivated by the vertex operator algebra construction of the monstrous moonshine module. In §3.1 we introduce vector
Revisiting the learning curve (once again)

The vast majority of published work in the field of associative learning seeks to test the adequacy of various theoretical accounts of the learning process using average data. Of course, averaging hides important information, but individual departures from the average are usually designated “error” and largely ignored. However, from the perspective of an individual differences approach, this error is the data of interest; and when associative models are applied to individual learning curves the error is substantial. To some extent individual differences can be reasonably understood in terms of parametric variations of the underlying model. Unfortunately, in many cases, the data cannot be accomodated in this way and the applicability of the underlying model can be called into question. Indeed several authors have proposed alternatives to associative models because of the poor fits between data and associative model. In the current paper a novel associative approach to the analysis of individual learning curves is presented. The Memory Environment Cue Array Model (MECAM) is described and applied to two human predictive learning datasets. The MECAM is predicated on the assumption that participants do not parse the trial sequences to which they are exposed into independent episodes as is often assumed when learning curves are modeled. Instead, the MECAM assumes that learning and responding on a trial may also be influenced by the events of the previous trial. Incorporating non-local information the MECAM produced better approximations to individual learning curves than did the Rescorla–Wagner Model (RWM) suggesting that further exploration of the approach is warranted.
Moral Awareness in Business Organizations: Influences of Issue-Related and Social Context Factors

Individuals' awarenessof moral issues is an important first step in the ethicaldecision-making process. Relying on research in social cognition andbusiness ethics, we hypothesized that moral awareness is influenced byissue-related factors (magnitude of consequences of the moral issueand issue framing in moral terms) and social contextrelated factors(competitive context and perceived social consensus that the issue isethically problematic). The hypotheses were tested in a fieldexperiment involving 291competitive intelligence practitioners.Results generally supported the hypotheses. Qualitative analysisyielded additional insights into the content of respondents' moralawareness.
Combining Iris and Periocular Recognition Using Light Field Camera

Iris and Periocular biometrics has proved its effectiveness in accurately verifying the subject of interest. Recent improvements in visible spectrum Iris and Periocular verification have further boosted its application to unconstrained scenarios. However existing visible Iris verification systems suffer from low quality samples because of the limited depth-of-field exhibited by the conventional Iris capture systems. In this work, we propose a robust Iris and Periocular erification scheme in visible spectrum using Light Field Camera (LFC). Since the light field camera can provide multiple focus images in single capture, we are motivated to investigate its applicability for robust Iris and Periocular verification by exploring its all-in-focus property. Further, the use of all-in-focus property will extend the depth-of-focus and overcome the problem of focus that plays a predominant role in robust Iris and Periocular verification. We first collect a new Iris and Periocular biometric database using both light field and conventional camera by simulating real life scenarios. We then propose a new scheme for feature extraction and classification by exploring the combination of Local Binary Patterns (LBP) and Sparse Reconstruction Classifier (SRC). Extensive experiments are carried out on the newly collected database to bring out the merits and demerits on applicability of light field camera for Iris and Periocular verification. Finally, we also present the results on combining the information from Iris and Periocular biometrics using weighted sum rule.
Multicast Beamforming for Scalable Videos in Cache-Enabled Heterogeneous Networks

This paper investigates multicast beamforming for scalable videos in cache-enabled heterogeneous networks, where a macro base station (MBS) and multiple small base stations (SBSs) serve multicast group users on-demand video services. Inspired by the main idea of the scalable video coding, the extension technology of the H.264#x002F;advanced video coding, each video is divided into a base layer (BL) and an enhancement layer (EL). The BL can provide the fundamental viewing quality and adding EL to the received BL can guarantee superior perceptual experience. The MBS and multiple SBSs cache the BLs and ELs of the most popular videos, respectively. Employing the transmission schemes in coordinated multi-point, i.e., parallel transmission and joint transmission, the MBS and SBSs parallelly transmit BLs and ELs while the SBSs cooperatively transmit the ELs if higher video qualities are required. To improve the quality of the received videos for users whose requirements can be satisfied locally, our aim is to maximize the weighted sum rate of them and this problem can be converted into an iterative second-order cone programming problem by successive convex optimization with low complexity. Numerical results demonstrate the advantage of our proposed scheme compared to the benchmark scheme, i.e., only MBS serving the end users, even when the transmit power of each SBS is kept low.
The problem of Area Under the Curve

Receiver Operating Characterics curves have recently received considerable attention as mechanisms for comparing different algorithms or experimental results. However the common approach of comparing Area Under the Curve has come in for some criticism with alternatives such as Area Under Kappa and H-measure being proposed as alternative measure. However, these measures have their own idiosyncracies and neglect certain advantages of RoC analysis that do not carry over to the proposed approach. In addition they suffer from the general desire for a one fits all mentality as opposed to a pareto approach. That is we want a single number to optimize, rather than a suite of numbers to trade off. The starting point for all of this research is the inadequacy and bias of traditional measures such as Accuracy, Recall and Precision, and F-measure - these should never be used singly or as a group, and ROC analysis is a very good alternative to them if used correctly, but treating it as a graph rather than trying to boil it down to a single number. Other measures that seek to remove the bias in traditional measures include Krippendorf's Alpha, Scott's Pi, Powers' Informedness and Markedness, as well as a great many variant Kappa statistics. The original Kappa statistics were intrinsically dichotomous but the family has been well generalized to allow for multiple classes and multiple sources of labels. We discuss the proper and improper use of ROC curves, the issues with AUC and Kappa, and make a recommendation as to the approach to take in comparing experimental algorithms or other kinds of tests.
Implementation of pharmacists’ interventions and assessment of medication errors in an intensive care unit of a Chinese tertiary hospital

Background Pharmacist interventions and medication errors potentially differ between the People’s Republic of China and other countries. This study aimed to report interventions administered by clinical pharmacists and analyze medication errors in an intensive care unit (ICU) in a tertiary hospital in People’s Republic of China. Method A prospective, noncomparative, 6-month observational study was conducted in a general ICU of a tertiary hospital in the People’s Republic of China. Clinical pharmacists performed interventions to prevent or resolve medication errors during daily rounds and documented all of these interventions and medication errors. Such interventions and medication errors were categorized and then analyzed. Results During the 6-month observation period, a total of 489 pharmacist interventions were reported. Approximately 407 (83.2%) pharmacist interventions were accepted by ICU physicians. The incidence rate of medication errors was 124.7 per 1,000 patient-days. Improper drug frequency or dosing (n=152, 37.3%), drug omission (n=83, 20.4%), and potential or actual occurrence of adverse drug reaction (n=54, 13.3%) were the three most commonly committed medication errors. Approximately 339 (83.4%) medication errors did not pose any risks to the patients. Antimicrobials (n=171, 35.0%) were the most frequent type of medication associated with errors. Conclusion Medication errors during prescription frequently occurred in an ICU of a tertiary hospital in the People’s Republic of China. Pharmacist interventions were also efficient in preventing medication errors.
Lexical status of inflectional and derivational suffixes: Evidence from Finnish

The present study tested whether Finnish inflectional or derivational suffixes have lexical access units separate from word roots. In three lexical decision experiments, pseudowords carrying a case-inflection required significantly longer rejection times than nonaffixed pseudowords. This suggests that case-inflections have separate lexical access units. Similar effects were obtained for productive derivational suffixes, too. A specific lexical architecture, being able to account for both the present pseudoword results as well as earlier ones obtained with real Finnish nouns, is proposed.
Noninvasive Procedure for Improving Efficiency of Three-Phase Induction Motors

In developing countries, three-phase squirrel cage induction motors, even though with load equal to their name plate rating, may operate at low efficiencies. This may be because their winding parameters are not optimized for lower running cost. To ascertain the cause, it is necessary to know the stator winding data and compare it with the results of the design. If a deviation exists, the winding is improper. However, it may be possible to improve efficiency by rewinding the motor. We propose a noninvasive procedure to determine the following winding data: number of parallel circuits in a phase, number of turns per coil, and conductor area. The procedure includes a new iterative algorithm for determining these winding data. We discuss experimental investigations to validate the proposed procedure and to test its effectiveness in energy conservation. We outline the usefulness of the procedure for designers and the advantages it offers to service providers and educational institutions.
A Factor Analysis of Barriers to Effective Listening

This study examined the most frequently encountered barriers that may affect listen ing effectiveness among business college students. A factor analysis indicated the presence of six listening barrier factors that concern students. In addition, the barrier "listen primarily for details or facts" was perceived to be the most frequently en countered barrier identifded by the students. The students' major or age had no bear ing on the level of frequency given to the barrier factors. However, two out of the six factors were significantly different based on the students'sex.
Racial Disparities in Pretrial Diversion

Living with a felony conviction exposes people to a host of negative life experiences, including unemployment, low income, and family instability. Pretrial diversions offer defendants a rare chance to escape this fate. Using data from the State Court Processing Statistics, this study examines racial disparities in pretrial diversion among men charged with felony crimes in metropolitan counties in the even years from 1990 to 2006. The study finds that prosecutors are more likely to grant pretrial diversions to White defendants than they are to grant these diversions to Black or Latino defendants with similar legal characteristics. Moreover, while disparities disadvantaging Latinos are only present among defendants whose most serious arrest charge is for a drug crime and who have no prior convictions, those disparities disadvantaging Blacks occur both among defendants whose most serious arrest charge is for a drug crime regardless of prior convictions and among defendants charged with violent crimes who have no prior convictions.
GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains

The automatic recognition of gene names and their associated database identifiers from biomedical text has been widely studied in recent years, as these tasks play an important role in many downstream text-mining applications. Despite significant previous research, only a small number of tools are publicly available and these tools are typically restricted to detecting only mention level gene names or only document level gene identifiers. In this work, we report GNormPlus: an end-to-end and open source system that handles both gene mention and identifier detection. We created a new corpus of 694 PubMed articles to support our development of GNormPlus, containing manual annotations for not only gene names and their identifiers, but also closely related concepts useful for gene name disambiguation, such as gene families and protein domains. GNormPlus integrates several advanced text-mining techniques, including SimConcept for resolving composite gene names. As a result, GNormPlus compares favorably to other state-of-the-art methods when evaluated on two widely used public benchmarking datasets, achieving 86.7% F1-score on the BioCreative II Gene Normalization task dataset and 50.1% F1-score on the BioCreative III Gene Normalization task dataset. The GNormPlus source code and its annotated corpus are freely available, and the results of applying GNormPlus to the entire PubMed are freely accessible through our web-based tool PubTator.
Periodic Stochastic Volatility and Fat Tails

This article provides a comprehensive analysis of the size and statistical significance of the day of the week, month of the year, and holiday effects in daily stock index returns and volatility. We employ data from the Dow Jones Industrial Average (DJIA), the S&P 500, the S&P MidCap 400, and the S&P SmallCap 600 in order to test whether the seasonal patterns of medium and small firms are similar to those of large firms. Using formal hypothesis tests based on bootstrapping, we demonstrate that there are more significant calendar effects in volatility than in expected returns, especially for the two large cap indices. More importantly, we introduce the periodic stochastic volatility (PSV) model for characterizing the observed seasonal patterns of daily financial market volatility. We analyze the interaction between seasonal heteroskedasticity and fat tails by comparing the performance of Gaussian PSV and fat-tailed PSVt specifications to the plain vanilla SV and SVt benchmarks. Consistent with our model-free results, we find strong evidence of seasonal periodicity in volatility, which essentially eliminates the need for a fat-tailed conditional distribution, and is robust to the exclusion of the crash of 1987 outliers. Copyright 2006, Oxford University Press.
Academic advising—traditional or developmental approaches?: Student perspectives

Abstract Academic advising schemes in higher educational institutions have received little research attention in the UK Two approaches to academic advising schemes are explored: the traditional and developmental approaches. The role of the adviser of studies system is examined and applied to one UK institution of higher education. Empirical research involving group discussions with final-year undergraduate students was conducted to assess the role of the advising scheme. This explored the approach students expected or desired of advisers of studies schemes. Analysis of the primary data concludes that students appear to favour a developmental approach to academic advising.
Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally

In this paper, we propose a novel large margin classifier, called the maxi-min margin machine (M4). This model learns the decision boundary both locally and globally. In comparison, other large margin classifiers construct separating hyperplanes only either locally or globally. For example, a state-of-the-art large margin classifier, the support vector machine (SVM), considers data only locally, while another significant model, the minimax probability machine (MPM), focuses on building the decision hyperplane exclusively based on the global information. As a major contribution, we show that SVM yields the same solution as M4 when data satisfy certain conditions, and MPM can be regarded as a relaxation model of M4. Moreover, based on our proposed local and global view of data, another popular model, the linear discriminant analysis, can easily be interpreted and extended as well. We describe the M4 model definition, provide a geometrical interpretation, present theoretical justifications, and propose a practical sequential conic programming method to solve the optimization problem. We also show how to exploit Mercer kernels to extend M4 for nonlinear classifications. Furthermore, we perform a series of evaluations on both synthetic data sets and real-world benchmark data sets. Comparison with SVM and MPM demonstrates the advantages of our new model.
HLA-DR Association in Papillary Thyroid Carcinoma

Objective: Papillary thyroid carcinoma (PTC) is the most frequent types of thyroid malignancies. Several genes may be involved in susceptibility of thyroid cancer including Human Leukocyte Antigens (HLA). The association of thyroid carcinoma with HLA alleles has been previously studied in other populations and certain HLA alleles were shown to be either predisposing or protective. The aim of this study was to determine the association between HLA-DR and papillary thyroid carcinoma in an Iranian population. Design: HLA-DR antigen frequencies were determined in patients with papillary thyroid carcinoma (N = 70) and non-related healthy controls (N=180) using PCR -SSP. Main Outcome: We found that HLA-DRB1*04 frequency was significantly higher in our patients compared to the controls [P = 0.02, OR; 1.9, 95% CI (1.04–3.57)]. Conclusions: Our results revealed HLA-DRB1*04 as predisposing factor in papillary thyroid carcinoma in Iranian population. This confirms the previous findings for associations between HLA-DRB1 and differentiated carcinomas in other populations.
Course Project Machine Learning Techniques for Quantifying Characteristic Geological Feature Difference

This paper presents a novel methodology of quantifying the model mismatch part in the Bayesian framework for reservoir model inversion. During model inversion, the updated models are required to obey the prior characteristic geological information, which poses the issue of violation quantification. Recent approaches for this penalty normally require covariance matrix of model parameters, which are often impractical for three reasons: 1) they require gaussian assumption and 2) the computing of the inverse of the covariance is very time consuming or the approximation of the covariance skews the estimation of the uncertainty. 3) quite large amounts of models are required to achieve a stationary covariance estimation. In this paper, we propose an alternative approach that while circumventing the computing or approximation of the covariance, can provide a reliable estimation on the model misfit penalty as well as relax gaussian assumption. In order to render the model misfit quantitative, it is argued that this paper relies on a statistical learning for two totally different sets of models: prior models and reference models. After defining within-model distance and between-model distance, misfit between any two models can be trained through machine learning technique. SVM algorithm is used in this paper and turns out to be very effective in quantifying the similarities with respect to both the number of training models required and relaxation of the gaussian assumption of the model.
TamagoCar: Using a Simulation App to explore Price Elasticity of Demand for Electricity of Electric Vehicle Users

The integration of electric vehicles (EV) into the smart grid is bounded by high variability of demand for electricity, unwillingness of consumers to change behavior as well as low degree of product familiarity. Especially the absence of real-time energy tariffs constrains the widespread use of EVs as sustainable alternatives to a combustion engine. We propose to explore the behavior of EV users with a specifically designed application, TamagoCar, which combines the users’ real world driving behavior with a simulated EV environment. We hypothesize that the EV users will adjust their demand for electricity as a function of uncertainty about future price, range anxiety, uncertainty about future travel and social influence. With our application we enable the end users to gain experience with using an EV as well as adjust their electricity demand in response to real-time price changes with respect to their individual preferences, social influence and macroeconomic conditions.