Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation models for paired comparison data

Binary paired comparison data are binary data that record which of two objects being compared is preferred. Applications arise in many contexts including biology, acoustics, genetics, consumer behaviour and sports tournaments. Most of traditional models, such as the Bradley-Terry model, are based on unrealistic independence assumptions. However, in many instances it is sensible to believe that the results of two paired comparisons involving a common object will be correlated. This thesis focuses on the potential presence of dependence among paired comparison data. Two novel models, either marginally or conditionally specified, are introduced in order to account for the dependence structure of the data. Although the proposed models appear to be more realistic than usual independence models, ordinary likelihood inference is made difficult by the need to approximate high dimensional integrals. Hence, in this thesis a composite likelihood inferential approach is proposed. Simulation studies are performed in order to assess the behaviour of the maximum composite likelihood estimators for the parameters of the correlation models for paired comparison data. The estimators, in case of single round robin tournaments, exhibit good properties. Moreover, even if not all matches of a round robin tournament are played, the estimates are not much affected at least until fifty per cent of the competitions takes place. The proposed methodology is illustrated by some applications to real data sets. A first application regards sports data, specifically the results of the Italian A1 league which is at the top of the Italian volleyball league system. In this case the interest lies in determining whether the geographical origin of the teams or their physical features influence the strength of the teams themselves. A further application is concerned with biological data. Biologists are interested in investigating the role of colours in sexual signalling and in determining whether specific colours are associated with stronger animals. For this purpose, they observed the results of contests between some lizards that belong to a particular species which displays three different patches of colours on their bodies. Finally, the problem of the evaluation and ranking of scientific journals is considered with an application to the main statistical journals.
Dynamic Programming Approach to Retrieving Similar Candlestick Charts for Short-Term Stock Price Prediction

— The paper describes a method for a short-term stock price prediction based on candlestick chart techniques that are popular among stock traders using technical analysis. While the techniques have long history, there is still no consistent conclusion on the predictability of the techniques. We focus on the fact that a trend of stock prices often continues after intervals of several days because stock prices tend to fluctuate according to announcements of important economic indicators, economic and political news, etc. Typically, stock price movements in the period without important news are small, resulting in generating a series of noisy candlesticks. To cope with the noisy candlesticks, this paper focuses on a dynamic programming algorithm that allows us to perform partial matches on sequences of stock prices. We propose a model consisting of six parameters for retrieving similar candlestick charts in order to take into account where the stock price occurs in high/low price zones, in addition to a price change and a length of candlestick body. Experiments are performed on the daily NASDAQ composite index. We choose the daily time frame since important news that affects stock prices occurs on a daily basis. The statistics of the candlesticks are calculated to determine the parameter values of the proposed model based on the average and the standard deviation. Experimental results show that the proposed method is effective in predicting both uptrend and downtrend. Strictly, the prediction of the downtrend is a little bit difficult than that of the uptrend, probably reflecting the fact that the NASDAQ stock market is constantly growing.
Acoustic waves in a strati(cid:12)ed atmosphere III. Temperature inhomogeneities

In a gravitationally strati(cid:12)ed atmosphere, small temperature variations distort the paths of acoustic waves from the rectilinear paths in an isothermal atmosphere. For temperature increasing upward, low-frequency waves near the acoustic cuto(cid:11) frequency propagating at a given polar angle are refracted towards the vertical direction (focused) and high-frequency waves, away from the vertical (defocused). Similarly, for temperature increasing towards the axis of a vertical cylinder, low-frequency waves are focused and high-frequency waves are defocused. This e(cid:11)ect of temperature inhomogeneities may be important for wave propagation in the chromospheric K 2v bright point phenomenon.
Mathematical programming approaches to machine learning and data mining

Machine learning problems of supervised classification, unsupervised clustering and parsimonious approximation are formulated as mathematical programs. The feature selection problem arising in the supervised classification task is effectively addressed by calculating a separating plane by minimizing separation error and the number of problem features utilized. The support vector machine approach is formulated using various norms to measure the margin of separation. The clustering problem of assigning m points in n-dimensional real space to k clusters is formulated as minimizing a piecewise-linear concave function over a polyhedral set. This problem is also formulated in a novel fashion by minimizing the sum of squared distances of data points to nearest cluster planes characterizing the k clusters. The problem of obtaining a parsimonious solution to a linear system where the right hand side vector may be corrupted by noise is formulated as minimizing the system residual plus either the number of nonzero elements in the solution vector or the norm of the solution vector. The feature selection problem, the clustering problem and the parsimonious approximation problem can all be stated as the minimization of a concave function over a polyhedral region and are solved by a theoretically justifiable, fast and finite successive linearization algorithm. Numerical tests indicate the utility and efficiency of these formulations on real-world databases. In particular, the feature selection approach via concave minimization computes a separating-plane based classifier that improves upon the generalization ability of a separating plane computed without feature suppression. This approach produces classifiers utilizing fewer original problem features than the support vector machine approaches, with comparable generalization ability. The clustering techniques are shown to be effective and efficient data mining tools in medical survival analysis applications. The parsimonious approximation methods yield improved results in a signal processing application, with high signal to noise ratio, over least squares and a lengthy combinatorial search. These results support the claim that mathematical programming is effective as the basis of data mining tools to extract patterns from a database which contain “knowledge” and thus achieve “knowledge discovery in databases”.
Selecting Data Mining Model for Web Advertising in Virtual Communities

—Marketing in web based social networks (virtual communities) became one of the crucial topics in contemporary business. There are three reasons why this area of marketing usage is interesting. Firstly, there are more and more users of social media in the Internet. Secondly, access to behavioral data of potential clients permits acquiring knowledge about their needs. Finally, there is a possibility of direct one-to-one marketing communication. In this article we will present a study concerning an advertising campaign in a popular social network. We will use data mining methods in order to adjust the message to different users, and we will also present a method of choosing an appropriate communication to a given user when a class imbalance problem occurs. The results are very promising and point out that there is a need for further studies in the area of using data mining in marketing.
A double-blind, placebo-controlled test of 2 d of calorie deprivation: effects on cognition, activity, sleep, and interstitial glucose concentrations 1–4

Background: Anecdotal information and limited research suggest that short-term caloric deprivation adversely affects cognition. However, this issue has not been studied using double-blind, placebo-controlledprocedures,becausetheformulationofacalorie-deficientfeedingregimenidenticaltoonewithcaloriesisimpossibleusingordinaryfoods.Therefore,testmealsvaryingincaloriccon-tent,butindistinguishableinsensorycharacteristics,wereformu-latedusinghydrocolloid-basedgelsastheprincipalstructuralcom-ponent. Objective: The purpose of this study was to examine the effects of 2 d of near-total caloric deprivation on cognitive function, satiety, activity, sleep, and glucose concentrations in a controlled environ-ment. Design: A double-blind, placebo-controlled crossover study of ca-loricdeprivationwasconducedinacontrolledenvironmentfor48h.Cognitivefunctionin27healthyyoungsubjectswasassessedre-peatedlywithstandardizedtestsofvigilance,reactiontime,learning,memory,logicalreasoning,mood,andsatiety.Wrist-wornmonitorswereusedtoassessambulatoryvigilance,activity,andsleep.Inter-stitialglucoseconcentrationswereassessedcontinuouslywithaminimallyinvasivemonitor. Results: When the subjects received the near calorie-free diets, meancalorieconsumptiontotaled1311kJ(313kcal)overthetestingperiod.Duringthefullyfedtreatmentsessions,thesubjectscon-sumedameanof9612kJ/d(2294kcal/d),whichmatchedtheirindividual,dailyenergyrequirements.Satietyandinterstitialglu-cose concentrations were lower during the calorie-deprived diet ( P (cid:1) 0.001) than during the fully fed diet. There were no detectable effects of calorie deprivation on any aspect of cognitive performance, ambulatory vigilance, activity, or sleep. The mood states assessed,includingfatigue,werenotaffectedbycaloriedeprivation. Conclusions: Cognitive performance, activity, sleep, and mood are not adversely affected in healthy humans by 2 d of calorie-deprivation when the subjects and investigators are unaware of the calorie content of the treatments.
OP-ICVA170482 1009..1015

OBJECTIVES: Cigarette smoking is a well-known cause of interstitial lung disease (ILD), pulmonary emphysema and lung cancer. Coexisting pulmonary disease can affect prognosis in patients with lung cancer. The aim of this study was to determine the influence of pulmonary disease on outcomes in patients with a smoking history who had undergone surgery for pathological Stage I non-small-cell lung cancer. METHODS: Medical records of 257 patients with a smoking history who underwent surgery for pathological Stage I non-small-cell lung cancer between June 2009 and December 2014 were reviewed. Coexisting ILDs were evaluated using high-resolution computed tomography. The degree of pulmonary emphysema was determined using image analysis software according to the Goddard classification. The impact of clinicopathological factors on outcome was evaluated. RESULTS: Among the 257 patients, ILDs were detected via high-resolution computed tomography in 60 (23.3%) patients; of these, usual interstitial pneumonia (UIP) patterns and non-UIP patterns were seen in 25 (9.7%) and 35 (13.6%) patients, respectively. The degree of pulmonary emphysema was classified as none, mild and moderate and included 50 (19.5%), 162 (63.0%) and 45 (17.5%) patients, respectively. The 5-year overall survival, cancer-specific survival and relapse-free survival were 80.7%, 88.0% and 74.9%, respectively, during a median follow-up period of 50.5 months. In multivariate analysis, the presence of a UIP pattern was shown to be an independent risk factor for poor outcome. CONCLUSIONS: The presence of a UIP-pattern ILD on high-resolution computed tomography images was shown to be a risk factor for poor outcome in patients with a smoking history who had undergone surgery for pathological Stage I non-small-cell lung cancer.
The Influence Of Information Technology On The Operational Performance Of Medium Scale Business Entities In East Java

— The utilization of Information Technology (IT) in the business entity is not detached from the need for decision making that requires fast and accurate information. IT plays a role in transforming the organization's transformation process, as it allows organizations to process and use information into decisions. But on the other hand, many business entities that are incapable of transforming IT assets have become competitive advantages that are sustained continuously in the long term. Another cause is inability converting IT assets into IT capabilities in a managerial process. In fact, the business entity has invested less funds for the procurement of IT resources. IT utilization is an ability that must be built in the business entity of any magnitude and type of business, regardless of the variety, but the fact that the adoption of IT in many business entities provides different outcomes or benefits. This research was conducted to determine the influence or contribution of IT in enhancing the performance of business in the medium scale enterprises in Indonesia, especially East Java. It should be done by analyzing the capabilities of medium-scale enterprises against: IT Adoption, IT Assimilation, and IT Strategic Alignment by looking at its impact on operational performance in the business entity.
Bridging the gap between the science of retail revenue management and its application

x 1. Introducción . 1 1.1. Revisión de la Literatura . 3 1.2. Hipótesis . 6 1.3. Estructura de la Tesis . 6 2. Modelos de Revenue Management . 7 2.1. Estimación de la demanda . 7 2.2. Optimización . 8 3. Diseño del Sistema . 11 3.1. Base de Datos . 11 3.1.1. Dimensiones. 11 3.1.2. Series. 12 3.1.3. Eventos, promociones y factores exógenos. 14 3.2. Pricing Core . 16 3.3. Interfaz de Usuario . 18 3.4. El proceso de toma de decisiones . 18 3.4. 1. Objetos comunes de interfaz . 20 3.4. 2. Módulos de la Aplicación . 22 3.4. 3. Flujo . 25
Molecular insights into the normal operation, regulation, and multisystemic roles of K (cid:2) -Cl (cid:3) cotransporter 3 (KCC3)

Isenring P. Molecular insights into the normal operation, regulation, and multisystemic roles of K (cid:2) -Cl (cid:3) cotransporter 3 (KCC3). Am Physiol Cell Physiol before the molecular identity of the Na (cid:2) -dependent K (cid:2) -Cl (cid:3) cotransporters was uncovered in the mid-nineties, a Na (cid:2) -independent K (cid:2) -Cl (cid:3) cotransport system was also known to exist. It was initially observed in sheep and goat red blood cells where it was shown to be ouabain-insensitive and to increase in the presence of N -ethylmaleimide (NEM). After it was established between the early and mid-nineties, the expressed sequence tag (EST) databank was found to include a sequence that was highly homologous to those of the Na (cid:2) -dependent K (cid:2) -Cl (cid:3) cotransporters. This sequence was eventually found to code for the Na (cid:2) -independent K (cid:2) -Cl (cid:3) cotransport function that was described in red blood cells several years before. It was KCC1 isoforms KCC2, KCC4. obvious that each one of these isoforms exhibits unique patterns of distribution and fulﬁlls distinct physiological roles. Among them, been the subject of great attention in view of its important role in the nervous system and its association with a rare hereditary sensorimotor neuropathy (called Andermann syndrome) that affects many individuals in Quebec province (Canada). It was also found to play important roles in the cardiovascular system, the organ of Corti, and circulating blood cells. As will be seen in this review, however, there are still a number of uncertainties regarding the transport properties, structural organization, and regulation of KCC3. The same is true regarding the mechanisms by which KCC3 accomplishes its numerous functions in animal cells.
World Representation for an Autonomous Driving Robot

Autonomous driving is the movement of an agent, robot or vehicle, from some point in space to another one, without any human intervention, in order to achieve predetermined goals. To drive autonomously using trajectory planning, it is vital to have an abstraction of the knowledge about the world, be it a priori or information that the agent acquires during the driving. For this, we developed a system capable of abstractly represent, not only the track for the Autonomous Driving Competition of the Portuguese Robotics Open, but also, tracks with similar characteristics. The system was developed in a flexible and modular manner, in order to allow the addition of new elements to the stated track and the easy expansion to support other types of tracks and circuits. The conclusion was that the most appropriate representation model for the system we were trying to develop was an hybrid model, in that, at a global level the representation would be topological and at a local level it would be metrical. In other words, dividing the track into sections, these are the basis for the topological representation, being each of the sections then mapped internally using a metrical representation. Integrating the work of this dissertation in the global system, one hoped to achieve a Autonomous Driving system capable of short and medium term planning, with the goal of improve the performance of the ROTA project robots, comparatively with the previous solution, which was based in a reactive system with some memory and to some degree stateful.
An Analysis of Habitual Mode Use in the Years of Rising Oil Prices

The existence of state dependence derived from panel data has played a very important role in studying employment and labor policies. This study is about state dependence of the transportation sector using retrospective panel survey data. The Ministry of Land, Infrastructure and Transport of Korea has conducted the survey to monitor changes in vehicle ownership and usage nationwide and to prepare measures when oil prices tend to rise sharply. From this data, we identify the existence of state dependence on passenger cars, public transportation, and nonmotorized modes. To do this, we estimate and analyze the dynamic random effects probit model that explains the selection of each transportation mode after controlling for the unobserved individual heterogeneity. Our results indicate that despite the rise of oil prices, behavior of habitual use (i.e., state dependence) of transportation modes is found in all three modes. The amount of state dependence of nonmotorized modes was the largest, followed by passenger cars and public transportation. From the estimated models, important policy implications can be drawn from the fact that the presence of state dependence and the importance of early habit formation are important not only in nonmotorized modes but also in public transportation. In other words, if policy makers want to encourage people to use public transportation in a new city, it suggests that a sufficient and convenient public transportation network should be built before people move to the city. Once cities are built without sufficient public transportation networks and people have become accustomed to using private cars, then it will be more difficult to change their transportation modes, requiring much more social efforts and costs.
Garbage In, Garbage Out: The Effect of Library Instruction on the Garbage In, Garbage Out: The Effect of Library Instruction on the Quality of Students’ Term Papers Quality of Students’ Term Papers

The authors report the results of a study which used citation analysis of students’ term papers to determine the effectiveness of a library instruction session. The research was conducted during the 2004-2005 school year. In each semester, two sections of the same class received a library instruction session, while the third section of the class did not. Bibliographies of the students’ term papers were then examined to determine if the numbers and types of sources cited differed between the two groups. Library instruction was determined to be effective, in that students receiving library instruction were significantly more likely to cite journal articles and other scholarly resources than those students not receiving the library instruction.
On the essential dimension of coherent sheaves

We characterize all fields of definition for a given coherent sheaf over a projective scheme in terms of projective modules over a finite-dimensional endomorphism algebra. This yields general results on the essential dimension of such sheaves. Applying them to vector bundles over a smooth projective curve C, we obtain an upper bound for the essential dimension of their moduli stack. The upper bound is sharp if the conjecture of Colliot-Th\'el\`ene, Karpenko and Merkurjev holds. We find that the genericity property proved for Deligne-Mumford stacks by Brosnan, Reichstein and Vistoli still holds for this Artin stack, unless the curve C is elliptic.
Neutrino search from gamma-ray bursts during the prompt and X-ray afterglow phases using 10 years of IceCube public data

Neutrino emission from gamma-ray bursts (GRBs) has been sought for a long time, and stringent limits on the most accredited GRB emission models have been obtained from IceCube. Multi-wavelength GRB observations of the last decades improved our knowledge of the GRB emission parameters, such as the Lorentz factor and the luminosity, which can vary from one GRB to another by several orders of magnitude. Empirical correlations among such parameters have been identified during the prompt phase, with direct implications on GRB models. In this work, we use the PSLab open-access code, developed for IceCube data analyses, to search for individual neutrino emission from the prompt and afterglow phases of selected GRBs, and for stacking emission from the ensemble of such GRBs. For the afterglow phase, we focus in particular on GRBs with X-ray flares and plateaus. While past stacking searches assumed the same GRB fluence at Earth, we present a stacking scheme based on physically motivated GRB weights. Moreover, we conceive a new methodology for the prompt phase that uses the empirical correlations to infer the GRB luminosity and Lorentz factor, when redshift measurements are not available. We do not observe any significant neutrino excess. Hence, we set constraints on the GRB neutrino fluxes and on relevant GRB parameters, including the magnetic field in the jet. Notably, the baryon loading is found to be<10 for typical GRB prompts, thus disfavoring a baryonic-dominated origin of the GRB ejecta.
Noisy Inliers Make Great Outliers: Out-of-Distribution Object Detection with Noisy Synthetic Outliers

Many high-performing works on out-of-distribution (OOD) detection use real or synthetically generated outlier data to regularise model conﬁdence; however, they often require retraining of the base network or specialised model architectures. Our work demonstrates that Noisy Inliers Make Great Outliers (NIMGO) in the challenging ﬁeld of OOD object detection. We hypothesise that synthetic out-liers need only be minimally perturbed variants of the in-distribution (ID) data in order to train a discriminator to identify OOD samples – without expensive retraining of the base network. To test our hypothesis, we generate a synthetic outlier set by applying an additive-noise perturbation to ID samples at the image or bounding-box level. An auxiliary feature monitoring multilayer perceptron (MLP) is then trained to detect OOD feature representations using the perturbed ID samples as a proxy. During testing, we demonstrate that the auxiliary MLP distinguishes ID samples from OOD samples at a state-of-the-art level, reducing the false positive rate by more than 20% (absolute) over the previous state-of-the-art on the OpenImages dataset. Extensive additional ablations provide empirical evidence in support of our hypothesis.