dataesr/scientific-paragraphs-categorization huggingface dataset:
They counted on the franchisor to find a global solution applicable to each franchisee that was economically viable. As with the first study, the problem identified was that of the potential effects on cash of adverse weather in the first weeks or months of half seasons (spring and autumn) when new collections are showcased and sold at full prices. Likewise, we formed a task force comprising old and new franchisees, and added financial partners at the request of the franchisor. Sales and weather data were purchased for the pilot cities in which new franchisees were considered.
The Intervention In the absence of historical sales, we searched for other independent data we could use for benchmarking. In this particular business, data vendors such as Nielsen or IRI did not offer data on ice cream parlour sales. As a first step, we selected among existing sales data for top-of-the-range individual ice cream categories sold in high-end food stores and supermarkets, the specific subcategory that was most correlated to sales of existing franchisees. Correlations ranged from. 73 to. 85, which was acceptable for the purpose of building a model providing financial compensation in case of adverse weather. Data were provided by one of the world's leading panelists on the FMCG industry. We used 5 years of weeks sales data for all cities in which a new implementation was planned.
We then purchased cleaned, recalibrated weather data such as temperature, precipitation, wind, and relative humidity, and collected additional control variables such as calendar information (i. e. holidays, school holidays and long weekends) and other market and economic data suggested by business owners as potential explanatory variables for sales. Next, we prepared a large set of possible explanatory weather variables, including an additional variable that was suggested by business owners, namely "perceived weather". Meteorologists call it Humidex. It links temperature of the ambient air and the relative humidity to better determine the temperature felt by the consumer. The least relevant variables were eliminated through batteries of correlation tests between sales and weather variables (Step 2 in Figure  2 ).
Purpose -This article studies the impact of micro and macro factors on firm performance in the context of an emerging economy just changed from a subsidized economy to a market economy. Design/methodology/approach -The authors carried out an investigation into 30 listed food processing companies in Vietnam from 2014 to 2019. The data are analyzed by using STATA software. In this study, beside the regression analytical technique, the Blinder-Oaxaca decompositionanalysis is used to study more deeply the effect of variables on financial performance of food processing companies, so its results are reliable base to give suggestions. Findings -The results of empirical research help us to have some following conclusion. First, two variables consisting of total assets turnover ratio (ATR) and growth in sales significantly influence financial performance, when it is measured by return on equity (ROE) or return on sales (ROS).
Data The study is analytical in nature and involved testing of hypotheses quantitatively. The main content of this research approach is to find out a concise answer to the research questions through the collection and analysis of information of firms. In order to evaluate the influence of independent variables on financial performance of food processing companies, thirty listed Vietnamese companies manufacturing food products are selected. For the purpose of completing research goals, dataset has to meet some requirements including: 30 selected companies must have been in business before 2014; there is no disruption in their business operation; dataset has both SMEs and non-SMEs. The study is mainly based on secondary financial data including income statements, balance sheets and cash flow statements for period of 2014-2019.
Discussion and conclusion Companies manufacturing food products play an important role in the Vietnamese economy, but the financial performance of firms in this industry is not as high as the expectation, therefore, studying determinants of their financial performance is a necessary activity. By using the data of 30 listed food processing companies in Vietnam, we have some following conclusions First, two variables consisting of total ATR and growth in sales significantly influence financial performance when it is measured by ROE or ROS. However, while GROWTH influences positively both ROE and ROS, the impact of ATR on ROE is in contrast to that on ROS. Those results are reasonable for companies manufacturing food products. For that industry, solutions to increase growth in sale are used by numerous leaders, because the profit on each product is rather low, so increasing sales can help companies ensure their financial performance.
Vineyard Experimentation was carried out across two consecutive years -during the 2014 and 2015 vintages -in two Shiraz (SH) and one Cabernet-Sauvignon (CS) vineyards (G1_SH, G2_SH and G2_CS respectively) located in the Geographical Indication (GI) Riverina, Griffith (New South Wales, Australia), and one Shiraz and Cabernet-Sauvignon vineyard (O1_SH and O_CS respectively) located in GI Orange (New South Wales, Australia). Experiments were also performed in three other Shiraz vineyards in 2015: two located in Griffith (G3_SH and G4_SH), and one in Orange region (O2_SH). Geographical and topographical data of the vineyards are detailed in previously published works  (Šuklje et al. , 2019b (Šuklje et al. , , Schmidtke et al. , 2020)). The basic vineyard characteristics are presented in Table  1.
In this study, a slight increase in these metabolites in Shiraz and Cabernet-Sauvignon grapes of greater maturity in both vintages was observed. This trend contrasted with the variations measured for EEBAs in Shiraz wines. Future research on grape maturity and yeast redox metabolism is therefore required. A high varietal effect was also measured for higher alcohol acetates. The average total HAAs concentrations increased from 250 to 1300 μg/L at the H1 to H3 stages respectively, with higher variability being observed in 2015. The differences were mainly due to isoamyl acetate and, to a lesser extent, phenylethyl and propyl acetate. Conversely, HAAs concentrations tended to slightly decrease in Cabernet-Sauvignon wines (Table  3  and Supplementary Data Table  S2  and S3 ). An increase in HAA concentrations with grape ripening has previously been reported for Australian Cabernet-Sauvignon wines  (Bindon et al.
Another report by Knight Frank (May 2019) stated that the total number of high net-worth individuals in UAE is expected to grow 15% by 2023. Finally, on May 08, 2018, a prominent local newspaper-gulfnews. com, reported that on an average individuals have approximately 99,000 US$ as private wealth. Hence, there is huge scope for wealth management companies to improve their net promoter scores by adopting a blended approach in their interactions with clients. Primary data collected through a survey have been analyzed to find evidence of behavioral biases & elements of religiosity. Subsequently, regression analysis of data has been done to develop a quantitative model to predict their impact on asset allocation decisions. Stated below are the research questions: • Is there evidence of cognitive/emotional biases and religiosity among Arab investors in UAE? • Do behavioral biases and religiosity impact asset allocation decisions?.
Research design The epistemological stance adopted is deductive, to verify existence of behavioral biases & religiosity, followed by developing a quantitative model to predict their impact on asset allocation decisions. The research has used primary data collected through a survey of individual Arab investors selected by simple random sampling. The raw data were analyzed by using software to perform regression analysis and develop the mathematical model. (a) Primary Data collection Primary data were collected by using Qualtrics. com and face to face surveys. 500 individuals were requested to take part. 161 responses were received, out of which 129 were completed fully, and used for data analysis. The survey questions collected the following information: • Demographics-age, gender, education, employment, and nationality; • Risk tolerance, return expectation, time horizon and asset allocations of portfolios.
ABSTRACT Thanks to the numerous machine learning tools available to us nowadays, it is easier than ever to derive a model from a dataset in the frame of a supervised learning problem. However, when this model behaves poorly compared with an expected performance, the underlying question of the existence of such a model is often underlooked and one might just be tempted to try different parameters or just choose another model architecture. This is why the quality of the learning examples should be considered as early as possible as it acts as a go/no go signal for the following potentially costly learning process. With ADESIT, we provide a way to evaluate the ability of a dataset to perform well for a given supervised learning problem through statistics and visual exploration. Notably, we base our work on recent studies proposing the use of functional dependencies and specifically counterexample analysis to provide dataset cleanliness statistics but also a theoretical upper bound on the prediction accuracy directly linked to the problem settings (measurement uncertainty, expected generalization.
INTRODUCTION We consider a supervised learning (SL) problem where the task is to predict a continuous or categorical target  from a set of features  using a set of examples. The goal is to find a function  (a. k. a. model) such that  ( ) ≃. This function must balance between error minimization on the provided examples and its ability to generalize well on unseen instances to avoid overfitting. To do so, a set of training examples is sent to a learning algorithm to infer such a function which can be evaluated afterwards against a testing dataset. Suppose the resulting accuracy is below expectations: what should be questioned? One might be tempted to try different training parameters or even change the learning algorithm. If this approach makes sense in some cases, challenging the very existence of  should also be one of the primary concerns to prevent an unsuccessful SL process or, conversely, to help the practitioners trust the computed model.
In practice, the quality and completeness of the learning examples is one of the primary factors of success in SL. Noisy, inaccurate, or incomplete data can lead to poor models, and it is sometimes even difficult to understand that the learning dataset itself is to blame. Indeed, if the examples given for training contradict themselves or do not contain sufficient information, even the most advanced algorithm will fail at understanding the workings of the phenomenon one wants to predict. In response to these concerns, multiple methods and metrics have been developed in the past allowing to evaluate, improve, and better understand the data and its predictive power. In particular, we focus on the recent studies proposing the use of functional dependencies (FDs) and specifically the analysis of counterexamples to find contradictions in the dataset  [3, 12].
For the specific case of SL, we understand intuitively that learning examples with equal causes ( ) and different outcomes () are likely to cause problems during the learning process. As a function,  needs to give a unique answer for a given input. This type of contradiction can be due to noise in the data (sensor precision, input error. ) but it might also reveal missing features or just the unpredictable nature of the phenomenon. Therefore, finding regions with high densities of counterexamples is of great interest for data preprocessing, to gain insight into the intrinsic limitations of the raw dataset and the problem statement itself but also to guide interactions with domain experts. If the limits given by counterexample analysis are not suitable for the problem at hand, the process must be refined by working on data acquisition or processing.
As our main proposition, we present ADESIT (Advanced Data Exploration and Selection Interactive Tool), a web-based intuitive graphical user interface to evaluate the limits of a dataset for a given SL problem. This evaluation is made through statistical measures based on counterexamples and an interactive visual exploration permits the user to see the variations of this measure on different regions of the data. Given a dataset, a set of features  and a prediction target , ADESIT helps the user to understand the predictive power of the data but also potential refinements and improvements in her features or data selection. We propose various statistics on the dataset up to tuple granularity along with an interactive representation of the data and found counterexamples. As shown in Figure  1 , we thought ADESIT as being part of an iterative refinement process.
In summary, for a given dataset and a specific SL problem, ADE-SIT proposes the following contributions: • Statistics derived from counterexamples analysis of the associated FD to characterize the ability of the dataset to perform well on the given problem. Notably, we extend previous indicators  [10]  with domain knowledge integration and propose their first known efficient implementations. One of those indicators serves as an upper bound on the maximum accuracy obtainable with any model on a given dataset  [12]. • Scalable counterexample enumeration along with fast approximate and exact computation of the associated indicators allowing fast analysis of large datasets. • Customizable counterexamples visualizations (scatter plot, table, graph) and selection up to tuple granularity. • Ways to find and export regions of data with good learning potential but also subsets which need refinements.
While  1 and  2 propose evaluations of the dataset purity and counterexamples distribution,  3 provides an appreciation of learnability by proposing an upper bound on the maximum accuracy obtainable with any model on a given dataset  [12]. Intuitively, this is true because a model cannot correctly classify a tuple and its associated counterexample at the same time. In the case of crisp FDs, the  3 error can be specifically expressed as follows: 𝑔 3 (𝑋 → 𝐶, 𝑟 ) = 1 - 𝑚𝑎𝑥 (|𝑠 | : 𝑠 ⊆ 𝑟, 𝑠 |= 𝑋 → 𝐶) |𝑟 | where | | is the number of tuples in  and  |=  →  means that  →  is satisfied in  (i. e. no pair of tuples in  is a VP). However, this definition of VP is known to be too strict as it lacks some crucial domain knowledge integration such as data uncertainty (sensor error, noisy acquisition. ).
Violating pairs enumeration. To enumerate VPs, every tuple has to be compared to each other and two tuples are added to the set of VPs if they agree on X and disagree on C in regard to the similarity measures defined for each attribute. As this operation is quadratic in the number of tuples, it does not scale easily. Nonetheless, such couple enumeration has been extensively studied for record linkage and similarity joins where optimisations have been proposed for massive datasets. Depending of the attribute type, techniques such as blocking  [2]  and/or a sliding window algorithm (similar to  [4] ) can be used for remarkable gains in reducing the processing time. Note that this approach is also highly scalable as many distributed approaches such as MapReduce  [11]  are presented in literature and could be easily implemented on top of the pipeline.
The bestknown constant-factor approximation algorithm for the | | upper bound | |  is described in  [7]  while the lower bound | |  is given by the size of maximum matching, such that: 0. 5|𝑀𝑉𝐶 | ≤ |𝑀𝑉𝐶 | 𝑙𝑏 ≤ |𝑀𝑉𝐶 | ≤ |𝑀𝑉𝐶 | 𝑢𝑏 ≤ 2|𝑀𝑉𝐶 | In addition, we also propose an exact algorithm which combines multiple graph techniques (kernelization, branch-and-reduce and intermediate local search solutions. )  [8]  at the price of exponential complexity in the number of edges, and thus only usable for datasets with a reasonable number of counterexamples. To the best of our knowledge, ADESIT implements for the first time an efficient computation of the generalized  3 measure, both exact and approximated, pointing out an interesting cross-fertilization from database core technologies to machine learning.
ADESIT ADESIT is a web-based application which proposes a machinelearning-focused analysis and visualization of a given dataset based on counterexample analysis and especially the  1 ,  2 and  3 measures. Notably, it provides a direct appreciation of the performances a prediction model cannot exceed and ways to increase the quality of input data and improve learning results by observing what tuples do not respect the FD associated to the SL problem at hand. As labelled in the screenshot available in Figure  2  nodes in contradiction get highlighted. Plots of counterexamples distribution are available for each axis combination also including attributes produced with dimensionality reduction techniques such as PCA. In addition to the ability to zoom, move and select tuple regions using lasso or square tools, the user can choose to filter all tuples, counterexamples, or pure tuples only.
This visualization allows to detect areas with high densities of counterexamples and helps to decide if the process of data selection for SL can continue or if new or better data is needed. An alternative mirroring table view can also be used to explore and export parts of the dataset. (C2) This last area proposes an interactive counterexample graph of variable depth. Nodes can be hovered to get more information on their corresponding tuples and clicked to be selected. A table summarizing the selected tuple and its direct neighborhood is also available. It is important to emphasize that we are upstream from the model creation itself. The  3 indicator acts as an upper-bound on the target accuracy but will not necessarily be the desired one as it might result in a lack of generalization. However, by making informed choices for the similarity measures (thresholds) and therefore assuming imperfections in the accuracy of the attributes, we produce a better reflection of the real-life phenomenon behind the data and therefore reduce the overall variance.
ADESIT is used to identify contexts where creating a good model will be difficult or promising and provides you with knowledge to explain why to potentially refine your data from acquisition (eg. Should we get new sensors or more precise ones?) to preprocessing. Also note that ADESIT proposes intuitive indicators in view of a given dataset and is not meant to analyze the unpredictability of the underlying phenomenon itself which is notably linked to the Bayes error  [6]. ADESIT is coded in Python using the Dash framework. We also use Pandas and Numpy. A beta version of ADESIT hosted on the LIRIS laboratory servers is available at adesit. datavalor. com.
DEMONSTRATION SCENARIO For our demonstration, we propose to analyse a real case issue proposed by the Compagnie Nationale du Rhône (CNR), a hydropower company in France. They have recorded various sensor data of one of their hydropower plant over the past ten years at frequencies up to the second and commissioned the following study from us: Is it possible to predict the power produced by a turbine with a precision of 1%? Notably, the given sensor features are rather imprecise with uncertainties up to 5%. Rather than giving them a questionable cryptic number obtained by the costly task of training a lot of different models to get the best accuracy possible, we propose the alternative of ADESIT, allowing to get a direct visual understanding of the limitations of the dataset, facilitating the dialogue with domain experts.
Moreover, ADESIT proposes numeric indicators allowing to also give them tangible numbers and notably a theoretical upper bound on the accuracy for their dataset. The analysis will be deeply linked to domain knowledge as we will use the information provided by the company such as validity domains or uncertainties. We will work with 5 attributes: the power produced (Megawatts) which is the target C we want to predict, the flow of the river ( 3  -1 ), the turbine's blades position (percents), the opening of the water supply (percents) and the elevation of the waterfall (m). We will play with those attributes to show the audience their respective usefulness in the SL problem at hand. Notably, we identify specific areas with more counterexamples which need to be studied closely for training. On Figure  2  for example, we observe that multiple counterexamples occur for openings between 50% and 60%.
After correlation with the operating curve of the plant and closer examination with experts from the CNR, this observation helped us identify situations of underproduction which could be corrected. Similarly, a range with high density of counterexamples across the blades position attribute helped us identify the degradation in time of a turbine. We will take advantage of the complexity of the problem to showcase the diverse functionalities of ADESIT. During our demonstration, the audience will play the role of the company and we will explain to them in detail what might and might not work with their project. We will notably encourage dialogue and questioning by giving them clear information about the data and the problematic. In addition, we will cover some datasets from the UCI Machine Learning Repository and link our results to the actual performances obtained by the community.
Are equity anomalies a product of p-hacking in the asset pricing literature? To shed new light on this question, we perform a true out-of-sample study of 30 well-known anomalies in the crosssection of returns. We replicate these anomalies in a novel hand-collected dataset of firms listed on the historical Stock Exchange of Melbourne in the years 1926 to 1987. The vast majority of return-predictive signals cannot be confirmed. Those which are observed are commonly driven by small firms with marginal economic significance. Only a handful of anomalies survive our tests, namely, the dividend yield, value uncertainty, and short-term residual reversal effects. Overall, our findings support the view that many anomalies are statistical artifacts resulting from data mining.
Introduction Should we believe in equity anomalies? Or are they just statistical artifacts born of p-hacking? To offer a new perspective on this issue, we perform a genuinely out-of-sample replication study. We examine a novel and unique dataset of companies listed on the historical Stock Exchange of Melbourne in the years 1926 to 1987. Using hand-collected security prices from stock exchange gazettes and records, we reproduce a broad array of return-predictive signals. Our findings cast substantial doubt on the validity of discoveries reported in the asset pricing literature. The past couple of decades have seen an impressive proliferation of equity anomalies documented in the finance literature. Fuelled partially by demand from the investment industry, which capitalizes on anomaly research to forge profitable strategies, the academic literature has documented a number of cross-sectional patterns in returns.
Investors cannot learn from academic papers that have not been published yet. With this method of verification, support for the data mining hypothesis clearly becomes more unequivocal. In this article, we perform an out-of-sample study of 30 well-known anomalies in a novel and unique dataset: historical stock market data from the former Stock Exchange of Melbourne (SEM). The SEM listed firms mainly from the state of Victoria in Australia, was one of the predecessors of the current Australian Securities Exchange (ASX), and was formally absorbed by the ASX in 1987. Our dataset contains hand-collected prices, dividend yields, share numbers, and corporate events for more than 3600 companies listed on the SEM in. All the data were retrieved from historical stock exchange gazettes and records and, to our knowledge, have not yet been published in the academic literature.