Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic Databases under Updates: Boolean Query Evaluation and Ranked Enumeration

We consider tuple-independent probabilistic databases in a dynamic setting, where tuples can be inserted or deleted. In this context we are interested in efficient data structures for maintaining the query result of Boolean as well as non-Boolean queries. For Boolean queries, we show how the known lifted inference rules can be made dynamic, so that they support single-tuple updates with only a constant number of arithmetic operations. As a consequence, we obtain that the probability of every safe UCQ can be maintained with constant update time. For non-Boolean queries, our task is to enumerate all result tuples ranked by their probability. We develop lifted inference rules for non-Boolean queries, and, based on these rules, provide a dynamic data structure that allows both log-time updates and ranked enumeration with logarithmic delay. As an application, we identify a fragment of non-repeating conjunctive queries that supports log-time updates as well as log-delay ranked enumeration. This characterisation is tight under the OMv-conjecture.
The local conserved quantities of the closed XXZ chain

Integrability of the XXZ model induces an extensive number of conserved quantities. In this paper we give a closed form expression for the series of local conserved charges of the XXZ model on a closed chain with or without a twist. We prove that each element of the series commutes with the Hamiltonian.
A Study on the Nutritional Requirement for Juvenile Flounder Paralichthys olivaceus

In this study the results indicated that the optimum protein, lipid and carbohydrate requirements of juvenile flounder are 52.78 %,8.78% and 15.80 % respectively. Combined the growth data with the analysis of fatty acid composition of muscle and liver, it was indicated that the optimum requirement of EPA and DHA are 0.5 % and 1.0～1.5 %. The highest growth rate and lowest hepatosomatic index were obtained in fish fed a diet containing an EPA/DHA ration of 1/2.The dietary fatty acid compositon seemed have no effect on the fatty acid compositon in the polar and nonpolar lipid from muscle and liver.It is also found that the EPA/DHA ration of the polar lipid from both liver and muscle appear to stay near a level of 1/2. The optimum requirements of trace elements in the practical diet for juvenile flounder should be:iron 374 mg/kg, zine 119.2 mg/kg, maganese 19.24～29.15 mg/kg, copper 5.62～7.24 mg/kg, cobalt 2.20mg/kg and selenium 1.94～2.19 mg/kg respectively. The optimum requirements of vitamin should be: VA8780 IU/kg, VD1880 IU/kg, VE 186 mg/kg,VK11.7 mg/kg,VB118.2 mg/kg,VB214.7 mg/kg,water soluble BV360 mg/kg, water nonsoluble VB345 mg/kg, VB5106 mg/kg, VB625.4 mg/kg, biotin 1.91 mg/kg,folic acid 9.4mg/kg, VC(LAPP)400 mg/kg, inositol 800～1200 mg/kg and choline chloride 2000 mg/kg respectively.
The Influence of Approved Budget on Promoting Outsourced Revenue Collection Performance of Local Government Authorities in Developing Countries

Local Revenue collection is very imperatives in sustainable economic development especially in developing countries. This study examined the influence of approved budget in promoting outsourced revenue collection performance of Local Government Authorities (LGAs) in developing countries. The study opted a time series research design where by secondary data were used. The population applied on the approved budgets and actual revenue collections from 2009/1
Exceptional Model Mining meets Multi-objective Optimization

Exceptional Model Mining (EMM) is a local pattern mining framework that generalizes subgroup discovery. In EMM, we look for subsets of objects-subgroups-whose model deviates significantly from the same model fitted on the overall dataset. Multi-objective Optimization (MOO) is an area of Multiple Criteria Decision Making where two or more functions need to be optimized at the same time and the goal is to find the best compromise between the concurrent objectives. We introduce a new model class for EMM in a MOO setting called Exceptional Pareto Front Mining. We design fitting quality measures that take into account both the distance between models and the relevance of the subgroups. We propose a beam search for top-K EMM whose added-value is studied on both synthetic and real life datasets. Among others, we discuss a use case on hyperparameter optimization in machine learning for both regression and multi-label classification.
Well-Being, Self-Actualization, and Fundamental Motives: An Evolutionary Perspective

From an evolutionary perspective, feelings of subjective well-being signal progress toward adaptive goals. We discuss life history theory and fundamental motives—integrating ideas from social psychology and evolutionary biology that offer a potentially useful perspective on subjective well-being. We discuss some preliminary research from this perspective, and outline implications for future research. In doing so, we suggest that different people may have very different notions of which behavior evokes feelings of subjective well-being, linked to their particular life history features (e.g., age, sex, relationship status). This perspective also suggests predictable variations in what evokes subjective well-being across geography and cultures. We end by addressing avenues for future work taking an evolutionary perspective to understanding happiness and satisfaction—as well as the positive side of unhappiness and dissatisfaction.
Detection of Peanut Allergen by Real-Time PCR: Looking for a Suitable Detection Marker as Affected by Processing

Peanut (Arachis hypogaea) contains allergenic proteins, which make it harmful to the sensitised population. The presence of peanut in foods must be indicated on label, to prevent accidental consumption by allergic population. In this work, we use chloroplast markers for specific detection of peanut by real-time PCR (Polymerase Chain Reaction), in order to increase the assay sensitivity. Binary mixtures of raw and processed peanut flour in wheat were performed at concentrations ranging from 100,000 to 0.1 mg/kg. DNA isolation from peanut, mixtures, and other legumes was carried out following three protocols for obtaining genomic and chloroplast-enrich DNA. Quantity and quality of DNA were evaluated, obtaining better results for protocol 2. Specificity and sensitivity of the method has been assayed with specific primers for three chloroplast markers (mat k, rpl16, and trnH-psbA) and Ara h 6 peanut allergen-coding region was selected as nuclear low-copy target and TaqMan probes. Efficiency and linear correlation of calibration curves were within the adequate ranges. Mat k chloroplast marker yielded the most sensitive and efficient detection for peanut. Moreover, detection of mat K in binary mixtures of processed samples was possible for up to 10 mg/kg even after boiling, and autoclave 121 °C 15 min, with acceptable efficiency and linear correlation. Applicability of the method has been assayed in several commercial food products.
Université de Montréal Estimating the probability of a fleet vehicle accident: A deep learning approach using Conditional Variational Auto-Encoders

Risk is the possibility of a negative or undesired outcome. In our work, we evaluate the risk of a fleet vehicle accident using the 1998 and 1999 records from the files of the Societe d’assurance automobiles du Quebec (SAAQ), where each observation in the data set corresponds to a truck carrier of merchandise, and where the number of accidents during the following year it had. For each vehicle, we have useful information such as the number and type of violations it had, as well as some of its characteristics like the number of axles or the number of cylinders. With our objective in mind, we propose a new approach using conditional variational auto-encoders (CVAE) considering two distributional assumptions, Negative Binomial and Poisson, to model the distribution of a fleet vehicle accident. Our main motivation for using a CVAE is to capture the joint distribution between the number of accidents of a fleet vehicle and the predictor variables of such accidents, and to extract latent features that help reconstruct the distribution of the number of fleet vehicle accidents. We compare the CVAE with other probabilistic methods, such as a simple MLP model that learns the distribution of the number of fleet vehicle accidents without extracting meaningful latent representations. We found that the CVAE marginally outperforms the MLP model, which suggests that a model able to learn latent features has added value over one that does not. We also compared the CVAE with another basic probabilistic model, the generalized linear model (GLM), as well as with classification models. We found that the CVAE and GLM using the Negative Binomial distribution tend to show better results. Moreover, we provide a feature engineering scheme that incorporates features related to the whole fleet in addition to individual features for each vehicle that translates into improved performances of all the models implemented in our work used to evaluate the probability of a fleet vehicle accident.
N6-methyladenosine (m6A) in pancreatic cancer: Regulatory mechanisms and future direction

N6-methyladenosine (m6A), the most abundant RNA modification in eukaryotes, plays a pivotal role in regulating many cellular and biological processes. Aberrant m6A modification has recently been involved in carcinogenesis in various cancers, including pancreatic cancer. Pancreatic cancer is one of the deadliest cancers. It is a heterogeneous malignant disease characterized by a plethora of diverse genetic and epigenetic events. Increasing evidence suggests that dysregulation of m6A regulatory factors, such as methyltransferases, demethylases, and m6A-binding proteins, profoundly affects the development and progression of pancreatic cancer. In addition, m6A regulators and m6A target transcripts may be promising early diagnostic and prognostic cancer biomarkers, as well as therapeutic targets. In this review, we highlight the biological functions and mechanisms of m6A in pancreatic cancer and discuss the potential of m6A modification in clinical applications.
Recognizing Surgically Altered Face Images Using SIFT and LDA

— we are currently witnessing a strong worldwide popular in plastic surgery. Plastic surgery is the process of reconstructing and repairing the part of the body by transfer of tissues. People take plastic surgery to correct feature defects and improve attractiveness. This paper focused on recognizing before and after plastic surgery face images. Nonlinear variation in plastic surgery is difficult task to recognize pre and post-surgery images. Linear Discriminant Analysis is proposed to match before and after plastic surgery images. The algorithm generates multiple face granules at three levels of granularity. Here SIFT is used as the feature extractor for extracting discriminating information from granules which unified in LDA. The proposed algorithm yields high identification accuracy and less time complexity as compared to existing algorithms.
Evolution of Uppsala Model, a Literature Review through the years

Internationalization of firms has been profusely studied to identify and understand the variables, stages and elements which promote or inhibit the consolidation of this process. Uppsala Model was proposed to explain the firms’ internationalization path in terms of variables of Change and State. Since its apparition in 1977, it was designed to be a general rather than exhaustive list of variables considered important by the auhors, Johanson and Vahlne. During this 4 decades the world business has been constantly evolving, thus the Uppsala Model has been revised several times; however, this paper is focused on presenting the versions of 1977, 2009 and 2017 to contrast the original version and 2 most modern ones offering a more visible appreciation of its evolution. As a result of this literature review, it is concluded that Uppsala Model has been important for the internationalization theory considering not only the quantity of updates and incorporation of new and relevant elements of the world businesses but also the additional research emerged from their proposals. The most important is the prevalence in the Model of the person, as the central element which, ultimately leads and develops the present and future variables of State and Change. Finally, the value of the Uppsala Model is found in its dynamic orientation to both, internal and external enterprise’s processes, as well as to the challenges generally faced by international firms.
Stoichiometry characteristics of carbon,nitrogen,phosphorus and anaerobic carbon decomposition of wetland soil of different texture

To clarify the stoichiometry characteristics of carbon,nitrogen,phosphorus and anaerobic carbon decomposition of wetland soil of different texture,the stoichiometry characteristics of carbon,nitrogen,phosphorus and anaerobic carbon decomposition of soil of two texture (loam and sandy soil) under Phragmites australis and Scirpus triqueter stands in Minjiang River estuary were analyzed. Loam carbon content was 14.61 mg/g,nitrogen was 1.01 mg/g and phosphorus was 0.64 mg/g,which were higer than that of sandy soil carbon(9.02 mg/g),nitrogen (0.16 mg/g) and phosphorus(0.42 mg/g). Averaged C/N ratios of loam was lower than sandy soil and C/P,N/P ratios were higer than sandy soil.The averaged methane production potential of 0～40 cm loam and sandy soil were 0.0072,0. 0019μg/(g·d) and carbon dioxide were 33.5134,4. 9239μg/(g·d),respectively.The potentials of two kinds of carbon decomposition production from loam were all higher than that of sandy soil. The stoichiometry characteristics of carbon,nitrogen,phosphorus had a preferable indication for anaerobic carbon decomposition.
Automatic Information Extraction and Inferencing System from Online News Sources for Substance Abuse Cases

Therisingnumberofsubstanceabusecasesisaserioussituationthatdemandssignificantattention. Gaininginsightsfromthe reported substance abuse cases will greatly help law enforcement authorities and policy makers. The unstructured nature of thepubliclyavailabledataisachallenge. Computationaltechniquescanbemadeuseinefficientlyextractingandsummarising these unstructured data. The proposed system extracts the news reported on substance abuse related crimes from Malayalam online news papers. The extracted data is then processed using Natural Language Processing (NLP) techniques to generate a set of information that can be helpful in generating valuable inferences. Results show that the proposed system provide good accuracy for the data extraction task.
Introduction to Wireless Sensor Networks its Applications and Use Cases

WSNs are infrastructure-less networks that are why these networks can be easily deployed in any disaster situations like floods, battlefields, etc. They are easy, fast, and cost-effective to deploy for some limited period [3]. Due to the limited energy and open nature of WSNs the energy and security of the network become the major challenge. The network energy can be saved with the help of efficient routing which will enhance the network lifetime. In this paper various routing protocols like LEACH (Low Energy Adaptive Clustering Hierarchy), PEGASIS (Power-Efficient Gathering in Sensor Information Systems), etc. LEACH routing protocols are reviewed that are designed based on clustering architecture where the network is divided into various clusters also known as zones based on their energy.
Arena: A Scalable and Conﬁgurable Benchmark for Policy Learning

We believe current benchmarks for policy learning lack two important properties: 1 scalability and conﬁgurability. The growing literature on modeling policies as 2 graph neural networks calls for an object-based benchmark where the number 3 of objects can be arbitrarily scaled and the mechanics can be freely conﬁgured. 4 We introduce the Arena benchmark 1 , a scalable and conﬁgurable benchmark for 5 policy learning. Arena provides an object-based game-like environment where the 6 number of objects can be arbitrarily scaled and the mechanics can be conﬁgured 7 with a large degree of freedom. In this way, arena is designed to be an all-in-one 8 environment that uses scaling and conﬁguration to smoothly interpolates multiple 9 dimensions of decision making that require different degrees of inductive bias. 10
A Methodology to Study the University’s Online Teaching Activity from Virtual Platform Indicators: The Effect of the Covid-19 Pandemic at Universitat Politècnica de Catalunya

The Covid-19 pandemic led Catalan universities to do all teaching and evaluation online from 11 March 2020 until the end of term on 30 July. Conventional universities made the transition to online teaching in just a few days and suddenly virtual platforms become the centre of interaction between lecturers and students. Data that were obtained from the virtual platforms gave extremely valuable information about what was being done in class. This paper analyses data taken from Atenea, the Moodle virtual platform at the Universitat Politècnica de Catalunya (UPC), during quarantine. The key indicators and a data analysis design for Moodle have been proposed, which reveal teaching developments at various levels (overall and at the centre and subject level). This is applied to study data from the UPC Moodle and the results are discussed. The methodology can be extrapolated to other universities with Moodle platforms because the UPC is a set of small campuses and centres.
Extinction vs. Abstinence: a review of the molecular and circuit consequences of post-cocaine extinction and abstinence

The intravenous cocaine self-administration model is widely used to characterize the neurobiology of cocaine seeking. When studies are aimed at understanding relapse to cocaine-seeking, a post-cocaine abstinence period is imposed, followed by “relapse” tests to assess the ability of drugrelated stimuli (“primes”) to evoke the resumption of the instrumental response previously made to obtain cocaine. Here we review this literature on the impact of post-cocaine abstinence procedures on neurobiology, finding that the prelimbic and infralimbic regions of the prefrontal cortex are recruited by extinction training, and are not part of the relapse circuitry when extinction training does not occur. Pairing cocaine infusions with discrete cues recruits the involvement of the NA which, together with the dorsal striatum, is a key part of the relapse circuit regardless of abstinence procedures. Differences in molecular adaptations in the NA core include increased expression of GluN1 and glutamate receptor signaling partners after extinction training. AMPA receptors and glutamate transporters are similarly affected by abstinence and extinction. Glutamate receptor antagonists show efficacy at reducing relapse following extinction and abstinence, with a modest increase in efficacy of compounds which restore glutamate homeostasis after extinction training. Imaging studies in humans reveal cocaine-induced adaptations that are similar to those produced after extinction training. Thus, while instrumental extinction training does not have face validity, its use does not produce adaptations distinct from human cocaine users.
Appenzeller to Brie: Efficient Zero-Knowledge Proofs for Mixed-Mode Arithmetic and Z2k

Zero-knowledge proofs are highly flexible cryptographic protocols that are an important building block for many secure systems. Typically, these are defined with respect to statements that are formulated as arithmetic operations over a fixed finite field. This inflexibility is a disadvantage when it comes to complex programs, as some fields are more amenable to express certain operations than others. At the same time, there do not seem to be many proofs with a programming model similar to those found in modern computer architectures that perform arithmetic with 32 or 64 bit integers. In this work, we present solutions to both of these problems. First, we show how to efficiently check consistency of secret values between different instances of zero-knowledge protocols based on the commit-and-prove paradigm. This allows a protocol user to easily switch to the most efficient representation for a given task. To achieve this, we modify the extended doubly-authenticated bits (edabits) approach by Escudero et al. (Crypto 2020), originally developed for MPC, and optimize it for the zero-knowledge setting. As an application of our consistency check, we also introduce protocols for efficiently verifying truncations and comparisons of shared values both modulo a large prime p and modulo 2k. Finally, we complement our conversion protocols with new protocols for verifying arithmetic statements in Z2k. Here, we build upon recent interactive proof systems based on information-theoretic MACs and vector oblivious linear evaluation (VOLE), and show how this paradigm can be adapted to the ring setting. In particular, we show that supporting such modular operations natively in a proof system can be almost as efficient as proofs over large fields or bits, and this also easily plugs into our framework for zero-knowledge conversions.
Data-Driven Sequential Dynamic Pricing in Mobility

This thesis deals with the problem of sequential dynamic pricing in mobility. It focuses on the domains with a limited number of resources, where the customers are buying products constructed from these resources. When we set a price for any product, we need to consider the possible revenue we might get from the resources that are included in the product if we did not sell that product. The problem is inspired by dynamic pricing for the long-haul bus routes with multiple stations. We model the problem using Markov Decision Process, and we use Monte Carlo Tree Search to solve the problem. We use domain knowledge to improve the standard Monte Carlo Tree Search al-gorithm. In this thesis, we propose heuristics that evaluate the states and replace the random rollout. We show that these heuristics improve the results when we have limited computational resources, or we solve large space problems.
The Role Of Women Labors In Indonesian Economic Growth

Indonesia has the fourth population rank in the world, the women’s population is greater than man. The increasing number of women’s population, along the growth of women’s labors in Indonesia who are active in the economy, it can be seen from the increasing number of Women's Labor Force Participation Rates from year to year. This is driven by the existence of the gender equality principle which is one of the targets in the Sustainable Development Goals (SDGs). The increasing population who work, both men and women have a role in economy. This research aims to study the role of women labors in Indonesia's economic growth 2015-2018. We used 34 provinces data from the publication of the National Labor Force Survey, the variables are economic growth, man labor, women’s labor and the number of poor people. The results in this study using panel data regression showed there is no spilover between women labors and man labors. In addition, the growth of women labors in Indonesia is not statistically significant on economic growth. While the growth of man labors and the government expenditure has a significant positive effect on economic growth and the growth of the poor population has a significant negative impact on economic growth.
International Journal of Social Science and Economic Research

Based on the current pressure of college entrance examination reform in Jiangsu Province, it is particularly necessary for high school students to master multiple forms of English learning strategies. According to learning strategy theory of O’ Malley and Chamot, this paper conducts a questionnaire survey of 93 students to explore the overall situation of high school students’ vocabulary learning strategies. The results show that: (1)High school students are proficient at using multiple strategies to learn vocabulary; (2) There exist differences in the use of vocabulary learning strategies between students of Grade ten and Grade twelve; (3) There is a correlation between vocabulary learning strategies and gender.