Miracle-dz/UniFAR-FTUs huggingface dataset:
Contextual Embeddings: When Are They Worth It?

We study the settings for which deep contextual embeddings (e.g., BERT) give large improvements in performance relative to classic pretrained embeddings (e.g., GloVe), and an even simpler baseline—random word embeddings—focusing on the impact of the training set size and the linguistic properties of the task. Surprisingly, we find that both of these simpler baselines can match contextual embeddings on industry-scale data, and often perform within 5 to 10% accuracy (absolute) on benchmark tasks. Furthermore, we identify properties of data for which contextual embeddings give particularly large gains: language containing complex structure, ambiguous word usage, and words unseen in training.
Experimental Evaluation of Three Value Recommendation Methods in Interactive Configuration

The present work deals with the recommendation of values in interactive configuration, with no prior knowledge about the user, but given a list of products previously configured and bought by other users ("sales histories"). The basic idea is to recommend, for a given variable at a given step of the configuration process, a value that has been chosen by other users in a similar context, where the context is defined by the variables that have already been decided, and the values that the current user has chosen for these variables. From this point, two directions have been explored. The first one is to select a set of similar configurations in the sales history (typically, the k closest ones, using a distance measure) and to compute the best recommendation from this set - this is the line proposed by [Coster et al., 2002]. The second one, that we propose here, is to learn a model from the entire sample as representation of the users' preferences, and to use it to recommend a pertinent value; three families of models are experimented: the Bayesian networks, the naive Bayesian networks and the lexicographic preferences trees.
The Determinants of Business Analytics Adoption: Does One-Size Fit All

Design/methodology/approach – Data was collected by means of a survey conducted among BA tools users working in a variety of industries in Turkey and 91 responses were obtained. The moderating effect of an organizational level contingency – analytical decision-making culture (ADMC) – on the relationships between individualand system-level factors was tested via the partial least squares (PLS) method.
Does Geographical Complexity of Colombian Financial Conglomerates Increase Banks’ Risk? The Role of Diversification, Regulatory Arbitrage and Funding Costs

During the last decade Colombian international financial conglomerates (IFC) expanded abroad, significantly increasing their geographical complexity. This paper analyzes the effect of this change in geographical complexity on the risk level of individual Colombian banks. We use monthly bank-level data on financial indicators and complexity measures for the period 20072018. We use the Z-score as a measure of bank risk and the number of countries in which a Colombian IFC has foreign banks subsidiaries as a measure of geographical complexity. Our results suggest that complexity is associated with higher levels of individual bank risk, as a result of an expansion to countries with large GDP co-movements and lower regulatory qualities. In addition, we find that banks with access to international funding respond differently to monetary policy changes. In particular, during periods of domestic monetary policy tightening (loosening), individual banks of complex IFCs present higher (lower) levels of risk, suggesting that the monetary policy risk taking channel is affected by the level of geographical complexity.
SOLID WASTE MIXTURES AS CONSTRUCTED WETLANDS FILLING: EFFECT OF HYDRAULIC LOADING RATE ON NUTRIENT REMOVAL FROM WASTEWATER

This study aims to contribute to constructed wetlands’ (CWs) eco-efficiency by applying the concepts of circular economy and waste to treat waste. Five sets of lab-scale CWs with different combinations of filling materials were evaluated and the effect of the hydraulic loading rate (HLR) on the nutrient removal efficiencies was studied. Each CW set consisted of two, duplicate, plastic pots with solid waste filling supporting Phragmites australis macrophyte plants. The filling materials were layer combinations of limestone rock fragments, a waste from construction activities, and one of four other solid wastes: cork granulates from the cork industry (LCG); snail shells from the food and catering industry (LSS); coal slag from coal power plants (LCS); and clay brick fragments from construction activities (LBF). A reference set (LO) was filled only with limestone fragments. The CWs were operated using a low-strength wastewater in successive fill-and-drain cycles with a retention time of one to eight days and a one-day rest. Their removal efficiency was evaluated for COD, total phosphorus (TP) and total nitrogen (TN). All four CWs with mixed filling showed COD removal efficiencies higher than the reference CW and above 79%. The highest removal efficiency was achieved by the LCS CW (91 to 97%). The reference LO CW showed the highest TP removal efficiency. With exception of the LSS CW, the mixed filling CWs showed removal efficiencies close to the reference CW (above 55%). All but the LSS CW showed higher TN removal efficiencies than the reference CW (above 51%). The observed effect of HLR depends on the type of CW. The effect on COD, TP and TN removal efficiencies averaged 9%, 15% and 20%, respectively, for a range of HLR from 0.005 to 0.087 m/day. From this study it can be concluded that all tested layer-packed mixed solid waste fillings are adequate substrate combinations for nutrient removal from wastewater. Moreover, high nutrient removal efficiencies were maintained over a wide range of hydraulic loading rates. This innovative combination of waste materials can improve the CW adaptability to specific types of wastewater and contribute to reducing solid waste disposal in landfills.
Low Density Lipoproteins May Actually Be Some Negative Acute Phase Proteins in the Plasma

There were 75 patients in the first and 118 patients in the second groups. Mean age (45.4 versus 47.9 years) and male ratio (53.3 versus 53.3%) were similar in both groups (p>0.05 for both). Smoking (34.6 versus 31.3%), body mass index (27.2 versus 26.7 kg/m2), fasting plasma glucose (119.4 versus 113.0 mg/dL), white coat hypertension (25.3 versus 32.2%), hypertension (10.6 versus 16.1%), and chronic obstructive pulmonary disease (14.6 versus 18.6%) were similar in both groups, too (p>0.05 for all). Although triglycerides (162.7 versus 125.4 mg/dL, p<0.001), diabetes mellitus (DM) (21.3 versus 12.7%, p<0.05), and coronary heart disease (CHD) (20.0 versus 11.0%, p<0.05) were higher, low density lipoproteins (LDL) (105.3 versus 126.2 mg/dL) and HDL (34.1 versus 50.0 mg/dL) were lower in patients with plasma HDL values of lower than 40 mg/dL, significantly (p<0.000 for both).
Implementing a C Memory Model Supporting Integer-Pointer Casts in CompCert

The ISO standard for the C programming language does not deﬁne semantics for integer-pointer casts. The certiﬁed C compiler CompCert uses an abstract memory model which allows for many optimizations, but in which the behavior of such casts is undeﬁned. In [10], Kang et al. present a formal memory model that supports integer-pointer casts semantics, while still allowing common optimizations. We show the relevance of this new memory model by implementing it in CompCert. We present the changes that need to be done, both in the way CompCert transforms C programs and in the proofs.
Using Closed Captions as Supervision for Video Activity Recognition

Recognizing activities in real-world videos is a difficult problem exacerbated by background clutter, changes in camera angle & zoom, and rapid camera movements. Large corpora of labeled videos can be used to train automated activity recognition systems, but this requires expensive human labor and time. This paper explores how closed captions that naturally accompany many videos can act as weak supervision that allows automatically collecting "labeled" data for activity recognition. We show that such an approach can improve activity retrieval in soccer videos. Our system requires no manual labeling of video clips and needs minimal human supervision. We also present a novel caption classifier that uses additional linguistic information to determine whether a specific comment refers to an ongoing activity. We demonstrate that combining linguistic analysis and automatically trained activity recognizers can significantly improve the precision of video retrieval.
Ensembling Visual Explanations for VQA

Explanations make AI systems more transparent and also justify their predictions. The top-ranked Visual Question Answering (VQA) systems are ensembles of multiple systems; however, there has been no work on generating explanations for such ensembles. In this paper, we propose different methods for ensembling visual explanations for VQA using the localization maps of the component systems. Our crowd-sourced human evaluation indicates that our ensemble visual explanation is superior to each of the individual system’s visual explanation, although the results vary depending on the individual system that the ensemble is compared against as well as the number of individual systems that agree with the ensemble model’s answer. Overall, our ensemble explanation is better 63% of the time when compared to any individual system’s explanation. Our algorithm is also efﬁcient and scales linearly in the number of component systems in the ensemble.
Avengers: Achieving Superhuman Performance for Question Answering on SQuAD 2.0 Using Multiple Data Augmentations, Randomized Mini-Batch Training and Architecture Ensembling

This project aims to achieve above human performance (EM/F1: 86.9/89.5) on the SQuAD 2.0 dataset. We ﬁrst augmented the training data by randomly sub-stituting with WordNet’s synonyms, followed by paraphrasing using the larger PPDB database. Then, in a novel application of GPT-2, we generated new sentences to augment the context paragraphs in a realistic and coherent manner. We further experimented with randomizing the mini-batches, which increased learning difﬁculty by sampling from different articles and topics. Our best single model, based on a ﬁne-tuned ALBERT-xxlarge-v2 with data augmentation, achieved a Dev EM/F1 score of 87.5/90.4 . By further ensembling multiple models and architectures, speciﬁcally across ALBERT, RoBERTa and XLNet, our ﬁnal ensemble achieved the top EM/F1 score of 89.4/91.7 on the Dev PCE leaderboard and the top EM/F1 score of 89.1/91.5 on the Test PCE leaderboard (as of Mar-17-2020) .
Integrating Blockchain and Broad Learning for Smart Energy Innovation: Design and Experiment

With the rapid development in recent years, the architecture and data sources of energy internet (EI) becomes increasingly complicated. It is a good opportunity and trend to combine energy equipment and information based on blockchain technology. However, high computational complexity inherently due to the blockchain must be addressed to achieve rapidly user electricity prediction. In this paper, an algorithm model of a blockchain technology combined with a broad learning data fusion algorithm is proposed. A novel and detailed combination model is introduced, mainly including CNN and LSTM model. Combination model considers energy data and blockchain data as parameter, and defines selection logic to guarantee user electricity forecast accuracy. Our proposed solution takes the advantage of CNN model in prediction accuracy and computation complexity, and advantage of LSTM model in long term memory. Further experiments illustrate that our solution achieves higher prediction accuracy of 94.1% (without considering the influence of real-time data), with the best result achieved ever. The experimental prediction results provide underlying insights to guide the direction of system resource planning.
The effects of interspecific interactions and hybridization on coastal cutthroat trout

Extended abstact. -This work includes both a literature review of the interspecific interactions among coastal cutthroat trout and other fishes and a preliminary research report of hybridization between coastal cutthroat trout and steelhead carried out by the author. Coastal cutthroat rout often coexist with other fishes, both in the marine and freshwater environments, and therefore may be affected by many types of interspecific interactions. They most often interact with other salmonids because of similar life histories and requirements. The decline of many coastal cuffhroat trout populations has been attributed in part to interactions with other salmonid species (Trotter 1987; Griffith 1988). Furthermore, such interactions can affect growth rate and other life-history characteristics (Nicholas l9Z8; Nilsson and Northcote 1981; Jonsson et al. 1984). These interactions can be grouped into three main categories: (l) predation on or by cutthroat trout, (2) competition by resource depletion, and (3) interference competition. Marine interactions may be limited because of variation in distributions, diets, and time-at-sea among the Pacific salmonids @earcy et al. 1990; Groot and Margolis 1991). The most common type of interspecific interaction involving coastal cutthroat trout is competition for food or space in fresh water.
Antibody Induced by Porphyromonas Gingivalis FimA-PVXCP DNA Vaccine Inhibit Host Cell Invasion and Enhance Phagocytosis

Porphyromonas gingivalis (P. gingivalis) is strongly correlated with periodontitis and systemic conditions such as aspiration pneumonia and cardiovascular disease. Vaccination against P. gingivalis will provide help to limit its pathogenicity. In order to investigate the function of antibody induced by DNA vaccine encoding P. gingivalis FimA fused with the immuno-enhancing molecule potato virus X coat protein (PVXCP), abilities of the induced antibody to inhibit host cell invasion and enhance phagocytosis were determined. Mice were vaccinated with the FimA-PVXCP fusion vaccine (pcDNA3.FimA-PVXCP) or FimA vaccine (pcDNA3.FimA). Antibody levels to FimA were measured by ELISA on day 14 after boosting. Both DNA vaccines induced anti-FimA antibody production at the comparable levels. Number of P. gingivalis invading into Caco-2 cells was significantly lowered when the bacteria were pre-incubated with serum from mice vaccinated with pcDNA3.FimA-PVXCP, comparing to the group that bacteria was not pre-incubated with serum or pre-incubated with normal mouse serum. In addition, when P. gingivalis was pre-incubated with serum from pcDNA3.FimA-PVXCP vaccinated mice, phagocytic index was significantly higher than the group that bacteria was not pre-incubated with serum or pre-incubated with normal mouse serum or serum from pcDNA3.FimA vaccinated mice. Therefore, anti-FimA antibody induced by FimA-PVXCP DNA vaccine could inhibit P. gingivalis cell invasion and also enhance phagocytosis. Experimental article (J Int Dent Med Res 2020; 13(1): 138-143)
Potential of Mid-Rise Social Residential Buildings to Reach Net Zero Energy Building Standard in Two Different Climates of Chile

The Net Zero Energy Buildings (NZEB) are a real solution to reduce the energy consumption and greenhouse gas (GHG) emissions generated by the building sector. Thus, many developed countries have conducted different studies for the development of public policies that encourage the implementation of NZEB. Chile adopted as environmental goal for the coming years reducing the GHG emissions by 30%, but the instruments to accomplish them have not been clearly defined. The NZEB might be a key strategy to reduce the GHG emissions. However, there are no studies that evaluate the performance of NZEB in Chile. The aim of this research is to determine the potential of mid-rise social residential buildings to reach the NZEB standard in Santiago and La Serena. In particular, mid-rise social buildings are selected as representative for this study through the use of a multi-criteria matrix. The electric consumption of buildings and photovoltaic production are simulated in EnergyPlus. The optimizations of the energy efficiency measures and the configurations of the photovoltaic systems are carried out in GenOpt. The results show that mid-rise social residential buildings are very close to reach the NZEB standard and through the optimized energy efficiency measures, the electric consumption of the building is reduced by up to 28%. Therefore, mid-rise social residential buildings have potential to reach NZEB standard but other measures must be studied.
Current Epidemiological Status of Bovine Theileriosis in Indian Scenario

Tick-borne diseases of livestock are responsible for heavy economic losses globally. The protozoan parasite Theileria annulata is the causative agent of the tick-borne disease tropical theileriosis (also known as Mediterranean fever) which causes morbidity in indigenous cattle and severe lethal disease in imported high-grade cattle and crossbreds in a wide geographical area ranging from the Mediterranean littoral regions of Europe and Africa to the near and Middle East to India. Mediterranean fever is an important bovine haemo protozoan disease in an economic point of view and spreads over North Africa, Southern Europe, The Middle East and Asia. Tropical theileriosis has long been recognized as a hindrance to the development of sound dairy industry in the India and is a cause of major economic losses. Serological surveys indicated that Theileria annulata infection is widespread in the country but the disease mostly affects exotic dairy breeds and their crosses with indigenous breeds. Knowledge of the of these diseases is important for the design and implementation of control strategies Long term carrier animals which were all recovered from previous exposure to the organisms pose thread as source of infection in Hyalomma anatolicum. The prevalence rate of theileriosis is based on the geographical region and several other associated factors like tick density, climatic conditions, age, gender, management practices and immunity of the host. Theileria caused by Theileri aannulata is economically important vector borne haemo protozoan disease of livestock. Theileria is responsible for causing theileriosis resulting in death of affected animals. The disease is endemic in warmer regions, it is seasonal and the incidence is higher during summer and rainy season when the ticks have higher activity although sporadic outbreaks have been recorded year round. It is a potential killer of livestock and causes economic losses in terms of mortality, morbidity, abortion, infertility, reduced milk yield etc. The disease is underestimated in cattle due to sub clinical nature. The conventional parasitological techniques are less sensitive. More than 80 percent of infections are cryptic and undetectable by direct.This paper briefly discussed about the occurrence, control and the economic importance of Theileria spp in WesternHimalayan region of Uttar Pradesh.
A computable and compositional semantics for hybrid automata

Hybrid Systems are systems having a mixed discrete and continuous behaviour that cannot be characterized faithfully using either only discrete or only continuous models. A good framework for hybrid systems should support their compositional description and analysis, since commonly systems are specified by a composition of smaller subsystems, to cope with the complexity of their monolithic representation. Moreover, since the reachability problem for hybrid systems is undecidable, one should investigate the conditions that guarantee approximate computability of composition, when only approximations to the exact problem data are available. In this paper, we propose an automata-based formalism (HIOA) for hybrid systems that is compositional and for which the evolution can be computed approximately. The main results are that the composition of compatible HIOA yields a pre-HIOA; a dominance result on the composition of HIOA by which we can replace any component in a composition by another one that exhibits the same external behaviour without affecting the behaviour of the composition; finally, the key result that the composition of two compatible upper(lower)-semicontinuous HIOA is a computable upper(lower)-semicontinuous pre-HIOA, which entails that the evolution of the composition is upper(lower)-semicomputable. A discussion on how compositionality/computability are handled in state-of-art libraries for reachability analysis closes the paper.
Research on Urban Resilience Evaluation Model and Its Application in China

Affected by the rapid progress of urbanization and the climatic environment, cities must continue to improve their resilience to enhance their “disaster-resistance” and “self-recovery” capabilities to cope with various types of disasters. Based on the urban resilience framework proposed by the Rockefeller Group, this study selected cities such as New York, New Orleans, Fukushima, Da Nang, and Athens as typical cases to analyze their resilience plans in detail, and discussed the construction of urban resilience evaluation models and their main impact factors, found that infrastructure resilience and social resilience are the two most important dimensions of urban resilience. At the same time, combined with the smart city index systems (standards) of Shanghai and Guangdong, China, to further optimize the evaluation model of urban resilience, and exploratively propose a set of urban resilience evaluation index systems suitable for urban applications in China.
Feature extraction and clustering of building energy profiles encoded as images

Buildings consume roughly 40% of the global energy demand, and an increasing amount of sensor data allows enhanced monitoring and analysis of the building energy systems. One typical application of data-driven analytics is the clustering of daily energy profiles in order to identify buildings that have similar temporal consumption patterns. A popular algorithm for this purpose is K-means. Despite being simple to implement, K-means is a very rigid algorithm and provides no insight on hidden non-linear features of the data, which in many cases are the most valuable. We propose a novel approach to cluster energy profiles using some extracted non-linear features. Given a time series of energy consumption, we transform it into three matrices: (1) a Gramian Difference matrix, (2) a Gramian Summation matrix, and (3) a Markov Transition Field matrix. We then interpret each matrix as the color channel of an image with three color channels. Using a Convolutional Neural Network, we construct a mapping from the encoded time series to a feature space of reduced dimensionality. Further, we apply a Spectral clustering technique within the feature space of the time series, resulting in successful non-linear clustering.
Aﬀordance-based Algorithms for Categorization of Road Network Data

We propose algorithms that can be used to automatically check road network data for consistency, and to categorize parts of it as junctions. The main idea is to use locomotion aﬀordances, i.e. restricted patterns of supported movement, in order to deﬁne the categories channel network and junction, and to motivate and derive tractable veriﬁcation and classiﬁcation strategies for them. Aﬀordance based category deﬁni-tions are part of a wider project of the authors to develop a grounded theory for geographic data, especially a theory of channel networks. The theory comes with a natural semantics in terms of reproducible observation procedures. It therefore provides a very general approach to map road network databases into it, and to check or categorize their features. The algorithms are applied using extracts from Open Street Map.
Fluid Queues and Their Applications in Telecommunications

2 Mathematical Background 2 2.1 Differential Equations . 2 2.2 Phase Type Distributions . 2 2.3 Renewal Theory . 2 2.4 Queueing Theory . 3 2.4. 1 Pollaczek-Khinchine Formulas . 3 2.4. 2 Mean Value Analysis for M/G/1 . 3