Miracle-dz/UniFAR-FTUs huggingface dataset:
Built Environment, Travel, Nutrition and Health in Chinese Cities: Evidence from the China Health and Nutrition Survey

Using a longitudinal survey of households and communities in China, this study examines the potential causal relationship between the built environment and travel behavior, nutrition intake, and health of urban residents. The results support that: (1) household income, size, and accessibility to transit and schools affect motor vehicle ownership, though differently depending on motor vehicle type and city size; (2) increase in household income and ownership of motor vehicle reduce the likelihood of cycling or walking by adult commuters; (3) availability of fast food restaurants in neighborhood and ownership of car increase children’s consumption of fast food; (4) the numbers of accessible supermarkets and free markets have opposite effects on urban residents’ food consumption, measured by intakes of calorie, protein, carbohydrate and fat; and (5) adult body mass index is affected positively by income and negatively by education level and participation in housework. Overall, neighborhood food environment, such as access to markets and fast food restaurants, shows significant effects on urban residents’ food and nutrition intake, but not significantly on their health outcomes. While some features of the built environment, such as accessibility to transit and schools, show little direct effect on travel behavior, nutrition intake, or health of Chinese urban residents. Nevertheless, evidence suggests that their effects may be indirect, through household vehicle ownership.
Inattentive Producers

I present and solve the problem of a producer who faces costs of acquiring, absorbing, and processing information. I establish a series of theoretical results describing the producer's behaviour. First, I find the conditions under which she prefers to set a plan for the price she charges, or instead prefers to set a plan for the quantity she sells. Second, I show that the agent rationally chooses to be inattentive to news, only sporadically updating her information. I solve for the optimal length of inattentiveness and characterize its determinants. Third, I explicitly aggregate the behaviour of many such producers. I apply these results to a model of inflation. I find that the model can fit the quantitative facts on post-war inflation remarkably well, that it is a good forecaster of future inflation, and that it survives the Lucas critique by fitting also the pre-war facts on inflation moderately well.
Learning Multilingual Semantic Parsers for Question Answering over Linked Data. A comparison of neural and probabilistic graphical model architectures

The task of answering natural language questions over structured data has received wide interest in recent years. Structured data in the form of knowledge bases has been available for public usage with coverage on multiple domains. DBpedia and Freebase are such knowledge bases that include encyclopedic data about multiple domains. However, querying such knowledge bases requires an understanding of a query language and the underlying ontology, which requires domain expertise. Querying structured data via question answering systems that understand natural language has gained popularity to bridge the gap between the data and the end user. In order to understand a natural language question, a question answering system needs to map the question into query representation that can be evaluated given a knowledge base. An important aspect that we focus in this thesis is the multilinguality. While most research focused on building monolingual solutions, mainly English, this thesis focuses on building multilingual question answering systems. The main challenge for processing language input is interpreting the meaning of questions in multiple languages. In this thesis, we present three different semantic parsing approaches that learn models to map questions into meaning representations, into a query in particular, in a supervised fashion. Each approach differs in the way the model is learned, the features of the model, the way of representing the meaning and how the meaning of questions is composed. The first approach learns a joint probabilistic model for syntax and semantics simultaneously from the labeled data. The second method learns a factorized probabilistic graphical model that builds on a dependency parse of the input question and predicts the meaning representation that is converted into a query. The last approach presents a number of different neural architectures that tackle the task of question answering in end-to-end fashion. We evaluate each approach using publicly available datasets and compare them with state-of-the-art QA systems. Gedruckt auf alterungsbeständigem Papier nach DIN-ISO 9706 Printed on non-aging paper according to DIN-ISO 9706
Association for Public Policy Analysis and Management Assessing OSHA Performance : New Evidence from the Construction Industry Author ( s ) :

The determinants of OSHA performance can be examined by breaking the regulatory process into three elements relating to enforcement, compliance behavior, and the adequacy of standards in addressing safety outcomes. This paper develops and applies this framework to the U.S. construction industry during the period 1987 to 1993. Enforcement activity among the firms in the sample was substantial, with firms facing a high probability of annual inspection. But, despite this significant enforcement effort, inspections have a modest effect on firm compliance with OSHA standards. Finally, the health and safety standards cited most frequently diverge from the major sources of fatalities and injuries on construction projects. These results suggest that historic enforcement policies toward construction make less sense as OSHA moves into its fourth decade of operation. More generally, the paper illustrates the problem offocusing enforcement resources on large, high-profile companies even though they often are not the major source of regulatory problems in an established area of public policy intervention. 2001 by the Association for Public Policy Analysis and Management.
Item Age E ff ect Against Global User Preference in Latent Model of Recommendation System

Due to highly competitive among various companies and widely use of recommendation systems, they need additional abilities to attract systems users, beside accuracy. In some cases, recommending items that may be interesting in near future is far more attractive, but analyzing effect of time is very complex and difficult. In this work, we starts at analyzing and stating assumptions about effect of time such as item age,on global user preference of item. Then they are proven by probabilistic modelling with a latent variable included for avoiding sparsity problem, using different distributions on discretized time data. The results of model show the significant improvement of accuracy compared to other traditional recommendation systems, for both individual and list recommendation. Furthermore, they also show that the accuracy, which is related to the number of possible values of the latent variable, is affected by cardinality of discretized time data.
Does NMT make a difference when post-editing closely related languages? The case of Spanish-Catalan

In the last years, we have witnessed an increase in the use of post-editing of machine translation (PEMT) in the translation industry. It has been included as part of the translation workﬂow because it increases productivity of translators. Currently, many Language Service Providers offer PEMT as a service. For many years now, (closely) related languages have been post-edited using rule-based and phrase-based machine translation (MT) systems because they present less challenges due to their morphological and syntactic similarities. Given the recent popularity of neural MT (NMT), this paper analyzes the performance of this approach compared to phrase-based statistical MT (PBSMT) on in-domain and general domain documents. We use standard automatic measures and temporal and technical effort to assess if NMT yields a real improvement when it comes to post-editing the Spanish-Catalan language pair.
Visualization of Protein Sorting at the Trans-Golgi Network and Endosomes Through Super-Resolution Imaging

The trans-Golgi network (TGN) and endosomes are essential protein sorting stations in the secretory transport pathway. Protein sorting is fundamentally a process of spatial segregation, but the spatial relationships among the proteins that constitute the sorting machinery have not been systematically analyzed at high resolution in mammalian cells. Here, using two-color STORM imaging, we show that the TGN/endosome-localized cargo adaptors, AP-1, GGA2 and epsinR, form elongated structures of over 250 nm in length at the juxta-nuclear Golgi area. Many of these structures are associated with clathrin. We found that AP-1 is spatially segregated from AP-3 and GGA2, whereas a fraction of AP-1 and GGA2 punctae are associated with epsinR. Moreover, we observed that the planar cell polarity cargo proteins, Vangl2 and Frizzled6 associate with different cargo adaptors—AP-1 and GGA2 or epsinR, respectively—when exiting the TGN. Knockdown analysis confirms the functional significance of this segregation. Our data indicates that TGN/endosome-localized cargo adaptors have distinct spatial relationships. The spatially segregated cargo adaptors GGA2 and AP-1 regulate sorting of Frizzled6 and Vangl2, respectively and spatially associated cargo adaptors can cooperatively regulate a specific sorting process.
Determination of Recharge from Stable Isotope Data to the Hydrological Systems in the Southern Negros Geothermal Field and its Environs , Philippines

The Philippine Meteoric Water Line equation of δ H = 8δ O + 12 is defined from the isotopic composition of rainfall in various geothermal areas of the Philippines. This equation suggests that recharge to the shallow and deeper groundwater systems in the Southern Negros Geothermal Field originates from precipitation at elevations that are about 250 meters lower than the recharge to the deep geothermal system. Rainwater in the rainy months of July to October effectively contribute to recharge, while rainwater in the dry months, although of significant amount, do not recharge the groundwater systems but run off to the surface water bodies. The identification of such areas promotes better management and protection of the recharge areas and guides reforestation programmes for watersheds.
Probabilistic Information Modulates the Timed Response Inhibition Deficit in Aging Mice

How interval timing is affected by aging constitutes one of the contemporary research questions. There is however a limited number of studies that investigate this research question in animal models of aging. The current study investigated how temporal decision-making is affected by aging. Initially, we trained young (2–3 month-old) and old C57BL/6J male mice (18–19 month-old) independently with short (3 s) and long (9 s) intervals by signaling, in each trial, the hopper associated with the interval that is in effect in that trial. The probability of short and long trials was manipulated (0.25 or 0.75) for different animals in each age group. During testing, both hoppers were illuminated, and thus active trial type was not differentiated. We expected mice to spontaneously combine the independently acquired time interval-location-probability information to adaptively guide their timing behavior in test trials. This adaptive ability and the resultant timing behavior were analyzed and compared between the age groups. Both young and old mice indeed adjusted their timing behavior in an abrupt fashion based on the independently acquired temporal-spatial-probabilistic information. The core timing ability of old mice was also intact. However, old mice had difficulty in terminating an ongoing timed response when the probability for the short trial was higher and this difference disappeared in the group that was exposed to a lower probability of short trials. These results suggest an inhibition problem in old mice as reflected through the threshold modulation process in timed decisions, which is cognitively penetrable to the probabilistic information.
Combinatorial Effect of ARTP Mutagenesis and Ribosome Engineering on an Industrial Strain of Streptomyces albus S12 for Enhanced Biosynthesis of Salinomycin

Salinomycin, an important polyketide, has been widely utilized in agriculture to inhibit growth of pathogenic bacteria. In addition, salinomycin has great potential in treatment of cancer cells. Due to inherited characteristics and beneficial potential, its demand is also inclining. Therefore, there is an urgent need to increase the current high demand of salinomycin. In order to obtain a high-yield mutant strain of salinomycin, the present work has developed an efficient breeding process of Streptomyces albus by using atmospheric and room temperature plasma (ARTP) combined with ribosome engineering. In this study, we investigate the presented method as it has the advantage of significantly shortening mutant screening duration by using an agar block diffusion method, as compared to other traditional strain breeding methods. As a result, the obtained mutant Tet30Chl25 with tetracycline and chloramphenicol resistance provided a salinomycin yield of 34,712 mg/L in shake flask culture, which was over 2.0-fold the parental strain S12. In addition, comparative transcriptome analysis of low and high yield mutants, and a parental strain revealed the mechanistic insight of biosynthesis pathways, in which metabolic pathways including butanoate metabolism, starch and sucrose metabolism and glyoxylate metabolism were closely associated with salinomycin biosynthesis. Moreover, we also confirmed that enhanced flux of glyoxylate metabolism via overexpression gene of isocitrate lyase (icl) promoted salinomycin biosynthesis. Based on these results, it has been successfully verified that the overexpression of crotonyl-CoA reductase gene (crr) and transcriptional regulator genes (orf 3 and orf 15), located in salinomycin synthesis gene cluster, is possibly responsible for the increase in salinomycin production in a typical strain Streptomyces albus DSM41398. Conclusively, a tentative regulatory model of ribosome engineering combined with ARTP in S. ablus is proposed to explore the roles of transcriptional regulators and stringent responses in the biosynthesis regulation of salinomycin.
Multilingual Indian Language Translation System at WAT 2018: Many-to-one Phrase-based SMT

This paper describes our trained models of phrase-based statistical machine translation (PBSMT) systems for Indic −→ English and English −→ Indic language-pairs, which has been submitted to the WAT 2018 shared task. In addition, we have introduced many-to-one statistical machine translation (SMT). This new approach produced comparable results in terms of translation accuracy with respect to the result of baseline SMT.
SEQUENTIAL-PARTIALLY PARALLEL ANALOG IMPLEMENTATION OF A SVM CLASSIFIER

The classification task in large data spaces can now relay on newly developed Support Vector Machine technique. A trade-off between the hardware complexity and processing time is a partially sequential – partially parallel implementation of the processing issues based on a description of the vectors as time-continuous signals. We present an analog implementation of a SVM classifier that uses an analog multiplier to compute the dot product between each support vector and the test vector, in a sequential manner. All the dot products necessary to classify a test vector are implemented in parallel. The correct operation and the accuracy of our analog SVM classifier is proven on the IRIS dataset.
MedNorm: A Corpus and Embeddings for Cross-terminology Medical Concept Normalisation

The medical concept normalisation task aims to map textual descriptions to standard terminologies such as SNOMED-CT or MedDRA. Existing publicly available datasets annotated using different terminologies cannot be simply merged and utilised, and therefore become less valuable when developing machine learning-based concept normalisation systems. To address that, we designed a data harmonisation pipeline and engineered a corpus of 27,979 textual descriptions simultaneously mapped to both MedDRA and SNOMED-CT, sourced from five publicly available datasets across biomedical and social media domains. The pipeline can be used in the future to integrate new datasets into the corpus and also could be applied in relevant data curation tasks. We also described a method to merge different terminologies into a single concept graph preserving their relations and demonstrated that representation learning approach based on random walks on a graph can efficiently encode both hierarchical and equivalent relations and capture semantic similarities not only between concepts inside a given terminology but also between concepts from different terminologies. We believe that making a corpus and embeddings for cross-terminology medical concept normalisation available to the research community would contribute to a better understanding of the task.
Raltegravir-Induced Adaptations of the HIV-1 Integrase: Analysis of Structure, Variability, and Mutation Co-occurrence

The human immunodeficiency virus type 1 (HIV-1) has several proteins of therapeutic importance, many of which are currently used as drug targets in antiretroviral therapy. Among these proteins is the integrase, which is responsible for the integration of the viral DNA into the host genome – a crucial step for HIV-1 replication. Given the importance of this protein in the replication process, three integrase inhibitors are currently used as an option for antiretroviral therapy: Raltegravir, Elvitegravir, and Dolutegravir. However, the crescent emergence of mutations that cause resistance to these drugs has become a worldwide health problem. In this study, we compared the variability of each position of the HIV-1 integrase sequence in clinical isolates of Raltegravir-treated and drug-naïve patients by calculating their Shannon entropies. A co-occurrence network was created to explore how mutations co-occur in patients treated with Raltegravir. Then, by building tridimensional models of the HIV-1 integrase intasomes, we investigated the relationship between variability, architecture, and co-occurrence. We observed that positions bearing some of the major resistance pathways are highly conserved among non-treated patients and variable among the treated ones. The residues involved in the three main resistance-related mutations could be identified in the same group when the positions were clustered according to their entropies. Analysis of the integrase architecture showed that the high-entropy residues S119, T124, and T125, are in contact with the host DNA, and their variations may have impacts in the protein-DNA recognition. The co-occurrence network revealed that the major resistance pathways N155H and Q148HR share more mutations with each other than with the Y143R pathway, this observation corroborates the fact that the N155H pathway is most commonly converted into Q148HRK than into Y143RCH pathway in patients’ isolates. The network and the structure analysis also support the hypothesis that the resistance-related E138K mutation may be a mechanism to compensate for mutations in neighbor lysine residues to maintain DNA binding. The present study reveals patterns by which the HIV-1 integrase adapts during Raltegravir therapy. This information can be useful to comprehend the impacts of the drug in the enzyme, as well as help planning new therapeutic approaches.
End-to-End Time-Sensitive Fact Check

Online social networks have become a prime target for the spread of fake news and misinformation. Despite advances in tools and techniques for the detection and verification of fake news, assessing the credibility of information remains a challenge. Previous work have extracted claims from social media post for credibility assessment. These claims typically describe the object, subject and predicate, and have no notion of time. We observe that the credibility of claims depends not just on the content, but also on the time period that the claim is purported to be valid for. We develop an end-to-end framework for evaluating time-sensitive claims. The framework generates alternate claims and takes into consideration relationships between these alternate claims and the target claim to performs a joint credibility assessment. Experiments results on two datasets shows the effectiveness of the proposed framework to increase the accuracy of claim assessment.
CpG IslandMethylation in Colorectal Cancer : Past , Present and Future

The concept of a CpG island methylator phenotype, or CIMP, quickly became the focus of several colorectal cancer studies describing its clinical and pathological features after its introduction in 1999 by Toyota and colleagues. Further characterization of CIMP in tumors lead to widespread acceptance of the concept, as expressed by Shen and Issa in their 2005 editorial, “CIMP, at last. ” Since that time, extensive research efforts have brought great insights into the epidemiology and prognosis of CIMP+ tumors and other epigenetic mechanisms underlying tumorigenesis. With the advances in technology and subsequent cataloging of the human methylome in cancer and normal tissue, new directions in research to understand CIMP and its role in complex biological systems yield hope for future epigenetically based diagnostics and treatments.
Using Models of Parallel Specialized Processors to Solve the Problem of Signal Separation

This paper considers models of highly efficient specialized processors used for parallel data processing as part of solving the problem of extracting individual signals from an additive mixture of several signals. The proposed models of recursive, nonrecursive, and regularization-based parallel specialized processors provide versatility in solving the problem of signal separation with various algorithms. An advantage of regularization-based processors is that they make the solution stable under conditions where the parameters of objects exhibit expected uncertainty when the inverse problem of signal separation is ill-posed. This paper presents the results we obtained from an asymptotic analysis of the computational complexity involved. The results identify the time it takes to solve problems by using specialized processors. The paper also identifies the conditions for the efficient use of specialized processors.