Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Scale Approach to Membrane Remodeling Processes

We present a multi-scale simulation procedure to describe membrane-related biological processes that span over a wide range of length scales. At macroscopic length-scale, a membrane is described as a flexible thin film modeled by a dynamic triangulated surface with its spatial conformations governed by an elastic energy containing only a few model parameters. An implicit protein model allows us to include complex effects of membrane-protein interactions in the macroscopic description. The gist of this multi-scale approach is a scheme to calibrate the implicit protein model using finer scale simulation techniques e.g., all atom and coarse grain molecular dynamics. We previously used this approach and properly described the formation of membrane tubular invaginations upon binding of B-subunit of Shiga toxin. Here, we provide a perspective of our multi-scale approach, summarizing its main features and sketching possible routes for future development.
ielab at the Open-Source IR Replicability Challenge 2019

We present our participating Docker image in the SIGIR Open-Source IR Replicability Challenge 2019. Our participation includes an Elasticsearch-based Docker image conforming to the specifications laid out by the challenge, a collection parser, and a topic parser. Our Docker image is capable of indexing and searching several commonly used Information Retrieval test collections including robust04 (TREC 2004 Robust Track Topics - disks 4/5), core17 (TREC NewYork Times Annotated Corpus), and core18 (TRECWashington Post Corpus). The Docker image and associated tools we present in this paper provide a solid foundation for replicable Information Retrieval experiments using Elasticsearch.
Multi-objective Relevance Ranking

In this paper, we introduce an Augmented Lagrangian based method in a search relevance ranking algorithm to incorporate the multi-dimensional nature of relevance and business constraints, both of which are the requirements for building relevance ranking models in production. The off-the-shelf solutions cannot handle such complex objectives and therefore, modelers are left hand-tuning of parameters that have only indirect impact to the objectives, at-tempting to incorporate multiple objectives (MO) in a model. This process is time-consuming and tends to face sub-optimality. The proposed method is designed to systematically solve the MO problem in a constrained optimization framework, which is integrated with a popular Boosting algorithm and is, by all means, a novel contribution. Furthermore, we propose a procedure to specify the constraints to achieve business goals and the exploration scales linearly in the number of constraints, while existing methodology can scale exponentially . The experimental results show that the method successfully builds models that achieve MO criteria much more efficiently than existing methods. The potential impact in-cludes significant reduction in model development time and allows for automation of model refresh even with presence of several MO criteria, in real world production system scale with hundreds of millions of records.
Flexible models for understanding and optimizing complex populations

Data analysis is often driven by the goals of understanding or optimizing some population of interest. The first of these two objectives aims to produce insights regarding characteristics of the underlying population, often to facilitate scientific understanding. Crucially, this requires models which produce results that are highly interpretable to the analyst. On the other hand, notions of interpretability are not necessarily as central for determining how to optimize populations, where the aim is to build datadriven systems which learn how to act upon individuals in a manner that maximally improves certain outcomes of interest across the population. In this thesis, we develop interpretable yet flexible modeling frameworks for addressing the former goal, as well as black-box nonparametric methods for addressing the latter. Throughout, we demonstrate various empirical applications of our algorithms, primarily in the biological context of modeling gene expression in large cell populations. For better understanding populations, we introduce two nonparametric models that can accurately reflect interesting characteristics of complex distributions without reliance on restrictive assumptions, while simultaneously remaining highly interpretable through their use of the Wasserstein (optimal transport) metric to summarize changes over an entire population. One approach is principal differences analysis, a projection-based technique that interpretably characterizes differences between two arbitrary high-dimensional probability distributions. Another approach is the TRENDS model, which quantifies the underlying effects of temporal progression in an evolving sequence of distributions that also vary due to confounding noise. While the aforementioned techniques fall under the frequentist regime, we subsequently present a Bayesian framework for the task of optimizing populations. Drawing upon the Gaussian process toolkit, our method learns how to best conservatively intervene upon heterogeneous populations in settings with limited data and substantial uncertainty about the underlying relationship between actions and outcomes. Thesis Supervisor: Tommi Jaakkola Thesis Supervisor: David Gifford Professor of EECS Professor of EECS
Unsupervised Construction of a Product Knowledge Graph

We describe techniques for generating a commercial knowledge graph with the goal of discovering relationships between brands, products, and categories. Using a diverse set of input data sources, we implement an unsupervised, efficient and scalable data pipeline that produces a brand-product graph. We also outline a number of challenges encountered when processing this type of data in the context of commercial products. ACM Reference format: Omar Alonso, Vasileios Kandylas, Rukmini Iyer. 2019. Unsupervised Construction of a Product Knowledge Graph. In Proceedings of the SIGIR 2019 Workshop on eCommerce (SIGIR 2019 eCom), 4 pages.
Parallel and distributed computation

Parallel computation: Tightly coupled processors that can communicate almost as quickly as perform a computation Distributed computation: Loosely couple processor for which communication is much slower than computation 2 PRAM Model A PRAM machine consists of m synchronous processors with shared memory. This model ignores synchronization problems and communication issues, and concentrates on the task of parallelization of the problem. One gets various variations of this model depending on how various processors are permitted to access the same memory location at the same time. • ER= Exclusive Read, only one processor can read a location in any 1 step • CR= Concurrent Read, any number of processors can read a location in a step • EW= Exclusive write, only one processor can write a location in any 1 step • CW= Concurrent Write, any number of processors can write a location in a step. What it 2 processors try to write different values? – Common: All processors must be trying to write the same value – Arbitrary: An arbitrary processor succeeds in the case of a write conflict – Priority: The lowest number processor succeeds The " right " model is probably an EREW PRAM, but we will study other models as academic exercises. We will sometimes refer to algorithms by the type of model that these algorithms are designed for, e.g. an EREW PRAM algorithm.
An Introduction to MultiAgent Systems, Second Edition

an introduction to multiagent systems second edition. Book lovers, when you need a new book to read, find the book here. Never worry not to find what you need. Is the an introduction to multiagent systems second edition your needed book now? That's true; you are really a good reader. This is a perfect book that comes from great author to share with you. The book offers the best experience and lesson to take, not only take, but also learn.
Using Docker to deploy computing software

There are many ways to facilitate the creation of large-scale projects. One of the most commonly used methods is to create virtual machines that contain the program environment. However, software has recently been created to make this process even easier. One example is Docker, a software for automating the deployment and management of applications in an operating system-level virtualization environment. This paper discusses the Docker software, its features and benefits, which allows you to create images that contain the program and all the necessary components for its operation. The purpose of this work is to study the capabilities of Docker. And also, the creation of a container containing a software implementation of the neural network for recognition of various handwritten characters. Training and test data is a database of handwritten numbers and letters "MNIST" and "EMNIST". To teach the neural network to recognize numbers, a training set containing 60 thousand copies was used, and the test set includes 10 thousand copies. For letters – the training set contains 88800 copies, and the test set includes 14800 copies. The project was created on the basis of the image tensorflow downloaded from public Docker-registry Docker Hub. The program is written in Python3, using the service for interactive computing Jupyter Notebook. 38
Dockerising Terrier for The Open-Source IR Replicability Challenge (OSIRRC 2019)

Reproducibility and replicability are key concepts in science, and it is therefore important for information retrieval (IR) platforms to aid in reproducing and replicating experiments. In this paper, we describe the creation of a Docker container for Terrier within the framework of the OSIRRC 2019 challenge, which allows typical runs to be reproduced on TREC Test Collections such as Robust04, GOV2, Core2018. In doing so, it is hoped that the produced Docker image can be of aid to other (re)producing baseline experiments on these test collections. Initiatives like OSIRRC are key in advancing these key concepts in the IR area. By making not only the source code available, but also the exact same environment and standardising inputs and outputs, it is possible to easily compare approaches and thereby improve the quality of the research for Information Retrieval.
Learning Embeddings for Product Size Recommendations

Despite significant recent growth in online fashion retail, choosing product sizes remains a major problem for customers. We tackle the problem of size recommendation in fashion e-commerce with the goal of improving customer experience and reducing financial and environmental costs from returned items. We propose a novel size recommendation system that learns a latent space for product sizes using only past purchases and brand information. Key to the success of our model is the application of transfer learning from a brand to a product level. We develop a neural collaborative filtering model that is applicable to every product, without requiring specific customer or product measurements or explicit customer feedback on the purchased sizes, which are not available for most customers or products. Offline experiments using data from a major retailer show improvements of between 4-40 % over the matrix factorisation baseline.
Modeling the energy consumption of smart buildings using artificial intelligence

Intelligent energy saving and energy efficiency technologies are the modern large-scale global trend in the energy systems development. The demand for smart buildings is growing not only in the world, but also in Russia, especially in the market of construction and operation of large business centers, shopping centers and other business projects. Accurate cost estimates are important for promoting energy efficiency construction projects and demonstrating their economic attractiveness. The growing number of digital measurement infrastructure, used in commercial buildings, has led to increased availability of high-frequency data that can be used for anomaly detection and diagnostics of equipment, heating, ventilation, and optimization of air conditioning. This led to the use of modern and efficient machine learning methods that provide promising opportunities to obtain more accurate forecasts of energy consumption of the building, and thus increase energy efficiency. In this paper, based on the gradient boosting model, a method of modeling and forecasting the energy consumption of buildings is proposed, and computer algorithms are developed to implement it. Energy consumption dataset of 300 commercial buildings was used to assess the effectiveness of the proposed algorithm. Computer simulations showed that the use of these algorithms has increased the accuracy of the prediction of energy consumption in more than 80 percent of cases compared to other machine learning algorithms.
Comprehensive Audience Expansion based on End-to-End Neural Prediction

In current online advertising applications, look-alike methods are valuable and commonly used to identify new potential users, tack-ling the difficulties of audience expansion. However, the demographic information and a variety of user behavior logs are high dimensional,noisy, and increasingly complex, which are challenging to extract suitable user profiles. Usually, rule-based and similarity-based approaches are proposed to profile the users’ interests and expand the audience. However, they are specific and limited in more complex scenarios. In this paper, we propose a new end-to-end solution, unifying the feature extraction and profile prediction stages. Specifically, we present a neural prediction framework and leverage it with the intuitive audience feature extraction stages. We conduct extensive study on a real and large advertisement dataset. The results demonstrate the advantage of the proposed approach, not only in accuracy but also generality.
Scaling CFL-reachability-based alias analysis: theory and practice

of thesis entitled: Scaling CFL-Reachability-Based Alias Analysis: Theory and Practice Submitted by ZHANG, Qirun for the degree of Doctor of Philosophy at The Chinese University of Hong Kong in September 2013 Alias analysis is a fundamental static analysis problem which aims at determining if two pointer variables can refer to the same memory location. Alias analysis is usually a prerequisite for many static analyses. The precision of alias analysis leaves a great impact on many subsequent static analyses. Alias analysis can be formulated as a context-free language (CFL) reachability problem on edge-labeled bidirected graphs. Solving all-pairs CFL-reachability is expensive. For a graph with n nodes and m edges, the traditional dynamic programming style algorithm exhibits an O(n/ log n) time complexity. It is also well-known that CFL-reachability-based alias analysis solving all-pairs CFL-reachability does not scale in practice. This thesis makes both theoretical and practical contributions
Bioinformatics analysis of microarray data to identify the candidate biomarkers of lung adenocarcinoma

Background Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and the most lethal malignant disease worldwide. However, the molecular mechanisms underlying LUAD are not fully understood. Methods Four datasets (GSE118370, GSE85841, GSE43458 and GSE32863) were obtained from the gene expression omnibus (GEO). Identification of differentially expressed genes (DEGs) and functional enrichment analysis were performed using the limma and clusterProfiler packages, respectively. A protein–protein interaction (PPI) network was constructed via Search Tool for the Retrieval of Interacting Genes (STRING) database, and the module analysis was performed by Cytoscape. Then, overall survival analysis was performed using the Kaplan–Meier curve, and prognostic candidate biomarkers were further analyzed using the Oncomine database. Results Totally, 349 DEGs were identified, including 275 downregulated and 74 upregulated genes which were significantly enriched in the biological process of extracellular structure organization, leukocyte migration and response to peptide. The mainly enriched pathways were complement and coagulation cascades, malaria and prion diseases. By extracting key modules from the PPI network, 11 hub genes were screened out. Survival analysis showed that except VSIG4, other hub genes may be involved in the development of LUAD, in which MYH10, METTL7A, FCER1G and TMOD1 have not been reported previously to correlated with LUAD. Briefly, novel hub genes identified in this study will help to deepen our understanding of the molecular mechanisms of LUAD carcinogenesis and progression, and to discover candidate targets for early detection and treatment of LUAD.
Unilateral cleft lip: evaluation and comparison of treatment outcome with two surgical techniques based on qualitative (subject/guardian and professional) assessment

Objectives The outcomes of the treatment of unilateral cleft lip can vary considerably due to variations in repair techniques. The aim of this study was to evaluate and compare treatment outcomes of surgical repair of unilateral cleft lip using either the Tennison–Randall or Millard technique based on (qualitative) parent/subject and professional assessments. Materials and Methods This was a prospective, randomized, controlled study conducted at Lagos University Teaching Hospital between January 2013 and July 2014. A total of 56 subjects with unilateral cleft lip presenting for primary surgery who satisfied the inclusion criteria were recruited for the study. Subjects were randomly allocated to surgical groups A or B through balloting. Group A underwent cleft repair with the Tennison–Randall technique, while group B underwent cleft repair with the Millard rotation advancement technique. Surgical outcome was assessed using qualitative evaluation by the guardian/subject and independent assessors based on a modified form of the criteria described by Christofides and colleagues. Results Of the 56 subjects enrolled in this study, 32 were male, with a male to female ratio of 1.3:1. Fifteen of the guardians/subjects in the Tennison–Randall group were most bothered about the lower part of the residual lip scar, while 12 guardians/subjects in the in the Millard group were most bothered about the upper part of the scar. More noses were judged to be flattened in the Millard group than in the Tennison–Randall group. Assessors observed a striking disparity in scar transgression of the philtral ridges between the two groups. Conclusion Essentially, there were no major difference in the overall results between Millard rotation-advancement and Tennison–Randall repairs. Both Millard and Tennison–Randall's techniques require significant improvements to improve the appearance of the scar on the upper part and lower part of the lip, respectively.
High School Mathematics Trajectories: Connecting Opportunities to Learn with Student Performance

This study is part of a larger multi-year comprehensive (K-12) mathematics and science curriculum reform initiative focusing on the connection between implemented and attained high school mathematics curriculum. Students’ opportunities to learn mathematics content in two geographically diverse school districts were studied to determine if these are linked with student performance in mathematics. All high school students and their mathematics teachers in both districts provided data for this study. Preliminary findings support the contention that curriculum differentiation exists at the high school level. The data suggest that different content trajectories offer very different opportunities to learn within and between school districts and these content trajectories are linked to levels of student performance in mathematics. The findings have implications for student learning outcomes and curriculum policy.
Comparative study of nursing curriculum in nursing faculties of Canada, Turkey, and Iran according to SPICES model

BACKGROUND: Innovation in the development and review of curriculums is one of the requirements of medical education in the present era. SPICES model has been taken into consideration by nursing faculties to promote quality of nursing education and to eliminate conventional curriculums. In Iran, for competency-based nursing education, review and development of curriculums are necessary. OBJECTIVE: The objectives of the present study were to determine the implementation of SPICES model in nursing curriculums of Tehran (Iran), Western (Canada), and Hacettepe (Turkey) nursing faculties and also to present recommendations to operationalize it in the nursing curriculum of Iran. MATERIALS AND METHODS: This study using comparative method with Brady's model was conducted in 2018. In this study, curriculums of nursing faculties of Tehran, Western Canada, and Hacettepe were compared. Data were extracted through texts and documents available at electronic pages of universities. Curriculums of these nursing faculties were compared from the perspective of SPICES model strategies (student-based, problem-based, integration, community-based, elective, and systematic) at four steps of description, interpretation, juxtaposition, and comparison. RESULTS: The results showed that curriculums in Western Canada reside at the end of the innovative spectrum of SPICES model. Curriculums in Tehran and Hacettepe in most of the strategies of this model reside at the beginning of the spectrum, and in most cases, conventional methods were used. CONCLUSIONS: According to successful experiences of Western Canada in the implementation of SPICES model's strategies and also considering this fact that still conventional approaches are prevalent in Iran, it is recommended to focus on experiences and, according to social conditions and facilities of the nursing community, to implement necessary changes in the curriculums based on this model.
Obesity and iron-deficiency anemia in women of reproductive age in northern Iran

BACKGROUND: Obesity and iron deficiency (ID) are two forms of the most usual nutritional disorders worldwide. Some studies have discovered a correlation between ID and obesity although more investigation is required. This study was aimed to determine the association between obesity and ID anemia (IDA) in Iranian childbearing age women. MATERIALS AND METHODS: This cross-sectional study was done on 256 women of reproductive age in northern Iran. The anthropometric measurements including height and weight were measured, and body mass index (BMI) was calculated. Low blood index of the hemoglobin (Hb), mean cell volume (MCV), and mean corpuscular hemoglobin (MCH) were evaluated with ferritin, serum iron, and total iron-binding capacity. Baseline data were expressed as means ± standard deviations. Chi-square test was applied to compare the categorical variable. Differences between the two groups were evaluated with independent samples t-test. A value of P < 0.05 was considered as statistically significant. RESULTS: Obesity was in urban women higher than rural women (55.1% vs. 44.9%), and this difference was significant (P < 0.021). There was found no association between hematological characteristics and BMI. The data showed that only 13.4% of obese women and 17.1% of the women with normal weight had IDA (odds ratio = 0.75; 95% confidence interval: 0.39–1.49, P > 0.05). CONCLUSIONS: According to the results of this study, it seems that the relationship between obesity and IDA is controversial. Hence, further studies are needed to be done.