Contagion Effect Estimation Using Proximal Embeddings

Contagion effect refers to the causal effect of peers' behavior on the outcome of an individual in social networks. Contagion can be confounded due to latent homophily which makes contagion effect estimation very hard: nodes in a homophilic network tend to have ties to peers with similar attributes and can behave similarly without influencing one another. One way to account for latent homophily is by considering proxies for the unobserved confounders. However, as we demonstrate in this paper, existing proxy-based methods for contagion effect estimation have a very high variance when the proxies are high-dimensional. To address this issue, we introduce a novel framework, Proximal Embeddings (ProEmb), that integrates variational autoencoders with adversarial networks to create low-dimensional representations of high-dimensional proxies and help with identifying contagion effects. While VAEs have been used previously for representation learning in causal inference, a novel aspect of our approach is the additional component of adversarial networks to balance the representations of different treatment groups, which is essential in causal inference from observational data where these groups typically come from different distributions. We empirically show that our method significantly increases the accuracy and reduces the variance of contagion effect estimation in observational network data compared to state-of-the-art methods

Minimizing Interference and Selection Bias in Network Experiment Design

Current approaches to A/B testing in networks focus on limiting interference, the concern that treatment effects can "spill over" from treatment nodes to control nodes and lead to biased causal effect estimation. Prominent methods for network experiment design rely on two-stage randomization, in which sparsely-connected clusters are identified and cluster randomization dictates the node assignment to treatment and control. Here, we show that cluster randomization does not ensure sufficient node randomization and it can lead to selection bias in which treatment and control nodes represent different populations of users. To address this problem, we propose a principled framework for network experiment design which jointly minimizes interference and selection bias. We introduce the concepts of edge spillover probability and cluster matching and demonstrate their importance for designing network A/B testing. Our experiments on a number of real-world datasets show that our proposed framework leads to significantly lower error in causal effect estimation than existing solutions.Comment: This paper has been accepted at the International AAAI Conference on Web and Social Media (ICWSM 2020

Estimation of the volume of distribution of some pharmacologically important compounds from their structural descriptor

Quantitative structure–activity relationship (QSAR) approaches were used to estimate the volume of distribution (Vd) using an artificial neural network (ANN). The data set consisted of the volume of distribution of 129 pharmacologically important compounds, i.e., benzodiazepines, barbiturates, nonsteroidal anti-inflammatory drugs (NSAIDs), tricyclic anti-depressants and some antibiotics, such as betalactams, tetracyclines and quinolones. The descriptors, which were selected by stepwise variable selection methods, were: the Moriguchi octanol–water partition coefficient; the 3D-MoRSE-signal 30, weighted by atomic van der Waals volumes; the fragment-based polar surface area; the d COMMA2 value, weighted by atomic masses; the Geary autocorrelation, weighted by the atomic Sanderson electronegativities; the 3D-MoRSE – signal 02, weighted by atomic masses, and the Geary autocorrelation – lag 5, weighted by the atomic van der Waals volumes. These descriptors were used as inputs for developing multiple linear regressions (MLR) and artificial neural network models as linear and non-linear feature mapping techniques, respectively. The standard errors in the estimation of Vd by the MLR model were: 0.104, 0.103 and 0.076 and for the ANN model: 0.029, 0.087 and 0.082 for the training, internal and external validation test, respectively. The robustness of these models were also evaluated by the leave-5-out cross validation procedure, that gives the statistics Q2 = 0.72 for the MLR model and Q2 = 0.82 for the ANN model. Moreover, the results of the Y-randomization test revealed that there were no chance correlations among the data matrix. In conclusion, the results of this study indicate the applicability of the estimation of the Vd value of drugs from their structural molecular descriptors. Furthermore, the statistics of the developed models indicate the superiority of the ANN over the MLR model

Combination of Adsorption-Diffusion Model with Computational Fluid Dynamics for Simulation of a Tubular Membrane Made from SAPO-34 Thin Layer Supported by Stainless Steel for Separation of CO2 from CH4

Modeling of CO2/CH4 separation using SAPO-34 tubular membrane was performed by computational fluid dynamics. The Maxwell-Stefan equations and Langmuir isotherms were used to describe the permeate flux through the membrane and the adsorption-diffusion, respectively. Three-dimensional Navier-Stokes momentum balances in feed and permeate side coupled with adsorption-diffusion equations from the membrane were simultaneously solved by ANSYS FLUENT software. The velocity and concentration profiles were determined in both feed and permeate sides. There was a good agreement between simulation and experimental results and root mean square deviation for CH4 and CO2 are 0.13 and 0.1 (mmol m-2 s-1), respectively. The concentration polarization effect was observed in the results. The effect of the process variables were investigated to find out the most influential parameters in permeation and purity. The impact of operating conditions on separation were studied and showed that for enhancement of separation efficiency of CO2 from CH4, feed pressure, feed flow rate and tube radius and number of membrane modules in series should be increased, whereas flow configuration has less significant effect

Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting

Existing studies addressing gender bias of pre-trained language models, usually build a small gender-neutral data set and conduct a second phase pre-training on the model with such data. However, given the limited size and concentrated focus of the gender-neutral data, catastrophic forgetting would occur during second-phase pre-training. Forgetting information in the original training data may damage the model's downstream performance by a large margin. In this work, we empirically show that catastrophic forgetting occurs in such methods by evaluating them with general NLP tasks in GLUE. Then, we propose a new method, GEnder Equality Prompt (GEEP), to improve gender fairness of pre-trained models with less forgetting. GEEP freezes the pre-trained model and learns gender-related prompts with gender-neutral data. Empirical results show that GEEP not only achieves SOTA performances on gender fairness tasks, but also forgets less and performs better on GLUE by a large margin.Comment: This paper has been accepted at the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023

Network experiment designs for inferring causal effects under interference

Current approaches to A/B testing in networks focus on limiting interference, the concern that treatment effects can “spill over” from treatment nodes to control nodes and lead to biased causal effect estimation. In the presence of interference, two main types of causal effects are direct treatment effects and total treatment effects. In this paper, we propose two network experiment designs that increase the accuracy of direct and total effect estimations in network experiments through minimizing interference between treatment and control units. For direct treatment effect estimation, we present a framework that takes advantage of independent sets and assigns treatment and control only to a set of non-adjacent nodes in a graph, in order to disentangle peer effects from direct treatment effect estimation. For total treatment effect estimation, our framework combines weighted graph clustering and cluster matching approaches to jointly minimize interference and selection bias. Through a series of simulated experiments on synthetic and real-world network datasets, we show that our designs significantly increase the accuracy of direct and total treatment effect estimation in network experiments

A Didactic Approach to Curriculum Renewal on the Basis of Market Demands: A Grounded Theory Study

This study aims to provide sufficient information on the issues of the current approaches, materials, and curricula employed in the field of Translation Studies. To do so, the researcher investigated the demands of the market and the vocational realities so as to come to an understanding of the curriculum drawbacks. Furthermore, this study provides a review on the current trends used by academic institutions and private sector inIran. As a phase of the adopted model, several semi-structured interviews were held with authorities in the market of translation, and then the gathered data. Having analyzed the data, a number of themes emerged, the most important of which were the skills pertinent to technology and computer assisted translation. Finally, a number of recommendations were made to improve the official curriculum of Translation Studies. To the future researchers, this study provides baseline information on the recent status of translator teaching trends

Candidemia in Febrile Neutropenic Patients; a Brief Report

Introduction: Febrile neutropenic patients are at risk of serious infections. The aim of the present study is to identify the frequency, species, and susceptibility patterns of candidemia in febrile neutropenic patients. Methods: This cross-sectional study was conducted on febrile neutropenic patients suspected with candidemia who had been referred to 3 educational hospitals during 9 months. Results: The blood samples of 80 febrile neutropenic patients with the mean age of 48§16.6 years were studied (60% female). Five (6.25%) episodes of candidemia were identified. The underlying disease was acute myeloid leukemia in 4 (80%) cases and all 5(100%) cases had central venous catheter and were receiving prophylactic ciprofloxacin and acyclovir. 100% of isolates were found to be susceptible to Voriconazole, 80% to Caspofungin, 60% to Amphotericin B, and 40% to Fluconazole. Conclusion: The frequency of candidemia among the studied febrile neutropenia patients was 6.25%, with 80% mortality rate, and themost frequently identified yeastwas Candida albicans (100% susceptible to Voriconazole)

Designing and Introducing a New Artificial Feeding Apparatus for Sand Fly Rearing

Background: Due to strict ethical rules, the risk of accidental disease transmission and the most importantly, inconven­ience regarding using of live animals, artificial feeding apparatus has been developed for colonization of haematophagous insects. Rearing of sandfly is more difficult than other haematophagous insects. Methods: In the current study, a new apparatus for membrane feeding of Phlebotomus papatasi was designed, made and compared with available apparatus in Sand Fly Insectary, Tehran University of Medical Sciences, Tehran, Iran, in 2014. Results: In comparison to other apparatus designed for artificial feeding of other arthropods, our designed apparatus had the highest performance which after up to 1h, the majority of sand flies landed and took blood and among tested membranes, chicken skin was proved the most efficient membrane. Conclusion: Sand fly artificial feeding apparatus can be used at least for rearing of Ph. papatasi