Miracle-dz/UniFAR-FTUs huggingface dataset:
To eWOM, or not? Investigating the Restricting Factors to Provide Online Ratings for Physicians

Information exchange concerning evaluation products and services has increased over the internet. This trend does not stop at medical treatment services and the patients’ evaluation of their physician. However, ratings on Amazon.com show low participation of consumers. In the case of physician ratings, this even more severe. The scarce of ratings implicate an insufficient information basis for patients to rely on. This research investigates factors which prevent patients from contributing their information of medical treatment experiences by partially applying Social Exchange Theory (SET) and the effects of patients’ general capabilities to exchange information via the internet. Results show that the ability to exchange information is one main determinant for the participation in eWOM, whereas the costs derived by SET did not influence the rating behavior. This suggests that the specifics of the medical treatment service call for additional theories to explain why patients are so restrictive in their rating behavior.
Math Self-Efficacy and STEM Intentions: A Person-Centered Approach

Research pertaining to STEM interest and persistence continues to be a top priority in the educational research arena. The current study employed a person-centered approach to examine the impact of math self-efficacy and various distal predictors, such as individuals’ demographic information, beliefs about math, and social group identification, on STEM interest and intentions. Specifically, we conducted a latent profile analysis (LPA), thereby inferring three homogeneous subgroups of individuals or latent classes from their response patterns on the 18-item sources of math self-efficacy measure. Our analyses showed that individuals’ ethnicity, implicit theories of math ability, and other group orientation were predictive of class membership (Mastery, Moderate, and Unconfident). We also found that there were significant differences in interest in STEM subjects, interest in STEM activities, individuals’ majors, and retention grade point average across the three latent classes. Our findings support the importance of math self-efficacy in choice of major as well as overall academic performance regardless of whether a student is in a STEM field or a non-STEM field.
A Look on the Generational Differences in IT Self-Service Engagement

Organizations are not only changing their IT service delivery model (e.g., Apple Genius Bar) but also are on the rise in adopting IT self-service portals solutions (e.g., IBM). As a byproduct of IT consumerization, individuals are becoming more and more tech-savvy and self-sufficient in making their own decisions about their choice of technology and how to solve any IT problem they might face. Engagement in IT self-service is a behavior where individuals try to resolve their technological issues without seeking the help of the organizations’ IT department personnel. Following an exploratory approach and applying the guidelines of grounded theory, this study aims to address the age-related divide in the workplace regarding IT self-service engagement behavior.
Phage-Borne Depolymerases Decrease Klebsiella pneumoniae Resistance to Innate Defense Mechanisms

Klebsiella pneumoniae produces capsular polysaccharides that are a crucial virulence factor protecting bacteria against innate response mechanisms of the infected host. Simultaneously, those capsules are targeted by specific bacteriophages equipped with virion-associated depolymerases able to recognize and degrade these polysaccharides. We show that Klebsiella phage KP32 produces two capsule depolymerases, KP32gp37 and KP32gp38, with a high specificity for the capsular serotypes K3 and K21, respectively. Together, they determine the host spectrum of bacteriophage KP32, which is limited to strains with serotype K3 and K21. Both depolymerases form a trimeric β-structure, display moderate thermostability and function optimally under neutral to alkaline conditions. We show that both depolymerases strongly affect the virulence of K. pneumoniae with the corresponding K3 and K21 capsular serotypes. Capsule degradation renders the otherwise serum-resistant cells more prone to complement-mediated killing with up to four log reduction in serum upon exposure to KP32gp37. Decapsulated strains are also sensitized for phagocytosis with a twofold increased uptake. In addition, the intracellular survival of phagocytized cells in macrophages was significantly reduced when bacteria were previously exposed to the capsule depolymerases. Finally, depolymerase application considerably increases the lifespan of Galleria mellonella larvae infected with K. pneumoniae in a time- and strain-dependent manner. In sum, capsule depolymerases are promising antivirulence compounds that act by defeating a major resistance mechanism of K. pneumoniae against the innate immunity.
Dependence of Present and Future European Temperature Extremes on the Location of Atmospheric Blocking

The impact of atmospheric blocking on European heat waves (HWs) and cold spells (CSs) is investigated for present and future conditions . A 50‐member ensemble of the second generation Canadian Earth System Model is used to quantify the role of internal variability in the response to blocking. We find that the present blocking‐extreme temperature link is well represented compared to ERA‐Interim, despite a significant underestimation of blocking frequency in most ensemble members. Our results show a strong correlation of blocking with northern European HWs in summer, spring, and fall. However, we also find a strong anticorrelation between blocking and HW occurrence in southern Europe in all seasons. Blocking increases the CS frequency particularly in southern Europe in fall, winter, and spring but reduces it in summer. For the future we find that blocking will continue to play an important role in the development of both CSs and HWs in all seasons.
Mean estimation with sub-Gaussian rates in polynomial time

We study polynomial time algorithms for estimating the mean of a heavy-tailed multivariate random vector. We assume only that the random vector $X$ has finite mean and covariance. In this setting, the radius of confidence intervals achieved by the empirical mean are large compared to the case that $X$ is Gaussian or sub-Gaussian. We offer the first polynomial time algorithm to estimate the mean with sub-Gaussian-size confidence intervals under such mild assumptions. Our algorithm is based on a new semidefinite programming relaxation of a high-dimensional median. Previous estimators which assumed only existence of finitely-many moments of $X$ either sacrifice sub-Gaussian performance or are only known to be computable via brute-force search procedures requiring time exponential in the dimension.
Role of reputation cues in trust formation for a developer's decision to join Open Source Software projects

Level of contributors' activity around an Open Source Software (OSS) project is one of the key factors in terms of its survival and success. There are several factors that affect a developer's decision to join an OSS project, yet little research examined the influence of third-party assessments on a developer's intention to join a project. Drawing on signaling theory, this manuscript explores how third-party assessment can influence a developers' decision to join an OSS project. In order to test it, vignette survey study was conducted manipulating reputation, development experience, and a number of current OSS projects of existing developers in the OSS project. The findings suggest that all three signals have a positive influence on developer's decision to join the OSS project. This suggests that projects seeking to expand the number of contributing developers should consider offering information about its "star developers".
Deep Reinforcement Learning for Six Degree-of-Freedom Planetary Powered Descent and Landing

Future Mars missions will require advanced guidance, navigation, and control algorithms for the powered descent phase to target specific surface locations and achieve pinpoint accuracy (landing error ellipse $<$ 5 m radius). The latter requires both a navigation system capable of estimating the lander's state in real-time and a guidance and control system that can map the estimated lander state to a commanded thrust for each lander engine. In this paper, we present a novel integrated guidance and control algorithm designed by applying the principles of reinforcement learning theory. The latter is used to learn a policy mapping the lander's estimated state directly to a commanded thrust for each engine, with the policy resulting in accurate and fuel-efficient trajectories. Specifically, we use proximal policy optimization, a policy gradient method, to learn the policy. Another contribution of this paper is the use of different discount rates for terminal and shaping rewards, which significantly enhances optimization performance. We present simulation results demonstrating the guidance and control system's performance in a 6-DOF simulation environment and demonstrate robustness to noise and system parameter uncertainty.
Polyphonic Sound Event Detection by Using Capsule Neural Networks

Artificial sound event detection (SED) aims to mimic the human ability to perceive and understand what is happening in the surroundings. Nowadays, deep learning offers valuable techniques for this goal, such as convolutional neural networks (CNNs). The capsule neural network (CapsNet) architecture has been recently introduced in the image processing field with the intent to overcome some of the known limitations of CNNs, specifically regarding the scarce robustness to affine transformations (i.e., perspective, size, and orientation) and the detection of overlapped images. This motivated the authors to employ CapsNets to deal with the polyphonic SED task, in which multiple sound events occur simultaneously. Specifically, we propose to exploit the capsule units to represent a set of distinctive properties for each individual sound event. Capsule units are connected through a so-called dynamic routing that encourages learning part-whole relationships and improves the detection performance in a polyphonic context. This paper reports extensive evaluations carried out on three publicly available datasets, showing how the CapsNet-based algorithm not only outperforms standard CNNs but also achieves the best results with respect to the state-of-the-art algorithms.
Better Trust Between Users in Sharing Economy Platforms

The widespread sharing of economy platforms has been recognized as a disruptive technology to every business sector. There is no doubt that sharing economy platforms created an innovative way for conducting business deals with anonyms individuals, yet, posse some new threats to the community that goes beyond monetary threat. The widespread sharing of economy platforms and their associated new threats motivated our study. In this study, I examined the impact of host’s and guest’s Identity Document (ID) verification on successful transaction completion in the context of sharing economy, specifically in accommodation services. In an empirical analysis of Airbnb's data, I found that verified hosts have the greatest impact on successful transaction completion among other groups, namely, recently-verified and never-verified groups, respectively
A Simple Baseline Algorithm for Graph Classification

Graph classification has recently received a lot of attention from various fields of machine learning e.g. kernel methods, sequential modeling or graph embedding. All these approaches offer promising results with different respective strengths and weaknesses. However, most of them rely on complex mathematics and require heavy computational power to achieve their best performance. We propose a simple and fast algorithm based on the spectral decomposition of graph Laplacian to perform graph classification and get a first reference score for a dataset. We show that this method obtains competitive results compared to state-of-the-art algorithms.
Taking Advantage of Multitask Learning for Fair Classification

A central goal of algorithmic fairness is to reduce bias in automated decision making. An unavoidable tension exists between accuracy gains obtained by using sensitive information as part of a statistical model, and any commitment to protect these characteristics. Often, due to biases present in the data, using the sensitive information in the functional form of a classifier improves classification accuracy. In this paper we show how it is possible to get the best of both worlds: optimize model accuracy and fairness without explicitly using the sensitive feature in the functional form of the model, thereby treating different individuals equally. Our method is based on two key ideas. On the one hand, we propose to use Multitask Learning (MTL), enhanced with fairness constraints, to jointly learn group specific classifiers that leverage information between sensitive groups. On the other hand, since learning group specific models might not be permitted, we propose to first predict the sensitive features by any learning method and then to use the predicted sensitive feature to train MTL with fairness constraints. This enables us to tackle fairness with a three-pronged approach, that is, by increasing accuracy on each group, enforcing measures of fairness during training, and protecting sensitive information during testing. Experimental results on two real datasets support our proposal, showing substantial improvements in both accuracy and fairness.
Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages

Measuring the semantic similarity between two sentences (or Semantic Textual Similarity - STS) is fundamental in many NLP applications. Despite the remarkable results in supervised settings with adequate labeling, little attention has been paid to this task in low-resource languages with insufficient labeling. Existing approaches mostly leverage machine translation techniques to translate sentences into rich-resource language. These approaches either beget language biases, or be impractical in industrial applications where spoken language scenario is more often and rigorous efficiency is required. In this work, we propose a multilingual framework to tackle the STS task in a low-resource language e.g. Spanish, Arabic , Indonesian and Thai, by utilizing the rich annotation data in a rich resource language, e.g. English. Our approach is extended from a basic monolingual STS framework to a shared multilingual encoder pretrained with translation task to incorporate rich-resource language data. By exploiting the nature of a shared multilingual encoder, one sentence can have multiple representations for different target translation language, which are used in an ensemble model to improve similarity evaluation. We demonstrate the superiority of our method over other state of the art approaches on SemEval STS task by its significant improvement on non-MT method, as well as an online industrial product where MT method fails to beat baseline while our approach still has consistently improvements.
Determinantal thinning of point processes with network learning applications

A new type of dependent thinning for point processes in continuous space is proposed, which leverages the advantages of determinantal point processes defined on finite spaces and, as such, is particularly amenable to statistical, numerical, and simulation techniques. It gives a new point process that can serve as a network model exhibiting repulsion. The properties and functions of the new point process, such as moment measures, the Laplace functional, the void probabilities, as well as conditional (Palm) characteristics can be estimated accurately by simulating the underlying (non-thinned) point process, which can be taken, for example, to be Poisson. This is in contrast (and preference to) finite Gibbs point processes, which, instead of thinning, require weighting the Poisson realizations, involving usually intractable normalizing constants. Models based on determinantal point processes are also well suited for statistical (supervised) learning techniques, allowing the models to be fitted to observed network patterns with some particular geometric properties. We illustrate this approach by imitating with determinantal thinning the well-known Matérn II hard-core thinning, as well as a soft-core thinning depending on nearest-neighbour triangles. These two examples demonstrate how the proposed approach can lead to new, statistically optimized, probabilistic transmission scheduling schemes.
Decoding of Non-Binary LDPC Codes using the Information Bottleneck Method

Recently, a novel lookup table based decoding method for binary low-density parity-check codes has attracted considerable attention. In this approach, mutual-information-maximizing lookup tables replace the conventional operations of the variable nodes and the check nodes in message passing decoding. Moreover, the exchanged messages are represented by integers with very small bit width. A machine learning framework termed the information bottleneck method is used to design the corresponding lookup tables. In this paper, we extend this decoding principle from binary to non-binary codes. This is not a straightforward extension but requires a more sophisticated lookup table design to cope with the arithmetic in higher order Galois fields. Provided bit error rate simulations show that our proposed scheme outperforms the log-max decoding algorithm and operates close to sum-product decoding.
Microbiomes Associated With Foods From Plant and Animal Sources

Food microbiome composition impacts food safety and quality. The resident microbiota of many food products is influenced throughout the farm to fork continuum by farming practices, environmental factors, and food manufacturing and processing procedures. Currently, most food microbiology studies rely on culture-dependent methods to identify bacteria. However, advances in high-throughput DNA sequencing technologies have enabled the use of targeted 16S rRNA gene sequencing to profile complex microbial communities including non-culturable members. In this study we used 16S rRNA gene sequencing to assess the microbiome profiles of plant and animal derived foods collected at two points in the manufacturing process; post-harvest/pre-retail (cilantro) and retail (cilantro, masala spice mixes, cucumbers, mung bean sprouts, and smoked salmon). Our findings revealed microbiome profiles, unique to each food, that were influenced by the moisture content (dry spices, fresh produce), packaging methods, such as modified atmospheric packaging (mung bean sprouts and smoked salmon), and manufacturing stage (cilantro prior to retail and at retail). The masala spice mixes and cucumbers were comprised mainly of Proteobacteria, Firmicutes, and Actinobacteria. Cilantro microbiome profiles consisted mainly of Proteobacteria, followed by Bacteroidetes, and low levels of Firmicutes and Actinobacteria. The two brands of mung bean sprouts and the three smoked salmon samples differed from one another in their microbiome composition, each predominated by either by Firmicutes or Proteobacteria. These data demonstrate diverse and highly variable resident microbial communities across food products, which is informative in the context of food safety, and spoilage where indigenous bacteria could hamper pathogen detection, and limit shelf life.
Rationale for an Association Between PD1 Checkpoint Inhibition and Therapeutic Vaccination Against HIV

The pathogenesis of HIV immunodeficiency is mainly dependent on the cytopatic effects exerted by the virus against infected CD4+ T cells. However, CD4+ T cell loss cannot be the only pathogenic factor since severe opportunistic infections may develop in HIV infected patients with normal CD4+ T cell counts and since the recent START study indicated that absolute CD4+ T cell counts are not predictive for AIDS and non-AIDS events. Recently our group demonstrated that CD8+CD28-CD127lowCD39+ regulatory T lymphocytes, previously found highly concentrated within tumor microenvironment, circulate with elevated frequency in the peripheral blood of HIV infected patients. Here, we show that these cells, that at least in part are HIV specific, express the PD1 immune checkpoint. Based on these evidences and considerations, in this Perspective article we speculate on the opportunity to treat HIV infected patients with anti-PD1 immune checkpoint inhibitors as a way to counteract the T regulatory cell compartment and to unleash virus-specific immune responses. In order to potentiate the immune responses against HIV we also propose the potential utility to associate immune checkpoint inhibition with HIV-specific therapeutic vaccination, reminiscent of what currently applied in oncologic protocols. We suggest that such an innovative strategy could permit drug-sparing regimens and, perhaps, lead to eradication of the infection in some patients.
Constituent Parsing as Sequence Labeling

We introduce a method to reduce constituent parsing to sequence labeling. For each word w_t, it generates a label that encodes: (1) the number of ancestors in the tree that the words w_t and w_{t+1} have in common, and (2) the nonterminal symbol at the lowest common ancestor. We first prove that the proposed encoding function is injective for any tree without unary branches. In practice, the approach is made extensible to all constituency trees by collapsing unary branches. We then use the PTB and CTB treebanks as testbeds and propose a set of fast baselines. We achieve 90% F-score on the PTB test set, outperforming the Vinyals et al. (2015) sequence-to-sequence parser. In addition, sacrificing some accuracy, our approach achieves the fastest constituent parsing speeds reported to date on PTB by a wide margin.
Actor-Expert: A Framework for using Action-Value Methods in Continuous Action Spaces

Value-based approaches can be difficult to use in continuous action spaces, because an optimization has to be solved to find the greedy action for the action-values. A common strategy has been to restrict the functional form of the action-values to be convex or quadratic in the actions, to simplify this optimization. Such restrictions, however, can prevent learning accurate action-values. In this work, we propose the Actor-Expert framework for value-based methods, that decouples action-selection (Actor) from the action-value representation (Expert). The Expert uses Q-learning to update the action-values towards the optimal action-values, whereas the Actor (learns to) output the greedy action for the current action-values. We develop a Conditional Cross Entropy Method for the Actor, to learn the greedy action for a generically parameterized Expert, and provide a two-timescale analysis to validate asymptotic behavior. We demonstrate in a toy domain with bimodal action-values that previous restrictive action-value methods fail whereas the decoupled Actor-Expert with a more general action-value parameterization succeeds. Finally, we demonstrate that Actor-Expert performs as well as or better than these other methods on several benchmark continuous-action domains.