Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Rank Social Bots

Software robots, or simply bots, have often been regarded as harmless programs confined within the cyberspace. However, recent events in our society proved that they can have important effects on real life as well. Bots have in fact become one of the key tools for disseminating information through online social networks (OSNs), influencing their members and eventually changing their opinions. With a focus on classification, social bot detection has lately emerged as a major topic in OSN analysis; nevertheless more research is needed to enhance our understanding of such automated behaviors, particularly to unveil the characteristics that better differentiate legitimate accounts from bots. We argue that this demands for learning behavioral models that should be trained using a large and heterogeneous set of behavioral features, so to detect and characterize OSN accounts according to their status as bots. Within this view, in this work we push forward research on bot analysis by proposing a machine-learning framework for identifying and ranking OSN accounts based on their degree of bot relevance. Our framework exploits the most known existing methods on bot detection for enhanced feature extraction, and state-of-the-art learning-to-rank methods, using different optimization and evaluation criteria. Results obtained on Twitter data show the significance and effectiveness of our approach in detecting and ranking bot accounts.
Argumentation-Based Explanations in Recommender Systems: Conceptual Framework and Empirical Results

Explaining automatically generated recommendations has shown to be an effective means for supporting the user's decision-making process and increasing system transparency. However, present methods mostly provide non-personalized explanations that are presented in an unstructured manner. We propose a framework based on Toulmin's model designed to generate explanations in an argumentative style by presenting supportive as well as critical information about recommended items and their features. Existing research suggests that argumentative explanations cannot be assumed as equally effective for everyone. People rather tend to either apply rational or intuitive decision-making styles that determine which kinds of information are preferably taken into account. In an experimental user study, we investigated the effectiveness of argumentative explanations while considering the moderating effect of these two different cognitive styles. The results indicate that argumentative explanations, as compared to baseline methods, lead to, among others, increased perceived explanation quality, information sufficiency and overall satisfaction with the system. However, this seems only to be true for intuitive thinkers who rely more on explanations in complex decision situations as compared to rational thinkers.
The Future of Islet Transplantation Is Now

Milestones in the history of diabetes therapy include the discovery of insulin and successful methods of beta cell replacement including whole pancreas and islet cell transplantation options. While pancreas transplantation remains the gold standard for patients who have difficulty controlling their symptoms with exogenous insulin, islet allotransplantation is now able to provide similar results with some advantages that make it an attractive potential alternative. The Edmonton Protocol, which incorporated a large dose of islets from multiple donors with steroid-free immunosuppression helped to establish the modern era of islet transplantation almost 20 years ago. While islet allotransplantation is recognized around the world as a powerful clinical therapy for type 1 diabetes it is not yet recognized by the Federal Drug Administration of the United States. Large-scale clinical trials administered by the Clinical Islet Transplantation Consortium have recently demonstrated that the well-regulated manufacture of a human islet product transplanted into patients with difficult to control type 1 diabetes and with a history of severe hyperglycemic episodes can safely and efficaciously maintain glycemic balance and eliminate the most severe complications associated with diabetes. The results of these clinical trials have established a strong basis for licensure of clinical islet allotransplantation in the US. Recognition by the Federal Drug Administration would likely lead to third party reimbursement for islet allotransplantation as a therapeutic option in the United States and would make the treatment available to many more patients. The high costs of rampant diabetes justify the expense of the treatment, which is in-line with the costs of clinical pancreas transplantation. While much enthusiasm and hope is raised toward the development and optimization of stem cell therapy, the islet transplantation community should push toward licensure, if that means broader access of this procedure to patients who may benefit from it. Even as we prepare to take the first steps in that direction, we must acknowledge the new challenges that a shift from the experimental to clinical will bring. Clinical islet allotransplantation in the United States would be a game-changing event in the treatment of type 1 diabetes and also generate enthusiasm for continued research.
VFunc: a Deep Generative Model for Functions

We introduce a deep generative model for functions. Our model provides a joint distribution p(f, z) over functions f and latent variables z which lets us efficiently sample from the marginal p(f) and maximize a variational lower bound on the entropy H(f). We can thus maximize objectives of the form E_{f~p(f)}[R(f)] + c*H(f), where R(f) denotes, e.g., a data log-likelihood term or an expected reward. Such objectives encompass Bayesian deep learning in function space, rather than parameter space, and Bayesian deep RL with representations of uncertainty that offer benefits over bootstrapping and parameter noise. In this short paper we describe our model, situate it in the context of prior work, and present proof-of-concept experiments for regression and RL.
Benchmarking the PSA-CMA-ES on the BBOB noiseless testbed

We evaluate the CMA-ES with population size adaptation mechanism (PSA-CMA-ES) on the BBOB noiseless testbed. On one hand, the PSA-CMA-ES with a simple restart strategy shows performance competitive with the best 2009 portfolio on most well-structured multimodal functions. On the other hand, it is not effective on weakly-structured multimodal functions. Moreover, on most uni-modal functions, the scale-up of performance measure w.r. t. the dimension tends to be worse than the default CMA-ES, implying that the population size is adapted greater than needed on the unimodal functions. To improve performance on unimodal functions and weakly-structured multimodal functions, we additionally propose a restart strategy for the PSA-CMA-ES. The proposed strategy consists of three search regimes. The resulted restart strategy shows improved performance on unimodal functions and weakly-structured multimodal functions with a little compromise in the performance on well-structured multimodal functions. The overall performance is competitive to the BIPOP-CMA-ES.
VTA: An Open Hardware-Software Stack for Deep Learning

Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility. Changes in algorithms, models, operators, or numerical systems threaten the viability of specialized hardware accelerators. We propose VTA, a programmable deep learning architecture template designed to be extensible in the face of evolving workloads. VTA achieves this flexibility via a parametrizable architecture, two-level ISA, and a JIT compiler. The two-level ISA is based on (1) a task-ISA that explicitly orchestrates concurrent compute and memory tasks and (2) a microcode-ISA which implements a wide variety of operators with single-cycle tensor-tensor operations. Next, we propose a runtime system equipped with a JIT compiler for flexible code-generation and heterogeneous execution that enables effective use of the VTA architecture. VTA is integrated and open-sourced into Apache TVM, a state-of-the-art deep learning compilation stack that provides flexibility for diverse models and divergent hardware backends. We propose a flow that performs design space exploration to generate a customized hardware architecture and software operator library that can be leveraged by mainstream learning frameworks. We demonstrate our approach by deploying optimized deep learning models used for object classification and style transfer on edge-class FPGAs.
Effects of estrogens and antiestrogens on gonadal sex differentiation and embryonic development in the domestic fowl (Gallus gallus domesticus)

Since it is known that environmental contaminants have the potential to cause endocrine disorders in humans and animals, there is an urgent need for in vivo tests to assess possible effects of these endocrine disrupting chemicals (EDCs). Although there is no standardized guideline, the avian embryo has proven to be particularly promising as it responds sensitively to a number of EDCs preferentially impacting the reproductive axis. In the present study we examined the effects of in ovo exposure to fulvestrant and tamoxifen as antiestrogenic model compounds and co-exposure to both substances and the potent estrogen 17α-ethinylestradiol (EE2) regarding sex differentiation and embryonic development of the domestic fowl (Gallus gallus domesticus). The substances were injected into the yolk of fertilized eggs on embryonic day 1. On embryonic day 19 sex genotype and phenotype were determined, followed by gross morphological and histological examination of the gonads. Sole EE2-treatment (20 ng/g egg) particularly affected male gonads and resulted in an increased formation of female-like gonadal cortex tissue and a reduction of seminiferous tubules. In ovo exposure to tamoxifen (0.1/1/10 µg/g egg) strongly impaired the differentiation of female gonads, led to a significant size reduction of the left ovary and induced malformations of the ovarian cortex, while fulvestrant (0.1/1/10 µg/g egg) did not affect sexual differentiation. However, both antiestrogens were able to antagonize the feminizing effects of EE2in genetic males when administered simultaneously. Since both estrogens and antiestrogens induce concentration-dependent morphological alterations of the sex organs, the chick embryo can be regarded as a promising model for the identification of chemicals with estrogenic and antiestrogenic activity.
Adaptive Problem Selection in a Mobile Python Tutor

We have developed PyKinetic, a mobile Python tutor for novices. We present our study on PyKinetic with various activities to target several skills: code tracing, debugging, code understanding and code writing. We compared a version with a fixed sequence of learning activities to an adaptive version, containing the same activities but with personalized problem selection. We had two hypotheses: (H1) the combination of activities is effective for learning, and (H2) the adaptive problem selection is beneficial. The results show that PyKinetic is effective for learning, and the adaptive version provides additional benefits for learners.
Is Q-learning Provably Efficient?

Model-free reinforcement learning (RL) algorithms, such as Q-learning, directly parameterize and update value functions or policies without explicitly modeling the environment. They are typically simpler, more flexible to use, and thus more prevalent in modern deep RL than model-based approaches. However, empirical work has suggested that model-free algorithms may require more samples to learn [Deisenroth and Rasmussen 2011, Schulman et al. 2015]. The theoretical question of "whether model-free algorithms can be made sample efficient" is one of the most fundamental questions in RL, and remains unsolved even in the basic scenario with finitely many states and actions. We prove that, in an episodic MDP setting, Q-learning with UCB exploration achieves regret $\tilde{O}(\sqrt{H^3 SAT})$, where $S$ and $A$ are the numbers of states and actions, $H$ is the number of steps per episode, and $T$ is the total number of steps. This sample efficiency matches the optimal regret that can be achieved by any model-based approach, up to a single $\sqrt{H}$ factor. To the best of our knowledge, this is the first analysis in the model-free setting that establishes $\sqrt{T}$ regret without requiring access to a "simulator. "
Oral Nutritional Supplementation in Children Treated for Cancer in Low− and Middle-Income Countries Is Feasible and Effective: the Experience of the Children’s Hospital Manuel De Jesus Rivera “La Mascota” in Nicaragua

Children with cancer are particularly vulnerable to malnutrition, which can affect their tolerance of chemotherapy and outcome. In Nicaragua approximately two-thirds of children diagnosed with cancer present with under-nutrition. A nutritional program for children with cancer has been developed at “La Mascota” Hospital. Results of this oral nutritional intervention including difficulties, benefits, and relevance for children treated for cancer in low− and middle-income countries are here reported and discussed.
Apelin/APJ axis improves angiotensin II-induced endothelial cell senescence through AMPK/SIRT1 signaling pathway

Introduction Previous studies have shown that endothelial cell senescence is involved in cardiovascular diseases such as cardiac fibrosis, atherosclerosis and heart failure. Accumulating evidence indicates that apelin exerts protective effects on ageing-related endothelial dysfunction. In this study, we aim to investigate the role of the apelin/APJ axis in angiotensin II (AngII)-induced endothelium senescence and its associated mechanisms. Material and methods Senescence-related β-gal activity assay and western blot were used to evaluate human umbilical vein endothelial cell (HUVEC) senescence. In addition, DCFH-DA staining was carried out to detect the generation of reactive oxygen species (ROS). A validated, high-sensitivity real-time quantitative telomeric repeat amplification protocol (RQ-TRAP) was applied to determine telomerase activity in HUVECs, and a CCK-8 assay was employed to measure cellular viability. Results AngII induced an increase in SA-β-Gal-positive cells and upregulation on expression of P21 and PAI-1 compared to the control group (p < 0.05), while apelin against this process (p < 0.05). The protective effects were attenuated when APJ, AMPK and SIRT1 expression was knocked down (p < 0.05). Furthermore, apelin reduced AngII-induced ROS generation and enhanced telomerase activity in HUVECs (p < 0.05), which contributed to increased HUVEC viability as assessed by the CCK-8 assay (p < 0.05). Conclusions The apelin/APJ axis improved AngII-induced HUVEC senescence via the AMPK/SIRT1 signaling pathway, and the underlying mechanisms might be associated with reduced ROS production and enhanced telomerase activity.
Event Detection and Retrieval on Social Media

In the recent years, we have witnessed the rapid adoption of social media platforms, such as Twitter, Facebook and YouTube, and their use as part of the everyday life of billions of people worldwide. Given the habit of people to use these platforms to share thoughts, daily activities and experiences it is not surprising that the amount of user generated content has reached unprecedented levels, with a substantial part of that content being related to real-world events, i.e. actions or occurrences taking place at a certain time and location. Given the key role of events in our life, the task of annotating and organizing social media content around them is of crucial importance for ensuring real-time and future access to multimedia content about an event of interest. In this chapter, we present several research efforts from recent years that tackle two main problems: a) event detection and b) event-based media retrieval and summarization. Given archived collections or live streams of social media items, the purpose of event detection methods is to identify previously unknown events in the form of sets of items that describe them. In general, the events could be of any type, but there are also approaches aiming at events of specific type. Given a target event the goal of event summarization is first to identify relevant content and then to represent it in a concise way, selecting the most appealing and representative content.
Erythrocyte Senescence in a Model of Rat Displaying Hutchinson-Gilford Progeria Syndrome

Background Increased oxidative stress is a major cause of aging and age-related diseases. Erythrocytes serve as good model for aging studies. Dihydrotachysterol is known to induce premature aging feature in rats mimicking Hutchinson-Gilford progeria syndrome. Aim In the present study, attempts have been made to explore the differential response of young and senescent erythrocytes separated by density gradient centrifugation from accelerated senescence model of rats mimicking Hutchinson-Gilford progeria syndrome and naturally aged rats. Methods The erythrocytes of naturally aged and progeroid rats were separated into distinct, young and old cells on the basis of their differential densities. The parameters of oxidative stress and membrane transport systems were studied. Discussion and Conclusion Our study provides evidence that organismal aging negatively affects oxidative stress markers and membrane transport systems in both young and old erythrocytes. This study further substantiates that the changes in progeria model of rats resemble natural aging in terms of erythrocyte senescence.
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach

Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-world problems is their lack of data-efficiency. To this end, we propose the Bottleneck Simulator: a model-based reinforcement learning method which combines a learned, factorized transition model of the environment with rollout simulations to learn an effective policy from few examples. The learned transition model employs an abstract, discrete (bottleneck) state, which increases sample efficiency by reducing the number of model parameters and by exploiting structural properties of the environment. We provide a mathematical analysis of the Bottleneck Simulator in terms of fixed points of the learned policy, which reveals how performance is affected by four distinct sources of error: an error related to the abstract space structure, an error related to the transition model estimation variance, an error related to the transition model estimation bias, and an error related to the transition model class bias. Finally, we evaluate the Bottleneck Simulator on two natural language processing tasks: a text adventure game and a real-world, complex dialogue response selection task. On both tasks, the Bottleneck Simulator yields excellent performance beating competing approaches.
Toward Convolutional Blind Denoising of Real Photographs

While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs. The main reason is that their learned models are easy to overfit on the simplified AWGN model which deviates severely from the complicated real-world noise model. In order to improve the generalization ability of deep CNN denoisers, we suggest training a convolutional blind denoising network (CBDNet) with more realistic noise model and real-world noisy-clean image pairs. On the one hand, both signal-dependent noise and in-camera signal processing pipeline is considered to synthesize realistic noisy images. On the other hand, real-world noisy photographs and their nearly noise-free counterparts are also included to train our CBDNet. To further provide an interactive strategy to rectify denoising result conveniently, a noise estimation subnetwork with asymmetric learning to suppress under-estimation of noise level is embedded into CBDNet. Extensive experimental results on three datasets of real-world noisy pho- tographs clearly demonstrate the superior performance of CBDNet over state-of-the-arts in terms of quantitative met- rics and visual quality. The code has been made available at https://github.com/GuoShi28/CBDNet.
Leadership styles in two Ghanaian hospitals in a challenging environment

Abstract Hospital managers’ power to exercise effective leadership in daily management can affect quality of care directly as well as through effects on frontline workers’ motivation. This paper explores the influence of contextual factors on hospital managers’ leadership styles and the motivation of frontline workers providing maternal and new born care in two public district hospitals in Ghana. It draws on data from an ethnographic study that involved participant observation, conversations and in-depth interviews conducted over 20 months, with frontline health workers and managers. Qualitative analysis software Nvivo 11 was used to facilitate coding, and common patterns emerging from the codes were grouped into themes. Ethical clearance was obtained from the Ghana Health Service Ethical Review Committee. Contextual factors such as institutional rules and regulations and funding constrained managers’ power, and influenced leadership styles and responses to expressed and observed needs of frontline workers and clients. The contextual constraints on mangers’ responses were a source of demotivation to both managers and frontline workers, as it hampered quality health service provision. Knowing what to do, but sometimes constrained by context, managers described ‘feeling sick’ and frustrated. On the other hand in the instances where managers’ were able to get round the constraints and respond effectively to frontline health workers and clients’ needs, they felt encouraged and motivated to work harder. Effective district hospital management and leadership is influenced by contextual factors; and not just individual manager’s knowledge and skills. Interventions to strengthen management and leadership of public sector hospitals in low- and middle-income countries like Ghana need to consider context and not just individual managers’ skills and knowledge strengthening.
Generative Adversarial Style Transfer Networks for Face Aging

How somebody looked like when younger? What could a person look like when 10 years older? In this paper we look at the problem of face aging, which relates to processing an image of a face to change its apparent age. This task involves synthesizing images and modeling the aging process, which both are problems that have recently enjoyed much research interest in the field of face and gesture recognition. We propose to look at the problem from the perspective of image style transfer, where we consider the age of the person as the underlying style of the image. We show that for large age differences, convincing face aging can be achieved by formulating the problem with a pairwise training of Cycle-consistent Generative Adversarial Networks (CycleGAN) over age groups. Furthermore, we propose a variant of CycleGAN which directly incorporates a pre-trained age prediction model, which performs better when the desired age difference is smaller. The proposed approaches are complementary in strengths and their fusion performs well for any desired level of aging effect. We quantitatively evaluate our proposed method through a user study and show that it outperforms prior state-of-the-art techniques for face aging.