Miracle-dz/UniFAR-FTUs huggingface dataset:
A contextual-bandit approach to personalized news article recommendation

Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation. In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks. The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.
Factorizing personalized Markov chains for next-basket recommendation

Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn the general taste of a user by factorizing the matrix over observed user-item preferences. On the other hand, MC methods model sequential behavior by learning a transition graph over items that is used to predict the next action based on the recent actions of a user. In this paper, we present a method bringing both approaches together. Our method is based on personalized transition graphs over underlying Markov chains. That means for each user an own transition matrix is learned - thus in total the method uses a transition cube. As the observations for estimating the transitions are usually very limited, our method factorizes the transition cube with a pairwise interaction model which is a special case of the Tucker Decomposition. We show that our factorized personalized MC (FPMC) model subsumes both a common Markov chain and the normal matrix factorization model. For learning the model parameters, we introduce an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data. Empirically, we show that our FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization.
Reply to Rossi et al.: Immunohistopathological findings in neuromyelitis optica concur with immunobiological observations in vitro

Aquaporin-4 (AQP4) water-channel–specific IgG unifies a spectrum of autoimmune astrocytopathies exemplified by neuromyelitis optica (NMO): relapsing inflammatory myelopathy, optic neuritis, and disorders affecting AQP4-rich circumventricular organs. AQP4-IgG detection in serum distinguishes these disorders from other inflammatory demyelinating disorders. The points of our study (1) were to emphasize that (i) IgG-AQP4 interaction has multiple molecular sequelae that plausibly account for diverse pathology (edema, inflammation, demyelination, and necrosis) and (ii) some outcomes are AQP4 isoform (M1 or M23)-specific. Our study involves immunocytochemistry and freeze-fracture EM of cultured rodent astrocytes, HEK293 cells transfected with non-tagged human AQP4s, Xenopus oocytes expressing GFP-tagged human AQP4s, and NMO patient brain immunohistopathology. Because serum IgG is polyclonal, we used high-titered sera (or IgG) pooled from at least 50 patients with NMO (binding > 1,000 nmol AQP4/L) to broadly investigate potentially pathogenic, AQP4-specific outcomes of IgG-AQP4 interaction in live cells. The correspondents question our data’s validity (2), citing conflicting data they obtained by using various AQP4 isoform-transfected cell types, sera from six individual patients with NMO, and mouse neuropathology.
A Survey of Query Auto Completion in Information Retrieval

In information retrieval, query auto completion (QAC), also known as type-ahead and auto-complete suggestion, refers to the following functionality: given a prex consisting of a number of characters entered into a search box, the user interface proposes alternative ways of extending the prex to a full query. QAC helps users to formulate their query when they have an intent in mind but not a clear way of expressing this in a query. It helps to avoid possible spelling mistakes, especially on devices with small screens. It saves keystrokes and cuts down the search duration of users which implies a lower load on the search engine, and results in savings in machine resources and maintenance. Because of the clear benets of QAC, a considerable number of algorithmic approaches to QAC have been proposed in the past few years. Query logs have proven to be a key asset underlying most of the recent research. This monograph surveys this research. It focuses on summarizing the literature on QAC and provides a general understanding of the wealth of QAC approaches that are currently available. A Survey of Query Auto Completion in Information Retrieval is an ideal reference on the topic. Its contributions can be summarized as follows: It provides researchers who are working on query auto completion or related problems in the eld of information retrieval with a good overview and analysis of state-of-the-art QAC approaches. In particular, for researchers new to the eld, the survey can serve as an introduction to the state-of-the-art. It also offers a comprehensive perspective on QAC approaches by presenting a taxonomy of existing solutions. In addition, it presents solutions for QAC under different conditions such as available high-resolution query logs, in-depth user interactions with QAC using eye-tracking, and elaborate user engagements in a QAC process. It also discusses practical issues related to QAC. Lastly, it presents a detailed discussion of core challenges and promising open directions in QAC.
Ventilación en posición prono prolongada como alternativa en el tratamiento del síndrome de distrés respiratorio agudo grave posneumonectomía. Caso clínico

Management of patients with severe respiratory failure is mainly supportive, and protective mechanical ventilation is the pivotal treatment. When conventional therapy is insufficient to improve oxygenation without deleterious effects, other strategies should be considered. We report a 53 year-old male who presented a severe respiratory failure refractory to conventional management after pneumonectomy. Prone position ventilation was used for 36 hours. Respiratory variables improved and he did not show hemodynamic instability. He was returned to the supine position without worsening of oxygenation parameters. Extended prone position ventilation could be considered in patients presenting with unresponsive severe respiratory failure after pulmonary resection.
Crowdsourced Data Management: Industry and Academic Perspectives

Crowdsourcing and human computation enable organizations to accomplish tasks that are currently not possible for fully automated techniques to complete, or require more flexibility and scalability than traditional employment relationships can facilitate. In the area of data processing, companies have benefited from crowd workers on platforms such as Amazon's Mechanical Turk or Upwork to complete tasks as varied as content moderation, web content extraction, entity resolution, and video/audio/image processing. Several academic researchers from diverse areas, ranging from the social sciences to computer science, have embraced crowdsourcing as a research area, resulting in algorithms and systems that improve crowd work quality, latency, and cost. Despite the relative nascence of the field, the academic and the practitioner communities have largely operated independently of each other for the past decade, rarely exchanging techniques and experiences. Crowdsourced Data Management: Industry and Academic Perspectives aims to narrow the gap between academics and practitioners. On the academic side, it summarizes the state of the art in crowd-powered algorithms and system design tailored to large-scale data processing. On the industry side, it surveys 13 industry users - such as Google, Facebook, and Microsoft - and four marketplace providers of crowd work - such as CrowdFlower and Upwork - to identify how hundreds of engineers and tens of million dollars are invested in various crowdsourcing solutions. Crowdsourced Data Management: Industry and Academic Perspectives simultaneously introduces academics to real problems that practitioners encounter every day, and provides a survey of the state of the art for practitioners to incorporate into their designs. Through the surveys, it also highlights the fact that crowdpowered data processing is a large and growing field. Over the next decade, most technical organizations are likely to benefit in some way from crowd work, and this monograph can help guide the effective adoption of crowdsourcing across these organizations.
Sharing letters with patients and their carers: problems and outcomes in elderly and dementia care

AIMS AND METHOD In a cross-sectional survey, we assessed the attitudes of older patients and their carers towards receiving copies of letters about them and the effects upon outcomes of sharing letters. We also studied the opinions of consultants onletter-sharing. RESULTS Few old age psychiatrists shared letters with patients or carers, and many had concerns about this practice. In contrast, letters were considered ‘ very welcome’ by 87% of patients and carers who received them, and 81% of those who did not would be ‘very pleased’ to receive them. Patients and carers who had received letters had significantly better knowledge of their care plan, whom to contact and ways of making contact with services. CLINICAL IMPLICATIONS Despite concerns expressed by psychiatrists, our findings support the sharing of letters with patients and carers of patients with dementia in old age psychiatry services.
Learning Deep Architectures for AI

Theoretical results strongly suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one needs deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult optimization task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
The Association Between Home and Vehicle Environmental Tobacco Smoke (ETS) and Chronic Bronchitis in a Canadian Population: The Canadian Community Health Survey, 2005

This study aimed to determine the association between home and vehicle exposure to environmental tobacco smoke (ETS), and chronic bronchitis based on data from 64,961 non-smokers aged 12 years and older who participated in the 2005 Canadian Community Health Survey (CCHS). The proportion of respondents who reported ETS exposure in the home and vehicle was 9.0% and 8.4% respectively. The prevalence of self-reported doctor diagnosed chronic bronchitis was 1.5%. When considered separately, home and vehicle ETS were both significantly associated with chronic bronchitis in children and adolescents aged 12–19 years, with adjusted odds ratios of 2.30 (95% CI 1.46–3.63) and 2.25 (95% CI 1.42–3.58), respectively. Neither home, nor vehicle ETS exposure was significantly associated with chronic bronchitis in age groups greater than 19 years. When home and vehicle ETS exposure were considered together, and sex, age, allergies, asthma, marital status, level of education, and race where controlled for, home ETS exposure was not a significant predictor of chronic bronchitis (P = 0.296), while vehicle ETS was. The correlation between exposure variables was fair, with a kappa coefficient of 0.40 (P < 0.0001). Females, older age groups, non-white individuals, asthmatic patients, as well as individuals with allergies, no high school diploma, and those who were previously married had higher odds of chronic bronchitis. In conclusion, vehicle exposure to ETS was significantly associated with chronic bronchitis, and children and adolescents exposed to ETS were more susceptible to the disease.
Fatigue and Fatigability in Persons With Multiple Sclerosis

We examine the hypothesis that in persons with multiple sclerosis, fatigue — measured with a questionnaire — is attributable to interactions between the central nervous system and the immune system, which exacerbate both perceived fatigability and performance fatigability. Performance fatigability is represented by the decline in voluntary force during sustained contractions as a consequence of the inability to maintain high levels of voluntary activation.
Individualized Exercise Interventions for Spinal Pain

Exercise is the most effective treatment for the management and prevention of spinal pain; yet on average, it delivers small to moderate treatment effects, which are rarely long lasting. This review examines the hypothesis that outcome of exercise interventions can be optimized when targeted toward the right patients and when tailored to address the neuromuscular impairments of each individual.
INSULIN RESISTANCE - WHAT IS MYTH AND WHAT IS REALITY?

Diabetes mellitus is a complex disorder of the energy metabolism of the human body. In the last WHO/ADA classification, the main forms of this disease are Type 1 (T1DM) and Type 2 (T2DM) diabetes mellitus. According to the same classification, the pathogenesis of T2DM is considered to include a progressive insulin secretory defect on the background of insulin resistance. Recently, controversies surround the concept of peripheral insulin resistance emerging in the 70's as an attempt to explain the differences from the T1DM phenotype whose autoimmune nature was as that time revealed. The insulin resistance hypothesis was based on the supposition that high plasma insulin levels are the result of a primary molecular defect against which the normal beta cells will react with an increased insulin secretion. This hypothesis has been used to explain both the pathogenesis of T2DM and the metabolic syndrome, named also for a short period of time "the insulin resistance syndrome". We will try to argue that what it is attributed to peripheral insulin resistance belongs in fact to obesity. Special emphasis is put on the role of the adipocytes, including the secretion of different adipokines with the secondary lipotoxicity, oxidative stress and pro-inflammatory reaction, explaining the complex relationship between obesity and diabetes.
Cannabinoid receptor 2 gene polymorphism was associated with bone mineral density and t-score in Chinese postmenopausal women

The objective of the study is to investigate the association of rs2229579 polymorphism of cannabinoid receptor 2 gene (CNR2) with bone mineral density (BMD) in Chinese Han postmenopausal women. 490 Chinese Han postmenopausal women were randomly recruited and their BMD were measured by dual-energy X-ray absorptiometry (DEXA) at total body, lumbar spine (L2-L4), femoral neck and trochanter. Genotypes for the CNR2 rs2229579 were determined by polymerase-chain-reaction with the confronting-two-primer (PCR-CTPP). The genotypes distribution of CNR2 rs2229579 was met with Hardy-Weinberg equilibrium (P>0.05). The allelic frequencies for the 490 Chinese Han postmenopausal women were 82.8% for C and 17.2% for T in CNR2 rs2229579 polymorphisms. The prevalence of each genotype in the study population was 67.8% CC, 30.0% CT, and 2.2% TT. BMD for both of femoral neck and total body in women with TT genotype was significantly higher than those with CC, CT and combined group of CC and CT genotypes, respectively (all the P values <0.05). T-score at femoral neck in women with TT genotype was higher than those with combined group of CC and CT genotypes (P<0.05). T-score at total body in women with CC genotype was lower than those with combined group of CT and TT genotypes (P<0.05). There were no genotypes or alleles distribution differences in CC, CT and TT genotypes between osteoporosis and control group (χ 2 = 0.750, P > 0.05; χ 2 = 1.804, P > 0.05). Our results suggest that there is a significant association between CNR2 rs2229579 genotype polymorphisms and BMD, as well as t-score in Chinese Han postmenopausal women. This suggests that CNR2 rs2229579 may play a role in osteoporosis pathogenesis.
Adaptation, Learning, and Optimization over Networks

This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and learning problems from streaming data through localized interactions among agents. The results derived in this work are useful in comparing network topologies against each other, and in comparing networked solutions against centralized or batch implementations. There are many good reasons for the peaked interest in distributed implementations, especially in this day and age when the word "network" has become commonplace whether one is referring to social networks, power networks, transportation networks, biological networks, or other types of networks. Some of these reasons have to do with the benefits of cooperation in terms of improved performance and improved resilience to failure. Other reasons deal with privacy and secrecy considerations where agents may not be comfortable sharing their data with remote fusion centers. In other situations, the data may already be available in dispersed locations, as happens with cloud computing. One may also be interested in learning through data mining from big data sets. Motivated by these considerations, this work examines the limits of performance of distributed solutions and discusses procedures that help bring forth their potential more fully. The presentation adopts a useful statistical framework and derives performance results that elucidate the mean-square stability, convergence, and steady-state behavior of the learning networks. At the same time, the work illustrates how distributed processing over graphs gives rise to some revealing phenomena due to the coupling effect among the agents. These phenomena are discussed in the context of adaptive networks, along with examples from a variety of areas including distributed sensing, intrusion detection, distributed estimation, online adaptation, network system theory, and machine learning.
The Cognitive Benefits of Exercise in Youth

As our schools and children struggle to meet ever-changing and mandated academic standards, challenges in the time spent on physical activity continue to arise. On the other hand, however, we continue to face a global climate mired in the midst of an obesity epidemic. It is widely accepted that the health benefits of exercise are wide-ranging and powerful. It appears that cognitive function and academic achievement are additional realms beyond the physical where exercise is beneficial. The school setting presents a unique public health opportunity to enact change on a variety of levels. As sports medicine and exercise specialists, we need to support efforts that increase access to quality physical fitness across the board for all children.
Characterizing cloud computing hardware reliability

Modern day datacenters host hundreds of thousands of servers that coordinate tasks in order to deliver highly available cloud computing services. These servers consist of multiple hard disks, memory modules, network cards, processors etc., each of which while carefully engineered are capable of failing. While the probability of seeing any such failure in the lifetime (typically 3-5 years in industry) of a server can be somewhat small, these numbers get magnified across all devices hosted in a datacenter. At such a large scale, hardware component failure is the norm rather than an exception. Hardware failure can lead to a degradation in performance to end-users and can result in losses to the business. A sound understanding of the numbers as well as the causes behind these failures helps improve operational experience by not only allowing us to be better equipped to tolerate failures but also to bring down the hardware cost through engineering, directly leading to a saving for the company. To the best of our knowledge, this paper is the first attempt to study server failures and hardware repairs for large datacenters. We present a detailed analysis of failure characteristics as well as a preliminary analysis on failure predictors. We hope that the results presented in this paper will serve as motivation to foster further research in this area.
TwitterMonitor: trend detection over the twitter stream

We present TwitterMonitor, a system that performs trend detection over the Twitter stream. The system identifies emerging topics (i.e. 'trends') on Twitter in real time and provides meaningful analytics that synthesize an accurate description of each topic. Users interact with the system by ordering the identified trends using different criteria and submitting their own description for each trend. We discuss the motivation for trend detection over social media streams and the challenges that lie therein. We then describe our approach to trend detection, as well as the architecture of TwitterMonitor. Finally, we lay out our demonstration scenario.
A Simple Way to Improve DVT Protocol Compliance

Objective: To assess the impact on deep vein thrombosis (DVT) protocol violations of the introduction of a label attached to the patient's drug chart, which specifically allows low-dose subcutaneous heparin or thromboembolic deterrent stockings (TEDS) to be prescribed as appropriate. Design: An audit study. Setting: Department of General Surgery of a District General Hospital in the United Kingdom. Method: All adult general surgical inpatients on a Weekday were studied. Staff were not forewarned of the studies. Patient details and risk factors for DVT were noted. Details of administered DVT prophylaxis were recorded. In total four separate studies were undertaken, namely: with original protocols (I), with refined protocol 1 and 3 years later (II, III) and finally after introduction of the label (IV). Results: Protocol violations were defined as being ‘acceptable’ or ‘unacceptable’. Raising awareness between studies I and II reduced acceptable violations to zero. There was no statistically significant reduction in unacceptable violations (24 in 80 patients, 1; 17 in 75, II; 13 in 60, III). In study IV, following introduction of the label, there were only 6 violations in 51 patients (p<0.02). Conclusion: Combining increased awareness with the attachment of a label to the drug chart reduced unacceptable violations by 63%.