Miracle-dz/UniFAR-FTUs huggingface dataset:
RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation

Recurrent neural networks for session-based recommendation have attracted a lot of attention recently because of their promising performance. repeat consumption is a common phenomenon in many recommendation scenarios (e.g., e-commerce, music, and TV program recommendations), where the same item is re-consumed repeatedly over time. However, no previous studies have emphasized repeat consumption with neural networks. An effective neural approach is needed to decide when to perform repeat recommendation. In this paper, we incorporate a repeat-explore mechanism into neural networks and propose a new model, called RepeatNet, with an encoder-decoder structure. RepeatNet integrates a regular neural recommendation approach in the decoder with a new repeat recommendation mechanism that can choose items from a user’s history and recommends them at the right time. We report on extensive experiments on three benchmark datasets. RepeatNet outperforms state-of-the-art baselines on all three datasets in terms of MRR and Recall. Furthermore, as the dataset size and the repeat ratio increase, the improvements of RepeatNet over the baselines also increase, which demonstrates its advantage in handling repeat recommendation scenarios.
Cockroaches, locusts, and envenomating arthropods: a promising source of antimicrobials

Objective(s): To present a brief overview of various natural sources of antimicrobials with the aim of highlighting invertebrates living in polluted environments as additional sources of antimicrobials. Materials and Methods: A PubMed search using antibacterials, antimicrobials, invertebrates, and natural products as keywords was carried out. In addition, we consulted conference proceedings, original unpublished research undertaken in our laboratories, and discussions in specific forums. Results: Representative of a stupefying 95% of the fauna, invertebrates are fascinating organisms which have evolved strategies to survive germ-infested environments, yet they have largely been ignored. Since invertebrates such as cockroaches inhabit hazardous environments which are rampant with pathogens, they must have developed defense mechanisms to circumvent infections. This is corroborated by the presence of antimicrobial molecules in the nervous systems and hemolymph of cockroaches. Antimicrobial compounds have also been unraveled from the nervous, adipose, and salivary glandular tissues of locusts. Interestingly, the venoms of arthropods including ants, scorpions, and spiders harbor toxins, but also possess multiple antimicrobials. Conclusion: These findings have rekindled the hopes for newer and enhanced therapeutic agents derived from a plentiful and diverse resource to combat fatal infectious diseases. Such antimicrobials from unusual sources can potentially be translated into clinical practice, however intensive research is needed over the next several years to realize these expectations.
Rift Valley fever virus induces fetal demise in Sprague-Dawley rats through direct placental infection

Rift Valley fever virus infection of pregnant rats results in fetal infection and death, even in dams with no signs of illness. Rift Valley fever virus (RVFV) infections in pregnant livestock cause high rates of fetal demise; miscarriage in pregnant women has also been associated with RVFV infection. To address how RVFV infection during pregnancy causes detrimental effects on the fetus, we developed a pregnant rodent model of RVFV infection. We found that pregnant rats were more susceptible to RVFV-induced death than their nonpregnant counterparts and that RVFV infection resulted in intrauterine fetal death and severe congenital abnormalities, even in pups from infected asymptomatic pregnant rats. Virus distribution in infected dams was widespread, with a previously unrecognized preference for infection, replication, and tissue damage in the placenta. In human mid-gestation placental tissue, RVFV directly infected placental chorionic villi, with replication detected in the outermost syncytial layer. Our work identifies direct placental infection by RVFV as a mechanism for vertical transmission. This is the first study to show vertical transmission of RVFV with a lethal outcome in a species other than livestock. This study highlights the potential impact of a future epidemic of this emerging mosquito-borne virus.
Confirming the pathogenicity of NECAP1 in early onset epileptic encephalopathy

Early onset epileptic encephalopathy (EOEE) has been used to encompass Ohtahara syndrome (early infantile epileptic encephalopathy [EIEE]), early myoclonic epilepsy, and many others. Multiple genes have been established to cause epileptic encephalopathy in the immature brain, and next‐generation sequencing has accelerated the process of novel gene discovery. Many of the previously published candidate genes are still pending confirmatory reports or functional studies. Although most of the genes involved are ion channels (channelopathies), multiple other pathways have been implicated as well. NECAP1 is a key element in clathrin‐mediated endocytosis and has been reported previously to cause EOEE in a Saudi family. We report another family with the same variant confirming the pathogenicity of this variant as a Saudi founder mutation, further delineate its phenotype, and propose that it causes EOEE instead of EIEE.
Pathological Evidence Exploration in Deep Retinal Image Diagnosis

Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch’s Postulates, a well-known strategy in medical research to identify the property of pathogen, we define a pathological descriptor that can be extracted from the activated neurons of a diabetic retinopathy detector. To visualize the symptom and feature encoded in this descriptor, we propose a GAN based method to synthesize pathological retinal image given the descriptor and a binary vessel segmentation. Besides, with this descriptor, we can arbitrarily manipulate the position and quantity of lesions. As verified by a panel of 5 licensed ophthalmologists, our synthesized images carry the symptoms that are directly related to diabetic retinopathy diagnosis. The panel survey also shows that our generated images is both qualitatively and quantitatively superior to existing methods.
Impact of Dumpsites on the Quality of Soil and Groundwater in Satellite Towns of the Federal Capital Territory, Abuja, Nigeria

Background. Urbanization, industrialization and changes in consumption patterns have compounded the problem of solid waste management in Nigeria. Poor waste management threatens the well-being and health of the local population, particularly those living adjacent to dumpsites. Objectives. An assessment of the impact of dumpsites in a satellite town of the Federal Capital Territory, Abuja, Nigeria was carried out to determine the level of biophysical/chemical parameters (pH, temperature, conductivity, nutrients (calcium and magnesium), heavy metals (lead, chromium, zinc), and microbial burden) on the quality of soil and groundwater and their impact on health and the environment. Methods. Soil and ground water samples were collected in four different dumpsites (Bwari, Gwagwalada, Kuje and Azhatta) with reference samples taken from the Federal Capital Territory, Abuja, and taken to the laboratory for biophysical/chemical analysis using standard methods. Results. The results were compared with the national and World Health Organization (WHO) standard limits for soil and water respectively. Except for zinc, the average concentrations for heavy metals in the soil samples were higher in all four dumpsites than the permissible levels. Soil and water parameters that exceed the standard limits pose significant health and environment risks to nearby residents. Conclusions. There is a need for raising the awareness of residents living close to dumpsites and those who use the well or nearby streams for domestic activities on the need to carry out adequate water treatment prior to its use.
Towards a Definition of Disentangled Representations

How can intelligent agents solve a diverse set of tasks in a data-efficient manner? The disentangled representation learning approach posits that such an agent would benefit from separating out (disentangling) the underlying structure of the world into disjoint parts of its representation. However, there is no generally agreed-upon definition of disentangling, not least because it is unclear how to formalise the notion of world structure beyond toy datasets with a known ground truth generative process. Here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world. In particular, we suggest that those transformations that change only some properties of the underlying world state, while leaving all other properties invariant, are what gives exploitable structure to any kind of data. Similar ideas have already been successfully applied in physics, where the study of symmetry transformations has revolutionised the understanding of the world structure. By connecting symmetry transformations to vector representations using the formalism of group and representation theory we arrive at the first formal definition of disentangled representations. Our new definition is in agreement with many of the current intuitions about disentangling, while also providing principled resolutions to a number of previous points of contention. While this work focuses on formally defining disentangling - as opposed to solving the learning problem - we believe that the shift in perspective to studying data transformations can stimulate the development of better representation learning algorithms.
EGFR Mutation Detection and Its Association With Clinicopathological Characters of Lung Cancer Patients

Background Lung cancer is the most common type of cancer worldwide with an estimation of 1.82 million new cancer cases diagnosed; and it is the leading cause of cancer-related deaths. Epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase identified as being highly expressed in cancer cells including lung cancers. The aim of the study is to determine the EGFR mutation status in non-small cell lung cancer (NSCLC) patients to investigate the association between the EGFR mutation status and clinicopathological characters of patients. Methods The tissue samples of the lung cancer patients were collected bronchoscopically. The EGFR mutations of 70 NSCLC patients were determined by the immunohistochemistry (IHC). Results EGFR mutations were present in 24 cases (34.29%), including 19 (79.13%) cases of exon 19 and five (20.83%) cases of exon 21 mutation. EGFR mutations were frequently associated with adenocarcinoma and non-smoker. Statistically significant association of EGFR mutations with adenocarcinoma subtypes and non-smokers was found (P < 0.05); and no significant association of EGFR mutation with the age of the patient (P = 0.4647) and the stage (P = 0.4578) of the tumor was found. When we compared between these two mutations, no significant association with age (P=0.614) and smoking status (P=0.127) was found in this study. Conclusions EGFR mutations were significantly associated with female sex, non-smoker and adenocarcinoma subtypes. The analysis of EGFR mutation by the IHC method is a potentially useful tool to guide clinicians for personalized treatment of NSCLC patients of adenocarcinoma subtype.
Rank-one matrix estimation: analysis of algorithmic and information theoretic limits by the spatial coupling method

Factorizing low-rank matrices is a problem with many applications in machine learning and statistics, ranging from sparse PCA to community detection and sub-matrix localization. For probabilistic models in the Bayes optimal setting, general expressions for the mutual information have been proposed using powerful heuristic statistical physics computations via the replica and cavity methods, and proven in few specific cases by a variety of methods. Here, we use the spatial coupling methodology developed in the framework of error correcting codes, to rigorously derive the mutual information for the symmetric rank-one case. We characterize the detectability phase transitions in a large set of estimation problems, where we show that there exists a gap between what currently known polynomial algorithms (in particular spectral methods and approximate message-passing) can do and what is expected information theoretically. Moreover, we show that the computational gap vanishes for the proposed spatially coupled model, a promising feature with many possible applications. Our proof technique has an interest on its own and exploits three essential ingredients: the interpolation method first introduced in statistical physics, the analysis of approximate message-passing algorithms first introduced in compressive sensing, and the theory of threshold saturation for spatially coupled systems first developed in coding theory. Our approach is very generic and can be applied to many other open problems in statistical estimation where heuristic statistical physics predictions are available.
Prevalence and Associated Factors of Overweight or Obesity and Abdominal Obesity in Iranian Population: A Population-based Study of Northwestern Iran

Background: This population-based study aimed at investigating the prevalence and associated factors of over-weight /obesity and abdominal obesity in Iran. Methods: The study population consists of 2818 inhabitant of the urban and regional area of East-Azerbaijan, Iran in 2015. The weight, height and waist circumferences were measured and the body mass index (BMI) and conicity index were calculated. The ANCOVA and logistic regression were used for statistical analysis. Results: Prevalence of overweight, obesity and abdominal obesity was 39.6%, 24%, and 76.4% respectively. Women showed the higher prevalence of obesity (32.2%) and abdominal obesity (81.4) than men (obesity: 15.1%; abdominal obesity: 68.6%). Age, marriage and family history of obesity were independent predictors of obesity in the population (P< 0.001). In men and women, nonsmokers (P<0.01) and subjects having more than two kids (P< 0.001) were also more expected to be overweight or obese and abdominally obese respectively. Conclusion: More actions mostly focusing on education and physical activity levels, and changing eating habits are required.
Towards Secure Network Computing Services for Lightweight Clients Using Blockchain

The emerging network computing technologies have significantly extended the abilities of the resource-constrained IoT devices through the network-based service sharing techniques. However, such a flexible and scalable service provisioning paradigm brings increased security risks to terminals due to the untrustworthy exogenous service codes loading from the open network. Many existing security approaches are unsuitable for IoT environments due to the high difficulty of maintenance or the dependencies upon extra resources like specific hardware. Fortunately, the rise of blockchain technology has facilitated the development of service sharing methods and, at the same time, it appears a viable solution to numerous security problems. In this paper, we propose a novel blockchain-based secure service provisioning mechanism for protecting lightweight clients from insecure services in network computing scenarios. We introduce the blockchain to maintain all the validity states of the off-chain services and edge service providers for the IoT terminals to help them get rid of untrusted or discarded services through provider identification and service verification. In addition, we take advantage of smart contracts which can be triggered by the lightweight clients to help them check the validities of service providers and service codes according to the on-chain transactions, thereby reducing the direct overhead on the IoT devices. Moreover, the adoptions of the consortium blockchain and the proof of authority consensus mechanism also help to achieve a high throughput. The theoretical security analysis and evaluation results show that our approach helps the lightweight clients get rid of untrusted edge service providers and insecure services effectively with acceptable latency and affordable costs.
Video Action Transformer Network

We introduce the Action Transformer model for recognizing and localizing human actions in video clips. We repurpose a Transformer-style architecture to aggregate features from the spatiotemporal context around the person whose actions we are trying to classify. We show that by using high-resolution, person-specific, class-agnostic queries, the model spontaneously learns to track individual people and to pick up on semantic context from the actions of others. Additionally its attention mechanism learns to emphasize hands and faces, which are often crucial to discriminate an action – all without explicit supervision other than boxes and class labels. We train and test our Action Transformer network on the Atomic Visual Actions (AVA) dataset, outperforming the state-of-the-art by a significant margin using only raw RGB frames as input.
Relative Entropy Regularized Policy Iteration

We present an off-policy actor-critic algorithm for Reinforcement Learning (RL) that combines ideas from gradient-free optimization via stochastic search with learned action-value function. The result is a simple procedure consisting of three steps: i) policy evaluation by estimating a parametric action-value function; ii) policy improvement via the estimation of a local non-parametric policy; and iii) generalization by fitting a parametric policy. Each step can be implemented in different ways, giving rise to several algorithm variants. Our algorithm draws on connections to existing literature on black-box optimization and 'RL as an inference' and it can be seen either as an extension of the Maximum a Posteriori Policy Optimisation algorithm (MPO) [Abdolmaleki et al., 2018a], or as an extension of Trust Region Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) [Abdolmaleki et al., 2017b; Hansen et al., 1997] to a policy iteration scheme. Our comparison on 31 continuous control tasks from parkour suite [Heess et al., 2017], DeepMind control suite [Tassa et al., 2018] and OpenAI Gym [Brockman et al., 2016] with diverse properties, limited amount of compute and a single set of hyperparameters, demonstrate the effectiveness of our method and the state of art results. Videos, summarizing results, can be found at goo.gl/HtvJKR .
Quantifying Generalization in Reinforcement Learning

In this paper, we investigate the problem of overfitting in deep reinforcement learning. Among the most common benchmarks in RL, it is customary to use the same environments for both training and testing. This practice offers relatively little insight into an agent's ability to generalize. We address this issue by using procedurally generated environments to construct distinct training and test sets. Most notably, we introduce a new environment called CoinRun, designed as a benchmark for generalization in RL. Using CoinRun, we find that agents overfit to surprisingly large training sets. We then show that deeper convolutional architectures improve generalization, as do methods traditionally found in supervised learning, including L2 regularization, dropout, data augmentation and batch normalization.
Improving Cognition to Increase Treatment Efficacy in Schizophrenia: Effects of Metabolic Syndrome on Cognitive Remediation's Outcome

Cognitive impairment, typically more severe in treatment resistant patients, is considered a hallmark of schizophrenia and the prime driver of functional disability. Recent evidence suggests that metabolic syndrome may contribute to cognitive deficits in schizophrenia, possibly through shared underlying mechanisms. However, results are still contradictory and no study has so far examined the influence of metabolic syndrome on cognitive outcome after cognitive remediation therapy (CRT). Based on these premises, this study aims to investigate the relationship between metabolic syndrome and cognition, specifically considering cognitive outcome after treatment. Secondary objectives include the analysis of the association between cognitive impairment and psychopathological status and, in a subgroup of patients, the evaluation of the effect of Sterol Regulatory Element Binding Transcription Factor 1 (SREBF-1) rs11868035 genetic polymorphism, previously associated with metabolic alterations, on both cognition and metabolic syndrome. One-hundred seventy-two outpatients with schizophrenia were assessed for metabolic parameters and neurocognitive measures and 138 patients, who completed CRT, were re-evaluated for cognition. A subsample of 51 patients was also genotyped for rs11868035 from peripheral blood sample. Results show a negative impact of metabolic syndrome on executive functions and global cognitive outcome after CRT. Data also revealed a significant effect of SREBF-1 polymorphism, with a higher prevalence of metabolic syndrome and worse processing speed performance among G/G homozygous subjects, compared the A allele carriers. Overall these findings support the hypothesis that metabolic alterations may hamper the capacity to restore cognitive deficits, as well as they highlight the need to further explore possible converging mechanisms underlying both cognitive and metabolic dysfunction. At the clinical level, results point to the importance of a comprehensive assessment including the metabolic status of patients and of individualized strategies addressing metabolic dysfunction in order to potentiate treatment outcome in schizophrenia.
Barriers between community screening for visual problems and treatments in a tertiary center

ABSTRACT OBJECTIVE To evaluate the effectiveness of mobile ophthalmic unit screenings and to investigate barriers between community care and resolution of the problem at a tertiary center. METHODS This prospective study evaluated a convenience sample from 10 municipalities in São Paulo State, Brazil. Patients were assessed in the municipality by a mobile ophthalmic unit and underwent a complete ophthalmic consultation. Patients were referred as warranted to a tertiary hospital. RESULTS The mobile ophthalmic unit screened 1,928 individuals and 714 (37%) were referred. The mean age of the referred patients was 57.12 (SD = 19.5) years with best corrected visual acuity of 0.37 (SD = 0.36) logMAR. Forty-seven (6.6%) patients were blind and 185 (26.5%) were visually impaired. Cataracts (44.7%) and pterygium (14.7%) accounted for most referrals. Of those referred, 67.1% presented to the tertiary center. The diagnosis by the mobile ophthalmic unit corresponded to the one by the tertiary center in 88.5% of the cases. There were a significantly higher number of blind and visually impaired persons among those who presented to the hospital. There was a significantly greater attendance among patients living in more distant municipalities from the reference center with a higher number of inhabitants and a greater number of ophthalmologists in the cities of origin (p < 0.05, all comparisons). Complete treatment was performed in 65.6% of patients, and loss to follow-up was the main cause of incomplete treatment in 50.7% of patients. A total of 313 cataract surgeries were performed, which reduced the number of blind patients from 20 to 2 and of visually impaired individuals from 87 to 2 (p < 0.001). CONCLUSIONS Only 37% of the patients assessed by a mobile ophthalmic unit required referral to a tertiary hospital. Among the referred patients, 67.1% presented to the hospital, and complete resolution after treatment was approximately 65.5%. There was a significant improvement in visual acuity and a reduction in the prevalence of blindness and visual impairment postoperatively.