Miracle-dz/UniFAR-FTUs huggingface dataset:
Human-like generalization in a machine through predicate learning

Humans readily generalize, applying prior knowledge to novel situations and stimuli. Advances in machine learning and artificial intelligence have begun to approximate and even surpass human performance, but machine systems reliably struggle to generalize information to untrained situations. We describe a neural network model that is trained to play one video game (Breakout) and demonstrates one-shot generalization to a new game (Pong). The model generalizes by learning representations that are functionally and formally symbolic from training data, without feedback, and without requiring that structured representations be specified a priori. The model uses unsupervised comparison to discover which characteristics of the input are invariant, and to learn relational predicates; it then applies these predicates to arguments in a symbolic fashion, using oscillatory regularities in network firing to dynamically bind predicates to arguments. We argue that models of human cognition must account for far-reaching and flexible generalization, and that in order to do so, models must be able to discover symbolic representations from unstructured data, a process we call predicate learning. Only then can models begin to adequately explain where human-like representations come from, why human cognition is the way it is, and why it continues to differ from machine intelligence in crucial ways.
Entropy Generation on Nanofluid Thin Film Flow of Eyring–Powell Fluid with Thermal Radiation and MHD Effect on an Unsteady Porous Stretching Sheet

This research paper investigates entropy generation analysis on two-dimensional nanofluid film flow of Eyring–Powell fluid with heat amd mass transmission over an unsteady porous stretching sheet in the existence of uniform magnetic field (MHD). The flow of liquid films are taken under the impact of thermal radiation. The basic time dependent equations of heat transfer, momentum and mass transfer are modeled and converted to a system of differential equations by employing appropriate similarity transformation with unsteady dimensionless parameters. Entropy analysis is the main focus in this work and the impact of physical parameters on the entropy profile are discussed in detail. The influence of thermophoresis and Brownian motion has been taken in the nanofluids model. An optima approach has been applied to acquire the solution of modeled problem. The convergence of the HAM (Homotopy Analysis Method) has been presented numerically. The disparity of the Nusslet number, Skin friction, Sherwood number and their influence on the velocity, heat and concentration fields has been scrutinized. Moreover, for comprehension, the physical presentation of the embedded parameters are explored analytically for entropy generation and discussed.
Wikipedia Culture Gap: Quantifying Content Imbalances Across 40 Language Editions

The online encyclopedia Wikipedia is the largest general information repository created through collaborative efforts from all over the globe. Despite the project’s goal being to achieve the sum of human knowledge, there are strong content imbalances across the language editions. In order to quantify and investigate these imbalances, we study the impact of cultural context in 40 language editions. To this purpose, we developed a computational method to identify articles that can be related to the editors' cultural context associated to each Wikipedia language edition. We employed a combination of strategies taking into account geolocated articles, specific keywords and categories, as well as links between articles. We verified the method’s quality with manual assessment and found an average precision of 0.92 and an average recall of 0.95. The results show that about a quarter of each Wikipedia language edition is dedicated to represent the corresponding cultural context. Although a considerable part of this content was created during the first years of the project, its creation is sustained over time. An analysis of cross-language coverage of this content shows that most of it is unique in its original language, and reveals special links between cultural contexts; at the same time, it highlights gaps where the encyclopaedia could extend its content. The approach and findings presented in this study can help to foster participation and inter-cultural enrichment of Wikipedias. The datasets produced are made available for further research.
Evidential Deep Learning to Quantify Classification Uncertainty

Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems. However, as the standard approach is to train the network to minimize a prediction loss, the resultant model remains ignorant to its prediction confidence. Orthogonally to Bayesian neural nets that indirectly infer prediction uncertainty through weight uncertainties, we propose explicit modeling of the same using the theory of subjective logic. By placing a Dirichlet distribution on the class probabilities, we treat predictions of a neural net as subjective opinions and learn the function that collects the evidence leading to these opinions by a deterministic neural net from data. The resultant predictor for a multi-class classification problem is another Dirichlet distribution whose parameters are set by the continuous output of a neural net. We provide a preliminary analysis on how the peculiarities of our new loss function drive improved uncertainty estimation. We observe that our method achieves unprecedented success on detection of out-of-distribution queries and endurance against adversarial perturbations.
Relational Deep Reinforcement Learning

We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy. Our results show that in a novel navigation and planning task called Box-World, our agent finds interpretable solutions that improve upon baselines in terms of sample complexity, ability to generalize to more complex scenes than experienced during training, and overall performance. In the StarCraft II Learning Environment, our agent achieves state-of-the-art performance on six mini-games -- surpassing human grandmaster performance on four. By considering architectural inductive biases, our work opens new directions for overcoming important, but stubborn, challenges in deep RL.
An Analysis of Pre-installed Android Software

The open-source nature of the Android OS makes it possible for manufacturers to ship custom versions of the OS along with a set of pre-installed apps, often for product differentiation. Some device vendors have recently come under scrutiny for potentially invasive private data collection practices and other potentially harmful or unwanted behavior of the preinstalled apps on their devices. Yet, the landscape of preinstalled software in Android has largely remained unexplored, particularly in terms of the security and privacy implications of such customizations. In this paper, we present the first large- scale study of pre-installed software on Android devices from more than 200 vendors. Our work relies on a large dataset of real-world Android firmware acquired worldwide using crowd-sourcing methods. This allows us to answer questions related to the stakeholders involved in the supply chain, from device manufacturers and mobile network operators to third- party organizations like advertising and tracking services, and social network platforms. Our study allows us to also uncover relationships between these actors, which seem to revolve primarily around advertising and data-driven services. Overall, the supply chain around Android's open source model lacks transparency and has facilitated potentially harmful behaviors and backdoored access to sensitive data and services without user consent or awareness. We conclude the paper with recommendations to improve transparency, attribution, and accountability in the Android ecosystem.
Molecular Characteristics of Community-Associated Staphylococcus aureus Isolates From Pediatric Patients With Bloodstream Infections Between 2012 and 2017 in Shanghai, China

Staphylococcus aureus is known as an invasive human pathogen, resulting in significant morbidity and mortality worldwide; however, information on community-associated S. aureus (CA-SA) from bloodstream infections (BSI) in children in China remains scarce. This study aimed to investigate the molecular characteristics of 78 CA-SA isolates recovered from pediatric patients with BSI between 2012 and 2017 in Shanghai. All isolates including 51 (65.4%) methicillin-susceptible S. aureus (MSSA) and 27 (34.6%) methicillin-resistant S. aureus (MRSA) isolates were characterized based on antimicrobial resistance, virulence genes, multilocus sequence typing (MLST), spa, and SCCmec typing. A total of 18 distinct sequence types (STs) and 44 spa types were identified. ST188 and ST7 were the predominant MSSA clones and ST59-MRSA-SCCmecIV/V was the most common MRSA clone. Spa t189 (9.0%, 7/78) was the most common spa type. SCCmec types IV and V were observed at frequencies of 59.3 and 40.7%, respectively. Notably, 40 (51.3%) S. aureus BSI strains were multidrug resistant (MDR), and these were mostly resistant to penicillin, erythromycin, and clindamycin. MRSA strains were associated with substantially higher rates of resistance to multiple antibiotics than MSSA strains. Fifty (64.1%, 50/78) isolates, including 19 (70.3%) MRSA isolates, harbored ≥ 10 tested virulence genes, as evaluated in this study. Ten (37.0%) MRSA isolates and four (7.8%) MSSA isolates harbored the gene encoding Panton–Valentine leukocidin (PVL). Virulence genes analysis showed diversity in different clones; the seb-sek-seq genes were present in all ST59 strains, whereas the seg-sei-sem-sen-seo genes were present in different clones including ST5, ST20, ST22, ST25, ST26, ST30, ST121, and ST487 strains. In conclusion, this study revealed that community-associated S. aureus strains from BSI in children demonstrated considerable genetic diversity, and identified major genotypes of CA-MRSA and CA-MSSA, with a high prevalence of CA-MRSA. Furthermore, major genotypes were frequently associated with specific antimicrobial resistance and toxin gene profiles. Understanding the molecular characteristics of those strains might provide further insights regarding the spread of BSI S. aureus among children between communities in China.
Melanoma response to anti-PD-L1 immunotherapy requires JAK1 signaling, but not JAK2

ABSTRACT Immunotherapies targeting programmed cell death protein 1 (PD-1) or its ligand, programmed cell death ligand 1 (PD-L1), dramatically improve the survival of melanoma patients. However, only ∼40% of treated patients demonstrate a clinical response to single-agent anti-PD-1 therapy. An intact tumor response to type-II interferon (i.e. IFN-γ) correlates with response to anti-PD-1, and patients with de novo or acquired resistance may harbor loss-of-function alterations in the JAK/STAT pathway, which lies downstream of the interferon gamma receptor (IFNGR1/2). In this study, we dissected the specific roles of individual JAK/STAT pathway members on the IFN-γ response, and identified JAK1 as the primary mediator of JAK/STAT signaling associated with IFN-γ-induced expression of antigen-presenting molecules MHC-I and MHC-II, as well as PD-L1 and the cytostatic response to IFN-γ. In contrast to the crucial role of JAK1, JAK2 was largely dispensable in mediating most IFN-γ effects. In a mouse melanoma model, inhibition of JAK1/2 in combination with anti-PD-L1 therapy partially blocked anti-tumor immunologic responses, while selective JAK2 inhibition appeared to augment therapy. Amplification of JAK/STAT signaling in tumor cells through genetic inhibition of the negative regulator PTPN2 potentiated IFN-γ response in vitro and in vivo, and may be a target to enhance immunotherapy efficacy.
Hierarchical Cosine Similarity Entropy for Feature Extraction of Ship-Radiated Noise

The classification performance of passive sonar can be improved by extracting the features of ship-radiated noise. Traditional feature extraction methods neglect the nonlinear features in ship-radiated noise, such as entropy. The multiscale sample entropy (MSE) algorithm has been widely used for quantifying the entropy of a signal, but there are still some limitations. To remedy this, the hierarchical cosine similarity entropy (HCSE) is proposed in this paper. Firstly, the hierarchical decomposition is utilized to decompose a time series into some subsequences. Then, the sample entropy (SE) is modified by utilizing Shannon entropy rather than conditional entropy and employing angular distance instead of Chebyshev distance. Finally, the complexity of each subsequence is quantified by the modified SE. Simulation results show that the HCSE method overcomes some limitations in MSE. For example, undefined entropy is not likely to occur in HCSE, and it is more suitable for short time series. Compared with MSE, the experimental results illustrate that the classification accuracy of real ship-radiated noise is significantly improved from 75% to 95.63% by using HCSE. Consequently, the proposed HCSE can be applied in practical applications.
Informational Power and Perceived Collective Benefit Affecting the Users’ Preference for a Mobile Technology: Evidences From a Survey Study

This study takes place from the idea that the personal usage of mobile technologies can bring positive outcomes to the user and to their society in an indirect way. Technologies studied in this work are defined as persuasive technologies (Fogg, 1997) because they are intentionally designed to modify the users’ attitude or behavior. This research is aimed to evaluate if the intention to use the application can be influenced by positive attitudes toward technology, by the persuasive power of the application and by the perceived fun. Participants (N = 118; M = 55; F = 63; mean age = 27.4; range age = 15–69) filled in an online questionnaire that was partly based on the Media and Technology Usage and Attitude Scale (MTUAS – Rosen et al., 2013). An additional eight items were added to the scale, aimed at evaluating participants’ technophobia, technophilia, perceived technology pervasiveness and perceived persuasive power of technology. By using linear regression analysis, it was found that the application’s informational power and the perceived entertainment positively influenced the usage intention. Another interesting result, obtained through ANOVA, concerns a generational difference: baby boomers tended to trust more the fact that the single individual action through the application can have an effective impact on the environment. These results represent a basis for future in-depth investigations about socially relevant use of the ICT.
The Use of Eye-Movement Desensitization Reprocessing (EMDR) Therapy in Treating Post-traumatic Stress Disorder—A Systematic Narrative Review

Aim: There is an extensive body of research examining the efficacy of Eye-Movement Desensitization Reprocessing (EMDR) therapy in treatment of Post-traumatic Stress Disorder (PTSD). This systematic narrative review aimed to systematically, and narratively, review robust evidence from Randomized-Controlled Trials examining the efficacy of EMDR therapy. Method: Eight databases were searched to identify studies relevant to the study aim. Two separate systematic searches of published, peer-reviewed evidence were carried out, considering relevant studies published prior to April 2017. After exclusion of all irrelevant, or non-robust, studies, a total of two meta-analyses and four Randomized-Controlled Trials were included for review. Results: Data from meta-analyses and Randomized-Controlled Trials included in this review evidence the efficacy of EMDR therapy as a treatment for PTSD. Specifically, EMDR therapy improved PTSD diagnosis, reduced PTSD symptoms, and reduced other trauma-related symptoms. EMDR therapy was evidenced as being more effective than other trauma treatments, and was shown to be an effective therapy when delivered with different cultures. However, limitations to the current evidence exist, and much current evidence relies on small sample sizes and provides limited follow-up data. Conclusions: This systematic narrative review contributes to the current evidence base, and provides recommendations for practice and future research. This review highlights the need for additional research to further examine the use of EMDR therapy for PTSD in a range of clinical populations and cultural contexts.
Backdrop: Stochastic Backpropagation

We introduce backdrop, a flexible and simple-to-implement method, intuitively described as dropout acting only along the backpropagation pipeline. Backdrop is implemented via one or more masking layers which are inserted at specific points along the network. Each backdrop masking layer acts as the identity in the forward pass, but randomly masks parts of the backward gradient propagation. Intuitively, inserting a backdrop layer after any convolutional layer leads to stochastic gradients corresponding to features of that scale. Therefore, backdrop is well suited for problems in which the data have a multi-scale, hierarchical structure. Backdrop can also be applied to problems with non-decomposable loss functions where standard SGD methods are not well suited. We perform a number of experiments and demonstrate that backdrop leads to significant improvements in generalization.
PD-1/PD-L1 pathway: an adaptive immune resistance mechanism to immunogenic chemotherapy in colorectal cancer

ABSTRACT Background: Chemotherapy is currently evaluated in order to enhance the efficacy of immune checkpoint blockade (ICB) therapy in colorectal cancer. However, the mechanisms by which these drugs could synergize with ICB remains unclear. The impact of chemotherapy on the PD-1/PD-L1 pathway and the resulting anticancer immune responses was assessed in two mouse models of colorectal cancer and validated in tumor samples from metastatic colorectal cancer patients that received neoadjuvant treatment. We demonstrated that 5-Fluorouracil plus Oxaliplatin (Folfox) drove complete tumor cure in mice when combined to anti-PD-1 treatment, while each monotherapy failed. This synergistic effect relies on the ability of Folfox to induce tumor infiltration by activated PD-1+ CD8 T cells in a T-bet dependent manner. This effect was concomitantly associated to the expression of PD-L1 on tumor cells driven by IFN-γ secreted by PD-1+ CD8 T cells, indicating that Folfox triggers tumor adaptive immune resistance. Finally, we observed an induction of PD-L1 expression and high CD8 T cell infiltration in the tumor microenvironment of colorectal cancer patients treated by Folfox regimen. Our study delineates a molecular pathway involved in Folfox-induced adaptive immune resistance in colorectal cancer. The results strongly support the use of immune checkpoint blockade therapy in combination with chemotherapies like Folfox.
Nursing professionals' attitudes toward biostatistics: an international web-based survey

Objective: The present study, an international web-based survey, was focused on four aims: to obtain nursing professional’s self-reported statistical knowledge levels and how this knowledge varies by research area, to investigate and specify when biostatistics courses should be taught in nursing education and to identify the key statistical methods relevant to nursing education. Methods: A total of 448 nursing professionals from five continents and 52 countries participated in our study. For the data comparison, the Kruskal-Wallis test, the Mann-Whitney U test, the Pearson Chi Square and Fisher-Freeman-Halton tests were applied. Results: The results indicate that while nursing professionals place an emphasis on biostatistics education, the majority state that biostatistics education should be taken both at the undergraduate and postgraduate level and the participants also believe that taking a biostatistics course is useful for their occupation. A biostatistics education should also emphasize the necessity of consulting to a biostatistician when planning a study. Conclusion: Our study provides information regarding self-reported levels of biostatistical knowledge of nursing professionals by research area and academic position, and provides guidance regarding the ideal semester for administering a biostatistics course.
Computational Analysis of Multi-Rotor Flows

Interactional aerodynamics of multi-rotor flows has been studied for a quadcopter representing a generic quad tilt-rotor aircraft in hover. The objective of the present study is to investigate the effects of the separation distances between rotors, and also fuselage and wings on the performance and efficiency of multirotor systems. Three-dimensional unsteady Navier-Stokes equations are solved using a spatially 5th order accurate scheme, dual-time stepping, and the Detached Eddy Simulation turbulence model. The results show that the separation distances as well as the wings have significant effects on the vertical forces of quadroror systems in hover. Understanding interactions in multi-rotor flows would help improve the design of next generation multi-rotor drones.
Accelerating CNN inference on FPGAs: A Survey

Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of problems, ranging from speech recognition to image classification and segmentation. The large amount of processing required by CNNs calls for dedicated and tailored hardware support methods. Moreover, CNN workloads have a streaming nature, well suited to reconfigurable hardware architectures such as FPGAs. The amount and diversity of research on the subject of CNN FPGA acceleration within the last 3 years demonstrates the tremendous industrial and academic interest. This paper presents a state-of-the-art of CNN inference accelerators over FPGAs. The computational workloads, their parallelism and the involved memory accesses are analyzed. At the level of neurons, optimizations of the convolutional and fully connected layers are explained and the performances of the different methods compared. At the network level, approximate computing and datapath optimization methods are covered and state-of-the-art approaches compared. The methods and tools investigated in this survey represent the recent trends in FPGA CNN inference accelerators and will fuel the future advances on effcient hardware deep learning.
Relational recurrent neural networks

Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods. It is unclear, however, whether they also have an ability to perform complex relational reasoning with the information they remember. Here, we first confirm our intuitions that standard memory architectures may struggle at tasks that heavily involve an understanding of the ways in which entities are connected -- i.e., tasks involving relational reasoning. We then improve upon these deficits by using a new memory module -- a \textit{Relational Memory Core} (RMC) -- which employs multi-head dot product attention to allow memories to interact. Finally, we test the RMC on a suite of tasks that may profit from more capable relational reasoning across sequential information, and show large gains in RL domains (e.g. Mini PacMan), program evaluation, and language modeling, achieving state-of-the-art results on the WikiText-103, Project Gutenberg, and GigaWord datasets.