Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory protection in associative learning: Humans maintain certain beliefs in a manner that violates prediction error.

Three experiments were conducted to investigate a possible role for certainty in human causal learning. In these experiments, human participants were initially trained with a set of cues, each of which was followed by the presence or absence of an outcome. In a subsequent training stage, 2 of these cues were trained in a causal compound, and the change in associative strength for each of the cues was compared, using a procedure based on Rescorla (2001). In each experiment, the cues differed in both their causal certainty (on the part of participants) and size of their prediction error (with respect to the outcome). The cue with the larger prediction error was always the cue with the more certain causal status. According to established prediction error models (Bush & Mosteller, 1951; Rescorla, 2001; Rescorla & Wagner, 1972), a larger prediction error should result in a greater updating of associative strength. However, the opposite was observed, as participants always learned more about the cue with the smaller prediction error. A plausible explanation is that participants engaged in a form of theory protection, in which they were resistant to updating their existing beliefs about cues with a certain causal status. Instead, participants appeared to attribute outcomes to cues with a comparatively uncertain causal status, in an apparent violation of prediction error. The potential role of attentional processes (Mackintosh, 1975; Pearce & Hall, 1980) in explaining these results is also discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Secure TDD MIMO Networks Against Training Sequence Based Eavesdropping Attack

Multi-User MIMO (MU-MIMO) has attracted much attention due to its significant advantage of increasing the utilization ratio of wireless channels. However, Frequency-Division Duplex (FDD) systems are vulnerable to eavesdropping, since the explicit CSI feedback can be manipulated. In this paper, we show that Time-Division Duplex (TDD) systems are insecure as well. In particular, we show that it is possible to eavesdrop on other users’ downloads by tuning training sequences. In order to defend MU-MIMO against such threats, we propose a secure CSI estimation scheme, which can provide correct estimates of CSI when adversarial users are in presence. We prove that our scheme is secure against training sequence based eavesdropping attack. We have implemented our scheme for TDD MU-MIMO systems and performed a series of experiments. Results demonstrate that our secure CSI estimation scheme is highly effective in protecting TDD MIMO networks against eavesdropping attack. Furthermore, we extend our scheme to support massive MU-MIMO networks, with a carefully redesigned uplink protocol and optimized power allocation to achieve higher spectral efficiency. To be more practical, we also take mismatch channel issue into our consideration. An enhancement scheme is proposed and we show that our scheme with enhancement is secure and correct under mismatch channel.
Generating Modern Poetry Automatically in Finnish

We present a novel approach for generating poetry automatically for the morphologically rich Finnish language by using a genetic algorithm. The approach improves the state of the art of the previous Finnish poem generators by introducing a higher degree of freedom in terms of structural creativity. Our approach is evaluated and described within the paradigm of computational creativity, where the fitness functions of the genetic algorithm are assimilated with the notion of aesthetics. The output is considered to be a poem 81.5% of the time by human evaluators.
General E(2)-Equivariant Steerable CNNs

The big empirical success of group equivariant networks has led in recent years to the sprouting of a great variety of equivariant network architectures. A particular focus has thereby been on rotation and reflection equivariant CNNs for planar images. Here we give a general description of E(2)-equivariant convolutions in the framework of Steerable CNNs. The theory of Steerable CNNs thereby yields constraints on the convolution kernels which depend on group representations describing the transformation laws of feature spaces. We show that these constraints for arbitrary group representations can be reduced to constraints under irreducible representations. A general solution of the kernel space constraint is given for arbitrary representations of the Euclidean group E(2) and its subgroups. We implement a wide range of previously proposed and entirely new equivariant network architectures and extensively compare their performances. E(2)-steerable convolutions are further shown to yield remarkable gains on CIFAR-10, CIFAR-100 and STL-10 when used as drop in replacement for non-equivariant convolutions.
AB Initio Tomography With Object Heterogeneity and Unknown Viewing Paramete

In this paper, we present an algorithm to automatically construct all the conformations of a heterogeneous planar object from their tomographic projections at random unknown view angles. Our statistically motivated approach can reveal and analyze the heterogeneity in the projection dataset and segregate the projections belonging to different structures without requiring prior structural information or templates, expert human intervention or even the knowledge of the number of conformations present in the sample. Even in the presence of high noise variance (low SNR) and a large number of conformations, our algorithm can estimate the structures of each conformation to a high degree of accuracy. We demonstrate the broad applicability of our algorithm by evaluating its performance on synthetic 2D datasets of well-known protein complexes such as Lipase under varying levels of noise and different number of conformations.
Electronic Word of Mouth engagement in social commerce platforms: An empirical study

The aim of this study is to investigate the factors that encourage customer engagement with Electronic Word of Mouth (eWOM) from the perspective of information characteristics, consumer behaviour, consumer motives, technological and social factors. Drawing from the Elaboration Likelihood Model (ELM), Theory of Reasoned Action (TRA) and Social Support Theory (SST) we propose a new model of eWOM engagement in s-commerce. This model was assessed using structural equation modeling (SEM) technique based on a survey of 218 s-commerce users in Nigeria. The findings confirmed that eWOM engagement is strongly determined by information credibility, information quality, social support, innovativeness, altruism, self-enhancement and sense of belonging. Additionally, using the Importance-Performance Map Analysis (IPMA), this study identifies information credibility, sense of belonging, attitude towards eWOM and social support as the most important factors that should be giving priority by managers in order to encourage consumer eWOM engagement in s-commerce platforms. Implications of the study and suggestions for future research are presented.
Resilient RMPC for Cyber-Physical Systems With Polytopic Uncertainties and State Saturation Under TOD Scheduling: An ADT Approach

This article presents a resilient robust model predictive control (RMPC) strategy for cyber-physical systems (CPSs) with polytopic uncertainties and state saturation nonlinearities under the try-once-discard (TOD) scheduling. To reduce the transmission burden, a so-called TOD protocol is adopted to schedule the nodes. The objective of the addressed problem is to design a set of resilient RMPC controllers such that, in the simultaneous presence of polytopic uncertainties and state saturations under the TOD protocol, the resilience, robustness, and exponential stability are guaranteed for the underlying CPSs. With the help of TOD-dependent Lyapunov-like function, and the average dwell-time (ADT) approach, sufficient conditions are obtained to guarantee the recursive feasibility of the proposed resilient RMPC approach and the exponential stability of the closed-loop system. Finally, two examples including a high-purity distillation process and a numerical simulation are used to demonstrate the effectiveness of the proposed methods.
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods

The EM algorithm is one of the most popular algorithm for inference in latent data models. The original formulation of the EM algorithm does not scale to large data set, because the whole data set is required at each iteration of the algorithm. To alleviate this problem, Neal and Hinton [1998] have proposed an incremental version of the EM (iEM) in which at each iteration the conditional expectation of the latent data (E-step) is updated only for a mini-batch of observations. Another approach has been proposed by Cappe and Moulines [2009] in which the E-step is replaced by a stochastic approximation step, closely related to stochastic gradient. In this paper, we analyze incremental and stochastic version of the EM algorithm as well as the variance reduced-version of [Chen et al., 2018] in a common unifying framework. We also introduce a new version incremental version, inspired by the SAGA algorithm by Defazio et al. [2014]. We establish non-asymptotic convergence bounds for global convergence. Numerical applications are presented in this article to illustrate our findings.
A mixed-methods investigation of the extent to which routinely collected information can help evaluate the implementaion of screening and brief alcohol interventions in primary health care

Aims: The aim of the study was to assess the cumulative evidence on the effectiveness of brief alcohol interventions in primary healthcare in order to highlight key knowledge gaps for further research. Methods: An overview of systematic reviews and meta-analyses of the effectiveness of brief alcohol intervention in primary healthcare published between 2002 and 2012. Findings: Twenty-four systematic reviews met the eligibility criteria (covering a total of 56 randomized controlled trials reported across 80 papers). Across the included studies, it was consistently reported that brief intervention was effective for addressing hazardous and harmful drinking in primary healthcare, particularly in middle-aged, male drinkers. Evidence gaps included: brief intervention effectiveness in key groups (women, older and younger drinkers, minority ethnic groups, dependent/co-morbid drinkers and those living in transitional and developing countries); and the optimum brief intervention length and frequency to maintain longer-term effectiveness. Conclusion: This overview highlights the large volume of primarily positive evidence supporting brief alcohol intervention effects as well as some unanswered questions with regards to the effectiveness of brief alcohol intervention across different cultural settings and in specific population groups, and in respect of the optimum content of brief interventions that might benefit from further research.
The genetic basis for response to the Ketogenic diet in drug-resistant epilepsy

The Ketogenic diet (KD) is an alternative treatment option for people with drug-resistant epilepsy. It can reduce seizure frequency, but it is resource-intensive and may cause adverse side effects. Predictors of response – which, in the absence of specific metabolic disorders, are unknown – would improve patient selection and may enhance understanding of how the KD exerts its antiseizure effect. This project is concerned with identifying possible genetic markers of response to the KD. DNA was extracted from capillary blood taken from individuals who were following the KD for their epilepsy, or who had done so in the past. Individuals were classed as responders if they achieved ≥50 seizure reduction. Response was classified at various follow-up points, as well as a summary of response over time. Association studies were conducted using candidate gene (KCNJ11 and BAD) sequencing, genome-wide single nucleotide polymorphism array, and whole exome sequencing data to determine whether there was an over-representation of specific gene variants in KD responders, compared to non-responders. Common variation in KCNJ11 and BAD was not significantly associated with KD response. rs12204701 reached significance in the array-based genome-wide association study including common variants, with 3-month diet response as the phenotype. No significant results were obtained when summary diet response was taken as the phenotype. Using the gene-based c-alpha test with the exome sequencing data, including all exonic and splicing variants, ANKRD36C reached significance; using a pathway-based count of case-unique alleles test, the ‘ERBB1 Internalisation’ pathway reached significance. No further significant results were obtained from the exome sequencing data when using other gene- and pathway-based tests or when variants were further filtered according to predicted functional consequence. Other genes with large differences in responder/non-responder minor or alternative allele counts are also of interest. It is unknown how these may contribute to variability in KD response. Some common themes were identified amongst the genes and pathways of significance and suggestive significance: cell cycling, apoptosis, glucose homeostasis, neurological processes and triglyceride biosynthesis. It is biologically plausible that these processes influence KD response, although it is likely that many genes play a role. A larger sample size is needed in order to improve power to detect genotypic-phenotypic associations and increase confidence in the importance of the genes of interest.
Cannygan: Edge-Preserving Image Translation with Disentangled Features

missing texture and edge information. In this paper, we proposed a framework to translate images while preserving more realistic textures and details. To this end, we disentangle the samples into shared content space and domain-specific style domain. Then, according to the blurred outlines and textures in the source domain, we introduce the classic canny edge detection algorithm to encode the boundary and edge information in the content latent space. We test the proposed method in the thermal to visible image translation scenario and the experimental results demonstrate that the proposed method outperforms several other state-of-the-art models.
Flexible information routing in neural populations through stochastic comodulation

Humans and animals are capable of flexibly switching between a multitude of tasks, each requiring rapid, sensory-informed decision making. Incoming stimuli are processed by a hierarchy of neural circuits consisting of millions of neurons with diverse feature selectivity. At any given moment, only a small subset of these carry task-relevant information. In principle, downstream processing stages could identify the relevant neurons through supervised learning, but this would require many example trials. Such extensive learning periods are inconsistent with the observed flexibility of humans or animals, who can adjust to changes in task parameters or structure almost immediately. Here, we propose a novel solution based on functionally-targeted stochastic modulation. It has been observed that trial-to-trial neural activity is modulated by a shared, low-dimensional, stochastic signal that introduces task-irrelevant noise. Counter-intuitively this noise is preferentially targeted towards task-informative neurons, corrupting the encoded signal. However, we hypothesize that this modulation offers a solution to the identification problem, labeling task-informative neurons so as to facilitate decoding. We simulate an encoding population of spiking neurons whose rates are modulated by a shared stochastic signal, and show that a linear decoder with readout weights approximating neuron-specific modulation strength can achieve near-optimal accuracy. Such a decoder allows fast and flexible task-dependent information routing without relying on hardwired knowledge of the task-informative neurons (as in maximum likelihood) or unrealistically many supervised training trials (as in regression).
Towards Unified Aesthetics and Emotion Prediction in Images

Aesthetics assessment and emotion recognition are two fundamental problems in user perception understanding. While the two tasks are correlated and mutually beneficial, they are usually solved separately in existing studies. In this paper, we resort to multi-task learning to deal with aesthetics assessment and emotion recognition for images in a unified framework. Towards this goal, we extend a large scale emotion dataset by further manually rating the aesthetic qualities of images. To our best knowledge, the new dataset is the first collection of images that are associated with both aesthetic and emotional labels. Besides, we present a novel Aesthetics-Emotion hybrid Network (AENet) for multi-task learning on aesthetics assessment and emotion recognition. Task-specific and shared features have been explicitly separated by different network streams, and effectively fused at multiple network levels. Experiments on our new and benchmark datasets verify the effectiveness of our approach for unified aesthetics and emotion prediction.
A Unifying Framework for Neuro-Inspired, Data-Driven Detection of Low-Level Auditory Features

Our understanding of hearing and speech recognition rests on controlled experiments requiring simple stimuli. However, these stimuli often lack the characteristics of complex sounds such as speech. We propose an approach that combines neural modelling with machine learning to determine relevant low-level auditory features. Our approach bridges the gap between detailed neuronal models that capture specific auditory responses, and research on the statistics of real-world speech data and speech recognition. First, we introduce a feature detection model with a modest number of parameters that is compatible with auditory physiology. In order to objectively determine relevant feature detectors within the model parameter space, the model is tested in a speech classification task, using a simple classifier that approximates the information bottleneck. This framework allows us to determine the best model parameters and their neurophysiological and psychoacoustic implications. We show that our model can capture a variety of well-studied features (such as amplitude modulations and onsets) and allows us to unify concepts from different areas of hearing research. Our approach has various potential applications. Firstly, it could lead to new, testable experimental hypotheses for understanding hearing. Moreover, promising features could be directly applied as a new acoustic front-end for speech recognition systems.
Hyperspectral Image Classification with Tensor-Based Rank-R Learning Models

In this paper, we present a general tensor-based nonlinear classifier, the Rank-R Feedforward Neural Network (FNN). In the proposed model, which is an extension of the Rank-1 FNN classifier, the network weights are constrained to satisfy a rank-R Canonical Polyadic Decomposition. By allowing a rank-R, instead of a rank-1, Canonical Polyadic Decomposition of the weights, the learning capacity of the model can be increased, which contributes to avoiding underfitting problems. The effectiveness of the proposed model is scrutinized on a hyperspectral image classification experimental setting, since hyperspectral data can naturally be represented as tensor objects. Performance evaluation results indicate that the proposed model outperforms other state-of-the-art models, including deep learning ones, especially in cases where the number of available training samples is small.
Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation

We consider the problem of estimating the mean of a set of vectors, which are stored in a distributed system. This is a fundamental task with applications in distributed SGD and many other distributed problems, where communication is a main bottleneck for scaling up computations. We propose a new sparsity-aware algorithm, which improves previous results both theoretically and empirically. The communication cost of our algorithm is characterized by Hoyer's measure of sparseness. Moreover, we prove that the communication cost of our algorithm is information-theoretic optimal up to a constant factor in all sparseness regime. We have also conducted experimental studies, which demonstrate the advantages of our method and confirm our theoretical findings.
Epiphyseal Growth Plate Fractures

Physeal Fractures: General Considerations. - Historical Review. - Anatomy and Growth. - Classification. - Epidemiology. - Evaluation. - Management. - Prognosis. - Complications. - Physeal Fractures: Anatomic Sites. - Phalanges of the Hand. - Distal Radius. - Distal Tibia. - Distal Fibula. - Metacarpal. - Phalanges of the Foot. - Distal Humerus. - Distal Ulna. - Proximal Humerus. - Distal Femur. - Metatarsals. - Proximal Tibia. - Proximal Radius. - Proximal Ulna. - Proximal Clavicle. - Pelvis: Triradiate Cartilage. - Distal Clavicle. - Proximal Femur. - Proximal Fibula. - Spine (Vertebral Physeal Endplate). - Ribs. - Partial Physeal Arrest. - Etiology. - Assessment. - Management: General. - Physeal Bar Excision. - Physeal Distraction. - Physeal Cartilage Transplantation. - Spontaneous Resolution.
Medical Concept Normalization in User-Generated Text

Online social networks have revolutionized the way people interact with each other nowadays. Users often share their experiences in various health - related topics like disease symptoms, drug treatments and other medical related issues in order to discuss with other patients dealing with similar conditions. During the production of a new drug, important drug properties like possible Adverse Drug Reactions (ADRs) are monitored through a phase of clinical trials. However, due to various factors that can not be easily measured in those trials, patients can potentially experience adverse events that were not related to their treatment before, or were related to it in a much smaller frequency. Therefore, the automatic detection of Adverse Events in online networks is gaining an increasing popularity among researchers in the biomedical community, as it can offer a valuable complementary source of information, next to the traditional approaches of reporting those events to the corresponding Food and Drug Association. From an NLP perspective, this task poses a significant challenge as there is a large gap between the informal language used in social media and the formal medical language used to officially describe a medical concept. Moreover, there is an absence of large annotated datasets, as the manual labeling of an adverse effect mentions is a time-consuming and often ambiguous procedure which also requires some sort of medical expertise. In this work we propose a novel machine learning approach to normalize Adverse Drug Effect mentions in user-generated text to a standard vocabulary from a medical Ontology. The evaluation results of the proposed model demonstrates a competitive performance among the current state of the art techniques, posing the potential feasibility of our model in the medical concept normalization domain.