Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain-inspired Balanced Tuning for Spiking Neural Networks

Due to the nature of Spiking Neural Networks (SNNs), it is challenging to be trained by biologically plausible learning principles. The multi-layered SNNs are with non-differential neurons, temporary-centric synapses, which make them nearly impossible to be directly tuned by back propagation. Here we propose an alternative biological inspired balanced tuning approach to train SNNs. The approach contains three main inspirations from the brain: Firstly, the biological network will usually be trained towards the state where the temporal update of variables are equilibrium (e.g. membrane potential); Secondly, specific proportions of excitatory and inhibitory neurons usually contribute to stable representations; Thirdly, the short-term plasticity (STP) is a general principle to keep the input and output of synapses balanced towards a better learning convergence. With these inspirations, we train SNNs with three steps: Firstly, the SNN model is trained with three brain-inspired principles; then weakly supervised learning is used to tune the membrane potential in the final layer for network classification; finally the learned information is consolidated from membrane potential into the weights of synapses by Spike-Timing Dependent Plasticity (STDP). The proposed approach is verified on the MNIST hand-written digit recognition dataset and the performance (the accuracy of 98.64%) indicates that the ideas of balancing state could indeed improve the learning ability of SNNs, which shows the power of proposed brain-inspired approach on the tuning of biological plausible SNNs.
Joint Extraction of Entities and Relations Based on a Novel Graph Scheme

Both entity and relation extraction can benefit from being performed jointly, allowing each task to correct the errors of the other. Most existing neural joint methods extract entities and relations separately and achieve joint learning through parameter sharing, leading to a drawback that information between output entities and relations cannot be fully exploited. In this paper, we convert the joint task into a directed graph by designing a novel graph scheme and propose a transition-based approach to generate the directed graph incrementally, which can achieve joint learning through joint decoding. Our method can model underlying dependencies not only between entities and relations, but also between relations. Experiments on NewYork Times (NYT) corpora show that our approach outperforms the state-of-the-art methods.
Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN-PSO

A novel hybrid algorithm that employs BP neural network (BPNN) and particle swarm optimization (PSO) algorithm is proposed for the kinematic parameter identification of industrial robots with an enhanced convergence response. The error model of the industrial robot is established based on a modified Denavit-Hartenberg method and Jacobian matrix. Then, the kinematic parameter identification of the industrial robot is transformed to a nonlinear optimization in which the unknown kinematic parameters are taken as optimal variables. A hybrid algorithm based on a BPNN and the PSO is applied to search for the optimal variables which are used to compensate for the error of the kinematic parameters and improve the positioning accuracy of the industrial robot. Simulations and experiments based on a realistic industrial robot are all provided to validate the efficacy of the proposed hybrid identification algorithm. The results show that the proposed parameter-identification method based on the BPNN and PSO has fewer iterations and faster convergence speed than the standard PSO algorithm.
Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer

Neural networks have been widely used for high resource language (e.g. English) named entity recognition (NER) and have shown state-of-the-art results.However, for low resource languages, such as Dutch, Spanish, due to the limitation of resources and lack of annotated data, taggers tend to have lower performances.To narrow this gap, we propose three novel strategies to enrich the semantic representations of low resource languages: we first develop neural networks to improve low resource word representations by knowledge transfer from high resource language using bilingual lexicons. Further, a lexicon extension strategy is designed to address out-of lexicon problem by automatically learning semantic projections.Thirdly, we regard word-level entity type distribution features as an external language-independent knowledge and incorporate them into our neural architecture. Experiments on two low resource languages (including Dutch and Spanish) demonstrate the effectiveness of these additional semantic representations (average 4.8\% improvement). Moreover, on Chinese OntoNotes 4.0 dataset, our approach achieved an F-score of 83.07\% with 2.91\% absolute gain compared to the state-of-the-art results.
Automatic Gating of Attributes in Deep Structure

Deep structure has been widely applied in a large variety of fields for its excellence of representing data. Attributes are a unique type of data descriptions that have been successfully utilized in numerous tasks to enhance performance. However, to introduce attributes into deep structure is complicated and challenging, because different layers in deep structure accommodate features of different abstraction levels, while different attributes may naturally represent the data in different abstraction levels. This demands adaptively and jointly modeling of attributes and deep structure by carefully examining their relationship. Different from existing works that treat attributes straightforwardly as the same level without considering their abstraction levels, we can make better use of attributes in deep structure by properly connecting them. In this paper, we move forward along this new direction by proposing a deep structure named Attribute Gated Deep Belief Network (AG-DBN) that includes a tunable attribute-layer gating mechanism and automatically learns the best way of connecting attributes to appropriate hidden layers. Experimental results on a manually-labeled subset of ImageNet, a-Yahoo and a-Pascal data set justify the superiority of AG-DBN against several baselines including CNN model and other AG-DBN variants. Specifically, it outperforms the CNN model, VGG19, by significantly reducing the classification error from 26.70% to 13.56% on a-Pascal.
An Electroelastic Solution for Functionally Graded Piezoelectric Circular Plates under the Action of Combined Mechanical Loads

In this study, we obtained an electroelastic solution for functionally graded piezoelectric circular plates under the action of combined mechanical loads which include the uniformly distributed loads on the upper surface of the plate and the radial force and bending moment at the periphery of the plate. All electroelastic materials parameters are assumed to vary according to the same gradient function along the thickness direction. The influence of different functionally graded parameters on the elastic displacement and elastic stress, as well as the electric displacement and electric potential, was discussed by a numerical example. The solution presented in this study is not only applicable to the case of combined loads, but also to the case of a single mechanical load. In addition, this solution reflects the influence of the function gradient on the pure piezoelectric plate, which is helpful to the refined analysis and optimization design of similar structures.
Neural dysfunction and retinopathy in persons with type 1 diabetes

ABSTRACT Objective: To determine associations of microvascular and neuropathic complications of diabetes cross-sectionally and longitudinally in persons with long-term type 1 diabetes (T1D). Research Design and Methods: Persons receiving care for T1D in South Central Wisconsin were identified in 1979–1980 and examined approximately every 5 years. Associations between neuropathic and microvascular complications were examined at most prior visits, when information on several neuropathic complications was collected. Temporal relationships were examined by modeling incidence between examinations across the visits. Results: Adjusting for duration of diabetes, glycated hemoglobin, and systolic blood pressure, the following were cross-sectionally associated with prevalent PDR (proliferative diabetic retinopathy): the presence of sensory neuropathy (SN) as reported at each Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) examination (odds ratio (OR) = 2.76, confidence interval (CI) = 1.71, 4.48) and the heartrate variability measures RMSD (square root of the mean of squared differences of successive RR intervals) (OR = 0.24, CI = 0.16, 0.37) and SDNN (standard deviation of successive RR intervals) (OR = 0.26, CI = 0.17, 0.39). Findings were similar for prevalent ME (macular edema) as assessed from spectral-domain optical coherence tomography (SD-OCT). The presence of PDR (OR = 2.13, CI = 1.63, 2.78) and ME (OR = 2.36, CI = 1.66, 3.34) were both significantly associated with incident WESDR SN. WESDR SN was associated with incident PDR (OR = 1.53, CI = 1.09, 2.15) but not incident ME (OR = 1.31, CI = 0.92, 1.87). Conclusions: Sensory neuropathy and heartrate variability were significantly associated with prevalent PDR and ME in people with long-term T1D. PDR and ME were significantly associated with incident sensory neuropathy, and sensory neuropathy was significantly associated with incident PDR. Studies using earliest detectable markers of microvascular and neurologic abnormalities are needed to determine which of the two systems ‘fails’ first. Such information might suggest a temporal sequence of diabetes complications.
Student-t Variational Autoencoder for Robust Density Estimation

We propose a robust multivariate density estimator based on the variational autoencoder (VAE). The VAE is a powerful deep generative model, and used for multivariate density estimation. With the original VAE, the distribution of observed continuous variables is assumed to be a Gaussian, where its mean and variance are modeled by deep neural networks taking latent variables as their inputs. This distribution is called the decoder. However, the training of VAE often becomes unstable. One reason is that the decoder of VAE is sensitive to the error between the data point and its estimated mean when its estimated variance is almost zero. We solve this instability problem by making the decoder robust to the error using a Bayesian approach to the variance estimation: we set a prior for the variance of the Gaussian decoder, and marginalize it out analytically, which leads to proposing the Student-t VAE. Numerical experiments with various datasets show that training of the Student-t VAE is robust, and the Student-t VAE achieves high density estimation performance.
Keeping in Touch with Collaborative UAVs: A Deep Reinforcement Learning Approach

Effective collaborations among autonomous unmanned aerial vehicles (UAVs) rely on timely information sharing. However, the time-varying flight environment and the intermittent link connectivity pose great challenges to message delivery. In this paper, we leverage the deep reinforcement learning (DRL) technique to address the UAVs' optimal links discovery and selection problem in uncertain environments. As the multi-agent learning efficiency is constrained by the high-dimensional and continuous action spaces, we slice the whole action spaces into a number of tractable fractions to achieve efficient convergences of optimal policies in continuous domains. Moreover, for the nonstationarity issue that particularly challenges the multi-agent DRL with local perceptions, we present a multi-agent mutual sampling method that jointly interacts the intra-agent and inter-agent state-action information to stabilize and expedite the training procedure. We evaluate the proposed algorithm on the UAVs' continuous network connection task. Results show that the associated UAVs can quickly select the optimal connected links, which facilitate the UAVs' teamwork significantly.
Discrete Network Embedding

Network embedding aims to seek low-dimensional vector representations for network nodes, by preserving the network structure. The network embedding is typically represented in continuous vector, which imposes formidable challenges in storage and computation costs, particularly in large-scale applications. To address the issue, this paper proposes a novel discrete network embedding (DNE) for more compact representations. In particular, DNE learns short binary codes to represent each node. The Hamming similarity between two binary embeddings is then employed to well approximate the ground-truth similarity. A novel discrete multi-class classifier is also developed to expedite classification. Moreover, we propose to jointly learn the discrete embedding and classifier within a unified framework to improve the compactness and discrimination of network embedding. Extensive experiments on node classification consistently demonstrate that DNE exhibits lower storage and computational complexity than state-of-the-art network embedding methods, while obtains competitive classification results.
Socioeconomic Status and Other Factors Associated with Childhood Obesity

Background: Childhood obesity in the United States is a critical public health issue. Although multiple child and parental factors are associated with childhood obesity, few models evaluate how socioeconomic status influences these risk factors. We aimed to create a model to examine how socioeconomic status modifies risk factors for child obesity. Methods: We conducted a secondary data analysis of the Early Childhood Longitudinal Birth Cohort. Using logistic regression, we modeled childhood obesity status from known parental and child risk factors for childhood obesity and tested interactions with socioeconomic status. Results: Compared with healthy-weight children, socioeconomic status, race, birth weight, parental smoking, and not eating dinner as a family were associated with kindergarten-aged children being overweight or obese. Parental smoking increased the odds of a child being overweight or obese by 40%, and eating dinner as a family reduced the odds of a child being overweight or obese by 4%. In addition, black or Hispanic children had a 60% increased odds of being overweight or obese when compared with their white counterparts. Native American children had almost double the odds of being overweight or obese compared with white children. Socioeconomic status did not modify any of these associations. Conclusion: Parental smoking, birth weight, and not eating dinner as a family were two modifiable factors associated with overweight and obesity in kindergarten-age children, regardless of socioeconomic status. Changing these life-style factors could reduce the child's risk for obesity.
Centralized Ranking Loss with Weakly Supervised Localization for Fine-Grained Object Retrieval

Fine-grained object retrieval has attracted extensive research focus recently. Its state-of-the-art schemesare typically based upon convolutional neural network (CNN) features. Despite the extensive progress, two issues remain open. On one hand, the deep features are coarsely extracted at image level rather than precisely at object level, which are interrupted by background clutters. On the other hand, training CNN features with a standard triplet loss is time consuming and incapable to learn discriminative features. In this paper, we present a novel fine-grained object retrieval scheme that conquers these issues in a unified framework. Firstly, we introduce a novel centralized ranking loss (CRL), which achieves a very efficient (1,000times training speedup comparing to the triplet loss) and discriminative feature learning by a ? centralized? global pooling. Secondly, a weakly supervised attractive feature extraction is proposed, which segments object contours with top-down saliency. Consequently, the contours are integrated into the CNN response map to precisely extract features ? within? the target object. Interestingly, we have discovered that the combination of CRL and weakly supervised learning can reinforce each other. We evaluate the performance ofthe proposed scheme on widely-used benchmarks including CUB200-2011 and CARS196. We havereported significant gains over the state-of-the-art schemes, e.g., 5.4% over SCDA [Wei et al., 2017]on CARS196, and 3.7% on CUB200-2011.
The role of the ventrolateral prefrontal cortex in emotional enhancement of memory: A TMS study

ABSTRACT Negative stimuli are often remembered better than neutral stimuli, which is called the emotional enhancement of memory (EEM). We tested whether the role of the ventrolateral prefrontal cortex (VLPFC) in the EEM depended on stimulus valence and/or arousal, and attentional resources. Continuous theta burst stimulation (cTBS) was applied over the left VLPFC, right VLPFC and vertex before encoding ‘negative arousing,’ ‘negative nonarousing,’ and ‘neutral’ words under full and divided attention, followed by a recognition test. The vertex condition showed the EEM effect for ‘negative arousing’ and ‘negative nonarousing’ words. However, the right VLPFC condition showed no evidence of the EEM effect for ‘negative arousing’ or ‘negative nonarousing’ words, under full attention. In contrast, the left VLPFC condition showed the EEM effect for ‘negative arousing’ words, but not ‘negative nonarousing’ words, under full attention. Thus, the left and right VLPFC have different roles in the EEM, depending on valence and arousal.
A Multi-Stakeholder Perspective on the Integration of Safety in University Nursing, Education, and Engineering Curricula

Improvement in workplace safety is dependent upon the active engagement of workforce leaders and designers. The university sector plays a key role in the education of these future leaders, and there is an expectation that safety education in universities will encompass more than just a safe learning environment—that is the nurturing of broader safety attitudes and awareness. However, with the exception of dedicated safety training programs, safety education is often delivered and assessed on an ad-hoc basis and at academic discretion. This is partly due to the absence of a simple tool with which curricula can be evaluated from a safety perspective. In a qualitative approach, semi-structured interviews were undertaken with multiple stakeholders (academics, professional organizations, and students) to determine their views on existing safety content in university curricula and on the level of preparedness, from a safety perspective, for workforce entry. University participants came from nursing, mechanical engineering, and education schools at three universities. A simple curriculum evaluative tool was also validated. Results indicated there were divergent views on the level of preparedness for workforce entry both between schools and stakeholder groups. However, the limitations of university curricula were acknowledged. The evaluation tool was shown to provide positive feedback on existing, but previously unacknowledged, safety content and also highlighted areas for future improvement and integration. However, voluntary utilization of the tool was a challenge for busy academics.
Multi-Turn Video Question Answering via Multi-Stream Hierarchical Attention Context Network

Conversational video question answering is a challenging task in visual information retrieval, which generates the accurate answer from the referenced video contents according to the visual conversation context and given question. However, the existing visual question answering methods mainly tackle the problem of single-turn video question answering, which may be ineffectively applied for multi-turn video question answering directly, due to the insufficiency of modeling the sequential conversation context. In this paper, we study the problem of multi-turn video question answering from the viewpoint of multi-step hierarchical attention context network learning. We first propose the hierarchical attention context network for context-aware question understanding by modeling the hierarchically sequential conversation context structure. We then develop the multi-stream spatio-temporal attention network for learning the joint representation of the dynamic video contents and context-aware question embedding. We next devise the hierarchical attention context network learning method with multi-step reasoning process for multi-turn video question answering. We construct two large-scale multi-turn video question answering datasets. The extensive experiments show the effectiveness of our method.
Evaluating and Complementing Vision-to-Language Technology for People who are Blind with Conversational Crowdsourcing

We study how real-time crowdsourcing can be used both for evaluating the value provided by existing automated approaches and for enabling workflows that provide scalable and useful alt text to blind users. We show that the shortcomings of existing AI image captioning systems frequently hinder a user's understanding of an image they cannot see to a degree that even clarifying conversations with sighted assistants cannot correct. Based on analysis of clarifying conversations collected from our studies, we design experiences that can effectively assist users in a scalable way without the need for real-time interaction. Our results provide lessons and guidelines that the designers of future AI captioning systems can use to improve labeling of social media imagery for blind users.
Parents’ Adverse Childhood Experiences and Their Children’s Behavioral Health Problems

Data from a national survey of families revealed that parents with a history of many ACEs are more likely to have children with behavioral health problems. BrightcoveDefaultPlayer10.1542/6138655490001PEDS-VA_2018-0023 Video Abstract BACKGROUND AND OBJECTIVES: Adverse childhood experiences (ACEs) include stressful and potentially traumatic events associated with higher risk of long-term behavioral problems and chronic illnesses. Whether parents’ ACE counts (an index of standard ACEs) confer intergenerational risk to their children’s behavioral health is unknown. In this study, we estimate the risk of child behavioral problems as a function of parent ACE counts. METHODS: We obtained retrospective information on 9 ACEs self-reported by parents and parent reports of their children’s (1) behavioral problems (using the Behavior Problems Index [BPI]), (2) attention-deficit/hyperactivity disorder diagnosis, and (3) emotional disturbance diagnosis from the 2013 Panel Study of Income Dynamics (PSID) core interview and the linked PSID Childhood Retrospective Circumstances Study and 2014 PSID Child Development Supplement. Multivariate linear and logistic regression models were used to estimate child behavioral health outcomes by parent retrospective ACE count. RESULTS: Children of parents with a history of 4 or more ACEs had on average a 2.3-point (95% confidence interval [CI]: 1.3–3.2) higher score on the BPI, 2.1 times (95% CI: 1.1–3.8) higher odds of hyperactivity, and 4.2 times (95% CI: 1.7–10.8) higher odds of an emotional disturbance diagnosis than children of parents with no ACEs. Maternal ACEs revealed a stronger association with child behavior problems than paternal ACEs. Relationships between parents’ 9 component ACEs individually and children’s BPI scores revealed consistently positive associations. Mediation by parent emotional distress and aggravation were observed. CONCLUSIONS: Parents with greater exposure to ACEs are more likely to have children with behavioral health problems.