Miracle-dz/UniFAR-FTUs huggingface dataset:
Impossibility in Belief Merging (Extended Abstract)

With the aim of studying social properties of belief merging and having a better understanding of impossibility, we extend in three ways the framework of logic-based merging introduced by Konieczny and Pino Perez. First, at the level of representation of the information, we pass from belief bases to complex epistemic states. Second, the profiles are represented as functions of finite societies to the set of epistemic states (a sort of vectors) and not as multisets of epistemic states. Third, we extend the set of rational postulates in order to consider the epistemic versions of the classical postulates of social choice theory: standard domain, Pareto property, independence of irrelevant alternatives and absence of dictator. These epistemic versions of social postulates are given, essentially, in terms of the finite propositional logic. We state some representation theorems for these operators. These extensions and representation theorems allow us to establish an epistemic and very general version of Arrow's impossibility theorem. One of the interesting features of our result, is that it holds for different representations of epistemic states; for instance conditionals, ordinal conditional functions and, of course, total preorders.
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference procedure of deep Convolutional Neural Networks (CNNs). Specifically, the proposed SFP enables the pruned filters to be updated when training the model after pruning. SFP has two advantages over previous works: (1) Larger model capacity. Updating previously pruned filters provides our approach with larger optimization space than fixing the filters to zero. Therefore, the network trained by our method has a larger model capacity to learn from the training data. (2) Less dependence on the pretrained model. Large capacity enables SFP to train from scratch and prune the model simultaneously. In contrast, previous filter pruning methods should be conducted on the basis of the pre-trained model to guarantee their performance. Empirically, SFP from scratch outperforms the previous filter pruning methods. Moreover, our approach has been demonstrated effective for many advanced CNN architectures. Notably, on ILSCRC-2012, SFP reduces more than 42% FLOPs on ResNet-101 with even 0.2% top-5 accuracy improvement, which has advanced the state-of-the-art. Code is publicly available on GitHub: https://github.com/he-y/softfilter-pruning
Genetic Diversity of Norovirus Infections, Coinfections, and Undernutrition in Children From Brazilian Semiarid Region

Background and Objective: Norovirus (NoV) infections are known to have high-morbidity and mortality rates and are a major health problem globally. The impact of NoV on child development is, however, poorly understood. We evaluated the distribution of NoV genotypes in children from a low-income Brazilian semiarid region, in relation with their clinical symptoms, nutritional status, and co-pathogens. Methods: The test population included children aged 2 to 36 months from 6 cities of the Brazilian semiarid region. Fecal samples were collected from each child, along with the information regarding their socioeconomic/clinical conditions using a standardized questionnaire. Detection and quantification of NoV were performed by reverse-transcription quantitative polymerase chain reaction, followed by molecular and phylogenetic analyses. Results: The NoV detection rate was 45.2%. Presence of NoV was associated with lower z scores for weight-for-age (P = 0.03), weight-for-height (P = 0.03), and body mass index-for-age (P = 0.03). NoV infection was associated with more frequent respiratory illnesses (P < 0.01). GII.P7 (polymerase) and GII.3 (capsid) were the most frequent NoV genotypes. Analysis of the open reading frame (ORF)1-2 junction identified recombinant NoV strains in 80% of the sequenced samples. Enteroaggregative Escherichia coli coinfection was the major predictor for diarrhea in NoV-positive samples (P < 0.02). Moreover, Shigella spp was also associated with NoV-positive diagnosis (P = 0.02). Conclusions: This study highlights the genetic variability of NoV and, associated co-infections and undernutrition in infants from low-income Brazilian semiarid region.
Reading or Listening to a Gain- or Loss-Framed Health Message: Effects of Message Framing and Communication Mode in the Context of Fruit and Vegetable Intake

Gain-framed health messages are found to be more effective when targeting prevention behaviors. However, framing research has only minimally investigated the role of communication mode, another important factor in health communication. This study explored the role of communication mode in interaction with message framing, and the influence of two individual differences related to involvement as conditions under which gain framing can lead to health behavior change. Participants (N = 258) were exposed to either an auditory or written health message concerning fruit and vegetable intake, with either gain- or loss-framed arguments. In addition, the online experiment consisted of baseline and posttest measures, among which intention to consume sufficient fruit and vegetables. Moderating effects of perceived baseline fruit and vegetable consumption and baseline intention were assessed. A significant interaction between message framing and communication mode was observed: In case of a gain-framed message, an auditory message resulted in a higher intention than a written message. This pattern was most explicitly found among those with a lower perceived fruit and vegetable intake at baseline. Although further research is warranted in health persuasion research, the findings can possibly be used to target health interventions better at specific groups of people who behave less healthy.
Learning to Recognize Transient Sound Events using Attentional Supervision

Making sense of the surrounding context and ongoing events through not only the visual inputs but also acoustic cues is critical for various AI applications. This paper presents an attempt to learn a neural network model that recognizes more than 500 different sound events from the audio part of user generated videos (UGV). Aside from the large number of categories and the diverse recording conditions found in UGV, the task is challenging because a sound event may occur only for a short period of time in a video clip. Our model specifically tackles this issue by combining a main subnet that aggregates information from the entire clip to make clip-level predictions, and a supplementary subnet that examines each short segment of the clip for segment-level predictions. As the labeled data available for model training are typically on the clip level, the latter subnet learns to pay attention to segments selectively to facilitate attentional segment-level supervision. We call our model the M&mnet, for it leverages both “M”acro (clip-level) supervision and “m”icro (segment-level) supervision derived from the macro one. Our experiments show that M&mnet works remarkably well for recognizing sound events, establishing a new state-of-theart for DCASE17 and AudioSet data sets. Qualitative analysis suggests that our model exhibits strong gains for short events. In addition, we show that the micro subnet is computationally light and we can use multiple micro subnets to better exploit information in different temporal scales.
Complementary Learning of Word Embeddings

Continuous bag-of-words (CB) and skip-gram (SG) models are popular approaches to training word embeddings. Conventionally they are two standing-alone techniques used individually. However, with the same goal of building embeddings by leveraging surrounding words, they are in fact a pair of complementary tasks where the output of one model can be used as input of the other, and vice versa. In this paper, we propose complementary learning of word embeddings based on the CB and SG model. Specifically, one round of learning first integrates the predicted output of a SG model with existing context, then forms an enlarged context as input to the CB model. Final models are obtained through several rounds of parameter updating. Experimental results indicate that our approach can effectively improve the quality of initial embeddings, in terms of intrinsic and extrinsic evaluations.
Commonsense Knowledge Aware Conversation Generation with Graph Attention

Commonsense knowledge is vital to many natural language processing tasks. In this paper, we present a novel open-domain conversation generation model to demonstrate how large-scale commonsense knowledge can facilitate language understanding and generation. Given a user post, the model retrieves relevant knowledge graphs from a knowledge base and then encodes the graphs with a static graph attention mechanism, which augments the semantic information of the post and thus supports better understanding of the post. Then, during word generation, the model attentively reads the retrieved knowledge graphs and the knowledge triples within each graph to facilitate better generation through a dynamic graph attention mechanism. This is the first attempt that uses large-scale commonsense knowledge in conversation generation. Furthermore, unlike existing models that use knowledge triples (entities) separately and independently, our model treats each knowledge graph as a whole, which encodes more structured, connected semantic information in the graphs. Experiments show that the proposed model can generate more appropriate and informative responses than state-of-the-art baselines.
Warm Handoffs and Attendance at Initial Integrated Behavioral Health Appointments

Though integrated behavioral health programs often encourage primary care physicians to refer patients by means of a personal introduction (warm handoff), data are limited regarding the benefits of warm handoffs. We conducted a retrospective study of adult primary care patients referred to behavioral health clinicians in an urban, safety-net hospital to investigate the association between warm handoffs and attendance rates at subsequent initial behavioral health appointments. In multivariable analyses, patients referred via warm handoffs were not more likely to attend initial appointments (OR = 0.96; 95% CI, 0.79-1.18; P = .71). A prospective study is necessary to confirm the role of warm handoffs.
Association of IL17 and IL23R gene polymorphisms with rheumatic heart disease in South Indian population

ABSTRACT Background: IL-23/Th17 signaling pathway plays a crucial role in the cell-mediated immune response against bacterial infections and also in the pathogenesis of inflammatory and autoimmune diseases. Recent studies indicate that Th17 cell-associated cytokines are involved in the progression and maintenance of valvular lesions in rheumatic heart disease (RHD). Variants in the genes of cytokines that are potentially involved in Th17 response may influence interindividual differences in their expression levels, thereby contributing to the pathogenesis of immune-mediated diseases such as RHD. Objective: The aim of the study is to investigate the association of IL17A, IL17F, and IL23R gene variants with the risk perception of RHD. Methods: A total of 225 individuals (99 RHD patients and 126 healthy siblings) were recruited for the study. The IL17A (rs2275913), IL17F (rs763780), and IL23R (rs10889677) polymorphisms were determined by polymerase chain reaction restriction fragment length polymorphisms and amplification-refractory mutation system-polymerase chain reaction methods, respectively. Results: The frequency of IL17A (rs2275913) A/A genotype was significantly high in pooled RHD patients (odds ratio [OR] = 2.76; pc = 0.021), rheumatic fever (RF) patients (OR = 14.5; pc = 0.0001), and mitral valvular lesions patients (OR = 2.74; pc = 0.039) when compared to healthy siblings. However, the IL17F (rs763780) and IL23R (rs10889677) polymorphisms did not show any association with RHD. Conclusions: The results suggest that IL17A (rs2275913) polymorphism is associated with the development of RF/RHD in South Indian population. Further studies are required to substantiate the association of these genes with the disease risk.
A Group-based Approach to Improve Multifactorial Evolutionary Algorithm

Multifactorial evolutionary algorithm (MFEA) exploits the parallelism of population-based evolutionaryalgorithm and provides an efficient way to evolve individuals for solving multiple tasks concurrently.Its efficiency is derived by implicitly transferring the genetic information among tasks.However, MFEA doesn? t distinguish the information quality in the transfer compromising the algorithmperformance. We propose a group-based MFEA that groups tasks of similar types and selectivelytransfers the genetic information only within the groups. We also develop a new selection criterionand an additional mating selection mechanism in order to strengthen the effectiveness andefficiency of the improved MFEA. We conduct the experiments in both the cross-domain and intra-domainproblems.
Optimization based Layer-wise Magnitude-based Pruning for DNN Compression

Layer-wise magnitude-based pruning (LMP) is a very popular method for deep neural network (DNN) compression. However, tuning the layer-specific thresholds is a difficult task, since the space of threshold candidates is exponentially large and the evaluation is very expensive. Previous methods are mainly by hand and require expertise. In this paper, we propose an automatic tuning approach based on optimization, named OLMP. The idea is to transform the threshold tuning problem into a constrained optimization problem (i.e., minimizing the size of the pruned model subject to a constraint on the accuracy loss), and then use powerful derivative-free optimization algorithms to solve it. To compress a trained DNN, OLMP is conducted within a new iterative pruning and adjusting pipeline. Empirical results show that OLMP can achieve the best pruning ratio on LeNet-style models (i.e., 114 times for LeNet-300-100 and 298 times for LeNet-5) compared with some state-of-the- art DNN pruning methods, and can reduce the size of an AlexNet-style network up to 82 times without accuracy loss.
Awareness, Risk Perception, and Protective Behaviors for Extreme Heat and Climate Change in New York City

Preventing heat-related illness and death requires an understanding of who is at risk and why, and options for intervention. We sought to understand the drivers of socioeconomic disparities in heat-related vulnerability in New York City (NYC), the perceived risk of heat exposure and climate change, and barriers to protective behaviors. A random digit dial telephone survey of 801 NYC adults aged 18 and older was conducted from 22 September–1 October, 2015. Thirteen percent of the population did not possess an air conditioner (AC), and another 15% used AC never/infrequently. In adjusted models, odds of not possessing AC were greater for non-Hispanic blacks compared with other races/ethnicities, odds ratio (OR) = 2.0 (95% CI: 1.1, 3.5), and for those with low annual household income, OR = 3.1 (95% CI: 1.8, 5.5). Only 12% reported going to a public place with AC if they could not keep cool at home. While low-income individuals were less likely to be aware of heat warnings, they were more likely to be concerned that heat could make them ill and that climate change would affect their health than participants with a higher household income, OR = 1.6 (95% CI: 1.0, 2.3). In NYC, lack of access to AC partially explains disparities in heat-related health outcomes. Our results point to opportunities for knowledge building and engagement on heat-health awareness and climate change adaptation that can be applied in NYC and other metropolitan areas to improve and target public health prevention efforts.
Adversarially Regularized Graph Autoencoder

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in real-world graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. The framework encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure. Furthermore, the latent representation is enforced to match a prior distribution via an adversarial training scheme. To learn a robust embedding, two variants of adversarial approaches, adversarially regularized graph autoencoder (ARGA) and adversarially regularized variational graph autoencoder (ARVGA), are developed. Experimental studies on real-world graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction, graph clustering, and graph visualization tasks.
Large-Scale Single-Cell RNA-Seq Reveals Molecular Signatures of Heterogeneous Populations of Human Induced Pluripotent Stem Cell-Derived Endothelial Cells

Rationale: Human-induced pluripotent stem cell–derived endothelial cells (iPSC-ECs) have risen as a useful tool in cardiovascular research, offering a wide gamut of translational and clinical applications. However, inefficiency of the currently available iPSC-EC differentiation protocol and underlying heterogeneity of derived iPSC-ECs remain as major limitations of iPSC-EC technology. Objective: Here, we performed droplet-based single-cell RNA sequencing (scRNA-seq) of the human iPSCs after iPSC-EC differentiation. Droplet-based scRNA-seq enables analysis of thousands of cells in parallel, allowing comprehensive analysis of transcriptional heterogeneity. Methods and Results: Bona fide iPSC-EC cluster was identified by scRNA-seq, which expressed high levels of endothelial-specific genes. iPSC-ECs, sorted by CD144 antibody–conjugated magnetic sorting, exhibited standard endothelial morphology and function including tube formation, response to inflammatory signals, and production of NO. Nonendothelial cell populations resulting from the differentiation protocol were identified, which included immature cardiomyocytes, hepatic-like cells, and vascular smooth muscle cells. Furthermore, scRNA-seq analysis of purified iPSC-ECs revealed transcriptional heterogeneity with 4 major subpopulations, marked by robust enrichment of CLDN5, APLNR, GJA5, and ESM1 genes, respectively. Conclusions: Massively parallel, droplet-based scRNA-seq allowed meticulous analysis of thousands of human iPSCs subjected to iPSC-EC differentiation. Results showed inefficiency of the differentiation technique, which can be improved with further studies based on identification of molecular signatures that inhibit expansion of nonendothelial cell types. Subtypes of bona fide human iPSC-ECs were also identified, allowing us to sort for iPSC-ECs with specific biological function and identity.
Multicriteria-Based Location Privacy Preservation in Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) are the preferable choice for Intelligent Transportation Systems (ITS) because of its prevailing significance in both safety and nonsafety applications. Information dissemination in a multihop fashion along with privacy preservation of source node is a serious but challenging issue. We have used the idea of the phantom node as the next forwarder for data dissemination. The phantom node (vehicle) hides the identity of actual source node thus preserving the location privacy. The selection of the phantom node among the set of alternatives’ candidate vehicles is considered as a multicriteria-based problem. The phantom node selection problem is solved by using an analytical network process (ANP) by considering different traffic scenarios. The selection is based on different parameters which are distance, speed, trust, acceleration, and direction. The best alternative (target phantom vehicle) is selected through an ANP where all the alternatives are ranked from best to worst. The vehicle having maximum weight is considered to be the best choice as a phantom node. In order to check the stability of the alternatives’ ranking, sensitivity analysis is performed by taking into account different traffic scenarios and interest level of candidate vehicles.
Comparison Between Modified Extracellular-Type Trehalose-Containing Kyoto Solution and University of Wisconsin Solution in 18-Hour Pancreas Preservation for Islet Transplantation.

To the Editor: I slet transplantation is a promising option for the treatment of patients with type 1 diabetes mellitus who have frequent severe hypoglycemia and a lack of hypoglycemia awareness. Donor pancreata are usually preserved with University of Wisconsin (UW) solution. However, UW solution is associated with some disadvantages. University of Wisconsin solution must be cold stored until use because it is chemically unstable. It is highly viscous, which may complicate the initial organ flush. We previously showed that modified extracellulartype trehalose-containing Kyoto (ETK) solution significantly improved the islet yield in comparison to UW preservation. The ETK solution has been shown to be
Improving Maximum Likelihood Estimation of Temporal Point Process via Discriminative and Adversarial Learning

Point process is an expressive tool in learning temporal event sequence which is ubiquitous in real-world applications. Traditional predictive models are based on maximum likelihood estimation (MLE). This paper aims to improve MLE by discriminative and adversarial learning. The initial model is learned by MLE explaining the joint distribution of the occurred event history. Then it is refined by devising a gradient based learning procedure with two complementary recipes: i) mean square error (MSE) that directly reflects the prediction accuracy of the model; ii) adversarial classification loss which induces the Wasserstein distance loss. The hope is that the adversarial loss can add sharpness to the smooth effect inherently caused by the MSE loss. The method is generic and compatible with different differentiable parametric forms of the intensity function. Empirical results via a variant of the Hawkes processes demonstrate its effectiveness of our method.
Harnessing Synthesized Abstraction Images to Improve Facial Attribute Recognition

Facial attribute recognition is an important and yet challenging research topic. Different from most previous approaches which predict attributes only based on the whole images, this paper leverages facial parts locations for better attribute prediction. A facial abstraction image which contains both local facial parts and facial texture information is introduced. This abstraction image is generated by a Generative Adversarial Network (GAN). Then we build a dual-path facial attribute recognition network to utilize features from the original face images and facial abstraction images. Empirically, the features of facial abstraction images are complementary to features of original face images. With the facial parts localized by the abstraction images, our method improves facial attributes recognition, especially the attributes located on small face regions. Extensive evaluations conducted on CelebA and LFWA benchmark datasets show that state-of-the-art performance is achieved.