bluuebunny/crossref_metadata_2025 huggingface dataset:
Congestive heart failure is the leading cause of pleural effusion in the north-west of Iran
Authors: Nazemiyeh Masoud , Dorraji Amirhossein , Nouri-Vaskeh Masoud , Sharifi Akbar
Published: September 2019

Introduction: Pleural effusion (PE) is a common manifestation of pulmonary and non–pulmonary diseases, and the first step for diagnosing the etiology is analysis of pleural fluid. The aim of this study was to determine the epidemiology of PE in a tertiary referral hospital in the North-West of Iran. Methods: All patients with PE who referred to the department of pulmonary diseases in tertiary centre of Tabriz University of Medical Sciences between 2015 and 2016 were enrolled. Complete enumeration method used for selection of patients. Required information including clinical findings, PE location, fluid appearance, and biochemical characteristics were recorded using a checklist and analyzed via appropriate statistical methods. Results: A total of 223 patients were included in this study. Congestive heart failure (CHF) was the most common cause of PE (n=67), followed by pulmonary thromboembolism and malignant diseases. PE fluid in all patients with CHF was transudative. Conclusion: According to the findings of this study, CHF was the most prevalent cause of PE.
Delayed diagnosis of myocardial infarction in a young man with a blunt chest trauma
Authors: Nikparvar Marzieh , Asghari Sayed Mohammadbagher , Farshidi Hossein
Published: September 2019

A 19 year old man was admitted to emergency department (ED) because of motorcycle to motorcycle accident. In ED he was evaluated as a multiple trauma patient and after overnight observation, was discharged with stable vital signs (VS) next morning. Twenty four hours later, he developed chest pain and dyspnea, and was admitted to ED for the second time. This time he was evaluated as a chest trauma patient and chest X ray and CT scanning were performed. According to cardiology consult, an electrocardiogram was recorded and extensive anterolateral ST elevation myocardial infarction (STEMI) was confirmed. Coronary angiography revealed total thrombotic occlusion of left anterior descending artery (LAD) from ostium. Percutaneous coronary intervention (PCI) was performed and LAD stented successfully. After 10 days of ICU admission, he was discharged with an ejection fraction (EF) of 35%.
Differential Effects of Dorsal and Ventral Medial Prefrontal Cortex Inactivation during Natural Reward Seeking, Extinction, and Cue-Induced Reinstatement
Authors: Caballero Jessica P. , Scarpa Garrett B. , Remage-Healey Luke , Moorman David E.
Published: September 2019

Rodent dorsal medial prefrontal cortex (mPFC), typically prelimbic cortex, is often described as promoting actions such as reward seeking, whereas ventral mPFC, typically infralimbic cortex, is thought to promote response inhibition. However, both dorsal and ventral mPFC are necessary for both expression and suppression of different behaviors, and each region may contribute to different functions depending on the specifics of the behavior tested. To better understand the roles of dorsal and ventral mPFC in motivated behavior we pharmacologically inactivated each area during operant fixed ratio 1 (FR1) seeking for a natural reward (sucrose), extinction, cue-induced reinstatement, and progressive ratio (PR) sucrose seeking in male Long–Evans rats. Bilateral inactivation of dorsal mPFC, but not ventral mPFC increased reward seeking during FR1. Inactivation of both dorsal and ventral mPFC decreased seeking during extinction. Bilateral inactivation of ventral mPFC, but not dorsal mPFC decreased reward seeking during cue-induced reinstatement. No effect of inactivation was found during PR. Our data contrast sharply with observations seen during drug seeking and fear conditioning, indicating that previously established roles of dorsal mPFC = going versus ventral mPFC = stopping are not applicable to all motivated behaviors and/or outcomes. Our results indicate that dichotomous functions of dorsal versus ventral mPFC, if they exist, may align better with other models, or may require the development of a new framework in which these multifaceted brain areas play different roles in action control depending on the behavioral context in which they are engaged.
Propionate promotes intestinal lipolysis and metabolic benefits via AMPK/LSD1 pathway in mice
Authors: Wang Dan , Liu Chu-Dan , Tian Meng-Li , Tan Cheng-Quan , Shu Gang , Jiang Qing-Yan , Zhang Lin , Yin Yu-Long
Published: September 2019

Dietary fibers and their microbial fermentation products short-chain fatty acids promote metabolic benefits, but the underlying mechanisms are still unclear. Recent studies indicate that intestinal lipid handling is under regulatory control and has broad influence on whole body energy homeostasis. Here we reported that dietary inulin and propionate significantly decreased whole body fat mass without affecting food intake in mice fed with chow diet. Meanwhile, triglyceride (TG) content was decreased and lipolysis gene expression, such as adipose triglyceride lipase (A
tgl), hormone-sensitive lipase (H
sl) and lysosomal acid lipase (L
al) was elevated in the jejunum and ileum of inulin- and propionate-treated mice. In vitro studies on Caco-2 cells showed propionate directly induced enterocyte Atgl, Hsl and Lal gene expression and decreased TG content, via activation of phosphorylation of AMP-activated protein kinase (p-AMPK) and lysine-specific demethylase 1 (LSD1). Moreover, inulin and propionate could increase intestinal lipolysis under high-fat diet (HFD)-fed condition which contributed to the prevention of HFD-induced obesity. Our study suggests that dietary fiber inulin and its microbial fermentation product propionate can regulate metabolic homeostasis through regulating intestinal lipid handling, which may provide a novel therapeutic target for both prevention and treatment of obesity.
Circadian gene PER1 senses progesterone signal during human endometrial decidualization
Authors: Zhang Ying , Meng Nan , Bao Haili , Jiang Yufei , Yang Ningjie , Wu Kejia , Wu Jinxiang , Wang Haibin , Kong Shuangbo , Zhang Yuanzhen
Published: September 2019

Progesterone is an important hormone for female reproduction; however, how the fluctuation of progesterone acts upon reproductive processes remains largely unknown. Mounting evidence indicates a pivotal role of the circadian clock in sensing hormone dynamics for homeostatic regulation of physiological functions. Therefore, we sought to determine whether clock genes respond to progesterone signaling in female reproductive system. In this study, we tested the hypothesis that the circadian system could respond to progesterone signaling during human endometrial decidual transformation. The expression of the circadian gene PER1 increased immediately and remained elevated during human endometrial decidualization. The progesterone receptor activated PER1 transcription by directly binding to its promoter from the onset of the stromal proliferation-differentiation transition. PER1 knockout significantly downregulated the expression of some PGR target genes, and attenuated human endometrial decidual transformation by expediting FOXO1 protein degradation. In conclusion, progesterone could control the female reproductive process through sustained feedback from the circadian gene PER1, which is probably involved to P4-PR signaling responsiveness in the initiation and maintenance of decidualization.
Endoscopic Ultrasound-Guided Fine Needle Aspiration of a PET/CT-Positive Small Hilar Lymph Node for the Diagnosis of Metastatic Merkel Cell Carcinoma
Authors: Antonini Fillipo , Vicentini Roberto , Giorgini Sara , Macarri Giampiero
Published: September 2019

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Response to lubiprostone in chronic constipation is associated with increased mucus and mucin output: a randomized clinical trial
Authors: Tarasiuk Aleksandra , Milad Mena , Ahmed Syed T , Majewski Marek , Wallner Grzegorz , Fichna Jakub , Bashashati Mohammad , Sarosiek Jerzy
Published: September 2019

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Socio-Cultural Potential of Rural Regions of Belarus in the Context of Cultural Services Consumption: A Sociological Analysis
Authors: Balich Natalia
Published: September 2019

The author considers socio-cultural potential of rural regions in the context of the cultural services consumption by the rural population. The researcher carries out the analysis of social and cultural infrastructure development of the village, gives numerical indicators of the activities of concert organizations, libraries, club institutions and formations, paid services in dynamics for 2010–2016. The paper reveals the degree of satisfaction of various social and age groups of the rural population with the quality of educational and cultural services at the place of residence, the availability of opportunities to visit cultural and leisure activities and cultural institutions. The given survey is based on the data of empirical research conducted by the Institute of Sociology of the National Academy of Sciences of Belarus in 2018. The author compares subjective assessments of their material well-being of groups with different social status with the average income per family member, the minimum subsistence budget in the context of opportunities to pay for educational paid clubs for their children, pre-school institutions. The researcher concludes that the most economically vulnerable group not having sufficient funds to pay for cultural services and activities are peasants, agricultural workers. Self-assessments of the modest financial situation and requests for cultural and leisure services correlate with the low level of income of this social group. The conducted study determines that the socio-cultural potential of rural regions largely depends on the specifics of the socio-cultural infrastructure of the modern village, the totality of social, cultural and economic resources. In contrast to the city, the socio-cultural infrastructure of rural regions is less developed and equipped with material and technical means. The author highlights that in successful realization of socio-cultural potential of different groups of rural population, a major role is played by the services of the population in the field of culture, which should be accessible regardless of social status and place of residence. The researcher underlines that it is necessary to create comfortable living conditions in rural areas, to provide local residents with not only material but also spiritual well-being. Ignoring the existing problems of the development of socio-cultural infrastructure and culture in rural areas will lead to a further outflow of young people and skilled working-age population to the city.
The system of unified training of scientific medical personnel of Sechenov university
Authors: Morozova Olga L. , Litvitskiy Petr F. , Andriutsa Natalya S. , Maltseva Larisa D. , Gribaleva Elizaveta O. , Emelyanova Elena S. , Arakelyan Arsen S.
Published: September 2019

Background: The article describes the issue of theoretical proof and experimental testing of the developed approach to optimization of fostering young generations of scientists and optimizing their individual career path. Aims: The aim of the article was to analyze the efficacy of the program of integration of high school students of medical school classes into the global system of training of young scientists.
Materials and methods: The general scientific methods were used, such as analysis and synthesis, field experiment and social research, which included survey conduction, education and control testing of high school students of medical preprofessional classes.
Results: Authors revealed the efficacy of implemented model of organization of educational process, which was based on the integration of high school students of medical classes into the global system of researchers training and which created a specialized environment for rapid individual professional development.
Conclusions: The conducted research proved the efficacy of the personalized approach to gradual teaching of basic research competencies.
The results of medical and psychophysiological examination performed during an arctic world oceanic international flight
Authors: Atkov Oleg Yu. , Gorokhova Svetlana G. , Serikov Vasiliy V. , Alchinova Irina B. , Polyakova Margarita V. , Pankova Nataliya N. , Karganov Mikhail Yu. , Baranov Viktor M.
Published: September 2019

Background: The development of the Arctic region is connected with extreme and strenuous work conditions and transmeridian shifts. The resulting stress changes the functional state of involved professionals irrespective of their age. Successful performance of job tasks depends on individual adaptation, which is an urgent issue to be studied with modern research methods.
Aims: The study was a complex research project on adaptation and changes of cardiovascular and psychophysiological functioning in response to long-term stress associated with rapid climate changes and transmeridian shifts experienced by the participants of the Arctic World Oceanic International Flight Sever Vash.
Materials and methods: The participants of Sever Vash expedition, which took place in July and August 2018, were examined. According to the design of the study, they underwent a complex laboratory and instrumental assessment before the start and immediately on the finish. Blood parameters, level of DNA damage in blood cells, cardiovascular parameters (heart rate, brachial and aortic blood pressure (BP), subendocardial blood flow etc.), allostatic load index (ALI), and data of psychophysiological tests with work capacity assessment were analyzed.
Results: All participants (7 males, 3969 y.o.) completed the planned route. The actual duration of the flight was 43 days. Comparative pairwise analysis on the finish revealed an increase of total blood protein (p0.000), creatinine (p0.000), bilirubine (p=0.038) and AcAT (p=0.031), and glucose (p0.000) levels, and shifts in peripheral blood parameters, which are typical for the process of adaptation to the Polar conditions and transmeridian shifts. An increase of DNA damage in white blood cells was found. Average values of hemodynamic parameters were in a normal range; heart rate increased (p0.000); mean brachial BP (p=0.003) and systolic aortic BP (p=0.001) decreased. Average values of pulse BP and subendocardial blood flow did not change (p0.05). ALI increased in four pilots; in others, no ALI dynamic was observed. Psychophysiological potential decreased after the flight. The number of errors and omissions of target signal (р0.000) increased, attention span and emotional stability deteriorated. Along with this, the mood parameter increased to a maximum level, and risk tolerance level decreased (р0.001).
Conclusions: Working in Arctic conditions is connected with stress factors and leads to multiple and deep changes required for urgent adaptation. Allostatic load index, which is an integral parameter of strained functioning in extreme conditions, can be a marker of individual adaptation. The achieved functional stability of body systems, particularly the cardiovascular system, ensures an acceptable level of work capacity in the considered conditions.
DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis
Authors: Lin Ziqian , Feng Jie , Lu Ziyang , Li Yong , Jin Depeng
Published: September 2019

Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds entering a region in a given time interval) and outflow (the traffic of crowds leaving a region for other places) of each region in the city with knowing the historical flow data. In this paper, we propose DeepSTN+, a deep learning-based convolutional model, to predict crowd flows in the metropolis. First, DeepSTN+ employs the ConvPlus structure to model the longrange spatial dependence among crowd flows in different regions. Further, PoI distributions and time factor are combined to express the effect of location attributes to introduce prior knowledge of the crowd movements. Finally, we propose an effective fusion mechanism to stabilize the training process, which further improves the performance. Extensive experimental results based on two real-life datasets demonstrate the superiority of our model, i.e., DeepSTN+ reduces the error of the crowd flow prediction by approximately 8%∼13% compared with the state-of-the-art baselines.
Difficulty-Aware Attention Network with Confidence Learning for Medical Image Segmentation
Authors: Nie Dong , Wang Li , Xiang Lei , Zhou Sihang , Adeli Ehsan , Shen Dinggang
Published: September 2019

Medical image segmentation is a key step for various applications, such as image-guided radiation therapy and diagnosis. Recently, deep neural networks provided promising solutions for automatic image segmentation; however, they often perform good on regular samples (i.e., easy-to-segment samples), since the datasets are dominated by easy and regular samples. For medical images, due to huge inter-subject variations or disease-specific effects on subjects, there exist several difficult-to-segment cases that are often overlooked by the previous works. To address this challenge, we propose a difficulty-aware deep segmentation network with confidence learning for end-to-end segmentation. The proposed framework has two main contributions: 1) Besides the segmentation network, we also propose a fully convolutional adversarial network for confidence learning to provide voxel-wise and region-wise confidence information for the segmentation network. We relax the adversarial learning to confidence learning by decreasing the priority of adversarial learning, so that we can avoid the training imbalance between generator and discriminator. 2) We propose a difficulty-aware attention mechanism to properly handle hard samples or hard regions considering structural information, which may go beyond the shortcomings of focal loss. We further propose a fusion module to selectively fuse the concatenated feature maps in encoder-decoder architectures. Experimental results on clinical and challenge datasets show that our proposed network can achieve state-of-the-art segmentation accuracy. Further analysis also indicates that each individual component of our proposed network contributes to the overall performance improvement.
PhoneMD: Learning to Diagnose Parkinson’s Disease from Smartphone Data
Authors: Schwab Patrick , Karlen Walter
Published: September 2019

Parkinson’s disease is a neurodegenerative disease that can affect a person’s movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson’s disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson’s disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson’s disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson’s disease.
A2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes
Authors: Xu Kui , Wang Zhe , Shi Jianping , Li Hongsheng , Zhang Qiangfeng Cliff
Published: September 2019

Constructing of molecular structural models from CryoElectron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form the molecular structure. This framework achieves 91% coverage on our newly proposed dataset and takes only a few minutes for a typical structure with a thousand amino acids. Our method is hundreds of times faster and several times more accurate than existing automated solutions without any human intervention.
Learning Phenotypes and Dynamic Patient Representations via RNN Regularized Collective Non-Negative Tensor Factorization
Authors: Yin Kejing , Qian Dong , Cheung William K. , Fung Benjamin C. M. , Poon Jonathan
Published: September 2019

Non-negative Tensor Factorization (NTF) has been shown effective to discover clinically relevant and interpretable phenotypes from Electronic Health Records (EHR). Existing NTF based computational phenotyping models aggregate data over the observation window, resulting in the learned phenotypes being mixtures of disease states appearing at different times. We argue that by separating the clinical events happening at different times in the input tensor, the temporal dynamics and the disease progression within the observation window could be modeled and the learned phenotypes will correspond to more specific disease states. Yet how to construct the tensor for data samples with different temporal lengths and properly capture the temporal relationship specific to each individual data sample remains an open challenge. In this paper, we propose a novel Collective Non-negative Tensor Factorization (CNTF) model where each patient is represented by a temporal tensor, and all of the temporal tensors are factorized collectively with the phenotype definitions being shared across all patients. The proposed CNTF model is also flexible to incorporate non-temporal data modality and RNN-based temporal regularization. We validate the proposed model using MIMIC-III dataset, and the empirical results show that the learned phenotypes are clinically interpretable. Moreover, the proposed CNTF model outperforms the state-of-the-art computational phenotyping models for the mortality prediction task.
TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding
Authors: Zhang Lei , Zhou Shengyuan , Zhi Tian , Du Zidong , Chen Yunji
Published: September 2019

Continuous-valued deep convolutional networks (DNNs) can be converted into accurate rate-coding based spike neural networks (SNNs). However, the substantial computational and energy costs, which is caused by multiple spikes, limit their use in mobile and embedded applications. And recent works have shown that the newly emerged temporal-coding based SNNs converted from DNNs can reduce the computational load effectively. In this paper, we propose a novel method to convert DNNs to temporal-coding SNNs, called TDSNN. Combined with the characteristic of the leaky integrate-andfire (LIF) neural model, we put forward a new coding principle Reverse Coding and design a novel Ticking Neuron mechanism. According to our evaluation, our proposed method achieves 42% total operations reduction on average in large networks comparing with DNNs with no more than 0.5% accuracy loss. The evaluation shows that TDSNN may prove to be one of the key enablers to make the adoption of SNNs widespread.
Interpretable Predictive Modeling for Climate Variables with Weighted Lasso
Authors: He Sijie , Li Xinyan , Sivakumar Vidyashankar , Banerjee Arindam
Published: September 2019

An important family of problems in climate science focus on finding predictive relationships between various climate variables. In this paper, we consider the problem of predicting monthly deseasonalized land temperature at different locations worldwide based on sea surface temperature (SST). Contrary to popular belief on the trade-off between (a) simple interpretable but inaccurate models and (b) complex accurate but uninterpretable models, we introduce a weighted Lasso model for the problem which yields interpretable results while being highly accurate. Covariate weights in the regularization of weighted Lasso are pre-determined, and proportional to the spatial distance of the covariate (sea surface location) from the target (land location). We establish finite sample estimation error bounds for weighted Lasso, and illustrate its superior empirical performance and interpretability over complex models such as deep neural networks (Deep nets) and gradient boosted trees (GBT). We also present a detailed empirical analysis of what went wrong with Deep nets here, which may serve as a helpful guideline for application of Deep nets to small sample scientific problems.
Deep Reinforcement Learning for Green Security Games with Real-Time Information
Authors: Wang Yufei , Shi Zheyuan Ryan , Yu Lantao , Wu Yi , Singh Rohit , Joppa Lucas , Fang Fei
Published: September 2019

Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing. However, real-time information such as footprints and agents’ subsequent actions upon receiving the information, e.g., rangers following the footprints to chase the poacher, have been neglected in previous work. To fill the gap, we first propose a new game model GSG-I which augments GSGs with sequential movement and the vital element of real-time information. Second, we design a novel deep reinforcement learning-based algorithm, DeDOL, to compute a patrolling strategy that adapts to the real-time information against a best-responding attacker. DeDOL is built upon the double oracle framework and the policy-space response oracle, solving a restricted game and iteratively adding best response strategies to it through training deep Q-networks. Exploring the game structure, DeDOL uses domain-specific heuristic strategies as initial strategies and constructs several local modes for efficient and parallelized training. To our knowledge, this is the first attempt to use Deep Q-Learning for security games.
A Nonconvex Projection Method for Robust PCA
Authors: Dutta Aritra , Hanzely Filip , Richtàrik Peter
Published: September 2019

Robust principal component analysis (RPCA) is a well-studied problem whose goal is to decompose a matrix into the sum of low-rank and sparse components. In this paper, we propose a nonconvex feasibility reformulation of RPCA problem and apply an alternating projection method to solve it. To the best of our knowledge, this is the first paper proposing a method that solves RPCA problem without considering any objective function, convex relaxation, or surrogate convex constraints. We demonstrate through extensive numerical experiments on a variety of applications, including shadow removal, background estimation, face detection, and galaxy evolution, that our approach matches and often significantly outperforms current state-of-the-art in various ways.
A Recursive Algorithm for Projected Model Counting
Authors: Lagniez Jean-Marie , Marquis Pierre
Published: September 2019

We present a recursive algorithm for projected model counting, i.e., the problem consisting in determining the number of models k∃X.Σk of a propositional formula Σ after eliminating from it a given set X of variables. Based on a ”standard” model counter, our algorithm projMC takes advantage of a disjunctive decomposition scheme of ∃X.Σ for computing k∃X.Σk. It also looks for disjoint components in its input for improving the computation. Our experiments show that in many cases projMC is significantly more efficient than the previous algorithms for projected model counting from the literature.
Learning Optimal Classification Trees Using a Binary Linear Program Formulation
Authors: Verwer Sicco , Zhang Yingqian
Published: September 2019

We provide a new formulation for the problem of learning the optimal classification tree of a given depth as a binary linear program. A limitation of previously proposed Mathematical Optimization formulations is that they create constraints and variables for every row in the training data. As a result, the running time of the existing Integer Linear programming (ILP) formulations increases dramatically with the size of data. In our new binary formulation, we aim to circumvent this problem by making the formulation size largely independent from the training data size. We show experimentally that our formulation achieves better performance than existing formulations on both small and large problem instances within shorter running time.
Asynchronous Proximal Stochastic Gradient Algorithm for Composition Optimization Problems
Authors: Wang Pengfei , Liu Risheng , Zheng Nenggan , Gong Zhefeng
Published: September 2019

In machine learning research, many emerging applications can be (re)formulated as the composition optimization problem with nonsmooth regularization penalty. To solve this problem, traditional stochastic gradient descent (SGD) algorithm and its variants either have low convergence rate or are computationally expensive. Recently, several stochastic composition gradient algorithms have been proposed, however, these methods are still inefficient and not scalable to large-scale composition optimization problem instances. To address these challenges, we propose an asynchronous parallel algorithm, named Async-ProxSCVR, which effectively combines asynchronous parallel implementation and variance reduction method. We prove that the algorithm admits the fastest convergence rate for both strongly convex and general nonconvex cases. Furthermore, we analyze the query complexity of the proposed algorithm and prove that linear speedup is accessible when we increase the number of processors. Finally, we evaluate our algorithm Async-ProxSCVR on two representative composition optimization problems including value function evaluation in reinforcement learning and sparse mean-variance optimization problem. Experimental results show that the algorithm achieves significant speedups and is much faster than existing compared methods.
Adding Constraints to Bayesian Inverse Problems
Authors: Wu Jiacheng , Wang Jian-Xun , Shadden Shawn C.
Published: September 2019

Using observation data to estimate unknown parameters in computational models is broadly important. This task is often challenging because solutions are non-unique due to the complexity of the model and limited observation data. However, the parameters or states of the model are often known to satisfy additional constraints beyond the model. Thus, we propose an approach to improve parameter estimation in such inverse problems by incorporating constraints in a Bayesian inference framework. Constraints are imposed by constructing a likelihood function based on fitness of the solution to the constraints. The posterior distribution of the parameters conditioned on (1) the observed data and (2) satisfaction of the constraints is obtained, and the estimate of the parameters is given by the maximum a posteriori estimation or posterior mean. Both equality and inequality constraints can be considered by this framework, and the strictness of the constraints can be controlled by constraint uncertainty denoting a confidence on its correctness. Furthermore, we extend this framework to an approximate Bayesian inference framework in terms of the ensemble Kalman filter method, where the constraint is imposed by re-weighing the ensemble members based on the likelihood function. A synthetic model is presented to demonstrate the effectiveness of the proposed method and in both the exact Bayesian inference and ensemble Kalman filter scenarios, numerical simulations show that imposing constraints using the method presented improves identification of the true parameter solution among multiple local minima.
Regular Boardgames
Authors: Kowalski Jakub , Mika Maksymilian , Sutowicz Jakub , Szykuła Marek
Published: September 2019

We propose a new General Game Playing (GGP) language called Regular Boardgames (RBG), which is based on the theory of regular languages. The objective of RBG is to join key properties as expressiveness, efficiency, and naturalness of the description in one GGP formalism, compensating certain drawbacks of the existing languages. This often makes RBG more suitable for various research and practical developments in GGP. While dedicated mostly for describing board games, RBG is universal for the class of all finite deterministic turn-based games with perfect information. We establish foundations of RBG, and analyze it theoretically and experimentally, focusing on the efficiency of reasoning. Regular Boardgames is the first GGP language that allows efficient encoding and playing games with complex rules and with large branching factor (e.g. amazons, arimaa, large chess variants, go, international checkers, paper soccer).
Unknown Agents in Friends Oriented Hedonic Games: Stability and Complexity
Authors: Barrot Nathanaël , Ota Kazunori , Sakurai Yuko , Yokoo Makoto
Published: September 2019

We study hedonic games under friends appreciation, where each agent considers other agents friends, enemies, or unknown agents. Although existing work assumed that unknown agents have no impact on an agent’s preference, it may be that her preference depends on the number of unknown agents in her coalition. We extend the existing preference, friends appreciation, by proposing two alternative attitudes toward unknown agents, extraversion and introversion, depending on whether unknown agents have a slightly positive or negative impact on preference. When each agent prefers coalitions with more unknown agents, we show that both core stable outcomes and individually stable outcomes may not exist. We also prove that deciding the existence of the core and the existence of an individual stable coalition structure are respectively NPNP-complete and NP-complete.
Popularity Prediction on Online Articles with Deep Fusion of Temporal Process and Content Features
Authors: Liao Dongliang , Xu Jin , Li Gongfu , Huang Weijie , Liu Weiqing , Li Jing
Published: September 2019

Predicting the popularity of online article sheds light to many applications such as recommendation, advertising and information retrieval. However, there are several technical challenges to be addressed for developing the best of predictive capability. (1) The popularity fluctuates under impacts of external factors, which are unpredictable and hard to capture. (2) Content and meta-data features, largely determining the online content popularity, are usually multi-modal and nontrivial to model. (3) Besides, it also needs to figure out how to integrate temporal process and content features modeling for popularity prediction in different lifecycle stages of online articles. In this paper, we propose a Deep Fusion of Temporal process and Content features (DFTC) method to tackle them. For modeling the temporal popularity process, we adopt the recurrent neural network and convolutional neural network. For multi-modal content features, we exploit the hierarchical attention network and embedding technique. Finally, a temporal attention fusion is employed for dynamically integrating all these parts. Using datasets collected from WeChat, we show that the proposed model significantly outperforms state-of-the-art approaches on popularity prediction.
SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning
Authors: Ma Jiaqi , Zhao Zhe , Chen Jilin , Li Ang , Hong Lichan , Chi Ed H.
Published: September 2019

Machine learning applications, such as object detection and content recommendation, often require training a single model to predict multiple targets at the same time. Multi-task learning through neural networks became popular recently, because it not only helps improve the accuracy of many prediction tasks when they are related, but also saves computation cost by sharing model architectures and low-level representations. The latter is critical for real-time large-scale machine learning systems.

However, classic multi-task neural networks may degenerate significantly in accuracy when tasks are less related. Previous works (Misra et al. 2016; Yang and Hospedales 2016; Ma et al. 2018) showed that having more flexible architectures in multi-task models, either manually-tuned or softparameter-sharing structures like gating networks, helps improve the prediction accuracy. However, manual tuning is not scalable, and the previous soft-parameter sharing models are either not flexible enough or computationally expensive.

In this work, we propose a novel framework called SubNetwork Routing (SNR) to achieve more flexible parameter sharing while maintaining the computational advantage of the classic multi-task neural-network model. SNR modularizes the shared low-level hidden layers into multiple layers of subnetworks, and controls the connection of sub-networks with learnable latent variables to achieve flexible parameter sharing. We demonstrate the effectiveness of our approach on a large-scale dataset YouTube8M. We show that the proposed method improves the accuracy of multi-task models while maintaining their computation efficiency.
Unsupervised Neural Machine Translation with SMT as Posterior Regularization
Authors: Ren Shuo , Zhang Zhirui , Liu Shujie , Zhou Ming , Ma Shuai
Published: September 2019

Without real bilingual corpus available, unsupervised Neural Machine Translation (NMT) typically requires pseudo parallel data generated with the back-translation method for the model training. However, due to weak supervision, the pseudo data inevitably contain noises and errors that will be accumulated and reinforced in the subsequent training process, leading to bad translation performance. To address this issue, we introduce phrase based Statistic Machine Translation (SMT) models which are robust to noisy data, as posterior regularizations to guide the training of unsupervised NMT models in the iterative back-translation process. Our method starts from SMT models built with pre-trained language models and word-level translation tables inferred from cross-lingual embeddings. Then SMT and NMT models are optimized jointly and boost each other incrementally in a unified EM framework. In this way, (1) the negative effect caused by errors in the iterative back-translation process can be alleviated timely by SMT filtering noises from its phrase tables; meanwhile, (2) NMT can compensate for the deficiency of fluency inherent in SMT. Experiments conducted on en-fr and en-de translation tasks show that our method outperforms the strong baseline and achieves new state-of-the-art unsupervised machine translation performance.
Incorporating Behavioral Constraints in Online AI Systems
Authors: Balakrishnan Avinash , Bouneffouf Djallel , Mattei Nicholas , Rossi Francesca
Published: September 2019

AI systems that learn through reward feedback about the actions they take are increasingly deployed in domains that have significant impact on our daily life. However, in many cases the online rewards should not be the only guiding criteria, as there are additional constraints and/or priorities imposed by regulations, values, preferences, or ethical principles. We detail a novel online agent that learns a set of behavioral constraints by observation and uses these learned constraints as a guide when making decisions in an online setting while still being reactive to reward feedback. To define this agent, we propose to adopt a novel extension to the classical contextual multi-armed bandit setting and we provide a new algorithm called Behavior Constrained Thompson Sampling (BCTS) that allows for online learning while obeying exogenous constraints. Our agent learns a constrained policy that implements the observed behavioral constraints demonstrated by a teacher agent, and then uses this constrained policy to guide the reward-based online exploration and exploitation. We characterize the upper bound on the expected regret of the contextual bandit algorithm that underlies our agent and provide a case study with real world data in two application domains. Our experiments show that the designed agent is able to act within the set of behavior constraints without significantly degrading its overall reward performance.
Session-Based Recommendation with Graph Neural Networks
Authors: Wu Shu , Tang Yuyuan , Zhu Yanqiao , Wang Liang , Xie Xing , Tan Tieniu
Published: September 2019

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.
Structured and Sparse Annotations for Image Emotion Distribution Learning
Authors: Xiong Haitao , Liu Hongfu , Zhong Bineng , Fu Yun
Published: September 2019

Label distribution learning methods effectively address the label ambiguity problem and have achieved great success in image emotion analysis. However, these methods ignore structured and sparse information naturally contained in the annotations of emotions. For example, emotions can be grouped and ordered due to their polarities and degrees. Meanwhile, emotions have the character of intensity and are reflected in different levels of sparse annotations. Motivated by these observations, we present a convolutional neural network based framework called Structured and Sparse annotations for image emotion Distribution Learning (SSDL) to tackle two challenges. In order to utilize structured annotations, the Earth Mover’s Distance is employed to calculate the minimal cost required to transform one distribution to another for ordered emotions and emotion groups. Combined with Kullback-Leibler divergence, we design the loss to penalize the mispredictions according to the dissimilarities of same emotions and different emotions simultaneously. Moreover, in order to handle sparse annotations, sparse regularization based on emotional intensity is adopted. Through combined loss and sparse regularization, SSDL could effectively leverage structured and sparse annotations for predicting emotion distribution. Experiment results demonstrate that our proposed SSDL significantly outperforms the state-of-the-art methods.
Dynamic Explainable Recommendation Based on Neural Attentive Models
Authors: Chen Xu , Zhang Yongfeng , Qin Zheng
Published: September 2019

Providing explanations in a recommender system is getting more and more attention in both industry and research communities. Most existing explainable recommender models regard user preferences as invariant to generate static explanations. However, in real scenarios, a user’s preference is always dynamic, and she may be interested in different product features at different states. The mismatching between the explanation and user preference may degrade costumers’ satisfaction, confidence and trust for the recommender system.

With the desire to fill up this gap, in this paper, we build a novel Dynamic Explainable Recommender (called DER) for more accurate user modeling and explanations. In specific, we design a time-aware gated recurrent unit (GRU) to model user dynamic preferences, and profile an item by its review information based on sentence-level convolutional neural network (CNN). By attentively learning the important review information according to the user current state, we are not only able to improve the recommendation performance, but also can provide explanations tailored for the users’ current preferences. We conduct extensive experiments to demonstrate the superiority of our model for improving recommendation performance. And to evaluate the explainability of our model, we first present examples to provide intuitive analysis on the highlighted review information, and then crowd-sourcing based evaluations are conducted to quantitatively verify our model’s superiority.
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Authors: Irvin Jeremy , Rajpurkar Pranav , Ko Michael , Yu Yifan , Ciurea-Ilcus Silviana , Chute Chris , Marklund Henrik , Haghgoo Behzad , Ball Robyn , Shpanskaya Katie , Seekins Jayne , Mong David A. , Halabi Safwan S. , Sandberg Jesse K. , Jones Ricky , Larson David B. , Langlotz Curtis P. , Patel Bhavik N. , Lungren Matthew P. , Ng Andrew Y.
Published: September 2019

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models.
Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks
Authors: Liang Shuailong , Nicol Olivia , Zhang Yue
Published: September 2019

Blame games tend to follow major disruptions, be they financial crises, natural disasters or terrorist attacks. To study how the blame game evolves and shapes the dominant crisis narratives is of great significance, as sense-making processes can affect regulatory outcomes, social hierarchies, and cultural norms. However, it takes tremendous time and efforts for social scientists to manually examine each relevant news article and extract the blame ties (A blames B). In this study, we define a new task, Blame Tie Extraction, and construct a new dataset related to the United States financial crisis (20072010) from The New York Times, The Wall Street Journal and USA Today. We build a Bi-directional Long Short-Term Memory (BiLSTM) network for contexts where the entities appear in and it learns to automatically extract such blame ties at the document level. Leveraging the large unsupervised model such as GloVe and ELMo, our best model achieves an F1 score of 70% on the test set for blame tie extraction, making it a useful tool for social scientists to extract blame ties more efficiently.