bluuebunny/crossref_metadata_2025 huggingface dataset:
Tatalaksana Pasien Post Ventriculo Peritoneal (VP) Shunt et causa Meningitis disertai Aspirasi Pneumonia dan Gagal Napas di Ruang Rawat Intensif
Authors: Kurniawan Arief , Zulfariansyah Ardi
Published: July 2020

Ventriculo Peritoneal (VP) Shunt adalah tindakan operasi pemasangan kateter yang menghubungkan ventrikel otak dan rongga peritoneum. Tindakan VP Shunt bertujuan menurunkan tekanan intrakranial yang tinggi pada hidrosefalus. Pasien hidrosefalus akibat meningitis sering mengalami gagal napas akibat gangguan otak atau aspirasi pneumonia. Terapi ventilasi mekanik diperlukan sebelum, selama dan setelah operasi VP Shunt. Laporan kasus laki-laki 21 tahun datang ke rumah sakit karena gangguan kesadaran dan didiagnosis dengan meningitis. Pasien mengalami gagal napas akibat aspirasi pneumonia dan mendapat terapi ventilasi mekanik, kemudian menjalani operasi VP Shunt atas indikasi hidrosefalus komunikan. Induksi anestesi menggunakan fentanil dan propofol, pemeliharaan anestesi menggunakan sevofluran dan analgesi pascabedah menggunakan fentanil kontinyu. Berdasarkan pemeriksaan cairan serebrospinal pasien didiagnosis meningitis serosa akibat Tuberkulosis. Pasien mendapat terapi antibiotik empirik untuk aspirasi pneumonia dan antituberkulosis. Setelah kesadaran membaik dan weaning ventilator pasien dipindahkan dari ruang perawatan intensif. Management Patient Post Ventriculo Peritoneal (VP) shunt et causa Meningitis with Pneumonia Aspiration and Respiratory Failure in ICUAbstractVentriculo Peritoneal (VP) Shunt is a surgery that connects the brain ventricles and the peritoneal cavity with a catheter. VP Shunt aims to reduce high intracranial pressure in hydrocephalus. Hydrocephalus patients due to meningitis often experience respiratory failure from brain disorder or pneumonia aspiration. Mechanical ventilation therapy is needed before, during and after VP Shunt surgery. Case report of 21-year-old came to the hospital with decreased of conciousness and diagnosed with meningitis. The patient experienced respiratory failure and received mechanical ventilation therapy in the intensive care unit. The patient underwent VP Shunt surgery for indications of communicant hydrocephalus. Induction of anesthesia used fentanyl and propofol, maintenance of anesthesia used sevoflurane and postoperative analgesia used continuous fentanyl. Based on the results of the examination of cerebrospinal fluid, the diagnosis leads to serous meningitis due to Tuberculosis bacteria. Empiric antibiotics therapy for aspiration pneumonia and antituberculosis were given even if no bacteria was found in the blood or cerebrospinal fluid. After improvement in clinical condition and weaning from mechanical ventilator, the patient discharge from the ICU and sent to the ward.
ANALISIS PERBADINGAN KINERJA KEUANGAN DAN ABNORMAL RETURN SEBELUM DAN SESDUAH AKUISISI PADA PERUSAHAAN YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2014
Authors: Sakinah Ulfa , Ikhsan Adhisyahfitri Evalina
Published: July 2020

The purpose of this study was to see a comparison of financial performance and abnormal return of companies before and after the acquisition of companies listed on the Stock Exchange in 2014. The number of samples used in this study were 10 companies, with a purposive sampling approach. The analysis technique used was paired sample t test and Wilcoxon signed rank test. The variables used are return on equity, current ratio, debt to equity ratio, total asset turn over, earnings per share, and abnormal return. Testing the hypothesis in this study using the sample t-test and Wilcoxon signed ranks test. The results of this study indicate that there are differences in the ratio of total asset turn over, while the return on equity ratio, current ratio, debt to equity ratio, earnings per share, and abnormal return there are no significant differences between before and after the company makes an acquisition.
PENERIMAAN DARI PEMERINTAH PUSAT, UKURAN LEGISLATIF, DAN TEMUAN AUDIT TERHADAP TINGKAT PENGUNGKAPAN LAPORAN KEUANGAN PEMERINTAH DAERAH (STUDI KASUS PADA PEMERINTAH DAERAH KABUPATEN/KOTA DI ACEH)
Authors: Fuadi M. Zahrul , Asmara Jhon Andra
Published: July 2020

This study aims to see the influence of regional distribution characteristics, legislative measures, and audit findingsagainst the level of disclosure of Preliminary Unaudited Regional Government Financial Report. The population that used in this study is the District and Urban District Government in Aceh Province during  2015-2017. This study usespurposive sampling techniques, so the selection of samples is done by according to specific considerations or goals.The independent variables examined in this study are regional government size, regional independence, intergovernmental revenue, legislative measures, and audit findings. The dependent variable is the level of disclosure of Preliminary Unaudited Regional Government Financial Report. The type of data used in this study is secondary data. The analysis used in this study is multiple linear regression. The data is processed by using the SPSS software test tool. The results showed that variables of regional government size, regional independence, intergovernmental revenue, legislative measures, and audit findings have positive affect on the level of disclosure of Preliminary Unaudited Regional Government Financial Report. Partially, intergovernmental revenue and audit findings have negative affect on the level of disclosure of Preliminary Unaudited Regional Government Financial Report, while regional government measures, regional independence and legislative measures have no significant effect on the level of disclosure of Preliminary Unaudited Regional Government Financial Report.
To the Anniversary of Group Psychology and Analysis of the Ego by Z. Freud
Authors: Dodeltsev R. F.
Published: July 2020

.
Definition as a Logical and Linguistic Category
Authors: Blagova A. R.
Published: July 2020

Definition is a logical and linguistic category that for a long time has been the subject of logicians’ and philosophers’ research. Today we understand a definition as a logical method that allows us to discover a word’s content, i.e. what it means in everyday use or what a speaker intends it to mean for this speaker’s own specific purposes. A definition consists of two parts: definiendum (that what is defined) and definiens (that which defines). The definiendum refers to the exact object, action, state etc., that is to be defined. The definiens contains the information necessary to define this object, action, state etc. This information is obtained during the process of definition. In the language, a definition can mean both the process of developing a sentence and the result of this process, i.e. the sentence itself. When composing an explanation of a word’s lexical meaning, we should draw on the vast experience of Russian lexicography. Practical lexicography gave us a wide range of kinds of word definitions which are now being extensively researched as theoretical generalizations and conclusions. This article explores methods of explanation of word meanings in the context of different possible logical relations between definiendum and definiens: inclusion, overlap, complementarity, adjacency. The existing word definitions (hyponymic, identifying, enumerative, synonymic, antonymic) can be distributed between those kinds of logical relations. Descriptive explanations are regarded as a specific kind of definition. Theoretical generalizations and conclusions are backed by examples from the main explanatory dictionaries of the Russian language.The conclusion is drawn that parts of a word definition can enter into different kinds of logical relations. Exploration of those relations is highly important for educational purposes as methodological basis for correct definitions of word meanings as well as for the composition of lexicographic explanations. At the same time, we shouldn’t forget that there are various ways to define a word’s lexical meaning. One and the same word can be defined in a number of ways, so the lexicographers’ main task is still to choose the best way or combination of ways that can fully and accurately discover the meaning of the word being defined.
V.D. Бonch-Bruyevich: «Professional» Revolutionary and One of the Founders of the Study of Religion in Soviet Union
Authors: Pyzikov D. D.
Published: July 2020

The article is devoted to the first stage of the life and work of the scientist and researcher of minority religious groups, V.D. Bonch-Bruyevich (1873-1955). This choice is not accidental, since the beginning of the scientist’s creative and research journey is inextricably linked with his development as an individual and personality, his human qualities, desires and aspirations. Based on the methodology of intellectual history, the article attempts to reconstruct the history of the life and work of Bonch-Bruyevich. Thanks to the friendship with V.I. Lenin and active revolutionary activities, an important role was played by organizational work, as well as propaganda (from the early years of the 20th century, when he was an editor, publisher and employee of social democratic and Bolshevik newspapers and magazines). V.D. Bonch-Bruyevich entered the nomenklatura elite of the new state and in 1918 occupied the position of business manager of the Council of People’s Commissars. But unlike many activists of the revolutionary movement who before the revolution of 1917 perceived representatives of minority religious groups as «companions», he defended the believers’ rights to freedom of conscience before and after the revolution. At the same time, Bonch-Bruyevich never left his scientific activity and continued to practise until his last days, combining organizational and scientific work. His huge contribution to the study of religion in Russia and the formation of Soviet religious studies can not be overestimated. Studying the life and work of Bonch-Bruyevich, like other researchers of the Soviet period, is quite important, especially in the context of modern debates about Soviet humanities.
Feature Augmented Memory with Global Attention Network for VideoQA
Authors: Cai Jiayin , Yuan Chun , Shi Cheng , Li Lei , Cheng Yangyang , Shan Ying
Published: July 2020

Recently, Recurrent Neural Network (RNN) based methods and Self-Attention (SA) based methods have achieved promising performance in Video Question Answering (VideoQA). Despite the success of these works, RNN-based methods tend to forget the global semantic contents due to the inherent drawbacks of the recurrent units themselves, while SA-based methods cannot precisely capture the dependencies of the local neighborhood, leading to insufficient modeling for temporal order. To tackle these problems, we propose a novel VideoQA framework which progressively refines the representations of videos and questions from fine to coarse grain in a sequence-sensitive manner. Specifically, our model improves the feature representations via the following two steps: (1) introducing two fine-grained feature-augmented memories to strengthen the information augmentation of video and text which can improve memory capacity by memorizing more relevant and targeted information. (2) appending the self-attention and co-attention module to the memory output thus the module is able to capture global interaction between high-level semantic informations. Experimental results show that our approach achieves state-of-the-art performance on VideoQA benchmark datasets.
Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering
Authors: Zhu Zihao , Yu Jing , Wang Yujing , Sun Yajing , Hu Yue , Wu Qi
Published: July 2020

Fact-based Visual Question Answering (FVQA) requires external knowledge beyond the visible content to answer questions about an image. This ability is challenging but indispensable to achieve general VQA. One limitation of existing FVQA solutions is that they jointly embed all kinds of information without fine-grained selection, which introduces unexpected noises for reasoning the final answer. How to capture the question-oriented and information-complementary evidence remains a key challenge to solve the problem. In this paper, we depict an image by a multi-modal heterogeneous graph, which contains multiple layers of information corresponding to the visual, semantic and factual features. On top of the multi-layer graph representations, we propose a modality-aware heterogeneous graph convolutional network to capture evidence from different layers that is most relevant to the given question. Specifically, the intra-modal graph convolution selects evidence from each modality and cross-modal graph convolution aggregates relevant information across different graph layers. By stacking this process multiple times, our model performs iterative reasoning across three modalities and predicts the optimal answer by analyzing all question-oriented evidence. We achieve a new state-of-the-art performance on the FVQA task and demonstrate the effectiveness and interpretability of our model with extensive experiments.
When Do GNNs Work: Understanding and Improving Neighborhood Aggregation
Authors: Xie Yiqing , Li Sha , Yang Carl , Wong Raymond Chi-Wing , Han Jiawei
Published: July 2020

Graph Neural Networks (GNNs) have been shown to be powerful in a wide range of graph-related tasks. While there exists various GNN models, a critical common ingredient is neighborhood aggregation, where the embedding of each node is updated by referring to the embedding of its neighbors. This paper aims to provide a better understanding of this mechanisms by asking the following question: Is neighborhood aggregation always necessary and beneficial? In short, the answer is no. We carve out two conditions under which neighborhood aggregation is not helpful: (1) when a node's neighbors are highly dissimilar and (2) when a node's embedding is already similar with that of its neighbors. We propose novel metrics that quantitatively measure these two circumstances and integrate them into an Adaptive-layer module. Our experiments show that allowing for node-specific aggregation degrees have significant advantage over current GNNs.
GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions
Authors: Wang Hanchen , Lian Defu , Zhang Ying , Qin Lu , Lin Xuemin
Published: July 2020

Entity interaction prediction is essential in many important applications such as chemistry, biology, material science, and medical science. The problem becomes quite challenging when each entity is represented by a complex structure, namely structured entity, because two types of graphs are involved: local graphs for structured entities and a global graph to capture the interactions between structured entities. We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model. In this paper, we propose a Graph of Graphs Neural Network, namely GoGNN, which extracts the features in both structured entity graphs and the entity interaction graph in a hierarchical way. We also propose the dual-attention mechanism that enables the model to preserve the neighbor importance in both levels of graphs. Extensive experiments on real-world datasets show that GoGNN outperforms the state-of-the-art methods on two representative structured entity interaction prediction tasks: chemical-chemical interaction prediction and drug-drug interaction prediction. Our code is available at Github.
JANE: Jointly Adversarial Network Embedding
Authors: Yang Liang , Wang Yuexue , Gu Junhua , Wang Chuan , Cao Xiaochun , Guo Yuanfang
Published: July 2020

Motivated by the capability of Generative Adversarial Network on exploring the latent semantic space and capturing semantic variations in the data distribution, adversarial learning has been adopted in network embedding to improve the robustness. However, this important ability is lost in existing adversarially regularized network embedding methods, because their embedding results are directly compared to the samples drawn from perturbation (Gaussian) distribution without any rectification from real data. To overcome this vital issue, a novel Joint Adversarial Network Embedding (JANE) framework is proposed to jointly distinguish the real and fake combinations of the embeddings, topology information and node features. JANE contains three pluggable components, Embedding module, Generator module and Discriminator module.

The overall objective function of JANE is defined in a min-max form, which can be optimized via alternating stochastic gradient. Extensive experiments demonstrate the remarkable superiority of the proposed JANE on link prediction (3% gains in both AUC and AP) and node clustering (5% gain in F1 score).
Bilinear Graph Neural Network with Neighbor Interactions
Authors: Zhu Hongmin , Feng Fuli , He Xiangnan , Wang Xiang , Li Yan , Zheng Kai , Zhang Yongdong
Published: July 2020

Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the representation of the target node. Nevertheless, the operation of weighted sum assumes the neighbor nodes are independent of each other, and ignores the possible interactions between them. When such interactions exist, such as the co-occurrence of two neighbor nodes is a strong signal of the target node's characteristics, existing GNN models may fail to capture the signal. In this work, we argue the importance of modeling the interactions between neighbor nodes in GNN. We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes. We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes. In particular, we specify two BGNN models named BGCN and BGAT, based on the well-known GCN and GAT, respectively. Empirical results on three public benchmarks of semi-supervised node classification verify the effectiveness of BGNN --- BGCN (BGAT) outperforms GCN (GAT) by 1.6% (1.5%) in classification accuracy. Codes are available at: https://github.com/zhuhm1996/bgnn.
Learning Regional Attention Convolutional Neural Network for Motion Intention Recognition Based on EEG Data
Authors: Fang Zhijie , Wang Weiqun , Ren Shixin , Wang Jiaxing , Shi Weiguo , Liang Xu , Fan Chen-Chen , Hou Zeng-Guang
Published: July 2020

Recent deep learning-based Brain-Computer Interface (BCI) decoding algorithms mainly focus on spatial-temporal features, while failing to explicitly explore spectral information which is one of the most important cues for BCI. In this paper, we propose a novel regional attention convolutional neural network (RACNN) to take full advantage of spectral-spatial-temporal features for EEG motion intention recognition. Time-frequency based analysis is adopted to reveal spectral-temporal features in terms of neural oscillations of primary sensorimotor. The basic idea of RACNN is to identify the activated area of the primary sensorimotor adaptively. The RACNN aggregates a varied number of spectral-temporal features produced by a backbone convolutional neural network into a compact fixed-length representation. Inspired by the neuroscience findings that functional asymmetry of the cerebral hemisphere, we propose a region biased loss to encourage high attention weights for the most critical regions. Extensive evaluations on two benchmark datasets and real-world BCI dataset show that our approach significantly outperforms previous methods.
Improving Knowledge Tracing via Pre-training Question Embeddings
Authors: Liu Yunfei , Yang Yang , Chen Xianyu , Shen Jian , Zhang Haifeng , Yu Yong
Published: July 2020

Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response. Although much research has been devoted to exploiting the question information, plentiful advanced information among questions and skills hasn't been well extracted, making it challenging for previous work to perform adequately. In this paper, we demonstrate that large gains on KT can be realized by pre-training embeddings for each question on abundant side information, followed by training deep KT models on the obtained embeddings. To be specific, the side information includes question difficulty and three kinds of relations contained in a bipartite graph between questions and skills. To pre-train the question embeddings, we propose to use product-based neural networks to recover the side information. As a result, adopting the pre-trained embeddings in existing deep KT models significantly outperforms state-of-the-art baselines on three common KT datasets.
Smart Voting
Authors: Colley Rachael , Grandi Umberto , Novaro Arianna
Published: July 2020

We propose a generalisation of liquid democracy in which a voter can either vote directly on the issues at stake, delegate her vote to another voter, or express complex delegations to a set of trusted voters. By requiring a ranking of desirable delegations and a backup vote from each voter, we are able to put forward and compare four algorithms to solve delegation cycles and obtain a final collective decision.
Reinforcement Learning with Dynamic Boltzmann Softmax Updates
Authors: Pan Ling , Cai Qingpeng , Meng Qi , Chen Wei , Huang Longbo
Published: July 2020

Value function estimation is an important task in reinforcement learning, i.e., prediction. The Boltzmann softmax operator is a natural value estimator and can provide several benefits. However, it does not satisfy the non-expansion property, and its direct use may fail to converge even in value iteration. In this paper, we propose to update the value function with dynamic Boltzmann softmax (DBS) operator, which has good convergence property in the setting of planning and learning. Experimental results on GridWorld show that the DBS operator enables better estimation of the value function, which rectifies the convergence issue of the softmax operator. Finally, we propose the DBS-DQN algorithm by applying the DBS operator, which outperforms DQN substantially in 40 out of 49 Atari games.
Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning
Authors: Feng Dieqiao , Gomes Carla , Selman Bart
Published: July 2020

Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems. Sokoban is a PSPACE-complete planning task and represents one of the hardest domains for current AI planners. Even domain-specific specialized search methods fail quickly due to the exponential search complexity on hard instances. Our approach based on deep reinforcement learning augmented with a curriculum-driven method is the first one to solve hard instances within one day of training while other modern solvers cannot solve these instances within any reasonable time limit. In contrast to prior efforts, which use carefully handcrafted pruning techniques, our approach automatically uncovers domain structure. Our results reveal that deep RL provides a promising framework for solving previously unsolved AI planning problems, provided a proper training curriculum can be devised.
Unsupervised Monocular Visual-inertial Odometry Network
Authors: Wei Peng , Hua Guoliang , Huang Weibo , Meng Fanyang , Liu Hong
Published: July 2020

Recently, unsupervised methods for monocular visual odometry (VO), with no need for quantities of expensive labeled ground truth, have attracted much attention. However, these methods are inadequate for long-term odometry task, due to the inherent limitation of only using monocular visual data and the inability to handle the error accumulation problem. By utilizing supplemental low-cost inertial measurements, and exploiting the multi-view geometric constraint and sequential constraint, an unsupervised visual-inertial odometry framework (UnVIO) is proposed in this paper. Our method is able to predict the per-frame depth map, as well as extracting and self-adaptively fusing visual-inertial motion features from image-IMU stream to achieve long-term odometry task. A novel sliding window optimization strategy, which consists of an intra-window and an inter-window optimization, is introduced for overcoming the error accumulation and scale ambiguity problem. The intra-window optimization restrains the geometric inferences within the window through checking the photometric consistency. And the inter-window optimization checks the 3D geometric consistency and trajectory consistency among predictions of separate windows. Extensive experiments have been conducted on KITTI and Malaga datasets to demonstrate the superiority of UnVIO over other state-of-the-art VO / VIO methods. The codes are open-source.
Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning
Authors: Zhu Hao , Huang Huaibo , Li Yi , Zheng Aihua , He Ran
Published: July 2020

Talking face generation aims to synthesize a face video with precise lip synchronization as well as a smooth transition of facial motion over the entire video via the given speech clip and facial image. Most existing methods mainly focus on either disentangling the information in a single image or learning temporal information between frames. However, cross-modality coherence between audio and video information has not been well addressed during synthesis. In this paper, we propose a novel arbitrary talking face generation framework by discovering the audio-visual coherence via the proposed Asymmetric Mutual Information Estimator (AMIE). In addition, we propose a Dynamic Attention (DA) block by selectively focusing the lip area of the input image during the training stage, to further enhance lip synchronization. Experimental results on benchmark LRW dataset and GRID dataset transcend the state-of-the-art methods on prevalent metrics with robust high-resolution synthesizing on gender and pose variations.
Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability
Authors: Pan Deng , Li Xiangrui , Li Xin , Zhu Dongxiao
Published: July 2020

Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items. Recently, explainable recommendation has attracted much attention from research community. However, trade-off exists between explainability and performance of the recommendation where metadata is often needed to alleviate the dilemma. We present a novel feature mapping approach that maps the uninterpretable general features onto the interpretable aspect features, achieving both satisfactory

accuracy and explainability in the recommendations by simultaneous minimization of rating prediction loss and interpretation loss. To evaluate

the explainability, we propose two new evaluation metrics specifically designed for aspect-level explanation using surrogate ground truth. Experimental results demonstrate a strong performance in both recommendation and explaining explanation, eliminating the need for metadata. Code is available from https://github.com/pd90506/AMCF.
KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction
Authors: Lin Xuan , Quan Zhe , Wang Zhi-Jie , Ma Tengfei , Zeng Xiangxiang
Published: July 2020

Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and clinical application, and effectively identifying potential DDIs during clinical trials is critical for patients and society. Most of existing computational models with AI techniques often concentrate on integrating multiple data sources and combining popular embedding methods together. Yet, researchers pay less attention to the potential correlations between drug and other entities such as targets and genes. Moreover, recent studies also adopted knowledge graph (KG) for DDI prediction. Yet, this line of methods learn node latent embedding directly, but they are limited in obtaining the rich neighborhood information of each entity in the KG. To address the above limitations, we propose an end-to-end framework, called Knowledge Graph Neural Network (KGNN), to resolve the DDI prediction. Our framework can effectively capture drug and its potential neighborhoods by mining their associated relations in KG. To extract both high-order structures and semantic relations of the KG, we learn from the neighborhoods for each entity in the KG as their local receptive, and then integrate neighborhood information with bias from representation of the current entity. This way, the receptive field can be naturally extended to multiple hops away to model high-order topological information and to obtain drugs potential long-distance correlations. We have implemented our method and conducted experiments based on several widely-used datasets. Empirical results show that KGNN outperforms the classic and state-of-the-art models.
Class Prior Estimation in Active Positive and Unlabeled Learning
Authors: Perini Lorenzo , Vercruyssen Vincent , Davis Jesse
Published: July 2020

Estimating the proportion of positive examples (i.e., the class prior) from positive and unlabeled (PU) data is an important task that facilitates learning a classifier from such data. In this paper, we explore how to tackle this problem when the observed labels were acquired via active learning. This introduces the challenge that the observed labels were not selected completely at random, which is the primary assumption underpinning existing approaches to estimating the class prior from PU data. We analyze this new setting and design an algorithm that is able to estimate the class prior for a given active learning strategy. Empirically, we show that our approach accurately recovers the true class prior on a benchmark of anomaly detection datasets and that it does so more accurately than existing methods.
Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs
Authors: Xue Bo , Wang Guanghui , Wang Yimu , Zhang Lijun
Published: July 2020

In this paper, we study the problem of stochastic linear bandits with finite action sets. Most of existing work assume the payoffs are bounded or sub-Gaussian, which may be violated in some scenarios such as financial markets. To settle this issue, we analyze the linear bandits with heavy-tailed payoffs, where the payoffs admit finite 1+epsilon moments for some epsilon in (0,1]. Through median of means and dynamic truncation, we propose two novel algorithms which enjoy a sublinear regret bound of widetilde{O}(d^(1/2)T^(1/(1+epsilon))), where d is the dimension of contextual information and T is the time horizon. Meanwhile, we provide an Omega(d^(epsilon/(1+epsilon))T^(1/(1+epsilon))) lower bound, which implies our upper bound matches the lower bound up to polylogarithmic factors in the order of d and T when epsilon=1. Finally, we conduct numerical experiments to demonstrate the effectiveness of our algorithms and the empirical results strongly support our theoretical guarantees.
Towards Explainable Conversational Recommendation
Authors: Chen Zhongxia , Wang Xiting , Xie Xing , Parsana Mehul , Soni Akshay , Ao Xiang , Chen Enhong
Published: July 2020

Recent studies have shown that both accuracy and explainability are important for recommendation. In this paper, we introduce explainable conversational recommendation, which enables incremental improvement of both recommendation accuracy and explanation quality through multi-turn user-model conversation. We show how the problem can be formulated, and design an incremental multi-task learning framework that enables tight collaboration between recommendation prediction, explanation generation, and user feedback integration. We also propose a multi-view feedback integration method to enable effective incremental model update. Empirical results demonstrate that our model not only consistently improves the recommendation accuracy but also generates explanations that fit user interests reflected in the feedbacks.
Towards Alleviating Traffic Congestion: Optimal Route Planning for Massive-Scale Trips
Authors: Li Ke , Chen Lisi , Shang Shuo
Published: July 2020

We investigate the problem of optimal route planning for massive-scale trips: Given a traffic-aware road network and a set of trip queries Q, we aim to find a route for each trip such that the global travel time cost for all queries in Q is minimized. Our problem is designed for a range of applications such as traffic-flow management, route planning and congestion prevention in rush hours. The exact algorithm bears exponential time complexity and is computationally prohibitive for application scenarios in dynamic traffic networks. To address the challenge, we propose a greedy algorithm and an epsilon-refining algorithm. Extensive experiments offer insight into the accuracy and efficiency of our proposed algorithms.
Community-Centric Graph Convolutional Network for Unsupervised Community Detection
Authors: He Dongxiao , Song Yue , Jin Di , Feng Zhiyong , Zhang Binbin , Yu Zhizhi , Zhang Weixiong
Published: July 2020

Community detection, aiming at partitioning a network into multiple substructures, is practically importance. Graph convolutional network (GCN), a new deep-learning technique, has recently been developed for community detection. Markov Random Fields (MRF) has been combined with GCN in the MRFasGCN method to improve accuracy. However, the existing GCN community-finding methods are semi-supervised, even though community finding is essentially an unsupervised learning problem. We developed a new GCN approach for unsupervised community detection under the framework of Autoencoder. We cast MRFasGCN as an encoder and then derived node community membership in the hidden layer of the encoder. We introduced a community-centric dual decoder to reconstruct network structures and node attributes separately in an unsupervised fashion, for faithful community detection in the input space. We designed a scheme of local enhancement to accommodate nodes to have more common neighbors and similar attributes with similar community memberships. Experimental results on real networks showed that our new method outperformed the best existing methods, showing the effectiveness of the novel decoding mechanism for generating links and attributes together over the commonly used methods for reconstructing links alone.
Triple-to-Text Generation with an Anchor-to-Prototype Framework
Authors: Li Ziran , Lin Zibo , Ding Ning , Zheng Hai-Tao , Shen Ying
Published: July 2020

Generating a textual description from a set of RDF triplets is a challenging task in natural language generation. Recent neural methods have become the mainstream for this task, which often generate sentences from scratch. However, due to the huge gap between the structured input and the unstructured output, the input triples alone are insufficient to decide an expressive and specific description. In this paper, we propose a novel anchor-to-prototype framework to bridge the gap between structured RDF triples and natural text. The model retrieves a set of prototype descriptions from the training data and extracts writing patterns from them to guide the generation process. Furthermore, to make a more precise use of the retrieved prototypes, we employ a triple anchor that aligns the input triples into groups so as to better match the prototypes. Experimental results on both English and Chinese datasets show that our method significantly outperforms the state-of-the-art baselines in terms of both automatic and manual evaluation, demonstrating the benefit of learning guidance from retrieved prototypes to facilitate triple-to-text generation.
Text Style Transfer via Learning Style Instance Supported Latent Space
Authors: Yi Xiaoyuan , Liu Zhenghao , Li Wenhao , Sun Maosong
Published: July 2020

Text style transfer pursues altering the style of a sentence while remaining its main content unchanged. Due to the lack of parallel corpora, most recent work focuses on unsupervised methods and has achieved noticeable progress. Nonetheless, the intractability of completely disentangling content from style for text leads to a contradiction of content preservation and style transfer accuracy. To address this problem, we propose a style instance supported method, StyIns. Instead of representing styles with embeddings or latent variables learned from single sentences, our model leverages the generative flow technique to extract underlying stylistic properties from multiple instances of each style, which form a more discriminative and expressive latent style space. By combining such a space with the attention-based structure, our model can better maintain the content and simultaneously achieve high transfer accuracy. Furthermore, the proposed method can be flexibly extended to semi-supervised learning so as to utilize available limited paired data. Experiments on three transfer tasks, sentiment modification, formality rephrasing, and poeticness generation, show that StyIns obtains a better balance between content and style, outperforming several recent baselines.
UniTrans : Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data
Authors: Wu Qianhui , Lin Zijia , Karlsson Börje F. , Huang Biqing , Lou Jian-Guang
Published: July 2020

Prior work in cross-lingual named entity recognition (NER) with no/little labeled data falls into two primary categories: model transfer- and data transfer-based methods. In this paper, we find that both method types can complement each other, in the sense that, the former can exploit context information via language-independent features but sees no task-specific information in the target language; while the latter generally generates pseudo target-language training data via translation but its exploitation of context information is weakened by inaccurate translations. Moreover, prior work rarely leverages unlabeled data in the target language, which can be effortlessly collected and potentially contains valuable information for improved results. To handle both problems, we propose a novel approach termed UniTrans to Unify both model and data Transfer for cross-lingual NER, and furthermore, leverage the available information from unlabeled target-language data via enhanced knowledge distillation. We evaluate our proposed UniTrans over 4 target languages on benchmark datasets. Our experimental results show that it substantially outperforms the existing state-of-the-art methods.
Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction
Authors: Zhao Shan , Hu Minghao , Cai Zhiping , Liu Fang
Published: July 2020

Joint extraction of entities and their relations benefits from the close interaction between named entities and their relation information. Therefore, how

to effectively model such cross-modal interactions is critical for the final performance. Previous works have used simple methods such as label-feature concatenation to perform coarse-grained semantic fusion among cross-modal instances, but fail to capture fine-grained correlations over token and label

spaces, resulting in insufficient interactions. In this paper, we propose a deep Cross-Modal Attention Network (CMAN) for joint entity and relation extraction. The network is carefully constructed by stacking multiple attention units in depth to fully model dense interactions over token-label spaces, in which two basic attention units are proposed to explicitly capture fine-grained correlations across

different modalities (e.g., token-to-token and labelto-token). Experiment results on CoNLL04 dataset show that our model obtains state-of-the-art results by achieving 90.62% F1 on entity recognition and 72.97% F1 on relation classification. In ADE dataset, our model surpasses existing approaches by

more than 1.9% F1 on relation classification. Extensive analyses further confirm the effectiveness of our approach.
Fast and Accurate Neural CRF Constituency Parsing
Authors: Zhang Yu , Zhou Houquan , Li Zhenghua
Published: July 2020

Estimating probability distribution is one of the core issues in the NLP field.

However, in both deep learning (DL) and pre-DL eras, unlike the vast applications of linear-chain CRF in sequence labeling tasks, very few works have applied tree-structure CRF to constituency parsing, mainly due to the complexity and inefficiency of the inside-outside algorithm.

This work presents a fast and accurate neural CRF constituency parser.

The key idea is to batchify the inside algorithm for loss computation by direct large tensor operations on GPU, and meanwhile avoid the outside algorithm for gradient computation via efficient back-propagation.

We also propose a simple two-stage bracketing-then-labeling parsing approach to improve efficiency further.

To improve the parsing performance, inspired by recent progress in dependency parsing, we introduce a new scoring architecture based on boundary representation and biaffine attention, and a beneficial dropout strategy.

Experiments on PTB, CTB5.1, and CTB7 show that our two-stage CRF parser achieves new state-of-the-art performance on both settings of w/o and w/ BERT, and can parse over 1,000 sentences per second.

We release our code at https://github.com/yzhangcs/crfpar.
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning
Authors: Wen Ying , Yang Yaodong , Wang Jun
Published: July 2020

Though limited in real-world decision making, most multi-agent reinforcement learning (MARL) models assume perfectly rational agents -- a property hardly met due to individual's cognitive limitation and/or the tractability of the decision problem. In this paper, we introduce generalized recursive reasoning (GR2) as a novel framework to model agents with different \emph{hierarchical} levels of rationality; our framework enables agents to exhibit varying levels of ``thinking'' ability thereby allowing higher-level agents to best respond to various less sophisticated learners. We contribute both theoretically and empirically. On the theory side, we devise the hierarchical framework of GR2 through probabilistic graphical models and prove the existence of a perfect Bayesian equilibrium. Within the GR2, we propose a practical actor-critic solver, and demonstrate its convergent property to a stationary point in two-player games through Lyapunov analysis. On the empirical side, we validate our findings on a variety of MARL benchmarks. Precisely, we first illustrate the hierarchical thinking process on the Keynes Beauty Contest, and then demonstrate significant improvements compared to state-of-the-art opponent modeling baselines on the normal-form games and the cooperative navigation benchmark.
Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks
Authors: Khajehnejad Moein , Asgharian Rezaei Ahmad , Babaei Mahmoudreza , Hoffmann Jessica , Jalili Mahdi , Weller Adrian
Published: July 2020

Influence maximization is a widely studied topic in network science, where the aim is to reach the maximum possible number of nodes, while only targeting a small initial set of individuals. It has critical applications in many fields, including viral marketing, information propagation, news dissemination, and vaccinations. However, the objective does not usually take into account whether the final set of influenced nodes is fair with respect to sensitive attributes, such as race or gender. Here we address fair influence maximization, aiming to reach minorities more equitably. We introduce Adversarial Graph Embeddings: we co-train an auto-encoder for graph embedding and a discriminator to discern sensitive attributes. This leads to embeddings which are similarly distributed across sensitive attributes. We then find a good initial set by clustering the embeddings. We believe we are the first to use embeddings for the task of fair influence maximization. While there are typically trade-offs between fairness and influence maximization objectives, our experiments on synthetic and real-world datasets show that our approach dramatically reduces disparity while remaining competitive with state-of-the-art influence maximization methods.
Generating Interpretable Poverty Maps using Object Detection in Satellite Images
Authors: Ayush Kumar , Uzkent Burak , Burke Marshall , Lobell David , Ermon Stefano
Published: July 2020

Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources. Recent computer vision advances in using satellite imagery to predict poverty have shown increasing accuracy, but they do not generate features that are interpretable to policymakers, inhibiting adoption by practitioners. Here we demonstrate an interpretable computational framework to accurately predict poverty at a local level by applying object detectors to high resolution (30cm) satellite images. Using the weighted counts of objects as features, we achieve 0.539 Pearson's r^2 in predicting village-level poverty in Uganda, a 31% improvement over existing (and less interpretable) benchmarks. Feature importance and ablation analysis reveal intuitive relationships between object counts and poverty predictions. Our results suggest that interpretability does not have to come at the cost of performance, at least in this important domain.
Multi-scale Two-way Deep Neural Network for Stock Trend Prediction
Authors: Liu Guang , Mao Yuzhao , Sun Qi , Huang Hailong , Gao Weiguo , Li Xuan , Shen Jianping , Li Ruifan , Wang Xiaojie
Published: July 2020

Stock Trend Prediction(STP) has drawn wide attention from various fields, especially Artificial Intelligence. Most previous studies are single-scale oriented which results in information loss from a multi-scale perspective. In fact, multi-scale behavior is vital for making intelligent investment decisions. A mature investor will thoroughly investigate the state of a stock market at various time scales. To automatically learn the multi-scale information in stock data, we propose a Multi-scale Two-way Deep Neural Network. It learns multi-scale patterns from two types of scale-information, wavelet-based and downsampling-based, by eXtreme Gradient Boosting and Recurrent Convolutional Neural Network, respectively. After combining the learned patterns from the two-way, our model achieves state-of-the-art performance on FI-2010 and CSI-2016, where the latter is our published long-range stock dataset to help future studies for STP task. Extensive experimental results on the two datasets indicate that multi-scale information can significantly improve the STP performance and our model is superior in capturing such information.
Relation-Aware Transformer for Portfolio Policy Learning
Authors: Xu Ke , Zhang Yifan , Ye Deheng , Zhao Peilin , Tan Mingkui
Published: July 2020

Portfolio selection is an important yet challenging task in AI for FinTech. One of the key issues is how to represent the non-stationary price series of assets in a portfolio, which is important for portfolio decisions. The existing methods, however, fall short of capturing: 1) the complicated sequential patterns for asset price series and 2) the price correlations among multiple assets. In this paper, under a deep reinforcement learning paradigm for portfolio selection, we propose a novel Relation-aware Transformer (RAT) to handle these aspects. Specifically, being equipped with our newly developed attention modules, RAT is structurally innovated to capture both sequential patterns and asset correlations for portfolio selection. Based on the extracted sequential features, RAT is able to make profitable portfolio decisions regarding each asset via a newly devised leverage operation. Extensive experiments on real-world crypto-currency and stock datasets verify the state-of-the-art performance of RAT.