pwc-archive/papers-with-abstracts huggingface dataset:
PageRank of integers
Authors: K. M. Frahm, A. D. Chepelianskii, D. L. Shepelyansky
Date: 2012-05-29 00:00:00

We build up a directed network tracing links from a given integer to its divisors and analyze the properties of the Google matrix of this network. The PageRank vector of this matrix is computed numerically and it is shown that its probability is inversely proportional to the PageRank index thus being similar to the Zipf law and the dependence established for the World Wide Web. The spectrum of the Google matrix of integers is characterized by a large gap and a relatively small number of nonzero eigenvalues. A simple semi-analytical expression for the PageRank of integers is derived that allows to find this vector for matrices of billion size. This network provides a new PageRank order of integers.
How to Sell Hard Information
Authors: S. Nageeb Ali, Nima Haghpanah, Xiao Lin, Ron Siegel
Date: 2020-10-15 00:00:00

The seller of an asset has the option to buy hard information about the value of the asset from an intermediary. The seller can then disclose the acquired information before selling the asset in a competitive market. We study how the intermediary designs and sells hard information to robustly maximize her revenue across all equilibria. Even though the intermediary could use an accurate test that reveals the asset's value, we show that robust revenue maximization leads to a noisy test with a continuum of possible scores that are distributed exponentially. In addition, the intermediary always charges the seller for disclosing the test score to the market, but not necessarily for running the test. This enables the intermediary to robustly appropriate a significant share of the surplus resulting from the asset sale even though the information generated by the test provides no social value.
Enhancing Keyphrase Extraction from Microblogs using Human Reading Time
Authors: Yingyi Zhang, Chengzhi Zhang
Date: 2020-10-20 00:00:00
Tasks: Keyphrase Extraction

The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human reading time, we can find the salient words in the corresponding content. However, previous studies on keyphrase extraction ignore human reading features. In this article, we aim to leverage human reading time to extract keyphrases from microblog posts. There are two main tasks in this study. One is to determine how to measure the time spent by a human on reading a word. We use eye fixation durations extracted from an open source eye-tracking corpus (OSEC). Moreover, we propose strategies to make eye fixation duration more effective on keyphrase extraction. The other task is to determine how to integrate human reading time into keyphrase extraction models. We propose two novel neural network models. The first is a model in which the human reading time is used as the ground truth of the attention mechanism. In the second model, we use human reading time as the external feature. Quantitative and qualitative experiments show that our proposed models yield better performance than the baseline models on two microblog datasets.
SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency
Authors: Sameer Dharur, Purva Tendulkar, Dhruv Batra, Devi Parikh, Ramprasaath R. Selvaraju
Date: 2020-10-20 00:00:00
Proceeding: NAACL 2021 4
Tasks: Question Answering, Visual Grounding, Visual Question Answering, Visual Question Answering (VQA)

Recent research in Visual Question Answering (VQA) has revealed state-of-the-art models to be inconsistent in their understanding of the world -- they answer seemingly difficult questions requiring reasoning correctly but get simpler associated sub-questions wrong. These sub-questions pertain to lower level visual concepts in the image that models ideally should understand to be able to answer the higher level question correctly. To address this, we first present a gradient-based interpretability approach to determine the questions most strongly correlated with the reasoning question on an image, and use this to evaluate VQA models on their ability to identify the relevant sub-questions needed to answer a reasoning question. Next, we propose a contrastive gradient learning based approach called Sub-question Oriented Tuning (SOrT) which encourages models to rank relevant sub-questions higher than irrelevant questions for an <image, reasoning-question> pair. We show that SOrT improves model consistency by upto 6.5% points over existing baselines, while also improving visual grounding.
Hierarchical Autoregressive Modeling for Neural Video Compression
Authors: Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt
Date: 2020-10-19 00:00:00
Proceeding: ICLR 2021 1
Tasks: Density Estimation, Video Compression

Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models. We draw a connection between such autoregressive generative models and the task of lossy video compression. Specifically, we view recent neural video compression methods (Lu et al., 2019; Yang et al., 2020b; Agustssonet al., 2020) as instances of a generalized stochastic temporal autoregressive transform, and propose avenues for enhancement based on this insight. Comprehensive evaluations on large-scale video data show improved rate-distortion performance over both state-of-the-art neural and conventional video compression methods.
Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram
Authors: Dongdong Zhang, Xiaohui Yuan, Ping Zhang
Date: 2020-10-20 00:00:00
Tasks: Deep Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Electrocardiogram (ECG) is a widely used reliable, non-invasive approach for cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the insufficiency of cardiologists, accurate and automatic diagnosis of ECG signals has become a hot research topic. Deep learning methods have demonstrated promising results in predictive healthcare tasks. In this paper, we developed a deep neural network for multi-label classification of cardiac arrhythmias in 12-lead ECG recordings. Experiments on a public 12-lead ECG dataset showed the effectiveness of our method. The proposed model achieved an average area under the receiver operating characteristic curve (AUC) of 0.970 and an average F1 score of 0.813. The deep model showed superior performance than 4 machine learning methods learned from extracted expert features. Besides, the deep models trained on single-lead ECGs produce lower performance than using all 12 leads simultaneously. The best-performing leads are lead I, aVR, and V5 among 12 leads. Finally, we employed the SHapley Additive exPlanations (SHAP) method to interpret the model's behavior at both patient level and population level. Our code is freely available at https://github.com/onlyzdd/ecg-diagnosis.
Federated Bayesian Optimization via Thompson Sampling
Authors: Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet
Date: 2020-10-20 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Bayesian Optimization, Computational Efficiency, Federated Learning, Thompson Sampling

Bayesian optimization (BO) is a prominent approach to optimizing expensive-to-evaluate black-box functions. The massive computational capability of edge devices such as mobile phones, coupled with privacy concerns, has led to a surging interest in federated learning (FL) which focuses on collaborative training of deep neural networks (DNNs) via first-order optimization techniques. However, some common machine learning tasks such as hyperparameter tuning of DNNs lack access to gradients and thus require zeroth-order/black-box optimization. This hints at the possibility of extending BO to the FL setting (FBO) for agents to collaborate in these black-box optimization tasks. This paper presents federated Thompson sampling (FTS) which overcomes a number of key challenges of FBO and FL in a principled way: We (a) use random Fourier features to approximate the Gaussian process surrogate model used in BO, which naturally produces the parameters to be exchanged between agents, (b) design FTS based on Thompson sampling, which significantly reduces the number of parameters to be exchanged, and (c) provide a theoretical convergence guarantee that is robust against heterogeneous agents, which is a major challenge in FL and FBO. We empirically demonstrate the effectiveness of FTS in terms of communication efficiency, computational efficiency, and practical performance.
ivadomed: A Medical Imaging Deep Learning Toolbox
Authors: Charley Gros, Andreanne Lemay, Olivier Vincent, Lucas Rouhier, Anthime Bucquet, Joseph Paul Cohen, Julien Cohen-Adad
Date: 2020-10-20 00:00:00
Tasks: Deep Learning, object-detection, Object Detection, Tumor Segmentation

ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data. The package includes APIs, command-line tools, documentation, and tutorials. ivadomed also includes pre-trained models such as spinal tumor segmentation and vertebral labeling. Original features of ivadomed include a data loader that can parse image metadata (e.g., acquisition parameters, image contrast, resolution) and subject metadata (e.g., pathology, age, sex) for custom data splitting or extra information during training and evaluation. Any dataset following the Brain Imaging Data Structure (BIDS) convention will be compatible with ivadomed without the need to manually organize the data, which is typically a tedious task. Beyond the traditional deep learning methods, ivadomed features cutting-edge architectures, such as FiLM and HeMis, as well as various uncertainty estimation methods (aleatoric and epistemic), and losses adapted to imbalanced classes and non-binary predictions. Each step is conveniently configurable via a single file. At the same time, the code is highly modular to allow addition/modification of an architecture or pre/post-processing steps. Example applications of ivadomed include MRI object detection, segmentation, and labeling of anatomical and pathological structures. Overall, ivadomed enables easy and quick exploration of the latest advances in deep learning for medical imaging applications. ivadomed's main project page is available at https://ivadomed.org.
CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation
Authors: Wenchang Ma, Ryuichi Takanobu, Minlie Huang
Date: 2020-10-20 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Conversational Recommendation, Recommendation Systems, Response Generation, Text Generation

Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate recommendation. However, there is still a lack of ability in existing CRS to (1) traverse multiple reasoning paths over background knowledge to introduce relevant items and attributes, and (2) arrange selected entities appropriately under current system intents to control response generation. To address these issues, we propose CR-Walker in this paper, a model that performs tree-structured reasoning on a knowledge graph, and generates informative dialog acts to guide language generation. The unique scheme of tree-structured reasoning views the traversed entity at each hop as part of dialog acts to facilitate language generation, which links how entities are selected and expressed. Automatic and human evaluations show that CR-Walker can arrive at more accurate recommendation, and generate more informative and engaging responses.
Growth of centimeter-scale perovskite single-crystalline thin flm via surface engineering
Authors: Yu-Hao Deng, Zhen-Qian Yang & Ren-Min Ma
Date: 2020-07-20 00:00:00

Modern electronic and photonic devices rely on single‑crystalline thin flm semiconductors for high performance
and reproducibility. The emerging halide perovskites have extraordinary electronic and photonic properties and can
be synthesized via low cost solution‑based methods. They have been used in a variety of devices with performance
approaching or over the devices based on conventional materials. However, their solution based growth method is
intrinsically challenge to grow large scale single‑crystalline thin flm due to the random nucleation and isotropous
growth of the crystal. Here, we report the growth of centimeter‑scale perovskite single‑crystalline thin flms by con‑
trolling the nucleation density and growth rate of the crystal under a spatially confned growth condition. The hydro‑
phobic treatment on substrates inhibits nucleation and accelerates the growth of single‑crystalline thin flm, provid‑
ing enough space for initial nucleus growing up quickly without touching each other. Single‑crystalline perovskite
thin‑flm with an aspect ratio of 1000 (1 cm in side length, 10 μm in thickness) has been successfully grown. The low
trap density and the high mobility of the as‑grown thin flm show a high crystallinity. The photodetector based on
the perovskite thin flm has achieved a gain ~ 104, beneftting from the short transit time of the carries due to the high
mobility and thin thickness of the active layer. Our work opens up a new route to grow large scale perovskite single‑
crystalline thin flms, providing a platform to develop high‑ performance devices.
A method of the transfer graphene based on sublimed method 2015 CN ZL201510962569.0
Authors: Yu-Hao Deng, Yi-Lun Wang, Xiao-wei Zhang, Lun Dai & Ren-Min Ma
Date: 2015-12-21 00:00:00

The invention discloses a kind of methods of transfer graphene.This method is using the substance easily to distil as the supporting layer of transfer graphene, first by heating easy sublimate, its graphene surface on being grown on initial substrate is set to sublimate to form fine and close supporting layer, then it removes initial substrate and graphene/supporting layer is transferred to target substrate, then supporting layer is distilled.Compared to residue glue problem existing for existing PMMA methods, PDMS methods and hot stamping, heat release tape method to target substrate planarization, adhesion requirement is higher the problems such as, the present invention uses easy sublimate as supporting layer, transfer process is simpler, convenient, can be achieved large area from graphene to arbitrary target substrate, it is lossless, shifted without residue glue, graphene will be expanded significantly in terms of flexible electronic, organic nano electronics, organic solar batteries, organic sensor, the application in the fields such as organic High-performance micro-nano electronic device, organic material energy stores.
Alerts in High-resolution TEM characterization of perovskite material
Authors: Yu-Hao Deng
Date: 2020-07-30 00:00:00
Tasks: Vocal Bursts Intensity Prediction

High-resolution TEM (HRTEM) is a powerful tool for structure characterization. However, MAPbI3 perovskite is highly sensitive to electron beams and easily decompose into PbI2. Universal mistakes that PbI2 is incorrectly labeled as perovskite are widely exist in HRTEM characterizations, which negatively affect the development of perovskite research field. Here complete errors in MAPbI3 perovskite calibration are statisticed, classified and identified based on corresponding electron diffraction (ED) simulations. Corrected material phases, ED patterns of original PbI2 and obvious crystallographic parameters are also presented. This approach paves the way to avoid misleadings in HRTEM characterization of perovskite and other electron beam-sensitive materials in the future.
Chelating agent: The health protector of researchers in lead halide perovskite laboratory
Authors: Yu-Hao Deng
Date: 2020-09-25 00:00:00

Lead halide perovskites have achieved substantial success in various optoelectronic devices owing to their remarkable physical properties. However, lead (Pb) as a heavy metal, long-lasting toxic to the body has become a health hazard for researchers. How to completely remove the residual lead in the laboratory and prevent lead from entering the human body have always been an important topic in laboratory safety. Here we develop an operable method to treat lead sources with low-cost and eco-friendly chelating agent (EDTA-2Na), which can reduce the concentration of free lead ions to 10-11 ppm theoretically. Moreover, experiments have demonstrated that the EDTA-2Na possess a strong ability on the removal of lead ions from the lab surface, gloves and lab coats. This approach paves the way to protect the health of researchers in lead halide perovskite laboratory.
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
Authors: Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, Zachary Ulissi
Date: 2020-10-20 00:00:00
Tasks: 3D Pose Estimation, Graph Neural Network

Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer production. Despite considerable effort by the catalysis community to apply machine learning models to the computational catalyst discovery process, it remains an open challenge to build models that can generalize across both elemental compositions of surfaces and adsorbate identity/configurations, perhaps because datasets have been smaller in catalysis than related fields. To address this we developed the OC20 dataset, consisting of 1,281,040 Density Functional Theory (DFT) relaxations (~264,890,000 single point evaluations) across a wide swath of materials, surfaces, and adsorbates (nitrogen, carbon, and oxygen chemistries). We supplemented this dataset with randomly perturbed structures, short timescale molecular dynamics, and electronic structure analyses. The dataset comprises three central tasks indicative of day-to-day catalyst modeling and comes with pre-defined train/validation/test splits to facilitate direct comparisons with future model development efforts. We applied three state-of-the-art graph neural network models (CGCNN, SchNet, Dimenet++) to each of these tasks as baseline demonstrations for the community to build on. In almost every task, no upper limit on model size was identified, suggesting that even larger models are likely to improve on initial results. The dataset and baseline models are both provided as open resources, as well as a public leader board to encourage community contributions to solve these important tasks.
Distributed Learning of Finite Gaussian Mixtures
Authors: Qiong Zhang, Jiahua Chen
Date: 2020-10-20 00:00:00

Advances in information technology have led to extremely large datasets that are often kept in different storage centers. Existing statistical methods must be adapted to overcome the resulting computational obstacles while retaining statistical validity and efficiency. Split-and-conquer approaches have been applied in many areas, including quantile processes, regression analysis, principal eigenspaces, and exponential families. We study split-and-conquer approaches for the distributed learning of finite Gaussian mixtures. We recommend a reduction strategy and develop an effective MM algorithm. The new estimator is shown to be consistent and retains root-n consistency under some general conditions. Experiments based on simulated and real-world data show that the proposed split-and-conquer approach has comparable statistical performance with the global estimator based on the full dataset, if the latter is feasible. It can even slightly outperform the global estimator if the model assumption does not match the real-world data. It also has better statistical and computational performance than some existing methods.
Action Sequence Augmentation for Early Graph-based Anomaly Detection
Authors: Tong Zhao, Bo Ni, Wenhao Yu, Zhichun Guo, Neil Shah, Meng Jiang
Date: 2020-10-20 00:00:00
Tasks: Anomaly Detection, Data Augmentation, Graph Anomaly Detection

The proliferation of web platforms has created incentives for online abuse. Many graph-based anomaly detection techniques are proposed to identify the suspicious accounts and behaviors. However, most of them detect the anomalies once the users have performed many such behaviors. Their performance is substantially hindered when the users' observed data is limited at an early stage, which needs to be improved to minimize financial loss. In this work, we propose Eland, a novel framework that uses action sequence augmentation for early anomaly detection. Eland utilizes a sequence predictor to predict next actions of every user and exploits the mutual enhancement between action sequence augmentation and user-action graph anomaly detection. Experiments on three real-world datasets show that Eland improves the performance of a variety of graph-based anomaly detection methods. With Eland, anomaly detection performance at an earlier stage is better than non-augmented methods that need significantly more observed data by up to 15% on the Area under the ROC curve.
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
Authors: Seyone Chithrananda, Gabriel Grand, Bharath Ramsundar
Date: 2020-10-19 00:00:00
Tasks: Molecular Property Prediction, molecular representation, Prediction, Property Prediction, Representation Learning

GNNs and chemical fingerprints are the predominant approaches to representing molecules for property prediction. However, in NLP, transformers have become the de-facto standard for representation learning thanks to their strong downstream task transfer. In parallel, the software ecosystem around transformers is maturing rapidly, with libraries like HuggingFace and BertViz enabling streamlined training and introspection. In this work, we make one of the first attempts to systematically evaluate transformers on molecular property prediction tasks via our ChemBERTa model. ChemBERTa scales well with pretraining dataset size, offering competitive downstream performance on MoleculeNet and useful attention-based visualization modalities. Our results suggest that transformers offer a promising avenue of future work for molecular representation learning and property prediction. To facilitate these efforts, we release a curated dataset of 77M SMILES from PubChem suitable for large-scale self-supervised pretraining.
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples
Authors: Jay Nandy, Wynne Hsu, Mong Li Lee
Date: 2020-10-20 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Out of Distribution (OOD) Detection

Among existing uncertainty estimation approaches, Dirichlet Prior Network (DPN) distinctly models different predictive uncertainty types. However, for in-domain examples with high data uncertainties among multiple classes, even a DPN model often produces indistinguishable representations from the out-of-distribution (OOD) examples, compromising their OOD detection performance. We address this shortcoming by proposing a novel loss function for DPN to maximize the \textit{representation gap} between in-domain and OOD examples. Experimental results demonstrate that our proposed approach consistently improves OOD detection performance.