pwc-archive/papers-with-abstracts huggingface dataset:
两院院士Nature被质疑，他感谢提出质疑的年轻科学家！
Authors: 纳米人
Date: 2021-06-11 00:00:00

We once again thank Deng for having motivated a fruitful dialogue and updates to the TEM studies of the original paper.【来源：Reply to: Perovskite decomposition and missing crystal planes in HRTEM】

加拿大科学院和工程院两院院士Edward H. Sargent教授是国际顶级科学家，Yu-Hao Deng老师敢于从科学的角度质疑他的这篇Nature论文，作者们敢于从科学的角度进行正面回应，对一些问题进行勘误，并对一些问题进行更进一步的探讨和阐述，这都是科学家该有的精神。
近日，根特大学邓玉豪（Yu-Hao Deng）提出，在2015年发表的这篇工作中，相关研究无法充分证明PbS修饰在钙钛矿，尤其是文章中展示的PbI2 TEM照片无法与钙钛矿、PbS之间显著区别。邓玉豪发现，通过对原文中的数据进行分析，对应的层间距应该为57°，而不是作者认为的60°，此外邓提出文章缺乏证明钙钛矿（112）晶面的衍射斑点，因此作者观测的PbS可能是钙钛矿降解的产物。
神仙打架！邓玉豪博士Nature发文，质疑著名学者Sargent院士6年前研究成果！
Authors: 高分子科学前沿
Date: 2021-06-09 00:00:00

2021年6月10日凌晨，根特大学邓玉豪博士（毕业于北京大学）在《Nature》发文质疑Sargent院士团队在2015年文章中的HRTEM结果。邓玉豪博士发现，文中MAPbI3钙钛矿材料的HRTEM数据中存在晶面缺失的现象！这意味着，Sargent院士团队文章图中的材料不是MAPbI3，而可能是其被电子束辐射分解的产物----碘化铅 (PbI2)。
最后，邓博士强调，这一质疑旨在提高研究人员的认识，为了避免科研人员未来在电子束敏感材料的 HRTEM 表征中可能出现的错误。
MRSCAtt: A Spatio-Channel Attention-Guided Network for Mars Rover Image Classification
Authors: Anirudh S Chakravarthy, Roshan Roy, Praveen Ravirathinam
Date: 2021-06-12 00:00:00
Tasks: image-classification, Image Classification

As the exploration of human beings pushes deeper into the galaxy, the classification of images from space and other planets is becoming an increasingly critical task. Image classification on these planetary images can be very challenging due to differences in hue, quality, illumination, and clarity when compared to images captured on Earth. In this work, we try to bridge this gap by developing a deep learning network, MRSCAtt (Mars Rover Spatial and Channel Attention), which jointly uses spatial and channel attention to accurately classify images. We use images taken by NASA's Curiosity rover on Mars as a dataset to show the superiority of our approach by achieving state-of-the-art results with 81.53% test set accuracy on the MSL Surface Dataset, outperforming other methods. To necessitate the use of spatial and channel attention, we perform an ablation study to show the effectiveness of each of the components. We further show robustness of our approach by validating with images taken aboard NASA's recently-landed Perseverance rover.
Joint Optimization for SSIM-Based CTU-Level Bit Allocation and Rate Distortion Optimization
Authors: Yang Li, Xuanqin Mou
Date: 2021-04-01 00:00:00
Tasks: Image Quality Assessment, SSIM, Video Compression, Video Quality Assessment

Structural similarity (SSIM)-based distortion Dssim is more consistent with human perception than the traditional mean squared error Dmse. To achieve better video encoding quality, many studies on optimal bit allocation (OBA) used Dssim as the distortion metric. However, the MSE-based rate distortion optimization (RDO) was still used in these studies. The inconsistency between the optimization goals of OBA and RDO results in a non-optimal SSIM-based encoding performance. To solve this problem, we propose an accurate coding tree unit level Dssim-Dmse model, which enables performing the SSIM-based RDO with simpler R-Dmse cost scaled by the SSIM-based Lagrangian parameter λssim. Moreover, based on this model, the R-Dssim model can be accurately estimated based on the joint relationship of R-Dssim-λssim. With the accurate R-Dssim model, the SSIM-based OBA problem is then solved. Accordingly, the SSIM-based OBA and SSIM-based RDO are unified together in our scheme, called SOSR. Compared with the HEVC reference encoder HM16.20, SOSR saves 5%, 11%, and 17% bitrate under the same SSIM in the commonly used all-intra, hierarchical and non-hierarchical low-delay-B configurations, which is superior to existing state-of-the-art SSIM-based OBA schemes.
Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)
Authors: Danial Yazdani, Michalis Mavrovouniotis, Changhe Li, Guoyu Chen, Wenjian Luo, Mohammad Nabi Omidvar, Juergen Branke, Shengxiang Yang, Xin Yao
Date: 2021-06-11 00:00:00

The Generalized Moving Peaks Benchmark (GMPB) is a tool for generating continuous dynamic optimization problem instances with controllable dynamic and morphological characteristics. GMPB has been used in recent Competitions on Dynamic Optimization at prestigious conferences, such as the IEEE Congress on Evolutionary Computation (CEC). This dynamic benchmark generator can create a wide variety of landscapes, ranging from simple unimodal to highly complex multimodal configurations and from symmetric to asymmetric forms. It also supports diverse surface textures, from smooth to highly irregular, and can generate varying levels of variable interaction and conditioning. This document provides an overview of GMPB, emphasizing how its parameters can be adjusted to produce landscapes with customizable characteristics. The MATLAB implementation of GMPB is available on the EDOLAB Platform.
Unsupervised Anomaly Detection Ensembles using Item Response Theory
Authors: Sevvandi Kandanaarachchi
Date: 2021-06-11 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

Constructing an ensemble from a heterogeneous set of unsupervised anomaly detection methods is challenging because the class labels or the ground truth is unknown. Thus, traditional ensemble techniques that use the response variable or the class labels cannot be used to construct an ensemble for unsupervised anomaly detection. We use Item Response Theory (IRT) -- a class of models used in educational psychometrics to assess student and test question characteristics -- to construct an unsupervised anomaly detection ensemble. IRT's latent trait computation lends itself to anomaly detection because the latent trait can be used to uncover the hidden ground truth. Using a novel IRT mapping to the anomaly detection problem, we construct an ensemble that can downplay noisy, non-discriminatory methods and accentuate sharper methods. We demonstrate the effectiveness of the IRT ensemble on an extensive data repository, by comparing its performance to other ensemble techniques.
Towards End-to-End Synthetic Speech Detection
Authors: Guang Hua, Andrew Beng Jin Teoh, Haijian Zhang
Date: 2021-06-11 00:00:00
Tasks: Synthetic Speech Detection

The constant Q transform (CQT) has been shown to be one of the most effective speech signal pre-transforms to facilitate synthetic speech detection, followed by either hand-crafted (subband) constant Q cepstral coefficient (CQCC) feature extraction and a back-end binary classifier, or a deep neural network (DNN) directly for further feature extraction and classification. Despite the rich literature on such a pipeline, we show in this paper that the pre-transform and hand-crafted features could simply be replaced by end-to-end DNNs. Specifically, we experimentally verify that by only using standard components, a light-weight neural network could outperform the state-of-the-art methods for the ASVspoof2019 challenge. The proposed model is termed Time-domain Synthetic Speech Detection Net (TSSDNet), having ResNet- or Inception-style structures. We further demonstrate that the proposed models also have attractive generalization capability. Trained on ASVspoof2019, they could achieve promising detection performance when tested on disjoint ASVspoof2015, significantly better than the existing cross-dataset results. This paper reveals the great potential of end-to-end DNNs for synthetic speech detection, without hand-crafted features.
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data
Authors: Haoyu Song, Yan Wang, Kaiyan Zhang, Wei-Nan Zhang, Ting Liu
Date: 2021-06-11 00:00:00
Proceeding: ACL 2021 5
Tasks: Decoder, Dialogue Generation, Response Generation

Maintaining consistent personas is essential for dialogue agents. Although tremendous advancements have been brought, the limited-scale of annotated persona-dense data are still barriers towards training robust and consistent persona-based dialogue models. In this work, we show how the challenges can be addressed by disentangling persona-based dialogue generation into two sub-tasks with a novel BERT-over-BERT (BoB) model. Specifically, the model consists of a BERT-based encoder and two BERT-based decoders, where one decoder is for response generation, and another is for consistency understanding. In particular, to learn the ability of consistency understanding from large-scale non-dialogue inference data, we train the second decoder in an unlikelihood manner. Under different limited data settings, both automatic and human evaluations demonstrate that the proposed model outperforms strong baselines in response quality and persona consistency.
SimSwap: An Efficient Framework For High Fidelity Face Swapping
Authors: Renwang Chen, Xuanhong Chen, Bingbing Ni, Yanhao Ge
Date: 2021-06-11 00:00:00
Tasks: Face Swapping, Vocal Bursts Intensity Prediction

We propose an efficient framework, called Simple Swap (SimSwap), aiming for generalized and high fidelity face swapping. In contrast to previous approaches that either lack the ability to generalize to arbitrary identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the target face. We overcome the above defects in the following two ways. First, we present the ID Injection Module (IIM) which transfers the identity information of the source face into the target face at feature level. By using this module, we extend the architecture of an identity-specific face swapping algorithm to a framework for arbitrary face swapping. Second, we propose the Weak Feature Matching Loss which efficiently helps our framework to preserve the facial attributes in an implicit way. Extensive experiments on wild faces demonstrate that our SimSwap is able to achieve competitive identity performance while preserving attributes better than previous state-of-the-art methods. The code is already available on github: https://github.com/neuralchen/SimSwap.
Online Continual Adaptation with Active Self-Training
Authors: Shiji Zhou, Han Zhao, Shanghang Zhang, Lianzhe Wang, Heng Chang, Zhi Wang, Wenwu Zhu
Date: 2021-06-11 00:00:00

Models trained with offline data often suffer from continual distribution shifts and expensive labeling in changing environments. This calls for a new online learning paradigm where the learner can continually adapt to changing environments with limited labels. In this paper, we propose a new online setting -- Online Active Continual Adaptation, where the learner aims to continually adapt to changing distributions using both unlabeled samples and active queries of limited labels. To this end, we propose Online Self-Adaptive Mirror Descent (OSAMD), which adopts an online teacher-student structure to enable online self-training from unlabeled data, and a margin-based criterion that decides whether to query the labels to track changing distributions. Theoretically, we show that, in the separable case, OSAMD has an $O({T}^{2/3})$ dynamic regret bound under mild assumptions, which is aligned with the $\Omega(T^{2/3})$ lower bound of online learning algorithms with full labels. In the general case, we show a regret bound of $O({T}^{2/3} + \alpha^* T)$, where $\alpha^*$ denotes the separability of domains and is usually small. Our theoretical results show that OSAMD can fast adapt to changing environments with active queries. Empirically, we demonstrate that OSAMD achieves favorable regrets under changing environments with limited labels on both simulated and real-world data, which corroborates our theoretical findings.
Preferential Temporal Difference Learning
Authors: Nishanth Anand, Doina Precup
Date: 2021-06-11 00:00:00

Temporal-Difference (TD) learning is a general and very useful tool for estimating the value function of a given policy, which in turn is required to find good policies. Generally speaking, TD learning updates states whenever they are visited. When the agent lands in a state, its value can be used to compute the TD-error, which is then propagated to other states. However, it may be interesting, when computing updates, to take into account other information than whether a state is visited or not. For example, some states might be more important than others (such as states which are frequently seen in a successful trajectory). Or, some states might have unreliable value estimates (for example, due to partial observability or lack of data), making their values less desirable as targets. We propose an approach to re-weighting states used in TD updates, both when they are the input and when they provide the target for the update. We prove that our approach converges with linear function approximation and illustrate its desirable empirical behaviour compared to other TD-style methods.
Policy Gradient Bayesian Robust Optimization for Imitation Learning
Authors: Zaynah Javed, Daniel S. Brown, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca D. Dragan, Ken Goldberg
Date: 2021-06-11 00:00:00
Tasks: Imitation Learning

The difficulty in specifying rewards for many real-world problems has led to an increased focus on learning rewards from human feedback, such as demonstrations. However, there are often many different reward functions that explain the human feedback, leaving agents with uncertainty over what the true reward function is. While most policy optimization approaches handle this uncertainty by optimizing for expected performance, many applications demand risk-averse behavior. We derive a novel policy gradient-style robust optimization approach, PG-BROIL, that optimizes a soft-robust objective that balances expected performance and risk. To the best of our knowledge, PG-BROIL is the first policy optimization algorithm robust to a distribution of reward hypotheses which can scale to continuous MDPs. Results suggest that PG-BROIL can produce a family of behaviors ranging from risk-neutral to risk-averse and outperforms state-of-the-art imitation learning algorithms when learning from ambiguous demonstrations by hedging against uncertainty, rather than seeking to uniquely identify the demonstrator's reward function.
Multi-Receiver Online Bayesian Persuasion
Authors: Matteo Castiglioni, Alberto Marchesi, Andrea Celli, Nicola Gatti
Date: 2021-06-11 00:00:00

Bayesian persuasion studies how an informed sender should partially disclose information to influence the behavior of a self-interested receiver. Classical models make the stringent assumption that the sender knows the receiver's utility. This can be relaxed by considering an online learning framework in which the sender repeatedly faces a receiver of an unknown, adversarially selected type. We study, for the first time, an online Bayesian persuasion setting with multiple receivers. We focus on the case with no externalities and binary actions, as customary in offline models. Our goal is to design no-regret algorithms for the sender with polynomial per-iteration running time. First, we prove a negative result: for any $0 < \alpha \leq 1$, there is no polynomial-time no-$\alpha$-regret algorithm when the sender's utility function is supermodular or anonymous. Then, we focus on the case of submodular sender's utility functions and we show that, in this case, it is possible to design a polynomial-time no-$(1 - \frac{1}{e})$-regret algorithm. To do so, we introduce a general online gradient descent scheme to handle online learning problems with a finite number of possible loss functions. This requires the existence of an approximate projection oracle. We show that, in our setting, there exists one such projection oracle which can be implemented in polynomial time.
Zero-Shot Controlled Generation with Encoder-Decoder Transformers
Authors: Devamanyu Hazarika, Mahdi Namazifar, Dilek Hakkani-Tür
Date: 2021-06-11 00:00:00
Tasks: Decoder, Document Summarization, Machine Translation, Text Generation

Controlling neural network-based models for natural language generation (NLG) has broad applications in numerous areas such as machine translation, document summarization, and dialog systems. Approaches that enable such control in a zero-shot manner would be of great importance as, among other reasons, they remove the need for additional annotated data and training. In this work, we propose novel approaches for controlling encoder-decoder transformer-based NLG models in zero-shot. This is done by introducing three control knobs, namely, attention biasing, decoder mixing, and context augmentation, that are applied to these models at generation time. These knobs control the generation process by directly manipulating trained NLG models (e.g., biasing cross-attention layers) to realize the desired attributes in the generated outputs. We show that not only are these NLG models robust to such manipulations, but also their behavior could be controlled without an impact on their generation performance. These results, to the best of our knowledge, are the first of their kind. Through these control knobs, we also investigate the role of transformer decoder's self-attention module and show strong evidence that its primary role is maintaining fluency of sentences generated by these models. Based on this hypothesis, we show that alternative architectures for transformer decoders could be viable options. We also study how this hypothesis could lead to more efficient ways for training encoder-decoder transformer models.
What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data
Authors: Yang Hu, Adriane Chapman, Guihua Wen, Dame Wendy Hall
Date: 2021-06-11 00:00:00
Tasks: BIG-bench Machine Learning

Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include: heavy reliance on massive training data, limited generalizability and poor expressiveness of high-level semantics. Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental factors of low-shot learning technologies, with a focus on the operation of structured knowledge under different low-shot conditions. We also introduce other techniques relevant to low-shot learning. Finally, we point out the limitations of low-shot learning, the prospects and gaps of industrial applications, and future research directions.
Small Object Detection for Near Real-Time Egocentric Perception in a Manual Assembly Scenario
Authors: Hooman Tavakoli, Snehal Walunj, Parsha Pahlevannejad, Christiane Plociennik, Martin Ruskowski
Date: 2021-06-11 00:00:00
Tasks: object-detection, Object Detection, Small Object Detection, Unity

Detecting small objects in video streams of head-worn augmented reality devices in near real-time is a huge challenge: training data is typically scarce, the input video stream can be of limited quality, and small objects are notoriously hard to detect. In industrial scenarios, however, it is often possible to leverage contextual knowledge for the detection of small objects. Furthermore, CAD data of objects are typically available and can be used to generate synthetic training data. We describe a near real-time small object detection pipeline for egocentric perception in a manual assembly scenario: We generate a training data set based on CAD data and realistic backgrounds in Unity. We then train a YOLOv4 model for a two-stage detection process: First, the context is recognized, then the small object of interest is detected. We evaluate our pipeline on the augmented reality device Microsoft Hololens 2.
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs
Authors: Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano
Date: 2021-06-11 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Question Generation, Question-Generation, Reinforcement Learning (RL)

Due to the discrete nature of words, language GANs require to be optimized from rewards provided by discriminator networks, via reinforcement learning methods. This is a much harder setting than for continuous tasks, which enjoy gradient flows from discriminators to generators, usually leading to dramatic learning instabilities. However, we claim that this can be solved by making discriminator and generator networks cooperate to produce output sequences during training. These cooperative outputs, inherently built to obtain higher discrimination scores, not only provide denser rewards for training, but also form a more compact artificial set for discriminator training, hence improving its accuracy and stability. In this paper, we show that our SelfGAN framework, built on this cooperative principle, outperforms Teacher Forcing and obtains state-of-the-art results on two challenging tasks, Summarization and Question Generation.
Survey of Image Based Graph Neural Networks
Authors: Usman Nazir, He Wang, Murtaza Taj
Date: 2021-06-11 00:00:00
Tasks: Classification, Superpixels, Survey

In this survey paper, we analyze image based graph neural networks and propose a three-step classification approach. We first convert the image into superpixels using the Quickshift algorithm so as to reduce 30% of the input data. The superpixels are subsequently used to generate a region adjacency graph. Finally, the graph is passed through a state-of-art graph convolutional neural network to get classification scores. We also analyze the spatial and spectral convolution filtering techniques in graph neural networks. Spectral-based models perform better than spatial-based models and classical CNN with lesser compute cost.
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
Authors: Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines
Date: 2021-06-11 00:00:00
Tasks: Bayesian Inference

Performing reliable Bayesian inference on a big data scale is becoming a keystone in the modern era of machine learning. A workhorse class of methods to achieve this task are Markov chain Monte Carlo (MCMC) algorithms and their design to handle distributed datasets has been the subject of many works. However, existing methods are not completely either reliable or computationally efficient. In this paper, we propose to fill this gap in the case where the dataset is partitioned and stored on computing nodes within a cluster under a master/slaves architecture. We derive a user-friendly centralised distributed MCMC algorithm with provable scaling in high-dimensional settings. We illustrate the relevance of the proposed methodology on both synthetic and real data experiments.
FedNLP: An interpretable NLP System to Decode Federal Reserve Communications
Authors: Jean Lee, Hoyoul Luis Youn, Nicholas Stevens, Josiah Poon, Soyeon Caren Han
Date: 2021-06-11 00:00:00
Tasks: Sentiment Analysis, Text Classification

The Federal Reserve System (the Fed) plays a significant role in affecting monetary policy and financial conditions worldwide. Although it is important to analyse the Fed's communications to extract useful information, it is generally long-form and complex due to the ambiguous and esoteric nature of content. In this paper, we present FedNLP, an interpretable multi-component Natural Language Processing system to decode Federal Reserve communications. This system is designed for end-users to explore how NLP techniques can assist their holistic understanding of the Fed's communications with NO coding. Behind the scenes, FedNLP uses multiple NLP models from traditional machine learning algorithms to deep neural network architectures in each downstream task. The demonstration shows multiple results at once including sentiment analysis, summary of the document, prediction of the Federal Funds Rate movement and visualization for interpreting the prediction model's result.