pwc-archive/papers-with-abstracts huggingface dataset:
Batch and high-yield production of high‐purity solid SiO2 nanodumbbells
Authors: Deng
Date: 2020-01-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Optically levitated nanodumbbells in vacuum are excellent candidates for thermodynamics, macroscopic quantum mechanics, precision measurements and quantum sensing. Silica (SiO2) material, with extremely low absorption of near-infrared light and super mechanical strength, has been the most potential material for optically levitated systems. Here we synthesize high-purity solid SiO2 nanodumbbells via Stöber method by introducing acetone for the induced aggregation of SiO2 nanospheres. The nanodumbbells show high uniformity and their sizes are tunable. Previous experimental results demonstrated that the synthetic nanodumbbells can be applied in GHz nanomechanical rotors and can withstand the tensile strength of over 13 GPa. This work supports batch production and high yield of SiO2 nanodumbbells, which engineers a new material platform to advance levitated optomechanics. 
【Cite this article：Deng, YH. Synthesis of High‐purity Solid SiO2 Nanodumbbells Via Induced Aggregation for Levitated Optomechanics. Silicon (2021). https://doi.org/10.1007/s12633-021-00952-5 ；DOI https://doi.org/10.1007/s12633-021-00952-5】
Relief and Stimulus in A Cross-sector Multi-product Scarce Resource Supply Chain Network
Authors: Xiaowei Hu, Peng Li
Date: 2021-01-22 00:00:00

In the era of a growing population, systemic changes to the world, and the rising risk of crises, humanity has been facing an unprecedented challenge of resource scarcity. Confronting and addressing the issues concerning the scarce resource's conservation, competition, and stimulation by grappling its characteristics and adopting viable policy instruments calls the decision-maker's attention with a paramount priority. In this paper, we develop the first general decentralized cross-sector supply chain network model that captures the unique features of scarce resources under a unifying fiscal policy scheme. We formulate the problem as a network equilibrium model with finite-dimensional variational inequality theories. We then characterize the network equilibrium with a set of classic theoretical properties, as well as with a set of properties that are novel to the network games application literature, namely, the lowest eigenvalue of the game Jacobian. Lastly, we provide a series of illustrative examples, including a medical glove supply network, to showcase how our model can be used to investigate the efficacy of the imposed policies in relieving supply chain distress and stimulating welfare. Our managerial insights inform and expand the political dialogues on fiscal policy design, public resource legislation, social welfare redistribution, and supply chain practice toward sustainability.
A Dual-branch Network for Infrared and Visible Image Fusion
Authors: Yu Fu, Xiao-Jun Wu
Date: 2021-01-24 00:00:00
Tasks: Generative Adversarial Network, Infrared And Visible Image Fusion, SSIM

Deep learning is a rapidly developing approach in the field of infrared and visible image fusion. In this context, the use of dense blocks in deep networks significantly improves the utilization of shallow information, and the combination of the Generative Adversarial Network (GAN) also improves the fusion performance of two source images. We propose a new method based on dense blocks and GANs , and we directly insert the input image-visible light image in each layer of the entire network. We use SSIM and gradient loss functions that are more consistent with perception instead of mean square error loss. After the adversarial training between the generator and the discriminator, we show that a trained end-to-end fusion network -- the generator network -- is finally obtained. Our experiments show that the fused images obtained by our approach achieve good score based on multiple evaluation indicators. Further, our fused images have better visual effects in multiple sets of contrasts, which are more satisfying to human visual perception.
The Two-Sided Market Network Analysis Based on Transfer Entropy & Labelr
Authors: Seung Bin Baik
Date: 2021-01-25 00:00:00
Tasks: Vocal Bursts Valence Prediction

This study more complex digital platforms in early stages in the two-sided market to produce powerful network effects. In this study, I use Transfer Entropy to look for super users who connect hominids in different networks to achieve higher network effects in the digital platform in the two-sided market, which has recently become more complex. And this study also aims to redefine the decision criteria of product managers by helping them define users with stronger network effects. With the development of technology, the structure of the industry is becoming more difficult to interpret and the complexity of business logic is increasing. This phenomenon is the biggest problem that makes it difficult for start-ups to challenge themselves. I hope this study will help product managers create new digital economic networks, enable them to make prioritized, data-driven decisions, and find users who can be the hub of the network even in small products.
Image Compression with Encoder-Decoder Matched Semantic Segmentation
Authors: Trinh Man Hoang, Jinjia Zhou, Yibo Fan
Date: 2021-01-24 00:00:00
Tasks: Decoder, Image Compression, Segmentation, Semantic Segmentation

In recent years, layered image compression is demonstrated to be a promising direction, which encodes a compact representation of the input image and apply an up-sampling network to reconstruct the image. To further improve the quality of the reconstructed image, some works transmit the semantic segment together with the compressed image data. Consequently, the compression ratio is also decreased because extra bits are required for transmitting the semantic segment. To solve this problem, we propose a new layered image compression framework with encoder-decoder matched semantic segmentation (EDMS). And then, followed by the semantic segmentation, a special convolution neural network is used to enhance the inaccurate semantic segment. As a result, the accurate semantic segment can be obtained in the decoder without requiring extra bits. The experimental results show that the proposed EDMS framework can get up to 35.31% BD-rate reduction over the HEVC-based (BPG) codec, 5% bitrate, and 24% encoding time saving compare to the state-of-the-art semantic-based image codec.
Multi-Time Attention Networks for Irregularly Sampled Time Series
Authors: Satya Narayan Shukla, Benjamin M. Marlin
Date: 2021-01-25 00:00:00
Tasks: Deep Learning, Time Series, Time Series Analysis

Irregular sampling occurs in many time series modeling applications where it presents a significant challenge to standard deep learning models. This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. In this paper, we propose a new deep learning framework for this setting that we call Multi-Time Attention Networks. Multi-Time Attention Networks learn an embedding of continuous-time values and use an attention mechanism to produce a fixed-length representation of a time series containing a variable number of observations. We investigate the performance of this framework on interpolation and classification tasks using multiple datasets. Our results show that the proposed approach performs as well or better than a range of baseline and recently proposed models while offering significantly faster training times than current state-of-the-art methods.
Architectural Implications of Function-as-a-Service Computing
Authors: Shahrad, Mohammad; Fonseca, Rodrigo; Goiri, Íñigo; Irfan Chaudhry, Gohar; Bianchini, Ricardo;
Date: 2019-10-16 00:00:00

Serverless computing is a rapidly growing cloud application model, popularized by Amazon’s Lambda platform. Serverless cloud services provide fine-grained provisioning of resources, which scale automatically with user demand. Function-as-a-Service (FaaS) applications follow this serverless model, with the developer providing their application as a set of functions which are executed in response to a user- or system-generated event. Functions are designed to be short-lived and execute inside containers or virtual machines, introducing a range of system-level overheads. This paper studies the architectural implications of this emerging paradigm. Using the commercial-grade Apache OpenWhisk FaaS platform on real servers, this work 
investigates and identifies the architectural implications of FaaS serverless computing. The workloads, along with the way that FaaS inherently interleaves short functions from many tenants frustrates many of the locality-preserving architectural structures common in modern processors. In particular, we find that: FaaS containerization brings up to 20x slowdown compared to native execution, cold-start can be over 10x a short function’s execution time, branch mispredictions per kilo-instruction are 20x
higher for short functions, memory bandwidth increases by 6x due to the invocation pattern, and IPC decreases by as much as 35% due to inter-function interference. We open-source FaaSProfiler, the FaaS testing and profiling platform that we developed for this work.
Identity-aware Graph Neural Networks
Authors: Jiaxuan You, Jonathan Gomes-Selman, Rex Ying, Jure Leskovec
Date: 2021-01-25 00:00:00
Tasks: Graph Classification, Graph Property Prediction, Link Prediction, Node Clustering, Property Prediction

Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs. Here we develop a class of message passing GNNs, named Identity-aware Graph Neural Networks (ID-GNNs), with greater expressive power than the 1-WL test. ID-GNN offers a minimal but powerful solution to limitations of existing GNNs. ID-GNN extends existing GNN architectures by inductively considering nodes' identities during message passing. To embed a given node, ID-GNN first extracts the ego network centered at the node, then conducts rounds of heterogeneous message passing, where different sets of parameters are applied to the center node than to other surrounding nodes in the ego network. We further propose a simplified but faster version of ID-GNN that injects node identity information as augmented node features. Altogether, both versions of ID-GNN represent general extensions of message passing GNNs, where experiments show that transforming existing GNNs to ID-GNNs yields on average 40% accuracy improvement on challenging node, edge, and graph property prediction tasks; 3% accuracy improvement on node and graph classification benchmarks; and 15% ROC AUC improvement on real-world link prediction tasks. Additionally, ID-GNNs demonstrate improved or comparable performance over other task-specific graph networks.
Accumulating Risk Capital Through Investing in Cooperation
Authors: Charlotte Roman, Michael Dennis, Andrew Critch, Stuart Russell
Date: 2021-01-25 00:00:00

Recent work on promoting cooperation in multi-agent learning has resulted in many methods which successfully promote cooperation at the cost of becoming more vulnerable to exploitation by malicious actors. We show that this is an unavoidable trade-off and propose an objective which balances these concerns, promoting both safety and long-term cooperation. Moreover, the trade-off between safety and cooperation is not severe, and you can receive exponentially large returns through cooperation from a small amount of risk. We study both an exact solution method and propose a method for training policies that targets this objective, Accumulating Risk Capital Through Investing in Cooperation (ARCTIC), and evaluate them in iterated Prisoner's Dilemma and Stag Hunt.
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach
Authors: Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim, Christopher G. Brinton, David J. Love
Date: 2021-01-25 00:00:00
Tasks: Denoising, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

In general, reliable communication via multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) requires accurate channel estimation at the receiver. The existing literature largely focuses on denoising methods for channel estimation that depend on either (i) channel analysis in the time-domain with prior channel knowledge or (ii) supervised learning techniques which require large pre-labeled datasets for training. To address these limitations, we present a frequency-domain denoising method based on a reinforcement learning framework that does not need a priori channel knowledge and pre-labeled data. Our methodology includes a new successive channel denoising process based on channel curvature computation, for which we obtain a channel curvature magnitude threshold to identify unreliable channel estimates. Based on this process, we formulate the denoising mechanism as a Markov decision process, where we define the actions through a geometry-based channel estimation update, and the reward function based on a policy that reduces mean squared error (MSE). We then resort to Q-learning to update the channel estimates. Numerical results verify that our denoising algorithm can successfully mitigate noise in channel estimates. In particular, our algorithm provides a significant improvement over the practical least squares (LS) estimation method and provides performance that approaches that of the ideal linear minimum mean square error (LMMSE) estimation with perfect knowledge of channel statistics.
Transferable Interactiveness Knowledge for Human-Object Interaction Detection
Authors: Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu, Xijie Huang, Liang Xu, Cewu Lu
Date: 2021-01-25 00:00:00
Tasks: Human-Object Interaction Detection, Object

Human-Object Interaction (HOI) detection is an important problem to understand how humans interact with objects. In this paper, we explore interactiveness knowledge which indicates whether a human and an object interact with each other or not. We found that interactiveness knowledge can be learned across HOI datasets and bridge the gap between diverse HOI category settings. Our core idea is to exploit an interactiveness network to learn the general interactiveness knowledge from multiple HOI datasets and perform Non-Interaction Suppression (NIS) before HOI classification in inference. On account of the generalization ability of interactiveness, interactiveness network is a transferable knowledge learner and can be cooperated with any HOI detection models to achieve desirable results. We utilize the human instance and body part features together to learn the interactiveness in hierarchical paradigm, i.e., instance-level and body part-level interactivenesses. Thereafter, a consistency task is proposed to guide the learning and extract deeper interactive visual clues. We extensively evaluate the proposed method on HICO-DET, V-COCO, and a newly constructed PaStaNet-HOI dataset. With the learned interactiveness, our method outperforms state-of-the-art HOI detection methods, verifying its efficacy and flexibility. Code is available at https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network.
Reinforcement Learning Based Temporal Logic Control with Soft Constraints Using Limit-deterministic Generalized Buchi Automata
Authors: Mingyu Cai, Shaoping Xiao, Zhijun Li, Zhen Kan
Date: 2021-01-25 00:00:00
Tasks: Motion Planning

This paper studies the control synthesis of motion planning subject to uncertainties. The uncertainties are considered in robot motions and environment properties, giving rise to the probabilistic labeled Markov decision process (PL-MDP). A Model-Free Reinforcement The learning (RL) method is developed to generate a finite-memory control policy to satisfy high-level tasks expressed in linear temporal logic (LTL) formulas. Due to uncertainties and potentially conflicting tasks, this work focuses on infeasible LTL specifications, where a relaxed LTL constraint is developed to allow the agent to revise its motion plan and take violations of original tasks into account for partial satisfaction. And a novel automaton is developed to improve the density of accepting rewards and enable deterministic policies. We proposed an RL framework with rigorous analysis that is guaranteed to achieve multiple objectives in decreasing order: 1) satisfying the acceptance condition of relaxed product MDP and 2) reducing the violation cost over long-term behaviors. We provide simulation and experimental results to validate the performance.
PAWLS: PDF Annotation With Labels and Structure
Authors: Mark Neumann, Zejiang Shen, Sam Skjonsberg
Date: 2021-01-25 00:00:00
Proceeding: ACL 2021 5

Adobe's Portable Document Format (PDF) is a popular way of distributing view-only documents with a rich visual markup. This presents a challenge to NLP practitioners who wish to use the information contained within PDF documents for training models or data analysis, because annotating these documents is difficult. In this paper, we present PDF Annotation with Labels and Structure (PAWLS), a new annotation tool designed specifically for the PDF document format. PAWLS is particularly suited for mixed-mode annotation and scenarios in which annotators require extended context to annotate accurately. PAWLS supports span-based textual annotation, N-ary relations and freeform, non-textual bounding boxes, all of which can be exported in convenient formats for training multi-modal machine learning models. A read-only PAWLS server is available at https://pawls.apps.allenai.org/ and the source code is available at https://github.com/allenai/pawls.
Emergent Communication under Competition
Authors: Michael Noukhovitch, Travis LaCroix, Angeliki Lazaridou, Aaron Courville
Date: 2021-01-25 00:00:00
Tasks: Misconceptions

The literature in modern machine learning has only negative results for learning to communicate between competitive agents using standard RL. We introduce a modified sender-receiver game to study the spectrum of partially-competitive scenarios and show communication can indeed emerge in a competitive setting. We empirically demonstrate three key takeaways for future research. First, we show that communication is proportional to cooperation, and it can occur for partially competitive scenarios using standard learning algorithms. Second, we highlight the difference between communication and manipulation and extend previous metrics of communication to the competitive case. Third, we investigate the negotiation game where previous work failed to learn communication between independent agents (Cao et al., 2018). We show that, in this setting, both agents must benefit from communication for it to emerge; and, with a slight modification to the game, we demonstrate successful communication between competitive agents. We hope this work overturns misconceptions and inspires more research in competitive emergent communication.
TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics
Authors: Yufang Hou, Charles Jochim, Martin Gleize, Francesca Bonin, Debasis Ganguly
Date: 2021-01-25 00:00:00
Proceeding: EACL 2021 2
Tasks: Data Augmentation

Tasks, Datasets and Evaluation Metrics are important concepts for understanding experimental scientific papers. However, most previous work on information extraction for scientific literature mainly focuses on the abstracts only, and does not treat datasets as a separate type of entity (Zadeh and Schumann, 2016; Luan et al., 2018). In this paper, we present a new corpus that contains domain expert annotations for Task (T), Dataset (D), Metric (M) entities on 2,000 sentences extracted from NLP papers. We report experiment results on TDM extraction using a simple data augmentation strategy and apply our tagger to around 30,000 NLP papers from the ACL Anthology. The corpus is made publicly available to the community for fostering research on scientific publication summarization (Erera et al., 2019) and knowledge discovery.
VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder
Authors: Matthew Ehrler, Neil Ernst
Date: 2021-01-25 00:00:00
Tasks: Time Series, Time Series Analysis

Remote sensing of Chlorophyll-a is vital in monitoring climate change. Chlorphyll-a measurements give us an idea of the algae concentrations in the ocean, which lets us monitor ocean health. However, a common problem is that the satellites used to gather the data are commonly obstructed by clouds and other artifacts. This means that time series data from satellites can suffer from spatial data loss. There are a number of algorithms that are able to reconstruct the missing parts of these images to varying degrees of accuracy, with Data INterpolating Empirical Orthogonal Functions (DINEOF) being the current standard. However, DINEOF is slow, suffers from accuracy loss in temporally homogenous waters, reliant on temporal data, and only able to generate a single potential reconstruction. We propose a machine learning approach to reconstruction of Chlorophyll-a data using a Variational Autoencoder (VAE). Our accuracy results to date are competitive with but slightly less accurate than DINEOF. We show the benefits of our method including vastly decreased computation time and ability to generate multiple potential reconstructions. Lastly, we outline our planned improvements and future work.
Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification
Authors: Anu Jagannath, Jithin Jagannath
Date: 2021-01-25 00:00:00
Tasks: General Classification, Management, Multi-Task Learning

Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) and beyond 5G communications, Internet of Things networks, among others. State-of-the-art studies in wireless signal recognition have only focused on a single task which in many cases is insufficient information for a system to act on. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks in conjunction with multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. Additionally, we consider the problem of heterogeneous wireless signals such as radar and communication signals in the electromagnetic spectrum. Accordingly, we have shown how the proposed MTL model outperforms several state-of-the-art single-task learning classifiers while maintaining a lighter architecture and performing two signal characterization tasks simultaneously. Finally, we also release the only known open heterogeneous wireless signals dataset that comprises of radar and communication signals with multiple labels.