OpenRaiser/Intern-Atlas huggingface dataset:
A Generalized Robotic Handwriting Learning System based on Dynamic Movement Primitives (DMPs)
Venue: arXiv cs.RO 2020
Year: 2020

Learning from demonstration (LfD) is a powerful learning method to enable a robot to infer how to perform a task given one or more human demonstrations of the desired task. By learning from end-user demonstration rather than requiring that a domain expert manually programming each skill, robots can more readily be applied to a wider range of real-world applications. Writing robots, as one application of LfD, has become a challenging research topic due to the complexity of human handwriting trajectories. In this paper, we introduce a generalized handwriting-learning system for a physical robot to learn from examples of humans' handwriting to draw alphanumeric characters. Our robotic system is able to rewrite letters imitating the way human demonstrators write and create new letters in a similar writing style. For this system, we develop an augmented dynamic movement primitive (DMP) algorithm, DMP*, which strengthens the robustness and generalization ability of our robotic system.
SuperCoder: Program Learning Under Noisy Conditions From Superposition of States
Venue: arXiv cs.LG 2020
Year: 2020

We propose a new method of program learning in a Domain Specific Language (DSL) which is based on gradient descent with no direct search. The first component of our method is a probabilistic representation of the DSL variables. At each timestep in the program sequence, different DSL functions are applied on the DSL variables with a certain probability, leading to different possible outcomes. Rather than handling all these outputs separately, whose number grows exponentially with each timestep, we collect them into a superposition of variables which captures the information in a single, but fuzzy, state. This state is to be contrasted at the final timestep with the ground-truth output, through a loss function. The second component of our method is an attention-based recurrent neural network, which provides an appropriate initialization point for the gradient descent that optimizes the probabilistic representation. The method we have developed surpasses the state-of-the-art for synthesising long programs and is able to learn programs under noise.
Improving Clinical Document Understanding on COVID-19 Research with Spark NLP
Venue: arXiv cs.CL 2020
Year: 2020

Following the global COVID-19 pandemic, the number of scientific papers studying the virus has grown massively, leading to increased interest in automated literate review. We present a clinical text mining system that improves on previous efforts in three ways. First, it can recognize over 100 different entity types including social determinants of health, anatomy, risk factors, and adverse events in addition to other commonly used clinical and biomedical entities. Second, the text processing pipeline includes assertion status detection, to distinguish between clinical facts that are present, absent, conditional, or about someone other than the patient. Third, the deep learning models used are more accurate than previously available, leveraging an integrated pipeline of state-of-the-art pretrained named entity recognition models, and improving on the previous best performing benchmarks for assertion status detection. We illustrate extracting trends and insights, e.g. most frequent disorders and symptoms, and most common vital signs and EKG findings, from the COVID-19 Open Research Dataset (CORD-19). The system is built using the Spark NLP library which natively supports scaling to use distributed clusters, leveraging GPUs, configurable and reusable NLP pipelines, healthcare specific embeddings, and the ability to train models to support new entity types or human languages with no code changes.
Battery Model Calibration with Deep Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Lithium-Ion (Li-I) batteries have recently become pervasive and are used in many physical assets. To enable a good prediction of the end of discharge of batteries, detailed electrochemical Li-I battery models have been developed. Their parameters are typically calibrated before they are taken into operation and are typically not re-calibrated during operation. However, since battery performance is affected by aging, the reality gap between the computational battery models and the real physical systems leads to inaccurate predictions. A supervised machine learning algorithm would require an extensive representative training dataset mapping the observation to the ground truth calibration parameters. This may be infeasible for many practical applications. In this paper, we implement a Reinforcement Learning-based framework for reliably and efficiently inferring calibration parameters of battery models. The framework enables real-time inference of the computational model parameters in order to compensate the reality-gap from the observations. Most importantly, the proposed methodology does not need any labeled data samples, (samples of observations and the ground truth calibration parameters). Furthermore, the framework does not require any information on the underlying physical model. The experimental results demonstrate that the proposed methodology is capable of inferring the model parameters with high accuracy and high robustness. While the achieved results are comparable to those obtained with supervised machine learning, they do not rely on the ground truth information during training.
An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth
Venue: arXiv cs.LG 2020
Year: 2020

Multi-step prediction is considered of major significance for time series analysis in many real life problems. Existing methods mainly focus on one-step-ahead forecasting, since multiple step forecasting generally fails due to accumulation of prediction errors. This paper presents a novel approach for predicting plant growth in agriculture, focusing on prediction of plant Stem Diameter Variations (SDV). The proposed approach consists of three main steps. At first, wavelet decomposition is applied to the original data, as to facilitate model fitting and reduce noise in them. Then an encoder-decoder framework is developed using Long Short Term Memory (LSTM) and used for appropriate feature extraction from the data. Finally, a recurrent neural network including LSTM and an attention mechanism is proposed for modelling long-term dependencies in the time series data. Experimental results are presented which illustrate the good performance of the proposed approach and that it significantly outperforms the existing models, in terms of error criteria such as RMSE, MAE and MAPE.
Rotation-Invariant Point Convolution With Multiple Equivariant Alignments
Venue: arXiv cs.CV 2020
Year: 2020

Recent attempts at introducing rotation invariance or equivariance in 3D deep learning approaches have shown promising results, but these methods still struggle to reach the performances of standard 3D neural networks. In this work we study the relation between equivariance and invariance in 3D point convolutions. We show that using rotation-equivariant alignments, it is possible to make any convolutional layer rotation-invariant. Furthermore, we improve this simple alignment procedure by using the alignment themselves as features in the convolution, and by combining multiple alignments together. With this core layer, we design rotation-invariant architectures which improve state-of-the-art results in both object classification and semantic segmentation and reduces the gap between rotation-invariant and standard 3D deep learning approaches.
Almost Optimal Bounds for Sublinear-Time Sampling of $k$-Cliques: Sampling Cliques is Harder Than Counting
Venue: arXiv cs.DS 2020
Year: 2020

In this work, we consider the problem of sampling a $k$-clique in a graph from an almost uniform distribution in sublinear time in the general graph query model. Specifically the algorithm should output each $k$-clique with probability $(1\pm ε)/n_k$, where $n_k$ denotes the number of $k$-cliques in the graph and $ε$ is a given approximation parameter. We prove that the query complexity of this problem is \[ Θ^*\left(\max\left\{ \left(\frac{(nα)^{k/2}}{ n_k}\right)^{\frac{1}{k-1}} ,\; \min\left\{nα,\frac{nα^{k-1}}{n_k} \right\}\right\}\right). \] where $n$ is the number of vertices in the graph, $α$ is its arboricity, and $Θ^*$ suppresses the dependence on $(\log n/ε)^{O(k)}$. Interestingly, this establishes a separation between approximate counting and approximate uniform sampling in the sublinear regime. For example, if $k=3$, $α= O(1)$, and $n_3$ (the number of triangles) is $Θ(n)$, then we get a lower bound of $Ω(n^{1/4})$ (for constant $ε$), while under these conditions, a $(1\pm ε)$-approximation of $n_3$ can be obtained by performing $\textrm{poly}(\log(n/ε))$ queries (Eden, Ron and Seshadhri, SODA20). Our lower bound follows from a construction of a family of graphs with arboricity $α$ such that in each graph there are $n_k$ cliques (of size $k$), where one of these cliques is "hidden" and hence hard to sample. Our upper bound is based on defining a special auxiliary graph $H_k$, such that sampling edges almost uniformly in $H_k$ translates to sampling $k$-cliques almost uniformly in the original graph $G$. We then build on a known edge-sampling algorithm (Eden, Ron and Rosenbaum, ICALP19) to sample edges in $H_k$, where the challenge is simulate queries to $H_k$ while being given access only to $G$.
Deformable Gabor Feature Networks for Biomedical Image Classification
Venue: arXiv cs.CV 2020
Year: 2020

In recent years, deep learning has dominated progress in the field of medical image analysis. We find however, that the ability of current deep learning approaches to represent the complex geometric structures of many medical images is insufficient. One limitation is that deep learning models require a tremendous amount of data, and it is very difficult to obtain a sufficient amount with the necessary detail. A second limitation is that there are underlying features of these medical images that are well established, but the black-box nature of existing convolutional neural networks (CNNs) do not allow us to exploit them. In this paper, we revisit Gabor filters and introduce a deformable Gabor convolution (DGConv) to expand deep networks interpretability and enable complex spatial variations. The features are learned at deformable sampling locations with adaptive Gabor convolutions to improve representativeness and robustness to complex objects. The DGConv replaces standard convolutional layers and is easily trained end-to-end, resulting in deformable Gabor feature network (DGFN) with few additional parameters and minimal additional training cost. We introduce DGFN for addressing deep multi-instance multi-label classification on the INbreast dataset for mammograms and on the ChestX-ray14 dataset for pulmonary x-ray images.
SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis
Venue: arXiv cs.CV 2020
Year: 2020

Advances in face rotation, along with other face-based generative tasks, are more frequent as we advance further in topics of deep learning. Even as impressive milestones are achieved in synthesizing faces, the importance of preserving identity is needed in practice and should not be overlooked. Also, the difficulty should not be more for data with obscured faces, heavier poses, and lower quality. Existing methods tend to focus on samples with variation in pose, but with the assumption data is high in quality. We propose a generative adversarial network (GAN) -based model to generate high-quality, identity preserving frontal faces from one or multiple low-resolution (LR) faces with extreme poses. Specifically, we propose SuperFront-GAN (SF-GAN) to synthesize a high-resolution (HR), frontal face from one-to-many LR faces with various poses and with the identity-preserved. We integrate a super-resolution (SR) side-view module into SF-GAN to preserve identity information and fine details of the side-views in HR space, which helps model reconstruct high-frequency information of faces (i.e., periocular, nose, and mouth regions). Moreover, SF-GAN accepts multiple LR faces as input, and improves each added sample. We squeeze additional gain in performance with an orthogonal constraint in the generator to penalize redundant latent representations and, hence, diversify the learned features space. Quantitative and qualitative results demonstrate the superiority of SF-GAN over others.
Deep Energy-Based NARX Models
Venue: arXiv cs.LG 2020
Year: 2020

This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data. In particular, our interest is in learning a conditional distribution of the current output based on a finite window of past inputs and outputs. To achieve this, we consider the use of so-called energy-based models, which have been developed in allied fields for learning unknown distributions based on data. This energy-based model relies on a general function to describe the distribution, and here we consider a deep neural network for this purpose. The primary benefit of this approach is that it is capable of learning both simple and highly complex noise models, which we demonstrate on simulated and experimental data.
Technology-driven Alteration of Nonverbal Cues and its Effects on Negotiation
Venue: arXiv cs.HC 2020
Year: 2020

A person's appearance, identity, and other nonverbal cues can substantially influence how one is perceived by a negotiation counterpart, potentially impacting the outcome of the negotiation. With recent advances in technology, it is now possible to alter such cues through real-time video communication. In many cases, a person's physical presence can explicitly be replaced by 2D/3D representations in live interactive media. In other cases, technologies such as deepfake can subtly and implicitly alter many nonverbal cues -- including a person's appearance and identity -- in real-time. In this article, we look at some state-of-the-art technological advances that can enable such explicit and implicit alteration of nonverbal cues. We also discuss the implications of such technology for the negotiation landscape and highlight ethical considerations that warrant deep, ongoing attention from stakeholders.
Stability and Identification of Random Asynchronous Linear Time-Invariant Systems
Venue: arXiv cs.LG 2020
Year: 2020

In many computational tasks and dynamical systems, asynchrony and randomization are naturally present and have been considered as ways to increase the speed and reduce the cost of computation while compromising the accuracy and convergence rate. In this work, we show the additional benefits of randomization and asynchrony on the stability of linear dynamical systems. We introduce a natural model for random asynchronous linear time-invariant (LTI) systems which generalizes the standard (synchronous) LTI systems. In this model, each state variable is updated randomly and asynchronously with some probability according to the underlying system dynamics. We examine how the mean-square stability of random asynchronous LTI systems vary with respect to randomization and asynchrony. Surprisingly, we show that the stability of random asynchronous LTI systems does not imply or is not implied by the stability of the synchronous variant of the system and an unstable synchronous system can be stabilized via randomization and/or asynchrony. We further study a special case of the introduced model, namely randomized LTI systems, where each state element is updated randomly with some fixed but unknown probability. We consider the problem of system identification of unknown randomized LTI systems using the precise characterization of mean-square stability via extended Lyapunov equation. For unknown randomized LTI systems, we propose a systematic identification method to recover the underlying dynamics. Given a single input/output trajectory, our method estimates the model parameters that govern the system dynamics, the update probability of state variables, and the noise covariance using the correlation matrices of collected data and the extended Lyapunov equation. Finally, we empirically demonstrate that the proposed method consistently recovers the underlying system dynamics with the optimal rate.
Unsupervised Label Refinement Improves Dataless Text Classification
Venue: arXiv cs.CL 2020
Year: 2020

Dataless text classification is capable of classifying documents into previously unseen labels by assigning a score to any document paired with a label description. While promising, it crucially relies on accurate descriptions of the label set for each downstream task. This reliance causes dataless classifiers to be highly sensitive to the choice of label descriptions and hinders the broader application of dataless classification in practice. In this paper, we ask the following question: how can we improve dataless text classification using the inputs of the downstream task dataset? Our primary solution is a clustering based approach. Given a dataless classifier, our approach refines its set of predictions using k-means clustering. We demonstrate the broad applicability of our approach by improving the performance of two widely used classifier architectures, one that encodes text-category pairs with two independent encoders and one with a single joint encoder. Experiments show that our approach consistently improves dataless classification across different datasets and makes the classifier more robust to the choice of label descriptions.
On Aadhaar Identity Management System
Venue: arXiv cs.CR 2020
Year: 2020

A unique identification for citizens can lead to effective governance to manage and provide citizen-centric services. While ensuring this service, privacy of the citizens needs to be preserved. Aadhaar, the identification system by UIDAI has faced some critics regarding its privacy preserving feature. This paper discusses those concerns in Aadhaar system and proposed a new model for the Aadhaar system. The proposed solution is aimed to address the issue of collusion of third party service providers and profiling of Aadhaar users. The proposed solution uses a distributed model capturing the Aadhaar system, in which data of users is decentralized and stored in zonal office's databases as well as the CIDR. The proposed solution provides the functioning of the authentication process of the Aadhaar system more effective, as it reduces the number of requests being handled directly by the CIDR and also tackles the concern of correlation of data.
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework
Venue: arXiv cs.LG 2020
Year: 2020

Deep Neural Networks (DNNs) have achieved extraordinary performance in various application domains. To support diverse DNN models, efficient implementations of DNN inference on edge-computing platforms, e.g., ASICs, FPGAs, and embedded systems, are extensively investigated. Due to the huge model size and computation amount, model compression is a critical step to deploy DNN models on edge devices. This paper focuses on weight quantization, a hardware-friendly model compression approach that is complementary to weight pruning. Unlike existing methods that use the same quantization scheme for all weights, we propose the first solution that applies different quantization schemes for different rows of the weight matrix. It is motivated by (1) the distribution of the weights in the different rows are not the same; and (2) the potential of achieving better utilization of heterogeneous FPGA hardware resources. To achieve that, we first propose a hardware-friendly quantization scheme named sum-of-power-of-2 (SP2) suitable for Gaussian-like weight distribution, in which the multiplication arithmetic can be replaced with logic shifter and adder, thereby enabling highly efficient implementations with the FPGA LUT resources. In contrast, the existing fixed-point quantization is suitable for Uniform-like weight distribution and can be implemented efficiently by DSP. Then to fully explore the resources, we propose an FPGA-centric mixed scheme quantization (MSQ) with an ensemble of the proposed SP2 and the fixed-point schemes. Combining the two schemes can maintain, or even increase accuracy due to better matching with weight distributions.
Optimal Survival Trees
Venue: arXiv cs.LG 2020
Year: 2020

Tree-based models are increasingly popular due to their ability to identify complex relationships that are beyond the scope of parametric models. Survival tree methods adapt these models to allow for the analysis of censored outcomes, which often appear in medical data. We present a new Optimal Survival Trees algorithm that leverages mixed-integer optimization (MIO) and local search techniques to generate globally optimized survival tree models. We demonstrate that the OST algorithm improves on the accuracy of existing survival tree methods, particularly in large datasets.
A Foundation for Spatio-Textual-Temporal Cube Analytics (Extended Version)
Venue: arXiv cs.DB 2020
Year: 2020

Large amounts of spatial, textual, and temporal data are being produced daily. This is data containing an unstructured component (text), a spatial component (geographic position), and a time component (timestamp). Therefore, there is a need for a powerful and general way of analyzing spatial, textual, and temporal data together. In this paper, we define and formalize the Spatio-Textual-Temporal Cube structure to enable combined effective and efficient analytical queries over spatial, textual, and temporal data. Our novel data model over spatio-textual-temporal objects enables novel joint and integrated spatial, textual, and temporal insights that are hard to obtain using existing methods. Moreover, we introduce the new concept of spatio-textual-temporal measures with associated novel spatio-textual-temporal-OLAP operators. To allow for efficient large-scale analytics, we present a pre-aggregation framework for the exact and approximate computation of spatio-textual-temporal measures. Our comprehensive experimental evaluation on a real-world Twitter dataset confirms that our proposed methods reduce query response time by 1-5 orders of magnitude compared to the No Materialization baseline and decrease storage cost between 97% and 99.9% compared to the Full Materialization baseline while adding only a negligible overhead in the Spatio-Textual-Temporal Cube construction time. Moreover, approximate computation achieves an accuracy between 90% and 100% while reducing query response time by 3-5 orders of magnitude compared to No Materialization.
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading Comprehension
Venue: arXiv cs.CL 2020
Year: 2020

Distant supervision (DS) is a promising approach for relation extraction but often suffers from the noisy label problem. Traditional DS methods usually represent an entity pair as a bag of sentences and denoise labels using multi-instance learning techniques. The bag-based paradigm, however, fails to leverage the inter-sentence-level and the entity-level evidence for relation extraction, and their denoising algorithms are often specialized and complicated. In this paper, we propose a new DS paradigm--document-based distant supervision, which models relation extraction as a document-based machine reading comprehension (MRC) task. By re-organizing all sentences about an entity as a document and extracting relations via querying the document with relation-specific questions, the document-based DS paradigm can simultaneously encode and exploit all sentence-level, inter-sentence-level, and entity-level evidence. Furthermore, we design a new loss function--DSLoss (distant supervision loss), which can effectively train MRC models using only $\langle$document, question, answer$\rangle$ tuples, therefore noisy label problem can be inherently resolved. Experiments show that our method achieves new state-of-the-art DS performance.