OpenRaiser/Intern-Atlas huggingface dataset:
Algorithms for $q$-ary Error-Correcting Codes with Limited Magnitude and Feedback
Venue: arXiv cs.IT 2020
Year: 2020

Berlekamp and Zigangirov completely determined the capacity error function for binary error correcting codes with noiseless feedback. It is still an unsolved problem if the upper bound for the capacity error function in the non-binary case of Ahlswede, Lebedev, and Deppe is sharp. We consider wraparound channels with limited magnitude and noiseless feedback. We completely determine the capacity error function for all $q$-ary wraparound channels with a magnitude of level $r$. All of our algorithms use partial noiseless feedback. Furthermore, a special case of the problem is equivalent to Shannon's zero-error problem.
A Method of Generating Measurable Panoramic Image for Indoor Mobile Measurement System
Venue: arXiv cs.CV 2020
Year: 2020

This paper designs a technique route to generate high-quality panoramic image with depth information, which involves two critical research hotspots: fusion of LiDAR and image data and image stitching. For the fusion of 3D points and image data, since a sparse depth map can be firstly generated by projecting LiDAR point onto the RGB image plane based on our reliable calibrated and synchronized sensors, we adopt a parameter self-adaptive framework to produce 2D dense depth map. For image stitching, optimal seamline for the overlapping area is searched using a graph-cuts-based method to alleviate the geometric influence and image blending based on the pyramid multi-band is utilized to eliminate the photometric effects near the stitching line. Since each pixel is associated with a depth value, we design this depth value as a radius in the spherical projection which can further project the panoramic image to the world coordinate and consequently produces a high-quality measurable panoramic image. The purposed method is tested on the data from our data collection platform and presents a satisfactory application prospects.
Optimization of Robot Grasping Forces and Worst Case Loading
Venue: arXiv cs.RO 2020
Year: 2020

We consider the optimization of the vector of grasping forces that support a known generalized force acting on the grasped object---a rigid body or a mechanism. Working in the framework of finite-dimensional normed vector spaces and their dual spaces, the cost function to be minimized is assumed to be a norm on the space of grasping forces. We present an expression for the optimum which depends on the external force and the kinematics of the grasping system. Next, assuming that optimal grasping forces are applied using force control, and assuming that there is a bound on the norm of the admissible grasping forces, we characterize the largest norm of an external force that the grasping system may support, that is, the norm of the worst-case loading that may be applied and still be supported. A few simple examples are given for the sake of illustration.
Improving Word Recognition using Multiple Hypotheses and Deep Embeddings
Venue: arXiv cs.CV 2020
Year: 2020

We propose a novel scheme for improving the word recognition accuracy using word image embeddings. We use a trained text recognizer, which can predict multiple text hypothesis for a given word image. Our fusion scheme improves the recognition process by utilizing the word image and text embeddings obtained from a trained word image embedding network. We propose EmbedNet, which is trained using a triplet loss for learning a suitable embedding space where the embedding of the word image lies closer to the embedding of the corresponding text transcription. The updated embedding space thus helps in choosing the correct prediction with higher confidence. To further improve the accuracy, we propose a plug-and-play module called Confidence based Accuracy Booster (CAB). The CAB module takes in the confidence scores obtained from the text recognizer and Euclidean distances between the embeddings to generate an updated distance vector. The updated distance vector has lower distance values for the correct words and higher distance values for the incorrect words. We rigorously evaluate our proposed method systematically on a collection of books in the Hindi language. Our method achieves an absolute improvement of around 10 percent in terms of word recognition accuracy.
COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Reinforcement learning has been applied to a wide variety of robotics problems, but most of such applications involve collecting data from scratch for each new task. Since the amount of robot data we can collect for any single task is limited by time and cost considerations, the learned behavior is typically narrow: the policy can only execute the task in a handful of scenarios that it was trained on. What if there was a way to incorporate a large amount of prior data, either from previously solved tasks or from unsupervised or undirected environment interaction, to extend and generalize learned behaviors? While most prior work on extending robotic skills using pre-collected data focuses on building explicit hierarchies or skill decompositions, we show in this paper that we can reuse prior data to extend new skills simply through dynamic programming. We show that even when the prior data does not actually succeed at solving the new task, it can still be utilized for learning a better policy, by providing the agent with a broader understanding of the mechanics of its environment. We demonstrate the effectiveness of our approach by chaining together several behaviors seen in prior datasets for solving a new task, with our hardest experimental setting involving composing four robotic skills in a row: picking, placing, drawer opening, and grasping, where a +1/0 sparse reward is provided only on task completion. We train our policies in an end-to-end fashion, mapping high-dimensional image observations to low-level robot control commands, and present results in both simulated and real world domains. Additional materials and source code can be found on our project website: https://sites.google.com/view/cog-rl
Characterizing Human Mobility Patterns During COVID-19 using Cellular Network Data
Venue: arXiv cs.SI 2020
Year: 2020

In this paper, our goal is to analyze and compare cellular network usage data from pre-lockdown, during lockdown, and post-lockdown phases surrounding the COVID-19 pandemic to understand and model human mobility patterns during the pandemic, and evaluate the effect of lockdowns on mobility. To this end, we collaborate with one of the main cellular network providers in Brazil, and collect and analyze cellular network connections from 1400 antennas for all users in the city of Rio de Janeiro and its suburbs from March 1, 2020 to July 1, 2020. Our analysis reveals that the total number of cellular connections decreases to 78% during the lockdown phase and then increases to 85% of the pre-COVID era as the lockdown eases. We observe that as more people work remotely, there is a shift in the antennas incurring top 10% of the total traffic, with the number of connections made to antennas in downtown Rio reducing drastically and antennas at other locations taking their place. We also observe that while nearly 40-45% users connected to only 1 antenna each day during the lockdown phase indicating no mobility, there are around 4% users (i.e., 80K users) who connected to more than 10 antennas, indicating very high mobility. Finally, we design an interactive tool that showcases mobility patterns in different granularities that can potentially help people and government officials understand the mobility of individuals and the number of COVID cases in a particular neighborhood. Our analysis, inferences, and interactive showcasing of mobility patterns based on large-scale data can be extrapolated to other cities of the world and has the potential to help in designing more effective pandemic management measures in the future.
Improved Algorithms for Edge Colouring in the W-Streaming Model
Venue: arXiv cs.DS 2020
Year: 2020

In the W-streaming model, an algorithm is given $O(n \mathrm{polylog} n)$ space and must process a large graph of up to $O(n^2)$ edges. In this short note we give two algorithms for edge colouring under the W-streaming model. For edge colouring in W-streaming, a colour for every edge must be determined by the time all the edges are streamed. Our first algorithm uses $Δ+ o(Δ)$ colours in $O(n \log^2 n)$ space when the edges arrive according to a uniformly random permutation. The second algorithm uses $(1 + o(1))Δ^2 / s$ colours in $\tilde{O}(n s)$ space when edges arrival adversarially.
Adversarial Dueling Bandits
Venue: arXiv cs.LG 2020
Year: 2020

We introduce the problem of regret minimization in Adversarial Dueling Bandits. As in classic Dueling Bandits, the learner has to repeatedly choose a pair of items and observe only a relative binary `win-loss' feedback for this pair, but here this feedback is generated from an arbitrary preference matrix, possibly chosen adversarially. Our main result is an algorithm whose $T$-round regret compared to the \emph{Borda-winner} from a set of $K$ items is $\tilde{O}(K^{1/3}T^{2/3})$, as well as a matching $Ω(K^{1/3}T^{2/3})$ lower bound. We also prove a similar high probability regret bound. We further consider a simpler \emph{fixed-gap} adversarial setup, which bridges between two extreme preference feedback models for dueling bandits: stationary preferences and an arbitrary sequence of preferences. For the fixed-gap adversarial setup we give an $\smash{ \tilde{O}((K/Δ^2)\log{T}) }$ regret algorithm, where $Δ$ is the gap in Borda scores between the best item and all other items, and show a lower bound of $Ω(K/Δ^2)$ indicating that our dependence on the main problem parameters $K$ and $Δ$ is tight (up to logarithmic factors).
HOPPY: An open-source and low-cost kit for dynamic robotics education
Venue: arXiv cs.RO 2020
Year: 2020

This letter introduces HOPPY, an open-source, low-cost, robust, and modular kit for robotics education. The robot dynamically hops around a rotating gantry with a fixed base. The kit lowers the entry barrier for studying dynamic robots and legged locomotion in real systems. The kit bridges the theoretical content of fundamental robotic courses and real dynamic robots by facilitating and guiding the software and hardware integration. This letter describes the topics which can be studied using the kit, lists its components, discusses best practices for implementation, presents results from experiments with the simulator and the real system, and suggests further improvements. A simple controller is described to achieve velocities up to 2m/s, navigate small objects, and mitigate external disturbances (kicks). HOPPY was utilized as the topic of a semester-long project for the Robot Dynamics and Control course at the University of Illinois at Urbana-Champaign. Students provided an overwhelmingly positive feedback from the hands-on activities during the course and the instructors will continue to improve the kit for upcoming semesters.
A Comprehensive Dictionary and Term Variation Analysis for COVID-19 and SARS-CoV-2
Venue: arXiv cs.DL 2020
Year: 2020

The number of unique terms in the scientific literature used to refer to either SARS-CoV-2 or COVID-19 is remarkably large and has continued to increase rapidly despite well-established standardized terms. This high degree of term variation makes high recall identification of these important entities difficult. In this manuscript we present an extensive dictionary of terms used in the literature to refer to SARS-CoV-2 and COVID-19. We use a rule-based approach to iteratively generate new term variants, then locate these variants in a large text corpus. We compare our dictionary to an extensive collection of terminological resources, demonstrating that our resource provides a substantial number of additional terms. We use our dictionary to analyze the usage of SARS-CoV-2 and COVID-19 terms over time and show that the number of unique terms continues to grow rapidly. Our dictionary is freely available at https://github.com/ncbi-nlp/CovidTermVar.
Deformable Convolutional LSTM for Human Body Emotion Recognition
Venue: arXiv cs.CV 2020
Year: 2020

People represent their emotions in a myriad of ways. Among the most important ones is whole body expressions which have many applications in different fields such as human-computer interaction (HCI). One of the most important challenges in human emotion recognition is that people express the same feeling in various ways using their face and their body. Recently many methods have tried to overcome these challenges using Deep Neural Networks (DNNs). However, most of these methods were based on images or on facial expressions only and did not consider deformation that may happen in the images such as scaling and rotation which can adversely affect the recognition accuracy. In this work, motivated by recent researches on deformable convolutions, we incorporate the deformable behavior into the core of convolutional long short-term memory (ConvLSTM) to improve robustness to these deformations in the image and, consequently, improve its accuracy on the emotion recognition task from videos of arbitrary length. We did experiments on the GEMEP dataset and achieved state-of-the-art accuracy of 98.8% on the task of whole human body emotion recognition on the validation set.
Political Geography and Representation: A Case Study of Districting in Pennsylvania
Venue: arXiv cs.CY 2020
Year: 2020

This preprint offers a detailed look, both qualitative and quantitative, at districting with respect to recent voting patterns in one state: Pennsylvania. We investigate how much the partisan playing field is tilted by political geography. In particular we closely examine the role of scale. We find that partisan-neutral maps rarely give seats proportional to votes, and that making the district size smaller tends to make it even harder to find a proportional map. This preprint was prepared as a chapter in the forthcoming edited volume Political Geometry, an interdisciplinary collection of essays on redistricting. (mggg.org/gerrybook)
From Artificial Intelligence to Brain Intelligence: The basis learning and memory algorithm for brain-like intelligence
Venue: arXiv cs.NE 2020
Year: 2020

The algorithm of brain learning and memory is still undetermined. The backpropagation algorithm of artificial neural networks was thought not suitable for brain cortex, and there is a lack of algorithm for memory engram. We designed a brain version of backpropagation algorithm, which are biologically plausible and could be implemented with virtual neurons to complete image classification task. An encoding algorithm that can automatically allocate engram cells is proposed, which is an algorithm implementation for memory engram theory, and could simulate how hippocampus achieve fast associative memory. The role of the LTP and LTD in the cerebellum is also explained in algorithm level. Our results proposed a method for the brain to deploy backpropagation algorithm, and sparse coding method for memory engram theory.
ElderSim: A Synthetic Data Generation Platform for Human Action Recognition in Eldercare Applications
Venue: arXiv cs.CV 2020
Year: 2020

To train deep learning models for vision-based action recognition of elders' daily activities, we need large-scale activity datasets acquired under various daily living environments and conditions. However, most public datasets used in human action recognition either differ from or have limited coverage of elders' activities in many aspects, making it challenging to recognize elders' daily activities well by only utilizing existing datasets. Recently, such limitations of available datasets have actively been compensated by generating synthetic data from realistic simulation environments and using those data to train deep learning models. In this paper, based on these ideas we develop ElderSim, an action simulation platform that can generate synthetic data on elders' daily activities. For 55 kinds of frequent daily activities of the elders, ElderSim generates realistic motions of synthetic characters with various adjustable data-generating options, and provides different output modalities including RGB videos, two- and three-dimensional skeleton trajectories. We then generate KIST SynADL, a large-scale synthetic dataset of elders' activities of daily living, from ElderSim and use the data in addition to real datasets to train three state-of the-art human action recognition models. From the experiments following several newly proposed scenarios that assume different real and synthetic dataset configurations for training, we observe a noticeable performance improvement by augmenting our synthetic data. We also offer guidance with insights for the effective utilization of synthetic data to help recognize elders' daily activities.
A short note on the decision tree based neural turing machine
Venue: arXiv cs.LG 2020
Year: 2020

Turing machine and decision tree have developed independently for a long time. With the recent development of differentiable models, there is an intersection between them. Neural turing machine(NTM) opens door for the memory network. It use differentiable attention mechanism to read/write external memory bank. Differentiable forest brings differentiable properties to classical decision tree. In this short note, we show the deep connection between these two models. That is: differentiable forest is a special case of NTM. Differentiable forest is actually decision tree based neural turing machine. Based on this deep connection, we propose a response augmented differential forest (RaDF). The controller of RaDF is differentiable forest, the external memory of RaDF are response vectors which would be read/write by leaf nodes.
PPG-based singing voice conversion with adversarial representation learning
Venue: arXiv cs.SD 2020
Year: 2020

Singing voice conversion (SVC) aims to convert the voice of one singer to that of other singers while keeping the singing content and melody. On top of recent voice conversion works, we propose a novel model to steadily convert songs while keeping their naturalness and intonation. We build an end-to-end architecture, taking phonetic posteriorgrams (PPGs) as inputs and generating mel spectrograms. Specifically, we implement two separate encoders: one encodes PPGs as content, and the other compresses mel spectrograms to supply acoustic and musical information. To improve the performance on timbre and melody, an adversarial singer confusion module and a mel-regressive representation learning module are designed for the model. Objective and subjective experiments are conducted on our private Chinese singing corpus. Comparing with the baselines, our methods can significantly improve the conversion performance in terms of naturalness, melody, and voice similarity. Moreover, our PPG-based method is proved to be robust for noisy sources.
Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation in Dense Mobile Crowds
Venue: arXiv cs.RO 2020
Year: 2020

We present a novel Deep Reinforcement Learning (DRL) based policy to compute dynamically feasible and spatially aware velocities for a robot navigating among mobile obstacles. Our approach combines the benefits of the Dynamic Window Approach (DWA) in terms of satisfying the robot's dynamics constraints with state-of-the-art DRL-based navigation methods that can handle moving obstacles and pedestrians well. Our formulation achieves these goals by embedding the environmental obstacles' motions in a novel low-dimensional observation space. It also uses a novel reward function to positively reinforce velocities that move the robot away from the obstacle's heading direction leading to significantly lower number of collisions. We evaluate our method in realistic 3-D simulated environments and on a real differential drive robot in challenging dense indoor scenarios with several walking pedestrians. We compare our method with state-of-the-art collision avoidance methods and observe significant improvements in terms of success rate (up to 33\% increase), number of dynamics constraint violations (up to 61\% decrease), and smoothness. We also conduct ablation studies to highlight the advantages of our observation space formulation, and reward structure.
Non-Asymptotic Capacity Upper Bounds for the Discrete-Time Poisson Channel with Positive Dark Current
Venue: arXiv cs.IT 2020
Year: 2020

We derive improved and easily computable upper bounds on the capacity of the discrete-time Poisson channel under an average-power constraint and an arbitrary constant dark current term. This is accomplished by combining a general convex duality framework with a modified version of the digamma distribution considered in previous work of the authors (Cheraghchi, J. ACM 2019; Cheraghchi, Ribeiro, IEEE Trans. Inf. Theory 2019). For most choices of parameters, our upper bounds improve upon previous results even when an additional peak-power constraint is imposed on the input.
Creating it from SCRATCh: A Practical Approach for Enhancing the Security of IoT-Systems in a DevOps-enabled Software Development Environment
Venue: arXiv cs.SE 2020
Year: 2020

DevOps describes a method to reorganize the way different disciplines in software engineering work together to speed up software delivery. However, the introduction of DevOps-methods to organisations is a complex task. A successful introduction results in a set of structured process descriptions. Despite the structure, this process leaves margin for error: Especially security issues are addressed in individual stages, without consideration of the interdependence. Furthermore, applying DevOps-methods to distributed entities, such as the Internet of Things (IoT) is difficult as the architecture is tailormade for desktop and cloud resources. In this work, an overview of tooling employed in the stages of DevOps processes is introduced. Gaps in terms of security or applicability to the IoT are derived. Based on these gaps, solutions that are being developed in the course of the research project SCRATCh are presented and discussed in terms of benefit to DevOps-environments.
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis
Venue: arXiv cs.CV 2020
Year: 2020

Generating images from textual descriptions has recently attracted a lot of interest. While current models can generate photo-realistic images of individual objects such as birds and human faces, synthesising images with multiple objects is still very difficult. In this paper, we propose an effective way to combine Text-to-Image (T2I) synthesis with Visual Question Answering (VQA) to improve the image quality and image-text alignment of generated images by leveraging the VQA 2.0 dataset. We create additional training samples by concatenating question and answer (QA) pairs and employ a standard VQA model to provide the T2I model with an auxiliary learning signal. We encourage images generated from QA pairs to look realistic and additionally minimize an external VQA loss. Our method lowers the FID from 27.84 to 25.38 and increases the R-prec. from 83.82% to 84.79% when compared to the baseline, which indicates that T2I synthesis can successfully be improved using a standard VQA model.