OpenRaiser/Intern-Atlas huggingface dataset:
FibAR: Embedding Optical Fibers in 3D Printed Objects for Active Markers in Dynamic Projection Mapping
Venue: arXiv cs.GR 2020
Year: 2020

This paper presents a novel active marker for dynamic projection mapping (PM) that emits a temporal blinking pattern of infrared (IR) light representing its ID. We used a multi-material three dimensional (3D) printer to fabricate a projection object with optical fibers that can guide IR light from LEDs attached on the bottom of the object. The aperture of an optical fiber is typically very small; thus, it is unnoticeable to human observers under projection and can be placed on a strongly curved part of a projection surface. In addition, the working range of our system can be larger than previous marker-based methods as the blinking patterns can theoretically be recognized by a camera placed at a wide range of distances from markers. We propose an automatic marker placement algorithm to spread multiple active markers over the surface of a projection object such that its pose can be robustly estimated using captured images from arbitrary directions. We also propose an optimization framework for determining the routes of the optical fibers in such a way that collisions of the fibers can be avoided while minimizing the loss of light intensity in the fibers. Through experiments conducted using three fabricated objects containing strongly curved surfaces, we confirmed that the proposed method can achieve accurate dynamic PMs in a significantly wide working range.
Generalized Pair Weights of Linear Codes and Linear Isomorphisms Preserving Pair Weights
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we first introduce the notion of generalized pair weights of an $[n, k]$-linear code over the finite field $\mathbb{F}_q$ and the notion of pair $r$-equiweight codes, where $1\le r\le k-1$. Some basic properties of generalized pair weights of linear codes over finite fields are derived. Then we obtain a necessary and sufficient condition for an $[n,k]$-linear code to be a pair equiweight code, and we characterize pair $r$-equiweight codes for any $1\le r\le k-1$. Finally, a necessary and sufficient condition for a linear isomorphism preserving pair weights between two linear codes is obtained.
Relational Neural Machines
Venue: arXiv cs.AI 2020
Year: 2020

Deep learning has been shown to achieve impressive results in several tasks where a large amount of training data is available. However, deep learning solely focuses on the accuracy of the predictions, neglecting the reasoning process leading to a decision, which is a major issue in life-critical applications. Probabilistic logic reasoning allows to exploit both statistical regularities and specific domain expertise to perform reasoning under uncertainty, but its scalability and brittle integration with the layers processing the sensory data have greatly limited its applications. For these reasons, combining deep architectures and probabilistic logic reasoning is a fundamental goal towards the development of intelligent agents operating in complex environments. This paper presents Relational Neural Machines, a novel framework allowing to jointly train the parameters of the learners and of a First--Order Logic based reasoner. A Relational Neural Machine is able to recover both classical learning from supervised data in case of pure sub-symbolic learning, and Markov Logic Networks in case of pure symbolic reasoning, while allowing to jointly train and perform inference in hybrid learning tasks. Proper algorithmic solutions are devised to make learning and inference tractable in large-scale problems. The experiments show promising results in different relational tasks.
Joint Deep Learning of Facial Expression Synthesis and Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Recently, deep learning based facial expression recognition (FER) methods have attracted considerable attention and they usually require large-scale labelled training data. Nonetheless, the publicly available facial expression databases typically contain a small amount of labelled data. In this paper, to overcome the above issue, we propose a novel joint deep learning of facial expression synthesis and recognition method for effective FER. More specifically, the proposed method involves a two-stage learning procedure. Firstly, a facial expression synthesis generative adversarial network (FESGAN) is pre-trained to generate facial images with different facial expressions. To increase the diversity of the training images, FESGAN is elaborately designed to generate images with new identities from a prior distribution. Secondly, an expression recognition network is jointly learned with the pre-trained FESGAN in a unified framework. In particular, the classification loss computed from the recognition network is used to simultaneously optimize the performance of both the recognition network and the generator of FESGAN. Moreover, in order to alleviate the problem of data bias between the real images and the synthetic images, we propose an intra-class loss with a novel real data-guided back-propagation (RDBP) algorithm to reduce the intra-class variations of images from the same class, which can significantly improve the final performance. Extensive experimental results on public facial expression databases demonstrate the superiority of the proposed method compared with several state-of-the-art FER methods.
Towards Semantic Noise Cleansing of Categorical Data based on Semantic Infusion
Venue: arXiv cs.CL 2020
Year: 2020

Semantic Noise affects text analytics activities for the domain-specific industries significantly. It impedes the text understanding which holds prime importance in the critical decision making tasks. In this work, we formalize semantic noise as a sequence of terms that do not contribute to the narrative of the text. We look beyond the notion of standard statistically-based stop words and consider the semantics of terms to exclude the semantic noise. We present a novel Semantic Infusion technique to associate meta-data with the categorical corpus text and demonstrate its near-lossless nature. Based on this technique, we propose an unsupervised text-preprocessing framework to filter the semantic noise using the context of the terms. Later we present the evaluation results of the proposed framework using a web forum dataset from the automobile-domain.
Social diversity and social preferences in mixed-motive reinforcement learning
Venue: arXiv cs.MA 2020
Year: 2020

Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity. In contrast, studies of reinforcement learning in mixed-motive games have primarily leveraged homogeneous approaches. Given the defining characteristic of mixed-motive games--the imperfect correlation of incentives between group members--we study the effect of population heterogeneity on mixed-motive reinforcement learning. We draw on interdependence theory from social psychology and imbue reinforcement learning agents with Social Value Orientation (SVO), a flexible formalization of preferences over group outcome distributions. We subsequently explore the effects of diversity in SVO on populations of reinforcement learning agents in two mixed-motive Markov games. We demonstrate that heterogeneity in SVO generates meaningful and complex behavioral variation among agents similar to that suggested by interdependence theory. Empirical results in these mixed-motive dilemmas suggest agents trained in heterogeneous populations develop particularly generalized, high-performing policies relative to those trained in homogeneous populations.
Situating Agent-Based Modelling in Population Health Research
Venue: arXiv cs.MA 2020
Year: 2020

Today's most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challenges. However, while ABM has been used occasionally in population health, we argue that for ABM to be most effective in the field it should be used as a means for answering questions normally inaccessible to the traditional epidemiological toolkit. In an effort to clearly illustrate the utility of ABM for population health research, and to clear up persistent misunderstandings regarding the method's conceptual underpinnings, we offer a detailed presentation of the core concepts of complex systems theory, and summarise why simulations are essential to the study of complex systems. We then examine the current state of the art in ABM for population health, and propose they are well-suited for the study of the `wicked' problems in population health, and could make significant contributions to theory and intervention development in these areas.
Conversational Structure Aware and Context Sensitive Topic Model for Online Discussions
Venue: arXiv cs.CL 2020
Year: 2020

Millions of online discussions are generated everyday on social media platforms. Topic modelling is an efficient way of better understanding large text datasets at scale. Conventional topic models have had limited success in online discussions, and to overcome their limitations, we use the discussion thread tree structure and propose a "popularity" metric to quantify the number of replies to a comment to extend the frequency of word occurrences, and the "transitivity" concept to characterize topic dependency among nodes in a nested discussion thread. We build a Conversational Structure Aware Topic Model (CSATM) based on popularity and transitivity to infer topics and their assignments to comments. Experiments on real forum datasets are used to demonstrate improved performance for topic extraction with six different measurements of coherence and impressive accuracy for topic assignments.
A comparison of mobile VR display running on an ordinary smartphone with standard PC display for P300-BCI stimulus presentation
Venue: arXiv cs.HC 2020
Year: 2020

A brain-computer interface (BCI) based on electroencephalography (EEG) is a promising technology for enhancing virtual reality (VR) applications-in particular, for gaming. We focus on the so-called P300-BCI, a stable and accurate BCI paradigm relying on the recognition of a positive event-related potential (ERP) occurring in the EEG about 300 ms post-stimulation. We implemented a basic version of such a BCI displayed on an ordinary and affordable smartphone-based head-mounted VR device: that is, a mobile and passive VR system (with no electronic components beyond the smartphone). The mobile phone performed the stimuli presentation, EEG synchronization (tagging) and feedback display. We compared the ERPs and the accuracy of the BCI on the VR device with a traditional BCI running on a personal computer (PC). We also evaluated the impact of subjective factors on the accuracy. The study was within-subjects, with 21 participants and one session in each modality. No significant difference in BCI accuracy was found between the PC and VR systems, although the P200 ERP was significantly wider and larger in the VR system as compared to the PC system.
Data hiding in speech signal using steganography and encryption
Venue: arXiv cs.MM 2020
Year: 2020

Data privacy and data security are always on highest priority in the world. We need a reliable method to encrypt the data so that it reaches the destination safely. Encryption is a simple yet effective way to protect our data while transmitting it to a destination. The proposed method has state of art technology of steganography and encryption. This paper puts forward a different approach for data hiding in speech signals. A ten-digit number within speech signal using audio steganography and encrypting it with a unique key for better security. At the receiver end the same unique key is used to decrypt the received signal and then hidden numbers are extracted. The proposed approach performance can be evaluated by PSNR, MSE, SSIM and bit-error rate. The simulation results give better performance compared to existing approach.
Algorithmic Aspects of 2-Secure Domination in Graphs
Venue: arXiv cs.DM 2020
Year: 2020

Let $G(V,E)$ be a simple, undirected and connected graph. A dominating set $S \subseteq V(G)$ is called a $2$-\textit{secure dominating set} ($2$-SDS) in $G$, if for every pair of distinct vertices $u_1,u_2 \in V(G)$ there exists a pair of distinct vertices $v_1,v_2 \in S$ such that $v_1 \in N[u_1]$, $v_2 \in N[u_2]$ and $(S \setminus \{v_1,v_2\}) \cup \{u_1,u_2 \}$ is a dominating set in $G$. The $2$\textit{-secure domination number} denoted by $γ_{2s}(G)$, equals the minimum cardinality of a $2$-SDS in $G$. Given a graph $ G$ and a positive integer $ k,$ the $ 2 $-Secure Domination ($ 2 $-SDM) problem is to check whether $ G $ has a $ 2 $-secure dominating set of size at most $ k.$ It is known that $ 2 $-SDM is NP-complete for bipartite graphs. In this paper, we prove that the $ 2 $-SDM problem is NP-complete for planar graphs and doubly chordal graphs, a subclass of chordal graphs. We strengthen the NP-complete result for bipartite graphs, by proving this problem is NP-complete for some subclasses of bipartite graphs namely, star convex bipartite, comb convex bipartite graphs. We prove that $ 2 $-SDM is linear time solvable for bounded tree-width graphs. We also show that the $ 2 $-SDM is W[2]-hard even for split graphs. The Minimum $ 2 $-Secure Dominating Set (M2SDS) problem is to find a $ 2 $-secure dominating set of minimum size in the input graph. We propose a $ Δ(G)+1 $ $ - $ approximation algorithm for M2SDS, where $ Δ(G) $ is the maximum degree of the input graph $ G $ and prove that M2SDS cannot be approximated within $ (1 - ε) \ln(| V | ) $ for any $ ε> 0 $ unless $ NP \subseteq DTIME(| V |^{ O(\log \log | V | )}) $. % even for bipartite graphs. A secure dominating set of a graph \textit{defends} one attack at any vertex of the graph. Finally, we show that the M2SDS is APX-complete for graphs with $Δ(G)=4.$
The utility of tactile force to autonomous learning of in-hand manipulation is task-dependent
Venue: arXiv cs.RO 2020
Year: 2020

Tactile sensors provide information that can be used to learn and execute manipulation tasks. Different tasks, however, might require different levels of sensory information; which in turn likely affect learning rates and performance. This paper evaluates the role of tactile information on autonomous learning of manipulation with a simulated 3-finger tendon-driven hand. We compare the ability of the same learning algorithm (Proximal Policy Optimization, PPO) to learn two manipulation tasks (rolling a ball about the horizontal axis with and without rotational stiffness) with three levels of tactile sensing: no sensing, 1D normal force, and 3D force vector. Surprisingly, and contrary to recent work on manipulation, adding 1D force-sensing did not always improve learning rates compared to no sensing---likely due to whether or not normal force is relevant to the task. Nonetheless, even though 3D force-sensing increases the dimensionality of the sensory input---which would in general hamper algorithm convergence---it resulted in faster learning rates and better performance. We conclude that, in general, sensory input is useful to learning only when it is relevant to the task---as is the case of 3D force-sensing for in-hand manipulation against gravity. Moreover, the utility of 3D force-sensing can even offset the added computational cost of learning with higher-dimensional sensory input.
Sim2Real2Sim: Bridging the Gap Between Simulation and Real-World in Flexible Object Manipulation
Venue: arXiv cs.RO 2020
Year: 2020

This paper addresses a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim) to bridge the gap between simulation and real-world, and automate a flexible object manipulation task. This strategy consists of three steps: (1) using the rough environment with the estimated models to develop the methods to complete the manipulation task in the simulation; (2) applying the methods from simulation to real-world and comparing their performance; (3) updating the models and methods in simulation based on the differences between the real world and the simulation. The Plug Task from the 2015 DARPA Robotics Challenge Finals is chosen to evaluate our Sim2Real2Sim strategy. A new identification approach for building the model of the linear flexible objects is derived from real-world to simulation. The automation of the DRC plug task in both simulation and real-world proves the success of the Sim2Real2Sim strategy. Numerical experiments are implemented to validate the simulated model.
JPLink: On Linking Jobs to Vocational Interest Types
Venue: arXiv cs.LG 2020
Year: 2020

Linking job seekers with relevant jobs requires matching based on not only skills, but also personality types. Although the Holland Code also known as RIASEC has frequently been used to group people by their suitability for six different categories of occupations, the RIASEC category labels of individual jobs are often not found in job posts. This is attributed to significant manual efforts required for assigning job posts with RIASEC labels. To cope with assigning massive number of jobs with RIASEC labels, we propose JPLink, a machine learning approach using the text content in job titles and job descriptions. JPLink exploits domain knowledge available in an occupation-specific knowledge base known as O*NET to improve feature representation of job posts. To incorporate relative ranking of RIASEC labels of each job, JPLink proposes a listwise loss function inspired by learning to rank. Both our quantitative and qualitative evaluations show that JPLink outperforms conventional baselines. We conduct an error analysis on JPLink's predictions to show that it can uncover label errors in existing job posts.
Autonomous Industrial Assembly using Force, Torque, and RGB-D sensing
Venue: arXiv cs.RO 2020
Year: 2020

We present algorithms and results for a robotic manipulation system that was designed to be easily programmable and adaptable to various tasks common to industrial setting, which is inspired by the Industrial Assembly Challenge at the 2018 World Robotics Summit in Tokyo. This challenge included assembly of standard, commercially available industrial parts into 2D and 3D assemblies. We demonstrate three tasks that can be classified into "peg-in-hole" and "hole-on-peg" tasks and identify two canonical algorithms: spiral-based search and tilting insertion. Both algorithms use hand-coded thresholds in the force and torque domains to detect critical points in the assembly. After briefly summarizing the state of the art in research, we describe the strategy and approach utilized by the tested system, how it's design bears on its performance, statistics on 20 experimental trials for each task, lessons learned during the development of the system, and open research challenges that still remain.
Object-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation
Venue: arXiv cs.CV 2020
Year: 2020

In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classification-based tracking methods exhibit excellent performance while their speeds are heavily limited by the expensive computation for massive proposal feature extraction. In contrast, matching-based tracking methods (such as Siamese networks) possess remarkable speed superiority. However, the absence of online updating renders these methods unadaptable to significant object appearance variations. In this paper, we propose a novel real-time visual tracking method, which adopts an object-adaptive LSTM network to effectively capture the video sequential dependencies and adaptively learn the object appearance variations. For high computational efficiency, we also present a fast proposal selection strategy, which utilizes the matching-based tracking method to pre-estimate dense proposals and selects high-quality ones to feed to the LSTM network for classification. This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classification-based tracking methods. In addition, to handle the problems of sample inadequacy and class imbalance during online tracking, we adopt a data augmentation technique based on the Generative Adversarial Network (GAN) to facilitate the training of the LSTM network. Extensive experiments on four visual tracking benchmarks demonstrate the state-of-the-art performance of our method in terms of both tracking accuracy and speed, which exhibits great potentials of recurrent structures for visual tracking.
Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach
Venue: arXiv cs.LG 2020
Year: 2020

We propose a new method to solve eigenvalue problems for linear and semilinear second order differential operators in high dimensions based on deep neural networks. The eigenvalue problem is reformulated as a fixed point problem of the semigroup flow induced by the operator, whose solution can be represented by Feynman-Kac formula in terms of forward-backward stochastic differential equations. The method shares a similar spirit with diffusion Monte Carlo but augments a direct approximation to the eigenfunction through neural-network ansatz. The criterion of fixed point provides a natural loss function to search for parameters via optimization. Our approach is able to provide accurate eigenvalue and eigenfunction approximations in several numerical examples, including Fokker-Planck operator and the linear and nonlinear Schrödinger operators in high dimensions.
Visual search over billions of aerial and satellite images
Venue: arXiv cs.CV 2020
Year: 2020

We present a system for performing visual search over billions of aerial and satellite images. The purpose of visual search is to find images that are visually similar to a query image. We define visual similarity using 512 abstract visual features generated by a convolutional neural network that has been trained on aerial and satellite imagery. The features are converted to binary values to reduce data and compute requirements. We employ a hash-based search using Bigtable, a scalable database service from Google Cloud. Searching the continental United States at 1-meter pixel resolution, corresponding to approximately 2 billion images, takes approximately 0.1 seconds. This system enables real-time visual search over the surface of the earth, and an interactive demo is available at https://search.descarteslabs.com.