OpenRaiser/Intern-Atlas huggingface dataset:
A Method to Generate High Precision Mesh Model and RGB-D Datasetfor 6D Pose Estimation Task
Venue: arXiv cs.CV 2020
Year: 2020

Recently, 3D version has been improved greatly due to the development of deep neural networks. A high quality dataset is important to the deep learning method. Existing datasets for 3D vision has been constructed, such as Bigbird and YCB. However, the depth sensors used to make these datasets are out of date, which made the resolution and accuracy of the datasets cannot full fill the higher standards of demand. Although the equipment and technology got better, but no one was trying to collect new and better dataset. Here we are trying to fill that gap. To this end, we propose a new method for object reconstruction, which takes into account the speed, accuracy and robustness. Our method could be used to produce large dataset with better and more accurate annotation. More importantly, our data is more close to the rendering data, which shrinking the gap between the real data and synthetic data further.
Tensor-Decomposition-based Hybrid Beamforming Design for mmWave OFDM Massive MIMO Communications
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we propose a novel joint hybrid precoder and combiner design for maximizing the average achievable sum-rate of single-user orthogonal frequency division multiplexing millimeter wave massive MIMO systems. We formulate the analog precoder and combiner design as a constrained Tucker2 decomposition and solve it by using the projected alternate least square method. Such a formulation allows maximizing the sum of the effective baseband gains over all subcarriers, while suppressing the interference among the data streams in the same subcarrier. In turn, the digital precoder and combiner are obtained from the effective baseband channel's singular value decomposition on a per-subcarrier basis. Numerical simulation results show that the proposed method outperforms other existing designs.
Answering Regular Path Queries Over SQ Ontologies
Venue: arXiv cs.LO 2020
Year: 2020

We study query answering in the description logic $\mathcal{SQ}$ supporting qualified number restrictions on both transitive and non-transitive roles. Our main contributions are a tree-like model property for $\mathcal{SQ}$ knowledge bases and, building upon this, an optimal automata-based algorithm for answering positive existential regular path queries in 2ExpTime.
TreeGen -- a Monte Carlo generator for data frames
Venue: arXiv cs.LG 2020
Year: 2020

The typical problem in Data Science is creating a structure that encodes the occurrence frequency of unique elements in rows and relations between different rows of a data frame. We present the probability tree abstract data structure, an extension of the decision tree, that facilitates more than two choices with assigned probabilities. Such a tree represents statistical relations between different rows of the data frame. The Probability Tree algorithmic structure is supplied with the Generator module that is a Monte Carlo generator that traverses through the tree. These two components are implemented in TreeGen Python package. The package can be used in increasing data multiplicity, compressing data preserving its statistical information, constructing hierarchical models, exploring data, and in feature extraction.
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem
Venue: arXiv cs.LG 2020
Year: 2020

In this work, we propose a multi-agent actor-critic reinforcement learning (RL) algorithm to accelerate the multi-level Monte Carlo Markov Chain (MCMC) sampling algorithms. The policies (actors) of the agents are used to generate the proposal in the MCMC steps; and the critic, which is centralized, is in charge of estimating the long term reward. We verify our proposed algorithm by solving an inverse problem with multiple scales. There are several difficulties in the implementation of this problem by using traditional MCMC sampling. Firstly, the computation of the posterior distribution involves evaluating the forward solver, which is very time consuming for a problem with heterogeneous. We hence propose to use the multi-level algorithm. More precisely, we use the generalized multiscale finite element method (GMsFEM) as the forward solver in evaluating a posterior distribution in the multi-level rejection procedure. Secondly, it is hard to find a function which can generate samplings which are meaningful. To solve this issue, we learn an RL policy as the proposal generator. Our experiments show that the proposed method significantly improves the sampling process
A Block-Permutation-Based Encryption Scheme with Independent Processing of RGB Components
Venue: arXiv cs.MM 2020
Year: 2020

This paper proposes a block-permutation-based encryption (BPBE) scheme for the encryption-then-compression (ETC) system that enhances the color scrambling. A BPBE image can be obtained through four processes, positional scrambling, block rotation/flip, negative-positive transformation, and color component shuffling, after dividing the original image into multiple blocks. The proposed scheme scrambles the R, G, and B components independently in positional scrambling, block rotation/flip, and negative-positive transformation, by assigning different keys to each color component. The conventional scheme considers the compression efficiency using JPEG and JPEG 2000, which need a color conversion before the compression process by default. Therefore, the conventional scheme scrambles the color components identically in each process. In contrast, the proposed scheme takes into account the RGB-based compression, such as JPEG-LS, and thus can increase the extent of the scrambling. The resilience against jigsaw puzzle solver (JPS) can consequently be increased owing to the wider color distribution of the BPBE image. Additionally, the key space for resilience against brute-force attacks has also been expanded exponentially. Furthermore, the proposed scheme can maintain the JPEG-LS compression efficiency compared to the conventional scheme. We confirm the effectiveness of the proposed scheme by experiments and analyses.
On the Relationship Between KR Approaches for Explainable Planning
Venue: arXiv cs.AI 2020
Year: 2020

In this paper, we build upon notions from knowledge representation and reasoning (KR) to expand a preliminary logic-based framework that characterizes the model reconciliation problem for explainable planning. We also provide a detailed exposition on the relationship between similar KR techniques, such as abductive explanations and belief change, and their applicability to explainable planning.
RustViz: Interactively Visualizing Ownership and Borrowing
Venue: arXiv cs.PL 2020
Year: 2020

Rust is a systems programming language that guarantees memory safety without the need for a garbage collector by statically tracking ownership and borrowing events. The associated rules are subtle and unique among industry programming languages, which can make learning Rust more challenging. Motivated by the challenges that Rust learners face, we are developing RustViz, a tool that allows teachers to generate an interactive timeline depicting ownership and borrowing events for each variable in a Rust code example. These visualizations are intended to help Rust learners develop an understanding of ownership and borrowing by example. This paper introduces RustViz by example, shows how teachers can use it to generate visualizations, describes learning goals, and proposes a study designed to evaluate RustViz based on these learning goals.
A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression
Venue: arXiv cs.DC 2020
Year: 2020

Deep neural networks (DNNs) are becoming increasingly deeper, wider, and non-linear due to the growing demands on prediction accuracy and analysis quality. When training a DNN model, the intermediate activation data must be saved in the memory during forward propagation and then restored for backward propagation. However, state-of-the-art accelerators such as GPUs are only equipped with very limited memory capacities due to hardware design constraints, which significantly limits the maximum batch size and hence performance speedup when training large-scale DNNs. In this paper, we propose a novel memory-driven high performance DNN training framework that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger networks. Different from the state-of-the-art solutions that adopt image-based lossy compressors such as JPEG to compress the activation data, our framework purposely designs error-bounded lossy compression with a strict error-controlling mechanism. Specifically, we provide theoretical analysis on the compression error propagation from the altered activation data to the gradients, and then empirically investigate the impact of altered gradients over the entire training process. Based on these analyses, we then propose an improved lossy compressor and an adaptive scheme to dynamically configure the lossy compression error-bound and adjust the training batch size to further utilize the saved memory space for additional speedup. We evaluate our design against state-of-the-art solutions with four popular DNNs and the ImageNet dataset. Results demonstrate that our proposed framework can significantly reduce the training memory consumption by up to 13.5x and 1.8x over the baseline training and state-of-the-art framework with compression, respectively, with little or no accuracy loss.
Viewpoint-aware Progressive Clustering for Unsupervised Vehicle Re-identification
Venue: arXiv cs.CV 2020
Year: 2020

Vehicle re-identification (Re-ID) is an active task due to its importance in large-scale intelligent monitoring in smart cities. Despite the rapid progress in recent years, most existing methods handle vehicle Re-ID task in a supervised manner, which is both time and labor-consuming and limits their application to real-life scenarios. Recently, unsupervised person Re-ID methods achieve impressive performance by exploring domain adaption or clustering-based techniques. However, one cannot directly generalize these methods to vehicle Re-ID since vehicle images present huge appearance variations in different viewpoints. To handle this problem, we propose a novel viewpoint-aware clustering algorithm for unsupervised vehicle Re-ID. In particular, we first divide the entire feature space into different subspaces according to the predicted viewpoints and then perform a progressive clustering to mine the accurate relationship among samples. Comprehensive experiments against the state-of-the-art methods on two multi-viewpoint benchmark datasets VeRi and VeRi-Wild validate the promising performance of the proposed method in both with and without domain adaption scenarios while handling unsupervised vehicle Re-ID.
Dichotomy Result on 3-Regular Bipartite Non-negative Functions
Venue: arXiv cs.CC 2020
Year: 2020

We prove a complexity dichotomy theorem for a class of Holant problems on 3-regular bipartite graphs. Given an arbitrary nonnegative weighted symmetric constraint function $f = [x_0, x_1, x_2, x_3]$, we prove that the bipartite Holant problem $\operatorname{Holant} \left( f \mid \left( =_3 \right) \right)$ is \emph{either} computable in polynomial time \emph{or} $\#$P-hard. The dichotomy criterion on $f$ is explicit.
Barycode-based GJK Algorithm
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we present a more efficient GJK algorithm to solve the collision detection and distance query problems in 2D. We contribute in two aspects: First, we propose a new barycode-based sub-distance algorithm that does not only provide a simple and unified condition to determine the minimum simplex but also improve the efficiency in distant, touching, and overlap cases in distance query. Second, we provide a highly efficient implementation subroutine for collision detection by optimizing the exit conditions of our GJK distance algorithm, which shows dramatic improvements in run-time for applications that only need binary results. We benchmark our methods along with that of the well-known open-source collision detection libraries, such as Bullet, FCL, OpenGJK, Box2D, and Apollo over a range of random datasets. The results indicate that our methods and implementations outperform the state-of-the-art in both collision detection and distance query.
Integration of Blockchain and IoT: An Enhanced Security Perspective
Venue: arXiv cs.CR 2020
Year: 2020

Blockchain (BC), a by-product of Bitcoin cryptocurrency, has gained immense and wide scale popularity for its applicability in various diverse domains - especially in multifaceted non-monetary systems. By adopting cryptographic techniques such as hashing and asymmetric encryption - along with distributed consensus approach, a Blockchain based distributed ledger not only becomes highly secure but also immutable and thus eliminates the need for any third-party intermediators. On the contrary, innumerable IoT (Internet of Things) devices are increasingly being added to the network. This phenomenon poses higher risk in terms of security and privacy. It is thus extremely important to address the security aspects of the growing IoT ecosystem. This paper explores the applicability of BC for ensuring enhanced security and privacy in the IoT ecosystem. Recent research articles and projects or applications were surveyed to assess the implementation of BC for IoT Security and identify associated challenges and propose solutions for BC enabled enhanced security for the IoT ecosystem.
Expanding Access to Music Technology -- Rapid Prototyping Accessible Instrument Solutions For Musicians With Intellectual Disabilities
Venue: arXiv cs.SD 2020
Year: 2020

Using open-source and creative coding frameworks, a teamof artist-engineers from Portland Community College work-ing with artists who experience Intellectual/Developmentaldisabilities prototyped an ensemble of adapted instrumentsand synthesizers that facilitate real-time in-key collabora-tion. The instruments employ a variety of sensors, send-ing the resulting musical controls to software sound gener-ators via MIDI. Careful consideration was given to the bal-ance between freedom of expression, and curating the pos-sible sonic outcomes as adaptation. Evaluation of adaptedinstrument design may differ greatly from frameworks forevaluating traditional instruments or products intended formass-market, though the results of such focused and indi-vidualised design have a variety of possible applications.
Using Unity to Help Solve Intelligence
Venue: arXiv cs.AI 2020
Year: 2020

In the pursuit of artificial general intelligence, our most significant measurement of progress is an agent's ability to achieve goals in a wide range of environments. Existing platforms for constructing such environments are typically constrained by the technologies they are founded on, and are therefore only able to provide a subset of scenarios necessary to evaluate progress. To overcome these shortcomings, we present our use of Unity, a widely recognized and comprehensive game engine, to create more diverse, complex, virtual simulations. We describe the concepts and components developed to simplify the authoring of these environments, intended for use predominantly in the field of reinforcement learning. We also introduce a practical approach to packaging and re-distributing environments in a way that attempts to improve the robustness and reproducibility of experiment results. To illustrate the versatility of our use of Unity compared to other solutions, we highlight environments already created using our approach from published papers. We hope that others can draw inspiration from how we adapted Unity to our needs, and anticipate increasingly varied and complex environments to emerge from our approach as familiarity grows.
Generic Ontology Design Patterns: Roles and Change over Time
Venue: arXiv cs.AI 2020
Year: 2020

In this chapter we propose Generic Ontology Design Patterns, GODPs, as a methodology for representing and instantiating ontology design patterns in a way that is adaptable, and allows domain experts (and other users) to safely use them without cluttering their ontologies.
Fast Motion Understanding with Spatiotemporal Neural Networks and Dynamic Vision Sensors
Venue: arXiv cs.CV 2020
Year: 2020

This paper presents a Dynamic Vision Sensor (DVS) based system for reasoning about high speed motion. As a representative scenario, we consider the case of a robot at rest reacting to a small, fast approaching object at speeds higher than 15m/s. Since conventional image sensors at typical frame rates observe such an object for only a few frames, estimating the underlying motion presents a considerable challenge for standard computer vision systems and algorithms. In this paper we present a method motivated by how animals such as insects solve this problem with their relatively simple vision systems. Our solution takes the event stream from a DVS and first encodes the temporal events with a set of causal exponential filters across multiple time scales. We couple these filters with a Convolutional Neural Network (CNN) to efficiently extract relevant spatiotemporal features. The combined network learns to output both the expected time to collision of the object, as well as the predicted collision point on a discretized polar grid. These critical estimates are computed with minimal delay by the network in order to react appropriately to the incoming object. We highlight the results of our system to a toy dart moving at 23.4m/s with a 24.73° error in $θ$, 18.4mm average discretized radius prediction error, and 25.03% median time to collision prediction error.
Mixed Nondeterministic-Probabilistic Interfaces
Venue: arXiv cs.LO 2020
Year: 2020

Interface theories are powerful frameworks supporting incremental and compositional design of systems through refinements and constructs for conjunction, and parallel composition. In this report we present a first Interface Theor -- |Modal Mixed Interfaces -- for systems exhibiting both non-determinism and randomness in their behaviour. The associated component model -- Mixed Markov Decision Processes -- is also novel and subsumes both ordinary Markov Decision Processes and Probabilistic Automata.
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
Venue: arXiv cs.AI 2020
Year: 2020

Most recently developed approaches to cooperative multi-agent reinforcement learning in the \emph{centralized training with decentralized execution} setting involve estimating a centralized, joint value function. In this paper, we demonstrate that, despite its various theoretical shortcomings, Independent PPO (IPPO), a form of independent learning in which each agent simply estimates its local value function, can perform just as well as or better than state-of-the-art joint learning approaches on popular multi-agent benchmark suite SMAC with little hyperparameter tuning. We also compare IPPO to several variants; the results suggest that IPPO's strong performance may be due to its robustness to some forms of environment non-stationarity.
Evolution of the political opinion landscape during electoral periods
Venue: arXiv cs.SI 2020
Year: 2020

We present a study of the evolution of the political landscape during the 2015 and 2019 presidential elections in Argentina, based on the data obtained from the micro-blogging platform Twitter. We build a semantic network based on the hashtags used by all the users following at least one of the main candidates. With this network we can detect the topics that are discussed in the society. At a difference with most studies of opinion on social media, we do not choose the topics a priori, they naturally emerge from the community structure of the semantic network instead. We assign to each user a dynamical topic vector which measures the evolution of her/his opinion in this space and allows us to monitor the similarities and differences among groups of supporters of different candidates. Our results show that the method is able to detect the dynamics of formation of opinion on different topics and, in particular, it can capture the reshaping of the political opinion landscape which has led to the inversion of result between the two rounds of the 2015 election.
Vector Embeddings with Subvector Permutation Invariance using a Triplet Enhanced Autoencoder
Venue: arXiv cs.LG 2020
Year: 2020

The use of deep neural network (DNN) autoencoders (AEs) has recently exploded due to their wide applicability. However, the embedding representation produced by a standard DNN AE that is trained to minimize only the reconstruction error does not always reveal more subtle patterns in the data. Sometimes, the autoencoder needs further direction in the form of one or more additional loss functions. In this paper, we use an autoencoder enhanced with triplet loss to promote the clustering of vectors that are related through permutations of constituent subvectors. With this approach, we can create an embedding of the vector that is nearly invariant to such permutations. We can then use these invariant embeddings as inputs to other problems, like classification and clustering, and improve detection accuracy in those problems.
Non-Linear Multiple Field Interactions Neural Document Ranking
Venue: arXiv cs.IR 2020
Year: 2020

Ranking tasks are usually based on the text of the main body of the page and the actions (clicks) of users on the page. There are other elements that could be leveraged to better contextualise the ranking experience (e.g. text in other fields, query made by the user, images, etc). We present one of the first in-depth analyses of field interaction for multiple field ranking in two separate datasets. While some works have taken advantage of full document structure, some aspects remain unexplored. In this work we build on previous analyses to show how query-field interactions, non-linear field interactions, and the architecture of the underlying neural model affect performance.
Patient-independent Epileptic Seizure Prediction using Deep Learning Models
Venue: arXiv cs.CV 2020
Year: 2020

Objective: Epilepsy is one of the most prevalent neurological diseases among humans and can lead to severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to mitigate injuries, and can be used to aid the treatment of patients with epilepsy. The purpose of a seizure prediction system is to successfully identify the pre-ictal brain stage, which occurs before a seizure event. Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset, and have been identified as a real-world solution to the seizure prediction problem. However, little attention has been given for designing such models to adapt to the high inter-subject variability in EEG data. Methods: We propose two patient-independent deep learning architectures with different learning strategies that can learn a global function utilizing data from multiple subjects. Results: Proposed models achieve state-of-the-art performance for seizure prediction on the CHB-MIT-EEG dataset, demonstrating 88.81% and 91.54% accuracy respectively. Conclusions: The Siamese model trained on the proposed learning strategy is able to learn patterns related to patient variations in data while predicting seizures. Significance: Our models show superior performance for patient-independent seizure prediction, and the same architecture can be used as a patient-specific classifier after model adaptation. We are the first study that employs model interpretation to understand classifier behavior for the task for seizure prediction, and we also show that the MFCC feature map utilized by our models contains predictive biomarkers related to interictal and pre-ictal brain states.