OpenRaiser/Intern-Atlas huggingface dataset:
Scan2CAD: Learning CAD Model Alignment in RGB-D Scans
Venue: arXiv cs.CV 2018
Year: 2018

We present Scan2CAD, a novel data-driven method that learns to align clean 3D CAD models from a shape database to the noisy and incomplete geometry of a commodity RGB-D scan. For a 3D reconstruction of an indoor scene, our method takes as input a set of CAD models, and predicts a 9DoF pose that aligns each model to the underlying scan geometry. To tackle this problem, we create a new scan-to-CAD alignment dataset based on 1506 ScanNet scans with 97607 annotated keypoint pairs between 14225 CAD models from ShapeNet and their counterpart objects in the scans. Our method selects a set of representative keypoints in a 3D scan for which we find correspondences to the CAD geometry. To this end, we design a novel 3D CNN architecture that learns a joint embedding between real and synthetic objects, and from this predicts a correspondence heatmap. Based on these correspondence heatmaps, we formulate a variational energy minimization that aligns a given set of CAD models to the reconstruction. We evaluate our approach on our newly introduced Scan2CAD benchmark where we outperform both handcrafted feature descriptor as well as state-of-the-art CNN based methods by 21.39%.
Calibrating Uncertainties in Object Localization Task
Venue: arXiv cs.LG 2018
Year: 2018

In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making. Specifically, such modules need to estimate the probability of each predicted object in a given region and the confidence interval for its bounding box. While recent Bayesian deep learning methods provide a principled way to estimate this uncertainty, the estimates for the bounding boxes obtained using these methods are uncalibrated. In this paper, we address this problem for the single-object localization task by adapting an existing technique for calibrating regression models. We show, experimentally, that the resulting calibrated model obtains more reliable uncertainty estimates.
Distributed traffic light control at uncoupled intersections with real-world topology by deep reinforcement learning
Venue: arXiv cs.AI 2018
Year: 2018

This work examines the implications of uncoupled intersections with local real-world topology and sensor setup on traffic light control approaches. Control approaches are evaluated with respect to: Traffic flow, fuel consumption and noise emission at intersections. The real-world road network of Friedrichshafen is depicted, preprocessed and the present traffic light controlled intersections are modeled with respect to state space and action space. Different strategies, containing fixed-time, gap-based and time-based control approaches as well as our deep reinforcement learning based control approach, are implemented and assessed. Our novel DRL approach allows for modeling the TLC action space, with respect to phase selection as well as selection of transition timings. It was found that real-world topologies, and thus irregularly arranged intersections have an influence on the performance of traffic light control approaches. This is even to be observed within the same intersection types (n-arm, m-phases). Moreover we could show, that these influences can be efficiently dealt with by our deep reinforcement learning based control approach.
Performance Analysis of Connectivity and Localization in Multi-Hop Underwater Optical Wireless Sensor Networks
Venue: arXiv cs.NI 2018
Year: 2018

Underwater optical wireless links have limited range and intermittent connectivity due to the hostile aquatic channel impairments and misalignment between the optical transceivers. Therefore, multi-hop communication can expand the communication range, enhance network connectivity, and provide a more precise network localization scheme. In this regard, this paper investigates the connectivity of underwater optical wireless sensor networks (UOWSNs) and its impacts on the network localization performance. Firstly, we model UOWSNs as randomly scaled sector graphs where the connection between sensors is established by point-to-point directed links. Thereafter, the probability of network connectivity is analytically derived as a function of network density, communication range, and optical transmitters' divergence angle. Secondly, the network localization problem is formulated as an unconstrained optimization problem and solved using the conjugate gradient technique. Numerical results show that different network parameters such as the number of nodes, divergence angle, and transmission range significantly influence the probability of a connected network. Furthermore, the performance of the proposed localization technique is compared to well-known network localization schemes and the results show that the localization accuracy of the proposed technique outperforms the literature in terms of network connectivity, ranging error, and number of anchors.
Challenges of measuring the impact of software: an examination of the lme4 R package
Venue: arXiv cs.DL 2018
Year: 2018

The rise of software as a research object is mirrored in the increasing interests towards quantitative studies of scientific software. However, due to the inconsistent practice of citing software, most of the existing studies analyzing the impact of scientific software are based on identification of software name mentions in full-text publications. Despite its limitations, citation data have a much larger quantity and broader coverage of scientific fields than full-text data and thus could support findings in much larger scopes. This paper presents an analysis aiming to evaluate the extent to which citations data can be used to reconstruct the impact of software. Specifically, we identified the variety of citable objects related to the lme4 R package and examined how the package's impact is scattered across these objects. Our results reveal a little-discussed challenge of using citation data to measure the impact of software, that even within the category of formal citation, there might be different forms in which the same software object is cited. This challenge can be mitigated by more carefully selecting objects as the proxy of software. However, it cannot be fully solved until we have one-software-one-proxy policy for software citation.
Target Driven Visual Navigation with Hybrid Asynchronous Universal Successor Representations
Venue: arXiv cs.AI 2018
Year: 2018

Being able to navigate to a target with minimal supervision and prior knowledge is critical to creating human-like assistive agents. Prior work on map-based and map-less approaches have limited generalizability. In this paper, we present a novel approach, Hybrid Asynchronous Universal Successor Representations (HAUSR), which overcomes the problem of generalizability to new goals by adapting recent work on Universal Successor Representations with Asynchronous Actor-Critic Agents. We show that the agent was able to successfully reach novel goals and we were able to quickly fine-tune the network for adapting to new scenes. This opens up novel application scenarios where intelligent agents could learn from and adapt to a wide range of environments with minimal human input.
Adventures in Formalisation: Financial Contracts, Modules, and Two-Level Type Theory
Venue: arXiv cs.PL 2018
Year: 2018

We present three projects concerned with applications of proof assistants in the area of programming language theory and mathematics. The first project is about a certified compilation technique for a domain-specific programming language for financial contracts (the CL language). The code in CL is translated into a simple expression language well-suited for integration with software components implementing Monte Carlo simulation techniques (pricing engines). The compilation procedure is accompanied with formal proofs of correctness carried out in Coq. The second project presents techniques that allow for formal reasoning with nested and mutually inductive structures built up from finite maps and sets. The techniques, which build on the theory of nominal sets combined with the ability to work with isomorphic representations of finite maps, make it possible to give a formal treatment, in Coq, of a higher-order module system, including the ability to eliminate at compile time abstraction barriers introduced by the module system. The development is based on earlier work on static interpretation of modules and provides the foundation for a higher-order module language for Futhark, an optimising compiler targeting data-parallel architectures. The third project presents an implementation of two-level type theory, a version of Martin-Lof type theory with two equality types: the first acts as the usual equality of homotopy type theory, while the second allows us to reason about strict equality. In this system, we can formalise results of partially meta-theoretic nature. We develop and explore in details how two-level type theory can be implemented in a proof assistant, providing a prototype implementation in the proof assistant Lean. We demonstrate an application of two-level type theory by developing some results on the theory of inverse diagrams using our Lean implementation.
Data Detection in Single User Massive MIMO Using Re-Transmissions
Venue: arXiv cs.IT 2018
Year: 2018

Single user massive multiple input multiple output (MIMO) can be used to increase the spectral efficiency, since the data is transmitted simultaneously from a large number of antennas located at both the base station and mobile. It is feasible to have a large number of antennas in the mobile, in the millimeter wave frequencies. However, the major drawback of single user massive MIMO is the high complexity of data recovery at the receiver. In this work, we propose a low complexity method of data detection with the help of re-transmissions. A turbo code is used to improve the bit-error-rate (BER). Simulation results indicate significant improvement in BER with just two re-transmissions as compared to the single transmission case. We also show that the minimum average SNR per bit required for error free propagation over a massive MIMO channel with re-transmissions is identical to that of the additive white Gaussian noise (AWGN) channel, which is equal to $-1.6$ dB.
A Deep Cascade Model for Multi-Document Reading Comprehension
Venue: arXiv cs.CL 2018
Year: 2018

A fundamental trade-off between effectiveness and efficiency needs to be balanced when designing an online question answering system. Effectiveness comes from sophisticated functions such as extractive machine reading comprehension (MRC), while efficiency is obtained from improvements in preliminary retrieval components such as candidate document selection and paragraph ranking. Given the complexity of the real-world multi-document MRC scenario, it is difficult to jointly optimize both in an end-to-end system. To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the document-level and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension. Specifically, irrelevant documents and paragraphs are first filtered out with simple functions for efficiency consideration. Then we jointly train three modules on the remaining texts for better tracking the answer: the document extraction, the paragraph extraction and the answer extraction. Experiment results show that the proposed method outperforms the previous state-of-the-art methods on two large-scale multi-document benchmark datasets, i.e., TriviaQA and DuReader. In addition, our online system can stably serve typical scenarios with millions of daily requests in less than 50ms.
Self Contained Relative Localization with a Low-Cost Multi-Robot System
Venue: arXiv cs.RO 2018
Year: 2018

A key limitation of current multi-robot systems is a lack of relative localization, particularly in environments without GPS or motion capture systems. This article presents a centralized method for relatively localizing a 2D swarm using sensors and beacons on the robots themselves. The UKF-based algorithm as well as the requisite novel and cost-effective sensing hardware are discussed. Comparisons with a motion capture system show that the method is capable of localization with errors on the order of the size of the robots.
Context-Aware Dialog Re-Ranking for Task-Oriented Dialog Systems
Venue: arXiv cs.CL 2018
Year: 2018

Dialog response ranking is used to rank response candidates by considering their relation to the dialog history. Although researchers have addressed this concept for open-domain dialogs, little attention has been focused on task-oriented dialogs. Furthermore, no previous studies have analyzed whether response ranking can improve the performance of existing dialog systems in real human-computer dialogs with speech recognition errors. In this paper, we propose a context-aware dialog response re-ranking system. Our system reranks responses in two steps: (1) it calculates matching scores for each candidate response and the current dialog context; (2) it combines the matching scores and a probability distribution of the candidates from an existing dialog system for response re-ranking. By using neural word embedding-based models and handcrafted or logistic regression-based ensemble models, we have improved the performance of a recently proposed end-to-end task-oriented dialog system on real dialogs with speech recognition errors.
GIRNet: Interleaved Multi-Task Recurrent State Sequence Models
Venue: arXiv cs.LG 2018
Year: 2018

In several natural language tasks, labeled sequences are available in separate domains (say, languages), but the goal is to label sequences with mixed domain (such as code-switched text). Or, we may have available models for labeling whole passages (say, with sentiments), which we would like to exploit toward better position-specific label inference (say, target-dependent sentiment annotation). A key characteristic shared across such tasks is that different positions in a primary instance can benefit from different `experts' trained from auxiliary data, but labeled primary instances are scarce, and labeling the best expert for each position entails unacceptable cognitive burden. We propose GITNet, a unified position-sensitive multi-task recurrent neural network (RNN) architecture for such applications. Auxiliary and primary tasks need not share training instances. Auxiliary RNNs are trained over auxiliary instances. A primary instance is also submitted to each auxiliary RNN, but their state sequences are gated and merged into a novel composite state sequence tailored to the primary inference task. Our approach is in sharp contrast to recent multi-task networks like the cross-stitch and sluice network, which do not control state transfer at such fine granularity. We demonstrate the superiority of GIRNet using three applications: sentiment classification of code-switched passages, part-of-speech tagging of code-switched text, and target position-sensitive annotation of sentiment in monolingual passages. In all cases, we establish new state-of-the-art performance beyond recent competitive baselines.
Enabling Communication Technologies for Automated Unmanned Vehicles in Industry 4.0
Venue: arXiv cs.RO 2018
Year: 2018

Within the context of Industry 4.0, mobile robot systems such as automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) are one of the major areas challenging current communication and localization technologies. Due to stringent requirements on latency and reliability, several of the existing solutions are not capable of meeting the performance required by industrial automation applications. Additionally, the disparity in types and applications of unmanned vehicle (UV) calls for more flexible communication technologies in order to address their specific requirements. In this paper, we propose several use cases for UVs within the context of Industry 4.0 and consider their respective requirements. We also identify wireless technologies that support the deployment of UVs as envisioned in Industry 4.0 scenarios.
Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts
Venue: arXiv cs.CL 2018
Year: 2018

In this work, we consider the medical concept normalization problem, i.e., the problem of mapping a disease mention in free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS). This task is challenging since medical terminology is very different when coming from health care professionals or from the general public in the form of social media texts. We approach it as a sequence learning problem, with recurrent neural networks trained to obtain semantic representations of one- and multi-word expressions. We develop end-to-end neural architectures tailored specifically to medical concept normalization, including bidirectional LSTM and GRU with an attention mechanism and additional semantic similarity features based on UMLS. Our evaluation over a standard benchmark shows that our model improves over a state of the art baseline for classification based on CNNs.
Document classification using a Bi-LSTM to unclog Brazil's supreme court
Venue: arXiv cs.IR 2018
Year: 2018

The Brazilian court system is currently the most clogged up judiciary system in the world. Thousands of lawsuit cases reach the supreme court every day. These cases need to be analyzed in order to be associated to relevant tags and allocated to the right team. Most of the cases reach the court as raster scanned documents with widely variable levels of quality. One of the first steps for the analysis is to classify these documents. In this paper we present a Bidirectional Long Short-Term Memory network (Bi-LSTM) to classify these pieces of legal document.
Approximate Schedules for Non-Migratory Parallel Jobs in Speed-Scaled Multiprocessor Systems
Venue: arXiv cs.DS 2018
Year: 2018

We consider a problem of scheduling rigid parallel jobs on variable speed processors so as to minimize the total energy consumption. Each job is specified by its processing volume and the required number of processors. We propose new constant factor approximation algorithms for the non-migratory cases when all jobs have a common release time and/or a common deadline.
A Generative Appearance Model for End-to-end Video Object Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

One of the fundamental challenges in video object segmentation is to find an effective representation of the target and background appearance. The best performing approaches resort to extensive fine-tuning of a convolutional neural network for this purpose. Besides being prohibitively expensive, this strategy cannot be truly trained end-to-end since the online fine-tuning procedure is not integrated into the offline training of the network. To address these issues, we propose a network architecture that learns a powerful representation of the target and background appearance in a single forward pass. The introduced appearance module learns a probabilistic generative model of target and background feature distributions. Given a new image, it predicts the posterior class probabilities, providing a highly discriminative cue, which is processed in later network modules. Both the learning and prediction stages of our appearance module are fully differentiable, enabling true end-to-end training of the entire segmentation pipeline. Comprehensive experiments demonstrate the effectiveness of the proposed approach on three video object segmentation benchmarks. We close the gap to approaches based on online fine-tuning on DAVIS17, while operating at 15 FPS on a single GPU. Furthermore, our method outperforms all published approaches on the large-scale YouTube-VOS dataset.
SegET: Deep Neural Network with Rich Contextual Features for Cellular Structures Segmentation in Electron Tomography Image
Venue: arXiv cs.CV 2018
Year: 2018

Electron tomography (ET) allows high-resolution reconstructions of macromolecular complexes at nearnative state. Cellular structures segmentation in the reconstruction data from electron tomographic images is often required for analyzing and visualizing biological structures, making it a powerful tool for quantitative descriptions of whole cell structures and understanding biological functions. However, these cellular structures are rather difficult to automatically separate or quantify from view owing to complex molecular environment and the limitations of reconstruction data of ET. In this paper, we propose a single end-to-end deep fully-convolutional semantic segmentation network dubbed SegET with rich contextual features which fully exploitsthe multi-scale and multi-level contextual information and reduces the loss of details of cellular structures in ET images. We trained and evaluated our network on the electron tomogram of the CTL Immunological Synapse from Cell Image library. Our results demonstrate that SegET can automatically segment accurately and outperform all other baseline methods on each individual structure in our ET dataset.
CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision
Venue: arXiv cs.CV 2018
Year: 2018

Knowledge of 3D properties of objects is a necessity in order to build effective computer vision systems. However, lack of large scale 3D datasets can be a major constraint for data-driven approaches in learning such properties. We consider the task of single image 3D point cloud reconstruction, and aim to utilize multiple foreground masks as our supervisory data to alleviate the need for large scale 3D datasets. A novel differentiable projection module, called 'CAPNet', is introduced to obtain such 2D masks from a predicted 3D point cloud. The key idea is to model the projections as a continuous approximation of the points in the point cloud. To overcome the challenges of sparse projection maps, we propose a loss formulation termed 'affinity loss' to generate outlier-free reconstructions. We significantly outperform the existing projection based approaches on a large-scale synthetic dataset. We show the utility and generalizability of such a 2D supervised approach through experiments on a real-world dataset, where lack of 3D data can be a serious concern. To further enhance the reconstructions, we also propose a test stage optimization procedure to obtain reconstructions that display high correspondence with the observed input image.
Adoção de Social CRM em Micro e Pequenas Empresas: Uma Análise do Mercado Santareno
Venue: arXiv cs.CY 2018
Year: 2018

Online social networks have changed the ways of communication and social interactions, especially in the Customer Relationship Management (CRM). In this sense, a new concept about business strategies involving CRM and social media has aroused, known as Social Customer Relationship Management. Despite to be an emergent and promising research field, it was perceived that Micro and Small Enterprises (MSE) have shown few or no process of Social CRM implemented. Aiming to test this hypothesis, this work conducts a market analysis in Santarém City, located in the Pará State, evaluating the adoption of Social CRM by MSE. The main contribution of this study is related to the understanding of the dynamics between Social CRM and MSE. As results, the construction of insights' list of products and solutions suitable for the implementation of Social CRM by MSE, with the potential to guide research and development projects in this area.