OpenRaiser/Intern-Atlas huggingface dataset:
Trajectory-Based Urban Air Mobility (UAM) Operations Simulator (TUS)
Venue: arXiv cs.CY 2019
Year: 2019

Nowadays, the demand for optimized services in urban environments to provide better society wellness is increasing. In this sense, ground transportation in dense urban environments has been facing challenges for many years (e.g., congestion and resilience). One import outcome of the effort made toward the creation of new concepts for enhancing urban transportation is the Urban Air Mobility (UAM) concept. UAM aims at enhancing city transportation services using manned and unmanned vehicles. However, these operations bring many challenges to be faced, e.g., the interaction between the controller agent and autonomous vehicles. Furthermore, trajectory planning is not a simple task due to several factors. Firstly, the trajectories must consider a reduced minimum separation as eVTOL vehicle are expected to operate in complex urban environments. This leads the trajectory planning process to observe safety primitives more restrictively once the airspace is expected to comport many vehicles that follow small minimum separation standards. Thereupon, the main goal of the Trajectory-Based UAM Operations Simulator (TUS) is to simulate the Trajectory-Based UAM operations in urban environments considering the presence of both manned and unmanned eVTOL vehicles. For this, a Discrete Event Simulation (DES) approach is adopted, which considers an input (i.e., the eVTOL vehicles, their origin and destination, and their respective trajectories) and produces an output (which describes if the trajectories are safe and the elapsed operation time). The main contribution of this simulation tool is to provide a simulated environment for testing and measuring the effectiveness (e.g., flight duration) of trajectories planned for eVTOL vehicles.
Building Critical Applications using Microservices
Venue: arXiv cs.CR 2019
Year: 2019

Microservices - combined with secure containers - facilitate new ways to build critical applications. These applications will benefit from many tools and services built for less critical software. The more stringent requirements of critical applications are addressed with the help of secure containers and compiler extensions. While this approach is sufficient for implementing fail-stop applications, there are still several open research questions regarding if and how fail-operational applications could be supported using this approach.
Ten years of research on ResearchGate, a scoping review using Google Scholar 2008_2017
Venue: arXiv cs.DL 2019
Year: 2019

Objective. To analyse quantitatively the articles published during 2008_2017 about the academic social networking site ResearchGate. Methods. A scoping bibliometric review of documents retrieved using Google Scholar was conducted, limited to publications that contained the word "ResearchGate" in their title and were published from 2008 to 2017. Results. The search yielded 159 documents, once a preliminary list of 386 documents retrieved from Google Scholar was filtered, which eliminated about 60% of the results that were bibliographic citations and not documents. Papers in journals were the most numerous type of documents (n73; 46%), followed by conference papers (n_31; 19.5 %). Contributing eight publications, two Spanish scholars (Delgado Lopez-Cozar and Orduna Malea, who were coauthors in each case) were the most prolific authors writing on this topic during the ten-year period. The keywords most used in the documents were "ResearchGate" and "Altmetrics". The publications were cited frequently since 2014 (more than 90% of the total cites fell in that period), and those with more than one author were the most cited ones. The authors of the documents were mainly librarians and information science professionals, who wrote primarily as co-authors with colleagues from their own institutions, mostly published in English. Conclusions. Interest in ResearchGate has grown since 2015, as evident from the number of articles published and the citations they received. Keywords. Academic social networks, bibliometrics, scholarly communication
Bayesian Receiver Operating Characteristic Metric for Linear Classifiers
Venue: arXiv cs.LG 2019
Year: 2019

We propose a novel classifier accuracy metric: the Bayesian Area Under the Receiver Operating Characteristic Curve (CBAUC). The method estimates the area under the ROC curve and is related to the recently proposed Bayesian Error Estimator. The metric can assess the quality of a classifier using only the training dataset without the need for computationally expensive cross-validation. We derive a closed-form solution of the proposed accuracy metric for any linear binary classifier under the Gaussianity assumption, and study the accuracy of the proposed estimator using simulated and real-world data. These experiments confirm that the closed-form CBAUC is both faster and more accurate than conventional AUC estimators.
Swarm Intelligence for Morphogenetic Engineering
Venue: arXiv cs.MA 2019
Year: 2019

We argue that embryological morphogenesis provides a model of how massive swarms of microscopic agents can be coordinated to assemble complex, multiscale hierarchical structures. This is accomplished by understanding natural morphogenetic processes in mathematical terms, abstracting from the biological specifics, and implementing these mathematical principles in artificial systems. We have developed a notation based on partial differential equations for artificial morphogenesis and have designed a prototype morphogenetic programming language, which permits precise description of morphogenetic algorithms and their automatic translation to simulation software. Morphogenetic programming is illustrated by two examples: (1) use of a modified flocking algorithm to route dense fiber bundles between regions of an artificial cortex while avoiding other bundles; (2) use of the clock-and-wavefront model of spinal segmentation for the assembly of the segmented spine of an insect-like robot body and for assembling segmented legs on the robot's spine. Finally, we show how a variation of smoothed particle hydrodynamics (SPH) swarm robotic control can be applied to the global-to-local compilation problem, that is, the derivation of individual agent control from global PDE specifications.
Multi-Spectral Visual Odometry without Explicit Stereo Matching
Venue: arXiv cs.CV 2019
Year: 2019

Multi-spectral sensors consisting of a standard (visible-light) camera and a long-wave infrared camera can simultaneously provide both visible and thermal images. Since thermal images are independent from environmental illumination, they can help to overcome certain limitations of standard cameras under complicated illumination conditions. However, due to the difference in the information source of the two types of cameras, their images usually share very low texture similarity. Hence, traditional texture-based feature matching methods cannot be directly applied to obtain stereo correspondences. To tackle this problem, a multi-spectral visual odometry method without explicit stereo matching is proposed in this paper. Bundle adjustment of multi-view stereo is performed on the visible and the thermal images using direct image alignment. Scale drift can be avoided by additional temporal observations of map points with the fixed-baseline stereo. Experimental results indicate that the proposed method can provide accurate visual odometry results with recovered metric scale. Moreover, the proposed method can also provide a metric 3D reconstruction in semi-dense density with multi-spectral information, which is not available from existing multi-spectral methods.
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors
Venue: arXiv cs.CV 2019
Year: 2019

Recently, single-image super-resolution has made great progress owing to the development of deep convolutional neural networks (CNNs). The vast majority of CNN-based models use a pre-defined upsampling operator, such as bicubic interpolation, to upscale input low-resolution images to the desired size and learn non-linear mapping between the interpolated image and ground truth high-resolution (HR) image. However, interpolation processing can lead to visual artifacts as details are over-smoothed, particularly when the super-resolution factor is high. In this paper, we propose a Deep Recurrent Fusion Network (DRFN), which utilizes transposed convolution instead of bicubic interpolation for upsampling and integrates different-level features extracted from recurrent residual blocks to reconstruct the final HR images. We adopt a deep recurrence learning strategy and thus have a larger receptive field, which is conducive to reconstructing an image more accurately. Furthermore, we show that the multi-level fusion structure is suitable for dealing with image super-resolution problems. Extensive benchmark evaluations demonstrate that the proposed DRFN performs better than most current deep learning methods in terms of accuracy and visual effects, especially for large-scale images, while using fewer parameters.
Multiple D2D Multicasts in Underlay Cellular Networks
Venue: arXiv cs.NI 2019
Year: 2019

Multicasting for disseminating popular data is an interesting solution for improving the energy and spectral efficiencies of cellular networks. To improve the achievable performance of such networks, underlay device-to-device (D2D) multicast communication offers a practical solution. However, despite significant potential for providing higher throughput and lower delay, implementing underlay D2D multicast communication poses several challenges, such as mutual interference among cellular users (CUs) and D2D multicast groups (MGs), and overhead signaling to provide channel state information, that may limit potential gains. We study a scenario where multiple D2D multicast groups may share a CU's uplink channel. We formulate an optimization problem to maximize the achievable system throughput while fulfilling quality of service (QoS) requirements of every CU and D2D MGs, subject to their corresponding maximum transmit power constraints. The formulated optimization problem is an instance of mixed integer non-linear programming (MINLP) problem, which is computationally intractable, in general. Therefore, to find a feasible solution, we propose a pragmatic two-step process of channel allocation and power allocation. In the first-step, we propose a channel allocation algorithm, which determines the subset of MGs that may share a channel subject to criteria based on two different parameters: interference and outage probabilities. Then, we propose an algorithm to allocate power to these MG subsets that maximizes the system throughput, while satisfying transmit power constraint. Numerical results show the efficacy of proposed approach in terms of higher achievable sum throughput and better spectrum efficiency with respect to various existing schemes.
Variants of the Segment Number of a Graph
Venue: arXiv cs.CG 2019
Year: 2019

The \emph{segment number} of a planar graph is the smallest number of line segments whose union represents a crossing-free straight-line drawing of the given graph in the plane. The segment number is a measure for the visual complexity of a drawing; it has been studied extensively. In this paper, we study three variants of the segment number: for planar graphs, we consider crossing-free polyline drawings in 2D; for arbitrary graphs, we consider crossing-free straight-line drawings in 3D and straight-line drawings with crossings in 2D. We first construct an infinite family of planar graphs where the classical segment number is asymptotically twice as large as each of the new variants of the segment number. Then we establish the $\exists\mathbb{R}$-completeness (which implies the NP-hardness) of all variants. Finally, for cubic graphs, we prove lower and upper bounds on the new variants of the segment number, depending on the connectivity of the given graph.
Cross-Enhancement Transform Two-Stream 3D ConvNets for Action Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Action recognition is an important research topic in computer vision. It is the basic work for visual understanding and has been applied in many fields. Since human actions can vary in different environments, it is difficult to infer actions in completely different states with a same structural model. For this case, we propose a Cross-Enhancement Transform Two-Stream 3D ConvNets algorithm, which considers the action distribution characteristics on the specific dataset. As a teaching model, stream with better performance in both streams is expected to assist in training another stream. In this way, the enhanced-trained stream and teacher stream are combined to infer actions. We implement experiments on the video datasets UCF-101, HMDB-51, and Kinetics-400, and the results confirm the effectiveness of our algorithm.
Linear response theory for Google matrix
Venue: arXiv cs.SI 2019
Year: 2019

We develop the linear response theory for the Google matrix PageRank algorithm with respect to a general weak perturbation and a numerical efficient and accurate algorithm, called LIRGOMAX algorithm, to compute the linear response of the PageRank with respect to this perturbation. We illustrate its efficiency on the example of the English Wikipedia network with more than 5 millions of articles (nodes). For a group of initial nodes (or simply a pair of nodes) this algorithm allows to identify the effective pathway between initial nodes thus selecting a particular subset of nodes which are most sensitive to the weak perturbation applied to them (injection or pumping at one node and absorption of probability at another node). The further application of the reduced Google matrix algorithm (REGOMAX) allows to determine the effective interactions between the nodes of this subset. General linear response theory already found numerous applications in various areas of science including statistical and mesoscopic physics. Based on these grounds we argue that the developed LIRGOMAX algorithm will find broad applications in the analysis of complex directed networks.
Efficient Deep Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

The success of deep neural networks (DNNs) is attributable to three factors: increased compute capacity, more complex models, and more data. These factors, however, are not always present, especially for edge applications such as autonomous driving, augmented reality, and internet-of-things. Training DNNs requires a large amount of data, which is difficult to obtain. Edge devices such as mobile phones have limited compute capacity, and therefore, require specialized and efficient DNNs. However, due to the enormous design space and prohibitive training costs, designing efficient DNNs for different target devices is challenging. So the question is, with limited data, compute capacity, and model complexity, can we still successfully apply deep neural networks? This dissertation focuses on the above problems and improving the efficiency of deep neural networks at four levels. Model efficiency: we designed neural networks for various computer vision tasks and achieved more than 10x faster speed and lower energy. Data efficiency: we developed an advanced tool that enables 6.2x faster annotation of a LiDAR point cloud. We also leveraged domain adaptation to utilize simulated data, bypassing the need for real data. Hardware efficiency: we co-designed neural networks and hardware accelerators and achieved 11.6x faster inference. Design efficiency: the process of finding the optimal neural networks is time-consuming. Our automated neural architecture search algorithms discovered, using 421x lower computational cost than previous search methods, models with state-of-the-art accuracy and efficiency.
Mixed Linear Layouts: Complexity, Heuristics, and Experiments
Venue: arXiv cs.DS 2019
Year: 2019

A $k$-page linear graph layout of a graph $G = (V,E)$ draws all vertices along a line $\ell$ and each edge in one of $k$ disjoint halfplanes called pages, which are bounded by $\ell$. We consider two types of pages. In a stack page no two edges should cross and in a queue page no edge should be nested by another edge. A crossing (nesting) in a stack (queue) page is called a conflict. The algorithmic problem is twofold and requires to compute (i) a vertex ordering and (ii) a page assignment of the edges such that the resulting layout is either conflict-free or conflict-minimal. While linear layouts with only stack or only queue pages are well-studied, mixed $s$-stack $q$-queue layouts for $s,q \ge 1$ have received less attention. We show NP-completeness results on the recognition problem of certain mixed linear layouts and present a new heuristic for minimizing conflicts. In a computational experiment for the case $s, q = 1$ we show that the new heuristic is an improvement over previous heuristics for linear layouts.
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
Venue: arXiv cs.CL 2019
Year: 2019

Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training. Due to the cost of applying such models to down-stream tasks, several model compression techniques on pre-trained language representations have been proposed (Sun et al., 2019; Sanh, 2019). However, surprisingly, the simple baseline of just pre-training and fine-tuning compact models has been overlooked. In this paper, we first show that pre-training remains important in the context of smaller architectures, and fine-tuning pre-trained compact models can be competitive to more elaborate methods proposed in concurrent work. Starting with pre-trained compact models, we then explore transferring task knowledge from large fine-tuned models through standard knowledge distillation. The resulting simple, yet effective and general algorithm, Pre-trained Distillation, brings further improvements. Through extensive experiments, we more generally explore the interaction between pre-training and distillation under two variables that have been under-studied: model size and properties of unlabeled task data. One surprising observation is that they have a compound effect even when sequentially applied on the same data. To accelerate future research, we will make our 24 pre-trained miniature BERT models publicly available.
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning
Venue: arXiv cs.LG 2019
Year: 2019

Various psychological factors affect how individuals express emotions. Yet, when we collect data intended for use in building emotion recognition systems, we often try to do so by creating paradigms that are designed just with a focus on eliciting emotional behavior. Algorithms trained with these types of data are unlikely to function outside of controlled environments because our emotions naturally change as a function of these other factors. In this work, we study how the multimodal expressions of emotion change when an individual is under varying levels of stress. We hypothesize that stress produces modulations that can hide the true underlying emotions of individuals and that we can make emotion recognition algorithms more generalizable by controlling for variations in stress. To this end, we use adversarial networks to decorrelate stress modulations from emotion representations. We study how stress alters acoustic and lexical emotional predictions, paying special attention to how modulations due to stress affect the transferability of learned emotion recognition models across domains. Our results show that stress is indeed encoded in trained emotion classifiers and that this encoding varies across levels of emotions and across the lexical and acoustic modalities. Our results also show that emotion recognition models that control for stress during training have better generalizability when applied to new domains, compared to models that do not control for stress during training. We conclude that is is necessary to consider the effect of extraneous psychological factors when building and testing emotion recognition models.
Optimizing Inter-Datacenter Tail Flow Completion Times using Best Worst-case Routing
Venue: arXiv cs.NI 2019
Year: 2019

Flow routing over inter-datacenter networks is a well-known problem where the network assigns a path to a newly arriving flow potentially according to the network conditions and the properties of the new flow. An essential system-wide performance metric for a routing algorithm is the flow completion times, which affect the performance of applications running across multiple datacenters. Current static and dynamic routing approaches do not take advantage of flow size information in routing, which is practical in a controlled environment such as inter-datacenter networks that are managed by the datacenter operators. In this paper, we discuss Best Worst-case Routing (BWR), which aims at optimizing the tail completion times of long-running flows over inter-datacenter networks with non-uniform link capacities. Since finding the path with the best worst-case completion time for a new flow is NP-Hard, we investigate two heuristics, BWRH and BWRHF, which use two different upper bounds on the worst-case completion times for routing. We evaluate BWRH and BWRHF against several real WAN topologies and multiple traffic patterns. Although BWRH better models the BWR problem, BWRH and BWRHF show negligible difference across various system-wide performance metrics, while BWRHF being significantly faster. Furthermore, we show that compared to other popular routing heuristics, BWRHF can reduce the mean and tail flow completion times by over $1.5\times$ and $2\times$, respectively.
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
Venue: arXiv cs.LG 2019
Year: 2019

Motivated by concerns surrounding the fairness effects of sharing and transferring fair machine learning tools, we propose two algorithms: Fairness Warnings and Fair-MAML. The first is a model-agnostic algorithm that provides interpretable boundary conditions for when a fairly trained model may not behave fairly on similar but slightly different tasks within a given domain. The second is a fair meta-learning approach to train models that can be quickly fine-tuned to specific tasks from only a few number of sample instances while balancing fairness and accuracy. We demonstrate experimentally the individual utility of each model using relevant baselines and provide the first experiment to our knowledge of K-shot fairness, i.e. training a fair model on a new task with only K data points. Then, we illustrate the usefulness of both algorithms as a combined method for training models from a few data points on new tasks while using Fairness Warnings as interpretable boundary conditions under which the newly trained model may not be fair.