OpenRaiser/Intern-Atlas huggingface dataset:
Computational Courtship: Understanding the Evolution of Online Dating through Large-scale Data Analysis
Venue: arXiv cs.CY 2020
Year: 2020

Have we become more tolerant of dating people of different social backgrounds compared to ten years ago? Has the rise of online dating exacerbated or alleviated gender inequalities in modern courtship? Are the most attractive people on these platforms necessarily the most successful? In this work, we examine the mate preferences and communication patterns of male and female users of the online dating site eHarmony over the past decade to identify how attitudes and behaviors have changed over this time period. While other studies have investigated disparities in user behavior between male and female users, this study is unique in its longitudinal approach. Specifically, we analyze how men and women differ in their preferences for certain traits in potential partners and how those preferences have changed over time. The second line of inquiry investigates to what extent physical attractiveness determines the rate of messages a user receives, and how this relationship varies between men and women. Thirdly, we explore whether online dating practices between males and females have become more equal over time or if biases and inequalities have remained constant (or increased). Fourthly, we study the behavioural traits in sending and replying to messages based on one's own experience of receiving messages and being replied to. Finally, we found that similarity between profiles is not a predictor for success except for the number of children and smoking habits. This work could have broader implications for shifting gender norms and social attitudes, reflected in online courtship rituals. Apart from the data-based research, we connect the results to existing theories that concern the role of ICTs in societal change. As searching for love online becomes increasingly common across generations and geographies, these findings may shed light on how people can build relationships through the Internet.
Complexity of Training ReLU Neural Network
Venue: arXiv cs.CC 2020
Year: 2020

In this paper, we explore some basic questions on the complexity of training neural networks with ReLU activation function. We show that it is NP-hard to train a two-hidden layer feedforward ReLU neural network. If dimension of the input data and the network topology is fixed, then we show that there exists a polynomial time algorithm for the same training problem. We also show that if sufficient over-parameterization is provided in the first hidden layer of ReLU neural network, then there is a polynomial time algorithm which finds weights such that output of the over-parameterized ReLU neural network matches with the output of the given data.
Collaborative target-tracking control using multiple autonomous fixed-wing UAVs with constant speeds
Venue: arXiv cs.SY 2020
Year: 2020

This paper considers a collaborative tracking control problem using a group of fixed-wing unmanned aerial vehicles (UAVs) with constant and non-identical speeds. The dynamics of fixed-wing UAVs are modelled by unicycle-type equations with nonholonomic constraints, assuming that UAVs fly at constant altitudes in the nominal operation mode. The controller is designed such that all fixed-wing UAVs as a group can collaboratively track a desired target's position and velocity. We first present conditions on the relative speeds of tracking UAVs and the target to ensure that the tracking objective can be achieved when UAVs are subject to constant speed constraints. We construct a reference velocity that includes both the target's velocity and position as feedback, which is to be tracked by the group centroid. In this way, all vehicles' headings are controlled such that the group centroid follows a reference trajectory that successfully tracks the target's trajectory. A spacing controller is further devised to ensure that all vehicles stay close to the group centroid trajectory. Trade-offs in the controller design and performance limitations of the target tracking control due to the constant-speed constraint are also discussed in detail. Experimental results with three fixed-wing UAVs tracking a target rotorcraft are provided.
CODIT: Code Editing with Tree-Based Neural Models
Venue: arXiv cs.SE 2020
Year: 2020

The way developers edit day-to-day code tends to be repetitive, often using existing code elements. Many researchers have tried to automate repetitive code changes by learning from specific change templates which are applied to limited scope. The advancement of deep neural networks and the availability of vast open-source evolutionary data opens up the possibility of automatically learning those templates from the wild. However, deep neural network based modeling for code changes and code in general introduces some specific problems that needs specific attention from research community. For instance, compared to natural language, source code vocabulary can be significantly larger. Further, good changes in code do not break its syntactic structure. Thus, deploying state-of-the-art neural network models without adapting the methods to the source code domain yields sub-optimal results. To this end, we propose a novel tree-based neural network system to model source code changes and learn code change patterns from the wild. Specifically, we propose a tree-based neural machine translation model to learn the probability distribution of changes in code. We realize our model with a change suggestion engine, CODIT, and train the model with more than 24k real-world changes and evaluate it on 5k patches. Our evaluation shows the effectiveness of CODITin learning and suggesting patches. CODIT can also learn specific bug fix pattern from bug fixing patches and can fix 25 bugs out of 80 bugs in Defects4J.
TZC: Efficient Inter-Process Communication for Robotics Middleware with Partial Serialization
Venue: arXiv cs.RO 2020
Year: 2020

Inter-process communication (IPC) is one of the core functions of modern robotics middleware. We propose an efficient IPC technique called TZC (Towards Zero-Copy). As a core component of TZC, we design a novel algorithm called partial serialization. Our formulation can generate messages that can be divided into two parts. During message transmission, one part is transmitted through a socket and the other part uses shared memory. The part within shared memory is never copied or serialized during its lifetime. We have integrated TZC with ROS and ROS2 and find that TZC can be easily combined with current open-source platforms. By using TZC, the overhead of IPC remains constant when the message size grows. In particular, when the message size is 4MB (less than the size of a full HD image), TZC can reduce the overhead of ROS IPC from tens of milliseconds to hundreds of microseconds and can reduce the overhead of ROS2 IPC from hundreds of milliseconds to less than 1 millisecond. We also demonstrate the benefits of TZC by integrating with TurtleBot2 that are used in autonomous driving scenarios. We show that by using TZC, the braking distance can be shortened by 16% than ROS.
Large batch size training of neural networks with adversarial training and second-order information
Venue: arXiv cs.LG 2020
Year: 2020

The most straightforward method to accelerate Stochastic Gradient Descent (SGD) computation is to distribute the randomly selected batch of inputs over multiple processors. To keep the distributed processors fully utilized requires commensurately growing the batch size. However, large batch training often leads to poorer generalization. A recently proposed solution for this problem is to use adaptive batch sizes in SGD. In this case, one starts with a small number of processes and scales the processes as training progresses. Two major challenges with this approach are (i) that dynamically resizing the cluster can add non-trivial overhead, in part since it is currently not supported, and (ii) that the overall speed up is limited by the initial phase with smaller batches. In this work, we address both challenges by developing a new adaptive batch size framework, with autoscaling based on the Ray framework. This allows very efficient elastic scaling with negligible resizing overhead (0.32\% of time for ResNet18 ImageNet training). Furthermore, we propose a new adaptive batch size training scheme using second order methods and adversarial training. These enable increasing batch sizes earlier during training, which leads to better training time. We extensively evaluate our method on Cifar-10/100, SVHN, TinyImageNet, and ImageNet datasets, using multiple neural networks, including ResNets and smaller networks such as SqueezeNext. Our method exceeds the performance of existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\% and $5\times$, respectively). Importantly, this is achieved without any additional hyper-parameter tuning to tailor our method in any of these experiments.
Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices
Venue: arXiv cs.CV 2020
Year: 2020

Internet of Things(IoT) devices, mobile phones, and robotic systems are often denied the power of deep learning algorithms due to their limited computing power. However, to provide time-critical services such as emergency response, home assistance, surveillance, etc, these devices often need real-time analysis of their camera data. This paper strives to offer a viable approach to integrate high-performance deep learning-based computer vision algorithms with low-resource and low-power devices by leveraging the computing power of the cloud. By offloading the computation work to the cloud, no dedicated hardware is needed to enable deep neural networks on existing low computing power devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the power of using cloud computing to perform real-time vision tasks. Furthermore, to reduce latency and improve real-time performance, compression algorithms are proposed and evaluated for streaming real-time video frames to the cloud.
GLAD: GLocalized Anomaly Detection via Human-in-the-Loop Learning
Venue: arXiv cs.LG 2020
Year: 2020

Human analysts that use anomaly detection systems in practice want to retain the use of simple and explainable global anomaly detectors. In this paper, we propose a novel human-in-the-loop learning algorithm called GLAD (GLocalized Anomaly Detection) that supports global anomaly detectors. GLAD automatically learns their local relevance to specific data instances using label feedback from human analysts. The key idea is to place a uniform prior on the relevance of each member of the anomaly detection ensemble over the input feature space via a neural network trained on unlabeled instances. Subsequently, weights of the neural network are tuned to adjust the local relevance of each ensemble member using all labeled instances. GLAD also provides explanations which can improve the understanding of end-users about anomalies. Our experiments on synthetic and real-world data show the effectiveness of GLAD in learning the local relevance of ensemble members and discovering anomalies via label feedback.
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
Venue: arXiv cs.CV 2020
Year: 2020

For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input. To induce invariance in CNNs to such transformations, we propose Probabilistic Augmentation of Data using Diffeomorphic Image Transformation (PADDIT) -- a systematic framework for generating realistic transformations that can be used to augment data for training CNNs. We show that CNNs trained with PADDIT outperforms CNNs trained without augmentation and with generic augmentation in segmenting white matter hyperintensities from T1 and FLAIR brain MRI scans.
Visual Designs for Binned Aggregation of Multi-Class Scatterplots
Venue: arXiv cs.HC 2020
Year: 2020

Point sets in 2D with multiple classes are a common type of data. A canonical visualization design for them are scatterplots, which do not scale to large collections of points. For these larger data sets, binned aggregation (or binning) is often used to summarize the data, with many possible design alternatives for creating effective visual representations of these summaries. There are a wide range of designs to show summaries of 2D multi-class point data, each capable of supporting different analysis tasks. In this paper, we explore the space of visual designs for such data, and provide design guidelines for different analysis scenarios. To support these guidelines, we compile a set of abstract tasks and ground them in concrete examples using multiple sample datasets. We then assess designs, and survey a range of design decisions, considering their appropriateness to the tasks. In addition, we provide a web-based implementation to experiment with design choices, supporting the validation of designs based on task needs.
Sanity Checks for Saliency Maps
Venue: arXiv cs.CV 2020
Year: 2020

Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed, often guided by visual appeal on image data. In this work, we propose an actionable methodology to evaluate what kinds of explanations a given method can and cannot provide. We find that reliance, solely, on visual assessment can be misleading. Through extensive experiments we show that some existing saliency methods are independent both of the model and of the data generating process. Consequently, methods that fail the proposed tests are inadequate for tasks that are sensitive to either data or model, such as, finding outliers in the data, explaining the relationship between inputs and outputs that the model learned, and debugging the model. We interpret our findings through an analogy with edge detection in images, a technique that requires neither training data nor model. Theory in the case of a linear model and a single-layer convolutional neural network supports our experimental findings.
On Learning and Learned Data Representation by Capsule Networks
Venue: arXiv cs.CV 2020
Year: 2020

In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks. Our investigation yielded the results some of which have been mentioned in the original paper of CapsNet, they are: 1) the routing operation determines the certainty with which a layer of capsules pass information to the layer above and the appropriate level of certainty is related to the model fitness; 2) in a designed experiment using data with a known 2D structure, capsule representations enable a more meaningful 2D manifold embedding than neurons do in a standard convolutional neural network (CNN), and; 3) compared with neurons of the standard CNN, capsules of successive layers are less coupled and more adaptive to new data distribution.
Towards Efficient Full Pose Omnidirectionality with Overactuated MAVs
Venue: arXiv cs.RO 2020
Year: 2020

Omnidirectional MAVs are a growing field, with demonstrated advantages for aerial interaction and uninhibited observation. While systems with complete pose omnidirectionality and high hover efficiency have been developed independently, a robust system that combines the two has not been demonstrated to date. This paper presents VoliroX: a novel omnidirectional vehicle that can exert a wrench in any orientation while maintaining efficient flight configurations. The system design is presented, and a 6 DOF geometric control that is robust to singularities. Flight experiments further demonstrate and verify its capabilities.
Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows
Venue: arXiv cs.LG 2020
Year: 2020

With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and evaluate a large number of trained neural networks with respect to their accuracy for the calculation of pressure and velocity distributions. In particular, we illustrate how training data size and the number of weights influence the accuracy of the solutions. With our best models we arrive at a mean relative pressure and velocity error of less than 3% across a range of previously unseen airfoil shapes. In addition all source code is publicly available in order to ensure reproducibility and to provide a starting point for researchers interested in deep learning methods for physics problems. While this work focuses on RANS solutions, the neural network architecture and learning setup are very generic, and applicable to a wide range of PDE boundary value problems on Cartesian grids.
Learning to Recognize Discontiguous Entities
Venue: arXiv cs.CL 2020
Year: 2020

This paper focuses on the study of recognizing discontiguous entities. Motivated by a previous work, we propose to use a novel hypergraph representation to jointly encode discontiguous entities of unbounded length, which can overlap with one another. To compare with existing approaches, we first formally introduce the notion of model ambiguity, which defines the difficulty level of interpreting the outputs of a model, and then formally analyze the theoretical advantages of our model over previous existing approaches based on linear-chain CRFs. Our empirical results also show that our model is able to achieve significantly better results when evaluated on standard data with many discontiguous entities.
Conflict complexity is lower bounded by block sensitivity
Venue: arXiv cs.CC 2020
Year: 2020

We show conflict complexity of every total Boolean function, recently introduced in [Swagato Sanyal. A composition theorem via conict complexity. arXiv preprint arXiv:1801.03285, 2018.] to prove a composition theorem of randomized decision tree complexity, is at least a half of its block sensitivity. We propose to compare conflict complexity with certificate complexity, and explain why it could be interesting.
Single Image Haze Removal using a Generative Adversarial Network
Venue: arXiv cs.CV 2020
Year: 2020

Traditional methods to remove haze from images rely on estimating a transmission map. When dealing with single images, this becomes an ill-posed problem due to the lack of depth information. In this paper, we propose an end-to-end learning based approach which uses a modified conditional Generative Adversarial Network to directly remove haze from an image. We employ the usage of the Tiramisu model in place of the classic U-Net model as the generator owing to its higher parameter efficiency and performance. Moreover, a patch based discriminator was used to reduce artefacts in the output. To further improve the perceptual quality of the output, a hybrid weighted loss function was designed and used to train the model. Experiments on synthetic and real world hazy images demonstrates that our model performs competitively with the state of the art methods.
Randomized Gradient Boosting Machine
Venue: arXiv cs.LG 2020
Year: 2020

Gradient Boosting Machine (GBM) introduced by Friedman is a powerful supervised learning algorithm that is very widely used in practice---it routinely features as a leading algorithm in machine learning competitions such as Kaggle and the KDDCup. In spite of the usefulness of GBM in practice, our current theoretical understanding of this method is rather limited. In this work, we propose Randomized Gradient Boosting Machine (RGBM) which leads to substantial computational gains compared to GBM, by using a randomization scheme to reduce search in the space of weak-learners. We derive novel computational guarantees for RGBM. We also provide a principled guideline towards better step-size selection in RGBM that does not require a line search. Our proposed framework is inspired by a special variant of coordinate descent that combines the benefits of randomized coordinate descent and greedy coordinate descent; and may be of independent interest as an optimization algorithm. As a special case, our results for RGBM lead to superior computational guarantees for GBM. Our computational guarantees depend upon a curious geometric quantity that we call Minimal Cosine Angle, which relates to the density of weak-learners in the prediction space. On a series of numerical experiments on real datasets, we demonstrate the effectiveness of RGBM over GBM in terms of obtaining a model with good training and/or testing data fidelity with a fraction of the computational cost.
Evolving Graphs with Semantic Neutral Drift
Venue: arXiv cs.NE 2020
Year: 2020

We introduce the concept of Semantic Neutral Drift (SND) for genetic programming (GP), where we exploit equivalence laws to design semantics preserving mutations guaranteed to preserve individuals' fitness scores. A number of digital circuit benchmark problems have been implemented with rule-based graph programs and empirically evaluated, demonstrating quantitative improvements in evolutionary performance. Analysis reveals that the benefits of the designed SND reside in more complex processes than simple growth of individuals, and that there are circumstances where it is beneficial to choose otherwise detrimental parameters for a GP system if that facilitates the inclusion of SND.
Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction
Venue: arXiv cs.CV 2020
Year: 2020

We address the problem of reconstructing high quality images from undersampled MRI data. This is a challenging task due to the highly ill-posed nature of the problem. In particular, in dynamic MRI scans, the interaction between the target structure and the physical motion affects the acquired measurements leading to blurring artefacts and loss of fine details. In this work, we propose a framework for dynamic MRI reconstruction framed under a new multi-task optimisation model called Compressed Sensing Plus Motion (CS+M). Firstly, we propose a single optimisation problem that simultaneously computes the MRI reconstruction and the physical motion. Secondly, we show our model can be efficiently solved by breaking it up into two more computationally tractable problems. The potentials and generalisation capabilities of our approach are demonstrated in different clinical applications including cardiac cine, cardiac perfusion and brain perfusion imaging. We show, through numerical and graphical experiments, that the proposed scheme reduces blurring artefacts and preserves the target shape and fine details. We also report the highest quality reconstruction under highly undersampling rates in comparison to several state of the art techniques.