OpenRaiser/Intern-Atlas huggingface dataset:
Temporal changes in stimulus perception improve bio-inspired source seeking
Venue: arXiv cs.RO 2019
Year: 2019

Braitenberg vehicles are well known qualitative models of sensor driven animal source seeking (biological taxes) that locally navigate a stimulus function. These models ultimately depend on the perceived stimulus values, while there is biological evidence that animals also use the temporal changes in the stimulus as information source for taxis behaviour. The time evolution of the stimulus values depends on the agent's (animal or robot) velocity, while simultaneously the velocity is typically the variable to control. This circular dependency appears, for instance, when using optical flow to control the motion of a robot, and it is solved by fixing the forward speed while controlling only the steering rate. This paper presents a new mathematical model of a bio-inspired source seeking controller that includes the rate of change of the stimulus in the velocity control mechanism. The above mentioned circular dependency results in a closed-loop model represented by a set of differential-algebraic equations (DAEs), which can be converted to non-linear ordinary differential equations (ODEs) under some assumptions. Theoretical results of the model analysis show that including a term dependent on the temporal evolution of the stimulus improves the behaviour of the closed-loop system compared to simply using the stimulus values. We illustrate the theoretical results through a set of simulations.
Evolving Academia/Industry Relations in Computing Research
Venue: arXiv cs.CY 2019
Year: 2019

In 2015, the CCC co-sponsored an industry round table that produced the document "The Future of Computing Research: Industry-Academic Collaborations". Since then, several important trends in computing research have emerged, and this document considers how those trends impact the interaction between academia and industry in computing fields. We reach the following conclusions: - In certain computing disciplines, such as currently artificial intelligence, we observe significant increases in the level of interaction between professors and companies, which take the form of extended joint appointments. - Increasingly, companies are highly motivated to engage both professors and graduate students working in specific technical areas because companies view computing research and technical talent as a core aspect of their business success. - There is also the further potential for principles and values from the academy (e.g., ethics, human-centered approaches, etc.) informing products and R&D roadmaps in new ways through these unique joint arrangements. - This increasing connection between faculty, students, and companies has the potential to change (either positively or negatively) numerous things, including: the academic culture in computing research universities, the research topics that faculty and students pursue, the ability of universities to train undergraduate and graduate students, etc. This report is the first step in engaging the broader computing research community, raising awareness of the opportunities, complexities and challenges of this trend but further work is required. We recommend follow-up to measure the degree and impact of this trend and to establish best practices that are shared widely among computing research institutions.
On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

In autonomous embedded systems, it is often vital to reduce the amount of actions taken in the real world and energy required to learn a policy. Training reinforcement learning agents from high dimensional image representations can be very expensive and time consuming. Autoencoders are deep neural network used to compress high dimensional data such as pixelated images into small latent representations. This compression model is vital to efficiently learn policies, especially when learning on embedded systems. We have implemented this model on the NVIDIA Jetson TX2 embedded GPU, and evaluated the power consumption, throughput, and energy consumption of the autoencoders for various CPU/GPU core combinations, frequencies, and model parameters. Additionally, we have shown the reconstructions generated by the autoencoder to analyze the quality of the generated compressed representation and also the performance of the reinforcement learning agent. Finally, we have presented an assessment of the viability of training these models on embedded systems and their usefulness in developing autonomous policies. Using autoencoders, we were able to achieve 4-5 $\times$ improved performance compared to a baseline RL agent with a convolutional feature extractor, while using less than 2W of power.
Yosys+nextpnr: an Open Source Framework from Verilog to Bitstream for Commercial FPGAs
Venue: arXiv cs.DC 2019
Year: 2019

This paper introduces a fully free and open source software (FOSS) architecture-neutral FPGA framework comprising of Yosys for Verilog synthesis, and nextpnr for placement, routing, and bitstream generation. Currently, this flow supports two commercially available FPGA families, Lattice iCE40 (up to 8K logic elements) and Lattice ECP5 (up to 85K elements) and has been hardware-proven for custom-computing machines including a low-power neural-network accelerator and an OpenRISC system-on-chip capable of booting Linux. Both Yosys and nextpnr have been engineered in a highly flexible manner to support many of the features present in modern FPGAs by separating architecture-specific details from the common mapping algorithms. This framework is demonstrated on a longest-path case study to find an atypical single source-sink path occupying up to 45% of all on-chip wiring.
Pressure and flow statistics of Darcy flow from simulated annealing
Venue: arXiv cs.CE 2019
Year: 2019

The pressure and flow statistics of Darcy flow through a random permeable medium are expressed in a form suitable for evaluation by the method of simulated annealing. There are several attractive aspects to using simulated annealing: (i) any probability distribution can be used for the permeability, (ii) there is no need to invert the transmissibility matrix which, while not a factor for single-phase flow, offers distinct advantages for the case of multiphase flow, and (iii) the action used for simulated annealing is eminently suitable for coarse graining by integrating over the short-wavelength degrees of freedom. In this paper, we show that the pressure and flow statistics obtained by simulated annealing are in excellent agreement with the more conventional finite-volume calculations.
Robotic Materials
Venue: arXiv cs.RO 2019
Year: 2019

The Computing Community Consortium (CCC) sponsored a workshop on "Robotic Materials" in Washington, DC, that was held from April 23-24, 2018. This workshop was the second in a series of interdisciplinary workshops aimed at transforming our notion of materials to become "robotic", that is have the ability to sense and impact their environment. Results of the first workshop held from March 10-12, 2017, at the University of Colorado have been summarized in a visioning paper (Correll, 2017) and have identified the key technological challenges of "Robotic Materials", namely the ability to create smart functionality with a minimum of additional wiring by relying on wireless power and communication. The goal of this second workshop was to turn these findings into recommendations for government action. Computation will become an important part of future material systems and will allow materials to analyze, change, store and communicate state in ways that are not possible using mechanical or chemical processes alone. What "computation" is and what is possibilities are, is unclear to most material scientists, while computer scientists are largely unaware of recent advances in so-called active and smart materials. This gap is currently shrinking, with computer scientists embracing neural networks and material scientists actively researching novel substrates such as memristors and other neuromorphic computing devices. Further pursuing these ideas will require an emphasis on interdisciplinary collaboration between chemists, engineers, and computer scientists, possibly elevating humankind to a new material age that is similarly disruptive as the leap from the stone to the plastic age.
DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction
Venue: arXiv cs.CV 2019
Year: 2019

A novel centerline extraction framework is reported which combines an end-to-end trainable multi-task fully convolutional network (FCN) with a minimal path extractor. The FCN simultaneously computes centerline distance maps and detects branch endpoints. The method generates single-pixel-wide centerlines with no spurious branches. It handles arbitrary tree-structured object with no prior assumption regarding depth of the tree or its bifurcation pattern. It is also robust to substantial scale changes across different parts of the target object and minor imperfections of the object's segmentation mask. To the best of our knowledge, this is the first deep-learning based centerline extraction method that guarantees single-pixel-wide centerline for a complex tree-structured object. The proposed method is validated in coronary artery centerline extraction on a dataset of 620 patients (400 of which used as test set). This application is challenging due to the large number of coronary branches, branch tortuosity, and large variations in length, thickness, shape, etc. The proposed method generates well-positioned centerlines, exhibiting lower number of missing branches and is more robust in the presence of minor imperfections of the object segmentation mask. Compared to a state-of-the-art traditional minimal path approach, our method improves patient-level success rate of centerline extraction from 54.3% to 88.8% according to independent human expert review.
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness
Venue: arXiv cs.LG 2019
Year: 2019

Adversarial examples are malicious inputs crafted to cause a model to misclassify them. Their most common instantiation, "perturbation-based" adversarial examples introduce changes to the input that leave its true label unchanged, yet result in a different model prediction. Conversely, "invariance-based" adversarial examples insert changes to the input that leave the model's prediction unaffected despite the underlying input's label having changed. In this paper, we demonstrate that robustness to perturbation-based adversarial examples is not only insufficient for general robustness, but worse, it can also increase vulnerability of the model to invariance-based adversarial examples. In addition to analytical constructions, we empirically study vision classifiers with state-of-the-art robustness to perturbation-based adversaries constrained by an $\ell_p$ norm. We mount attacks that exploit excessive model invariance in directions relevant to the task, which are able to find adversarial examples within the $\ell_p$ ball. In fact, we find that classifiers trained to be $\ell_p$-norm robust are more vulnerable to invariance-based adversarial examples than their undefended counterparts. Excessive invariance is not limited to models trained to be robust to perturbation-based $\ell_p$-norm adversaries. In fact, we argue that the term adversarial example is used to capture a series of model limitations, some of which may not have been discovered yet. Accordingly, we call for a set of precise definitions that taxonomize and address each of these shortcomings in learning.
Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution
Venue: arXiv cs.CV 2019
Year: 2019

Spectral super-resolution (SSR) aims at generating a hyperspectral image (HSI) from a given RGB image. Recently, a promising direction for SSR is to learn a complicated mapping function from the RGB image to the HSI counterpart using a deep convolutional neural network. This essentially involves mapping the RGB context within a size-specific receptive field centered at each pixel to its spectrum in the HSI. The focus thereon is to appropriately determine the receptive field size and establish the mapping function from RGB context to the corresponding spectrum. Due to their differences in category or spatial position, pixels in HSIs often require different-sized receptive fields and distinct mapping functions. However, few efforts have been invested to explicitly exploit this prior. To address this problem, we propose a pixel-aware deep function-mixture network for SSR, which is composed of a new class of modules, termed function-mixture (FM) blocks. Each FM block is equipped with some basis functions, i.e., parallel subnets of different-sized receptive fields. Besides, it incorporates an extra subnet as a mixing function to generate pixel-wise weights, and then linearly mixes the outputs of all basis functions with those generated weights. This enables us to pixel-wisely determine the receptive field size and the mapping function. Moreover, we stack several such FM blocks to further increase the flexibility of the network in learning the pixel-wise mapping. To encourage feature reuse, intermediate features generated by the FM blocks are fused in late stage, which proves to be effective for boosting the SSR performance. Experimental results on three benchmark HSI datasets demonstrate the superiority of the proposed method.
GEVR: An Event Venue Recommendation System for Groups of Mobile Users
Venue: arXiv cs.HC 2019
Year: 2019

In this paper, we present GEVR, the first Group Event Venue Recommendation system that incorporates mobility via individual location traces and context information into a "social-based" group decision model to provide venue recommendations for groups of mobile users. Our study leverages a real-world dataset collected using the OutWithFriendz mobile app for group event planning, which contains 625 users and over 500 group events. We first develop a novel "social-based" group location prediction model, which adaptively applies different group decision strategies to groups with different social relationship strength to aggregate each group member's location preference, to predict where groups will meet. Evaluation results show that our prediction model not only outperforms commonly used and state-of-the-art group decision strategies with over 80% accuracy for predicting groups' final meeting location clusters, but also provides promising qualities in cold-start scenarios. We then integrate our prediction model with the Foursquare Venue Recommendation API to construct an event venue recommendation framework for groups of mobile users. Evaluation results show that GEVR outperforms the comparative models by a significant margin.
Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach
Venue: arXiv cs.LG 2019
Year: 2019

Cancer is one of the leading cause of death, worldwide. Many believe that genomic data will enable us to better predict the survival time of these patients, which will lead to better, more personalized treatment options and patient care. As standard survival prediction models have a hard time coping with the high-dimensionality of such gene expression (GE) data, many projects use some dimensionality reduction techniques to overcome this hurdle. We introduce a novel methodology, inspired by topic modeling from the natural language domain, to derive expressive features from the high-dimensional GE data. There, a document is represented as a mixture over a relatively small number of topics, where each topic corresponds to a distribution over the words; here, to accommodate the heterogeneity of a patient's cancer, we represent each patient (~document) as a mixture over cancer-topics, where each cancer-topic is a mixture over GE values (~words). This required some extensions to the standard LDA model eg: to accommodate the "real-valued" expression values - leading to our novel "discretized" Latent Dirichlet Allocation (dLDA) procedure. We initially focus on the METABRIC dataset, which describes breast cancer patients using the r=49,576 GE values, from microarrays. Our results show that our approach provides survival estimates that are more accurate than standard models, in terms of the standard Concordance measure. We then validate this approach by running it on the Pan-kidney (KIPAN) dataset, over r=15,529 GE values - here using the mRNAseq modality - and find that it again achieves excellent results. In both cases, we also show that the resulting model is calibrated, using the recent "D-calibrated" measure. These successes, in two different cancer types and expression modalities, demonstrates the generality, and the effectiveness, of this approach.
Recognizing Arrow Of Time In The Short Stories
Venue: arXiv cs.CL 2019
Year: 2019

Recognizing arrow of time in short stories is a challenging task. i.e., given only two paragraphs, determining which comes first and which comes next is a difficult task even for humans. In this paper, we have collected and curated a novel dataset for tackling this challenging task. We have shown that a pre-trained BERT architecture achieves reasonable accuracy on the task, and outperforms RNN-based architectures.
Deep Learning for Localization in the Lung
Venue: arXiv cs.CV 2019
Year: 2019

Lung cancer is the leading cause of cancer-related death worldwide, and early diagnosis is critical to improving patient outcomes. To diagnose cancer, a highly trained pulmonologist must navigate a flexible bronchoscope deep into the branched structure of the lung for biopsy. The biopsy fails to sample the target tissue in 26-33% of cases largely because of poor registration with the preoperative CT map. We developed two deep learning approaches to localize the bronchoscope in the preoperative CT map in real time and tested the algorithms across 13 trajectories in a lung phantom and 68 trajectories in 11 human cadaver lungs. In the lung phantom, we observe performance reaching 95% precision and recall of visible airways and 3 mm average position error. On a successful cadaver lung sequence, the algorithms trained on simulation alone achieved 77%-94% precision and recall of visible airways and 4-6 mm average position error. We also compare the effect of GAN-stylizing images and we look at aggregate statistics over the entire set of trajectories.
Q-Learning for Continuous Actions with Cross-Entropy Guided Policies
Venue: arXiv cs.AI 2019
Year: 2019

Off-Policy reinforcement learning (RL) is an important class of methods for many problem domains, such as robotics, where the cost of collecting data is high and on-policy methods are consequently intractable. Standard methods for applying Q-learning to continuous-valued action domains involve iteratively sampling the Q-function to find a good action (e.g. via hill-climbing), or by learning a policy network at the same time as the Q-function (e.g. DDPG). Both approaches make tradeoffs between stability, speed, and accuracy. We propose a novel approach, called Cross-Entropy Guided Policies, or CGP, that draws inspiration from both classes of techniques. CGP aims to combine the stability and performance of iterative sampling policies with the low computational cost of a policy network. Our approach trains the Q-function using iterative sampling with the Cross-Entropy Method (CEM), while training a policy network to imitate CEM's sampling behavior. We demonstrate that our method is more stable to train than state of the art policy network methods, while preserving equivalent inference time compute costs, and achieving competitive total reward on standard benchmarks.
Bayesian Estimation Based Parameter Estimation for Composite Load
Venue: arXiv cs.SY 2019
Year: 2019

Accurate identification of parameters of load models is essential in power system computations, including simulation, prediction, and stability and reliability analysis. Conventional point estimation based composite load modeling approaches suffer from disturbances and noises and provide limited information of the system dynamics. In this work, a statistic (Bayesian Estimation) based distribution estimation approach is proposed for both static (ZIP) and dynamic (Induction Motor) load modeling. When dealing with multiple parameters, Gibbs sampling method is employed. In each iteration, the proposal samples each parameter while keeps others fixed. The proposed method provides a distribution estimation of load models coefficients and is robust to measurement errors.
Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters
Venue: arXiv cs.SD 2019
Year: 2019

In this article we explore how the different semantics of spectrograms' time and frequency axes can be exploited for musical tempo and key estimation using Convolutional Neural Networks (CNN). By addressing both tasks with the same network architectures ranging from shallow, domain-specific approaches to deep variants with directional filters, we show that axis-aligned architectures perform similarly well as common VGG-style networks developed for computer vision, while being less vulnerable to confounding factors and requiring fewer model parameters.
High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks
Venue: arXiv cs.CV 2019
Year: 2019

Predicting human perceptual similarity is a challenging subject of ongoing research. The visual process underlying this aspect of human vision is thought to employ multiple different levels of visual analysis (shapes, objects, texture, layout, color, etc). In this paper, we postulate that the perception of image similarity is not an explicitly learned capability, but rather one that is a byproduct of learning others. This claim is supported by leveraging representations learned from a diverse set of visual tasks and using them jointly to predict perceptual similarity. This is done via simple feature concatenation, without any further learning. Nevertheless, experiments performed on the challenging Totally-Looks-Like (TLL) benchmark significantly surpass recent baselines, closing much of the reported gap towards prediction of human perceptual similarity. We provide an analysis of these results and discuss them in a broader context of emergent visual capabilities and their implications on the course of machine-vision research.
The Meta Distribution of the SIR in Linear Motorway VANETs
Venue: arXiv cs.IT 2019
Year: 2019

The meta distribution of the signal-to-interference-ratio (SIR) is an important performance indicator for wireless networks because, for ergodic point processes, it describes the fraction of scheduled links that achieve certain reliability, conditionally on the point process. The calculation of the moments of the meta distribution requires the probability generating functional (PGFL) of the point process. In vehicular ad hoc networks (VANETs) along high-speed motorways, the Poisson point process (PPP) is a poor deployment model, because the drivers, due to the high speeds, maintain large safety distances. In this paper, we model the distribution of inter-vehicle distance equal to the sum of a constant hardcore distance and an exponentially distributed random variable. We design a novel \emph{discretization model} for the locations of vehicles which can be used to approximate well the PGFL due to the hardcore point process and the meta distribution of the SIR generated from synthetic motorway traces. On the other hand, the PPP overestimates significantly the coefficient-of-variation of the meta distribution due to the hardcore process, and its predictions fail. In addition, we show that the calculation of the meta distribution becomes especially meaningful in the upper tail of the SIR distribution.