OpenRaiser/Intern-Atlas huggingface dataset:
Transportation Proof of an inequality by Anantharam, Jog and Nair
Venue: arXiv cs.IT 2019
Year: 2019

Anantharam, Jog and Nair recently put forth an entropic inequality which simultaneously generalizes the Shannon-Stam entropy power inequality and the Brascamp-Lieb inequality in entropic form. We give a brief proof of their result based on optimal transport.
Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems
Venue: arXiv cs.MA 2019
Year: 2019

Many real-world systems such as taxi systems, traffic networks and smart grids involve self-interested actors that perform individual tasks in a shared environment. However, in such systems, the self-interested behaviour of agents produces welfare inefficient and globally suboptimal outcomes that are detrimental to all - some common examples are congestion in traffic networks, demand spikes for resources in electricity grids and over-extraction of environmental resources such as fisheries. We propose an incentive-design method which modifies agents' rewards in non-cooperative multi-agent systems that results in independent, self-interested agents choosing actions that produce optimal system outcomes in strategic settings. Our framework combines multi-agent reinforcement learning to simulate (real-world) agent behaviour and black-box optimisation to determine the optimal modifications to the agents' rewards or incentives given some fixed budget that results in optimal system performance. By modifying the reward functions and generating agents' equilibrium responses within a sequence of offline Markov games, our method enables optimal incentive structures to be determined offline through iterative updates of the reward functions of a simulated game. Our theoretical results show that our method converges to reward modifications that induce system optimality. We demonstrate the applications of our framework by tackling a challenging problem within economics that involves thousands of selfish agents and tackle a traffic congestion problem.
Understanding spatial correlation in eye-fixation maps for visual attention in videos
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we present an analysis of recorded eye-fixation data from human subjects viewing video sequences. The purpose is to better understand visual attention for videos. Utilizing the eye-fixation data provided in the CRCNS (Collaborative Research in Computational Neuroscience) dataset, this paper focuses on the relation between the saliency of a pixel and that of its direct neighbors, without making any assumption about the structure of the eye-fixation maps. By employing some basic concepts from information theory, the analysis shows substantial correlation between the saliency of a pixel and the saliency of its neighborhood. The analysis also provides insights into the structure and dynamics of the eye-fixation maps, which can be very useful in understanding video saliency and its applications.
Bootstrapping Robotic Ecological Perception from a Limited Set of Hypotheses Through Interactive Perception
Venue: arXiv cs.RO 2019
Year: 2019

To solve its task, a robot needs to have the ability to interpret its perceptions. In vision, this interpretation is particularly difficult and relies on the understanding of the structure of the scene, at least to the extent of its task and sensorimotor abilities. A robot with the ability to build and adapt this interpretation process according to its own tasks and capabilities would push away the limits of what robots can achieve in a non controlled environment. A solution is to provide the robot with processes to build such representations that are not specific to an environment or a situation. A lot of works focus on objects segmentation, recognition and manipulation. Defining an object solely on the basis of its visual appearance is challenging given the wide range of possible objects and environments. Therefore, current works make simplifying assumptions about the structure of a scene. Such assumptions reduce the adaptivity of the object extraction process to the environments in which the assumption holds. To limit such assumptions, we introduce an exploration method aimed at identifying moveable elements in a scene without considering the concept of object. By using the interactive perception framework, we aim at bootstrapping the acquisition process of a representation of the environment with a minimum of context specific assumptions. The robotic system builds a perceptual map called relevance map which indicates the moveable parts of the current scene. A classifier is trained online to predict the category of each region (moveable or non-moveable). It is also used to select a region with which to interact, with the goal of minimizing the uncertainty of the classification. A specific classifier is introduced to fit these needs: the collaborative mixture models classifier. The method is tested on a set of scenarios of increasing complexity, using both simulations and a PR2 robot.
Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM
Venue: arXiv cs.NE 2019
Year: 2019

Many long short-term memory (LSTM) applications need fast yet compact models. Neural network compression approaches, such as the grow-and-prune paradigm, have proved to be promising for cutting down network complexity by skipping insignificant weights. However, current compression strategies are mostly hardware-agnostic and network complexity reduction does not always translate into execution efficiency. In this work, we propose a hardware-guided symbiotic training methodology for compact, accurate, yet execution-efficient inference models. It is based on our observation that hardware may introduce substantial non-monotonic behavior, which we call the latency hysteresis effect, when evaluating network size vs. inference latency. This observation raises question about the mainstream smaller-dimension-is-better compression strategy, which often leads to a sub-optimal model architecture. By leveraging the hardware-impacted hysteresis effect and sparsity, we are able to achieve the symbiosis of model compactness and accuracy with execution efficiency, thus reducing LSTM latency while increasing its accuracy. We have evaluated our algorithms on language modeling and speech recognition applications. Relative to the traditional stacked LSTM architecture obtained for the Penn Treebank dataset, we reduce the number of parameters by 18.0x (30.5x) and measured run-time latency by up to 2.4x (5.2x) on Nvidia GPUs (Intel Xeon CPUs) without any accuracy degradation. For the DeepSpeech2 architecture obtained for the AN4 dataset, we reduce the number of parameters by 7.0x (19.4x), word error rate from 12.9% to 9.9% (10.4%), and measured run-time latency by up to 1.7x (2.4x) on Nvidia GPUs (Intel Xeon CPUs). Thus, our method yields compact, accurate, yet execution-efficient inference models.
The Semialgebraic Orbit Problem
Venue: arXiv cs.CC 2019
Year: 2019

The Semialgebraic Orbit Problem is a fundamental reachability question that arises in the analysis of discrete-time linear dynamical systems such as automata, Markov chains, recurrence sequences, and linear while loops. An instance of the problem comprises a dimension $d\in\mathbb{N}$, a square matrix $A\in\mathbb{Q}^{d\times d}$, and semialgebraic source and target sets $S,T\subseteq \mathbb{R}^d$. The question is whether there exists $x\in S$ and $n\in\mathbb{N}$ such that $A^nx \in T$. The main result of this paper is that the Semialgebraic Orbit Problem is decidable for dimension $d\leq 3$. Our decision procedure relies on separation bounds for algebraic numbers as well as a classical result of transcendental number theory---Baker's theorem on linear forms in logarithms of algebraic numbers. We moreover argue that our main result represents a natural limit to what can be decided (with respect to reachability) about the orbit of a single matrix. On the one hand, semialgebraic sets are arguably the largest general class of subsets of $\mathbb{R}^d$ for which membership is decidable. On the other hand, previous work has shown that in dimension $d=4$, giving a decision procedure for the special case of the Orbit Problem with singleton source set $S$ and polytope target set $T$ would entail major breakthroughs in Diophantine approximation.
Software solutions for form-based collection of data and the semantic enrichment of form data
Venue: arXiv cs.CY 2019
Year: 2019

Data collection is an important part of many citizen science projects as well as other fields of research, particularly in life sciences. Mobile applications with form-based surveys are increasingly used to support this, due to the large number of mobile devices and their growing number of built-in sensors. Since the composition of form-based surveys from scratch can be a tedious task, multiple tools have been published that can help with their design and distribution as well as the data collection via mobile devices and the data storage. Some even support simple data analysis. With this increasing number of software options project leaders will often face the question, which tool is most suitable for their current use case. With that in mind, this project pursues two main objectives: 1. To present an overview of a selection of survey design tools and their capabilities in order to provide a clear foundation for such a decision. 2. To examine if any tool provides the capability to collect and export data in a way that can easily be used and interpreted by other applications or persons. This aspect includes the supply of metadata about the data collection process and the data itself, information about the meaning of the data as well as an export format that can easily be processed.
Delay Analysis of Spatially-Coded MIMO-ZFBF with Retransmissions in Random Networks
Venue: arXiv cs.IT 2019
Year: 2019

For a low-mobile Poisson bipolar network and under line-of-sight/non-line-of-sight (LOS/NLOS) path-loss model, we study repetitive retransmissions (RR) and blocked incremental redundancy (B-IR). We consider spatially-coded multiple-input multiple-output (MIMO) zero-forcing beamforming (ZFBF) multiplexing system, whereby the packet success reception is determined based on the aggregate data rate across spatial dimensions of the MIMO system. Characterization of retransmission performance in this low-mobile configuration is practically important, but inherently complex due to a substantial rate correlation across retransmissions and intractability of evaluating the probability density function (pdf) of aggregate data rate. Adopting tools of stochastic geometry, we firstly characterize the rate correlation coefficient (RCC) for both schemes. Our results show that, compared to RR scheme, B-IR scheme has higher RCC while its coverage probability is substantially larger. We demonstrate that the spotted contention between coverage probability and RCC causes the mean transmission delay (MTD) of B-IR to become either smaller or larger than the MTD of RR scheme. Finally, we develop a numerical approximation of MTD, and evaluate the effective spatial throughput (EST), which is reciprocal to MTD, of RR and B-IR schemes. Our numerical results highlight fundamental tradeoffs between densification, multiplexing gain, block length, and activity factor of nodes. We further observe that for dense networks 1) LOS component is considerably instrumental to enhance EST; 2) EST of B-IR scheme can be much higher than that of RR scheme; 3) When Doppler spread exists, it can improve MTD of B-IR while it does not cast any meaningful effect on the MTD of RR.
Neuroevolution with Perceptron Turing Machines
Venue: arXiv cs.NE 2019
Year: 2019

We introduce the perceptron Turing machine and show how it can be used to create a system of neuroevolution. Advantages of this approach include automatic scaling of solutions to larger problem sizes, the ability to experiment with hand-coded solutions, and an enhanced potential for understanding evolved solutions. Hand-coded solutions may be implemented in the low-level language of Turing machines, which is the genotype used in neuroevolution, but a high-level language called Lopro is introduced to make the job easier.
ExceLint: Automatically Finding Spreadsheet Formula Errors
Venue: arXiv cs.PL 2019
Year: 2019

Spreadsheets are one of the most widely used programming environments, and are widely deployed in domains like finance where errors can have catastrophic consequences. We present a static analysis specifically designed to find spreadsheet formula errors. Our analysis directly leverages the rectangular character of spreadsheets. It uses an information-theoretic approach to identify formulas that are especially surprising disruptions to nearby rectangular regions. We present ExceLint, an implementation of our static analysis for Microsoft Excel. We demonstrate that ExceLint is fast and effective: across a corpus of 70 spreadsheets, ExceLint takes a median of 5 seconds per spreadsheet, and it significantly outperforms the state of the art analysis.
Similar Image Search for Histopathology: SMILY
Venue: arXiv cs.CV 2019
Year: 2019

The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Though these datasets typically have associated metadata such as diagnosis or clinical notes, even carefully curated datasets rarely contain annotations of the location of regions of interest on each image. Because pathology images are extremely large (up to 100,000 pixels in each dimension), further laborious visual search of each image may be needed to find the feature of interest. In this paper, we introduce a deep learning based reverse image search tool for histopathology images: Similar Medical Images Like Yours (SMILY). We assessed SMILY's ability to retrieve search results in two ways: using pathologist-provided annotations, and via prospective studies where pathologists evaluated the quality of SMILY search results. As a negative control in the second evaluation, pathologists were blinded to whether search results were retrieved by SMILY or randomly. In both types of assessments, SMILY was able to retrieve search results with similar histologic features, organ site, and prostate cancer Gleason grade compared with the original query. SMILY may be a useful general-purpose tool in the pathologist's arsenal, to improve the efficiency of searching large archives of histopathology images, without the need to develop and implement specific tools for each application.
The Evolved Transformer
Venue: arXiv cs.LG 2019
Year: 2019

Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models. Our goal is to apply NAS to search for a better alternative to the Transformer. We first construct a large search space inspired by the recent advances in feed-forward sequence models and then run evolutionary architecture search with warm starting by seeding our initial population with the Transformer. To directly search on the computationally expensive WMT 2014 English-German translation task, we develop the Progressive Dynamic Hurdles method, which allows us to dynamically allocate more resources to more promising candidate models. The architecture found in our experiments -- the Evolved Transformer -- demonstrates consistent improvement over the Transformer on four well-established language tasks: WMT 2014 English-German, WMT 2014 English-French, WMT 2014 English-Czech and LM1B. At a big model size, the Evolved Transformer establishes a new state-of-the-art BLEU score of 29.8 on WMT'14 English-German; at smaller sizes, it achieves the same quality as the original "big" Transformer with 37.6% less parameters and outperforms the Transformer by 0.7 BLEU at a mobile-friendly model size of 7M parameters.
Human-Centered Artificial Intelligence and Machine Learning
Venue: arXiv cs.AI 2019
Year: 2019

Humans are increasingly coming into contact with artificial intelligence and machine learning systems. Human-centered artificial intelligence is a perspective on AI and ML that algorithms must be designed with awareness that they are part of a larger system consisting of humans. We lay forth an argument that human-centered artificial intelligence can be broken down into two aspects: (1) AI systems that understand humans from a sociocultural perspective, and (2) AI systems that help humans understand them. We further argue that issues of social responsibility such as fairness, accountability, interpretability, and transparency.
Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework
Venue: arXiv cs.CV 2019
Year: 2019

Iris segmentation and localization in non-cooperative environment is challenging due to illumination variations, long distances, moving subjects and limited user cooperation, etc. Traditional methods often suffer from poor performance when confronted with iris images captured in these conditions. Recent studies have shown that deep learning methods could achieve impressive performance on iris segmentation task. In addition, as iris is defined as an annular region between pupil and sclera, geometric constraints could be imposed to help locating the iris more accurately and improve the segmentation results. In this paper, we propose a deep multi-task learning framework, named as IrisParseNet, to exploit the inherent correlations between pupil, iris and sclera to boost up the performance of iris segmentation and localization in a unified model. In particular, IrisParseNet firstly applies a Fully Convolutional Encoder-Decoder Attention Network to simultaneously estimate pupil center, iris segmentation mask and iris inner/outer boundary. Then, an effective post-processing method is adopted for iris inner/outer circle localization.To train and evaluate the proposed method, we manually label three challenging iris datasets, namely CASIA-Iris-Distance, UBIRIS.v2, and MICHE-I, which cover various types of noises. Extensive experiments are conducted on these newly annotated datasets, and results show that our method outperforms state-of-the-art methods on various benchmarks. All the ground-truth annotations, annotation codes and evaluation protocols are publicly available at https://github.com/xiamenwcy/IrisParseNet.
A Data Driven Method of Optimizing Feedforward Compensator for Autonomous Vehicle
Venue: arXiv cs.RO 2019
Year: 2019

A reliable controller is critical and essential for the execution of safe and smooth maneuvers of an autonomous vehicle.The controller must be robust to external disturbances, such as road surface, weather, and wind conditions, and so on.It also needs to deal with the internal parametric variations of vehicle sub-systems, including power-train efficiency, measurement errors, time delay,so on.Moreover, as in most production vehicles, the low-control commands for the engine, brake, and steering systems are delivered through separate electronic control units.These aforementioned factors introduce opaque and ineffectiveness issues in controller performance.In this paper, we design a feed-forward compensate process via a data-driven method to model and further optimize the controller performance.We apply the principal component analysis to the extraction of most influential features.Subsequently,we adopt a time delay neural network and include the accuracy of the predicted error in a future time horizon.Utilizing the predicted error,we then design a feed-forward compensate process to improve the control performance.Finally,we demonstrate the effectiveness of the proposed feed-forward compensate process in simulation scenarios.
A Platform Architecture for Multi-Tenant Blockchain-Based Systems
Venue: arXiv cs.SE 2019
Year: 2019

Blockchain has attracted a broad range of interests from start-ups, enterprises and governments to build next generation applications in a decentralized manner. Similar to cloud platforms, a single blockchain-based system may need to serve multiple tenants simultaneously. However, design of multi-tenant blockchain-based systems is challenging to architects in terms of data and performance isolation, as well as scalability. First, tenants must not be able to read other tenants' data and tenants with potentially higher workload should not affect read/write performance of other tenants. Second, multi-tenant blockchain-based systems usually require both scalability for each individual tenant and scalability with number of tenants. Therefore, in this paper, we propose a scalable platform architecture for multi-tenant blockchain-based systems to ensure data integrity while maintaining data privacy and performance isolation. In the proposed architecture, each tenant has an individual permissioned blockchain to maintain their own data and smart contracts. All tenant chains are anchored into a main chain, in a way that minimizes cost and load overheads. The proposed architecture has been implemented in a proof-of-concept prototype with our industry partner, Laava ID Pty Ltd (Laava). We evaluate our proposal in a three-fold way: fulfilment of the identified requirements, qualitative comparison with design alternatives, and quantitative analysis. The evaluation results show that the proposed architecture can achieve data integrity, performance isolation, data privacy, configuration flexibility, availability, cost efficiency and scalability.
Capturing Object Detection Uncertainty in Multi-Layer Grid Maps
Venue: arXiv cs.RO 2019
Year: 2019

We propose a deep convolutional object detector for automated driving applications that also estimates classification, pose and shape uncertainty of each detected object. The input consists of a multi-layer grid map which is well-suited for sensor fusion, free-space estimation and machine learning. Based on the estimated pose and shape uncertainty we approximate object hulls with bounded collision probability which we find helpful for subsequent trajectory planning tasks. We train our models based on the KITTI object detection data set. In a quantitative and qualitative evaluation some models show a similar performance and superior robustness compared to previously developed object detectors. However, our evaluation also points to undesired data set properties which should be addressed when training data-driven models or creating new data sets.
Distributed Correlation-Based Feature Selection in Spark
Venue: arXiv cs.LG 2019
Year: 2019

CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed version of the CFS algorithm, capable of dealing with the large volumes of data typical of big data applications. Two versions of the algorithm were implemented and compared using the Apache Spark cluster computing model, currently gaining popularity due to its much faster processing times than Hadoop's MapReduce model. We tested our algorithms on four publicly available datasets, each consisting of a large number of instances and two also consisting of a large number of features. The results show that our algorithms were superior in terms of both time-efficiency and scalability. In leveraging a computer cluster, they were able to handle larger datasets than the non-distributed WEKA version while maintaining the quality of the results, i.e., exactly the same features were returned by our algorithms when compared to the original algorithm available in WEKA.