OpenRaiser/Intern-Atlas huggingface dataset:
How Will the Presence of Autonomous Vehicles Affect the Equilibrium State of Traffic Networks?
Venue: arXiv cs.GT 2019
Year: 2019

It is known that connected and autonomous vehicles are capable of maintaining shorter headways and distances when they form platoons of vehicles. Thus, such technologies can result in increases in the capacities of traffic networks. Consequently, it is envisioned that their deployment will boost the network mobility. In this paper, we verify the validity of this impact under selfish routing behavior of drivers in traffic networks with mixed autonomy, i.e. traffic networks with both regular and autonomous vehicles. We consider a nonatomic routing game on a network with inelastic (fixed) demands for the set of network O/D pairs, and study how replacing a fraction of regular vehicles by autonomous vehicles will affect the mobility of the network. Using the well known US bureau of public roads (BPR) traffic delay models, we show that the resulting Wardrop equilibrium is not necessarily unique even in its weak sense for networks with mixed autonomy. We state the conditions under which the total network delay is guaranteed not to increase as a result of autonomy increase. However, we show that when these conditions do not hold, counter intuitive behaviors may occur: the total delay can grow by increasing the network autonomy. In particular, we prove that for networks with a single O/D pair, if the road degrees of asymmetry are homogeneous, the total delay is 1) unique, and 2) a nonincreasing continuous function of network autonomy fraction. We show that for heterogeneous degrees of asymmetry, the total delay is not unique, and it can further grow with autonomy increase. We demonstrate that similar behaviors may be observed in networks with multiple O/D pairs. We further bound such performance degradations due to the introduction of autonomy in homogeneous networks.
Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems
Venue: arXiv cs.IT 2019
Year: 2019

The vehicular edge computing (VEC) system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states and computing workloads. These uncertainties bring extra challenges to task offloading. In this work, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning-based task offloading (ALTO) algorithm based on the multi-armed bandit (MAB) theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to 30% compared with the existing upper confidence bound based learning algorithm.
Timely Negotiation and Correction of Shared Intentions With Body Motion
Venue: arXiv cs.RO 2019
Year: 2019

Current robot architectures for modeling interaction behavior are not well suited to the dual task of sequencing discrete actions and incorporating information instantly. Additionally, for communication based on body motion, actions also serve as cues for negotiating interaction alternatives and to enable timely interventions. The paper presents a dynamical system based on the stable heteroclinic channel network, which provides a rich set of parameters to isntantly modulate motions, while maintaining a compact state graph abstraction suitable for reasoning, planning and inference.
Asymptotic Analysis on LDPC-BICM Scheme for Compute-and-Forward Relaying
Venue: arXiv cs.IT 2019
Year: 2019

The compute-and-forward (CAF) scheme has attracted great interests due to its high band-width efficiency on two-way relay channels. In the CAF scheme, a relay attempts to decode a linear combination of transmitted messages from other terminals or relays. It is a crucial issue to study practical error-correcting codes in order to realize the CAF scheme with low computational complexity. In this paper, we present an efficient bit-interleaved coded modulation (BICM) scheme for the CAF scheme with phase shift keying (PSK) modulations. In particular, we examine the asymptotic decoding performance of the BICM scheme with low-density parity-check (LDPC) codes by using the density evolution (DE) method. Based on the asymmetric nature of the channel model, we utilize the population dynamics method for the DE equations without the all-zero codeword assumption. The results show that, for two-way relay channels with QPSK and 8PSK modulations, the LDPC-BICM scheme provides higher achievable rate compared with an alternative separation decoding scheme.
Solving Simple Stochastic Games with few Random Nodes faster using Bland's Rule
Venue: arXiv cs.DS 2019
Year: 2019

The best algorithm so far for solving Simple Stochastic Games is Ludwig's randomized algorithm which works in expected $2^{O(\sqrt{n})}$ time. We first give a simpler iterative variant of this algorithm, using Bland's rule from the simplex algorithm, which uses exponentially less random bits than Ludwig's version. Then, we show how to adapt this method to the algorithm of Gimbert and Horn whose worst case complexity is $O(k!)$, where $k$ is the number of random nodes. Our algorithm has an expected running time of $2^{O(k)}$, and works for general random nodes with arbitrary outdegree and probability distribution on outgoing arcs.
Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions
Venue: arXiv cs.CV 2019
Year: 2019

Underwater image enhancement is such an important low-level vision task with many applications that numerous algorithms have been proposed in recent years. These algorithms developed upon various assumptions demonstrate successes from various aspects using different data sets and different metrics. In this work, we setup an undersea image capturing system, and construct a large-scale Real-world Underwater Image Enhancement (RUIE) data set divided into three subsets. The three subsets target at three challenging aspects for enhancement, i.e., image visibility quality, color casts, and higher-level detection/classification, respectively. We conduct extensive and systematic experiments on RUIE to evaluate the effectiveness and limitations of various algorithms to enhance visibility and correct color casts on images with hierarchical categories of degradation. Moreover, underwater image enhancement in practice usually serves as a preprocessing step for mid-level and high-level vision tasks. We thus exploit the object detection performance on enhanced images as a brand new task-specific evaluation criterion. The findings from these evaluations not only confirm what is commonly believed, but also suggest promising solutions and new directions for visibility enhancement, color correction, and object detection on real-world underwater images.
A review of domain adaptation without target labels
Venue: arXiv cs.LG 2019
Year: 2019

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.
Lightweight Markerless Monocular Face Capture with 3D Spatial Priors
Venue: arXiv cs.CV 2019
Year: 2019

We present a simple lightweight markerless facial performance capture framework using just a monocular video input that combines Active Appearance Models for feature tracking and prior constraints on 3D shapes into an integrated objective function. 2D monocular inputs inherently lack information along the depth axis and can lead to physically implausible solutions. In order to address this loss of information, we enforce a constraint on our objective function within a probabilistic framework that uses preexisting animations obtained from accurate 3D tracking systems, thus achieving more plausible results. Our system fits a Blendshape model to tracked 2D features while also handling noise in estimation of features and camera parameters. We learn separate constraints for the upper and lower regions of the face thus maintaining flexibility. We show that using this approach, we can obtain significant improvement in tracking especially along the depth dimension. Our method uses easily obtainable prior animation data. We show that our method can generate convincing animations using only a monocular video input. We quantitatively evaluate our results comparing it with an approach using a monocular input without our spatial constraints and show that our results are closer to the ground-truth geometry. Finally, we also evaluate the effect that the choice of the Blendshape set has on the results of the solver by solving for a different set of Blendshapes and quantitatively comparing it with our previous results and to the ground truth. We show that while the choice of Blendshapes does make a difference, the use of our spatial constraints generates results that are closer to the ground truth.
Soft Constraints for Inference with Declarative Knowledge
Venue: arXiv cs.AI 2019
Year: 2019

We develop a likelihood free inference procedure for conditioning a probabilistic model on a predicate. A predicate is a Boolean valued function which expresses a yes/no question about a domain. Our contribution, which we call predicate exchange, constructs a softened predicate which takes value in the unit interval [0, 1] as opposed to a simply true or false. Intuitively, 1 corresponds to true, and a high value (such as 0.999) corresponds to "nearly true" as determined by a distance metric. We define Boolean algebra for soft predicates, such that they can be negated, conjoined and disjoined arbitrarily. A softened predicate can serve as a tractable proxy to a likelihood function for approximate posterior inference. However, to target exact inference, we temper the relaxation by a temperature parameter, and add a accept/reject phase use to replica exchange Markov Chain Mont Carlo, which exchanges states between a sequence of models conditioned on predicates at varying temperatures. We describe a lightweight implementation of predicate exchange that it provides a language independent layer that can be implemented on top of existingn modeling formalisms.
TrABin: Trustworthy Analyses of Binaries
Venue: arXiv cs.PL 2019
Year: 2019

Verification of microkernels, device drivers, and crypto routines requires analyses at the binary level. In order to automate these analyses, in the last years several binary analysis platforms have been introduced. These platforms share a common design: the adoption of hardware-independent intermediate representations, a mechanism to translate architecture dependent code to this representation, and a set of architecture independent analyses that process the intermediate representation. The usage of these platforms to verify software introduces the need for trusting both the correctness of the translation from binary code to intermediate language (called transpilation) and the correctness of the analyses. Achieving a high degree of trust is challenging since the transpilation must handle (i) all the side effects of the instructions, (ii) multiple instruction encodings (e.g. ARM Thumb), and (iii) variable instruction length (e.g. Intel). Similarly, analyses can use complex transformations (e.g. loop unrolling) and simplifications (e.g. partial evaluation) of the artifacts, whose bugs can jeopardize correctness of the results. We overcome these problems by developing a binary analysis platform on top of the interactive theorem prover HOL4. First, we formally model a binary intermediate language and we prove correctness of several supporting tools (i.e. a type checker). Then, we implement two proof-producing transpilers, which respectively translate ARMv8 and CortexM0 programs to the intermediate language and generate a certificate. This certificate is a HOL4 proof demonstrating correctness of the translation. As demonstrating analysis, we implement a proof-producing weakest precondition generator, which can be used to verify that a given loop-free program fragment satisfies a contract. Finally, we use an AES encryption implementation to benchmark our platform.
Permutation Decoding of Polar Codes
Venue: arXiv cs.IT 2019
Year: 2019

A new permutation decoding approach for polar codes is presented. The complexity of the algorithm is similar to that of a successive cancellation list (SCL) decoder, while it can be implemented with the latency of a successive cancellation decoder. As opposed to the SCL algorithm, the sorting operation is not used in the proposed method. It is shown that the error correction performance of the algorithm is similar to that of the SCL decoder for polar codes. Moreover, a new construction aiming to improve the error correction performance of polar codes under the proposed algorithm is presented.
Fleet Prognosis with Physics-informed Recurrent Neural Networks
Venue: arXiv cs.CE 2019
Year: 2019

Services and warranties of large fleets of engineering assets is a very profitable business. The success of companies in that area is often related to predictive maintenance driven by advanced analytics. Therefore, accurate modeling, as a way to understand how the complex interactions between operating conditions and component capability define useful life, is key for services profitability. Unfortunately, building prognosis models for large fleets is a daunting task as factors such as duty cycle variation, harsh environments, inadequate maintenance, and problems with mass production can lead to large discrepancies between designed and observed useful lives. This paper introduces a novel physics-informed neural network approach to prognosis by extending recurrent neural networks to cumulative damage models. We propose a new recurrent neural network cell designed to merge physics-informed and data-driven layers. With that, engineers and scientists have the chance to use physics-informed layers to model parts that are well understood (e.g., fatigue crack growth) and use data-driven layers to model parts that are poorly characterized (e.g., internal loads). A simple numerical experiment is used to present the main features of the proposed physics-informed recurrent neural network for damage accumulation. The test problem consist of predicting fatigue crack length for a synthetic fleet of airplanes subject to different mission mixes. The model is trained using full observation inputs (far-field loads) and very limited observation of outputs (crack length at inspection for only a portion of the fleet). The results demonstrate that our proposed hybrid physics-informed recurrent neural network is able to accurately model fatigue crack growth even when the observed distribution of crack length does not match with the (unobservable) fleet distribution.
Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles
Venue: arXiv cs.AI 2019
Year: 2019

This article reviews the applications of Bayesian Networks to Intelligent Autonomous Vehicles (IAV) from the decision making point of view, which represents the final step for fully Autonomous Vehicles (currently under discussion). Until now, when it comes making high level decisions for Autonomous Vehicles (AVs), humans have the last word. Based on the works cited in this article and analysis done here, the modules of a general decision making framework and its variables are inferred. Many efforts have been made in the labs showing Bayesian Networks as a promising computer model for decision making. Further research should go into the direction of testing Bayesian Network models in real situations. In addition to the applications, Bayesian Network fundamentals are introduced as elements to consider when developing IAVs with the potential of making high level judgement calls.
The pH sensitivity of solvated tectomer electronics
Venue: arXiv cs.ET 2019
Year: 2019

Colloidal liquid robotics with embodied intelligence solutions mimicking biologic systems, in response to the future increasingly distributed sensing and the resulting data to be managed, has been proposed as the next cybernetic paradigm. Solutions for data storage and readout in liquido require a physical structure able to change configuration under electrical stimuli. We propose tectomers as a candidate for such an adaptive structure. A tectomer is a oligomer made of few oligoglycine units with a common centre. Tectomers undergo pH dependent assembly in a single layer supramers. Tectomers represent a stable paradigm, in their amorphous or crystalline forms, reversibly influenced by solution pH, whose electronic properties are studied herein.
Computational Design of Lightweight Trusses
Venue: arXiv cs.GR 2019
Year: 2019

Trusses are load-carrying light-weight structures consisting of bars connected at joints ubiquitously applied in a variety of engineering scenarios. Designing optimal trusses that satisfy functional specifications with a minimal amount of material has interested both theoreticians and practitioners for more than a century. In this paper, we introduce two main ideas to improve upon the state of the art. First, we formulate an alternating linear programming problem for geometry optimization. Second, we introduce two sets of complementary topological operations, including a novel subdivision scheme for global topology refinement inspired by Michell's famed theoretical study. Based on these two ideas, we build an efficient computational framework for the design of lightweight trusses. \AD{We illustrate our framework with a variety of functional specifications and extensions. We show that our method achieves trusses with smaller volumes and is over two orders of magnitude faster compared with recent state-of-the-art approaches.
Deep Learning for Joint MIMO Detection and Channel Decoding
Venue: arXiv cs.IT 2019
Year: 2019

We propose a deep-learning approach for the joint MIMO detection and channel decoding problem. Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners. However, due to the complex MIMO signal model, the optimal solution to the joint MIMO detection and channel decoding problem (i.e., the maximum likelihood decoding of the transmitted codewords from the received MIMO signals) is computationally infeasible. As a practical measure, the current model-based MIMO receivers all use suboptimal MIMO decoding methods with affordable computational complexities. This work applies the latest advances in deep learning for the design of MIMO receivers. In particular, we leverage deep neural networks (DNN) with supervised training to solve the joint MIMO detection and channel decoding problem. We show that DNN can be trained to give much better decoding performance than conventional MIMO receivers do. Our simulations show that a DNN implementation consisting of seven hidden layers can outperform conventional model-based linear or iterative receivers. This performance improvement points to a new direction for future MIMO receiver design.
Image Enhancement Network Trained by Using HDR images
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, a novel image enhancement network is proposed, where HDR images are used for generating training data for our network. Most of conventional image enhancement methods, including Retinex based methods, do not take into account restoring lost pixel values caused by clipping and quantizing. In addition, recently proposed CNN based methods still have a limited scope of application or a limited performance, due to network architectures. In contrast, the proposed method have a higher performance and a simpler network architecture than existing CNN based methods. Moreover, the proposed method enables us to restore lost pixel values. Experimental results show that the proposed method can provides higher-quality images than conventional image enhancement methods including a CNN based method, in terms of TMQI and NIQE.
Multilevel Coupled Model Transformations for Precise and Reusable Definition of Model Behaviour
Venue: arXiv cs.SE 2019
Year: 2019

The use of Domain-Specific Languages (DSLs) is a promising field for the development of tools tailored to specific problem spaces, effectively diminishing the complexity of hand-made software. With the goal of making models as precise, simple and reusable as possible, we augment DSLs with concepts from multilevel modelling, where the number of abstraction levels are not limited. This is particularly useful for DSL definitions with behaviour, whose concepts inherently belong to different levels of abstraction. Here, models can represent the state of the modelled system and evolve using model transformations. These transformations can benefit from a multilevel setting, becoming a precise and reusable definition of the semantics for behavioural modelling languages. We present in this paper the concept of Multilevel Coupled Model Transformations, together with examples, formal definitions and tools to assess their conceptual soundness and practical value.
SAFE: Scale Aware Feature Encoder for Scene Text Recognition
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we address the problem of having characters with different scales in scene text recognition. We propose a novel scale aware feature encoder (SAFE) that is designed specifically for encoding characters with different scales. SAFE is composed of a multi-scale convolutional encoder and a scale attention network. The multi-scale convolutional encoder targets at extracting character features under multiple scales, and the scale attention network is responsible for selecting features from the most relevant scale(s). SAFE has two main advantages over the traditional single-CNN encoder used in current state-of-the-art text recognizers. First, it explicitly tackles the scale problem by extracting scale-invariant features from the characters. This allows the recognizer to put more effort in handling other challenges in scene text recognition, like those caused by view distortion and poor image quality. Second, it can transfer the learning of feature encoding across different character scales. This is particularly important when the training set has a very unbalanced distribution of character scales, as training with such a dataset will make the encoder biased towards extracting features from the predominant scale. To evaluate the effectiveness of SAFE, we design a simple text recognizer named scale-spatial attention network (S-SAN) that employs SAFE as its feature encoder, and carry out experiments on six public benchmarks. Experimental results demonstrate that S-SAN can achieve state-of-the-art (or, in some cases, extremely competitive) performance without any post-processing.