OpenRaiser/Intern-Atlas huggingface dataset:
Transfer Learning for High-Precision Trajectory Tracking Through $\mathcal{L}_1$ Adaptive Feedback and Iterative Learning
Venue: arXiv cs.RO 2018
Year: 2018

Robust and adaptive control strategies are needed when robots or automated systems are introduced to unknown and dynamic environments where they are required to cope with disturbances, unmodeled dynamics, and parametric uncertainties. In this paper, we demonstrate the capabilities of a combined $\mathcal{L}_1$ adaptive control and iterative learning control (ILC) framework to achieve high-precision trajectory tracking in the presence of unknown and changing disturbances. The $\mathcal{L}_1$ adaptive controller makes the system behave close to a reference model; however, it does not guarantee that perfect trajectory tracking is achieved, while ILC improves trajectory tracking performance based on previous iterations. The combined framework in this paper uses $\mathcal{L}_1$ adaptive control as an underlying controller that achieves a robust and repeatable behavior, while the ILC acts as a high-level adaptation scheme that mainly compensates for systematic tracking errors. We illustrate that this framework enables transfer learning between dynamically different systems, where learned experience of one system can be shown to be beneficial for another different system. Experimental results with two different quadrotors show the superior performance of the combined $\mathcal{L}_1$-ILC framework compared with approaches using ILC with an underlying proportional-derivative controller or proportional-integral-derivative controller. Results highlight that our $\mathcal{L}_1$-ILC framework can achieve high-precision trajectory tracking when unknown and changing disturbances are present and can achieve transfer of learned experience between dynamically different systems. Moreover, our approach is able to achieve precise trajectory tracking in the first attempt when the initial input is generated based on the reference model of the adaptive controller.
Numerical Simulations of Polymer Flooding Process in Porous Media on Distributed-memory Parallel Computers
Venue: arXiv cs.CE 2018
Year: 2018

Polymer flooding is a mature enhanced oil recovery technique that has been successfully applied in many field projects. By injecting polymer into a reservoir, the viscosity of water is increased, and the efficiency of water flooding is improved. As a result, more oil can be recovered. This paper presents numerical simulations of a polymer flooding process using parallel computers, where the numerical modeling of polymer retention, inaccessible pore volumes, a permeability reduction and polymer absorption are considered. Darcy's law is employed to model the behavoir of a fluid in porous media, and the upstream finite difference (volume) method is applied to discretize the mass conservation equations. Numerical methods, including discretization schemes, linear solver methods, nonlinearization methods and parallel techniques are introduced. Numerical experiments show that, on one hand, computed results match those from the commercial simulator, Schlumberger-Eclipse, which is widely applied by the petroleum industry, and, on the other hand, our simulator has excellent scalability, which is demonstrated by field applications with up to 27 million grid blocks using up to 2048 CPU cores.
Gamma Spaces and Information
Venue: arXiv cs.IT 2018
Year: 2018

We investigate the role of Segal's Gamma-spaces in the context of classical and quantum information, based on categories of finite probabilities with stochastic maps and density matrices with quantum channels. The information loss functional extends to the setting of probabilistic Gamma-spaces considered here. The Segal construction of connective spectra from Gamma-spaces can be used in this setting to obtain spectra associated to certain categories of gapped systems.
LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Recent work has shown that Field-Programmable Gate Arrays (FPGAs) play an important role in the acceleration of Machine Learning applications. Initial specification of machine learning applications are often done using a high-level Python-oriented framework such as Tensorflow, followed by a manual translation to either C or RTL for synthesis using vendor tools. This manual translation step is time-consuming and requires expertise that limit the applicability of FPGAs in this important domain. In this paper, we present an open-source tool-flow that maps numerical computation models written in Tensorflow to synthesizable hardware. Unlike other tools, which are often constrained by a small number of inflexible templates, our flow uses Google's XLA compiler which emits LLVM code directly from a Tensorflow specification. This LLVM code can then be used with a high-level synthesis tool to automatically generate hardware. We show that our flow allows users to generate Deep Neural Networks with very few lines of Python code.
Real-Time Shape Tracking of Facial Landmarks
Venue: arXiv cs.CV 2018
Year: 2018

Detection of facial landmarks and accurate tracking of their shape are essential in real-time virtual makeup applications, where users can see the makeups effect by moving their face in different directions. Typical face tracking techniques detect diverse facial landmarks and track them using a point tracker such as the Kanade-Lucas-Tomasi (KLT) point tracker. Typically, 5 or 64 points are used for tracking a face. Even though these points are sufficient to track the approximate locations of facial landmarks, they are not sufficient to track the exact shape of facial landmarks. In this paper, we propose a method that can track the exact shape of facial landmarks in real-time by combining a deep learning technique and a point tracker. We detect facial landmarks accurately using SegNet, which performs semantic segmentation based on deep learning. Edge points of detected landmarks are tracked using the KLT point tracker. In spite of its popularity, the KLT point tracker suffers from the point loss problem. We solve this problem by executing SegNet periodically to calculate the shape of facial landmarks. That is, by combining the two techniques, we can avoid the computational overhead of SegNet for real-time shape tracking and the point loss problem of the KLT point tracker. We performed several experiments to evaluate the performance of our method and report some of the results herein.
Recurrent Stacking of Layers for Compact Neural Machine Translation Models
Venue: arXiv cs.CL 2018
Year: 2018

In neural machine translation (NMT), the most common practice is to stack a number of recurrent or feed-forward layers in the encoder and the decoder. As a result, the addition of each new layer improves the translation quality significantly. However, this also leads to a significant increase in the number of parameters. In this paper, we propose to share parameters across all the layers thereby leading to a recurrently stacked NMT model. We empirically show that the translation quality of a model that recurrently stacks a single layer 6 times is comparable to the translation quality of a model that stacks 6 separate layers. We also show that using pseudo-parallel corpora by back-translation leads to further significant improvements in translation quality.
SAT encodings for sorting networks, single-exception sorting networks and $ε-$halvers
Venue: arXiv cs.DS 2018
Year: 2018

Sorting networks are oblivious sorting algorithms with many practical applications and rich theoretical properties. Propositional encodings of sorting networks are a key tool for proving concrete bounds on the minimum number of comparators or depth (number of parallel steps) of sorting networks. In this paper, we present new SAT encodings that reduce the number of variables and clauses of the sorting constraint of optimality problems. Moreover, the proposed SAT encodings can be applied to a broader class of problems, such as the search of optimal single-exception sorting networks and $ε-$halvers. We obtain optimality results for single-exception sorting networks on $n \le 10$ inputs.
3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space
Venue: arXiv cs.CV 2018
Year: 2018

Tremendous amounts of expensive annotated data are a vital ingredient for state-of-the-art 3d hand pose estimation. Therefore, synthetic data has been popularized as annotations are automatically available. However, models trained only with synthetic samples do not generalize to real data, mainly due to the gap between the distribution of synthetic and real data. In this paper, we propose a novel method that seeks to predict the 3d position of the hand using both synthetic and partially-labeled real data. Accordingly, we form a shared latent space between three modalities: synthetic depth image, real depth image, and pose. We demonstrate that by carefully learning the shared latent space, we can find a regression model that is able to generalize to real data. As such, we show that our method produces accurate predictions in both semi-supervised and unsupervised settings. Additionally, the proposed model is capable of generating novel, meaningful, and consistent samples from all of the three domains. We evaluate our method qualitatively and quantitively on two highly competitive benchmarks (i.e., NYU and ICVL) and demonstrate its superiority over the state-of-the-art methods. The source code will be made available at https://github.com/masabdi/LSPS.
Stochastic Stability in Schelling's Segregation Model with Markovian Asynchronous Update
Venue: arXiv cs.GT 2018
Year: 2018

We investigate the dependence of steady-state properties of Schelling's segregation model on the agents' activation order. Our basic formalism is the Pollicott-Weiss version of Schelling's segregation model. Our main result modifies this baseline scenario by incorporating contagion in the decision to move: (pairs of) agents are connected by a second, agent influence network. Pair activation is specified by a random walk on this network. The considered schedulers choose the next pair nonadaptively. We can complement this result by an example of adaptive scheduler (even one that is quite fair) that is able to preclude maximal segregation. Thus scheduler nonadaptiveness seems to be required for the validity of the original result under arbitrary asynchronous scheduling. The analysis (and our result) are part of an adversarial scheduling approach we are advocating to evolutionary games and social simulations.
Timing Driven C-Slow Retiming on RTL for MultiCores on FPGAs
Venue: arXiv cs.AR 2018
Year: 2018

In this paper C-Slow Retiming (CSR) on RTL is discussed. CSR multiplies the functionality of cores by adding the same number of registers into each path. The technique is ideal for FPGAs with their already existing registers. Previously publications are limited to adding registers on netlist level, which generates a lot of system verification problems and which is assumed to be the major drawback to use this technology in the modern multicore times. The paper shows how CSR can efficiently be done with timing driven automatic RTL modification. The methodology provided with this paper can be used as guidance for using CSR in high level synthesis (HLS). The paper shows the results of a CSR-ed complex RISC core on RTL implemented on FPGAs.
Syllabification by Phone Categorization
Venue: arXiv cs.CL 2018
Year: 2018

Syllables play an important role in speech synthesis, speech recognition, and spoken document retrieval. A novel, low cost, and language agnostic approach to dividing words into their corresponding syllables is presented. A hybrid genetic algorithm constructs a categorization of phones optimized for syllabification. This categorization is used on top of a hidden Markov model sequence classifier to find syllable boundaries. The technique shows promising preliminary results when trained and tested on English words.
A Survey on Expert Recommendation in Community Question Answering
Venue: arXiv cs.SI 2018
Year: 2018

Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time, on the other hand, an answerer might face numerous new questions without being able to identify their questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.
Maximizing Ergodic Throughput in Wireless Powered Communication Networks
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers a single-antenna wirelesspowered communication network (WPCN) over a flat-fading channel. We show that, by using our probabilistic harvestand-transmit (PHAT) strategy, which requires the knowledge of instantaneous full channel state information (CSI) and fading probability distribution, the ergodic throughput of this system may be greatly increased relative to that achieved by the harvestthen-transmit (HTT) protocol. To do so, instead of dividing every frame to the uplink (UL) and downlink (DL), the channel is allocated to the UL wireless information transmission (WIT) and DL wireless power transfer (WPT) based on the estimated channel power gain. In other words, based on the fading probability distribution, we will derive some thresholds that determine the association of a frame to the DL WPT or UL WIT. More specifically, if the channel gain falls below or goes over these thresholds, the channel will be allocated to WPT or WIT. Simulation results verify the performance of our proposed scheme.
Briefly on Bottom-up
Venue: arXiv cs.FL 2018
Year: 2018

These short notes are meant as a quick reference for the construction of SLR(1), of LR(1), and of LALR(1) parsing tables.
Semantic Search by Latent Ontological Features
Venue: arXiv cs.IR 2018
Year: 2018

Both named entities and keywords are important in defining the content of a text in which they occur. In particular, people often use named entities in information search. However, named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. We propose ontology-based extensions of the traditional Vector Space Model that explore different combinations of those latent ontological features with keywords for text retrieval. Our experiments on benchmark datasets show better search quality of the proposed models as compared to the purely keyword-based model, and their advantages for both text retrieval and representation of documents and queries.
Aligning Technical Debt Prioritization with Business Objectives: A Multiple-Case Study
Venue: arXiv cs.SE 2018
Year: 2018

Technical debt (TD) is a metaphor to describe the trade-off between short-term workarounds and long-term goals in software development. Despite being widely used to explain technical issues in business terms, industry and academia still lack a proper way to manage technical debt while explicitly considering business priorities. In this paper, we report on a multiple-case study of how two big software development companies handle technical debt items, and we show how taking the business perspective into account can improve the decision making for the prioritization of technical debt. We also propose a first step toward an approach that uses business process management (BPM) to manage technical debt. We interviewed a set of IT business stakeholders, and we collected and analyzed different sets of technical debt items, comparing how these items would be prioritized using a purely technical versus a business-oriented approach. We found that the use of business process management to support technical debt management makes the technical debt prioritization decision process more aligned with business expectations. We also found evidence that the business process management approach can help technical debt management achieve business objectives.
Improved Person Re-Identification Based on Saliency and Semantic Parsing with Deep Neural Network Models
Venue: arXiv cs.CV 2018
Year: 2018

Given a video or an image of a person acquired from a camera, person re-identification is the process of retrieving all instances of the same person from videos or images taken from a different camera with non-overlapping view. This task has applications in various fields, such as surveillance, forensics, robotics, multimedia. In this paper, we present a novel framework, named Saliency-Semantic Parsing Re-Identification (SSP-ReID), for taking advantage of the capabilities of both clues: saliency and semantic parsing maps, to guide a backbone convolutional neural network (CNN) to learn complementary representations that improves the results over the original backbones. The insight of fusing multiple clues is based on specific scenarios in which one response is better than another, thus favoring the combination of them to increase performance. Due to its definition, our framework can be easily applied to a wide variety of networks and, in contrast to other competitive methods, our training process follows simple and standard protocols. We present extensive evaluation of our approach through five backbones and three benchmarks. Experimental results demonstrate the effectiveness of our person re-identification framework. In addition, we combine our framework with re-ranking techniques to achieve state-of-the-art results on three benchmarks.
CLEF: Limiting the Damage Caused by Large Flows in the Internet Core (Technical Report)
Venue: arXiv cs.NI 2018
Year: 2018

The detection of network flows that send excessive amounts of traffic is of increasing importance to enforce QoS and to counter DDoS attacks. Large-flow detection has been previously explored, but the proposed approaches can be used on high-capacity core routers only at the cost of significantly reduced accuracy, due to their otherwise too high memory and processing overhead. We propose CLEF, a new large-flow detection scheme with low memory requirements, which maintains high accuracy under the strict conditions of high-capacity core routers. We compare our scheme with previous proposals through extensive theoretical analysis, and with an evaluation based on worst-case-scenario attack traffic. We show that CLEF outperforms previously proposed systems in settings with limited memory.
LineNet: a Zoomable CNN for Crowdsourced High Definition Maps Modeling in Urban Environments
Venue: arXiv cs.CV 2018
Year: 2018

High Definition (HD) maps play an important role in modern traffic scenes. However, the development of HD maps coverage grows slowly because of the cost limitation. To efficiently model HD maps, we proposed a convolutional neural network with a novel prediction layer and a zoom module, called LineNet. It is designed for state-of-the-art lane detection in an unordered crowdsourced image dataset. And we introduced TTLane, a dataset for efficient lane detection in urban road modeling applications. Combining LineNet and TTLane, we proposed a pipeline to model HD maps with crowdsourced data for the first time. And the maps can be constructed precisely even with inaccurate crowdsourced data.
QoS management mechanisms for Enhanced Living Environments in IoT
Venue: arXiv cs.DC 2018
Year: 2018

The Internet of Things (IoT) paradigm is expected to bring ubiquitous intelligence through new applications in order to enhance living and other environments. Several research and standardization studies are now focused on the Middleware level of the underlying communication system. For this level, several challenges need to be considered, among them the Quality of Service (QoS) issue. The Autonomic Computing paradigm is now recognized as a promising approach to help communication and other systems to self-adapt when the context is changing. With the aim to promote the vision of an autonomic Middleware-level QoS management for IoT-based systems, this paper proposes a set of QoS-oriented mechanisms that can be dynamically executed at the Middleware level to correct QoS degradation. The benefits of the proposed mechanisms are also illustrated for a concrete case of Enhanced Living Environment.
Fast Witness Counting
Venue: arXiv cs.CC 2018
Year: 2018

We study the witness-counting problem: given a set of vectors $V$ in the $d$-dimensional vector space over $\mathbb{F}_2$, a target vector $t$, and an integer $k$, count all ways to sum-up exactly $k$ different vectors from $V$ to reach $t$. The problem is well-known in coding theory and received considerable attention in complexity theory. Recently, it appeared in the context of hardware monitoring. Our contribution is an algorithm for witness counting that is optimal in the sense of fine-grained complexity. It runs in time $\mathcal{O}^*(2^d)$ with only a logarithmic dependence on $m=|V|$. The algorithm makes use of the Walsh-Hadamard transform to compute convolutions over $\mathbb{F}_2^d$. The transform, however, overcounts the solutions. Inspired by the inclusion-exclusion principle, we introduce correction terms. The correction leads to a recurrence that we show how to solve efficiently. The correction terms are obtained from equivalence relations over $\mathbb{F}_2^d$. We complement our upper bound with two lower bounds on the problem. The first relies on $\# ETH$ and prohibits an $2^{o(d)}$-time algorithm. The second bound states the non-existence of a polynomial kernel for the decision version of the problem.
Mileage-responsive Wind Power Smoothing
Venue: arXiv cs.SY 2018
Year: 2018

This paper proposes a novel wind power smoothing control paradigm in context of performance-based regulation service. Conventional methods aim at adjusting wind power output using hard-coded filtering algorithms that can result in visually smoothed power output with unmeasurable impacts on system generation-demand balance. Distinguished from conventional methods, the newly proposed control method smooths wind power output from a power system perspective by using the regulation mileage as a key performance indicator. To simultaneously address the system needs and maximize wind energy harvesting, a mileage-responsive framework is developed to enable wind farms to optimally generate smoothing power. The effectiveness of the proposed method is well demonstrated through case studies, of which the simulation results shows a great potential for practical applications.