OpenRaiser/Intern-Atlas huggingface dataset:
Deformable Stacked Structure for Named Entity Recognition
Venue: arXiv cs.CL 2018
Year: 2018

Neural architecture for named entity recognition has achieved great success in the field of natural language processing. Currently, the dominating architecture consists of a bi-directional recurrent neural network (RNN) as the encoder and a conditional random field (CRF) as the decoder. In this paper, we propose a deformable stacked structure for named entity recognition, in which the connections between two adjacent layers are dynamically established. We evaluate the deformable stacked structure by adapting it to different layers. Our model achieves the state-of-the-art performances on the OntoNotes dataset.
Modern Convex Optimization to Medical Image Analysis
Venue: arXiv cs.CV 2018
Year: 2018

Recently, diagnosis, therapy and monitoring of human diseases involve a variety of imaging modalities, such as magnetic resonance imaging(MRI), computed tomography(CT), Ultrasound(US) and Positron-emission tomography(PET) as well as a variety of modern optical techniques. Over the past two decade, it has been recognized that advanced image processing techniques provide valuable information to physicians for diagnosis, image guided therapy and surgery, and monitoring of the treated organ to the therapy. Many researchers and companies have invested significant efforts in the developments of advanced medical image analysis methods; especially in the two core studies of medical image segmentation and registration, segmentations of organs and lesions are used to quantify volumes and shapes used in diagnosis and monitoring treatment; registration of multimodality images of organs improves detection, diagnosis and staging of diseases as well as image-guided surgery and therapy, registration of images obtained from the same modality are used to monitor progression of therapy. These challenging clinical-motivated applications introduce novel and sophisticated mathematical problems which stimulate developments of advanced optimization and computing methods, especially convex optimization attaining optimum in a global sense, hence, bring an enormous spread of research topics for recent computational medical image analysis. Particularly, distinct from the usual image processing, most medical images have a big volume of acquired data, often in 3D or 4D (3D + t) along with great noises or incomplete image information, and form the challenging large-scale optimization problems; how to process such poor 'big data' of medical images efficiently and solve the corresponding optimization problems robustly are the key factors of modern medical image analysis.
Almost optimal algorithms for diameter-optimally augmenting trees
Venue: arXiv cs.DS 2018
Year: 2018

We consider the problem of augmenting an $n$-vertex tree with one shortcut in order to minimize the diameter of the resulting graph. The tree is embedded in an unknown space and we have access to an oracle that, when queried on a pair of vertices $u$ and $v$, reports the weight of the shortcut $(u,v)$ in constant time. Previously, the problem was solved in $O(n^2 \log^3 n)$ time for general weights [Oh and Ahn, ISAAC 2016], in $O(n^2 \log n)$ time for trees embedded in a metric space [Große et al., {\tt arXiv:1607.05547}], and in $O(n \log n)$ time for paths embedded in a metric space [Wang, WADS 2017]. Furthermore, a $(1+\varepsilon)$-approximation algorithm running in $O(n+1/\varepsilon^{3})$ has been designed for paths embedded in $\mathbb{R}^d$, for constant values of $d$ [Große et al., ICALP 2015]. The contribution of this paper is twofold: we address the problem for trees (not only paths) and we also improve upon all known results. More precisely, we design a {\em time-optimal} $O(n^2)$ time algorithm for general weights. Moreover, for trees embedded in a metric space, we design (i) an exact $O(n \log n)$ time algorithm and (ii) a $(1+\varepsilon)$-approximation algorithm that runs in $O\big(n+ \varepsilon^{-1}\log \varepsilon^{-1}\big)$ time.
Constrained Exploration and Recovery from Experience Shaping
Venue: arXiv cs.LG 2018
Year: 2018

We consider the problem of reinforcement learning under safety requirements, in which an agent is trained to complete a given task, typically formalized as the maximization of a reward signal over time, while concurrently avoiding undesirable actions or states, associated to lower rewards, or penalties. The construction and balancing of different reward components can be difficult in the presence of multiple objectives, yet is crucial for producing a satisfying policy. For example, in reaching a target while avoiding obstacles, low collision penalties can lead to reckless movements while high penalties can discourage exploration. To circumvent this limitation, we examine the effect of past actions in terms of safety to estimate which are acceptable or should be avoided in the future. We then actively reshape the action space of the agent during reinforcement learning, so that reward-driven exploration is constrained within safety limits. We propose an algorithm enabling the learning of such safety constraints in parallel with reinforcement learning and demonstrate its effectiveness in terms of both task completion and training time.
Lexical Bias In Essay Level Prediction
Venue: arXiv cs.CL 2018
Year: 2018

Automatically predicting the level of non-native English speakers given their written essays is an interesting machine learning problem. In this work I present the system "balikasg" that achieved the state-of-the-art performance in the CAp 2018 data science challenge among 14 systems. I detail the feature extraction, feature engineering and model selection steps and I evaluate how these decisions impact the system's performance. The paper concludes with remarks for future work.
SDN-Based Resource Management for Autonomous Vehicular Networks: A Multi-Access Edge Computing Approach
Venue: arXiv cs.NI 2018
Year: 2018

Enabling high-definition (HD)-map-assisted cooperative driving among autonomous vehicles (AVs) to improve the navigation safety faces technical challenges due to increased communication traffic volume for data dissemination and increased number of computing/storing tasks on AVs. In this article, a new architecture that combines multi-access edge computing (MEC) and software-defined networking (SDN) is proposed for flexible resource management and enhanced resource utilization. With MEC, the interworking of multiple wireless access technologies can be realized to exploit the diversity gain over a wide range of radio spectrum, and at the same time, computing/storing tasks of an AV are collaboratively processed by servers and other AVs. Moreover, by enabling SDN and network function virtualization (NFV) control modules at each cloud-computing and MEC server, an efficient resource allocation framework is proposed to enhance global resource sharing among different network infrastructures. A case study is presented to demonstrate the effectiveness of the proposed resource allocation framework.
Optimality of Linear Sketching under Modular Updates
Venue: arXiv cs.CC 2018
Year: 2018

We study the relation between streaming algorithms and linear sketching algorithms, in the context of binary updates. We show that for inputs in $n$ dimensions, the existence of efficient streaming algorithms which can process $Ω(n^2)$ updates implies efficient linear sketching algorithms with comparable cost. This improves upon the previous work of Li, Nguyen and Woodruff [LNW14] and Ai, Hu, Li and Woodruff [AHLW16] which required a triple-exponential number of updates to achieve a similar result for updates over integers. We extend our results to updates modulo $p$ for integers $p \ge 2$, and to approximation instead of exact computation.
Implicit Maximum Likelihood Estimation
Venue: arXiv cs.LG 2018
Year: 2018

Implicit probabilistic models are models defined naturally in terms of a sampling procedure and often induces a likelihood function that cannot be expressed explicitly. We develop a simple method for estimating parameters in implicit models that does not require knowledge of the form of the likelihood function or any derived quantities, but can be shown to be equivalent to maximizing likelihood under some conditions. Our result holds in the non-asymptotic parametric setting, where both the capacity of the model and the number of data examples are finite. We also demonstrate encouraging experimental results.
Text Summarization as Tree Transduction by Top-Down TreeLSTM
Venue: arXiv cs.IR 2018
Year: 2018

Extractive compression is a challenging natural language processing problem. This work contributes by formulating neural extractive compression as a parse tree transduction problem, rather than a sequence transduction task. Motivated by this, we introduce a deep neural model for learning structure-to-substructure tree transductions by extending the standard Long Short-Term Memory, considering the parent-child relationships in the structural recursion. The proposed model can achieve state of the art performance on sentence compression benchmarks, both in terms of accuracy and compression rate.
BubbleTouch: A Quasi-Static Tactile Skin Simulator
Venue: arXiv cs.RO 2018
Year: 2018

We present BubbleTouch, an open source quasi-static simulator for robotic tactile skins. BubbleTouch can be used to simulate contact with a robot's tactile skin patches as it interacts with humans and objects. The simulator creates detailed traces of contact forces that can be used in experiments in tactile contact activities. We summarize the design of BubbleTouch and highlight our recent work that uses BubbleTouch for experiments with tactile object recognition.
Quantitative bisimulations using coreflections and open morphisms
Venue: arXiv cs.LO 2018
Year: 2018

We investigate a canonical way of defining bisimilarity of systems when their semantics is given by a coreflection, typically in a category of transition systems. We use the fact, from Joyal et al., that coreflections preserve open morphisms situations in the sense that a coreflection induces a path subcategory in the category of systems in such a way that open bisimilarity with respect to the induced path category coincides with usual bisimilarity of their semantics. We prove that this method is particularly well-suited for systems with quantitative information: we canonically recover the path category of probabilistic systems from Cheng et al., and of timed systems from Nielsen et al., and, finally, we propose a new canonical path category for hybrid systems.
Tree-Based Optimization: A Meta-Algorithm for Metaheuristic Optimization
Venue: arXiv cs.NE 2018
Year: 2018

Designing search algorithms for finding global optima is one of the most active research fields, recently. These algorithms consist of two main categories, i.e., classic mathematical and metaheuristic algorithms. This article proposes a meta-algorithm, Tree-Based Optimization (TBO), which uses other heuristic optimizers as its sub-algorithms in order to improve the performance of search. The proposed algorithm is based on mathematical tree subject and improves performance and speed of search by iteratively removing parts of the search space having low fitness, in order to minimize and purify the search space. The experimental results on several well-known benchmarks show the outperforming performance of TBO algorithm in finding the global solution. Experiments on high dimensional search spaces show significantly better performance when using the TBO algorithm. The proposed algorithm improves the search algorithms in both accuracy and speed aspects, especially for high dimensional searching such as in VLSI CAD tools for Integrated Circuit (IC) design.
DRIVESHAFT: Improving Perceived Mobile Web Performance
Venue: arXiv cs.NI 2018
Year: 2018

With mobiles overtaking desktops as the primary vehicle of Internet consumption, mobile web performance has become a crucial factor for websites as it directly impacts their revenue. In principle, improving web performance entails squeezing out every millisecond of the webpage delivery, loading, and rendering. However, on a practical note, an illusion of faster websites suffices. This paper presents DriveShaft, a system envisioned to be deployed in Content Delivery Networks, which improves the perceived web performance on mobile devices by reducing the time taken to show visually complete web pages, without requiring any changes in websites, browsers, or any actions from end-user. DriveShaft employs (i) crowdsourcing, (ii) on-the-fly JavaScript injection, (iii) privacy preserving desensitization, and (iv) automatic HTML generation to achieve its goals. Experimental evaluations using 200 representative websites on different networks (Wi-Fi and 4G), different devices (high-end and low-end phones) and different browsers, show a reduction of 5x in the time required to see a visually complete website while giving a perception of 5x-6x faster page loading.
Gradient-Based Low-Light Image Enhancement
Venue: arXiv cs.CV 2018
Year: 2018

A low-light image enhancement is a highly demanded image processing technique, especially for consumer digital cameras and cameras on mobile phones. In this paper, a gradient-based low-light image enhancement algorithm is proposed. The key is to enhance the gradients of dark region, because the gradients are more sensitive for human visual system than absolute values. In addition, we involve the intensity-range constraints for the image integration. By using the intensity-range constraints, we can integrate the output image with enhanced gradients preserving the given gradient information while enforcing the intensity range of the output image within a certain intensity range. Experiments demonstrate that the proposed gradient-based low-light image enhancement can effectively enhance the low-light images.
Towards Automated Let's Play Commentary
Venue: arXiv cs.AI 2018
Year: 2018

We introduce the problem of generating Let's Play-style commentary of gameplay video via machine learning. We propose an analysis of Let's Play commentary and a framework for building such a system. To test this framework we build an initial, naive implementation, which we use to interrogate the assumptions of the framework. We demonstrate promising results towards future Let's Play commentary generation.
Incremental Adversarial Learning for Optimal Path Planning
Venue: arXiv cs.RO 2018
Year: 2018

Path planning plays an essential role in many areas of robotics. Various planning techniques have been presented, either focusing on learning a specific task from demonstrations or retrieving trajectories by optimizing for hand-crafted cost functions which are well defined a priori. In this work, we present an incremental adversarial learning-based framework that allows inferring implicit behaviour, i.e. the natural characteristic of a set of given trajectories. To achieve adversarial learning, a zero-sum game is constructed between a planning algorithm and an adversary - the discriminator. We employ the discriminator within an optimal motion planning algorithm, such that costs can be learned and optimized iteratively, improving the integration of implicit behavior. By combining a cost-based planning approach with trained intrinsic behaviour, this can be be integrated also with other constraints such as obstacles or general cost factors within a single planning framework. We demonstrate the proposed method on a dataset for collision avoidance, as well as for the generation of human-like trajectories from motion capture data. Our results show that incremental adversarial learning is able to generate paths that reflect the natural implicit behaviour of a dataset, with the ability to improve on performance using iterative learning and generation.
Memory Management in Successive-Cancellation based Decoders for Multi-Kernel Polar Codes
Venue: arXiv cs.IT 2018
Year: 2018

Multi-kernel polar codes have recently been proposed to construct polar codes of lengths different from powers of two. Decoder implementations for multi-kernel polar codes need to account for this feature, that becomes critical in memory management. We propose an efficient, generalized memory management framework for implementation of successivecancellation decoding of multi-kernel polar codes. It can be used on many types of hardware architectures and different flavors of SC decoding algorithms. We illustrate the proposed solution for small kernel sizes, and give complexity estimates for various kernel combinations and code lengths.
Network Coding for Critical Infrastructure Networks
Venue: arXiv cs.NI 2018
Year: 2018

The applications in the critical infrastructure systems pose simultaneous resilience and performance requirements to the underlying computer network. To meet such requirements, the networks that use the store-and-forward paradigm poses stringent conditions on the redundancy in the network topology and results in problems that becoming computationally challenging to solve at scale. However, with the advent of programmable data-planes, it is now possible to use linear network coding (NC) at the intermediate network nodes (i.e. hardware and software switches) to meet resilience requirements of the applications. To that end, we propose an architecture that realizes linear NC in programmable networks by decomposing the linear NC functions into the atomic coding primitives. We designed and implemented the primitives using the features offered by the P4 ecosystem. Using an empirical evaluation of an open-source prototype, we show that the theoretical gains promised by linear network coding can be realized with a per-packet processing cost.
Earning Maximization with Quality of Charging Service Guarantee for IoT Devices
Venue: arXiv cs.SY 2018
Year: 2018

Resonant Beam Charging (RBC) is a promising Wireless Power Transfer (WPT) technology to provide long-range, high-power, mobile and safe wireless power for the Internet of Things (IoT) devices. The Point-to-Multipoint (PtMP) RBC system can charge multiple receivers simultaneously similar to WiFi communications. To guarantee the Quality of Charging Service (QoCS) for each receiver and maximize the overall earning in the PtMP RBC service, we specify the Charging Pricing Strategy (CPS) and develop the High Priority Charge (HPC) scheduling algorithm to control the charging order and power allocation. Each receiver is assigned a priority, which is updated dynamically based on its State of Charging (SOC) and specified charging power. The receivers with high priorities are scheduled to be charged in each time slot. We present the pseudo code of the HPC algorithm based on quantifying the receiver's SOC, discharging energy and various relevant parameters. Relying on simulation analysis, we demonstrate that the HPC algorithm can achieve better QoCS and earning than the Round-Robin Charge (RRC) scheduling algorithm. Based on the performance evaluation, we illustrate that the methods to improve the PtMP RBC service are: 1) limiting the receiver number within a reasonable range and 2) prolonging the charging duration as long as possible. In summary, the HPC scheduling algorithm provides a practical strategy to maximize the earning of the PtMP RBC service with each receiver's QoCS guarantee.
Fair Stochastic Interference Orchestration with Cellular Throughput Boosted via Outband Sidelinks
Venue: arXiv cs.NI 2018
Year: 2018

Time-domain Inter-Cell Interference Coordination (ICIC) is recognized as the main driver towards efficient and effective ultra-dense network deployments. Almost Blank Subframe (ABS), as key-example of ICIC, has been recently standardized so as to achieve high spectral efficiency. As we show in this article, adopting ABS implies non-trivial complexity to be effective in multicellular environments with heterogeneous cell coverage and user density. Nonetheless, no fairness determinism is guaranteed by ICIC and ABS in particular. Instead, we analytically show that a compound exploitation of ABS with outband sidelinks used for Device-to-Device (D2D) communications on unlicensed bands not only allows to abate the complexity of operating ABS, but also results in unexpectedly high levels of fairness. Based on the analysis, we formulate a convex optimization problem to stochastically make ABS decisions while providing proportional fairness guarantees. Our results prove that, compared to a legacy system, stochastically orchestration of ABS largely boosts fairness while retaining a notable throughput gain offered by mmWave outband sidelinks used for relay.
Coordinating and Integrating Faceted Classification with Rich Semantic Modeling
Venue: arXiv cs.DL 2018
Year: 2018

Faceted classifications define dimensions for the types of entities included. In effect, the facets provide an "ontological commitment". We compare a faceted thesaurus, the Art and Architecture Thesaurus (AAT), with ontologies derived from the Basic Formal Ontology (BFO2), which is an upper (or formal) ontology widely used to describe entities in biomedicine. We consider how the AAT and BFO2-based ontologies could be coordinated and integrated into a Human Activity and Infrastructure Foundry (HAIF). To extend the AAT to enable this coordination and integration, we describe how a wider range of relationships among its terms could be introduced. Using these extensions, we explore richer modeling of topics from AAT that deal with Technology. Finally, we consider how ontology-based frames and semantic role frames can be integrated to make rich semantic statements about changes in the world.
Pulser: Fast Congestion Response using Explicit Incast Notifications for Datacenter Networks
Venue: arXiv cs.NI 2018
Year: 2018

Datacenter applications frequently cause incast congestion, which degrades both flow completion times of short flows and throughput of long flows. Without isolating incast, existing congestion control schemes (e.g., DCTCP) rely on existing ECN signal to react to general congestion, and they lose performance due to their slow, cautious, and inaccurate reaction to incast. We propose to isolate incast using Explicit Incast Notifications (EIN) that are generated by switches, similar to ECN. Our incast detection is fast and accurate. Further, we present our congestion control scheme, called Pulser, which drastically backs off during incast based on EIN, but restores sending rate once incast ends. Our real experiments and ns-3 simulations show that Pulser outperforms prior schemes, DCTCP and ICTCP, in both flow completion times and throughput.
DSR: Direct Self-rectification for Uncalibrated Dual-lens Cameras
Venue: arXiv cs.CV 2018
Year: 2018

With the developments of dual-lens camera modules,depth information representing the third dimension of thecaptured scenes becomes available for smartphones. It isestimated by stereo matching algorithms, taking as input thetwo views captured by dual-lens cameras at slightly differ-ent viewpoints. Depth-of-field rendering (also be referred toas synthetic defocus or bokeh) is one of the trending depth-based applications. However, to achieve fast depth estima-tion on smartphones, the stereo pairs need to be rectified inthe first place. In this paper, we propose a cost-effective so-lution to perform stereo rectification for dual-lens camerascalled direct self-rectification, short for DSR1. It removesthe need of individual offline calibration for every pair ofdual-lens cameras. In addition, the proposed solution isrobust to the slight movements, e.g., due to collisions, ofthe dual-lens cameras after fabrication. Different with ex-isting self-rectification approaches, our approach computesthe homography in a novel way with zero geometric distor-tions introduced to the master image. It is achieved by di-rectly minimizing the vertical displacements of correspond-ing points between the original master image and the trans-formed slave image. Our method is evaluated on both real-istic and synthetic stereo image pairs, and produces supe-rior results compared to the calibrated rectification or otherself-rectification approaches