OpenRaiser/Intern-Atlas huggingface dataset:
Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection
Venue: arXiv cs.CV 2017
Year: 2017

The fast growing deep learning technologies have become the main solution of many machine learning problems for medical image analysis. Deep convolution neural networks (CNNs), as one of the most important branch of the deep learning family, have been widely investigated for various computer-aided diagnosis tasks including long-term problems and continuously emerging new problems. Image contour detection is a fundamental but challenging task that has been studied for more than four decades. Recently, we have witnessed the significantly improved performance of contour detection thanks to the development of CNNs. Beyond purusing performance in existing natural image benchmarks, contour detection plays a particularly important role in medical image analysis. Segmenting various objects from radiology images or pathology images requires accurate detection of contours. However, some problems, such as discontinuity and shape constraints, are insufficiently studied in CNNs. It is necessary to clarify the challenges to encourage further exploration. The performance of CNN based contour detection relies on the state-of-the-art CNN architectures. Careful investigation of their design principles and motivations is critical and beneficial to contour detection. In this paper, we first review recent development of medical image contour detection and point out the current confronting challenges and problems. We discuss the development of general CNNs and their applications in image contours (or edges) detection. We compare those methods in detail, clarify their strengthens and weaknesses. Then we review their recent applications in medical image analysis and point out limitations, with the goal to light some potential directions in medical image analysis. We expect the paper to cover comprehensive technical ingredients of advanced CNNs to enrich the study in the medical image domain.
Control and Data Flow Execution of Java Programs
Venue: arXiv cs.SE 2017
Year: 2017

Since decade understanding of programs has become a compulsory task for the students as well as for others who are involved in the process of developing software and providing solutions to open problems. In that aspect showing the problem in a pictorial presentation in a best manner is a key advantage to better understand it. We provide model and structure for Java programs to understand the control and data flow analysis of execution. Especially it helps to understand the static analysis of Java programs, which is an uttermost important phase for software maintenance. We provided information and model for visualization of Java programs that may help better understanding of programs for a learning and analysis purpose. The idea provided for building visualization tool is extracting data and control analysis from execution of Java programs. We presented case studies to prove that our idea is most important for better understanding of Java programs which may help towards static analysis, software debugging and software maintenance.
Cyber-Physical War Gaming
Venue: arXiv cs.CR 2017
Year: 2017

This paper presents general strategies for cyber war gaming of Cyber-Physical Systems (CPSs) that are used for cyber security research at the U.S. Army Research Laboratory (ARL). Since Supervisory Control and Data Acquisition (SCADA) and other CPSs are operational systems, it is difficult or impossible to perform security experiments on actual systems. The authors describe how table-top strategy sessions and realistic, live CPS war games are conducted at ARL. They also discuss how the recorded actions of the war game activity can be used to test and validate cyber-defence models, such as game-theoretic security models.
The Price of Uncertainty in Present-Biased Planning
Venue: arXiv cs.GT 2017
Year: 2017

The tendency to overestimate immediate utility is a common cognitive bias. As a result people behave inconsistently over time and fail to reach long-term goals. Behavioral economics tries to help affected individuals by implementing external incentives. However, designing robust incentives is often difficult due to imperfect knowledge of the parameter $β\in (0,1]$ quantifying a person's present bias. Using the graphical model of Kleinberg and Oren, we approach this problem from an algorithmic perspective. Based on the assumption that the only information about $β$ is its membership in some set $B \subset (0,1]$, we distinguish between two models of uncertainty: one in which $β$ is fixed and one in which it varies over time. As our main result we show that the conceptual loss of efficiency incurred by incentives in the form of penalty fees is at most $2$ in the former and $1 + \max B/\min B$ in the latter model. We also give asymptotically matching lower bounds and approximation algorithms.
Chisio: A Compound Graph Editing and Layout Framework
Venue: arXiv cs.HC 2017
Year: 2017

We introduce a new free, open-source compound graph editing and layout framework named Chisio, based on the Eclipse Graph Editing Framework (GEF) and written in Java. Chisio can be used as a finished graph editor with its easy-to-use graphical interface. The framework has an architecture suitable for easy customization of the tool for end-user's specific needs as well. Chisio comes with a variety of graph layout algorithms, most supporting compound structures and non-uniform node dimensions. Furthermore, new algorithms are straightforward to add, making Chisio an ideal test environment for layout algorithm developers.
Non-FPT lower bounds for structural restrictions of decision DNNF
Venue: arXiv cs.AI 2017
Year: 2017

We give a non-FPT lower bound on the size of structured decision DNNF and OBDD with decomposable AND-nodes representing CNF-formulas of bounded incidence treewidth. Both models are known to be of FPT size for CNFs of bounded primal treewidth. To the best of our knowledge this is the first parameterized separation of primal treewidth and incidence treewidth for knowledge compilation models.
Low Cost, Open-Source Testbed to Enable Full-Sized Automated Vehicle Research
Venue: arXiv cs.SY 2017
Year: 2017

An open-source vehicle testbed to enable the exploration of automation technologies for road vehicles is presented. The platform hardware and software, based on the Robot Operating System (ROS), are detailed. Two methods are discussed for enabling the remote control of a vehicle (in this case, an electric 2013 Ford Focus). The first approach used digital filtering of Controller Area Network (CAN) messages. In the case of the test vehicle, this approach allowed for the control of acceleration from a tap-point on the CAN bus and the OBD-II port. The second approach, based on the emulation of the analog output(s) of a vehicle's accelerator pedal, brake pedal, and steering torque sensors, is more generally applicable and, in the test vehicle, allowed for the full control vehicle acceleration, braking, and steering. To demonstrate the utility of the testbed for vehicle automation research, system identification was performed on the test vehicle and speed and steering controllers were designed to allow the vehicle to follow a predetermined path. The resulting system was shown to be differentially flat, and a high level path following algorithm was developed using the differentially flat properties and state feedback. The path following algorithm is experimentally validated on the automation testbed developed in the paper.
Information-Theoretic Attacks in the Smart Grid
Venue: arXiv cs.IT 2017
Year: 2017

Gaussian random attacks that jointly minimize the amount of information obtained by the operator from the grid and the probability of attack detection are presented. The construction of the attack is posed as an optimization problem with a utility function that captures two effects: firstly, minimizing the mutual information between the measurements and the state variables; secondly, minimizing the probability of attack detection via the Kullback-Leibler divergence between the distribution of the measurements with an attack and the distribution of the measurements without an attack. Additionally, a lower bound on the utility function achieved by the attacks constructed with imperfect knowledge of the second order statistics of the state variables is obtained. The performance of the attack construction using the sample covariance matrix of the state variables is numerically evaluated. The above results are tested in the IEEE 30-Bus test system.
Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Applications
Venue: arXiv cs.LG 2017
Year: 2017

Recently, convex formulations of low-rank matrix factorization problems have received considerable attention in machine learning. However, such formulations often require solving for a matrix of the size of the data matrix, making it challenging to apply them to large scale datasets. Moreover, in many applications the data can display structures beyond simply being low-rank, e.g., images and videos present complex spatio-temporal structures that are largely ignored by standard low-rank methods. In this paper we study a matrix factorization technique that is suitable for large datasets and captures additional structure in the factors by using a particular form of regularization that includes well-known regularizers such as total variation and the nuclear norm as particular cases. Although the resulting optimization problem is non-convex, we show that if the size of the factors is large enough, under certain conditions, any local minimizer for the factors yields a global minimizer. A few practical algorithms are also provided to solve the matrix factorization problem, and bounds on the distance from a given approximate solution of the optimization problem to the global optimum are derived. Examples in neural calcium imaging video segmentation and hyperspectral compressed recovery show the advantages of our approach on high-dimensional datasets.
Network Essence: PageRank Completion and Centrality-Conforming Markov Chains
Venue: arXiv cs.SI 2017
Year: 2017

Jiří Matoušek (1963-2015) had many breakthrough contributions in mathematics and algorithm design. His milestone results are not only profound but also elegant. By going beyond the original objects --- such as Euclidean spaces or linear programs --- Jirka found the essence of the challenging mathematical/algorithmic problems as well as beautiful solutions that were natural to him, but were surprising discoveries to the field. In this short exploration article, I will first share with readers my initial encounter with Jirka and discuss one of his fundamental geometric results from the early 1990s. In the age of social and information networks, I will then turn the discussion from geometric structures to network structures, attempting to take a humble step towards the holy grail of network science, that is to understand the network essence that underlies the observed sparse-and-multifaceted network data. I will discuss a simple result which summarizes some basic algebraic properties of personalized PageRank matrices. Unlike the traditional transitive closure of binary relations, the personalized PageRank matrices take "accumulated Markovian closure" of network data. Some of these algebraic properties are known in various contexts. But I hope featuring them together in a broader context will help to illustrate the desirable properties of this Markovian completion of networks, and motivate systematic developments of a network theory for understanding vast and ubiquitous multifaceted network data.
Distributed Bundle Adjustment
Venue: arXiv cs.CV 2017
Year: 2017

Most methods for Bundle Adjustment (BA) in computer vision are either centralized or operate incrementally. This leads to poor scaling and affects the quality of solution as the number of images grows in large scale structure from motion (SfM). Furthermore, they cannot be used in scenarios where image acquisition and processing must be distributed. We address this problem with a new distributed BA algorithm. Our distributed formulation uses alternating direction method of multipliers (ADMM), and, since each processor sees only a small portion of the data, we show that robust formulations improve performance. We analyze convergence of the proposed algorithm, and illustrate numerical performance, accuracy of the parameter estimates, and scalability of the distributed implementation in the context of synthetic 3D datasets with known camera position and orientation ground truth. The results are comparable to an alternate state-of-the-art centralized bundle adjustment algorithm on synthetic and real 3D reconstruction problems. The runtime of our implementation scales linearly with the number of observed points.
Plausible Deniability for Privacy-Preserving Data Synthesis
Venue: arXiv cs.CR 2017
Year: 2017

Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic because of their dependence on the background knowledge of adversaries. On the other hand, rigorous methods such as the exponential mechanism for differential privacy are often computationally impractical to use for releasing high dimensional data or cannot preserve high utility of original data due to their extensive data perturbation. This paper presents a criterion called plausible deniability that provides a formal privacy guarantee, notably for releasing sensitive datasets: an output record can be released only if a certain amount of input records are indistinguishable, up to a privacy parameter. This notion does not depend on the background knowledge of an adversary. Also, it can efficiently be checked by privacy tests. We present mechanisms to generate synthetic datasets with similar statistical properties to the input data and the same format. We study this technique both theoretically and experimentally. A key theoretical result shows that, with proper randomization, the plausible deniability mechanism generates differentially private synthetic data. We demonstrate the efficiency of this generative technique on a large dataset; it is shown to preserve the utility of original data with respect to various statistical analysis and machine learning measures.
Synthesising Wider Field Images from Narrow-Field Retinal Video Acquired Using a Low-Cost Direct Ophthalmoscope (Arclight) Attached to a Smartphone
Venue: arXiv cs.CV 2017
Year: 2017

Access to low cost retinal imaging devices in low and middle income countries is limited, compromising progress in preventing needless blindness. The Arclight is a recently developed low-cost solar powered direct ophthalmoscope which can be attached to the camera of a smartphone to acquire retinal images and video. However, the acquired data is inherently limited by the optics of direct ophthalmoscopy, resulting in a narrow field of view with associated corneal reflections, limiting its usefulness. In this work we describe the first fully automatic method utilizing videos acquired using the Arclight attached to a mobile phone camera to create wider view, higher quality still images comparable with images obtained using much more expensive and bulky dedicated traditional retinal cameras.
Capital flow constrained lot sizing problem with loss of goodwill and loan
Venue: arXiv cs.CE 2017
Year: 2017

We introduce capital flow constraints, loss of good will and loan to the lot sizing problem. Capital flow constraint is different from traditional capacity constraints: when a manufacturer launches production, its present capital should not be less than its present total production cost; otherwise, it must decrease production quantity or suspend production. Unsatisfied demand in one period may cause customer's demand to shrink in the next period considering loss of goodwill. Fixed loan can be adopted in the starting period for production. A mixed integer model for a deterministic single-item problem is constructed. Based on the analysis about the structure of optimal solutions, we approximate it to a traveling salesman problem, and divide it into sub-linear programming problems without integer variables. A forward recursive algorithm with heuristic adjustments is proposed to solve it. When unit variable production costs are equal and goodwill loss rate is zero, the algorithm can obtain optimal solutions. Under other situations, numerical comparisons with CPLEX 12.6.2 show our algorithm can reach optimal in most cases and has computation time advantage for large-size problems. Numerical tests also demonstrate that initial capital availability as well as loan interest rate can substantially affect the manufacturer's optimal lot sizing decisions.
Plain stopping time and conditional complexities revisited
Venue: arXiv cs.CC 2017
Year: 2017

In this paper we analyze the notion of "stopping time complexity", informally defined as the amount of information needed to specify when to stop while reading an infinite sequence. This notion was introduced by Vovk and Pavlovic (2016). It turns out that plain stopping time complexity of a binary string $x$ could be equivalently defined as (a) the minimal plain complexity of a Turing machine that stops after reading $x$ on a one-directional input tape; (b) the minimal plain complexity of an algorithm that enumerates a prefix-free set containing $x$; (c)~the conditional complexity $C(x|x*)$ where $x$ in the condition is understood as a prefix of an infinite binary sequence while the first $x$ is understood as a terminated binary string; (d) as a minimal upper semicomputable function $K$ such that each binary sequence has at most $2^n$ prefixes $z$ such that $K(z)<n$; (e) as $\max C^X(x)$ where $C^X(z)$ is plain Kolmogorov complexity of $z$ relative to oracle $X$ and the maximum is taken over all extensions $X$ of $x$. We also show that some of these equivalent definitions become non-equivalent in the more general setting where the condition $y$ and the object $x$ may differ. We also answer an open question from Chernov, Hutter and~Schmidhuber.
The Convergence of Machine Learning and Communications
Venue: arXiv cs.NI 2017
Year: 2017

The areas of machine learning and communication technology are converging. Today's communications systems generate a huge amount of traffic data, which can help to significantly enhance the design and management of networks and communication components when combined with advanced machine learning methods. Furthermore, recently developed end-to-end training procedures offer new ways to jointly optimize the components of a communication system. Also in many emerging application fields of communication technology, e.g., smart cities or internet of things, machine learning methods are of central importance. This paper gives an overview over the use of machine learning in different areas of communications and discusses two exemplar applications in wireless networking. Furthermore, it identifies promising future research topics and discusses their potential impact.
Automatic Discovery and Geotagging of Objects from Street View Imagery
Venue: arXiv cs.CV 2017
Year: 2017

Many applications such as autonomous navigation, urban planning and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper we propose to automatically detect and compute the GPS coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov Random Field model to perform objects triangulation. The novelty of the resulting pipeline is the combined use of monocular depth estimation and triangulation to enable automatic mapping of complex scenes with multiple visually similar objects of interest. We validate experimentally the effectiveness of our approach on two object classes: traffic lights and telegraph poles. The experiments report high object recall rates and GPS accuracy within 2 meters, which is comparable with the precision of single-frequency GPS receivers.
Generating Sentence Planning Variations for Story Telling
Venue: arXiv cs.CL 2017
Year: 2017

There has been a recent explosion in applications for dialogue interaction ranging from direction-giving and tourist information to interactive story systems. Yet the natural language generation (NLG) component for many of these systems remains largely handcrafted. This limitation greatly restricts the range of applications; it also means that it is impossible to take advantage of recent work in expressive and statistical language generation that can dynamically and automatically produce a large number of variations of given content. We propose that a solution to this problem lies in new methods for developing language generation resources. We describe the ES-Translator, a computational language generator that has previously been applied only to fables, and quantitatively evaluate the domain independence of the EST by applying it to personal narratives from weblogs. We then take advantage of recent work on language generation to create a parameterized sentence planner for story generation that provides aggregation operations, variations in discourse and in point of view. Finally, we present a user evaluation of different personal narrative retellings.
Safe Reinforcement Learning via Shielding
Venue: arXiv cs.LO 2017
Year: 2017

Reinforcement learning algorithms discover policies that maximize reward, but do not necessarily guarantee safety during learning or execution phases. We introduce a new approach to learn optimal policies while enforcing properties expressed in temporal logic. To this end, given the temporal logic specification that is to be obeyed by the learning system, we propose to synthesize a reactive system called a shield. The shield is introduced in the traditional learning process in two alternative ways, depending on the location at which the shield is implemented. In the first one, the shield acts each time the learning agent is about to make a decision and provides a list of safe actions. In the second way, the shield is introduced after the learning agent. The shield monitors the actions from the learner and corrects them only if the chosen action causes a violation of the specification. We discuss which requirements a shield must meet to preserve the convergence guarantees of the learner. Finally, we demonstrate the versatility of our approach on several challenging reinforcement learning scenarios.
Discovering Gender Differences in Facial Emotion Recognition via Implicit Behavioral Cues
Venue: arXiv cs.HC 2017
Year: 2017

We examine the utility of implicit behavioral cues in the form of EEG brain signals and eye movements for gender recognition (GR) and emotion recognition (ER). Specifically, the examined cues are acquired via low-cost, off-the-shelf sensors. We asked 28 viewers (14 female) to recognize emotions from unoccluded (no mask) as well as partially occluded (eye and mouth masked) emotive faces. Obtained experimental results reveal that (a) reliable GR and ER is achievable with EEG and eye features, (b) differential cognitive processing especially for negative emotions is observed for males and females and (c) some of these cognitive differences manifest under partial face occlusion, as typified by the eye and mouth mask conditions.