OpenRaiser/Intern-Atlas huggingface dataset:
Challenges in Reconstructing Shapes from Euler Characteristic Curves
Venue: arXiv cs.CG 2018
Year: 2018

Shape recognition and classification is a problem with a wide variety of applications. Several recent works have demonstrated that topological descriptors can be used as summaries of shapes and utilized to compute distances. In this abstract, we explore the use of a finite number of Euler Characteristic Curves (ECC) to reconstruct plane graphs. We highlight difficulties that occur when attempting to adopt approaches for reconstruction with persistence diagrams to reconstruction with ECCs. Furthermore, we highlight specific arrangements of vertices that create problems for reconstruction and present several observations about how they affect the ECC-based reconstruction. Finally, we show that plane graphs without degree two vertices can be reconstructed using a finite number of ECCs.
Energy Optimization of Automatic Hybrid Sailboat
Venue: arXiv cs.RO 2018
Year: 2018

Autonomous Surface Vehicles (ASVs) provide an effective way to actualize applications such as environment monitoring, search and rescue, and scientific researches. However, the conventional ASVs depends overly on the stored energy. Hybrid Sailboat, mainly powered by the wind, can solve this problem by using an auxiliary propulsion system. The electric energy cost of Hybrid Sailboat needs to be optimized to achieve the ocean automatic cruise mission. Based on adjusted setting on sails and rudders, this paper seeks the optimal trajectory for autonomic cruising to reduce the energy cost by changing the heading angle of sailing upwind. The experiment results validate the heading angle accounts for energy cost and the trajectory with the best heading angle saves up to 23.7% than other conditions. Furthermore, the energy-time line can be used to predict the energy cost for long-time sailing.
MeshNet: Mesh Neural Network for 3D Shape Representation
Venue: arXiv cs.CV 2018
Year: 2018

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation.
Semi-supervised learning with Bidirectional GANs
Venue: arXiv cs.LG 2018
Year: 2018

In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model (BiGAN) in a semi-supervised manner. The presented method utilizes triplet loss function as an additional component of the objective function used to train discriminative data representation in the latent space of the BiGAN model. This representation can be further used as a seed for generating artificial images, but also as a good feature embedding for classification and image retrieval tasks. We evaluate the quality of the proposed method in the two mentioned challenging tasks using two benchmark datasets: CIFAR10 and SVHN.
Fixed-length Bit-string Representation of Fingerprint by Normalized Local Structures
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a method to represent a fingerprint image by an ordered, fixed-length bit-string providing improved accuracy performance, faster matching time and compressibility. First, we devise a novel minutia-based local structure modeled by a mixture of 2D elliptical Gaussian functions in the pixel space. Each local structure is mapped to the Euclidean space by normalizing the local structure with the number of minutiae that associates to it. This simple yet crucial crux enables fast dissimilarity computation of two local structures with Euclidean distance without distortion. A complementary texture-based local structure to the minutia-based local structure is also introduced whereby both can be compressed via principal component analysis and fused easily in the Euclidean space. The fused local structure is then converted to a K-bit ordered string via a K-means clustering algorithm. This chain of computation with sole use of Euclidean distance is vital for speedy and discriminative bit-string conversion. The accuracy can be further improved by a finger-specific bit-training algorithm in which two criteria are leveraged to select useful bit positions for matching. Experiments are performed on Fingerprint Verification Competition (FVC) databases for comparison with existing techniques to show the superiority of the proposed method.
A randomized gradient-free attack on ReLU networks
Venue: arXiv cs.LG 2018
Year: 2018

It has recently been shown that neural networks but also other classifiers are vulnerable to so called adversarial attacks e.g. in object recognition an almost non-perceivable change of the image changes the decision of the classifier. Relatively fast heuristics have been proposed to produce these adversarial inputs but the problem of finding the optimal adversarial input, that is with the minimal change of the input, is NP-hard. While methods based on mixed-integer optimization which find the optimal adversarial input have been developed, they do not scale to large networks. Currently, the attack scheme proposed by Carlini and Wagner is considered to produce the best adversarial inputs. In this paper we propose a new attack scheme for the class of ReLU networks based on a direct optimization on the resulting linear regions. In our experimental validation we improve in all except one experiment out of 18 over the Carlini-Wagner attack with a relative improvement of up to 9\%. As our approach is based on the geometrical structure of ReLU networks, it is less susceptible to defences targeting their functional properties.
Counting Complexity for Reasoning in Abstract Argumentation
Venue: arXiv cs.AI 2018
Year: 2018

In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics. When asking for projected counts we are interested in counting the number of extensions of a given argumentation framework while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming (DP). Our algorithms run in time double or triple exponential in the treewidth depending on the considered semantics. Finally, we take the exponential time hypothesis (ETH) into account and establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension.
Automatic Liver Segmentation with Adversarial Loss and Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

Automatic segmentation of medical images is among most demanded works in the medical information field since it saves time of the experts in the field and avoids human error factors. In this work, a method based on Conditional Adversarial Networks and Fully Convolutional Networks is proposed for the automatic segmentation of the liver MRIs. The proposed method, without any post-processing, is achieved the second place in the SIU Liver Segmentation Challenge 2018, data of which is provided by Dokuz Eylül University. In this paper, some improvements for the post-processing step are also proposed and it is shown that with these additions, the method outperforms other baseline methods.
Distributed Impedance Control of Latency-Prone Robotic Systems with Series Elastic Actuation
Venue: arXiv cs.RO 2018
Year: 2018

Robotic systems are increasingly relying on distributed feedback controllers to tackle complex and latency-prone sensing and decision problems. These demands come at the cost of a growing computational burden and, as a result, larger controller latencies. To maximize robustness to mechanical disturbances and achieve high control performance, we emphasize the necessity for executing damping feedback in close proximity to the control plant while allocating stiffness feedback in a latency-prone centralized control process. Additionally, series elastic actuators (SEAs) are becoming prevalent in torque-controlled robots during recent years to achieve compliant interactions with environments and humans. However, designing optimal impedance controllers and characterizing impedance performance for SEAs with time delays and filtering are still under-explored problems. The presented study addresses the optimal controller design problem by devising a critically-damped gain design method for a class of SEA cascaded control architectures, which is composed of outer-impedance and inner-torque feedback loops. Via the proposed controller design criterion, we adopt frequency-domain methods to thoroughly analyze the effects of time delays, filtering and load inertia on SEA impedance performance. These results are further validated through the analysis, simulation, and experimental testing on high-performance actuators and on an omnidirectional mobile base.
A Structure-aware Online Learning Algorithm for Markov Decision Processes
Venue: arXiv cs.LG 2018
Year: 2018

To overcome the curse of dimensionality and curse of modeling in Dynamic Programming (DP) methods for solving classical Markov Decision Process (MDP) problems, Reinforcement Learning (RL) algorithms are popular. In this paper, we consider an infinite-horizon average reward MDP problem and prove the optimality of the threshold policy under certain conditions. Traditional RL techniques do not exploit the threshold nature of optimal policy while learning. In this paper, we propose a new RL algorithm which utilizes the known threshold structure of the optimal policy while learning by reducing the feasible policy space. We establish that the proposed algorithm converges to the optimal policy. It provides a significant improvement in convergence speed and computational and storage complexity over traditional RL algorithms. The proposed technique can be applied to a wide variety of optimization problems that include energy efficient data transmission and management of queues. We exhibit the improvement in convergence speed of the proposed algorithm over other RL algorithms through simulations.
Multiple source direction of arrival estimation using subspace pseudointensity vectors
Venue: arXiv cs.SD 2018
Year: 2018

The recently proposed subspace pseudointensity method for direction of arrival estimation is applied in the context of Tasks 1 and 2 of the LOCATA Challenge using the Eigenmike recordings. Specific implementation details are described and results reported for the development dataset, for which the ground truth source directions are available. For both single and multiple source scenarios, the average absolute error angle is about 9 degrees.
Racial categories in machine learning
Venue: arXiv cs.LG 2018
Year: 2018

Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexity challenges the paradigm of considering fairness to be a formal property of supervised learning with respect to protected personal attributes. Racial identity is not simply a personal subjective quality. For people labeled "Black" it is an ascribed political category that has consequences for social differentiation embedded in systemic patterns of social inequality achieved through both social and spatial segregation. In the United States, racial classification can best be understood as a system of inherently unequal status categories that places whites as the most privileged category while signifying the Negro/black category as stigmatized. Social stigma is reinforced through the unequal distribution of societal rewards and goods along racial lines that is reinforced by state, corporate, and civic institutions and practices. This creates a dilemma for society and designers: be blind to racial group disparities and thereby reify racialized social inequality by no longer measuring systemic inequality, or be conscious of racial categories in a way that itself reifies race. We propose a third option. By preceding group fairness interventions with unsupervised learning to dynamically detect patterns of segregation, machine learning systems can mitigate the root cause of social disparities, social segregation and stratification, without further anchoring status categories of disadvantage.
Disease phenotyping using deep learning: A diabetes case study
Venue: arXiv cs.LG 2018
Year: 2018

Characterization of a patient clinical phenotype is central to biomedical informatics. ICD codes, assigned to inpatient encounters by coders, is important for population health and cohort discovery when clinical information is limited. While ICD codes are assigned to patients by professionals trained and certified in coding there is substantial variability in coding. We present a methodology that uses deep learning methods to model coder decision making and that predicts ICD codes. Our approach predicts codes based on demographics, lab results, and medications, as well as codes from previous encounters. We are able to predict existing codes with high accuracy for all three of the test cases we investigated: diabetes, acute renal failure, and chronic kidney disease. We employed a panel of clinicians, in a blinded manner, to assess ground truth and compared the predictions of coders, model and clinicians. When disparities between the model prediction and coder assigned codes were reviewed, our model outperformed coder assigned ICD codes.
Design and Control of A Hybrid Sailboat for Enhanced Tacking Maneuver
Venue: arXiv cs.RO 2018
Year: 2018

Sailing robots provide a low-cost solution to conduct the ocean missions such as marine exploration, pollution detection, and border surveillance, etc. However, compared with other propeller-driven surface vessels, sailboat suffers in complex marine wind field due to its low mobility. Especially in tacking, sailboats are required to head upwind, and need to make a zig-zag path. In this trajectory, a series of turnings, which will cross the challenging no-go zone, place significant challenge as it will reduce speed greatly and consequently result in unsuccessful turning. This paper presents a hybrid sailboat design to solve this issue. Electric propellers and control system are added to a model sailboat. We have further designed the control strategy and tuned the parameters (PWM-time) experimentally. Finally, the system and control can complete the tacking maneuver with average speed approximately 10% higher and enhanced success rate, though the sailboat weight is much heavier.
From C to Interaction Trees: Specifying, Verifying, and Testing a Networked Server
Venue: arXiv cs.PL 2018
Year: 2018

We present the first formal verification of a networked server implemented in C. Interaction trees, a general structure for representing reactive computations, are used to tie together disparate verification and testing tools (Coq, VST, and QuickChick) and to axiomatize the behavior of the operating system on which the server runs (CertiKOS). The main theorem connects a specification of acceptable server behaviors, written in a straightforward "one client at a time" style, with the CompCert semantics of the C program. The variability introduced by low-level buffering of messages and interleaving of multiple TCP connections is captured using network refinement, a variant of observational refinement.
Joint Design of Convolutional Code and CRC under Serial List Viterbi Decoding
Venue: arXiv cs.IT 2018
Year: 2018

This paper studies the joint design of optimal convolutional codes (CCs) and CRC codes when serial list Viterbi algorithm (S-LVA) is employed in order to achieve the target frame error rate (FER). We first analyze the S-LVA performance with respect to SNR and list size, repsectively, and prove the convergence of the expected number of decoding attempts when SNR goes to the extreme. We then propose the coded channel capacity as the criterion to jointly design optimal CC-CRC pair and optimal list size and show that the optimal list size of S-LVA is always the cardinality of all possible CCs. With the maximum list size, we choose the design metric of optimal CC-CRC pair as the SNR gap to random coding union (RCU) bound and the optimal CC-CRC pair is the one that achieves a target SNR gap with the least complexity. Finally, we show that a weaker CC with a strong optimal CRC code could be as powerful as a strong CC with no CRC code.
Distributed Augmented Reality with 3D Lung Dynamics -- A Planning Tool Concept
Venue: arXiv cs.DC 2018
Year: 2018

Augmented Reality (AR) systems add visual information to the world by using advanced display techniques. The advances in miniaturization and reduced costs make some of these systems feasible for applications in a wide set of fields. We present a potential component of the cyber infrastructure for the operating room of the future; a distributed AR based software-hardware system that allows real-time visualization of 3D lung dynamics superimposed directly on the patient's body. Several emergency events (e.g. closed and tension pneumothorax) and surgical procedures related to the lung (e.g. lung transplantation, lung volume reduction surgery, surgical treatment of lung infections, lung cancer surgery) could benefit from the proposed prototype.
Sensors in Distributed Mixed Reality Environments
Venue: arXiv cs.NI 2018
Year: 2018

With the advances in sensors and computer networks an increased number of Mixed Reality (MR) applications require large amounts of information from the real world. Such information is collected through sensors (e.g. position and orientation tracking sensors). These sensors collect data from the physical environment in real-time at different locations and a distributed system connecting them must assure data distribution among collaborative sites at interactive speeds. We propose a new architecture for sensor based interactive distributed environments that falls in-between the atomistic peer-to-peer model and the traditional client-server model. Each node in the system is autonomous and fully manages its resources and connectivity. The dynamic behavior of the nodes is triggered by the human participants that manipulate the sensors attached to the nodes.
FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing
Venue: arXiv cs.DC 2018
Year: 2018

The requirement of supporting both latency sensitive and computing intensive Internet of Things (IoT) applications is consistently boosting the necessity for integrating Edge, Fog and Cloud infrastructure. Although there are a number of real-world frameworks attempt to support such integration, they have many limitations from various perspectives including platform independence, security, resource management and multi-application assistance. To address these limitations, we propose a simplified but effective framework, named FogBus for facilitating end-to-end IoT-Fog(Edge)-Cloud integration. FogBus offers a platform independent interface to IoT applications and computing instances for execution and interaction. It not only assists developers in building applications but also helps users in running multiple applications at a time and service providers to manage their resources. In addition, FogBus applies Blockchain, authentication and encryption techniques to secure operations on sensitive data. Because of its lightweight and cross platform software systems, it is easy to deploy, scalable and cost e_cient. We demonstrate the effectiveness of our framework by creating a computing environment with it that integrates finger pulse oximeter as IoT devices with Smartphone-based gateway and Raspberry Pi-based Fog nodes for Sleep Apnea analysis. We also run several experiments on this computing environment varying FogBus settings. The experimental results show that different FogBus settings can improve latency, energy, network and CPU usage of the computing infrastructure.
Grid R-CNN
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditional regression based methods, the Grid R-CNN captures the spatial information explicitly and enjoys the position sensitive property of fully convolutional architecture. Instead of using only two independent points, we design a multi-point supervision formulation to encode more clues in order to reduce the impact of inaccurate prediction of specific points. To take the full advantage of the correlation of points in a grid, we propose a two-stage information fusion strategy to fuse feature maps of neighbor grid points. The grid guided localization approach is easy to be extended to different state-of-the-art detection frameworks. Grid R-CNN leads to high quality object localization, and experiments demonstrate that it achieves a 4.1% AP gain at IoU=0.8 and a 10.0% AP gain at IoU=0.9 on COCO benchmark compared to Faster R-CNN with Res50 backbone and FPN architecture.
"Only the Initiates Will Have the Secrets Revealed": Computational Chemists and the Openness of Scientific Software
Venue: arXiv cs.CY 2018
Year: 2018

Computational chemistry is a scientific field within which the computer is a pivotal element. This scientific community emerged in the 1980s and was involved with two major industries: the computer manufacturers and the pharmaceutical industry, the latter becoming a potential market for the former through molecular modeling software packages. We aim to address the difficult relationships between scientific modeling methods and the software implementing these methods throughout the 1990s. Developing, using, licensing, and distributing software leads to multiple tensions among the actors in intertwined academic and industrial contexts. The Computational Chemistry mailing List (CCL), created in 1991, constitutes a valuable corpus for revealing the tensions associated with software within the community. We analyze in detail two flame wars that exemplify these tensions. We conclude that models and software must be addressed together. Interrelations between both imply that openness in computational science is complex.