OpenRaiser/Intern-Atlas huggingface dataset:
Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging
Venue: arXiv cs.CV 2020
Year: 2020

We present a novel methodology to detect imperfect bilateral symmetry in CT of human anatomy. In this paper, the structurally symmetric nature of the pelvic bone is explored and is used to provide interventional image augmentation for treatment of unilateral fractures in patients with traumatic injuries. The mathematical basis of our solution is on the incorporation of attributes and characteristics that satisfy the properties of intrinsic and extrinsic symmetry and are robust to outliers. In the first step, feature points that satisfy intrinsic symmetry are automatically detected in the Möbius space defined on the CT data. These features are then pruned via a two-stage RANSAC to attain correspondences that satisfy also the extrinsic symmetry. Then, a disparity function based on Tukey's biweight robust estimator is introduced and minimized to identify a symmetry plane parametrization that yields maximum contralateral similarity. Finally, a novel regularization term is introduced to enhance similarity between bone density histograms across the partial symmetry plane, relying on the important biological observation that, even if injured, the dislocated bone segments remain within the body. Our extensive evaluations on various cases of common fracture types demonstrate the validity of the novel concepts and the robustness and accuracy of the proposed method.
Approximating Optimal Bidirectional Macro Schemes
Venue: arXiv cs.DS 2020
Year: 2020

Lempel-Ziv is an easy-to-compute member of a wide family of so-called macro schemes; it restricts pointers to go in one direction only. Optimal bidirectional macro schemes are NP-complete to find, but they may provide much better compression on highly repetitive sequences. We consider the problem of approximating optimal bidirectional macro schemes. We describe a simulated annealing algorithm that usually converges quickly. Moreover, in some cases, we obtain bidirectional macro schemes that are provably a 2-approximation of the optimal. We test our algorithm on a number of artificial repetitive texts and verify that it is efficient in practice and outperforms Lempel-Ziv, sometimes by a wide margin.
Order-Preserving Key Compression for In-Memory Search Trees
Venue: arXiv cs.DB 2020
Year: 2020

We present the High-speed Order-Preserving Encoder (HOPE) for in-memory search trees. HOPE is a fast dictionary-based compressor that encodes arbitrary keys while preserving their order. HOPE's approach is to identify common key patterns at a fine granularity and exploit the entropy to achieve high compression rates with a small dictionary. We first develop a theoretical model to reason about order-preserving dictionary designs. We then select six representative compression schemes using this model and implement them in HOPE. These schemes make different trade-offs between compression rate and encoding speed. We evaluate HOPE on five data structures used in databases: SuRF, ART, HOT, B+tree, and Prefix B+tree. Our experiments show that using HOPE allows the search trees to achieve lower query latency (up to 40\% lower) and better memory efficiency (up to 30\% smaller) simultaneously for most string key workloads.
TIME: A Transparent, Interpretable, Model-Adaptive and Explainable Neural Network for Dynamic Physical Processes
Venue: arXiv cs.LG 2020
Year: 2020

Partial Differential Equations are infinite dimensional encoded representations of physical processes. However, imbibing multiple observation data towards a coupled representation presents significant challenges. We present a fully convolutional architecture that captures the invariant structure of the domain to reconstruct the observable system. The proposed architecture is significantly low-weight compared to other networks for such problems. Our intent is to learn coupled dynamic processes interpreted as deviations from true kernels representing isolated processes for model-adaptivity. Experimental analysis shows that our architecture is robust and transparent in capturing process kernels and system anomalies. We also show that high weights representation is not only redundant but also impacts network interpretability. Our design is guided by domain knowledge, with isolated process representations serving as ground truths for verification. These allow us to identify redundant kernels and their manifestations in activation maps to guide better designs that are both interpretable and explainable unlike traditional deep-nets.
Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective
Venue: arXiv cs.IR 2020
Year: 2020

Recommender systems (RSs) now play a very important role in the online lives of people as they serve as personalized filters for users to find relevant items from an array of options. Owing to their effectiveness, RSs have been widely employed in consumer-oriented e-commerce platforms. However, despite their empirical successes, these systems still suffer from two limitations: data noise and data sparsity. In recent years, generative adversarial networks (GANs) have garnered increased interest in many fields, owing to their strong capacity to learn complex real data distributions; their abilities to enhance RSs by tackling the challenges these systems exhibit have also been demonstrated in numerous studies. In general, two lines of research have been conducted, and their common ideas can be summarized as follows: (1) for the data noise issue, adversarial perturbations and adversarial sampling-based training often serve as a solution; (2) for the data sparsity issue, data augmentation--implemented by capturing the distribution of real data under the minimax framework--is the primary coping strategy. To gain a comprehensive understanding of these research efforts, we review the corresponding studies and models, organizing them from a problem-driven perspective. More specifically, we propose a taxonomy of these models, along with their detailed descriptions and advantages. Finally, we elaborate on several open issues and current trends in GAN-based RSs.
Parameterized Algorithms for Generalizations of Directed Feedback Vertex Set
Venue: arXiv cs.DS 2020
Year: 2020

The Directed Feedback Vertex Set (DFVS) problem takes as input a directed graph~$G$ and seeks a smallest vertex set~$S$ that hits all cycles in $G$. This is one of Karp's 21 $\mathsf{NP}$-complete problems. Resolving the parameterized complexity status of DFVS was a long-standing open problem until Chen et al. [STOC 2008, J. ACM 2008] showed its fixed-parameter tractability via a $4^kk! n^{\mathcal{O}(1)}$-time algorithm, where $k = |S|$. Here we show fixed-parameter tractability of two generalizations of DFVS: - Find a smallest vertex set $S$ such that every strong component of $G - S$ has size at most~$s$: we give an algorithm solving this problem in time $4^k(ks+k+s)!\cdot n^{\mathcal{O}(1)}$. This generalizes an algorithm by Xiao [JCSS 2017] for the undirected version of the problem. - Find a smallest vertex set $S$ such that every non-trivial strong component of $G - S$ is 1-out-regular: we give an algorithm solving this problem in time $2^{\mathcal{O}(k^3)}\cdot n^{\mathcal{O}(1)}$. We also solve the corresponding arc versions of these problems by fixed-parameter algorithms.
MEC-enhanced Information Freshness for Safety-critical C-V2X Communications
Venue: arXiv cs.IT 2020
Year: 2020

Information freshness is a status update timeliness indicator of utmost importance to several real-time applications, such as connected and autonomous driving. The Ageof- Information (AoI) metric is widely considered as useful to quantify the information freshness of delivered messages to the involved entities. Recently, the advent of Multi-access Edge Computing (MEC) promises several performance benefits for Vehicular-to-Everything (V2X) communications, emphasizing on the experienced End-to-End (E2E) message delay. In this paper, we argue that, when it comes to safety-critical use cases, such as the one of Vulnerable Road User (VRU), additional metrics can be more insightful to evaluate and address scalability issues in dense urban environments. In particular, the impact of the packet inter-arrival time on the timeliness of VRU messages arriving at nearby vehicles can be directly assessed by exploiting the AoI metric. For that purpose, assuming a MEC-enabled multi-VRU system setting, we model the AoI and, by means of a performance comparison to the state-of-the-art network architecture based on numerical evaluations, we provide evidence of the information freshness and system scalability enhancements offered by MEC infrastructure deployment for different system parameter settings involving a large number of connected entities.
Dual link failure survivability with recovery time constraint: A Parallel cross connection backup route recovery strategy
Venue: arXiv cs.NI 2020
Year: 2020

In this paper, we proposed a fast recovery strategy for a dual link failure in elastic optical network. The elastic optical network is a promising solution to meet the next generation higher bandwidth demand. The survivability of high speed network is very crucial. As the network size increases the probability of the dual link failure and node failure also increases. Here, we proposed a parallel cross connection backup recovery strategy for dual link failure in the network. Proposed strategy shows lower bandwidth blocking probability (BBP), fast connection recovery, and bandwidth provisioning ratio when compared with the existing shared path protection (SPP) and dedicated path protection (DPP) approaches. Simulation is performed on ARPANET and COST239 topology networks.
Optimal Sampling Cost in Wireless Networks with Age of Information Constraints
Venue: arXiv cs.NI 2020
Year: 2020

We consider the problem of minimizing the time average cost of sampling and transmitting status updates by users over a wireless channel subject to average Age of Information constraints (AoI). Errors in the transmission may occur and the scheduling algorithm has to decide if the users sample a new packet or attempt for retransmission of the packet sampled previously. The cost consists of both sampling and transmission costs. The sampling of a new packet after a failure imposes an additional cost in the system. We formulate a stochastic optimization problem with time average cost in the objective under time average AoI constraints. To solve this problem, we apply tools from Lyapunov optimization theory and develop a dynamic algorithm that takes decisions in a slot-by-slot basis. The algorithm decides if a user: a) samples a new packet, b) transmits the old one, c) remains silent. We provide optimality guarantees of the algorithm and study its performance in terms of time average cost and AoI through simulation results.
Some Aspects of a Software Reliability Problem
Venue: arXiv cs.SE 2020
Year: 2020

Obviously, the dynamism of software reliability research has speeded up significantly in the last period, and we can state the fact that its intensity is approaching, and in some cases is ahead of the information systems hardware reliability research intensity. Reliability of software is much more important than its other characteristics, such as runtime, and although the absolute reliability of modern software is apparently unattainable, there is still no generally accepted measure of reliability of computer programs. The article analyzes the reasons for the situation and offers an approach to solving the problem. The article touches upon the issue of general characteristics of information systems software life cycle. Considered software application reliability questions and use of fail-safe ensuring programming. Also presented basic types of so-called virus programs that lead to abnormal functioning of information systems. Much attention is given to presenting some known models used for software debugging and operating. So, this review paper consists of four sections: information systems software process creation, reliability of information systems software, using of fail-safe programs and estimation of software reliability according the results of adjusting and normal operation.
Dimensionality Reduction and Motion Clustering during Activities of Daily Living: 3, 4, and 7 Degree-of-Freedom Arm Movements
Venue: arXiv cs.RO 2020
Year: 2020

The wide variety of motions performed by the human arm during daily tasks makes it desirable to find representative subsets to reduce the dimensionality of these movements for a variety of applications, including the design and control of robotic and prosthetic devices. This paper presents a novel method and the results of an extensive human subjects study to obtain representative arm joint angle trajectories that span naturalistic motions during Activities of Daily Living (ADLs). In particular, we seek to identify sets of useful motion trajectories of the upper limb that are functions of a single variable, allowing, for instance, an entire prosthetic or robotic arm to be controlled with a single input from a user, along with a means to select between motions for different tasks. Data driven approaches are used to obtain clusters as well as representative motion averages for the full-arm 7 degree of freedom (DOF), elbow-wrist 4 DOF, and wrist-only 3 DOF motions. The proposed method makes use of well-known techniques such as dynamic time warping (DTW) to obtain a divergence measure between motion segments, DTW barycenter averaging (DBA) to obtain averages, Ward's distance criterion to build hierarchical trees, batch-DTW to simultaneously align multiple motion data, and functional principal component analysis (fPCA) to evaluate cluster variability. The clusters that emerge associate various recorded motions into primarily hand start and end location for the full-arm system, motion direction for the wrist-only system, and an intermediate between the two qualities for the elbow-wrist system. The proposed clustering methodology is justified by comparing results against alternative approaches.
Modified Bee Colony optimization algorithm for computational parameter identification for pore scale transport in periodic porous media
Venue: arXiv cs.CE 2020
Year: 2020

This paper discusses an optimization method called Modified Bee Colony algorithm (MBC) based on a particular intelligent behavior of honeybee swarms. The algorithm was checked in a few benchmarks like Shekel, Rozenbroke, Himmelblau and Rastrigin functions, then was applied to parameter identification for reactive flow problems in periodic porous media. The simulation results show that the performance and efficiency of MBC algorithm are comparable to the other parameter identification methods and strategies, at the same time it is able to better capture local minima for the considered class of problems. The proposed identification approach is applicable for different geometries (random and periodic) and for a range of process parameters. In this paper the potential of the approach is demonstrated in identifying parameters of Langmuir isotherm for low Peclet and high Damkoler numbers reactive flow in a 2D periodic porous media with circular inclusions. Finite element approximation in space and implicit time discretization are exploited.
Implementation of a Natural User Interface to Command a Drone
Venue: arXiv cs.HC 2020
Year: 2020

In this work, we propose the use of a Natural User Interface (NUI) through body gestures using the open source library OpenPose, looking for a more dynamic and intuitive way to control a drone. For the implementation, we use the Robotic Operative System (ROS) to control and manage the different components of the project. Wrapped inside ROS, OpenPose (OP) processes the video obtained in real-time by a commercial drone, allowing to obtain the user's pose. Finally, the keypoints from OpenPose are obtained and translated, using geometric constraints, to specify high-level commands to the drone. Real-time experiments validate the full strategy.
Modeling the Popularity of Twitter Hashtags with Master Equations
Venue: arXiv cs.SI 2020
Year: 2020

In this work we introduce a model based on master equations to describe the time evolution of the popularity of topics and hashtags on the Twitter social network. Specifically, we model the number of times a certain hashtag appears on the network as a function of time. In our model, the behavior of this quantity depends on the degree distribution of the network and the extrinsic interest the community has for the topic or hashtag. From the master equation, we are able to obtain explicit solutions for the mean and variance. We propose a gamma kernel function to model the topic popularity, which is quite simple and yields reasonable results. Finally, we validate the plausibility of the model by analyzing actual Twitter data obtained through the public API.
A deep learning framework for solution and discovery in solid mechanics
Venue: arXiv cs.LG 2020
Year: 2020

We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to learning and discovery in solid mechanics. We explain how to incorporate the momentum balance and constitutive relations into PINN, and explore in detail the application to linear elasticity, and illustrate its extension to nonlinear problems through an example that showcases von~Mises elastoplasticity. While common PINN algorithms are based on training one deep neural network (DNN), we propose a multi-network model that results in more accurate representation of the field variables. To validate the model, we test the framework on synthetic data generated from analytical and numerical reference solutions. We study convergence of the PINN model, and show that Isogeometric Analysis (IGA) results in superior accuracy and convergence characteristics compared with classic low-order Finite Element Method (FEM). We also show the applicability of the framework for transfer learning, and find vastly accelerated convergence during network re-training. Finally, we find that honoring the physics leads to improved robustness: when trained only on a few parameters, we find that the PINN model can accurately predict the solution for a wide range of parameters new to the network---thus pointing to an important application of this framework to sensitivity analysis and surrogate modeling.
Workload Scheduling on heterogeneous Mobile Edge Cloud in 5G networks to Minimize SLA Violation
Venue: arXiv cs.DC 2020
Year: 2020

Smart devices have become an indispensable part of our lives and gain increasing applicability in almost every area. Latency-aware applications such as Augmented Reality (AR), autonomous driving, and online gaming demand more resources such as network bandwidth and computational capabilities. Since the traditional mobile networks cannot fulfill the required bandwidth and latency, Mobile Edge Cloud (MEC) emerged to provide cloud computing capabilities in the proximity of users on 5G networks. In this paper, we consider a heterogeneous MEC network with numerous mobile users that send their tasks to MEC servers. Each task has a maximum acceptable response time. Non-uniform distribution of users makes some MEC servers hotspots that cannot take more. A solution is to relocate the tasks among MEC servers, called Workload Migration. We formulate this problem of task scheduling as a mixed-integer non-linear optimization problem to minimize the number of Service Level Agreement (SLA) violations. Since solving this optimization problem has high computational complexity, we introduce a greedy algorithm called MESA, Migration Enabled Scheduling Algorithm, which reaches a near-optimal solution quickly. Our experiments show that in the term of SLA violation, MESA is only 8% and 11% far from the optimal choice on the average and the worst-case, respectively. Moreover, the migration enabled solution can reduce SLA violations by about 30% compare to assigning tasks to MEC servers without migration.
Towards a generalization of information theory for hierarchical partitions
Venue: arXiv cs.IT 2020
Year: 2020

Complex systems often exhibit multiple levels of organization covering a wide range of physical scales, so the study of the hierarchical decomposition of their structure and function is frequently convenient. To better understand this phenomenon, we introduce a generalization of information theory that works with hierarchical partitions. We begin revisiting the recently introduced Hierarchical Mutual Information (HMI), and show that it can be written as a level by level summation of classical conditional mutual information terms. Then, we prove that the HMI is bounded from above by the corresponding hierarchical joint entropy. In this way, in analogy to the classical case, we derive hierarchical generalizations of many other classical information-theoretic quantities. In particular, we prove that, as opposed to its classical counterpart, the hierarchical generalization of the Variation of Information is not a metric distance, but it admits a transformation into one. Moreover, focusing on potential applications of the existing developments of the theory, we show how to adjust by chance the HMI. We also corroborate and analyze all the presented theoretical results with exhaustive numerical computations, and include an illustrative application example of the introduced formalism. Finally, we mention some open problems that should be eventually addressed for the proposed generalization of information theory to reach maturity.
Statistical Beamforming for FDD Downlink Massive MIMO via Spatial Information Extraction and Beam Selection
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we study the beamforming design problem in frequency-division duplexing (FDD) downlink massive MIMO systems, where instantaneous channel state information (CSI) is assumed to be unavailable at the base station (BS). We propose to extract the information of the angle-of-departures (AoDs) and the corresponding large-scale fading coefficients (a.k.a. spatial information) of the downlink channel from the uplink channel estimation procedure, based on which a novel downlink beamforming design is presented. By separating the subpaths for different users based on the spatial information and the hidden sparsity of the physical channel, we construct near-orthogonal virtual channels in the beamforming design. Furthermore, we derive a sum-rate expression and its approximations for the proposed system. Based on these closed-form rate expressions, we develop two low-complexity beam selection schemes and carry out asymptotic analysis to provide valuable insights on the system design. Numerical results demonstrate a significant performance improvement of our proposed algorithm over the state-of-the-art beamforming approach.
CNN-based Repetitive self-revised learning for photos' aesthetics imbalanced classification
Venue: arXiv cs.CV 2020
Year: 2020

Aesthetic assessment is subjective, and the distribution of the aesthetic levels is imbalanced. In order to realize the auto-assessment of photo aesthetics, we focus on using repetitive self-revised learning (RSRL) to train the CNN-based aesthetics classification network by imbalanced data set. As RSRL, the network is trained repetitively by dropping out the low likelihood photo samples at the middle levels of aesthetics from the training data set based on the previously trained network. Further, the retained two networks are used in extracting highlight regions of the photos related with the aesthetic assessment. Experimental results show that the CNN-based repetitive self-revised learning is effective for improving the performances of the imbalanced classification.