OpenRaiser/Intern-Atlas huggingface dataset:
Prostate Segmentation from Ultrasound Images using Residual Fully Convolutional Network
Venue: arXiv cs.CV 2019
Year: 2019

Medical imaging based prostate cancer diagnosis procedure uses intra-operative transrectal ultrasound (TRUS) imaging to visualize the prostate shape and location to collect tissue samples. Correct tissue sampling from prostate requires accurate prostate segmentation in TRUS images. To achieve this, this study uses a novel residual connection based fully convolutional network. The advantage of this segmentation technique is that it requires no pre-processing of TRUS images to perform the segmentation. Thus, it offers a faster and straightforward prostate segmentation from TRUS images. Results show that the proposed technique can achieve around 86% Dice Similarity accuracy using only few TRUS datasets.
Contingency Model Predictive Control for Automated Vehicles
Venue: arXiv cs.SY 2019
Year: 2019

We present Contingency Model Predictive Control (CMPC), a novel and implementable control framework which tracks a desired path while simultaneously maintaining a contingency plan -- an alternate trajectory to avert an identified potential emergency. In this way, CMPC anticipates events that might take place, instead of reacting when emergencies occur. We accomplish this by adding an additional prediction horizon in parallel to the classical receding MPC horizon. The contingency horizon is constrained to maintain a feasible avoidance solution; as such, CMPC is selectively robust to this emergency while tracking the desired path as closely as possible. After defining the framework mathematically, we demonstrate its effectiveness experimentally by comparing its performance to a state-of-the-art deterministic MPC. The controllers drive an automated research platform through a left-hand turn which may be covered by ice. Contingency MPC prepares for the potential loss of friction by purposefully and intuitively deviating from the prescribed path to approach the turn more conservatively; this deviation significantly mitigates the consequence of encountering ice.
Neural Networks in Hybrid Precoding for Millimeter Wave Massive MIMO Systems
Venue: arXiv cs.NI 2019
Year: 2019

Neural networks have been applied to the physical layer of wireless communication systems to solve complex problems. In millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid precoding has been considered as an energy-efficient technology to replace fully-digital precoding. The way of designing hybrid precoding in mmWave massive MIMO systems by multi-layer neural networks has not been investigated. Based on further decomposing the baseband precoding matrix, an idea is proposed in this paper to map hybrid precoding structure to a multi-layer neural network. Considering the deterioration in the throughput and energy efficiency of mmWave massive MIMO systems, the feasibility of the proposed idea is analyzed. Moreover, a singular value decomposition (SVD) based decomposing (SVDDE) algorithm is proposed to evaluate the feasibility of the proposed idea. Simulation results indicate that there is an optimal number of users which can minimize the performance deterioration. Moreover, the simulation results also show that slight deterioration in the throughput and energy efficiency of mmWave massive MIMO systems is caused by further decomposing the baseband precoding matrix. In other words, further decomposing the baseband precoding matrix is a feasible way to map the hybrid precoding structure to a multi-layer neural network.
Simplex space-time meshes in two-phase flow simulations
Venue: arXiv cs.CE 2019
Year: 2019

In this paper, we present the numerical solution of two-phase flow problems of engineering significance with a space-time finite element method that allows for local temporal refinement. Our basis is the method presented in [3], which allows for arbitrary temporal refinement in preselected regions of the mesh. It has been extended to adaptive temporal refinement that is governed by a quantity that is part of the solution process, namely, the interface position in two-phase flow. Due to local effects such as surface tension, jumps in material properties, etc., the interface can, in general, be considered a region that requires high flexibility and high resolution, both in space and in time. The new method, which leads to tetrahedral (for 2D problems) and pentatope (for 3D problems) meshes, offers an efficient yet accurate approach to the underlying two-phase flow problems.
Volumetric Untrimming: Precise decomposition of trimmed trivariates into tensor products
Venue: arXiv cs.CG 2019
Year: 2019

3D objects, modeled using Computer Aided Geometric Design tools, are traditionally represented using a boundary representation (B-rep), and typically use spline functions to parameterize these boundary surfaces. However, recent development in physical analysis, in isogeometric analysis (IGA) in specific, necessitates a volumetric parametrization of the interior of the object. IGA is performed directly by integrating over the spline spaces of the volumetric spline representation of the object. Typically, tensor-product B-spline trivariates are used to parameterize the volumetric domain. A general 3D object, that can be modeled in contemporary B-rep CAD tools, is typically represented using trimmed B-spline surfaces. In order to capture the generality of the contemporary B-rep modeling space, while supporting IGA needs, Massarwi and Elber (2016) proposed the use of trimmed trivariates volumetric elements. However, the use of trimmed geometry makes the integration process more difficult since integration over trimmed B-spline basis functions is a highly challenging task. In this work, we propose an algorithm that precisely decomposes a trimmed B-spline trivariate into a set of (singular only on the boundary) tensor-product B-spline trivariates, that can be utilized to simplify the integration process in IGA. The trimmed B-spline trivariate is first subdivided into a set of trimmed Bézier trivariates, at all its internal knots. Then, each trimmed Bézier trivariate, is decomposed into a set of mutually exclusive tensor-product B-spline trivariates, that precisely cover the entire trimmed domain. This process, denoted untrimming, can be performed in either the Euclidean space or the parametric space of the trivariate. We present examples on complex trimmed trivariates' based geometry, and we demonstrate the effectiveness of the method by applying IGA over the (untrimmed) results.
An Energy-Efficient Resource Management System for a Mobile Ad Hoc Cloud
Venue: arXiv cs.DC 2019
Year: 2019

Recently, mobile ad hoc clouds have emerged as a promising technology for mobile cyber-physical system applications, such as mobile intelligent video surveillance and smart homes. Resource management plays a key role in maximizing resource utilization and application performance in mobile ad hoc clouds. Unlike resource management in traditional distributed computing systems, such as clouds, resource management in a mobile ad hoc cloud poses numerous challenges owing to the node mobility, limited battery power, high latency, and the dynamic network environment. The real-time requirements associated with mobile cyber-physical system applications make the problem even more challenging. Currently, existing resource management systems for mobile ad hoc clouds are not designed to support mobile cyber-physical system applications and energy-efficient communication between application tasks. In this paper, we propose a new energy-efficient resource management system for mobile ad hoc clouds. The proposed system consists of two layers: a network layer and a middleware layer. The network layer provides ad hoc network and communication services to the middleware layer and shares the collected information in order to allow efficient and robust resource management decisions. It uses (1) a transmission power control mechanism to improve energy efficiency and network capacity, (2) link lifetimes to reduce communication and energy consumption costs, and (3) link quality to estimate data transfer times. The middleware layer is responsible for the discovery, monitoring, migration, and allocation of resources. It receives application tasks from users and allocates tasks to nodes on the basis of network and node-level information.
Multi-Task Time Series Analysis applied to Drug Response Modelling
Venue: arXiv cs.LG 2019
Year: 2019

Time series models such as dynamical systems are frequently fitted to a cohort of data, ignoring variation between individual entities such as patients. In this paper we show how these models can be personalised to an individual level while retaining statistical power, via use of multi-task learning (MTL). To our knowledge this is a novel development of MTL which applies to time series both with and without control inputs. The modelling framework is demonstrated on a physiological drug response problem which results in improved predictive accuracy and uncertainty estimation over existing state-of-the-art models.
Quotienting Impertinent Camera Kinematics for 3D Video Stabilization
Venue: arXiv cs.CV 2019
Year: 2019

With the recent advent of methods that allow for real-time computation, dense 3D flows have become a viable basis for fast camera motion estimation. Most importantly, dense flows are more robust than the sparse feature matching techniques used by existing 3D stabilization methods, able to better handle large camera displacements and occlusions similar to those often found in consumer videos. Here we introduce a framework for 3D video stabilization that relies on dense scene flow alone. The foundation of this approach is a novel camera motion model that allows for real-world camera poses to be recovered directly from 3D motion fields. Moreover, this model can be extended to describe certain types of non-rigid artifacts that are commonly found in videos, such as those resulting from zooms. This framework gives rise to several robust regimes that produce high-quality stabilization of the kind achieved by prior full 3D methods while avoiding the fragility typically present in feature-based approaches. As an added benefit, our framework is fast: the simplicity of our motion model and efficient flow calculations combine to enable stabilization at a high frame rate.
On the Impact of Satellite Communications over Mobile Networks: An Experimental Analysis
Venue: arXiv cs.NI 2019
Year: 2019

Future telecommunication systems are expected to co-exist with different backhauling nodes such as terrestrial or satellite systems. Satellite connectivity can add flexibility to backhauling networks and provide an alternative route for transmission. This paper presents experimental comparisons of satellite and terrestrial cellular networks and evaluates their performances in terms of different Key Performance Indicators (KPIs) including Channel Quality Index (CQI), Modulation Coding Scheme (MCS) index, Downlink throughput, Frame Utilization (FU) and number of Resource Block (RB) utilization ratios. Our experimental satellite network system uses a real satellite backhaul deployment and works in Ka band (with two specific sub-bands on 19 Ghz in downlink and 29 Ghz in uplink). As a benchmark, we compare our system with live terrestrial network in which backhaul connection is cellular backhaul. Our experiments reveal three main observations: First observation is that there exists FU and number of RB utilization problems in the satellite link even though there exists a single test user equipment (UE) with high CQI and MCS index values. Second observation is that in satellite link relatively low number of Protocol Data Units (PDUs) are generated at Radio Link Controller (RLC) layer compared to the Packet Data Convergence Control (PDCP) layer. Finally, our third observation concludes that the excessive existence of PDCP PDUs can be due to existence of General Packet Radio Service (GPRS) Tunneling Protocol-User Plane (GTP-U) accelerator where an optimal balance between the caching size and the number of UEs using satellite eNodeB is needed. For this reason, our experimental results reveal the existence of a trade-off between the supported number of users on satellite link and the GTP-U acceleration rate.
Finite Sample Analysis of Stochastic System Identification
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we analyze the finite sample complexity of stochastic system identification using modern tools from machine learning and statistics. An unknown discrete-time linear system evolves over time under Gaussian noise without external inputs. The objective is to recover the system parameters as well as the Kalman filter gain, given a single trajectory of output measurements over a finite horizon of length $N$. Based on a subspace identification algorithm and a finite number of $N$ output samples, we provide non-asymptotic high-probability upper bounds for the system parameter estimation errors. Our analysis uses recent results from random matrix theory, self-normalized martingales and SVD robustness, in order to show that with high probability the estimation errors decrease with a rate of $1/\sqrt{N}$. Our non-asymptotic bounds not only agree with classical asymptotic results, but are also valid even when the system is marginally stable.
Scalable Similarity Joins of Tokenized Strings
Venue: arXiv cs.IR 2019
Year: 2019

This work tackles the problem of fuzzy joining of strings that naturally tokenize into meaningful substrings, e.g., full names. Tokenized-string joins have several established applications in the context of data integration and cleaning. This work is primarily motivated by fraud detection, where attackers slightly modify tokenized strings, e.g., names on accounts, to create numerous identities that she can use to defraud service providers, e.g., Google, and LinkedIn. To detect such attacks, all the accounts are pair-wise compared, and the resulting similar accounts are considered suspicious and are further investigated. Comparing the tokenized-string features of a large number of accounts requires an intuitive tokenized-string distance that can detect subtle edits introduced by an adversary, and a very scalable algorithm. This is not achievable by existing distance measure that are unintuitive, hard to tune, and whose join algorithms are serial and hence unscalable. We define a novel intuitive distance measure between tokenized strings, Normalized Setwise Levenshtein Distance (NSLD). To the best of our knowledge, NSLD is the first metric proposed for comparing tokenized strings. We propose a scalable distributed framework, Tokenized-String Joiner (TSJ), that adopts existing scalable string-join algorithms as building blocks to perform NSLD-joins. We carefully engineer optimizations and approximations that dramatically improve the efficiency of TSJ. The effectiveness of the TSJ framework is evident from the evaluation conducted on tens of millions of tokenized-string names from Google accounts. The superiority of the tokenized-string-specific TSJ framework over the general-purpose metric-spaces joining algorithms has been established.
Repairing mappings under policy views
Venue: arXiv cs.DB 2019
Year: 2019

The problem of data exchange involves a source schema, a target schema and a set of mappings from transforming the data between the two schemas. We study the problem of data exchange in the presence of privacy restrictions on the source. The privacy restrictions are expressed as a set of policy views representing the information that is safe to expose over all instances of the source. We propose a protocol that provides formal privacy guarantees and is data-independent, i.e., if certain criteria are met, then the protocol guarantees that the mappings leak no sensitive information independently of the data that lies in the source. We also propose an algorithm for repairing an input mapping w.r.t. a set of policy views, in cases where the input mapping leaks sensitive information. The empirical evaluation of our work shows that the proposed algorithm is quite efficient, repairing sets of 300 s-t tgds in an average time of 5s on a commodity machine. To the best of our knowledge, our work is the first one that studies the problems of exchanging data and repairing mappings under such privacy restrictions. Furthermore, our work is the first to provide practical algorithms for a logical privacy-preservation paradigm, described as an open research challenge in previous work on this area.
Low Resource Text Classification with ULMFit and Backtranslation
Venue: arXiv cs.CL 2019
Year: 2019

In computer vision, virtually every state-of-the-art deep learning system is trained with data augmentation. In text classification, however, data augmentation is less widely practiced because it must be performed before training and risks introducing label noise. We augment the IMDB movie reviews dataset with examples generated by two families of techniques: random token perturbations introduced by Wei and Zou [2019] and backtranslation -- translating to a second language then back to English. In low resource environments, backtranslation generates significant improvement on top of the state of-the-art ULMFit model. A ULMFit model pretrained on wikitext103 and then fine-tuned on only 50 IMDB examples and 500 synthetic examples generated by backtranslation achieves 80.6% accuracy, an 8.1% improvement over the augmentation-free baseline with only 9 minutes of additional training time. Random token perturbations do not yield any improvements but incur equivalent computational cost. The benefits of training with backtranslated examples decreases with the size of the available training data. On the full dataset, neither augmentation technique improves upon ULMFit's state of the art performance. We address this by using backtranslations as a form of test time augmentation as well as ensembling ULMFit with other models, and achieve small improvements.
A Novel Framework for Software Defined Wireless Body Area Network
Venue: arXiv cs.NI 2019
Year: 2019

Software Defined Networking (SDN) has gained huge popularity in replacing traditional network by offering flexible and dynamic network management. It has drawn significant attention of the researchers from both academia and industries. Particularly, incorporating SDN in Wireless Body Area Network (WBAN) applications indicates promising benefits in terms of dealing with challenges like traffic management, authentication, energy efficiency etc. while enhancing administrative control. This paper presents a novel framework for Software Defined WBAN (SDWBAN), which brings the concept of SDN technology into WBAN applications. By decoupling the control plane from data plane and having more programmatic control would assist to overcome the current lacking and challenges of WBAN. Therefore, we provide a conceptual framework for SDWBAN with packet flow model and a future direction of research pertaining to SDWBAN.
Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records
Venue: arXiv cs.LG 2019
Year: 2019

Electronic medical records (EMRs) supports the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But insofar most algorithms have been centralized, taking little account of the decentralized, non-identically independently distributed (non-IID), and privacy-sensitive characteristics of EMRs that can complicate data collection, sharing and learning. To address this challenge, we introduced a community-based federated machine learning (CBFL) algorithm and evaluated it on non-IID ICU EMRs. Our algorithm clustered the distributed data into clinically meaningful communities that captured similar diagnoses and geological locations, and learnt one model for each community. Throughout the learning process, the data was kept local on hospitals, while locally-computed results were aggregated on a server. Evaluation results show that CBFL outperformed the baseline FL algorithm in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC), Area Under the Precision-Recall Curve (PR AUC), and communication cost between hospitals and the server. Furthermore, communities' performance difference could be explained by how dissimilar one community was to others.
Automatically Generating Engaging Presentation Slide Decks
Venue: arXiv cs.CY 2019
Year: 2019

Talented public speakers have thousands of hours of practice. One means of improving public speaking skills is practice through improvisation, e.g. presenting an improvised presentation using an unseen slide deck. We present TEDRIC, a novel system capable of generating coherent slide decks based on a single topic suggestion. It combines semantic word webs with text and image data sources to create an engaging slide deck with an overarching theme. We found that audience members perceived the quality of improvised presentations using these generated slide decks to be on par with presentations using human created slide decks for the Improvised TED Talk performance format. TEDRIC is thus a valuable new creative tool for improvisers to perform with, and for anyone looking to improve their presentation skills.
A WCET-aware cache coloring technique for reducing interference in real-time systems
Venue: arXiv cs.OS 2019
Year: 2019

The predictability of a system is the condition to give saferbound on worst case execution timeof real-time tasks which are running on it. Commercial off-the-shelf(COTS) processors are in-creasingly used in embedded systems and contain shared cache memory. This component hasa hard predictable behavior because its state depends of theexecution history of the systems.To increase predictability of COTS component we use cache coloring, a technique widely usedto partition cache memory. Our main contribution is a WCET aware heuristic which parti-tion task according to the needs of each task. Our experiments are made with CPLEX an ILPsolver with random tasks set generated running on preemptive system scheduled with earliestdeadline first(EDF).
Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs
Venue: arXiv cs.CR 2019
Year: 2019

This paper proposes a privacy protocol for distributed average consensus algorithms on bounded real-valued inputs that guarantees statistical privacy of honest agents' inputs against colluding (passive adversarial) agents, if the set of colluding agents is not a vertex cut in the underlying communication network. This implies that privacy of agents' inputs is preserved against $t$ number of arbitrary colluding agents if the connectivity of the communication network is at least $(t+1)$. A similar privacy protocol has been proposed for the case of bounded integral inputs in our previous paper~\cite{gupta2018information}. However, many applications of distributed consensus concerning distributed control or state estimation deal with real-valued inputs. Thus, in this paper we propose an extension of the privacy protocol in~\cite{gupta2018information}, for bounded real-valued agents' inputs, where bounds are known apriori to all the agents.