On Two-Pass Streaming Algorithms for Maximum Bipartite Matching

We study two-pass streaming algorithms for \textsf{Maximum Bipartite Matching} (\textsf{MBM}). All known two-pass streaming algorithms for \textsf{MBM} operate in a similar fashion: They compute a maximal matching in the first pass and find 3-augmenting paths in the second in order to augment the matching found in the first pass. Our aim is to explore the limitations of this approach and to determine whether current techniques can be used to further improve the state-of-the-art algorithms. We give the following results: We show that every two-pass streaming algorithm that solely computes a maximal matching in the first pass and outputs a $(2/3+\epsilon)$-approximation requires $n^{1+\Omega(\frac{1}{\log \log n})}$ space, for every $\epsilon > 0$, where $n$ is the number of vertices of the input graph. This result is obtained by extending the Ruzsa-Szemer\'{e}di graph construction of [GKK, SODA'12] so as to ensure that the resulting graph has a close to perfect matching, the key property needed in our construction. This result may be of independent interest. Furthermore, we combine the two main techniques, i.e., subsampling followed by the \textsc{Greedy} matching algorithm [Konrad, MFCS'18] which gives a $2-\sqrt{2} \approx 0.5857$-approximation, and the computation of \emph{degree-bounded semi-matchings} [EHM, ICDMW'16][KT, APPROX'17] which gives a $\frac{1}{2} + \frac{1}{12} \approx 0.5833$-approximation, and obtain a meta-algorithm that yields Konrad's and Esfandiari et al.'s algorithms as special cases. This unifies two strands of research. By optimizing parameters, we discover that Konrad's algorithm is optimal for the implied class of algorithms and, perhaps surprisingly, that there is a second optimal algorithm. We show that the analysis of our meta-algorithm is best possible. Our results imply that further improvements, if possible, require new techniques

A user-friendly guide to using distance measures to compare time series in ecology

Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g., assigning species to individual bird calls); clustering (e.g., clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g., accuracy of model predictions to original time series data); and anomaly detection (e.g., detecting possible catastrophic events from population time series). These common tasks in ecological research all rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored. Here, we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology. Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well suited to comparing noisy time series and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends. The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series and help us answer new ecological questions

Developing a cationic contrast agent for computed tomographic imaging of articular cartilage and synthetic biolubricants for early diagnosis and treatment of osteoarthritis

Osteoarthritis (OA) causes debilitating pain for millions of people, yet OA is typically diagnosed late in the disease process after severe damage to the articular cartilage has occurred and few treatment options exist. Furthermore, destructive techniques are required to measure cartilage biochemical and mechanical properties for studying cartilage function and changes during OA. Hence, research and clinical needs exist for non-destructive measures of cartilage properties. Various arthroscopic (e.g., ultrasound probes) and imaging (e.g., MRI or CT) techniques are available for assessing cartilage less destructively. However, arthroscopic methods are limited by patient anesthesia/infection risks and cost, and MRI is hindered by high cost, long image acquisition times and low resolution. Contrast-enhanced CT (CECT) is a promising diagnostic tool for early-stage OA, yet most of its development work utilizes simplified and ideal cartilage models, and rarely intact, pre-clinical animal or human models. To advance CECT imaging for articular cartilage, this dissertation describes further development of a new cationic contrast agent (CA4+) for minimally-invasive assessment of cartilage biochemical and mechanical properties, including glycosaminoglycan content, compressive modulus, and coefficient of friction. Specifically, CA4+ enhanced CT is compared to these three cartilage properties initially using an ideal bovine osteochondral plug model, then the technique is expanded to examine human finger joints and both euthanized and live mouse knees. Furthermore, CECT attenuations with CA4+ map bovine meniscal GAG content and distribution, signifying CECT can evaluate multiple tissues involved in OA. CECT's sensitivity to critical cartilage and meniscal properties demonstrates its applicability as both a non-destructive research tool as well as a method for diagnosing and monitoring early-stage OA. Additionally, CECT enables evaluation of efficacy for a new biolubricant (2M TEG) for early-stage OA treatment. In particular, CECT can detect the reduced wear on cartilage surfaces for samples tested in 2M TEG compared to samples tested in saline (negative control). With its sensitivity to cartilage GAG content, surface roughness, and mechanical properties, CA4+ enhanced CT will serve as a valuable tool for subsequent in vivo animal and clinical use

Machine learning for Internet of Things data analysis: A survey

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.Comment: Digital Communications and Networks (2017

GPU Resource Optimization and Scheduling for Shared Execution Environments

General purpose graphics processing units have become a computing workhorse for a variety of data- and compute-intensive applications, from large supercomputing systems for massive data analytics to small, mobile embedded devices for autonomous vehicles. Making effective and efficient use of these processors traditionally relies on extensive programmer expertise to design and develop kernel methods which simultaneously trade off task decomposition and resource exploitation. Often, new architecture designs force code refinements in order to continue to achieve optimal performance. At the same time, not all applications require full utilization of the system to achieve that optimal performance. In this case, the increased capability of new architectures introduces an ever-widening gap between the level of resources necessary for optimal performance and the level necessary to maintain system efficiency. The ability to schedule and execute multiple independent tasks on a GPU, known generally as concurrent kernel execution, enables application programmers and system developers to balance application performance and system efficiency. Various approaches to develop both coarse- and fine-grained scheduling mechanisms to achieve a high degree of resource utilization and improved application performance have been studied. Most of these works focus on mechanisms for the management of compute resources, while a small percentage consider the data transfer channels. In this dissertation, we propose a pragmatic approach to scheduling and managing both types of resources – data transfer and compute – that is transparent to an application programmer and capable of providing near-optimal system performance. Furthermore, the approaches described herein rely on reinforcement learning methods, which enable the scheduling solutions to be flexible to a variety of factors, such as transient application behaviors, changing system designs, and tunable objective functions. Finally, we describe a framework for the practical implementation of learned scheduling policies to achieve high resource utilization and efficient system performance

MODELLING AND IN VIVO MONITORING OF THE TIME DEPENDENT MECHANICAL PROPERTIES OF TISSUE ENGINEERING SCAFFOLDS

When organs and tissue fail either due to pre-existing disease progression or by accidental damage, current state of the art treatment involves the replacement of the damaged or diseased tissue with new donor derived organs/tissue. The limitations of these current approaches include a limited supply of tissue for treatments and the immune response of the patient’s own body against the new implanted tissue/organs. To solve these issues, tissue engineering aims to develop artificial analogs derived from a patient’s own cells instead of donor tissue/organs for treatment. To this end, a promising approach, known as scaffold-based tissue engineering, is to seed engineered constructs or scaffolds with cells to form artificial analogs, which then develop with time into new tissue/organs for implantation. The mechanical properties of the scaffold play a critical role in the success of scaffold-based treatments, as the scaffold is expected to provide a temporary support for the generation of new tissue/organs without causing failure at any time during the treatment process. It is noted that due to the degradation of scaffold in the treatment process, the mechanical properties of the scaffold are not constant but change with time dynamically. This raises two scientific issues; one is the representation of the time-dependent mechanical properties and the other one is the monitoring of these properties, especially in the in vivo environments (i.e., upon the implantation of scaffolds into animal/patient bodies). To address these issues, this research is aimed at performing a novel study on the modelling and in vivo monitoring of the time dependent mechanical properties of tissue engineering scaffolds. To represent the time-dependent mechanical properties of a scaffold, a novel model based on the concept of finite element model updating is developed. The model development involves three steps: (1) development of a finite element model for the effective mechanical properties of the scaffold, (2) parametrizing the finite element model by selecting parameters associated with the scaffold microstructure and/or material properties, which vary with scaffold degradation, and (3) identifying selected parameters as functions of time based on measurements from the tests on the scaffold mechanical properties as they degrade. To validate the developed model, scaffolds were made from the biocompatible polymer polycaprolactone (PCL) mixed with hydroxyapatite (HA) nanoparticles and their mechanical properties were examined in terms of the Young modulus. Based on the bulk degradation exhibited by the PCL/HA scaffold, the molecular weight was selected for model updating. With the identified molecular weight, the finite element model v developed was effective for predicting the time-dependent mechanical properties of PCL/HA scaffolds during degradation . To monitor and characterize scaffold mechanical properties in vivo, novel methods based on synchrotron-based phase contrast imaging and finite element modeling were developed. The first method is to represent the scaffold mechanical properties from the measured deflection. In this method, the phase contrast imaging is used to characterize the scaffold deflection caused by ultrasound radiation forces; and the finite element modelling is used to represent the ultrasonic loading on the scaffold, thus predicting the mechanical properties from the measured deflection. The second method is to characterize the scaffold degradation due to surface erosion, which involves the remote sensing of the time dependent morphology of tissue scaffolds by phase contrast imaging and the estimation of time dependent mass loss of the scaffolds from the sensed morphology. The last method is to relate the elastic mechanical property and nonlinear stress-strain behavior to the scaffold geometry, both changing with time during surface erosion. To validate the above methods, scaffolds was made from varying biomaterials (PLGA and PCL) and their mechanical properties (degradation, mass loss, and elastic modulus) were examined experimentally. The results obtained illustrate the methods developed in this research are effective to monitor and characterize scaffold mechanical properties. The significance of this research is that the model developed for the scaffold mechanical properties can be used in the design of scaffolds with the desired mechanical properties, instead of the trial and error methods typical in current scaffold design; and that these novel monitoring methods based on synchrotron imaging can be used to characterize the scaffold time-dependent mechanical properties in the in vivo environments, representing an important advance in tissue engineering

Applying the repeated game framework to multiparty networked applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 145-154).This thesis presents repeated game analysis as an important and practical tool for networked application and protocol designers. Incentives are a potential concern for a large number of networked applications. Well-studied examples include routing and peer-to-peer networks. To the extent that incentives significantly impact the outcome of a system, system designers require tools and frameworks to better understand how their design decisions impact these incentive concerns. Repetition is a prevalent and critical aspect of many networking applications and protocols. Most networked protocols and architectures seek to optimize performance over a longer timescale and many have explicit support for repetition. Similarly, most players in networked applications are interested in longer horizons, whether they be firms building a business or typical individuals trying to use a system. Fortunately, the study of repeated interaction between multiple self-interested parties, repeated games, is a well-understood and developed area of economic and game theoretic research. A key conclusion from that literature is that the outcome of the repeated game can differ qualitatively from that of the one-shot game. Nonetheless, the tools of repeated games have rarely if ever been brought to bear on networking problems. Our work presents the descriptive and prescriptive power of repeated game analysis by making specific contributions to several relevant networking problems.(cont.) The applications considered are inherently repeated in practice, yet our research is the first to consider the repeated model for each particular problem. In the case of interdomain routing, we first show that user-directed routing (e.g., overlays) transforms routing into a meaningfully repeated game. This motivates us to consider protocols that integrate incentives into routing systems. In designing such a routing protocol, we again use repeated games to identify important properties including the protocol period and the format of certain protocol fields. Leveraging this insight, we show how it is possible to address the problem of the repeated dynamic and arrive at a more desirable outcome. In the case of multicast overlay networks, we show how repeated games can be used to explain the paradox of cooperative user behavior. In contrast to prior models, our repeated model explains the scaling properties of these networks in an endogenous fashion. This enables meaningful examination of the impact architecture and protocol design decisions have on the system outcome. We therefore use this model, with simulation, to descry system parameters and properties important in building robust networks. These examples demonstrate the important and practical insights that repeated game analysis can yield. Further, we argue that the results obtained in the particular problems stem from properties fundamental to networked applications - and their natural relationship with properties of repeated games.(cont.) This strongly suggests that the tools and techniques of this research can be applied more generally. Indeed, we hope that these results represent the beginning of an increased use of repeated games for the study and design of networked applications.by Michael Moïse Afergan.Ph.D

LOW INSERTION-LOSS NANOPHOTONIC MODULATORS THROUGH EPSILON-NEAR-ZERO MATERIAL-BASED PLASMON-ASSISTED APPROACH FOR INTEGRATED PHOTONICS

Electro-optic/absorption Modulators (EOM/EAMs) encode high-frequency electrical signals into optical signals. With the requirement of large packing density, device miniaturization is possible by confining light in a sub-wavelength dimension by utilizing the plasmonic phenomenon. In plasmon, energy gets transferred from light to the form of oscillation of free electrons on a surface of a metal at an interface between the metal and a dielectric. Plasmonic provides increased light-matter interaction (LMI) and thus making the light more sensitive to local refractive index change. Plasmonic-based integrated nanophotonic modulators, despite their promising features, have one key limiting factor of large Insertion Loss (IL) which limits their practical potential. To combat this, this research utilizes a plasmon-assisted approach through the lens of surface-to-volume ratio to realize a 4-slot-based EAM with an extinction ratio (ER) of 2.62 dB/μm and insertion loss (IL) of 0.3 dB/μm operating at ~1 GHz and a single slot design with ER of 1.4 dB/ μm and IL of 0.25 dB/ μm operating at ~20 GHz, achieved by replacing the traditional metal contact with heavily doped Indium Tin Oxide (ITO). Furthermore, the analysis imposes realistic fabrication constraints, and material properties, and illustrates trade-offs in the performance that must be carefully optimized for a given scenario. Besides the research investigates optical and electrical properties of constituent materials through techniques such as atomic layer deposition (ALD) for depositing thin films, spectroscopic ellipsometry (SE), and Hall measurements for optical and electrical characterization respectively

Structured Parallelism by Composition - Design and implementation of a framework supporting skeleton compositionality

This thesis is dedicated to the efficient compositionality of algorithmic skeletons, which are abstractions of common parallel programming patterns. Skeletons can be implemented in the functional parallel language Eden as mere parallel higher order functions. The use of algorithmic skeletons facilitates parallel programming massively. This is because they already implement the tedious details of parallel programming and can be specialised for concrete applications by providing problem specific functions and parameters. Efficient skeleton compositionality is of particular importance because complex, specialised skeletons can be compound of simpler base skeletons. The resulting modularity is especially important for the context of functional programming and should not be missing in a functional language. We subdivide composition into three categories: -Nesting: A skeleton is instantiated from another skeleton instance. Communication is tree shaped, along the call hierarchy. This is directly supported by Eden. -Composition in sequence: The result of a skeleton is the input for a succeeding skeleton. Function composition is expressed in Eden by the ( . ) operator. For performance reasons the processes of both skeletons should be able to exchange results directly instead of using the indirection via the caller process. We therefore introduce the remote data concept. -Iteration: A skeleton is called in sequence a variable number of times. This can be defined using recursion and composition in sequence. We optimise the number of skeleton instances, the communication in between the iteration steps and the control of the loop. To this end, we developed an iteration framework where iteration skeletons are composed from control and body skeletons. Central to our composition concept is remote data. We send a remote data handle instead of ordinary data, the data handle is used at its destination to request the referenced data. Remote data can be used inside arbitrary container types for efficient skeleton composition similar to ordinary distributed data types. The free combinability of remote data with arbitrary container types leads to a high degree of flexibility. The programmer is not restricted by using a predefined set of distributed data types and (re-)distribution functions. Moreover, he can use remote data with arbitrary container types to elegantly create process topologies. For the special case of skeleton iteration we prevent the repeated construction and deconstruction of skeleton instances for each single iteration step, which is common for the recursive use of skeletons. This minimises the parallel overhead for process and channel creation and allows to keep data local on persistent processes. To this end we provide a skeleton framework. This concept is independent of remote data, however the use of remote data in combination with the iteration framework makes the framework more flexible. For our case studies, both approaches perform competitively compared to programs with identical parallel structure but which are implemented using monolithic skeletons - i.e. skeleton not composed from simpler ones. Further, we present extensions of Eden which enhance composition support: generalisation of overloaded communication, generalisation of process instantiation, compositional process placement and extensions of Box types used to adapt communication behaviour