Deploying Software-Defined Networks: a Telco Perspective

Software-Defined Networking (SDN) proposes a new network architecture in which the control plane and forwarding plane are decoupled. SDN can improve network efficiency and ease of management through the centralization of the control and policy decisions. However, SDN deployments are currently limited to data-center and experimental environments. This thesis surveys the deployment of SDN from the perspective of a telecommunication network operator. We discuss the strategies which enable the operator to migrate to a network in which both SDN and legacy devices interoperate. As a synthesis of existing technologies and protocols, we formulate an automated process for the bootstrapping of newly deployed forwarding devices. Furthermore, we review solutions for programming the forwarding devices and for performing topology discovery. The functional correctness of the proposed bootstrapping process is evaluated in an emulated environment

A Case for Pharmacogenomics in Management of Cardiac Arrhythmias

Disorders of the cardiac rhythm are quite prevalent in clinical practice. Though the variability in drug response between individuals has been extensively studied, this information has not been widely used in clinical practice. Rapid advances in the field of pharmacogenomics have provided us with crucial insights on inter-individual genetic variability and its impact on drug metabolism and action. Technologies for faster and cheaper genetic testing and even personal genome sequencing would enable clinicians to optimize prescription based on the genetic makeup of the individual, which would open up new avenues in the area of personalized medicine. We have systematically looked at literature evidence on pharmacogenomics markers for anti-arrhythmic agents from the OpenPGx consortium collection and reason the applicability of genetics in the management of arrhythmia. We also discuss potential issues that need to be resolved before personalized pharmacogenomics becomes a reality in regular clinical practice

Machine learning tools for mRNA isoform function prediction

This dissertation is focused on improving mRNA isoform characterization in terms of functional networks, function prediction and tissue-specificity. There are three major challenges in solving these problems. The first is the unavailability of mRNA isoform level functional data which is required to develop machine learning tools. However, the available data, even at the gene level doesn’t include all genes, further complicating the matter. The second challenge is the lack of information about tissue-specificity in functional databases such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and UniProt. The third challenge is the lack of mRNA isoform level “ground truth” functional annotation data. The scope of this dissertation includes using mRNA isoform and protein sequences, high-throughput RNA-sequencing data and functional annotations at the gene level to develop computational methods for predicting functions for alternative spliced mRNA isoforms in mouse. To address these challenges, this dissertation develops and describes two computational tools. The first is a supervised learning-based machine learning framework for predicting tissue-specific mRNA isoform functional networks. Tissue-spEcific mrNa iSoform functIonal Networks (TENSION) makes use of single mRNA producing gene annotations and gene annotations tagged with “NOT” to create a high-quality mRNA isoform level functional data. We use these mRNA isoform level functional data to train random forest algorithms to develop mRNA isoform functional network prediction models. By using a leave-one-tissue-out approach and incorporating tissue-specific mRNA isoform level predictors along with those obtained from mRNA isoform and protein sequences, we have developed mRNA isoform level functional networks for 17 mouse tissues. We identify about 10.6 million tissue-specific functional mRNA isoform interactions and demonstrate the ability of our networks to reveal tissue-specific functional differences of the isoforms of the same genes. We validate our models and predictions by using a series of tests such as 10-fold stratified cross validation, comparison with published method and validating against literature datasets. As a result, we have also generated a high-quality mRNA isoform level functional dataset that can be used for benchmarking future methods. Next, we describe mRNA Function Recommendation System (mFRecSys), a recommendation system for making tissue-specific function recommendations for mRNA isoforms. In mFRecSys, we consider mRNA isoforms as “users” and Gene Ontology biological process terms as “items”. By using explicit contexts for mRNA isoforms, Gene Ontology biological process terms and tissue-specific mRNA isoform expression, mFRecSys is able to make tissue-specific mRNA isoform function recommendations. This work emphasizes the significance of incorporating diverse biological context to develop better machine learning tools for biology. It also highlights the use of simplified supervised learning methods for biological network prediction. The machine learning models and recommendation systems developed as part of this work also draw attention to the power of simple mRNA isoform sequence-based predictors to improve mRNA isoform function prediction. The methods developed have potential practical applications, for instance as predictive models for distinguishing the functions of different mRNA isoforms of the same gene or identifying tissue-specific functions of mRNA isoforms

The Influence of Maturity Offset, Strength, and Movement Competency on Motor Skill Performance in Adolescent Males

This study aimed to examine the extent to which maturity offset, strength, and movement competency influences motor skill performance in adolescent boys. One hundred and eight secondary school boys completed anthropometric and physical testing on two non-consecutive days for the following variables: maturity offset, isometric mid-thigh pull absolute (IMTPABS) and relative (IMTPREL) peak force, resistance training skills quotient, 10-, 20- and 30-meter sprint time, countermovement jump height, horizontal jump distance, anaerobic endurance performance, and seated medicine ball throw (SMBT). The IMTPREL displayed significant small to large correlations with all performance variables (r = 0.27-0.61) whereas maturity offset was significantly correlated with IMTPABS (r = 0.69), sprint (r = 0.29-0.33), jump (r = 0.23-0.34), and SMBT (r = 0.32). Absolute and relative strength were the strongest predictors of all performance variables and combined with maturity to explain 21-76% of the variance. Low and average relative strength boys were nearly eight times (odds ratio: 7.80, confidence interval: 1.48-41.12, p < 0.05) and nearly four times (odds ratio: 3.86, confidence interval: 0.95-15.59, p < 0.05) more likely to be classified as lower competency compared to high relative strength boys. Relative strength has more influence on motor skill performance than maturity when compared with movement competency

TempTabQA: Temporal Question Answering for Semi-Structured Tables

Semi-structured data, such as Infobox tables, often include temporal information about entities, either implicitly or explicitly. Can current NLP systems reason about such information in semi-structured tables? To tackle this question, we introduce the task of temporal question answering on semi-structured tables. We present a dataset, TempTabQA, which comprises 11,454 question-answer pairs extracted from 1,208 Wikipedia Infobox tables spanning more than 90 distinct domains. Using this dataset, we evaluate several state-of-the-art models for temporal reasoning. We observe that even the top-performing LLMs lag behind human performance by more than 13.5 F1 points. Given these results, our dataset has the potential to serve as a challenging benchmark to improve the temporal reasoning capabilities of NLP models.Comment: EMNLP 2023(Main), 23 Figures, 32 Table

Effects of combined resistance training and weightlifting on injury risk factors and resistance training skill of adolescent males

The purpose of this study was to investigate the effects of resistance training with or without weightlifting on risk factors for injury and resistance training skill in circa-peak height velocity boys. Sixty-seven boys (age 12-14 years) from a local secondary school were divided into three groups: combined resistance training (CRT), combined resistance training with weightlifting movements (CRT&WL), or a control group (CON). Experimental groups completed twice-weekly training programs over the course of an academic year. The tuck jump assessment, asymmetry measures for single-leg horizontal jump, isometric mid-thigh pull, and the Star Excursion Balance Test, and resistance training skill were measured pre-, mid-, and post-intervention. Only the CRT group significantly improved tuck jump assessment score pre- to post-test (p = 0.006, -20.4%, d = -0.39) but there were no clear effects on asymmetry measures for any group. Both groups significantly improved resistance training skill from pre- to post-test (CRT&WL: p = 0.002, 17.6%, d = 1.00; CRT: p = 0.026, 9.2%, d = 0.53). This study suggests that a school-based CRT program may provide significant improvements in jump landing kinematics, whereas the inclusion of weightlifting movements may provide greater improvements in resistance training skill

Pemanfaatan Bioteknologi Fermentasi Jerami Padi Sebagai Pakan Ternak

Desa Wonokerso, Kecamatan Pakisaji memiliki lahan pertanian dan perkebunan dengan komoditi utamanya adalah tanaman padi, jagung dan tebu. Limbah dari hasil pertanian terutama limbah tanaman padi masih banyak yang belum termanfaatkan. Oleh karena itu, tujuan kegiatan pengabdian ini adalah mensosialisasikan dan dan mendemonstrasikan pengolahan limbah jerami padi dengan menggunakan metode bioteknologi fermentasi untuk pakan ternak ruminansia (sapi, kambing, dan domba). Khalayak sasaran kegiatan ini kelompok peternak desa Wonokerso. Metode pelaksanaan kegiatan yang dilakukan meliputi observasi, sosialisasi, demonstrasi, dan evaluasi.  Berdasarkan hasil pelaksanaan kegiatan diketahui bahwa 1) masyarakat petani dan peternak desa Wonokerso mulai memahami keuntungan menggunakan fermentasi jerami sebagai pakan ternak, 2) peternak mulai tertarik untuk membuat dan mencoba memberikan fermentasi Jerami sebagai pakan ternak

Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges

[EN] If last decade viewed computational services as a utility then surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of ¿platform as a service¿ or ¿software as a service¿, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.Garcia Valls, MS.; Dubey, A.; Botti, V. (2018). Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges. Journal of Systems Architecture. 91:83-102. https://doi.org/10.1016/j.sysarc.2018.05.007S831029