Sailing the Archipelago in a Boat of Rhymes Pantun in the Malay World

The extremely popular poetic form from Insular Southeast Asia, the pantun, travelled from its unknown source throughout the Malay Archipelago, first in Malay, then in the languages of Southeast Asia. In the ports and states where they were received, local colour, other idiosyncrasies, references, and linguistic characteristics have been added, and in fact, special forms with special names developed. This basic form is known, composed, and loved in at least 40 dialects of Malay, and 35 non-Malay languages, in the Peninsula and many of the islands of Malaysia and Indonesia. It spread through trade routes, ports, and also via diasporas and colonial economic projects which caused numerous peoples to move, who in turn brought the pantun along with them. It is now the most dynamic single literary form and has the longest history

Distributed Beamforming with Wirelessly Powered Relay Nodes

This paper studies a system where a set of $N$ relay nodes harvest energy from the signal received from a source to later utilize it when forwarding the source's data to a destination node via distributed beamforming. To this end, we derive (approximate) analytical expressions for the mean SNR at destination node when relays employ: i) time-switching based energy harvesting policy, ii) power-splitting based energy harvesting policy. The obtained results facilitate the study of the interplay between the energy harvesting parameters and the synchronization error, and their combined impact on mean SNR. Simulation results indicate that i) the derived approximate expressions are very accurate even for small $N$ (e.g., $N=15$), ii) time-switching policy by the relays outperforms power-splitting policy by at least $3$ dB.Comment: 4 pages, 3 figures, accepted for presentation at IEEE VTC 2017 Spring conferenc

Experimental Analysis of Ultra Wideband In Vivo Radio Channel

In this paper, we present the experimental analysis of in vivo wireless channel response on Ultra-Wideband (UWB) with the frequencies between 3.1-10.6 GHz. The analysis proves the location dependent based characteristics of in vivo channel. The results clearly show the highly multipath scenario. It can also be observed that the multipath effect of the channel is much higher in the denser areas, i.e. an antenna placed within the intestine area or inside the stomach. Results prove that in vivo channel is different from a conventional communication channel and therefore extensive studies need to be done to understand the channel

Molecular Simulations of the Ribosome and Associated Translation Factors

The ribosome is a macromolecular complex which is responsible for protein synthesis in all living cells according to their transcribed genetic information. Using X-ray crystallography and, more recently, cryo-electron microscopy (cryo-EM), the structure of the ribosome was resolved at atomic resolution in many functional and conformational states. Molecular dynamics simulations have added information on dynamics and energetics to the available structural information, thereby have bridged the gap to the kinetics obtained from single-molecule and bulk experiments. Here, we review recent computational studies that brought notable insights into ribosomal structure and function.Comment: 11 pages, 3 figures, to be published in Current Opinion in Structural Biolog

Exact Byzantine Consensus on Arbitrary Directed Graphs Under Local Broadcast Model

We consider Byzantine consensus in a synchronous system where nodes are connected by a network modeled as a directed graph, i.e., communication links between neighboring nodes are not necessarily bi-directional. The directed graph model is motivated by wireless networks wherein asymmetric communication links can occur. In the classical point-to-point communication model, a message sent on a communication link is private between the two nodes on the link. This allows a Byzantine faulty node to equivocate, i.e., send inconsistent information to its neighbors. This paper considers the local broadcast model of communication, wherein transmission by a node is received identically by all of its outgoing neighbors, effectively depriving the faulty nodes of the ability to equivocate. Prior work has obtained sufficient and necessary conditions on undirected graphs to be able to achieve Byzantine consensus under the local broadcast model. In this paper, we obtain tight conditions on directed graphs to be able to achieve Byzantine consensus with binary inputs under the local broadcast model. The results obtained in the paper provide insights into the trade-off between directionality of communication links and the ability to achieve consensus

Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach

Many algorithms in workflow scheduling and resource provisioning rely on the performance estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the execution of tasks and predicting their runtime accurately, therefore, becomes an essential part of any Workflow Management System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS) platforms that use clouds for deploying scientific workflows, task runtime prediction becomes more challenging because it requires the processing of a significant amount of data in a near real-time scenario while dealing with the performance variability of cloud resources. Hence, relying on methods such as profiling tasks' execution data using basic statistical description (e.g., mean, standard deviation) or batch offline regression techniques to estimate the runtime may not be suitable for such environments. In this paper, we propose an online incremental learning approach to predict the runtime of tasks in scientific workflows in clouds. To improve the performance of the predictions, we harness fine-grained resources monitoring data in the form of time-series records of CPU utilization, memory usage, and I/O activities that are reflecting the unique characteristics of a task's execution. We compare our solution to a state-of-the-art approach that exploits the resources monitoring data based on regression machine learning technique. From our experiments, the proposed strategy improves the performance, in terms of the error, up to 29.89%, compared to the state-of-the-art solutions.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM International Conference on Utility and Cloud Computin