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This study, conducted in 2017, explores the use of Machine learning algorithms to predict Characteristics of Transmission Lines such as Impedance or resonance frequency using design parameters of Transmission Lines. Using formulas and equations that define the characteristics of Transmission lines, training data was generated. We trained different models for this dataset. The extent of deviation of predicted output from the actual output was measured in terms of maximum error and average error. This helped determine how well an algorithm worked for a particular transmission line. Further, the best-suited algorithm for each transmission line under consideration was found based on the error, Comment: 6 pages, 8 figures, 2 tables

Data-driven approaches, including deep learning, have shown great promise as surrogate models across many domains. These extend to various areas in sustainability. An interesting direction for which data-driven methods have not been applied much yet is in the quick quantitative evaluation of urban layouts for planning and design. In particular, urban designs typically involve complex trade-offs between multiple objectives, including limits on urban build-up and/or consideration of urban heat island effect. Hence, it can be beneficial to urban planners to have a fast surrogate model to predict urban characteristics of a hypothetical layout, e.g. pedestrian-level wind velocity, without having to run computationally expensive and time-consuming high-fidelity numerical simulations. This fast surrogate can then be potentially integrated into other design optimization frameworks, including generative models or other gradient-based methods. Here we present the use of CNNs for urban layout characterization that is typically done via high-fidelity numerical simulation. We further apply this model towards a first demonstration of its utility for data-driven pedestrian-level wind velocity prediction. The data set in this work comprises results from high-fidelity numerical simulations of wind velocities for a diverse set of realistic urban layouts, based on randomized samples from a real-world, highly built-up urban city. We then provide prediction results obtained from the trained CNN, demonstrating test errors of under 0.1 m/s for previously unseen urban layouts. We further illustrate how this can be useful for purposes such as rapid evaluation of pedestrian wind velocity for a potential new layout. It is hoped that this data set will further accelerate research in data-driven urban AI, even as our baseline model facilitates quantitative comparison to future methods.

One of the major roadblocks for the establishment of 2D semiconductor technology for CMOS integrated circuits is lack of industry scalable doping techniques that lead to 2D FETs with comparable n-type and p-type behavior. Here we demonstrate a Chalcogen based technique to alter the surface of WSe2 to realize enhanced ambipolar behavior along with a complete transition from n to p-type behavior of WSe2 FETs. The technique involves dry chemistry between Chalcogen atom and TMDC surface which leads to surface states that cause improved hole and electron injection through the FETs. We propose such a technique for realization of all WSe2 based CMOS integrated circuits and therefore unveil its potential towards technology.

We study the power of fractional allocations of resources to maximize influence in a network. This work extends in a natural way the well-studied model by Kempe, Kleinberg, and Tardos (2003), where a designer selects a (small) seed set of nodes in a social network to influence directly, this influence cascades when other nodes reach certain thresholds of neighbor influence, and the goal is to maximize the final number of influenced nodes. Despite extensive study from both practical and theoretical viewpoints, this model limits the designer to a binary choice for each node, with no way to apply intermediate levels of influence. This model captures some settings precisely, e.g. exposure to an idea or pathogen, but it fails to capture very relevant concerns in others, for example, a manufacturer promoting a new product by distributing five "20% off" coupons instead of giving away one free product. While fractional versions of problems tend to be easier to solve than integral versions, for influence maximization, we show that the two versions have essentially the same computational complexity. On the other hand, the two versions can have vastly different solutions: the added flexibility of fractional allocation can lead to significantly improved influence. Our main theoretical contribution is to show how to adapt the major positive results from the integral case to the fractional case. Specifically, Mossel and Roch (2006) used the submodularity of influence to obtain their integral results; we introduce a new notion of continuous submodularity, and use this to obtain matching fractional results. We conclude that we can achieve the same greedy $(1-1/e-\epsilon)$-approximation for the fractional case as the integral case. In practice, we find that the fractional model performs substantially better than the integral model, according to simulations on real-world social network data.

In the raise of many global organizations deploying their data centers and content services in India, the prefix reachability performance study from global destinations garners our attention. The events such as failures and attacks occurring in the Internet topology have impact on Autonomous System (AS) paths announced in the control plane and reachability of prefixes from spatially distributed ASes. As a consequence the customer reachability to the services in terms of increased latency and outages for a short or long time are experienced. The challenge in control plane event detection is when the data plane traffic is able to reach the intended destinations correctly. However detection of such events are crucial for the operations of content and data center industries. By monitoring the spatially distributed routing table features like AS path length distributions, spatial prefix reachability distribution and covering to overlap route ratio, we can detect the control plane events. In our work, we study prefix AS paths from the publicly available route-view data and analyze the global reachability as well as reachability to Indian AS topology. To capture the spatial events in a single temporal pattern, we propose a counting based measure using prefixes announced by x % of spatial peers. Employing statistical characteristics change point detection and temporal aberration algorithm on the time series of the proposed measure, we identify the occurrence of long and stochastic control plane events. The impact and duration of the events are also quantified. We validate the mechanisms over the proposed measure using the SEA-Me-We4 cable cut event manifestations in the control plane of Indian AS topology. The cable cut events occurred on 6th June 2012 (long term event) and 17th April 2012 (stochastic event) are considered for validation., Comment: 19 pages, 8 figures

At present Internet has emerged as a country's predominant and viable data communication infrastructure. The Autonomous System (AS) resources which are building blocks of the Internet are AS numbers, IPv4 and IPv6 Prefixes. AS number growth is one of Internet infrastructure development indicators. Hence understanding on long term trend and stochastic variation behaviour are essential to detect significant events during the growth. In this work, time series based approximation is considered for mathematical modelling and forecast the yearly AS growth. The AS data of five countries namely India, China, Japan, South Korea and Taiwan are extracted from APNIC archive. ARIMA models with different Auto Regressive and Moving Average parameters are identified for forecasting. Model validation, parameter estimation, point forecast and prediction intervals with 95 % confidence levels for the five countries are reported in the paper. The significant level change in variations, positive growth percentage in Inter Annual Absolute Variations (IAAV) and higher percentage of advertised ASes when compared to other countries indicate India's fast growth and wider global reachability of Internet infrastructure from 2007 onwards. The correlation between IAAV change point and GDP growth period indicates that service sector industry growth is the driving force behind significant yearly changes., Comment: 22 pages, 15 figures

Colloidal dispersions of Laponite platelets are known to age slowly from viscous sols to colloidal glasses. We follow this aging process by monitoring the diffusion of probe particles embedded in the sample via dynamic light scattering. Our results show that the time-dependent diffusion of the probe particles scales with their size. This implies that the fluctuation-dissipation theorem can be generalized for this out-of-equilibrium system by replacing the bath temperature with an effective temperature. Simultaneous dynamic rheological measurements reveal that this effective temperature increases as a function of aging time and frequency. This suggests the existence of two regimes: at probed time scales longer than the characteristic relaxation time of the Laponite dispersion, the system thermalizes with the bath, whereas at shorter time scales, the system is out-of-equilibrium with an effective temperature greater than the bath temperature., Comment: 4 pages, 4 figures