Abstract

Across the world billions of dollars of damage are attributed to rodents, resulting in them being classified collectively as the biggest animal pest in the world. At a commercial scale most pest control companies employ the labour intensive approach of deploying and manually monitoring rodenticide bait stations. In this paper was present, RatSpy, a visual, low-power bait station monitoring system which wirelessly reports both on bait station levels and intruders entering the bait station. The smart bait stations report data back to a custom designed cloud platform. The system performance was evaluated under realistic field conditions (on an active cattle farm) with initial results showing significant potential in terms of reducing manual labour, improving scalability and data.

Abstract

A plethora of program analysis and optimization techniques rely on linear programming at their heart. However, such techniques are often considered too slow for production use. While today’s best solvers are optimized for complex problems with thousands of dimensions, linear programming, as used in compilers, is typically applied to small and seemingly trivial problems, but to many instances in a single compilation run. As a result, compilers do not benefit from decades of research on optimizing large-scale linear programming. We design a simplex solver targeted at compilers. A novel theory of transprecision computation applied from individual elements to full data-structures provides the computational foundation. By carefully combining it with optimized representations for small and sparse matrices and specialized small-coefficient algorithms, we (1) reduce memory traffic, (2) exploit wide vectors, and (3) use low-precision arithmetic units effectively. We evaluate our work by embedding our solver into a state-of-the-art integer set library and implement one essential operation, coalescing, on top of our transprecision solver. Our evaluation shows more than an order-of-magnitude speedup on the core simplex pivot operation and a mean speedup of 3.2x (vs. GMP) and 4.6x (vs. IMath) for the optimized coalescing operation. Our results demonstrate that our optimizations exploit the wide SIMD instructions of modern microarchitectures effectively. We expect our work to provide foundations for a future integer set library that uses transprecision arithmetic to accelerate compiler analyses

Abstract

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Abstract

Cellular processes are inherently noisy, and the selection for accurate responses in presence of noise has likely shaped signalling networks. Here, we investigate the trade-off between accuracy of information transmission and its energetic cost for a mitogen-activated protein kinase (MAPK) signalling cascade. Our analysis of the pheromone response pathway of budding yeast suggests that dose-dependent induction of the negative transcriptional feedbacks in this network maximizes the information per unit energetic cost, rather than the information transmission capacity itself. We further demonstrate that futile cycling of MAPK phosphorylation and dephosphorylation has a measurable effect on growth fitness, with energy dissipation within the signalling cascade thus likely being subject to evolutionary selection. Considering optimization of accuracy versus the energetic cost of information processing, a concept well established in physics and engineering, may thus offer a general framework to understand the regulatory design of cellular signalling systems.

Abstract

n this study, a 3D seismic response of soil deposit, soil-ash deposit and soil-ash-foundation system was investigated. Homogeneous sand deposit of 80m × 9m × 20m was initially analyzed. A pond ash layer is on top of the sand deposit with varying thicknesses and the efficiency of the pond ash layer on the sand deposit was evaluated for its best suitability. The optimum sand-ash deposit overlain by a shallow foundation has been analysed under the excitation of the Nepal (Mw:7.8) and North East India earthquake (Mw:7.5). A seismic response analysis was performed using finite element software PLAXIS3D. The finite element model adopted for the present study has been validated using 1D nonlinear ground response analysis programs. e.g. DEEPSOIL and Cyclic1D. Results of the response analysis have been determined in terms of acceleration, displacement, excess pore pressure, and excess pore pressure ratio. It was observed that, the sand-pond ash-foundation system experienced liquefaction when excited under the Nepal earthquake motion whereas it is safe against the North East India earthquake.

Abstract

ndian Musicology defines 22 distinct shrutis (microtones), 12 swa- rasthanas (semitones) and 7 swaras (tones). By implementing the mathematical concepts like cycle of fourths and fifths, these shrutis acquire a discrete frequency ratio which justifies the Bharatha’s 22 shrutis experiment. Indian classical music defines the 7 swaras and each swara is associated to an animal/bird call. The Grama system in Indian music distributes the 22 shrutis amongst the 7 swaras. We based our work on the Shadja Grama system. The traversal of pitches in a swara are more important in understanding the swara as compared to the fre- quency ratio. This shows us that swaras have peculiar properties and an identity independent of the octave.

Abstract

Requirements Engineering in the industry is expertise-driven, heavily manual, and centered around various types of requirement specification documents being prepared and maintained. These specification documents are in diverse formats and vary depending on whether it is a business requirement document, functional specification, interface specification, client specification, and so on. These diverse specification documents embed crucial product knowledge such as functional decomposition of the domain into features, feature hierarchy, feature types and their specific feature characteristics, dependencies, business context, etc. Moreover, in a product development scenario, thousands of pages of requirement specification documentation is created over the years. Comprehending functionality and its associated context from large volumes of specification documents is a highly complex task. To address this problem, we propose to digitalize the requirement specification documents into processable models. This paper discusses the salient aspects involved in the digitalization of requirements knowledge from diverse requirement specification documents. It proposes an AI engine for the automatic transformation of diverse text-based requirement specifications into machine-processable models using NLP techniques and the generation of context-sensitive user stories. The paper describes the key requirement abstractions and concepts essential in an industrial scenario, the conceptual meta-model, and DizReq engine (AI engine for digitalizing requirements) implementation for automatically transforming diverse requirement specifications into user stories embedding the business context. The evaluation results from digitalizing specifications of an IT product suite are discussed: mean feature extraction efficiency is 40 features/file, mean user story extraction efficiency is 71 user stories/file, feature extraction accuracy is 94%, and requirement extraction accuracy is 98%. Keywords: NLP, NLP4RE, model extraction, requirements digitalization, requireme

Abstract

RF bio-sensing is often utilized for the detection of bio-samples by using the concept of shift in resonant frequency. It is widely reported that the shift in resonant frequency is narrower compared to the bandwidth of the device used for measuring this shift. As a result, wideband Vector Network Analyzer (VNA) proves to be an overkill and instead a narrowband measurement setup can fulfill this requirement. Therefore, in this paper, an integrated solution for the detection of materials having varying permittivities is presented. A DualBand Six Port Reflectometer (DBSPR) is proposed and designed using planer microstrip line technology for the purpose of characterizing the sensor using reflection and transmission coefficient. A Complimentary Split Ring Resonator (CSRR) based sensor having an unloaded resonance frequency of 2.3 GHz is utilized as a test set. The designed DBSPR operates at 1.12 GHz and 2.26 GHz with a 10dB bandwidth of 254 MHz and 215 MHz respectively. The DBSPR and CSRR sensor are integrated using a microstrip line. The integrated device is tested for two different materials having a permittivity of 2.3 and 4.3. A shift of 118 MHz is observed between these materials and thus shows the potential usefulness of the proposed setup.

Abstract

L-Lysine is an essential amino acid and bio-sample observing major significance in food processing, pharmaceutical and agricultural industries. Conventional sensing techniques require longer pre-processing times and are sensitive to ambient conditions. However, radio frequency (RF) sensing based on Complementary Split Ring Resonator (CSRR) exhibits a significant shift in the resonant frequency and is highly desirable for the L-Lysine’s quantitative analysis. The frequency shifts of 498.4 MHz, 482.9 MHz, 471.4 MHz, 459.9 MHz and 452.3 MHz are obtained through varying concentrations from 0 mg/ml to 40 mg/ml in step size 10 mg/ml of L-Lysine solution. Therefore, experimental results and analysis presented in this work indicate the proposed radio frequency sensor’s linearity in the abovereported concentration range.

Abstract

Problems involving code-mixed language are often plagued by a lack of resources and an absence of materials to perform sophisticated transfer learning with. In this paper we describe our submission to the Sentimix Hindi-English task involving sentiment classification of code-mixed texts, and with an F1 score of 67.1%, we demonstrate that simple convolution and attention may well produce reasonable results.