International Institute of Information Technology, Hyderabad
Recent publications
Считается, что детерминистская переформулировка квантовой механики позволяет обойтись без обычных философских интерпретаций вероятности и стохастичности стандартных квантово-механических сценариев. Недавно 'т Хофт предложил другую точку зрения, основанную на онтологической формулировке квантовой механики, которая получается путем записи гамильтониана квантовой системы таким образом, чтобы сделать ее математически эквивалентной детерминированной системе. Онтологические детерминированные модели состоят из элементарных ячеек, также называемых клеточными автоматами, внутри которых описывающие динамику величины колеблются по периодическим орбитам. Это обобщает и заменяет классический язык квантовой механики, основанный на гармонических осцилляторах. Показано, что структуре наборов клеточных автоматов можно придать ясную физическую интерпретацию, используя уравнение Майораны с бесконечным числом компонент: клеточные автоматы являются элементарными строительными блоками, порожденными группой Пуанкаре преобразований пространства-времени, с положительной энергией вплоть до планковского масштаба. Этот подход тесно связан с гипотезой Римана.
Agriculture is the backbone of any developing or developed country that makes any living to survive. To make food available throughout the year, it is necessary to know about agriculture and the work and strategies involved. Hence, agricultural courses have to be introduced to higher education students. Additionally, agriculture-related methods are available in many higher education institutions for longer. However, students and teachers will face difficulties in real-time practical classes during certain challenging circumstances. These situations require the teacher to utilize trending technologies to improve the teaching and learning process and to make it more manageable. In this study, for this process, a novel neural network-based recognition algorithm (NN-RA) is implemented that works similarly to a backpropagation neural network (BP-NN) to provide a practical agriculture course. The proposed BP-NN is compared with the existing NN-RA, I-SC, and I-VDT algorithms based on the data transfer and signal-to-noise ratio. From the results, it can be observed that the proposed BP-NN attains a higher accuracy in data transfer of 99%.
Nucleobase π-π stacking is one of the crucial organizing interactions within three-dimensional (3D) RNA architectures. Characterizing the structural variability of these contacts in RNA crystal structures will help delineate their subtleties and their role in determining function. This analysis of different stacking geometries found in RNA X-ray crystal structures is the largest such survey to date; coupled with quantum-mechanical calculations on typical representatives of each possible stacking arrangement, we determined the distribution of stacking interaction energies. A total of 1,735,481 stacking contacts, spanning 359 of the 384 theoretically possible distinct stacking geometries, were identified. Our analysis reveals preferential occurrences of specific consecutive stacking arrangements in certain regions of RNA architectures. Quantum chemical calculations suggest that 88 of the 359 contacts possess intrinsically stable stacking geometries, whereas the remaining stacks require the RNA backbone or surrounding macromolecular environment to force their formation and maintain their stability. Our systematic analysis of π-π stacks in RNA highlights trends in the occurrence and localization of these noncovalent interactions and may help better understand the structural intricacies of functional RNA-based molecular architectures.
Living organisms are required to sense the environment accurately in order to ensure appropriate responses. The accuracy of estimating the environmental input is severely limited by noise stemming from inherent stochasticity of the chemical reactions involved in signaling pathways. Cells employ multiple strategies to improve the accuracy by tuning the reaction rates, for instance amplifying the response, reducing the noise etc.. However, the pathway also consumes energy through incorporating ATP in phosphorylating key signaling proteins involved in the reaction pathways. In many instances, improvements in accuracy elicit extra energetic cost. For example, higher deactivation rate suppresses the basal pathway activity effectively amplifying the dynamic range of the response which leads to improvement in accuracy. Higher deactivation rate also enhances the energy dissipation rate. Here, we employed a theoretical approach based on thermodynamics of information to explore the role of accuracy and energetic cost in the performance of a Mitogen Activated Protein Kinase signaling system. Our study shows that the accuracy-energy trade-off can explain the optimality of the reaction rates of the reaction pathways rather than accuracy alone. Our analysis elucidates the role of interplay between accuracy and energetic cost in evolutionary shaping of the parameter space of signaling pathways.
River valley projects have a lot of promise in the seismically active Himalayan orogenic region. Some hydroelectric projects are now operational, some are in the planning stages, and a few more will be built shortly. Knowing the nature of ground motion at these locations is critical. The present study uses a probabilistic seismic hazard analysis (PSHA) technique to estimate Peak Ground Acceleration (PGA) for the three hydropower projects in Uttarakhand, Himachal Pradesh, and Jammu and Kashmir (India). Given all potential earthquakes, the aim of probabilistic seismic hazard analysis (PSHA) is to quantify the rate of surpassing certain ground motion levels at the project site. Hazard curves may be used to determine the seismic design input for a location, and they can also be used to analyze the tunnel seismic reaction. The fundamental methods of PSHA are presented in this article in an attempt to offer a clear and brief introduction to the theoretical basis and implementation of PSHA in today’s engineering practice.
Recent advancements in deep learning have enabled 3D human body reconstruction from a monocular image, which has broad applications in multiple domains. In this paper, we propose SHARP (SHape Aware Reconstruction of People in loose clothing), a novel end-to-end trainable network that accurately recovers the 3D geometry and appearance of humans in loose clothing from a monocular image. SHARP uses a sparse and efficient fusion strategy to combine parametric body prior with a non-parametric 2D representation of clothed humans. The parametric body prior enforces geometrical consistency on the body shape and pose, while the non-parametric representation models loose clothing and handles self-occlusions as well. We also leverage the sparseness of the non-parametric representation for faster training of our network while using losses on 2D maps. Another key contribution is 3DHumans, our new life-like dataset of 3D human body scans with rich geometrical and textural details. We evaluate SHARP on 3DHumans and other publicly available datasets, and show superior qualitative and quantitative performance than existing state-of-the-art methods.
We determine the strange quark mass (ms) and quark mixing element |Vus|, and their joint determination from the Cabibbo suppressed hadronic τ decays in various perturbative schemes. We improve this analysis compared to the previous analysis based on the optimal renormalization or the renormalization group summed perturbation theory (RGSPT) scheme by replacing the theoretical longitudinal contributions with phenomenological parametrization; the RGSPT coefficients are used for the dimension-4 Adler functions. The improved analysis results in the extraction of ms(2 GeV)=98±19 MeV and |Vus|=0.2191±0.0043 from the RGSPT scheme.
Machine learning (ML) has recently facilitated many advances in solving problems related to many-body physical systems. Given the intrinsic quantum nature of these problems, it is natural to speculate that quantum-enhanced machine learning will enable us to unveil even greater details than we currently have. With this motivation, this paper examines a quantum machine learning approach based on shallow variational ansatz inspired by tensor networks for supervised learning tasks. In particular, we first look at the standard image classification tasks using the Fashion-MNIST dataset and study the effect of repeating tensor network layers on ansatz’s expressibility and performance. Finally, we use this strategy to tackle the problem of quantum phase recognition for the transverse-field Ising and Heisenberg spin models in one and two dimensions, where we were able to reach \(\ge 98\%\) test-set accuracies with both multi-scale entanglement renormalization ansatz (MERA) and tree tensor network (TTN) inspired parametrized quantum circuits.
A tool Smart Glazing Simulator (SGLSim), has been developed to perform parametric simulation analysis of different window systems with several window-to-wall ratios and orientations to compute and compare the annual energy performance. The net annual energy performance of the building is based on the electricity consumption in heating, cooling, interior lighting, and appliances, along with the electricity generation by the photovoltaic (PV) glazing, which is used to evaluate the energy performance of smart glazing. Performing parametric energy simulations and calculating the net annual electricity consumption of different combinations requires building modeling and energy simulation expertise. A web-based parametric tool can assist the user in carrying out the desired studies without requiring extensive technical knowledge. A case study is prepared for India’s warm and humid climatic zone. This study examines the benefits of double pane semi-transparent photovoltaics (STPV) glazing, STPV glazing with dynamic internal blind, and electrochromic (EC) glazing over other traditional glazing systems. The study shows that the optimal net annual electricity consumption in the case of STPV windows is 10–12% less than the optimal value obtained in a simple glazing case. Additionally, the result suggested that glare-controlled interior blinds in the STPV window further reduce the net annual electricity consumption by up to 15% compared to conventional glazing. Similarly, installing the EC glazing reduces the yearly electricity consumption by up to 5% compared to standard glazing.
When the Indian government declared the first lockdown on 25 March 2020 to control the increasing number of COVID-19 cases, people were forced to stay and work from home. The aim of this study is to quantify the impact of stay-at-home orders on residential Air Conditioning (AC) energy and household electricity consumption (excluding AC energy). This was done using monitored data from 380 homes in a group of five buildings in Hyderabad, India. We gathered AC energy and household electricity consumption data at a 30-min interval for each home individually in April 2019 and April 2020. Descriptive and inferential statistical analysis was done on this data. To offset the difference in temperatures for the month of April in 2019 and 2020, only those weekdays were selected where the average temperature in 2019 was same as the average temperature in 2020. The study establishes that the average number of hours the AC was used per day in each home increased in the range 4.90–7.45% depending on the temperature for the year 2020. Correspondingly, the overall AC consumption increased in the range 3.60–4.5%, however the daytime (8:00 AM to 8:00 PM) AC energy consumption increased in the range 22–26% and nighttime (8:00 PM to 8:00 AM) AC energy consumption decreased by 5–7% in the year 2020. The study showed a rise in household electricity consumption of about 15% for the entire day in the year 2020. The household electricity consumption increased during daytime by 22- 27.50% and 1.90- 6.6% during the nighttime. It was observed that the morning household electricity peak demand shifted from 7:00 AM in 2019 to 9:00 AM in 2020. Conversely, the evening peak demand shifted from 9:00 PM in 2019 to 7:00 PM in 2020. An additional peak was observed during afternoon hours in the lockdown.
The prevalence of anxiety among university students is increasing, resulting in the negative impact on their academic and social (behavioral and emotional) development. In order for students to have competitive academic performance, the cognitive function should be strengthened by detecting and handling anxiety. Over a period of 6 weeks, this study examined how to detect anxiety and how Mano Shakti Yoga (MSY) helps reduce anxiety. Relying on cardiac signals, this study follows an integrated detection-estimation-reduction framework for anxiety using the Intelligent Internet of Medical Things (IIoMT) and MSY. IIoMT is the integration of Internet of Medical Things (wearable smart belt) and machine learning algorithms (Decision Tree (DT), Random Forest (RF), and AdaBoost (AB)). Sixty-six eligible students were selected as experiencing anxiety detected based on the results of self-rating anxiety scale (SAS) questionnaire and a smart belt. Then, the students were divided randomly into two groups: experimental and control. The experimental group followed an MSY intervention for one hour twice a week, while the control group followed their own daily routine. Machine learning algorithms are used to analyze the data obtained from the smart belt. MSY is an alternative improvement for the immune system that helps reduce anxiety. All the results illustrate that the experimental group reduced anxiety with a significant (p < 0.05) difference in group × time interaction compared to the control group. The intelligent techniques achieved maximum accuracy of 80% on using RF algorithm. Thus, students can practice MSY and concentrate on their objectives by improving their intelligence, attention, and memory.
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Abhijit Mitra
  • Centre for Computational Natural Sciences and Bioinformatics (CCNSB)
Sachin Chaudhari
  • Signal Processing and Communications Research Center (SPCRC)
Zia Abbas
  • Centre for VLSI and Embedded System Technologies (CVEST)
Antarip Halder
  • Centre for Computational Natural Sciences and Bioinformatics (CCNSB)
Vishnu Sreekumar
  • Centre for Cognitive Science (CogSci)
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