Indian Institute of Technology Ropar

About: Indian Institute of Technology Ropar is a education organization based out in Ropar, India. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 1014 authors who have published 2878 publications receiving 35715 citations.

Modelling human preference in evolutionary art

Abstract: Creative activities including arts are characteristic to humankind. Our understanding of creativity is limited, yet there is substantial research trying to mimic human creativity in artificial systems and in particular to produce systems that automatically evolve art appreciated by humans. We propose here to model human visual preference by a set of aesthetic measures identified through observation of human selection of images and then use these for automatic evolution of aesthetic images.

Systematic study of low-energy incomplete fusion: Role of entrance channel parameters

Abstract: An attempt has been made to investigate the role of various entrance channel parameters in low-energy ($\ensuremath{\approx}4\text{--}7$ MeV/nucleon) incomplete fusion reactions through excitation function measurements. The analysis of measured excitation functions, in the framework of statistical model code pace4, reveals that the $xn$/$pxn$ channels are populated, predominantly, via complete fusion processes. However, in the production of $\ensuremath{\alpha}$-emitting channels, even after correcting for the precursor decay contribution, a significant enhancement as compared to statistical model predictions has been observed, which may be attributed due to the contribution of breakup processes. The observed enhancement is found to increase with projectile energy. Further, the comparison of present work with literature data reveals the dependence of incomplete fusion on mass-asymmetry of interacting partners, $\ensuremath{\alpha}\text{\ensuremath{-}}Q$ value of the projectile, and also on ${Z}_{P}{Z}_{T}$ (the Coulomb factor). From the present analysis, it may be concluded that a single entrance channel parameter (i.e., mass asymmetry or ${Z}_{P}{Z}_{T}$ or $\ensuremath{\alpha}\text{\ensuremath{-}}Q$ value) is not able to explain, completely, the yields of the low-energy incomplete fusion component. Therefore, a combination of these parameters and/or a parameter which can incorporate all gross features of interacting partners should be chosen to get a systematics for such reactions.

Investigating the temporal dynamics of electroencephalogram (EEG) microstates using recurrent neural networks.

Abstract: Electroencephalogram (EEG) microstates that represent quasi-stable, global neuronal activity are considered as the building blocks of brain dynamics. Therefore, the analysis of microstate sequences is a promising approach to understand fast brain dynamics that underlie various mental processes. Recent studies suggest that EEG microstate sequences are non-Markovian and nonstationary, highlighting the importance of the sequential flow of information between different brain states. These findings inspired us to model these sequences using Recurrent Neural Networks (RNNs) consisting of long-short-term-memory (LSTM) units to capture the complex temporal dependencies. Using an LSTM-based auto encoder framework and different encoding schemes, we modeled the microstate sequences at multiple time scales (200-2,000 ms) aiming to capture stably recurring microstate patterns within and across subjects. We show that RNNs can learn underlying microstate patterns with high accuracy and that the microstate trajectories are subject invariant at shorter time scales (≤400 ms) and reproducible across sessions. Significant drop in the reconstruction accuracy was observed for longer sequence lengths of 2,000 ms. These findings indirectly corroborate earlier studies which indicated that EEG microstate sequences exhibit long-range dependencies with finite memory content. Furthermore, we find that the latent representations learned by the RNNs are sensitive to external stimulation such as stress while the conventional univariate microstate measures (e.g., occurrence, mean duration, etc.) fail to capture such changes in brain dynamics. While RNNs cannot be configured to identify the specific discriminating patterns, they have the potential for learning the underlying temporal dynamics and are sensitive to sequence aberrations characterized by changes in metal processes. Empowered with the macroscopic understanding of the temporal dynamics that extends beyond short-term interactions, RNNs offer a reliable alternative for exploring system level brain dynamics using EEG microstate sequences.

Reductive Formylation of Nitroarenes using HCOOH over Bimetallic C−N Framework Derived from the Integration of MOF and COF

Abstract: CoZn embedded C−N framework is prepared by the carbonization of CoZn containing MOF integrated with COF porous architecture in Ar atmosphere. The graphitic nature of porous carbon is confirmed from Raman analysis. The porosity and nanostructure information are retrieved from N2‐sorption and transmission electron microscopic analysis, respectively. The incorporation of different metals and their oxidation states and types of nitrogen present in the C−N framework are confirmed from X‐ray photoelectron spectroscopy. The basicity of the materials is determined from a CO2‐temperature programmed desorption. ZnCo embedded C−N framework exhibits excellent activity in the selective reductive formylation using HCOOH. For comparison, more than 15 materials are prepared, and their activities are compared. Several control experiments are performed to establish a structure‐activity relation. The recycling experiment, hot‐filtration test, and poisoning experiment demonstrate the metal embedded porous C−N framework‘s recyclability and stability. A reaction mechanism for the reductive N‐formylation of nitroaromatics is presented based on structure‐activity relationship, control reactions, and physicochemical characterizations. The development of interesting MOF‐COF‐derived metal nanoclusters embedded C−N framework for selective reductive formylation of nitroaromatics using formic acid will be highly attractive to catalysis researchers and industrialists.