Department of Humanities and Social Sciences

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

For p∈ (1 , N) and Ω ⊆ RN open, the Beppo-Levi space D01,p(Ω) is the completion of Cc∞(Ω) with respect to the norm [∫Ω|∇u|pdx]1p. Using the p-capacity, we define a norm and then identify the Banach function space H(Ω) with the set of all g in Lloc1(Ω) that admits the following Hardy–Sobolev type inequality: ∫Ω|g||u|pdx≤C∫Ω|∇u|pdx,∀u∈D01,p(Ω),for some C&gt; 0. Further, we characterize the set of all g in H(Ω) for which the map G(u)=∫Ωg|u|pdx is compact on D01,p(Ω). We use a variation of the concentration compactness lemma to give a sufficient condition on g∈ H(Ω) so that the best constant in the above inequality is attained in D01,p(Ω). © 2021, Fondazione Annali di Matematica Pura ed Applicata and Springer-Verlag GmbH Germany, part of Springer Nature.

Department of Mathematics

Fulltext

Annali di Matematica Pura ed Applicata

The compactness and the concentration compactness via p-capacity

The increasing demand for food and clean energy, such as biofuel calls for a sustainable food-energy nexus in the agriculture sector. Mixed cropping pattern of food and biofuel crops is a viable strategy to meet the escalating demands of the biofuel production at the cost of food production. The implementation of the proposed solutions of simulation–optimization frameworks, at larger spatial scales, is a challenging task. One of the commonly adopted approaches is to implement the solution initially in critical zones that are sensitive to the land management practices and are critical for achieving the objectives. Despite the different techniques to identify the critical zones, this study proposes a new approach to identify the critical zones within a watershed, where the land use changes are essential to improve the social and physical environment while meeting the concurring demands for food and biofuel production. A decision support system (DSS), utilizing the concept of analytical hierarchy process (AHP) is developed to choose the number of optimal solutions from the Pareto-optimal Front to reduce the uncertainty involved in solution adaption by the decision-maker and identification of the critical zone. The results from the study indicate how solution strategies can influence the objective of optimal balance between crop demand and nutrient minimization using different cases. The proposed land use using the developed framework reduced the Total Nitrogen and Total Phosphorous loads by 29% and 38%, respectively from the watershed by converting about 44% of the baseline land use to different cropping patterns with the restriction on minimal food grain and biomass production. The outcome of the framework indicates that the adaptation of more robust objective function for spatial optimization through the developed DSS can reduce the nutrient load in the downstream water. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

Department Of Civil Engineering

Department of Aerospace Engineering

Stochastic Environmental Research and Risk Assessment

A decision support system for the identification of critical zones in a watershed to implement land management practices

Electroencephalography (EEG) signals are promising as alternatives to other biometrics owing to their protection against spoofing. Previous studies have focused on capturing individual variability by analyzing task/condition-specific EEG. This work attempts to model biometric signatures independent of task/condition by normalizing the associated variance. Toward this goal, the paper extends ideas from subspace-based text-independent speaker recognition and proposes novel modifications for modeling multi-channel EEG data. The proposed techniques assume that biometric information is present in the entire EEG signal and accumulate statistics across time in a high dimensional space. These high dimensional statistics are then projected to a lower dimensional space where the biometric information is preserved. The lower dimensional embeddings obtained using the proposed approach are shown to be task-independent. The best subspace system identifies individuals with accuracies of 86.4% and 35.9% on datasets with 30 and 920 subjects, respectively, using just nine EEG channels. The paper also provides insights into the subspace model's scalability to unseen tasks and individuals during training and the number of channels needed for subspace modeling. © 2005-2012 IEEE.

Department of Computer Science and Engineering

IEEE Transactions on Information Forensics and Security

Evidence of Task-Independent Person-Specific Signatures in EEG Using Subspace Techniques

Most business cycle theories originate from a paradigmatic vision with a two fold simplicity. The first of which is that firms are bound through simple relations in which they influence each other anonymously via prices but not directly through non-price rivalrous competition. And the second is that firms have immutable and transparent cores. These presumptions yield an economy whose natural state is one of stability. Macroeconomic dynamics, or change more generally, is brought about by exogenous shocks. This paper develops an alternate vision for understanding business cycle dynamics. We consider the dynamics that emerge from micro changes that originate from deep within firms. Each firm is surprised by externally visible innovations of other firms because they cannot observe each others’ high dimensional interiors. Innovations can cascade because of the complex microeconomic interrelations between the plans of firms. The interaction between external-observable attributes and internal-unobservable active cores of firms is capable of generating change without exogenous shocks. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Department of Mechanical Engineering

Review of Austrian Economics

Business cycles and the internal dynamics of firms

This paper proposes a novel design for a hermetically sealable device, consisting of charged linear and nonlinear membranes driven in the gigahertz range in vacuum setting, as a source of antibunched single phonons. Constraints for effecting phonon antibunching are found using the stationary Liouville-von Neumann master equation. Using analytical calculations and material and geometry optimization, we show that sizes of the proposed system can be upscaled to the near-micrometer range in a trade-off with the system operating temperature. The results are significant to realize quantum phononics, which has much promise as a modality for sensing and computing applications. © 2021 Author(s).

World University root node

AIP Advances

Novel hermetically sealed device to realize unconventional phonon blockade at near-micron dimensions and milliKelvin temperatures

Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result in slowly-varying fluorescence signals with low temporal resolution. Estimating the temporal positions of the neuronal action potentials from these signals is a challenging problem. In the literature, several generative model-based and data-driven algorithms have been studied with varied levels of success. This article proposes a neural network-based signal-to-signal conversion approach, where it takes as input raw-fluorescence signal and learns to estimate the spike information in an end-to-end fashion. Theoretically, the proposed approach formulates the spike estimation as a single channel source separation problem with unknown mixing conditions. The source corresponding to the action potentials at a lower resolution is estimated at the output. Experimental studies on the spikefinder challenge dataset show that the proposed signal-to-signal conversion approach significantly outperforms state-of-the-art-methods in terms of Pearson’s correlation coefficient, Spearman’s rank correlation coefficient and yields comparable performance for the area under the receiver operating characteristics measure. We also show that the resulting system: (a) has low complexity with respect to existing supervised approaches and is reproducible; (b) is layer-wise interpretable, and (c) has the capability to generalize across different calcium indicators. Copyright: © 2021 Sebastian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PLoS Computational Biology

Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Ambient PM10 (particulate matter with aerodynamic diameter ≤ 10 µm) samples were collected and characterized from July 2012 to August 2013 with the objective to evaluate the variation in elemental concentration and use the same as markers for source apportionment and health risk assessment for the first time over Bhubaneswar, India. The yearly average mass of PM10 was 82.28 µg/m3, which was   37% higher than the national ambient air quality (NAAQ) standards. Maximum PM10 concentration was observed during winter season followed by post-monsoon, pre-monsoon, and monsoon months. Acid soluble components in the PM10 samples were analyzed using ICP-OES (inductive coupled plasma optical emission spectroscopy), and 19 different elements including heavy metals were determined. Enrichment factor analysis attributed the source to either crustal or non-crustal origin. Principal component analysis (PCA) revealed that crustal sources, industrial activities, and vehicular emissions were significant contributors to PM mass. The contribution of total average elemental concentration showed a seasonal variation with the lowest (11.96 µg/m3) and highest (17.77 µg/m3) during monsoon and winter, respectively, which is relatively less significant than the variation in total PM10 mass that ranged between 48.43 µg/m3 in monsoon and 138.24 µg/m3 during the winter season. This observation evidences the predominant contribution of local/regional emission sources to the metallic components in coarse PM10 mass, which is corroborated by the wind pattern studies carried out using polar plots and a Lagrangian Particle Dispersion Model (LPDM) FLEXPART. Further, carcinogenic and non-carcinogenic health risk assessments of the measured elements that find their way into the human body through different exposure pathways have been calculated using United State Environmental Protection Agency (USEPA) standards. The carcinogenic risk of most of the elements was insignificant. The potential risk assessment study revealed that regular exposure to heavy metals through the ingestion pathway caused detrimental health effects. These effects were observed to be more severe in children in comparison to adults. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Environmental Monitoring and Assessment

Metals in coarse ambient aerosol as markers for source apportionment and their health risk assessment over an eastern coastal urban atmosphere in India

This study analyses the spatial and sectoral distribution of Greenfield Foreign Direct Investment (GFDI) and its impact on industrial agglomeration in India for the time period 2006–2015. We employ a unique dataset obtained by merging GFDI data from FDI Markets and plant level data from Annual Survey of Industries (ASI). We find that the Indian manufacturing sector shows signs of industrial dispersion rather than agglomeration throughout the sample period. Likewise, the spatial distribution pattern of GFDI also shows a decline in regional concentration and, interestingly, GFDI also seems to target new destinations. We also find that the increased spatial spread of GFDI leads to an industrial dispersion in Indian manufacturing industries. © 2020 Elsevier B.V.

Economic Systems

Foreign direct investment and industrial agglomeration: Evidence from India

Industrial performance is an essential element of economic progress. In this study, we examine the impact of outsourcing on industrial performance using the firm-level data of 191 textile companies in India over the period 2000–2015. First, we follow the conventional non-parametric two-stage procedure and analyse the nexus between outsourcing and firm performance under a single-objective setting. We then test the influence of outsourcing on the performance of multiple-objective firms using reverse directional distance function scores. To address the bias in efficiency estimation and the serial correlation issue in the second-stage regression, we use truncated regression and the double-bootstrap procedure for panel data analysis. Our results show an improvement in industrial performance over the study period. Our analysis following the conventional two-stage procedure shows that the outsourcing of manufacturing activities and professional jobs contributes to industrial performance. The relation between outsourcing and firm performance essentially remains the same in a more reliable analysis using a panel double bootstrap procedure. © 2020 Elsevier B.V.

Research in International Business and Finance

Outsourcing and firm performance nexus: An analysis using the conventional and panel double-bootstrap procedure

Although asset pricing theories predict a positive relation between risk and returns, empirical findings on credit risk-return relationship are mixed. And, observed negative relation between the two in this regard is referred to as credit risk-return puzzle. Using credit rating as a measure of credit risk, we have investigated into the existence of this puzzle in India during July 2011 to March 2019. We have used information for the companies listed on the National Stock Exchange for this purpose. Our results validate the presence of this puzzle in the Indian stock market. Moreover, credit risk is observed to be a systematic risk, which has not been captured in the traditional asset pricing models. We have also observed partial evidence favoring both behavioural and rational pricing explanations—the two widely acknowledged explanations in the literature behind this puzzle. On our further query in this connection, we have not seen any significant change in the puzzle due to the recent enactment of the Insolvency and Bankruptcy Code.&nbsp;