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In the context of four-dimensional ${\cal N} = 1$ type IIB superstring compactifications, the U-dual completion of the holomorphic flux superpotential leads to four S-dual pairs of fluxes, namely $(F, H), (Q, P), (P', Q')$ and $(H', F')$. It has been observed that the scalar potentials induced by such generalized superpotentials typically have an enormous amount of terms, making it hard to study any phenomenological aspects such as moduli stabilization. In this regard, we present a set of generic master formulae which not only formulate the scalar potential in a compact way but also enable one to {\it read-off} the various scalar potential pieces by simply knowing a set of topological data of the compactifying Calabi Yau and its mirror threefold. We demonstrate the applicability of our master formulae by {\it reading-off} the scalar potentials for five explicit models, and using a set of {\it axionic flux} combinations we show that 76276 terms arising from the flux superpotential in a ${\mathbb T}^6/({\mathbb Z}_2\times{\mathbb Z}_2)$-based model can be equivalently expressed by using 2816 terms, while 11212 terms arising from the flux superpotential in a Quintic-based model can be equivalently expressed by 668 terms! We argue that the master formulae presented in this work can be useful in an analytic exploration of the rich landscape of the non-geometric flux vacua., Comment: 43 pages, 5 tables, typos fixed

Within the machine learning community, reconstruction attacks are a principal attack of concern and have been identified even in federated learning, which was designed with privacy preservation in mind. In federated learning, it has been shown that an adversary with knowledge of the machine learning architecture is able to infer the exact value of a training element given an observation of the weight updates performed during stochastic gradient descent. In response to these threats, the privacy community recommends the use of differential privacy in the stochastic gradient descent algorithm, termed DP-SGD. However, DP has not yet been formally established as an effective countermeasure against reconstruction attacks. In this paper, we formalise the reconstruction threat model using the information-theoretic framework of quantitative information flow. We show that the Bayes' capacity, related to the Sibson mutual information of order infinity, represents a tight upper bound on the leakage of the DP-SGD algorithm to an adversary interested in performing a reconstruction attack. We provide empirical results demonstrating the effectiveness of this measure for comparing mechanisms against reconstruction threats.

Decentralized learning (DL) faces increased vulnerability to privacy breaches due to sophisticated attacks on machine learning (ML) models. Secure aggregation is a computationally efficient cryptographic technique that enables multiple parties to compute an aggregate of their private data while keeping their individual inputs concealed from each other and from any central aggregator. To enhance communication efficiency in DL, sparsification techniques are used, selectively sharing only the most crucial parameters or gradients in a model, thereby maintaining efficiency without notably compromising accuracy. However, applying secure aggregation to sparsified models in DL is challenging due to the transmission of disjoint parameter sets by distinct nodes, which can prevent masks from canceling out effectively. This paper introduces CESAR, a novel secure aggregation protocol for DL designed to be compatible with existing sparsification mechanisms. CESAR provably defends against honest-but-curious adversaries and can be formally adapted to counteract collusion between them. We provide a foundational understanding of the interaction between the sparsification carried out by the nodes and the proportion of the parameters shared under CESAR in both colluding and non-colluding environments, offering analytical insight into the working and applicability of the protocol. Experiments on a network with 48 nodes in a 3-regular topology show that with random subsampling, CESAR is always within 0.5% accuracy of decentralized parallel stochastic gradient descent (D-PSGD), while adding only 11% of data overhead. Moreover, it surpasses the accuracy on TopK by up to 0.3% on independent and identically distributed (IID) data.

Decentralized learning (DL) enables collaborative learning without a server and without training data leaving the users' devices. However, the models shared in DL can still be used to infer training data. Conventional defenses such as differential privacy and secure aggregation fall short in effectively safeguarding user privacy in DL, either sacrificing model utility or efficiency. We introduce Shatter, a novel DL approach in which nodes create virtual nodes (VNs) to disseminate chunks of their full model on their behalf. This enhances privacy by (i) preventing attackers from collecting full models from other nodes, and (ii) hiding the identity of the original node that produced a given model chunk. We theoretically prove the convergence of Shatter and provide a formal analysis demonstrating how Shatter reduces the efficacy of attacks compared to when exchanging full models between nodes. We evaluate the convergence and attack resilience of Shatter with existing DL algorithms, with heterogeneous datasets, and against three standard privacy attacks. Our evaluation shows that Shatter not only renders these privacy attacks infeasible when each node operates 16 VNs but also exhibits a positive impact on model utility compared to standard DL. In summary, Shatter enhances the privacy of DL while maintaining the utility and efficiency of the model., Comment: Accepted at PETS 2025

This paper introduces ZIP-DL, a novel privacy-aware decentralized learning (DL) algorithm that exploits correlated noise to provide strong privacy protection against a local adversary while yielding efficient convergence guarantees for a low communication cost. The progressive neutralization of the added noise during the distributed aggregation process results in ZIP-DL fostering a high model accuracy under privacy guarantees. ZIP-DL further uses a single communication round between each gradient descent, thus minimizing communication overhead. We provide theoretical guarantees for both convergence speed and privacy guarantees, thereby making ZIP-DL applicable to practical scenarios. Our extensive experimental study shows that ZIP-DL significantly outperforms the state-of-the-art in terms of vulnerability/accuracy trade-off. In particular, ZIP-DL (i) reduces the efficacy of linkability attacks by up to 52 percentage points compared to baseline DL, (ii) improves accuracy by up to 37 percent w.r.t. the state-of-the-art privacy-preserving mechanism operating under the same threat model as ours, when configured to provide the same protection against membership inference attacks, and (iii) reduces communication by up to 10.5x against the same competitor for the same level of protection.

Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their data stored locally, sharing only the model updates obtained by minimizing a cost function over their local inputs. FL was proposed as a stepping-stone towards privacy-preserving machine learning, but it has been shown vulnerable to issues such as leakage of private information, lack of personalization of the model, and the possibility of having a trained model that is fairer to some groups than to others. In this paper, we address the triadic interaction among personalization, privacy guarantees, and fairness attained by models trained within the FL framework. Differential privacy and its variants have been studied and applied as cutting-edge standards for providing formal privacy guarantees. However, clients in FL often hold very diverse datasets representing heterogeneous communities, making it important to protect their sensitive information while still ensuring that the trained model upholds the aspect of fairness for the users. To attain this objective, a method is put forth that introduces group privacy assurances through the utilization of $d$-privacy (aka metric privacy). $d$-privacy represents a localized form of differential privacy that relies on a metric-oriented obfuscation approach to maintain the original data's topological distribution. This method, besides enabling personalized model training in a federated approach and providing formal privacy guarantees, possesses significantly better group fairness measured under a variety of standard metrics than a global model trained within a classical FL template. Theoretical justifications for the applicability are provided, as well as experimental validation on real-world datasets to illustrate the working of the proposed method.

With recent advancements in technology, the threats of privacy violations of individuals' sensitive data are surging. Location data, in particular, have been shown to carry a substantial amount of sensitive information. A standard method to mitigate the privacy risks for location data consists in adding noise to the true values to achieve geo-indistinguishability (geo-ind). However, geo-ind alone is not sufficient to cover all privacy concerns. In particular, isolated locations are not sufficiently protected by the state-of-the-art Laplace mechanism (LAP) for geo-ind. In this paper, we focus on a mechanism based on the Blahut-Arimoto algorithm (BA) from the rate-distortion theory. We show that BA, in addition to providing geo-ind, enforces an elastic metric that mitigates the problem of isolation. Furthermore, BA provides an optimal trade-off between information leakage and quality of service. We then proceed to study the utility of BA in terms of the statistics that can be derived from the reported data, focusing on the inference of the original distribution. To this purpose, we de-noise the reported data by applying the iterative Bayesian update (IBU), an instance of the expectation-maximization method. It turns out that BA and IBU are dual to each other, and as a result, they work well together, in the sense that the statistical utility of BA is quite good and better than LAP for high privacy levels. Exploiting these properties of BA and IBU, we propose an iterative method, PRIVIC, for a privacy-friendly incremental collection of location data from users by service providers. We illustrate the soundness and functionality of our method both analytically and with experiments.

Federated learning (FL) is a type of collaborative machine learning where participating peers/clients process their data locally, sharing only updates to the collaborative model. This enables to build privacy-aware distributed machine learning models, among others. The goal is the optimization of a statistical model's parameters by minimizing a cost function of a collection of datasets which are stored locally by a set of clients. This process exposes the clients to two issues: leakage of private information and lack of personalization of the model. On the other hand, with the recent advancements in various techniques to analyze data, there is a surge of concern for the privacy violation of the participating clients. To mitigate this, differential privacy and its variants serve as a standard for providing formal privacy guarantees. Often the clients represent very heterogeneous communities and hold data which are very diverse. Therefore, aligned with the recent focus of the FL community to build a framework of personalized models for the users representing their diversity, it is also of utmost importance to protect the clients' sensitive and personal information against potential threats. To address this goal we consider $d$-privacy, also known as metric privacy, which is a variant of local differential privacy, using a a metric-based obfuscation technique that preserves the topological distribution of the original data. To cope with the issue of protecting the privacy of the clients and allowing for personalized model training to enhance the fairness and utility of the system, we propose a method to provide group privacy guarantees exploiting some key properties of $d$-privacy which enables personalized models under the framework of FL. We provide theoretical justifications to the applicability and experimental validation on real datasets to illustrate the working of our method.

Electric vehicles (EVs) are gaining popularity due to the growing awareness for a sustainable future. However, since there are disproportionately fewer charging stations than EVs, range anxiety plays a major role in the rise in the number of queries made along the journeys to find an available charging station. On the other hand, the use of personal data in various types of analytics is increasing at an unprecedented rate. Hence, the risks of privacy violation are also surging. Geo-indistinguishability is one of the standards for formalising location privacy as a generalisation of the local differential privacy. However, the noise has to be carefully calibrated considering the implications of potential utility-loss. In this paper, we introduce approximate geo-indistinguishability (AGeoI) which allows the EVs to obfuscate the individual query-locations while ensuring that they remain within their preferred area of interest. It is vital because journeys are often sensitive to a sharp drop in QoS, which has a high cost for the extra distance to be covered. We apply AGeoI and dummy data generation to protect the privacy of EVs during their journeys and preserve the QoS. Analytical insights and experiments are used to demonstrate that a very high percentage of EVs get privacy for free and that the utility-loss caused by the privacy-gain is minuscule. Using the iterative Bayesian update, our method allows for a private and highly accurate prediction of charging station occupancy without disclosing query locations and vehicle trajectories, which is vital in unprecedented traffic congestion scenarios and efficient route-planning.

With the recent bloom of data, there is a huge surge in threats against individuals' private information. Various techniques for optimizing privacy-preserving data analysis are at the focus of research in the recent years. In this paper, we analyse the impact of sampling on the utility of the standard techniques of frequency estimation, which is at the core of large-scale data analysis, of the locally deferentially private data-release under a pure protocol. We study the case in a distributed environment of data sharing where the values are reported by various nodes to the central server, e.g., cross-device Federated Learning. We show that if we introduce some random sampling of the nodes in order to reduce the cost of communication, the standard existing estimators fail to remain unbiased. We propose a new unbiased estimator in the context of sampling each node with certain probability and compute various statistical summaries of the data using it. We propose a way of sampling each node with personalized sampling probabilities as a step to further generalisation, which leads to some interesting open questions in the end. We analyse the accuracy of our proposed estimators on synthetic datasets to gather some insight on the trade-off between communication cost, privacy, and utility.

Most differentially private (DP) algorithms assume a central model in which a reliable third party inserts noise to queries made on datasets, or a local model where the users locally perturb their data. However, the central model is vulnerable via a single point of failure, and in the local model, the utility of the data deteriorates significantly. The recently proposed shuffle model is an intermediate framework between the central and the local paradigms where the users send their locally privatized data to a server where messages are shuffled, effacing the link between a privatized message and the corresponding user, giving a better trade-off between privacy and utility than the local model, as its privacy gets amplified without adding more noise. In this paper, we theoretically derive the strictest known bound for DP guarantee for the shuffle models with $k$-Randomized Response local randomizers. There on, we focus on the utility of the shuffle model for histogram queries. Leveraging on the matrix inversion method, which is used to approximate the original distribution from the empirical one produced by the $k$-RR mechanism, we de-noise the histogram produced by the shuffle model to evaluate the total variation distance of the resulting histogram from the true one, which we regard as the measure of utility of the privacy mechanism. We perform experiments on both synthetic and real data to compare the privacy-utility trade-off of the shuffle model with that of the central one privatized by adding the state-of-the-art Gaussian noise to each bin. Although the experimental results stay consistent with the literature that favour the central model, we see that, the difference in statistical utilities between the central and the shuffle models is very small, showing that they are almost comparable under the same level of DP.

With the recent bloom of focus on digital economy, the importance of personal data has seen a massive surge of late. Keeping pace with this trend, the model of data market is starting to emerge as a process to obtain high-quality personal information in exchange of incentives. To have a formal guarantee to protect the privacy of the sensitive data involved in digital economy, \emph{differential privacy (DP)} is the go-to technique, which has gained a lot of attention by the community recently. However, it is essential to optimize the privacy-utility trade-off by ensuring the highest level of privacy protection is ensured while preserving the utility of the data. In this paper, we theoretically derive sufficient and necessary conditions to have tight $(\epsilon,\,\delta)$-DP blankets for the shuffle model, which, to the best of our knowledge, have not been proven before, and, thus, characterize the best possible DP protection for shuffle models which can be implemented in data markets to ensure privacy-preserving trading of digital economy., Comment: Extended Abstract

Secondary positrons produced inside Galactic Molecular Clouds (GMCs) can significantly contribute to the observed positron spectrum on Earth. Multi-wavelength data of GMCs are particularly useful in building this model. A very recent survey implemented the optical/IR dust extinction measurements to trace 567 GMCs within 4 kpc of Earth, residing in the Galactic plane. We use the updated catalog of GMCs reported in recent papers, distributed in the Galactic plane, to find the secondary positrons produced in them in interactions of cosmic rays with molecular hydrogen. Moreover, by analyzing the \textit{Fermi}-LAT data, new GMCs have been discovered near the Galactic plane. We also include some of these GMCs closest to the Earth, where cosmic ray interactions produce secondaries. It has been speculated earlier that cosmic rays may be reaccelerated in some GMCs. We select 7 GMCs out of 567 GMCs recently reported, within 4 kpc of Earth, where reacceleration due to magnetized turbulence is assumed. We include a hardened component of secondary positrons produced from the interaction of reaccelerated CRs in those 7 GMCs. We use publicly available code \texttt{DRAGON} for our simulation setup to study CR propagation in the Galaxy and show that the observed positron spectrum can be well explained in the energy range of 1 to 1000 GeV by our self-consistent model., Comment: Proceedings of the 37th International Cosmic Ray Conference (ICRC 2021)

Personal data is becoming one of the most essential resources in today's information-based society. Accordingly, there is a growing interest in data markets, which operate data trading services between data providers and data consumers. One issue the data markets have to address is that of the potential threats to privacy. Usually some kind of protection must be provided, which generally comes to the detriment of utility. A correct pricing mechanism for private data should therefore depend on the level of privacy. In this paper, we propose a model of data federation in which data providers, who are, generally, less influential on the market than data consumers, form a coalition for trading their data, simultaneously shielding against privacy threats by means of differential privacy. Additionally, we propose a technique to price private data, and an revenue-distribution mechanism to distribute the revenue fairly in such federation data trading environments. Our model also motivates the data providers to cooperate with their respective federations, facilitating a fair and swift private data trading process. We validate our result through various experiments, showing that the proposed methods provide benefits to both data providers and consumers.

With the proliferation of the digital data economy, digital data is considered as the crude oil in the twenty-first century, and its value is increasing. Keeping pace with this trend, the model of data market trading between data providers and data consumers, is starting to emerge as a process to obtain high-quality personal information in exchange for some compensation. However, the risk of privacy violations caused by personal data analysis hinders data providers' participation in the data market. Differential privacy, a de-facto standard for privacy protection, can solve this problem, but, on the other hand, it deteriorates the data utility. In this paper, we introduce a pricing mechanism that takes into account the trade-off between privacy and accuracy. We propose a method to induce the data provider to accurately report her privacy price and, we optimize it in order to maximize the data consumer's profit within budget constraints. We show formally that the proposed mechanism achieves these properties, and also, validate them experimentally.

The recently observed data by AMS-02 clearly confirms that the positron flux rises with energy and shows a peak near a few hundred GeV. This rising positron flux cannot be explained by interactions of cosmic rays with interstellar hydrogen gas. In this paper, our goal is to study whether secondary production due to cosmic ray interactions in nearby Galactic Molecular Clouds (GMCs) can contribute significantly to the observed positron spectrum on Earth. Due to the progress in multi-wavelength astronomy, many new GMCs have been discovered in our Galaxy recently. Using large scale CO survey, 1064 GMCs were detected in the Galaxy, which reside in the Galactic plane. Very recent survey implemented the optical/IR dust extinction measurements, to trace 567 GMCs within 4 kpc of Earth, also residing in the Galactic plane. We use the updated list of GMCs reported in recent papers, which are distributed in the Galactic plane, to find the secondary positrons produced in them in interactions of cosmic rays with molecular hydrogen. Moreover, by analysing the \textit{Fermi}-LAT data, new GMCs have been discovered near the Galactic plane. We also include some of these GMCs closest to the Earth where cosmic ray interactions are producing secondaries. It has been speculated earlier that cosmic rays may be reaccelerated in some GMCs. We select 7 GMCs out of 567 GMCs recently reported, within 4 kpc of Earth, where reacceleration due to magnetized turbulence is assumed. We include a hardened component of secondary positrons, produced from interaction of reaccelerated CRs in those 7 GMCs. We use publicly available code \textbf{DRAGON} for our simulation setup to study CR propagation in the Galaxy and show that the observed positron spectrum can be well explained in the energy range of 1 to 1000 GeV by our self consistent model., Comment: 22 pages, 14 figures, 8 tables; accepted for publication in Journal of High Energy Astrophysics (JHEAp)

Low Surface Brightness (LSB) galaxies have very diffuse, low surface density stellar disks which appear faint in optical images. They are very rich in neutral hydrogen (HI) gas, which extends well beyond the stellar disks. Their extended HI rotation curves and stellar disks indicate that they have very massive dark matter (DM) halos compared to normal bright galaxies. Hence, LSB galaxies may represent valuable laboratories for the indirect detection of DM. In this paper, we search for WIMP annihilation signatures in four LSB galaxies and present an analysis of nearly nine years of data from the Fermi Large Area Telescope (LAT). Above 500 MeV, no excess emission was detected from the LSB galaxies. We obtain constraints on the DM cross-section for different annihilation channels, for both individual and stacked targets. In addition to this, we use radio data from the Very Large Array (VLA) radio telescope in order to derive DM constraints, following a multiwavelength approach. The constraints obtained from the four considered LSB galaxies are nearly 3 orders of magnitude weaker than the predicted limits for the thermal relic abundances and the combined limits achieved from Fermi-LAT observations of dwarf spheroidal galaxies. Finally, we discuss the possibility of detecting emission from LSB galaxies using the upcoming ground-based $\gamma$-ray and radio observatories, namely the Cherenkov Telescope Array (CTA) and the Square Kilometre Array (SKA)., Comment: To be published in Monthly Notices of the Royal Astronomical Society (accepted in 2020 December 8)

Various studies firmly establish the fact that gamma-ray observations can act as a unique probe to detect the possible cosmic ray (CR) sources, study the CR density distribution and explore the average properties of interstellar medium (ISM) such as the gas density profile of ISM. We use the DRAGON code to study different propagation models by incorporating realistic source distribution, Galactic magnetic field (GMF) and gas density profile, and finally obtain the proton distribution (both spatial and energy) in the Galaxy by fitting the locally observed CR spectra. The uncertainties in the model parameters used to study the CR propagation are also shown here. Our obtained proton distribution is then used to calculate the diffuse gamma-ray flux produced by proton-proton interactions in the Galactic halo. We compare our diffuse gamma-ray fluxes with the previous results and the isotropic gamma-ray background (IGRB) at sub -TeV energy regime measured by \textit{Fermi}-LAT. It is found to be much less than IGRB, which suggests IGRB is mostly of extragalactic origin. Finally, we use our obtained proton distribution to calculate gamma-ray fluxes from individual Galactic Molecular Clouds (GMCs) and those fluxes are compared with the \textit{Fermi}-LAT observations of GMCs., Comment: 22 pages, 15 figures

Tucana-II (Tuc-II), a recently discovered and confirmed Ultra Faint Dwarf Spheroidal galaxy, has a high mass to light ratio as well as a large line-of-sight stellar velocity dispersion, thus making it an ideal candidate for an indirect dark matter (DM) search. In this paper, we have analyzed nine years of $\gamma$-ray data obtained from the \textit{Fermi}-LAT instrument from the direction of Tuc-II. The fact that a very weak significant $\gamma$-ray excess ($2.2\sigma$) over the background of Tuc-II have been detected from the location of this galaxy. We have observed that this excess of $\gamma$-ray emission from the of location Tuc-II rises with longer periods of data. If WIMP pair annihilation is assumed for this faint emission, for $b\bar{b}$ annihilation channel the test statistics (TS) value peaks at DM mass $\sim$ 14 GeV and for $\tau^{+}\tau^{-}$ annihilation channel it peaks at DM mass 4 GeV. It is then called for an estimation of the $95\%$ confidence level upper limit of the possible velocity weighted self-annihilation cross-section of the DM particles (WIMPs) within Tuc-II by fitting the observed $\gamma$-ray flux with spectra expected for DM annihilation. The estimated upper limits of the cross-sections from Tuc-II are then compared with two other dwarf galaxies that are considered to be good DM candidates in several studies. We have also compared our results with the cross-sections obtained in various popular theoretical models of the WIMPs to find that our results impose reasonable tight constraints on the parameter spaces of those DM models. In the concluding section, we compared our results with the similar results obtained from a combined dSph analysis by the \textit{Fermi}-LAT collaboration as well as the results obtained from the studies of DM in the dwarf galaxies by the major ground-based Cherenkov experiments., Comment: 23 pages, 16 figures, 7 tables

Galactic magnetic field (GMF) and secondary cosmic rays (CRs) (e.g. $^{10}$beryllium, boron, antiproton) are important components to understand the propagation of CRs in the Milky Way Galaxy. Realistic modeling of GMF is based on the Faraday rotation measurements of various Galactic and extragalactic radio sources and synchrotron emission from CR leptons in the radio frequency range, thereby providing information of halo height. On the other hand, diffusion coefficient and halo height are also estimated from the $^{10}$Be/$^{9}$Be and B/C ratios. Moreover, density distribution of gaseous components of interstellar medium (ISM) also plays an important role as secondary CRs are produced due to interaction of primary CRs with the gaseous components of ISM. We consider mainly molecular, atomic, and ionized components of hydrogen gas for our study. Recent observations and hydrodynamical simulations provide new forms of density profiles of hydrogen gas in Milky Way Galaxy. In the \texttt{DRAGON} code, we have implemented our chosen density profiles, based on realistic observations in radio, X-ray and $\gamma$-ray wavebands, and hydrodynamical simulations of interstellar hydrogen gas to study the variation in the height of the halo required to fit the observed CR spectra. Our results show the halo height ($z_{t}$) varies in the range of 2 to 6 kpc for the density profiles considered in our work., Comment: 18 pages, 6 captioned figures. Accepted in JCAP

Triangulum-II, a newly discovered dwarf spheroidal galaxy, is a strong candidate for the indirect search of dark matter through the detection of $\gamma$-ray emission that could originate from the pair- annihilation of weakly interacting massive particles (WIMPs). We here report on the analysis of almost seven years Fermi Gamma-Ray Space Telescope data of Triangulum-II which was taken during its all sky survey operation mode. No excess $\gamma$-ray emission has been detected above 100 MeV from Triangulum-II. We derive the upper limits on $\gamma$-ray flux assuming both the power-law spectra and the spectra related to WIMP annihilation. In this work, we have considered several theoretical WIMP (neutralinos here) models envisioning both thermal and non-thermal production of WIMPs, and put limits on pair-annihilation cross-section of WIMPs to constrain the parameter space related to those theoretical models., Comment: It has been accepted for publication in JCAP. 12 pages, 4 tables, 7 figures

The term jitter and shimmer has long been used in the domain of speech and acoustic signal analysis as a parameter for speaker identification and other prosodic features. In this study, we look forward to use the same parameters in neural domain to identify and categorize emotional cues in different musical clips. For this, we chose two ragas of Hindustani music which are conventionally known to portray contrast emotions and EEG study was conducted on 5 participants who were made to listen to 3 min clip of these two ragas with sufficient resting period in between. The neural jitter and shimmer components were evaluated for each experimental condition. The results reveal interesting information regarding domain specific arousal of human brain in response to musical stimuli and also regarding trait characteristics of an individual. This novel study can have far reaching conclusions when it comes to modeling of emotional appraisal. The results and implications are discussed in detail., Comment: 6 pages, 12 figures, Presented in 4th International Conference on Signal Processing and Integrated Networks (SPIN) 2017

The present study reports interesting findings in regard to emotional arousal based activities while listening to two Hindustani classical ragas of contrast emotion. EEG data was taken on 5 naive listeners while they listened to two ragas Bahar and Mia ki Malhar which are conventionally known to portray contrast emotions. The EEG data were analyzed with the help of two robust non linear tools viz. Adaptive Fractal Analysis (AFA) and Detrended Fluctuation Analysis (DFA). A comparative study of the Hurst Exponents obtained from the two methods have been shown which shows that DFA provides more rigorous results compared to AFA when it comes to the scaling analysis of biosignal data. The results and implications have been discussed in detail., Comment: 5 pages, 9 figures, Presented in 4th International Conference on Signal Processing and Integrated Networks (SPIN) 2017

What happens inside the performers brain when he is performing and composing a particular raga. Are there some specific regions in brain which are activated when an artist is creating or imaging a raga in his brain. Do the regions remain the same when the artist is listening to the same raga sung by him. These are the questions that perplexed neuroscientists for a long time. In this study we strive to answer these questions by using latest state of the art techniques to assess brain response. An EEG experiment was conducted for two eminent performers of Indian classical music, when they mentally created the imagery of a raga Jay Jayanti in their mind, as well as when they listened to the same raga. The beauty of Hindustani music lies in the fact that the musician is himself the composer and recreates the imagery of the raga in his mind while performing, hence the scope of creative improvisations are immense. The alpha and theta frequency rhythms were segregated from each of the time series data and analyzed using robust non MFDXA technique to quantitatively assess the degree of cross-correlation of each EEG frequency rhythm in different combination of electrodes from frontal, occipital and temporal lobes. A strong response was found in the occipital and fronto occipital region during mental improvisation of the raga, which is an interesting revelation of this study. Strong retentive features were obtained in regard to both alpha and theta rhythms in musical listening in the fronto temporal and occipital temporal region while the features were almost absent in the thinking part. Further, other specific regions have been identified separately for the two separate conditions in which the correlations among the different lobes were the strongest., Comment: 14 pages, 5 figures

The term gestalt has been widely used in the field of psychology which defined the perception of human mind to group any object not in part but as a unified whole. Music in general is polytonic i.e. a combination of a number of pure tones (frequencies) mixed together in a manner that sounds harmonius. The study of human brain response due to different frequency groups of acoustic signal can give us an excellent insight regarding the neural and functional architecture of brain functions. In this work we have tried to analyze the effect of different frequency bands of music on the various frequency rhythms of human brain obtained from EEG data of 5 participants. Four (4) widely popular Rabindrasangeet clips were subjected to Wavelet Transform method for extracting five resonant frequency bands from the original music signal. These resonant frequency bands were presented to the subjects as auditory stimulus and EEG signals recorded simultaneously in 19 different locations of the brain. The recorded EEG signals were noise cleaned and subjected to Multifractal Detrended Fluctuation Analysis (MFDFA) technique on the alpha, theta and gamma frequency range. Thus, we obtained the complexity values (in the form of multifractal spectral width) in alpha, theta and gamma EEG rhythms corresponding to different frequency bands of music. We obtain frequency specific arousal based response in different lobes of brain as well as in specific EEG bands corresponding to musical stimuli. This revelation can be of immense importance when it comes to the field of cognitive music therapy., Comment: 14 Pages, 5 Figures, Presented in International Conference on Creativity and Cognition in Art and Design, NIMHANS, Bangalore; 19-21 January, 2017

Neuro-electronic hybrid promises to bring up a model architecture for computing. Such computing architecture could help to bring the power of biological connection and electronic circuits together for better computing paradigm. Such paradigms for solving real world tasks with higher accuracy is on demand now. A robot as a autonomous system is modeled here to navigate following a particular line. Sensory inputs from robot is directed as input to the cell culture in response to which motor commands are generated from the culture., Comment: 6 pages, 10 Figures