arXiv subjects

Gaurav Gupta

Publications and source records attributed to Gaurav Gupta.

48 records · Page 3Linked to original sources

arXiv2019

Learning Latent Fractional dynamics with Unknown Unknowns

Despite significant effort in understanding complex systems (CS), we lack a theory for modeling, inference, analysis and efficient control of time-varying complex networks (TVCNs) in uncertain environments. From brain activity dynamics to microbiome, and even chromatin interactions within the genome architecture, many such TVCNs exhibits a pronounced spatio-temporal fractality. Moreover, for many TVCNs only limited information (e.g., few variables) is accessible for modeling, which hampers the capabilities of analytical tools to uncover the true degrees of freedom and infer the CS model, the hidden states and their parameters. Another fundamental limitation is that of understanding and unveiling of unknown drivers of the dynamics that could sporadically excite the network in ways that straightforward modeling does not work due to our inability to model non-stationary processes. Towards addressing these challenges, in this paper, we consider the problem of learning the fractional dynamical complex networks under unknown unknowns (i.e., hidden drivers) and partial observability (i.e., only partial data is available). More precisely, we consider a generalized modeling approach of TVCNs consisting of discrete-time fractional dynamical equations and propose an iterative framework to determine the network parameterization and predict the state of the system. We showcase the performance of the proposed framework in the context of task classification using real electroencephalogram data.

cs.LG↗

arXiv2019

Data-driven Perception of Neuron Point Process with Unknown Unknowns

Identification of patterns from discrete data time-series for statistical inference, threat detection, social opinion dynamics, brain activity prediction has received recent momentum. In addition to the huge data size, the associated challenges are, for example, (i) missing data to construct a closed time-varying complex network, and (ii) contribution of unknown sources which are not probed. Towards this end, the current work focuses on statistical neuron system model with multi-covariates and unknown inputs. Previous research of neuron activity analysis is mainly limited with effects from the spiking history of target neuron and the interaction with other neurons in the system while ignoring the influence of unknown stimuli. We propose to use unknown unknowns, which describes the effect of unknown stimuli, undetected neuron activities and all other hidden sources of error. The maximum likelihood estimation with the fixed-point iteration method is implemented. The fixed-point iterations converge fast, and the proposed methods can be efficiently parallelized and offer computational advantage especially when the input spiking trains are over long time-horizon. The developed framework provides an intuition into the meaning of having extra degrees-of-freedom in the data to support the need for unknowns. The proposed algorithm is applied to simulated spike trains and on real-world experimental data of mouse somatosensory, mouse retina and cat retina. The model shows a successful increasing of system likelihood with respect to the conditional intensity function, and it also reveals the convergence with iterations. Results suggest that the neural connection model with unknown unknowns can efficiently estimate the statistical properties of the process by increasing the network likelihood.

cs.LG↗

arXiv2018

Anonymizing k-Facial Attributes via Adversarial Perturbations

A face image not only provides details about the identity of a subject but also reveals several attributes such as gender, race, sexual orientation, and age. Advancements in machine learning algorithms and popularity of sharing images on the World Wide Web, including social media websites, have increased the scope of data analytics and information profiling from photo collections. This poses a serious privacy threat for individuals who do not want to be profiled. This research presents a novel algorithm for anonymizing selective attributes which an individual does not want to share without affecting the visual quality of images. Using the proposed algorithm, a user can select single or multiple attributes to be surpassed while preserving identity information and visual content. The proposed adversarial perturbation based algorithm embeds imperceptible noise in an image such that attribute prediction algorithm for the selected attribute yields incorrect classification result, thereby preserving the information according to user's choice. Experiments on three popular databases i.e. MUCT, LFWcrop, and CelebA show that the proposed algorithm not only anonymizes k-attributes, but also preserves image quality and identity information.

cs.CV↗

arXiv2018

Dealing with Unknown Unknowns: Identification and Selection of Minimal Sensing for Fractional Dynamics with Unknown Inputs

This paper focuses on analysis and design of time-varying complex networks having fractional order dynamics. These systems are key in modeling the complex dynamical processes arising in several natural and man made systems. Notably, examples include neurophysiological signals such as electroencephalogram (EEG) that captures the variation in potential fields, and blood oxygenation level dependent (BOLD) signal, which serves as a proxy for neuronal activity. Notwithstanding, the complex networks originated by locally measuring EEG and BOLD are often treated as isolated networks and do not capture the dependency from external stimuli, e.g., originated in subcortical structures such as the thalamus and the brain stem. Therefore, we propose a paradigm-shift towards the analysis of such complex networks under unknown unknowns (i.e., excitations). Consequently, the main contributions of the present paper are threefold: (i) we present an alternating scheme that enables to determine the best estimate of the model parameters and unknown stimuli; (ii) we provide necessary and sufficient conditions to ensure that it is possible to retrieve the state and unknown stimuli; and (iii) upon these conditions we determine a small subset of variables that need to be measured to ensure that both state and input can be recovered, while establishing sub-optimality guarantees with respect to the smallest possible subset. Finally, we present several pedagogical examples of the main results using real data collected from an EEG wearable device.

eess.SP↗

arXiv2018

Approximate Submodular Functions and Performance Guarantees

We consider the problem of maximizing non-negative non-decreasing set functions. Although most of the recent work focus on exploiting submodularity, it turns out that several objectives we encounter in practice are not submodular. Nonetheless, often we leverage the greedy algorithms used in submodular functions to determine a solution to the non-submodular functions. Hereafter, we propose to address the original problem by \emph{approximating} the non-submodular function and analyze the incurred error, as well as the performance trade-offs. To quantify the approximation error, we introduce a novel concept of $δ$-approximation of a function, which we used to define the space of submodular functions that lie within an approximation error. We provide necessary conditions on the existence of such $δ$-approximation functions, which might not be unique. Consequently, we characterize this subspace which we refer to as \emph{region of submodularity}. Furthermore, submodular functions are known to lead to different sub-optimality guarantees, so we generalize those dependencies upon a $δ$-approximation into the notion of \emph{greedy curvature}. Finally, we used this latter notion to simplify some of the existing results and efficiently (i.e., linear complexity) determine tightened bounds on the sub-optimality guarantees using objective functions commonly used in practical setups and validate them using real data.

cs.DS↗

arXiv2018

Re-thinking EEG-based non-invasive brain interfaces: modeling and analysis

Brain interfaces are cyber-physical systems that aim to harvest information from the (physical) brain through sensing mechanisms, extract information about the underlying processes, and decide/actuate accordingly. Nonetheless, the brain interfaces are still in their infancy, but reaching to their maturity quickly as several initiatives are released to push forward their development (e.g., NeuraLink by Elon Musk and `typing-by-brain' by Facebook). This has motivated us to revisit the design of EEG-based non-invasive brain interfaces. Specifically, current methodologies entail a highly skilled neuro-functional approach and evidence-based \emph{a priori} knowledge about specific signal features and their interpretation from a neuro-physiological point of view. Hereafter, we propose to demystify such approaches, as we propose to leverage new time-varying complex network models that equip us with a fractal dynamical characterization of the underlying processes. Subsequently, the parameters of the proposed complex network models can be explained from a system's perspective, and, consecutively, used for classification using machine learning algorithms and/or actuation laws determined using control system's theory. Besides, the proposed system identification methods and techniques have computational complexities comparable with those currently used in EEG-based brain interfaces, which enable comparable online performances. Furthermore, we foresee that the proposed models and approaches are also valid using other invasive and non-invasive technologies. Finally, we illustrate and experimentally evaluate this approach on real EEG-datasets to assess and validate the proposed methodology. The classification accuracies are high even on having less number of training samples.

q-bio.NC↗

arXiv2017

Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents

In this digital era, one thing that still holds the convention is a printed archive. Printed documents find their use in many critical domains such as contract papers, legal tenders and proof of identity documents. As more advanced printing, scanning and image editing techniques are becoming available, forgeries on these legal tenders pose a serious threat. Ability to easily and reliably identify source printer of a printed document can help a lot in reducing this menace. During printing procedure, printer hardware introduces certain distortions in printed characters' locations and shapes which are invisible to naked eyes. These distortions are referred as geometric distortions, their profile (or signature) is generally unique for each printer and can be used for printer classification purpose. This paper proposes a set of features for characterizing text-line-level geometric distortions, referred as geometric distortion signatures and presents a novel system to use them for identification of the origin of a printed document. Detailed experiments performed on a set of thirteen printers demonstrate that the proposed system achieves state of the art performance and gives much higher accuracy under small training size constraint. For four training and six test pages of three different fonts, the proposed method gives 99\% classification accuracy.

cs.MM↗

arXiv2017

Switching based Spin Transfer Torque Oscillator with zero-bias field and large tuning-ratio

We propose a novel concept of obtaining oscillations with frequencies in very-high frequency (VHF) and ultra-high frequency (UHF) bands. A traditional spin torque nano-oscillator (STNO) consists of at least one pinned layer (PL) and one free layer (FL) which precesses in a fixed orbit, defined by a precession angle, which results in magneto-resistance (MR) oscillations. In STNO, with aligned or even orthogonal easy-axis of the magnetic layers and with or without external bias magnetic field, it is not possible to attain full MR swing. The constringed MR swing jeopardizes the extracted output power. Furthermore, the orbit is strongly disturbed by the thermal fluctuations resulting in strong magnetic noise. In stark contrast to the operation principle of a STNO, we theoretically demonstrate, with the practical parameters from the experiments, that with a unidirectional current in a dual asymmetric free-layers (with no pinned layer) based perpendicular magnetic tunnel junction (pMTJ), both of the free layers can attain a complete and out-of-phase self-sustained switching without the aid of any external magnetic field. This design facilitates a switching based spin torque oscillator (SW-STO) with a full MR swing, and hence a larger output power, for stable and more thermally robust free-running oscillations. Furthermore, the integration with the n-type metal-oxide-semiconductor (NMOS) field-effect transistors at 130, 65 and 14 nm node is appraised to expound its effect on the oscillator performance, controllability with DC bias and the design constraints, to demonstrate the viability of the design as a dynamically controllable oscillator for practical on-chip implementation.

cond-mat.mes-hall↗

arXiv2017

Ultrafast and low-energy switching in voltage-controlled elliptical pMTJ

Switching magnetization in a perpendicular magnetic tunnel junction (pMTJ) via voltage controlled magnetic anisotropy (VCMA) has shown the potential to markedly reduce the switching energy. However, the requirement of an external magnetic field poses a critical bottleneck for its practical applications. In this work, we propose an elliptical-shaped pMTJ to eliminate the requirement of providing an external field by an additional circuit. We demonstrate that a 10 nm thick in-plane magnetized bias layer (BL) separated by a metallic spacer of 3 nm from the free layer (FL) can be engineered within the MTJ stack to provide the 50 mT bias magnetic field for switching. By conducting macrospin simulation, we find that a fast switching in 0.38 ns with energy consumption as low as 0.3 fJ at a voltage of 1.6 V can be achieved. Furthermore, we study the phase diagram of switching probability, showing that a pulse duration margin of 0.15 ns is obtained and a low-voltage operation (~ 1 V) is favored. Finally, the MTJ scalability is considered, and it is found that scaling-down may not be appealing in terms of both the energy consumption and the switching time for the precession based VCMA switching.

cond-mat.mes-hall↗

arXiv2014

User Selection in MIMO Interfering Broadcast Channels

Interference alignment aims to achieve maximum degrees of freedom in an interference system. For achieving Interference alignment in interfering broadcast systems a closed-form solution is proposed in [1] which is an extension of the grouping scheme in [2]. In a downlink scenario where there are a large number of users, the base station is required to select a subset of users such that the sum rate is maximized. To search for the optimal user subset using brute-force approach is computationally exhaustive because of the large number of possible user subset combinations. We propose a user selection algorithm achieving sum rate close to that of optimal solution. The algorithm employs coordinate ascent approach and exploits orthogonality between the desired signal space and the interference channel space in the reciprocal system to select the user at each step. For the sake of completeness, we have also extended the sum rate approach based algorithm to Interfering broadcast channel. The complexity of both these algorithms is shown to be linear with respect to the total number of users as compared to exponential in brute-force search.

cs.IT↗

arXiv2013

Conditional Entropy based User Selection for Multiuser MIMO Systems

We consider the problem of user subset selection for maximizing the sum rate of downlink multi-user MIMO systems. The brute-force search for the optimal user set becomes impractical as the total number of users in a cell increase. We propose a user selection algorithm based on conditional differential entropy. We apply the proposed algorithm on Block diagonalization scheme. Simulation results show that the proposed conditional entropy based algorithm offers better alternatives than the existing user selection algorithms. Furthermore, in terms of sum rate, the solution obtained by the proposed algorithm turns out to be close to the optimal solution with significantly lower computational complexity than brute-force search.

cs.IT↗

arXiv2002

The SN 1006 Remnant: Optical Proper Motions, Deep Imaging, Distance, and Brightness at Maximum

We report the first measurement of proper motions in the SN1006 remnant (G327.6+14.6) based entirely on digital images. CCD images from three epochs spanning a period of 11 years are used: 1987 from Las Campanas, and 1991 and 1998 from CTIO. Measuring the shift of delicate Balmer filaments along the northwest rim of the remnant, we obtain proper motions of 280 +/- 8 mas/yr along the entire length where the filaments are well defined, with little systematic variation along the filaments. We also report very deep Halpha imaging observations of the entire remnant that clearly show very faint emission surrounding almost the entire shell, as well as some diffuse emission regions in the (projected) interior. Combining the proper motion measurement with a recent measurement of the shock velocity based on spectra of the same filaments by Ghavamian et al. leads to a distance of 2.17 +/- 0.08 kpc to SN1006. Several lines of argument suggest that SN1006 was a Type Ia event, so the improved distance measurement can be combined with the peak luminosity for SNeIa, as determined for events in galaxies with Cepheid-based distances, to calculate the apparent brightness of the spectacular event that drew wide attention in the eleventh century. The result, V_max = -7.5 =/- 0.4, lies squarely in the middle of the wide range of estimates based on the historical observations.

astro-ph↗