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Data analysis has become a necessity in the modern era of cricket. Everything from effective team management to match win predictions use some form of analytics. Meaningful data representations are necessary for efficient analysis of data. In this study we investigate the use of adaptive (learnable) embeddings to represent inter-related features (such as players, teams, etc). The data used for this study is collected from a classical T20 tournament IPL (Indian Premier League). To naturally facilitate the learning of meaningful representations of features for accurate data analysis, we formulate a deep representation learning framework which jointly learns a custom set of embeddings (which represents our features of interest) through the minimization of a contrastive loss. We base our objective on a set of classes obtained as a result of hierarchical clustering on the overall run rate of an innings. It's been assessed that the framework ensures greater generality in the obtained embeddings, on top of which a task based analysis of overall run rate prediction was done to show the reliability of the framework.

Earth's magnetosphere is vital for today's technologically dependent society. To date, numerous design studies have been conducted and over a dozen science missions have own to study the magnetosphere. However, a majority of these solutions relied on large monolithic satellites, which limited the spatial resolution of these investigations, as did the technological limitations of the past. To counter these limitations, we propose the use of a satellite swarm carrying numerous and distributed payloads for magnetospheric measurements. Our mission is named APIS (Applications and Potentials of Intelligent Swarms), which aims to characterize fundamental plasma processes in the Earth's magnetosphere and measure the effect of the solar wind on our magnetosphere. We propose a swarm of 40 CubeSats in two highly-elliptical orbits around the Earth, which perform radio tomography in the magnetotail at 8-12 Earth Radii (RE) downstream, and the subsolar magnetosphere at 8-12RE upstream. In addition, in-situ measurements of the magnetic and electric fields, plasma density will be performed by on-board instruments. In this article, we present an outline of previous missions and designs for magnetospheric studies, along with the science drivers and motivation for the APIS mission. Furthermore, preliminary design results are included to show the feasibility of such a mission. The science requirements drive the APIS mission design, the mission operation and the system requirements. In addition to the various science payloads, critical subsystems of the satellites are investigated e.g., navigation, communication, processing and power systems. We summarize our findings, along with the potential next steps to strengthen our design study., Comment: Accepted in Acta Astronautica

Research in adversarial learning has primarily focused on homogeneous unstructured datasets, which often map into the problem space naturally. Inverting a feature space attack on heterogeneous datasets into the problem space is much more challenging, particularly the task of finding the perturbation to perform. This work presents a formal search strategy: the `Feature Importance Guided Attack' (FIGA), which finds perturbations in the feature space of heterogeneous tabular datasets to produce evasion attacks. We first demonstrate FIGA in the feature space and then in the problem space. FIGA assumes no prior knowledge of the defending model's learning algorithm and does not require any gradient information. FIGA assumes knowledge of the feature representation and the mean feature values of defending model's dataset. FIGA leverages feature importance rankings by perturbing the most important features of the input in the direction of the target class. While FIGA is conceptually similar to other work which uses feature selection processes (e.g., mimicry attacks), we formalize an attack algorithm with three tunable parameters and investigate the strength of FIGA on tabular datasets. We demonstrate the effectiveness of FIGA by evading phishing detection models trained on four different tabular phishing datasets and one financial dataset with an average success rate of 94%. We extend FIGA to the phishing problem space by limiting the possible perturbations to be valid and feasible in the phishing domain. We generate valid adversarial phishing sites that are visually identical to their unperturbed counterpart and use them to attack six tabular ML models achieving a 13.05% average success rate.

Statistical distances (SDs), which quantify the dissimilarity between probability distributions, are central to machine learning and statistics. A modern method for estimating such distances from data relies on parametrizing a variational form by a neural network (NN) and optimizing it. These estimators are abundantly used in practice, but corresponding performance guarantees are partial and call for further exploration. In particular, there seems to be a fundamental tradeoff between the two sources of error involved: approximation and estimation. While the former needs the NN class to be rich and expressive, the latter relies on controlling complexity. This paper explores this tradeoff by means of non-asymptotic error bounds, focusing on three popular choices of SDs -- Kullback-Leibler divergence, chi-squared divergence, and squared Hellinger distance. Our analysis relies on non-asymptotic function approximation theorems and tools from empirical process theory. Numerical results validating the theory are also provided.

The Cadmium Zinc Telluride (CZT) Imager on board AstroSat is a hard X-ray imaging spectrometer operating in the energy range of 20 $-$ 100 keV. It also acts as an open hard X-ray monitor above 100 keV capable of detecting transient events like the Gamma-ray Bursts (GRBs). Additionally, the instrument has the sensitivity to measure hard X-ray polarization in the energy range of 100 $-$ 400 keV for bright on-axis sources like Crab and Cygnus X-1 and bright GRBs. As hard X-ray instruments like CZTI are sensitive to cosmic rays in addition to X-rays, it is required to identify and remove particle induced or other noise events and select events for scientific analysis of the data. The present CZTI data analysis pipeline includes algorithms for such event selection, but they have certain limitations. They were primarily designed for the analysis of data from persistent X-ray sources where the source flux is much less than the background and thus are not best suited for sources like GRBs. Here, we re-examine the characteristics of noise events in CZTI and present a generalized event selection method that caters to the analysis of data for all types of sources. The efficacy of the new method is reviewed by examining the Poissonian behavior of the selected events and the signal to noise ratio for GRBs., Comment: Accepted for publication in JAA

Gravitational-wave observations became commonplace in Advanced LIGO-Virgo's recently concluded third observing run. 56 non-retracted candidates were identified and publicly announced in near real time. Gravitational waves from binary neutron star mergers, however, remain of special interest since they can be precursors to high-energy astrophysical phenomena like $\gamma$-ray bursts and kilonovae. While late-time electromagnetic emissions provide important information about the astrophysical processes within, the prompt emission along with gravitational waves uniquely reveals the extreme matter and gravity during - and in the seconds following - merger. Rapid communication of source location and properties from the gravitational-wave data is crucial to facilitate multi-messenger follow-up of such sources. This is especially enabled if the partner facilities are forewarned via an early-warning (pre-merger) alert. Here we describe the commissioning and performance of such a low-latency infrastructure within LIGO-Virgo. We present results from an end-to-end mock data challenge that detects binary neutron star mergers and alerts partner facilities before merger. We set expectations for these alerts in future observing runs.

Over the last 5 years, UVIT has completed observations of more than 500 proposals with ~ 800 unique pointings. In addition, regular planned monitoring observations have been made and from their analysis various key parameters related to in orbit performance of UVIT have been quantified. The sensitivities of the UV channels have remained steady indicating no effect of potential molecular contamination confirming the adequacy of all the protocols implemented for avoiding contamination. The quality of the PSF through the years confirms adequacy of thermal control measures. The early calibrations obtained during the Performance Verification (PV) phase have been further revised for more subtle effects. These include flat fields and detector distortions with greater precision. The operations of UVIT have also evolved through in orbit experience, e.g. tweaking of operational sequencing, protocol for recovery from bright object detection (BOD) shutdowns, parameters for BOD thresholds, etc. Finally, some effects of charged particle hits on electronics led to opimised strategy for regular resetting. The Near-UV channel was lost in one of such operations. All the above in-orbit experiences are presented here., Comment: Accepted for publication in Journal of Astrophysics & Astronomy, 11 pages, 10 figures, 1 table

Elemental abundances in the solar corona are known to be different from those observed in the solar photosphere. The ratio of coronal to photospheric abundance shows a dependence on the first ionisation potential (FIP) of the element. We estimate FIP bias from direct measurements of elemental abundances from soft X-ray spectra using data from multiple space missions covering a range of solar activity levels. This comprehensive analysis shows clear evidence for a decrease in FIP bias around maximum intensity of the X-ray flare with coronal abundances briefly tending to photospheric values and a slow recovery as the flare decays. The departure from coronal abundances are larger for the low FIP elements Ca, Fe and Si than for S which have a mid FIP value. These changes in the degree of fractionation might provide inputs to model wave propagation through the chromosphere during flares., Comment: accepted for publication in Solar Physics

Bringing the success of modern machine learning (ML) techniques to mobile devices can enable many new services and businesses, but also poses significant technical and research challenges. Two factors that are critical for the success of ML algorithms are massive amounts of data and processing power, both of which are plentiful, yet highly distributed at the network edge. Moreover, edge devices are connected through bandwidth- and power-limited wireless links that suffer from noise, time-variations, and interference. Information and coding theory have laid the foundations of reliable and efficient communications in the presence of channel imperfections, whose application in modern wireless networks have been a tremendous success. However, there is a clear disconnect between the current coding and communication schemes, and the ML algorithms deployed at the network edge. In this paper, we challenge the current approach that treats these problems separately, and argue for a joint communication and learning paradigm for both the training and inference stages of edge learning., Comment: 13 pages, 5 figures

Neural architecture search (NAS) has emerged as a promising avenue for automatically designing task-specific neural networks. Existing NAS approaches require one complete search for each deployment specification of hardware or objective. This is a computationally impractical endeavor given the potentially large number of application scenarios. In this paper, we propose Neural Architecture Transfer (NAT) to overcome this limitation. NAT is designed to efficiently generate task-specific custom models that are competitive under multiple conflicting objectives. To realize this goal we learn task-specific supernets from which specialized subnets can be sampled without any additional training. The key to our approach is an integrated online transfer learning and many-objective evolutionary search procedure. A pre-trained supernet is iteratively adapted while simultaneously searching for task-specific subnets. We demonstrate the efficacy of NAT on 11 benchmark image classification tasks ranging from large-scale multi-class to small-scale fine-grained datasets. In all cases, including ImageNet, NATNets improve upon the state-of-the-art under mobile settings ($\leq$ 600M Multiply-Adds). Surprisingly, small-scale fine-grained datasets benefit the most from NAT. At the same time, the architecture search and transfer is orders of magnitude more efficient than existing NAS methods. Overall, the experimental evaluation indicates that, across diverse image classification tasks and computational objectives, NAT is an appreciably more effective alternative to conventional transfer learning of fine-tuning weights of an existing network architecture learned on standard datasets. Code is available at https://github.com/human-analysis/neural-architecture-transfer, Comment: Code is available at https://github.com/human-analysis/neural-architecture-transfer

In many information-theoretic channel coding problems, adding an input cost constraint to the operational setup amounts to restricting the optimization domain in the capacity formula. This paper shows that, in contrast to common belief, such a simple modification does not hold for the cost-constrained (CC) wiretap channel (WTC). The secrecy-capacity of the discrete memoryless (DM) WTC without cost constraints is described by a single auxiliary random variable. For the CC DM-WTC, however, we show that two auxiliaries are necessary to achieve capacity. Specifically, we first derive the secrecy-capacity formula, proving the direct part via superposition coding. Then, we provide an example of a CC DM-WTC whose secrecy-capacity cannot be achieved using a single auxiliary. This establishes the fundamental role of superposition coding over CC WTCs.

A distributed binary hypothesis testing (HT) problem over a noisy (discrete and memoryless) channel studied previously by the authors is investigated from the perspective of the strong converse property. It was shown by Ahlswede and Csisz\'{a}r that a strong converse holds in the above setting when the channel is rate-limited and noiseless. Motivated by this observation, we show that the strong converse continues to hold in the noisy channel setting for a special case of HT known as testing against independence (TAI). The proof utilizes the blowing up lemma and the recent change of measure technique of Tyagi and Watanabe as the key tools.

The Cadmium Zinc Telluride (CZT) Imager onboard AstroSat, consists of pixelated CZT detectors, which are sensitive to hard X-rays above 20 keV. The individual pixels are triggered by ionising events occurring in them, and the detectors operate in a self-triggered mode, recording each event separately with information about its time of incidence, detector co-ordinates, and channel that scales with the amount of ionisation. The detectors are sensitive not only to photons from astrophysical sources of interest, but also prone to a number of other events like background X-rays, cosmic rays, and noise in detectors or the electronics. In this work a detailed analysis of the effect of cosmic rays on the detectors is made and it is found that cosmic rays can trigger multiple events which are closely packed in time (called 'bunches'). Higher energy cosmic rays, however, can also generate delayed emissions, a signature previously seen in the PICsIT detector on-board INTEGRAL. An algorithm to automatically detect them based on their spatial clustering properties is presented. Residual noise events are examined using examples of Gamma Ray Bursts as target sources., Comment: 16 pages, 12 figures

A two-terminal distributed binary hypothesis testing problem over a noisy channel is studied. The two terminals, called the observer and the decision maker, each has access to $n$ independent and identically distributed samples, denoted by $\mathbf{U}$ and $\mathbf{V}$, respectively. The observer communicates to the decision maker over a discrete memoryless channel, and the decision maker performs a binary hypothesis test on the joint probability distribution of $(\mathbf{U},\mathbf{V})$ based on $\mathbf{V}$ and the noisy information received from the observer. The trade-off between the exponents of the type I and type II error probabilities is investigated. Two inner bounds are obtained, one using a separation-based scheme that involves type-based compression and unequal error-protection channel coding, and the other using a joint scheme that incorporates type-based hybrid coding. The separation-based scheme is shown to recover the inner bound obtained by Han and Kobayashi for the special case of a rate-limited noiseless channel, and also the one obtained by the authors previously for a corner point of the trade-off. Finally, we show via an example that the joint scheme achieves a strictly tighter bound than the separation-based scheme for some points of the error-exponents trade-off.

In this work, we analyse and predict the stability of communities in complex networks. We use a variant of closeness centrality, known as profile closeness, to measure the loyalty of a member towards its community. We show that the profile closeness is an adequate indicator of how communities evolve in a network. We investigate this in static as well as dynamic (temporal) networks and establish the relevance of profile closeness in predicting the evolution of a complex network. Keywords: Small world networks , Centrality , Community , Closeness , Clustering