Private Incremental Regression

Data is continuously generated by modern data sources, and a recent challenge in machine learning has been to develop techniques that perform well in an incremental (streaming) setting. In this paper, we investigate the problem of private machine learning, where as common in practice, the data is not given at once, but rather arrives incrementally over time. We introduce the problems of private incremental ERM and private incremental regression where the general goal is to always maintain a good empirical risk minimizer for the history observed under differential privacy. Our first contribution is a generic transformation of private batch ERM mechanisms into private incremental ERM mechanisms, based on a simple idea of invoking the private batch ERM procedure at some regular time intervals. We take this construction as a baseline for comparison. We then provide two mechanisms for the private incremental regression problem. Our first mechanism is based on privately constructing a noisy incremental gradient function, which is then used in a modified projected gradient procedure at every timestep. This mechanism has an excess empirical risk of $\approx\sqrt{d}$, where $d$ is the dimensionality of the data. While from the results of [Bassily et al. 2014] this bound is tight in the worst-case, we show that certain geometric properties of the input and constraint set can be used to derive significantly better results for certain interesting regression problems.Comment: To appear in PODS 201

In-orbit Performance of UVIT on ASTROSAT

We present the in-orbit performance and the first results from the ultra-violet Imaging telescope (UVIT) on ASTROSAT. UVIT consists of two identical 38cm coaligned telescopes, one for the FUV channel (130-180nm) and the other for the NUV (200-300nm) and VIS (320-550nm) channels, with a field of view of 28 $arcmin$. The FUV and the NUV detectors are operated in the high gain photon counting mode whereas the VIS detector is operated in the low gain integration mode. The FUV and NUV channels have filters and gratings, whereas the VIS channel has filters. The ASTROSAT was launched on 28th September 2015. The performance verification of UVIT was carried out after the opening of the UVIT doors on 30th November 2015, till the end of March 2016 within the allotted time of 50 days for calibration. All the on-board systems were found to be working satisfactorily. During the PV phase, the UVIT observed several calibration sources to characterise the instrument and a few objects to demonstrate the capability of the UVIT. The resolution of the UVIT was found to be about 1.4 - 1.7 $arcsec$ in the FUV and NUV. The sensitivity in various filters were calibrated using standard stars (white dwarfs), to estimate the zero-point magnitudes as well as the flux conversion factor. The gratings were also calibrated to estimate their resolution as well as effective area. The sensitivity of the filters were found to be reduced up to 15\% with respect to the ground calibrations. The sensitivity variation is monitored on a monthly basis. UVIT is all set to roll out science results with its imaging capability with good resolution and large field of view, capability to sample the UV spectral region using different filters and capability to perform variability studies in the UV.Comment: 10 pages, To appear in SPIE conference proceedings, SPIE conference paper, 201

The number of matchings in random graphs

We study matchings on sparse random graphs by means of the cavity method. We first show how the method reproduces several known results about maximum and perfect matchings in regular and Erdos-Renyi random graphs. Our main new result is the computation of the entropy, i.e. the leading order of the logarithm of the number of solutions, of matchings with a given size. We derive both an algorithm to compute this entropy for an arbitrary graph with a girth that diverges in the large size limit, and an analytic result for the entropy in regular and Erdos-Renyi random graph ensembles.Comment: 17 pages, 6 figures, to be published in Journal of Statistical Mechanic

Identification of ORC1/CDC6-Interacting Factors in Trypanosoma brucei Reveals Critical Features of Origin Recognition Complex Architecture

DNA Replication initiates by formation of a pre-replication complex on sequences termed origins. In eukaryotes, the pre-replication complex is composed of the Origin Recognition Complex (ORC), Cdc6 and the MCM replicative helicase in conjunction with Cdt1. Eukaryotic ORC is considered to be composed of six subunits, named Orc1–6, and monomeric Cdc6 is closely related in sequence to Orc1. However, ORC has been little explored in protists, and only a single ORC protein, related to both Orc1 and Cdc6, has been shown to act in DNA replication in Trypanosoma brucei. Here we identify three highly diverged putative T. brucei ORC components that interact with ORC1/CDC6 and contribute to cell division. Two of these factors are so diverged that we cannot determine if they are eukaryotic ORC subunit orthologues, or are parasite-specific replication factors. The other we show to be a highly diverged Orc4 orthologue, demonstrating that this is one of the most widely conserved ORC subunits in protists and revealing it to be a key element of eukaryotic ORC architecture. Additionally, we have examined interactions amongst the T. brucei MCM subunits and show that this has the conventional eukaryotic heterohexameric structure, suggesting that divergence in the T. brucei replication machinery is limited to the earliest steps in origin licensing

Overexpression of DNA Polymerase Zeta Reduces the Mitochondrial Mutability Caused by Pathological Mutations in DNA Polymerase Gamma in Yeast

In yeast, DNA polymerase zeta (Rev3 and Rev7) and Rev1, involved in the error-prone translesion synthesis during replication of nuclear DNA, localize also in mitochondria. We show that overexpression of Rev3 reduced the mtDNA extended mutability caused by a subclass of pathological mutations in Mip1, the yeast mitochondrial DNA polymerase orthologous to human Pol gamma. This beneficial effect was synergistic with the effect achieved by increasing the dNTPs pools. Since overexpression of Rev3 is detrimental for nuclear DNA mutability, we constructed a mutant Rev3 isoform unable to migrate into the nucleus: its overexpression reduced mtDNA mutability without increasing the nuclear one

Corals in the carbide industry

The carbide industry in India had till very recently depended for raw material largely on imported petroleum coke. Nearly a decade ago Industrial Chemicals Ltd., Sankarnagar, in Tirunelveli District, the pioneer manufacturers of calcium carbide in the country, started looking for indigenous raw material. In processing calcium carbide from lime and carbon, they knew they could very well put local wood charcoal to lime, but the samples of limestone from all over India tried8 were found far from adequate to make first-class carbide conforming to the rigid international standard Ultimately, the corals available in the seas around Rameswaram, on analysis, were found to conform to all the requirements as per standard specification to make ' A ' grade calcium carbide

Mechanical Properties of Partial Replacement of Cement by adding Neem Gum/Accaacia Gum a Naturally Available Polymeric Fillers

The use of natural polymeric fillers in concrete has been gaining popularity in recent years due to their environmental and economic benefits. Neem gum and acacia gum are two such fillers that have been shown to improve the mechanical properties of concrete. In this experimental study, the effects of partial replacement of cement with neem gum and acacia gum on the compressive strength, splitting tensile strength, flexural strength, elastic modulus, and water absorption of concrete were investigated. The results showed that the addition of neem gum and acacia gum to concrete can improve its mechanical properties. The compressive strength of concrete increased by up to 10% when 0.5% of neem gum or acacia gum was added. The splitting tensile strength and flexural strength of concrete also increased by up to 15% and 10%, respectively, when 0.5% of neem gum or acacia gum was added. The improvement in the mechanical properties of concrete with the addition of neem gum and acacia gum is attributed to the following factors. The significantly higher than the compressive strength of the control mix withacacia gum 23.5 MPa, but for 1 weight % of neem gum is about 34.5 MPa. The Split tensile strength was also significantly higher than the split tensile strength of the control mix without acacia gum 3.1 MPa and the best result seen in the 1.5 weight percentage of acacia gum with 3.8 MPa. The higher flexural strength of the control mix with acacia gum is about 5.1 MPa, for neem gum 5.9 MPa and elastic modulus 24 GPa for 1 % of acacia, 38GPa for neem gum was noted. Significantly lower than the water absorption rate of the control mix with acacia gum 2.8% and with neem gum for 1.5 % of neem gum is about 1.4 percentages. The use of these fillers can improve the mechanical properties of concrete and reduce the environmental impact of concrete production

Radial Basis Function Artificial Neural Network: Spread Selection

descent algorithm, K-means clustering algorithm, radial basis function artificial neural network (RBFANN), Abstract � Modeling and simulation of hydrological processes are important for the efficient management and planning of water resources. In recent years, radial basis function artificial neural network (RBFANN) models are significantly used in the field of water resources and hydrological applications due to their good convergance ability. The RBFANN has flexible mathematical structure which is capable of identifying the non-linear relationship between input and output data sets. The RBFANN model is motivated by the locally tuned response. Due to this nature, the networks are easily trained by a sufficiently large data set to learn the physical process to be approximated. The training of RBFANN can be split into an unsupervised part and a supervised part. The K-means clustering algorithm is used in unsupervised learning and gradient descent algorithm is used in supervised learning part. This paper illustrates about the model setting parameters which are to be estimated carefully in RBFANN while modeling the rainfall-runoff process. In RBFANN, spread and center values are the model parameters which are estimated by inducing the suitable weight values. The spread value is selected based on the minimum error crieteria of the developed model. Fortnightly rainfall-runoff values of Kovilar reservoir in Vaipar basin in Tamilnadu, India has been used for the rainfall-runoff modeling. The results showing that the selection of spread is an important parameter in the model performace. It was found that the magnitude of spread value has an impact of model perforance and it should be selected with care. 1

A novel two-stage multi-step dynamic error correction model for improving streamflow forecast accuracy

The occurrences of floods in the recent past have significantly increased due to climate change and anthropogenic activities. Hence, reliable streamflow forecasts are crucial for minimizing the detrimental effects of flooding. However, forecast accuracy deteriorates besides elevated uncertainty when the lead time increases. Therefore, streamflow forecast should have improved accuracy with simultaneous uncertainty quantification to increase the model confidence for effective decision-making. The study proposes a novel two-stage multi-step dynamic error correction model to forecast up to 7 days ahead of streamflow, with the objective of no significant deterioration in accuracy. The framework is developed by integrating the process-based hydrological HBV model with the Bayesian-based Particle filter (PF) and machine learning-based Random Forest algorithm (RF). This facilitates combining the advantages of each model, i.e., process understanding ability of the HBV model, robust uncertainty quantifying ability of the PF technique, and relatively superior predictive ability of the RF algorithm. The model performance is quantified through several statistical performance error measures and uncertainty indices, with graphical performance indicators. The framework tested on the Beas and Sunkoshi river basins of India and Nepal exemplified the NSE of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation respectively for the 7-day ahead streamflow forecast. Hence, the proposed dynamic modeling framework can be considered as a potential tool to forecast streamflow without significant deterioration in the model accuracy even at increased lead times

COVID-19 Lockdown Disruptions on Water Resources, Wastewater, and Agriculture in India

10.3389/frwa.2021.603531Frontiers in Water360353