Evaluation of Seed Quality in Naturally Aged Seed Lots of Coriander

Three seed lots of fifteen genotypes of coriander were subjected to study the effect of natural ageing on different seed quality parameters. Results revealed that all the genotypes showed the germination percentage above the Minimum Seed Certification Standards (65%) in Lot-1 (freshly harvested seed) and Lot-2 (1 year old seed). Standard germination (%), seedling length (cm), seedling dry weight (mg), seedling vigor index-I & II and accelerated ageing test (%) revealed that quality of seeds declined with faster rate inLot-3 (2 years old seed). Among all the genotypes, maximum germination was retained by genotype DH-339 (75.5%) followed by Hisar Surbhi (74.5%) and maximum loss of germination was observed in genotype DH 352-1 (61.2%). Hence, the genotypes DH-339 and Hisar Surbhi were found superior in terms of viability, vigor and storability whereas genotype DH 352-1 was found poor under ambient conditions

Using Jet Substructure at the LHC to Search for the Light Higgs Bosons of the CP-Violating MSSM

The CP-violating version of the Minimal Supersymmetric Standard Model (MSSM) is an example of a model where experimental data do not preclude the presence of light Higgs bosons in the range around 10 -- 110 GeV. Such light Higgs bosons, decaying almost wholly to b-bbar pairs, may be copiously produced at the LHC, but would remain inaccessible to conventional Higgs searches because of intractable QCD backgrounds. We demonstrate that a significant number of these light Higgs bosons would be boosted strongly enough for the pair of daughter $b$-jet pairs to appear as a single `fat' jet with substructure. Tagging such jets could extend the discovery potential at the LHC into the hitherto-inaccessible region for light Higgs bosons.Comment: LaTeX, 33 pages, 5 eps figures and 5 tables embedded. minor changes, to appear in Physical Review

Workload Prediction for Adaptive Power Scaling Using Deep Learning

We apply hierarchical sparse coding, a form of deep learning, to model user-driven workloads based on on-chip hardware performance counters. We then predict periods of low instruction throughput, during which frequency and voltage can be scaled to reclaim power. Using a multi-layer coding structure, our method progressively codes counter values in terms of a few prominent features learned from data, and passes them to a Support Vector Machine (SVM) classifier where they act as signatures for predicting future workload states. We show that prediction accuracy and look-ahead range improve significantly over linear regression modeling, giving more time to adjust power management settings. Our method relies on learning and feature extraction algorithms that can discover and exploit hidden statistical invariances specific to workloads. We argue that, in addition to achieving superior prediction performance, our method is fast enough for practical use. To our knowledge, we are the first to use deep learning at the instruction level for workload prediction and on-chip power adaptation.Engineering and Applied Science

Stability prediction of Himalayan residual soil slope using artificial neural network

In the past decade, advances in machine learning (ML) techniques have resulted in developing sophisticated models that are capable of modelling extremely complex multi-factorial problems like slope stability analysis. The literature review indicates that considerable works have been done in slope stability using ML, but none of them covers the analysis of residual soil slope. The present study aims to develop an artificial neural network (ANN) model that can be employed for evaluating the factor of safety of Shiwalik Slopes in the Himalayan Region. Data obtained from numerical analysis of a residual soil slope were used to develop two ANN models (ANN1 and ANN2 utilising eleven input parameters, and scaled-down number of parameters based on correlation coefficient, respectively). A four-layer, feed-forward back-propagation neural network having the optimum number of hidden neurons is developed based on trial-and-error method. The results derived from ANN models were compared with those achieved from numerical analysis. Additionally, several performance indices such as coefficient of determination (R2), root mean square error, variance account for, and residual error were employed to evaluate the predictive performance of the developed ANN models. Both the ANN models have shown good prediction performance; however, the overall performance of the ANN2 model is better than the ANN1 model. It is concluded that the ANN models are reliable, valid, and straightforward computational tools that can be employed for slope stability analysis during the preliminary stage of designing infrastructure projects in residual soil slope. © 2020, Springer Nature B.V

Treatment of Pseudo Class III Malocclusion with Multiple Loop Protraction Utility Arch

Pseudo Class III malocclusion has been characterized by an anterior crossbite in the presence of a forward mandibular displacement. There are various methods to correct pseudo Class III malocclusion, e.g., Inclined planes, reverse stainless steel crown, bonded composite resin slopes, tongue blade, the removable appliance with auxiliary springs, and maxillary lingual arch with finger springs. In this article, we are presenting a case of pseudo Class III malocclusion treated with multiple loop protraction utility arch. Patient had functional mandibular anterior deviation resulting into traumatic anterior cross bite and concave profile. We fabricated multiple loop arch wire (0.016”×0.022” blue elgiloy) which was activated at four 90° bends without disturbing other segments of the arch

A compositional account of motifs, mechanisms, and dynamics in biochemical regulatory networks

Regulatory networks depict promoting or inhibiting interactions between molecules in a biochemical system. We introduce a category-theoretic formalism for regulatory networks, using signed graphs to model the networks and signed functors to describe occurrences of one network in another, especially occurrences of network motifs. With this foundation, we establish functorial mappings between regulatory networks and other mathematical models in biochemistry. We construct a functor from reaction networks, modeled as Petri nets with signed links, to regulatory networks, enabling us to precisely define when a reaction network could be a physical mechanism underlying a regulatory network. Turning to quantitative models, we associate a regulatory network with a Lotka-Volterra system of differential equations, defining a functor from the category of signed graphs to a category of parameterized dynamical systems. We extend this result from closed to open systems, demonstrating that Lotka-Volterra dynamics respects not only inclusions and collapsings of regulatory networks, but also the process of building up complex regulatory networks by gluing together simpler pieces. Formally, we use the theory of structured cospans to produce a lax double functor from the double category of open signed graphs to that of open parameterized dynamical systems. Throughout the paper, we ground the categorical formalism in examples inspired by systems biology.Comment: 33 pages. Added several examples, plus minor revision

Static and dynamical quantum correlations in phases of an alternating field XY model

We investigate the static and dynamical patterns of entanglement in an anisotropic XY model with an alternating transverse magnetic field, which is equivalent to a two-component one-dimensional Fermi gas on a lattice, a system realizable with current technology. Apart from the antiferromagnetic and paramagnetic phases, the model possesses a dimer phase which is not present in the transverse XY model. At zero temperature, we find that the first derivative of bipartite entanglement can detect all the three phases. We analytically show that the model has a "factorization line" on the plane of system parameters, in which the zero temperature state is separable. Along with investigating the effect of temperature on entanglement in a phase plane, we also report a non-monotonic behavior of entanglement with respect to temperature in the anti-ferromagnetic and paramagnetic phases, which is surprisingly absent in the dimer phase. Since the time dynamics of entanglement in a realizable physical system plays an important role in quantum information processing tasks, the evolutions of entanglement at small as well as large time are examined. Consideration of large time behavior of entanglement helps us to prove that in this model, entanglement is always ergodic. We observe that other quantum correlation measures can qualitatively show similar features in zero and finite temperatures. However, unlike nearest-neighbor entanglement, the nearest-neighbor information theoretic measures can be both ergodic as well as non-ergodic, depending on the system parameters.Comment: 20 Pages, 13 Figures, 2 Tables, Published versio

Reducing Computational Complexity of Quantum Correlations

We address the issue of reducing the resource required to compute information-theoretic quantum correlation measures like quantum discord and quantum work deficit in two qubits and higher dimensional systems. We show that determination of the quantum correlation measure is possible even if we utilize a restricted set of local measurements. We find that the determination allows us to obtain a closed form of quantum discord and quantum work deficit for several classes of states, with a low error. We show that the computational error caused by the constraint over the complete set of local measurements reduces fast with an increase in the size of the restricted set, implying usefulness of constrained optimization, especially with the increase of dimensions. We perform quantitative analysis to investigate how the error scales with the system size, taking into account a set of plausible constructions of the constrained set. Carrying out a comparative study, we show that the resource required to optimize quantum work deficit is usually higher than that required for quantum discord. We also demonstrate that minimization of quantum discord and quantum work deficit is easier in the case of two-qubit mixed states of fixed ranks and with positive partial transpose in comparison to the corresponding states having non-positive partial transpose. Applying the methodology to quantum spin models, we show that the constrained optimization can be used with advantage in analyzing such systems in quantum information-theoretic language. For bound entangled states, we show that the error is significantly low when the measurements correspond to the spin observables along the three Cartesian coordinates, and thereby we obtain expressions of quantum discord and quantum work deficit for these bound entangled states.Comment: 19 pages, 14 figures, 3 table

Workload Prediction for Adaptive Power Scaling Using Deep Learning

Abstract-We apply hierarchical sparse coding, a form of deep learning, to model user-driven workloads based on on-chip hardware performance counters. We then predict periods of low instruction throughput, during which frequency and voltage can be scaled to reclaim power. Using a multi-layer coding structure, our method progressively codes counter values in terms of a few prominent features learned from data, and passes them to a Support Vector Machine (SVM) classifier where they act as signatures for predicting future workload states. We show that prediction accuracy and look-ahead range improve significantly over linear regression modeling, giving more time to adjust power management settings. Our method relies on learning and feature extraction algorithms that can discover and exploit hidden statistical invariances specific to workloads. We argue that, in addition to achieving superior prediction performance, our method is fast enough for practical use. To our knowledge, we are the first to use deep learning at the instruction level for workload prediction and on-chip power adaptation