Back Reaction of Hawking Radiation on Black Hole Geometry

We propose a model for the geometry of a dynamical spherical shell in which the metric is asymptotically Schwarzschild, but deviates from Ricci-flatness in a finite neighbourhood of the shell. Hence, the geometry corresponds to a `hairy' black hole, with the hair originating on the shell. The metric is regular for an infalling shell, but it bifurcates, leading to two disconnected Schwarzschild-like spacetime geometries. The shell is interpreted as either collapsing matter or as Hawking radiation, depending on whether or not the shell is infalling or outgoing. In this model, the Hawking radiation results from tunnelling between the two geometries. Using this model, the back reaction correction from Hawking radiation is calculated.Comment: Latex file, 15 pages, 4 figures enclosed, uses eps

Molecular hydrogen in the disk of the Herbig Ae star HD97048

We present high-resolution spectroscopic mid-infrared observations of the circumstellar disk around the Herbig Ae star HD97048 obtained with the VLT Imager and Spectrometer for the mid-InfraRed (VISIR). We conducted observations of mid-infrared pure rotational lines of molecular hydrogen (H2) as a tracer of warm gas in the disk surface layers. In a previous paper, we reported the detection of the S(1) pure rotational line of H2 at 17.035 microns and argued it is arising from the inner regions of the disk around the star. We used VISIR on the VLT for a more comprehensive study based on complementary observations of the other mid-infrared molecular transitions, namely S(2) and S(4) at 12.278 microns and 8.025 microns respectively, to investigate the physical properties of the molecular gas in the circumstellar disk around HD97048. We do not detect neither the S(2) line nor the S(4) H2 line from the disk of HD97048, but we derive upper limits on the integrated line fluxes which allows us to estimate an upper limit on the gas excitation temperature, T_ex < 570 K. This limit on the temperature is consistent with the assumptions previously used in the analysis of the S(1) line, and allows us to set stronger contraints on the mass of warm gas in the inner regions of the disk. Indeed, we estimate the mass of warm gas to be lower than 0.1 M_Jup. We also discuss the probable physical mechanisms which could be responsible of the excitation of H2 in the disk of HD97048.Comment: accepted for publication in Ap

Bounds on the Capacity of the Relay Channel with Noncausal State Information at Source

We consider a three-terminal state-dependent relay channel with the channel state available non-causally at only the source. Such a model may be of interest for node cooperation in the framework of cognition, i.e., collaborative signal transmission involving cognitive and non-cognitive radios. We study the capacity of this communication model. One principal problem in this setup is caused by the relay's not knowing the channel state. In the discrete memoryless (DM) case, we establish lower bounds on channel capacity. For the Gaussian case, we derive lower and upper bounds on the channel capacity. The upper bound is strictly better than the cut-set upper bound. We show that one of the developed lower bounds comes close to the upper bound, asymptotically, for certain ranges of rates.Comment: 5 pages, submitted to 2010 IEEE International Symposium on Information Theor

Non supersymmetric strong coupling background from the large N quantum mechanics of two matrices coupled via a Yang-Mills interaction

We derive the planar large N non-supersymmetric background of the quantum mechanical hamiltonian of two hermitean matrices coupled via a Yang-Mills interaction, in terms of the density of eigenvalues of one of the matrices. This background satisfies an implicit non linear integral equation, with a perturbative small coupling expansion and a solvable large coupling solution, which is obtained. The energy of system and the expectation value of several correlators are obtained in this strong coupling limit. They are free of infrared divergences.Comment: Latex, 13 page

Biotoxic effects of the herbicides on growth, seed yield, and grain protein of greengram

We studied the effects of atrazine, isoproturon, metribuzin and sulfosulfuron on plant vigour, nodulation, chlorophyll content, seed yield and protein content in seeds, in greengram inoculated with Bradyrhizobium sp. (vigna). The pre-emergence application of the four herbicides at 400 µg kg-1 of soil adversely affected the measured parameters. The average maximum increase of 10 % in seed yield occurred at 200 µg kg-1 of sulfosulfuron, while atrazine at 200 and 400 µg kg-1 of soil decreased the seed yield by 25 % and 40%, respectively. The average maximum chlorophyll content of 1.2 mg g-1 was obtained at 200 µg kg-1 of sulfosulfuron which declined consistently for all herbicides and increasing dose rates. Sulfosulfuron at 200 µg kg-1 increased the number of nodules found per plant by 7 % at 45 days after seeding the greengram. In contrast, the tested dose rates of atrazine, isoproturon and metribuzin significantly reduced the nodulation (nodule number and dry mass). The average maximum grain protein of 182 mg g-1 was obtained for sulfosulfuron at 400 µg kg-1, while minimum grain protein was obtained at 400 µg kg-1- of isoproturon (124 mg g-1) and atrazine (125 mg g-1) application. Among the herbicides tested, atrazine and metribuzin showed a large degree of phytotoxicity to the crop, inhibiting its vegetative growth and was thus incompatible with greengram. Journal of Applied Sciences and Environmental Management Vol. 10(3) 2006: 141-14

Missile Guidance Law Design via Backstepping Technique

In this paper a Back-stepping Control technique is proposed for command to line-of-sight missile guidance law design. In this design, the three-dimensional (3-D) CLOS guidance problem is formulated as a tracking problem of a time-varying nonlinear system. Simulation results for different engagement scenarios illustrate the validity of the proposed Backstepping-based Guidance Law

A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography

Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative and transferable features from surface electromyography (sEMG). However, muscle contractions have strong temporal dependencies but conventional CNN can only exploit spatial correlations. Considering that long short-term memory neural network (LSTM) is able to capture long-term and non-linear dynamics of time-series data, in this paper we propose a CNN-LSTM hybrid model to fully explore the temporal-spatial information in sEMG. Firstly, CNN is utilized to extract deep features from sEMG spectrum, then these features are processed via LSTM-based sequence regression to estimate wrist kinematics. Six healthy participants are recruited for the participatory collection and motion analysis under various experimental setups. Estimation results in both intra-session and inter-session evaluations illustrate that CNN-LSTM significantly outperforms CNN, LSTM and several representative machine learning approaches, particularly when complex wrist movements are activated

Interplay of Aharonov-Bohm, chirality, and aspect ratio effects in the axial conductance of a nanotube

A magnetic flux applied along the axis of a nanotube can counteract the effect of the tube chirality and dramatically affect its conductance, leading to a way to determine the chirality of a nanotube. The effect of the applied flux is strongest in the long tube limit where the conductance is (i) either a sequence of sharp $4e^{2}/h$ height peaks located at integer (in units of the flux quantum) values of the flux (for an armchair tube) or (ii) a periodic sequence of pairs of $2e^{2}/h$ height peaks for a chiral tube, with the spacing determined by the chirality. In the short tube limit the conductance takes on the value that gives the universal conductivity of an undoped graphene sheet, with a small amplitude modulation periodic in the flux.Comment: 5 pages, 2 figures, version to be published in Phys. Rev.

Skills embeddings: A neural approach to multicomponent representations of students and tasks

Educational systems use models of student skill to inform decision-making processes. Defining such a model manually is challenging due to the large number of relevant factors. We introduce an alternative approach by learning multidimensional representations (embeddings) from student activity data. Such embeddings are fixed-length real vectors with three desirable characteristics: co-location of similar students and items in a vector space; magnitude increases with skill, and that absence of a skill can be represented. Based on the Multicomponent Latent Trait Model, we use a neural network with complementary trainable weights to learn these embeddings by backpropagation in an unsupervised manner. We evaluate using synthetic student activity data that provides a ground-truth of student skills in order to understand the impact of number of students, question items and knowledge components in the domain. We find that our data-mined parameter values can recreate the synthetic datasets up to the accuracy of the model that generated them, for domains containing up to 10 simultaneously active knowledge components, which can be effectively mined using relatively small quantities of data (1000 students, 100 items). We describe a procedure to estimate the number of components in a domain, and propose a component-masking logic mechanism that improves performance on high-dimensional datasets.Cambridge Assessmen