Statistics of Mesoscopic Fluctuations of Quantum Capacitance

The Thouless formula $G = (e^2/h)(E_c/\Delta)$ for the two-probe dc conductance $G$ of a d-dimensional mesoscopic cube is re-analysed to relate its quantum capacitance $C_Q$ to the reciprocal of the level spacing $\Delta$. To this end, the escape time-scale $\tau$ occurring in the Thouless correlation energy $E_c = \hbar/\tau$ is interpreted as the {\em time constant} $\tau = RC_Q$ with $RG \equiv$ 1, giving at once $C_Q = (e^2/2\pi \Delta)$. Thus, the statistics of the quantum capacitance is directly related to that of the level spacing, which is well known from the Random Matrix Theory for all the three universality classes of statistical ensembles. The basic questions of how intrinsic this quantum capacitance can arise purely quantum-resistively, and of its observability {\em vis-a-vis} the external geometric capacitance that combines with it in series, are discussed

DSDV, DYMO, OLSR: Link Duration and Path Stability

In this paper, we evaluate and compare the impact of link duration and path stability of routing protocols; Destination Sequence Distance vector (DSDV), Dynamic MANET On- Demand (DYMO) and Optimized Link State Routing (OLSR) at different number of connections and node density. In order to improve the efficiency of selected protocols; we enhance DYMO and OLSR. Simulation and comparison of both default and enhanced routing protocols is carried out under the performance parameters; Packet Delivery Ratio (PDR), Average End-to End Delay (AE2ED) and Normalized Routing Overhead (NRO). From the results, we observe that DYMO performs better than DSDV, MOD-OLSR and OLSR in terms of PDR, AE2ED, link duration and path stability at the cost of high value of NRO

On the Limits of Depth Reduction at Depth 3 Over Small Finite Fields

Recently, Gupta et.al. [GKKS2013] proved that over Q any $n^{O(1)}$-variate and $n$-degree polynomial in VP can also be computed by a depth three $\Sigma\Pi\Sigma$ circuit of size $2^{O(\sqrt{n}\log^{3/2}n)}$. Over fixed-size finite fields, Grigoriev and Karpinski proved that any $\Sigma\Pi\Sigma$ circuit that computes $Det_n$ (or $Perm_n$) must be of size $2^{\Omega(n)}$ [GK1998]. In this paper, we prove that over fixed-size finite fields, any $\Sigma\Pi\Sigma$ circuit for computing the iterated matrix multiplication polynomial of $n$ generic matrices of size $n\times n$, must be of size $2^{\Omega(n\log n)}$. The importance of this result is that over fixed-size fields there is no depth reduction technique that can be used to compute all the $n^{O(1)}$-variate and $n$-degree polynomials in VP by depth 3 circuits of size $2^{o(n\log n)}$. The result [GK1998] can only rule out such a possibility for depth 3 circuits of size $2^{o(n)}$. We also give an example of an explicit polynomial ($NW_{n,\epsilon}(X)$) in VNP (not known to be in VP), for which any $\Sigma\Pi\Sigma$ circuit computing it (over fixed-size fields) must be of size $2^{\Omega(n\log n)}$. The polynomial we consider is constructed from the combinatorial design. An interesting feature of this result is that we get the first examples of two polynomials (one in VP and one in VNP) such that they have provably stronger circuit size lower bounds than Permanent in a reasonably strong model of computation. Next, we prove that any depth 4 $\Sigma\Pi^{[O(\sqrt{n})]}\Sigma\Pi^{[\sqrt{n}]}$ circuit computing $NW_{n,\epsilon}(X)$ (over any field) must be of size $2^{\Omega(\sqrt{n}\log n)}$. To the best of our knowledge, the polynomial $NW_{n,\epsilon}(X)$ is the first example of an explicit polynomial in VNP such that it requires $2^{\Omega(\sqrt{n}\log n)}$ size depth four circuits, but no known matching upper bound

Landau diamagnetism revisited

The problem of diamagnetism, solved by Landau, continues to pose fascinating issues which have relevance even today. These issues relate to inherent quantum nature of the problem, the role of boundary and dissipation, the meaning of thermodynamic limits, and above all, the quantum-classical crossover occasioned by environment-induced decoherence. The Landau Diamagnetism provides a unique paradigm for discussing these issues, the significance of which are far-reaching. Our central result is a remarkable one as it connects the mean orbital magnetic moment, a thermodynamic property, with the electrical resistivity, which characterizes transport properties of materials.Comment: 4 pages, 1 figur

Laminar and turbulent flows over spherically blunted cone and hyperboloid with massive surface blowing

Numerical solutions are presented for the flow over a spherically blunted cone and hyperboloid with massive surface blowing. Time-dependent viscous shock-layer equations are used to describe the flow field. The boundary conditions on the body surface include a prescribed blowing-rate distribution. The governing equations are solved by a time-asymptotic finite-difference method. Results presented here are only for a perfect gas-type flow at zero angle of attack. Both laminar and turbulent flow solutions are obtained. It is found that the effect of the surface blowing on the laminar flow field is to smooth out the curvature discontinuity at the sphere-cone juncture point, which results in a positive pressure gradient over the body. The shock slope increases on the downstream portion of the body as the surface blowing rate is increased. The turbulent flow with surface blowing is found to redevelop a boundary-layer-like region near the surface. The effects of this boundary-layer region on the flow field and heating rates are discussed

Toolboxes and handing students a hammer: The effects of cueing and instruction on getting students to think critically

Developing critical thinking skills is a common goal of an undergraduate physics curriculum. How do students make sense of evidence and what do they do with it? In this study, we evaluated students' critical thinking behaviors through their written notebooks in an introductory physics laboratory course. We compared student behaviors in the Structured Quantitative Inquiry Labs (SQILabs) curriculum to a control group and evaluated the fragility of these behaviors through procedural cueing. We found that the SQILabs were generally effective at improving the quality of students' reasoning about data and making decisions from data. These improvements in reasoning and sensemaking were thwarted, however, by a procedural cue. We describe these changes in behavior through the lens of epistemological frames and task orientation, invoked by the instructional moves

On Link Availability Probability of Routing Protocols for Urban Scenario in VANETs

This paper presents the link availability probability. We evaluate and compare the link availability probability for routing protocols; Ad hoc On-demand Distance vector (AODV), Dynamic Source Routing (DSR) and Fisheye State Routing (FSR) for different number of connections and node density. A novel contribution of this work is enhancement in existing parameters of routing protocols; AODV, DSR and FSR as MOD-AODV, MOD-DSR and MOD-FSR. From the results, we observe that MOD-DSR and DSR outperform MOD-AODV, AODV, MODOLSR and OLSR in terms of Packet Delivery Ratio (PDR), Average End-to End Delay (AE2ED), link availability probability at the cost of high value of Normalized Routing Overhead (NRO).Comment: IEEE Conference on Open Systems (ICOS2012)", Kuala Lumpur, Malaysia, 201

MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes

Attribute recognition, particularly facial, extracts many labels for each image. While some multi-task vision problems can be decomposed into separate tasks and stages, e.g., training independent models for each task, for a growing set of problems joint optimization across all tasks has been shown to improve performance. We show that for deep convolutional neural network (DCNN) facial attribute extraction, multi-task optimization is better. Unfortunately, it can be difficult to apply joint optimization to DCNNs when training data is imbalanced, and re-balancing multi-label data directly is structurally infeasible, since adding/removing data to balance one label will change the sampling of the other labels. This paper addresses the multi-label imbalance problem by introducing a novel mixed objective optimization network (MOON) with a loss function that mixes multiple task objectives with domain adaptive re-weighting of propagated loss. Experiments demonstrate that not only does MOON advance the state of the art in facial attribute recognition, but it also outperforms independently trained DCNNs using the same data. When using facial attributes for the LFW face recognition task, we show that our balanced (domain adapted) network outperforms the unbalanced trained network.Comment: Post-print of manuscript accepted to the European Conference on Computer Vision (ECCV) 2016 http://link.springer.com/chapter/10.1007%2F978-3-319-46454-1_