Graph Kernels

We present a unified framework to study graph kernels, special cases of which include the random walk (Gärtner et al., 2003; Borgwardt et al., 2005) and marginalized (Kashima et al., 2003, 2004; Mahé et al., 2004) graph kernels. Through reduction to a Sylvester equation we improve the time complexity of kernel computation between unlabeled graphs with n vertices from O(n^6) to O(n^3). We find a spectral decomposition approach even more efficient when computing entire kernel matrices. For labeled graphs we develop conjugate gradient and fixed-point methods that take O(dn^3) time per iteration, where d is the size of the label set. By extending the necessary linear algebra to Reproducing Kernel Hilbert Spaces (RKHS) we obtain the same result for d-dimensional edge kernels, and O(n^4) in the infinite-dimensional case; on sparse graphs these algorithms only take O(n^2) time per iteration in all cases. Experiments on graphs from bioinformatics and other application domains show that these techniques can speed up computation of the kernel by an order of magnitude or more. We also show that certain rational kernels (Cortes et al., 2002, 2003, 2004) when specialized to graphs reduce to our random walk graph kernel. Finally, we relate our framework to R-convolution kernels (Haussler, 1999) and provide a kernel that is close to the optimal assignment kernel of Fröhlich et al. (2006) yet provably positive semi-definite

Mpemba effect and phase transitions in the adiabatic cooling of water before freezing

An accurate experimental investigation on the Mpemba effect (that is, the freezing of initially hot water before cold one) is carried out, showing that in the adiabatic cooling of water a relevant role is played by supercooling as well as by phase transitions taking place at 6 +/- 1 oC, 3.5 +/- 0.5 oC and 1.3 +/- 0.6 oC, respectively. The last transition, occurring with a non negligible probability of 0.21, has not been detected earlier. Supported by the experimental results achieved, a thorough theoretical analysis of supercooling and such phase transitions, which are interpreted in terms of different ordering of clusters of molecules in water, is given.Comment: revtex, 4 pages, 2 figure

Developing printable thermoelectric materials based on graphene nanoplatelet/ethyl cellulose nanocomposites

Thermoelectric (TE) materials have drawn a lot of attention as a promising technology to harvest waste heat and convert it into electrical energy. However, the toxicity and expense of inorganic TE materials along with high-temperature fabrication processes have limited their application. Additionally, the reduction of raw material resources, such as metals and petroleum is another limiting factor. Hence, developing low-cost, stable, and easily-created TE materials from renewable resources is attracting more and more interest for a wide range of applications including the internet of things and self-powered sensors. Herein, an efficacious processing strategy to fabricate printable TE materials has been developed with Ethyl cellulose (EC), a non-conducting polymer, as the polymer matrix and with Graphene nanoplatelets (GNPs) as fillers. EC, one of the cellulose's derivatives, has been widely used as a binder in the printing pastes. The conductive pastes with different filler contents have been fabricated. The weight ratio of GNPs and EC were ranged from 0.2 to 0.7. These conductive pastes have been deposited by blade coating on glass substrates. The electrical conductivity of the composites has increased polynomially as the filler content increased, whereas the Seebeck coefficient did not change significantly with the increased electrical conductivity. The highest electrical conductivity at room temperature (355.4 S m−1) was obtained for the ratio of 0.7. This ratio also had the maximum power factor value. Moreover, a 3D structure form (cylindrical pellet) from the highest conductive paste was also fabricated. The proposed technique demonstrates an industrially feasible approach to fabricate different geometries and structures for organic TE modules. So, this approach could provide a good reference for the production of high efficiency, low-temperature, lightweight, low-cost, TE materials

A convergent decomposition algorithm for support vector machines.

In this work we consider nonlinear minimization problems with a single linear equality constraint and box constraints. In particular we are interested in solving problems where the number of variables is so huge that traditional optimization methods cannot be directly applied. Many interesting real world problems lead to the solution of large scale constrained problems with this structure. For example, the special subclass of problems with convex quadratic objective function plays a fundamental role in the training of Support Vector Machine, which is a technique for machine learning problems. For this particular subclass of convex quadratic problem, some convergent decomposition methods, based on the solution of a sequence of smaller subproblems, have been proposed. In this paper we define a new globally convergent decomposition algorithm that differs from the previous methods in the rule for the choice of the subproblem variables and in the presence of a proximal point modification in the objective function of the subproblems. In particular, the new rule for sequentially selecting the subproblems appears to be suited to tackle large scale problems, while the introduction of the proximal point term allows us to ensure the global convergence of the algorithm for the general case of nonconvex objective function. Furthermore, we report some preliminary numerical results on support vector classification problems with up to 100 thousands variables

Domain wall dynamics in stepped magnetic nanowire with perpendicular magnetic anisotropy

Micromagnetic simulation is carried out to investigate the current-driven domain wall (DW) in a nanowire with perpendicular magnetic anisotropy (PMA). A stepped nanowire is proposed to pin DW and achieve high information storage capacity based on multi-bit per cell scheme. The DW speed is found to increase for thicker and narrower nanowires. For depinning DW from the stepped region, the current density Jdep is investigated with emphasis on device geometry and materials intrinsic properties. The Jdep could be analytically determined as a function of the nanocontriction dimension and the thickness of the nanowire. Furthermore, Jdep is found to exponential dependent on the anisotropy energy and saturation magnetization, offering thus more flexibility in adjusting the writing current for memory applications

Deep interactive evolution

This paper describes an approach that combines generative adversarial networks (GANs) with interactive evolutionary computation (IEC). While GANs can be trained to produce lifelike images, they are normally sampled randomly from the learned distribution, providing limited control over the resulting output. On the other hand, interactive evolution has shown promise in creating various artifacts such as images, music and 3D objects, but traditionally relies on a hand-designed evolvable representation of the target domain. The main insight in this paper is that a GAN trained on a specific target domain can act as a compact and robust genotype-to-phenotype mapping (i.e. most produced phenotypes do resemble valid domain artifacts). Once such a GAN is trained, the latent vector given as input to the GAN's generator network can be put under evolutionary control, allowing controllable and high-quality image generation. In this paper, we demonstrate the advantage of this novel approach through a user study in which participants were able to evolve images that strongly resemble specific target images.Comment: 16 pages, 5 figures, Published at EvoMUSART EvoStar 201