t1/3t^{1/3} Superdiffusivity of Finite-Range Asymmetric Exclusion Processes on Z\mathbb Z

We consider finite-range asymmetric exclusion processes on $\mathbb Z$ with non-zero drift. The diffusivity $D(t)$ is expected to be of ${\mathcal O}(t^{1/3})$. We prove that $D(t)\ge Ct^{1/3}$ in the weak (Tauberian) sense that $\int_0^\infty e^{-\lambda t}tD(t)dt \ge C\lambda^{-7/3}$ as $\lambda\to 0$. The proof employs the resolvent method to make a direct comparison with the totally asymmetric simple exclusion process, for which the result is a consequence of the scaling limit for the two-point function recently obtained by Ferrari and Spohn. In the nearest neighbor case, we show further that $tD(t)$ is monotone, and hence we can conclude that $D(t)\ge Ct^{1/3}(\log t)^{-7/3}$ in the usual sense.Comment: Version 3. Statement of Theorem 3 is correcte

Superdiffusivity of Asymmetric Energy Model in Dimension One and Two

We discuss an asymmetric energy model (AEM) introduced by Giardina et al. in \cite{7}. This model is expected to belong to the KPZ class. We obtain lower bounds for the diffusion coefficient. In particular, the diffusion coefficient is diverging in dimension one and two as it is expected in the KPZ picture

From Physical to Cyber: Escalating Protection for Personalized Auto Insurance

Nowadays, auto insurance companies set personalized insurance rate based on data gathered directly from their customers' cars. In this paper, we show such a personalized insurance mechanism -- wildly adopted by many auto insurance companies -- is vulnerable to exploit. In particular, we demonstrate that an adversary can leverage off-the-shelf hardware to manipulate the data to the device that collects drivers' habits for insurance rate customization and obtain a fraudulent insurance discount. In response to this type of attack, we also propose a defense mechanism that escalates the protection for insurers' data collection. The main idea of this mechanism is to augment the insurer's data collection device with the ability to gather unforgeable data acquired from the physical world, and then leverage these data to identify manipulated data points. Our defense mechanism leveraged a statistical model built on unmanipulated data and is robust to manipulation methods that are not foreseen previously. We have implemented this defense mechanism as a proof-of-concept prototype and tested its effectiveness in the real world. Our evaluation shows that our defense mechanism exhibits a false positive rate of 0.032 and a false negative rate of 0.013.Comment: Appeared in Sensys 201

Additive manufacturing of soft magnets for electrical machines—a review

With growing interest in electrification from clean energy technologies, such as wind power and use of pure electric powertrains in various applications, the demand for next-generation, high-performance magnetic materials has risen significantly. Electrical machine design for these applications is facing challenges in terms of meeting very demanding metrics for power densities and conversion efficiencies, thereby motivating the exploration of advanced materials and manufacturing for the next generation of lightweight ultraefficient electric machines. Additive manufacturing (AM), a layer-by-layer three dimensional (3D) printing technology, opens up new venues of improvements for industrial manufacturing of electrical machines via near-net shape printing of complex geometries, reduction of parts count and production lead time, and conservation of expensive critical materials such as rare-earth magnets as well as nanocrystalline and amorphous soft magnetic composites, allowing their use in only critical regions required by desired properties of the printed parts. The magnetic, electrical, thermal, and mechanical properties of the magnetic materials are also greatly influenced by the selection of the AM method. Among the seven major American Standard Testing and Materials-defined standard modes of 3D printing, selective laser melting, fused deposition modeling, and binder jetting technology dominate the AM processing of soft magnetic materials and their integration in electrical machines. In this work, the state of the art in printability and performance characteristics of soft magnetic materials for electric machines is summarized and discussed. The prospects of soft magnetic materials selection in terms of price, printability, weight, and performance of the electrical machines are also discussed. This review highlights the current status of AM of large electrical machines, AM process selection guidelines, hybrid printing technologies, and the associated opportunities and challenges. An emphasis is put on multimaterial processing that is essential for electrical machines. Hybrid printing technologies that combine multiple AM processes with adequate automation and enable simultaneous multimaterials dispensing, real-time quality control, postprocessing, and surface finish with integrated subtractive computer numeric control machining are the requirements for progressing toward the end-user electrical machines

Transport Properties of a Chain of Anharmonic Oscillators with random flip of velocities

We consider the stationary states of a chain of $n$ anharmonic coupled oscillators, whose deterministic hamiltonian dynamics is perturbed by random independent sign change of the velocities (a random mechanism that conserve energy). The extremities are coupled to thermostats at different temperature $T_\ell$ and $T_r$ and subject to constant forces $\tau_\ell$ and $\tau_r$. If the forces differ $\tau_\ell \neq \tau_r$ the center of mass of the system will move of a speed $V_s$ inducing a tension gradient inside the system. Our aim is to see the influence of the tension gradient on the thermal conductivity. We investigate the entropy production properties of the stationary states, and we prove the existence of the Onsager matrix defined by Green-kubo formulas (linear response). We also prove some explicit bounds on the thermal conductivity, depending on the temperature.Comment: Published version: J Stat Phys (2011) 145:1224-1255 DOI 10.1007/s10955-011-0385-

On the asymmetric zero-range in the rarefaction fan

We consider the one-dimensional asymmetric zero-range process starting from a step decreasing profile. In the hydrodynamic limit this initial condition leads to the rarefaction fan of the associated hydrodynamic equation. Under this initial condition and for totally asymmetric jumps, we show that the weighted sum of joint probabilities for second class particles sharing the same site is convergent and we compute its limit. For partially asymmetric jumps we derive the Law of Large Numbers for the position of a second class particle under the initial configuration in which all the positive sites are empty, all the negative sites are occupied with infinitely many first class particles and with a single second class particle at the origin. Moreover, we prove that among the infinite characteristics emanating from the position of the second class particle, this particle chooses randomly one of them. The randomness is given in terms of the weak solution of the hydrodynamic equation through some sort of renormalization function. By coupling the zero-range with the exclusion process we derive some limiting laws for more general initial conditions.Comment: 22 pages, to appear in Journal of Statistical Physic

Location Dependent Dirichlet Processes

Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arising from space and time. In this paper, we propose location dependent Dirichlet processes (LDDP) which incorporate nonparametric Gaussian processes in the DP modeling framework to model such dependencies. We develop the LDDP in the context of mixture modeling, and develop a mean field variational inference algorithm for this mixture model. The effectiveness of the proposed modeling framework is shown on an image segmentation task