Orbital magnetization in periodic insulators

Working in the Wannier representation, we derive an expression for the orbital magnetization of a periodic insulator. The magnetization is shown to be comprised of two contributions, an obvious one associated with the internal circulation of bulk-like Wannier functions in the interior, and an unexpected one arising from net currents carried by Wannier functions near the surface. Each contribution can be expressed as a bulk property in terms of Bloch functions in a gauge-invariant way. Our expression is verified by comparing numerical tight-binding calculations for finite and periodic samples.Comment: submitted to PRL; signs corrected in Eqs. (11), (12), (19), and (20

Sputtered Gold as an Effective Schottky Gate for Strained Si/SiGe Nanostructures

Metallization of Schottky surface gates by sputtering Au on strained Si/SiGe heterojunctions enables the depletion of the two dimensional electron gas (2DEG) at a relatively small voltage while maintaining an extremely low level of leakage current. A fabrication process has been developed to enable the formation of sub-micron Au electrodes sputtered onto Si/SiGe without the need of a wetting layer.Comment: 3 pages, 3 figure

Exact Algorithms for Maximum Independent Set

We show that the maximum independent set problem (MIS) on an $n$-vertex graph can be solved in $1.1996^nn^{O(1)}$ time and polynomial space, which even is faster than Robson's $1.2109^{n}n^{O(1)}$-time exponential-space algorithm published in 1986. We also obtain improved algorithms for MIS in graphs with maximum degree 6 and 7, which run in time of $1.1893^nn^{O(1)}$ and $1.1970^nn^{O(1)}$, respectively. Our algorithms are obtained by using fast algorithms for MIS in low-degree graphs in a hierarchical way and making a careful analyses on the structure of bounded-degree graphs

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

Hölder properties of local times for fractional Brownian motions

We study the local times of fractional Brownian motions for all temporal dimensions, N, spatial dimensions d and Hurst parameters H for which local times exist. We establish a Hölder continuity result that is a refinement of Xiao (Probab Th Rel Fields 109:129-157, 1997). Our approach is an adaptation of the more general attack of Xiao (Probab Th Rel Fields 109:129-157, 1997) using ideas of Baraka and Mountford (1997, to appear), the principal result of this latter paper is contained in this articl

Free Quarks and Antiquarks versus Hadronic Matter

Meson-meson reactions A(q_1 \bar{q}_1) + B(q_2 \bar{q}_2) to q_1 + \bar{q}_1 + q_2 + \bar{q}_2 in high-temperature hadronic matter are found to produce an appreciable amount of quarks and antiquarks freely moving in hadronic matter and to establish a new mechanism for deconfinement of quarks and antiquarks in hadronic matter.Comment: 9 pages, 3 figure

An Intrinsic Integrity-Driven Rating Model for a Sustainable Reputation System

In the era of digital markets, the challenge for consumers is discerning quality amidst information asymmetry. While traditional markets use brand mechanisms to address this issue, transferring such systems to internet-based P2P markets, where misleading practices like fake ratings are rampant, remains challenging. Current internet platforms strive to counter this through verification algorithms, but these efforts find themselves in a continuous tug-of-war with counterfeit actions. Exploiting the transparency, immutability, and traceability of blockchain technology, this paper introduces a robust reputation voting system grounded in it. Unlike existing blockchain-based reputation systems, our model harnesses an intrinsically economically incentivized approach to bolster agent integrity. We optimize this model to mirror real-world user behavior, preserving the reputation system's foundational sustainability. Through Monte-Carlo simulations, using both uniform and power-law distributions enabled by an innovative inverse transform method, we traverse a broad parameter landscape, replicating real-world complexity. The findings underscore the promise of a sustainable, transparent, and formidable reputation mechanism. Given its structure, our framework can potentially function as a universal, sustainable oracle for offchain-onchain bridging, aiding entities in perpetually cultivating their reputation. Future integration with technologies like Ring Signature and Zero Knowledge Proof could amplify the system's privacy facets, rendering it particularly influential in the ever-evolving digital domain.Comment: 36 pages,13 figure

Quartification with T' Flavor

In the simplest (non-quiver) unified theories, fermion families are often treated sequentially and a flavor symmetry may act similarly. As an alternative with non-sequential flavor symmetry, we consider a model based on the group (T'*Z_2)_global * [SU(3)^4]_local which combines the predictions of T' flavor symmetry with the features of a unified quiver gauge theory. The model accommodates the relationships between mixing angles separately for neutrinos, and for quarks, which have been previously predicted with T'. This quiver unification theory makes predictions of several additional gauge bosons and bifundamental fermions at the TeV scale.Comment: 8 pages, LaTex; added references and clarifie