Volume Modeling for Rapid Prototyping

The expanding workspace of Rapid Prototyping will draw on the new developments in geometric modeling. Volume modeling has substantial advantages over other modeling schemes to meet the emerging requirements of Rapid Prototyping technology. It provides us with a new approach to design complex geometry and topology. The integration of the volume modeling and Rapid Prototyping technology will help us to fully exploit RP's ability to fabricate objects with complex structures. This paper addresses our research and practice in a volume modeling system toward Rapid Prototyping. Novel techniques in volumetric data manipulation, NURBS volume models and triangular facet generation over solid models are presented. Computer models designed by this system and their corresponding DTM products are also shown atthe end of this paper.Mechanical Engineerin

Concurrently Non-Malleable Zero Knowledge in the Authenticated Public-Key Model

We consider a type of zero-knowledge protocols that are of interest for their practical applications within networks like the Internet: efficient zero-knowledge arguments of knowledge that remain secure against concurrent man-in-the-middle attacks. In an effort to reduce the setup assumptions required for efficient zero-knowledge arguments of knowledge that remain secure against concurrent man-in-the-middle attacks, we consider a model, which we call the Authenticated Public-Key (APK) model. The APK model seems to significantly reduce the setup assumptions made by the CRS model (as no trusted party or honest execution of a centralized algorithm are required), and can be seen as a slightly stronger variation of the Bare Public-Key (BPK) model from \cite{CGGM,MR}, and a weaker variation of the registered public-key model used in \cite{BCNP}. We then define and study man-in-the-middle attacks in the APK model. Our main result is a constant-round concurrent non-malleable zero-knowledge argument of knowledge for any polynomial-time relation (associated to a language in $\mathcal{NP}$), under the (minimal) assumption of the existence of a one-way function family. Furthermore,We show time-efficient instantiations of our protocol based on known number-theoretic assumptions. We also note a negative result with respect to further reducing the setup assumptions of our protocol to those in the (unauthenticated) BPK model, by showing that concurrently non-malleable zero-knowledge arguments of knowledge in the BPK model are only possible for trivial languages

Charm meson scattering cross sections by pion and rho meson

Using the local flavor SU(4) gauge invariance in the limit of vanishing vector meson masses, we extend our previous study of charm meson scattering cross sections by pion and rho meson, which is based only on the pseudoscalar-pseudoscalar-vector meson couplings, to include also contributions from the couplings among three vector mesons and among four particles. We find that diagrams with light meson exchanges usually dominate the cross sections. For the processes considered previously, the additional interactions lead only to diagrams involving charm meson exchanges and contact interactions, and the cross sections for these processes are thus not much affected. Nevertheless, these additional interactions introduce new processes with light meson exchanges and increase significantly the total scattering cross sections of charm mesons by pion and rho meson.Comment: 14 pages, revtex, 6 figures, added a figure on the effects of on-shell divergence, final version to appear in Nucl. Phys.

Exploring the Learnability of Numeric Datasets

When doing classification, it has often been observed that datasets may exhibit different levels of difficulty with respect to how accurately they can be classified. That is, there are some datasets which can be classified very accurately by many classification algorithms, and there also exist some other datasets that no classifier can classify them with high accuracy. Based on this observation, we try to address the following problems: a)what are the factors that make a dataset easy or difficult to be accurately classified? b) how to use such factors to predict the difficulties of unclassified datasets? and c) how to use such factors to improve classification. It turns out that the monotonic features of the datasets, along with some other closely related structural properties, play an important role in determining how difficult datasets can be accurately classified. More importantly, datasets which are comprised of highly monotonic data, can usually be classified more accurately than datasets with low monotonically distributed data. By further exploring these monotonicity based properties, we observed that datasets can always be decomposed into a family of subsets while each of them is highly monotonic locally. Moreover, it is proposed in this dissertation a methodology to use the classification models inferred from the smaller but highly monotonic subsets to construct a highly accurate classification model for the original dataset. Two groups of experiments were implemented in this dissertation. The first group of experiments were performed to discover the relationships between the data difficulty and data monotonic properties, and represent such relationships in regression models. Such models were later used to predict the classification difficulty of unclassified datasets. It seems that in more than 95% of the predictions, the deviations between the predicted value and the real difficulty are smaller than 2.4%. The second group of experiments focused on the performance of the proposed meta-learning approach. According to the experimental results, the proposed approach can consistently achieve significant improvements

Efficient conversion to radial polarization in the two-micron band using a continuously space-variant half-waveplate

We demonstrate efficient conversion of a linearly-polarized Gaussian beam to a radially-polarised doughnut beam in the two-micron band using a continuously space-variant half-waveplate created by femtosecond writing of subwavelength gratings. The low scattering loss (<0.07) of this device indicates that it would be suitable for use with high power lasers

AI’s Role in Enhancing the Construction of Regional Primary and Secondary School Teachers

This paper investigates and analyzes the current situation of regional teaching staff and explores the path of AI in enhancing the construction of primary and secondary school teachers. Recent state documents have emphasized the urgent need for AI to improve teaching staff development. The study reveals issues in the construction of primary and secondary school teachers' teams in teaching, management, and training. For instance, classroom teaching is limited by time and space, making it difficult to achieve personalized learning for students and differentiated teaching for teachers. Teacher training lacks individual and group diagnosis, which does not consider teacher individuality, differences, and hierarchy. The evaluation of teachers in regional management and service tends to be subjective, and the awareness and application of data are inadequate. The study proposes addressing these problems by building and applying teacher big data, developing an AI assistant for teachers, and upgrading the quality of teaching intelligent monitoring systems. The proposed measures aim to promote high-quality development of regional teaching staff by establishing the main path for AI to enhance the construction of primary and secondary school teachers