Combining semantic and syntactic generalization in example-based machine translation

In this paper, we report our experiments in combining two EBMT systems that rely on generalized templates, Marclator and CMU-EBMT, on an English–German translation task. Our goal was to see whether a statistically significant improvement could be achieved over the individual performances of these two systems. We observed that this was not the case. However, our system consistently outperformed a lexical EBMT baseline system

Reliable Identification of RFID Tags Using Multiple Independent Reader Sessions

Radio Frequency Identification (RFID) systems are gaining momentum in various applications of logistics, inventory, etc. A generic problem in such systems is to ensure that the RFID readers can reliably read a set of RFID tags, such that the probability of missing tags stays below an acceptable value. A tag may be missing (left unread) due to errors in the communication link towards the reader e.g. due to obstacles in the radio path. The present paper proposes techniques that use multiple reader sessions, during which the system of readers obtains a running estimate of the probability to have at least one tag missing. Based on such an estimate, it is decided whether an additional reader session is required. Two methods are proposed, they rely on the statistical independence of the tag reading errors across different reader sessions, which is a plausible assumption when e.g. each reader session is executed on different readers. The first method uses statistical relationships that are valid when the reader sessions are independent. The second method is obtained by modifying an existing capture-recapture estimator. The results show that, when the reader sessions are independent, the proposed mechanisms provide a good approximation to the probability of missing tags, such that the number of reader sessions made, meets the target specification. If the assumption of independence is violated, the estimators are still useful, but they should be corrected by a margin of additional reader sessions to ensure that the target probability of missing tags is met.Comment: Presented at IEEE RFID 2009 Conferenc

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

Mutual statistics, braid group, and the fractional quantum Hall effect

We show that the notion of mutual statistics arises naturally from the representation theory of the braid group over the multi-sheeted surface. A Hamiltonian which describes particles moving on the double-sheeted surface is proposed as a model for the bilayered fractional quantum Hall effect (FQHE) discovered recently. We explicitly show that the quasi-holes of the bilayered Hall fluid display fractional mutual statistics. A model for 3-dimensional FQHE using the multi-layered sample is suggested.Comment: LaTex 26 page

Adaptive Latency Insensitive Protocols

Latency-insensitive design copes with excessive delays typical of global wires in current and future IC technologies. It achieves its goal via encapsulation of synchronous logic blocks in wrappers that communicate through a latency-insensitive protocol (LIP) and pipelined interconnects. Previously proposed solutions suffer from an excessive performance penalty in terms of throughput or from a lack of generality. This article presents an adaptive LIP that outperforms previous static implementations, as demonstrated by two relevant cases — a microprocessor and an MPEG encoder — whose components we made insensitive to the latencies of their interconnections through a newly developed wrapper. We also present an informal exposition of the theoretical basis of adaptive LIPs, as well as implementation detail

Estimating the Number of Stable Configurations for the Generalized Thomson Problem

Given a natural number N, one may ask what configuration of N points on the two-sphere minimizes the discrete generalized Coulomb energy. If one applies a gradient-based numerical optimization to this problem, one encounters many configurations that are stable but not globally minimal. This led the authors of this manuscript to the question, how many stable configurations are there? In this manuscript we report methods for identifying and counting observed stable configurations, and estimating the actual number of stable configurations. These estimates indicate that for N approaching two hundred, there are at least tens of thousands of stable configurations.Comment: The final publication is available at Springer via http://dx.doi.org/10.1007/s10955-015-1245-