This paper presents a deep architecture for learning a similarity metric on variablelength character sequences. The model combines a stack of character-level bidirectional LSTM's with a Siamese architecture. It learns to project variablelength strings into a fixed-dimensional embedding space by using only information about the similarity between pairs of strings. This model is applied to the task of job title normalization based on a manually annotated taxonomy. A small data set is incrementally expanded and augmented with new sources of variance. The model learns a representation that is selective to differences in the input that reflect semantic differences (e.g., "Java developer" vs. "HR manager") but also invariant to nonsemantic string differences (e.g., "Java developer" vs. "Java programmer").

The paper explores kriging surrogate modelling combined with expected improvement approach for the design of electromagnetic devices. A novel algorithm based on the concept of rewards is proposed, tested and demonstrated in the context of TEAM Workshop Problem 22. Balancing exploration and exploitation is emphasized and robustness of the design considered.

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Abstract-In this paper, a novel method of fabricating threedimensional (3-D) system-in-package (SiP) using a silicon carrier that can integrate known good dice with an integrated cooling solution is presented. The backbone of this stacked module is the fabrication of a silicon carrier with through-hole conductive interconnects. The design, process, and assembly to fabricate silicon through-hole interconnect using a wet silicon etching method is discussed in this paper. The process optimization to fabricate silicon carriers with solder through-hole interconnect within the design tolerance has been achieved. The design and modeling methodology to optimize the package in terms of electrical aspects of the stacked module is carried out to achieve less interconnect parasitics. An integrated cooling solution for 3-D stacked modules using single-phase and two-phase cooling solutions is also demonstrated for high-power applications. Known good thin flip-chip devices with daisy chain are fabricated and attached to the silicon carrier by flip-chip processes making it a known good carrier after electrical testing. Individual known good carriers are vertically integrated to form 3-D SiP.Index Terms-Three-dimensional system-in-package (3-D SiP), stacked modules, through wafer interconnection, wafer thinning, integrated cooling solution.

We hypothesize that student affect is a useful predictor of spoken dialogue system performance, relative to other parameters. We test this hypothesis in the context of our spoken dialogue tutoring system, where student learning is the primary performance metric. We first present our system and corpora, which have been annotated with several student affective states, student correctness and discourse structure. We then discuss unigram and bigram parameters derived from these annotations. The unigram parameters represent each annotation type individually, as well as systemgeneric features. The bigram parameters represent annotation combinations, including student state sequences and student states in the discourse structure context. We then use these parameters to build learning models. First, we build simple models based on correlations between each of our parameters and learning. Our results suggest that our affect parameters are among our most useful predictors of learning, particularly in specific discourse structure contexts. Next, we use the PARADISE framework (multiple linear regression) to build complex learning models containing only the most useful subset of parameters. Our approach is a value-added one; we perform a number of model-building experiments, both with and without including our affect parameters, and then compare the performance of the models on the training and the test sets. Our results show that when included as inputs, our affect parameters are selected as predictors in most models, and many of these models show high generalizability in Kate Forbes-Riley and Mihai Rotaru contributed equally to this work. 123 12 K. Forbes-Riley et al.testing. Our results also show that overall, the affect-included models significantly outperform the affect-excluded models.

-This paper discusses the use of kriging surrogate modelling in multiobjective design optimisation in electromagnetics. The importance of achieving appropriate balance between exploration and exploitation is emphasised when searching for the global optimum. It is argued that this approach will yield a procedure to solve time consuming electromagnetic design problems efficiently and will also assist the decision making process to achieve a robust design of practical devices considering tolerances and uncertainties.

In this paper we study the utility of discourse structure for spoken dialogue performance modeling. We experiment with various ways of exploiting the discourse structure: in isolation, as context information for other factors (correctness and certainty) and through trajectories in the discourse structure hierarchy. Our correlation and PARADISE results show that, while the discourse structure is not useful in isolation, using the discourse structure as context information for other factors or via trajectories produces highly predictive parameters for performance analysis.

The paper presents new developments in modeling AC losses in high-temperature superconducting (HTS) tapes as a highly nonlinear diffusion process. Following successful 1D formulation for a bulk superconductor and subsequent inclusion of the presence of silver in a tape using a 'sandwich' model, a 2D scheme has now been developed using time-stepping finite difference formulation and is briefly described. Edge effects and importance of nonlinearity are emphasised.Index Terms-AC losses, field diffusion, computational magnetics, high temperature superconductivity, time dependent magnetic fields.