Thomas Demeester

Quasi-TM Transmission Line Parameters of Coupled Lossy Lines Based on the Dirichlet to Neumann Boundary Operator

This paper presents a new multiconductor transmission line model for general 2-D lossy configurations based on mode reciprocity. Particular attention is devoted to elucidate the validity of the quasi-TM model and the approximations that have to be invoked to obtain this model. A new derivation of the complex capacitance matrix is given, especially taking into account the presence of semiconductors. This derivation automatically leads to a nonclassical circuit signal current definition and demands for a formulation of the complex inductance problem consistent with that definition. The relevant resistance, inductance, conductance, and capacitance circuit matrices are obtained by solving boundary integral equations only, making use of the Dirichlet to Neumann boundary operator for the different materials. This allows to simulate complex metal-insulator-semiconductor structures, as shown in the numerical examples.

Federated search in the wild: the combined power of over a hundred search engines

Federated search has the potential of improving web search: the user becomes less dependent on a single search provider and parts of the deep web become available through a unified interface, leading to a wider variety in the retrieved search results. However, a publicly available dataset for federated search reflecting an actual web environment has been absent. As a result, it has been difficult to assess whether proposed systems are suitable for the web setting. We introduce a new test collection containing the results from more than a hundred actual search engines, ranging from large general web search engines such as Google and Bing to small domain-specific engines. We discuss the design and analyze the effect of several sampling methods. For a set of test queries, we collected relevance judgements for the top 10 results of each search engine. The dataset is publicly available and is useful for researchers interested in resource selection for web search collections, result merging and size estimation of uncooperative resources.

Taily: shard selection using the tail of score distributions

Search engines can improve their efficiency by selecting only few promising shards for each query. State-of-the-art shard selection algorithms first query a central index of sampled documents, and their effectiveness is similar to searching all shards. However, the search in the central index also hurts efficiency. Additionally, we show that the effectiveness of these approaches varies substantially with the sampled documents. This paper proposes Taily, a novel shard selection algorithm that models a querys score distribution in each shard as a Gamma distribution and selects shards with highly scored documents in the tail of the distribution. Taily estimates the parameters of score distributions based on the mean and variance of the score functions features in the collections and shards. Because Taily operates on term statistics instead of document samples, it is efficient and has deterministic effectiveness. Experiments on large web collections (Gov2, CluewebA and CluewebB) show that Taily achieves similar effectiveness to sample-based approaches, and improves upon their efficiency by roughly 20% in terms of used resources and response time.

Internal Impedance of Composite Conductors with Arbitrary Cross Section

A new way to calculate the internal inductance and resistance per unit length for an inhomogeneous conductor with arbitrary cross-sectional geometry is presented, based on the surface admittance boundary operator. The method is formulated in such a way, that the physical meaning of the internal impedance is clarified, as obtained by disregarding the external magnetic field. A comparison is made with the definitions known in literature to determine the internal impedance. In a number of numerical examples, the differences between those definitions are elucidated, and some physical properties of the internal impedance are investigated.

Exploiting user disagreement for web search evaluation: an experimental approach

To express a more nuanced notion of relevance as compared to binary judgments, graded relevance levels can be used for the evaluation of search results. Especially in Web search, users strongly prefer top results over less relevant results, and yet they often disagree on which are the top results for a given information need. Whereas previous works have generally considered disagreement as a negative effect, this paper proposes a method to exploit this user disagreement by integrating it into the evaluation procedure. First, we present experiments that investigate the user disagreement. We argue that, with a high disagreement, lower relevance levels might need to be promoted more than in the case where there is global consensus on the top results. This is formalized by introducing the User Disagreement Model, resulting in a weighting of the relevance levels with a probabilistic interpretation. A validity analysis is given, and we explain how to integrate the model with well-established evaluation metrics. Finally, we discuss a specific application of the model, in the estimation of suitable weights for the combined relevance of Web search snippets and pages.

Lifted Rule Injection for Relation Embeddings.

Methods based on representation learning currently hold the state-of-the-art in many natural language processing and knowledge base inference tasks. Yet, a major challenge is how to efficiently incorporate commonsense knowledge into such models. A recent approach regularizes relation and entity representations by propositionalization of first-order logic rules. However, propositionalization does not scale beyond domains with only few entities and rules. In this paper we present a highly efficient method for incorporating implication rules into distributed representations for automated knowledge base construction. We map entity-tuple embeddings into an approximately Boolean space and encourage a partial ordering over relation embeddings based on implication rules mined from WordNet. Surprisingly, we find that the strong restriction of the entity-tuple embedding space does not hurt the expressiveness of the model and even acts as a regularizer that improves generalization. By incorporating few commonsense rules, we achieve an increase of 2 percentage points mean average precision over a matrix factorization baseline, while observing a negligible increase in runtime.

Representation learning for very short texts using weighted word embedding aggregation

We create text representations by weighing word embeddings using idf information.A novel median-based loss is designed to mitigate the negative effect of outliers.A dataset of semantically related textual pairs from Wikipedia and Twitter is made.Our method outperforms all word embedding baselines in a semantic similarity task.Our method is out-of-the-box and thus requires no retraining in different contexts. Short text messages such as tweets are very noisy and sparse in their use of vocabulary. Traditional textual representations, such as tf-idf, have difficulty grasping the semantic meaning of such texts, which is important in applications such as event detection, opinion mining, news recommendation, etc. We constructed a method based on semantic word embeddings and frequency information to arrive at low-dimensional representations for short texts designed to capture semantic similarity. For this purpose we designed a weight-based model and a learning procedure based on a novel median-based loss function. This paper discusses the details of our model and the optimization methods, together with the experimental results on both Wikipedia and Twitter data. We find that our method outperforms the baseline approaches in the experiments, and that it generalizes well on different word embeddings without retraining. Our method is therefore capable of retaining most of the semantic information in the text, and is applicable out-of-the-box.

What Snippets Say About Pages in Federated Web Search

What is the likelihood that a Web page is considered relevant to a query, given the relevance assessment of the corresponding snippet? Using a new federated IR test collection that contains search results from over a hundred search engines on the internet, we are able to investigate such research questions from a global perspective. Our test collection covers the main Web search engines like Google, Yahoo!, and Bing, as well as a number of smaller search engines dedicated to multimedia, shopping, etc., and as such reflects a realistic Web environment. Using a large set of relevance assessments, we are able to investigate the connection between snippet quality and page relevance. The dataset is strongly inhomogeneous, and although the assessors’ consistency is shown to be satisfying, care is required when comparing resources. To this end, a number of probabilistic quantities, based on snippet and page relevance, are introduced and evaluated.

Construction of the Dirichlet to Neumann Boundary Operator for Triangles and Applications in the Analysis of Polygonal Conductors

This paper introduces a fast and accurate method to investigate the broadband inductive and resistive behavior of conductors with a nonrectangular cross section. The presented iterative combined waveguide mode (ICWM) algorithm leads to an expansion of the longitudinal electric field inside a triangle using a combination of parallel-plate waveguide modes in three directions, each perpendicular to one of the triangle sides. This expansion is used to calculate the triangles Dirichlet to Neumann boundary operator. Subsequently, any polygonal conductor can be modeled as a combination of triangles. The method is especially useful to investigate current crowding effects near sharp conductor corners. In a number of numerical examples, the accuracy of the ICWM algorithm is investigated, and the method is applied to some polygonal conductor configurations.

Modeling the Broadband Inductive and Resistive Behavior of Composite Conductors

Accurately modeling interconnect structures is an important issue in high-frequency chip design. Conductors have a finite thickness and conductivity, and are often composed of different metals. It is shown that the Dirichlet-to-Neumann technique can be used to model the inductive and resistive behavior of such structures, up to high frequencies at which the skin effect is well-developed. Furthermore, the method can be used for the accurate and fast calculation of the longitudinal current distribution in the composite conductors.