Cost-based attribute selection for GRE (GRAPH-SC/GRAPH-FP)

In this paper we discuss several approaches to the problem of content determination for the generation of referring expressions (GRE) using the Graphbased framework of Krahmer et al. (2003). This work was carried out in the context of the First NLG Shared Task and Evaluation Challenge on Attribute Selection for Referring Expression Generation

Presupposition projection as proof construction

Even though Van der Sandt's presuppositions as anaphora approach is empirically successful, it fails to give a formal account of the interaction between world-knowledge and presuppositions. In this paper, an algorithm is sketched which is based on the idea of presuppositions as anaphora. It improves on this approach by employing a deductive system, Constructive Type Theory (CTT), to get a formal handle on the way world-knowledge influences presupposition projection. In CTT, proofs for expressions are explicitly represented as objects. These objects can be seen as a generalization of DRT's discourse markers. They are useful in dealing with presuppositional phenomena which require world-knowledge, such as Clark's bridging examples and Beaver's conditional presuppositions

Plug and Play Conversations: The Micro-Conversation Scheme for Modular Development of Hybrid Conversational Agent

Conversational agents (CAs) for psychotherapy pose unique challenges (e.g., reliance on domain experts to pre-script large amounts of therapeutical dialogues). To tackle these challenges, we propose a modular approach to develop such CA, called Micro-Conversation Scheme (MCS). Conversations can be algorithmically extended in MCS by combining different micro-conversations (MC), which isolate single therapeutical topic. The sequencing of MC is managed by a Connector component, connecting MC into longer conversations with context. Additionally, MCS integrates natural language generation (NLG) models as plugins for generating counseling-style utterances (e.g., reflections). Moreover, MCS adopts interactive learning to continuously improve CA based on human feedback. MCS provides a solution to the challenges of complex-to-design and difficult-to-extend conversations, and inability of CA to flexibly generate context-appropriate counseling-style utterances for psychotherapy. MCS is expected to benefit the community by promoting the collaboration between conversational designers and developers while preserve their independence during the development of CAs.</p

Quantization and Compressive Sensing

Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This book chapter explores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next, we consider several forms of scalar quantizers, namely uniform, non-uniform, and 1-bit. We provide performance bounds and fundamental analysis, as well as practical quantizer designs and reconstruction algorithms that account for quantization. Furthermore, we provide an overview of Sigma-Delta ($\Sigma\Delta$) quantization in the compressed sensing context, and also discuss implementation issues, recovery algorithms and performance bounds. As we demonstrate, proper accounting for quantization and careful quantizer design has significant impact in the performance of a compressive acquisition system.Comment: 35 pages, 20 figures, to appear in Springer book "Compressed Sensing and Its Applications", 201

Differences in Internet use and eHealth needs of adolescent and young adult versus older cancer patients:Results from the PROFILES registry

SIMPLE SUMMARY: The internet has become an important health information source for patients with cancer. AYAs (adolescents and young adults; 18–39 years at time of diagnosis) can be considered as digital natives; they work with the internet and related technologies in their daily lives. It is likely that AYAs are more used to using the internet, while older cancer patients might prefer former ways of information provision to obtain health-related information. The question arises whether internet use and eHealth needs of AYA cancer patients are comparable to those of older ones. By conducting a cross-sectional survey, we evaluated differences in cancer-related internet patterns between AYAs and older cancer patients (40+ years at time of diagnosis). A better understanding of differences between generations will help inform healthcare providers on how to guide cancer patients of different ages regarding cancer-related internet use. ABSTRACT: Background: Our aim was to evaluate differences in cancer-related internet patterns between AYAs (adolescents and young adults; 18–39 years at time of diagnosis) and older adult cancer patients (40+ years). Methods: Cross-sectional surveys were distributed among AYA and older adult cancer patients regarding cancer-related internet use and eHealth needs. Results: 299 AYAs (mean age 31.8 years) and 270 older adults (mean age 55.8 years) participated. AYAs searched significantly more often on the internet on a daily basis just before diagnosis (45% vs. 37%), right after diagnosis (71% vs. 62%) and during treatment (65% vs. 59%) compared to older adults. During follow up, there was a trend that AYAs searched less often on the internet compared to older adults (15% vs. 17%). AYAs searched more often on topics, such as alternative or complementary therapies, treatment guidelines, fertility, end of life, sexuality and intimacy, lifestyle and insurance. AYAs felt significantly better informed (75%) after searching for cancer-related information compared to older adults (65%) and had significantly less unmet needs regarding access to their own medical information (22% vs. 47%). AYAs search more on the internet on a daily basis/several times per week in the diagnosis and treatment phase than older cancer patients. They search on different topics than older adults and seems to have less unmet eHealth needs.It is important that these are easy to find and reliable

On Deterministic Sketching and Streaming for Sparse Recovery and Norm Estimation

We study classic streaming and sparse recovery problems using deterministic linear sketches, including l1/l1 and linf/l1 sparse recovery problems (the latter also being known as l1-heavy hitters), norm estimation, and approximate inner product. We focus on devising a fixed matrix A in R^{m x n} and a deterministic recovery/estimation procedure which work for all possible input vectors simultaneously. Our results improve upon existing work, the following being our main contributions: * A proof that linf/l1 sparse recovery and inner product estimation are equivalent, and that incoherent matrices can be used to solve both problems. Our upper bound for the number of measurements is m=O(eps^{-2}*min{log n, (log n / log(1/eps))^2}). We can also obtain fast sketching and recovery algorithms by making use of the Fast Johnson-Lindenstrauss transform. Both our running times and number of measurements improve upon previous work. We can also obtain better error guarantees than previous work in terms of a smaller tail of the input vector. * A new lower bound for the number of linear measurements required to solve l1/l1 sparse recovery. We show Omega(k/eps^2 + klog(n/k)/eps) measurements are required to recover an x' with |x - x'|_1 <= (1+eps)|x_{tail(k)}|_1, where x_{tail(k)} is x projected onto all but its largest k coordinates in magnitude. * A tight bound of m = Theta(eps^{-2}log(eps^2 n)) on the number of measurements required to solve deterministic norm estimation, i.e., to recover |x|_2 +/- eps|x|_1. For all the problems we study, tight bounds are already known for the randomized complexity from previous work, except in the case of l1/l1 sparse recovery, where a nearly tight bound is known. Our work thus aims to study the deterministic complexities of these problems

Restricted Isometries for Partial Random Circulant Matrices

In the theory of compressed sensing, restricted isometry analysis has become a standard tool for studying how efficiently a measurement matrix acquires information about sparse and compressible signals. Many recovery algorithms are known to succeed when the restricted isometry constants of the sampling matrix are small. Many potential applications of compressed sensing involve a data-acquisition process that proceeds by convolution with a random pulse followed by (nonrandom) subsampling. At present, the theoretical analysis of this measurement technique is lacking. This paper demonstrates that the $s$th order restricted isometry constant is small when the number $m$ of samples satisfies $m \gtrsim (s \log n)^{3/2}$, where $n$ is the length of the pulse. This bound improves on previous estimates, which exhibit quadratic scaling