User attitudes towards news content personalization

Personalizing news content requires to choose the appropriate depth of personalization and to assess the extent to which readers’ explicit expressions of interest in general and specific news topics can be used as the basis for personalization. A preliminary survey examined 117 respondents’ attitudes towards news content personalization and their interest in various news topics and subtopics. The second survey examined 23 participants’ declared and actual interests. Participants preferred personalization based on general news topics. Declared interest in general news topics adequately predicted the actual interests in some topics, while in others users’ interests differed between general news topics and subtopics. The variance in interest in items also differed among topics. Thus, different personalization methods should be used for different topics. For some, such as ‘Sports’, users show either high interest or no interest at all. In the latter case most articles related to the topic should be removed, with the exception of items that refer to unique events that may raise general interest according to the expressed interest. In other topics, such as ‘Science & Technology’, most users are interested in important articles, even if they are not interested in the general news topic. Here, the filtering technique should identify the important articles and present them to all readers. The results can be used to develop effective and simple personalization mechanisms which can be applied to the personalization of news, as well as to other domains.     

General Geometric Good Continuation: From Taylor to Laplace via Level Sets

Good continuation is the Gestalt observation that parts often group in particular ways to form coherent wholes. Perceptual integration of edges, for example, involves orientation good continuation, a property which has been exploited computationally very extensively. But more general local-global relationships, such as for shading or color, have been elusive. While Taylor’s Theorem suggests certain modeling and smoothness criteria, the consideration of level set geometry indicates a different approach. Using such first principles we derive, for the first time, a generalization of good continuation to all those visual structures that can be abstracted as scalar functions over the image plane. Based on second order differential constraints that reflect good continuation, our analysis leads to a unique class of harmonic models and a cooperative algorithm for structure inference. Among the different applications of good continuation, here we apply these results to the denoising of shading and intensity distributions and demonstrate how our approach eliminates spurious measurements while preserving both singularities and regular structure, a property that facilitates higher level processes which depend so critically on both of these classes of visual structures.     

Stability and model selection in k-means clustering

Clustering stability methods are a family of widely used model selection techniques for data clustering. Their unifying theme is that an appropriate model should result in a clustering which is robust with respect to various kinds of perturbations. Despite their relative success, not much is known theoretically on why or when do they work, or even what kind of assumptions they make in choosing an ‘appropriate’ model. Moreover, recent theoretical work has shown that they might ‘break down’ for large enough samples. In this paper, we focus on the behavior of clustering stability using k-means clustering. Our main technical result is an exact characterization of the distribution to which suitably scaled measures of instability converge, based on a sample drawn from any distribution in ? ⁿ satisfying mild regularity conditions. From this, we can show that clustering stability does not ‘break down’ even for arbitrarily large samples, at least for the k-means framework. Moreover, it allows us to identify the factors which eventually determine the behavior of clustering stability. This leads to some basic observations about what kind of assumptions are made when using these methods. While often reasonable, these assumptions might also lead to unexpected consequences.     

LIQUID-LIQUID EXTRACTION OF METAL IONS BY NEUTRAL PHOSPHOR- AMIDES. PART I,EXTRACTION OF URANYL IONS FROM NITRATE AND SULPHATE MEDIA. EXAMINATION OF EXTRACTED SPECIES BY UV/V1S AND sip NMR SPECTROSCOPY.

ABSTRACT

The extraction of uranyl nitrate and uranyl sulphate from aqueous media by the neutral chelating diphosphoramides CH₃-N[P(OKNMe₂)₂] (NIPA) and its less hydrophilic homologues R-N[P(O)(NMe₂)₂]₂ (R = -C₁₂H₂₅ (OD1PA) or -C₁₆H₃₃ (OHDIPA)), diluted in CH₃NO₂ or toluene, has been studied. In the presei ce of HNO₃, NaNO₃, NaCl, and Na₂SO₄ as salting-out agents, extraction is generally excellent. Some of the extracted complex species have been identified by comparing their ³¹P NMR and UV/vis spectra with those of pure complexes of known structure. The results are compared with extractions using tri-n-butyl phosphate (TBP) as complexing agent.     

Learning and generalization with the information bottleneck

The Information Bottleneck is an information theoretic framework that finds concise representations for an ‘input’ random variable that are as relevant as possible for an ‘output’ random variable. This framework has been used successfully in various supervised and unsupervised applications. However, its learning theoretic properties and justification remained unclear as it differs from standard learning models in several crucial aspects, primarily its explicit reliance on the joint input–output distribution. In practice, an empirical plug-in estimate of the underlying distribution has been used, so far without any finite sample performance guarantees. In this paper we present several formal results that address these difficulties. We prove several finite sample bounds, which show that the information bottleneck can provide concise representations with good generalization, based on smaller sample sizes than needed to estimate the underlying distribution. The bounds are non-uniform and adaptive to the complexity of the specific model chosen. Based on these results, we also present a preliminary analysis on the possibility of analyzing the information bottleneck method as a learning algorithm in the familiar performance-complexity tradeoff framework. In addition, we formally describe the connection between the information bottleneck and minimal sufficient statistics.     

Unsupervised natural image patch learning

Computational Visual Media - A metric for natural image patches is an important tool for analyzing images. An efficient means of learning one is to train a deep network to map an image patch to a...     

Work measurement for estimating food preparation time of a bioregenerative diet

During space missions, such as the prospective Mars mission, crew labor time is a strictly limited resource. The diet for such a mission (based on crops grown in a bioregenerative life support system) will require astronauts to prepare their meals essentially from raw ingredients. Time spent on food processing and preparation is time lost for other purposes. Recipe design and diet planning for a space mission should therefore incorporate the time required to prepare the recipes as a critical factor. In this study, videotape analysis of an experienced chef was used to develop a database of recipe preparation time. The measurements were highly consistent among different measurement teams. Data analysis revealed a wide variation between the active times of different recipes, underscoring the need for optimization of diet planning. Potential uses of the database developed in this study are discussed and illustrated in this work.     

Efficient Detection of Vacuity in Temporal Model Checking

The ability to generate a counter-example is an important feature of model checking tools, because a counter-example provides information to the user in the case that the formula being checked is found to be non-valid. In this paper, we turn our attention to providing similar feedback to the user in the case that the formula is found to be valid, because valid formulas can hide real problems in the model. For instance, propositional logic formulas containing implications can suffer from antecedent failure, in which the formula is trivially valid because the pre-condition of the implication is not satisfiable. We call this vacuity, and extend the definition to cover other kinds of trivial validity. For non-vacuously valid formulas, we define an interesting witness as a non-trivial example of the validity of the formula. We formalize the notions of vacuity and interesting witness, and show how to detect vacuity and generate interesting witnesses in temporal model checking. Finally, we provide a practical solution for a useful subset of ACTL formulas.