Identifying Retweetable Tweets with a Personalized Global Classifier

In this paper we present a method to identify tweets that a user may find interesting enough to retweet. The method is based on a global, but personalized classifier, which is trained on data from several users, represented in terms of user-specific features. Thus, the method is trained on a sufficient volume of data, while also being able to make personalized decisions, i.e., the same post received by two different users may lead to different classification decisions. Experimenting with a collection of approx.\ 130K tweets received by 122 journalists, we train a logistic regression classifier, using a wide variety of features: the content of each tweet, its novelty, its text similarity to tweets previously posted or retweeted by the recipient or sender of the tweet, the network influence of the author and sender, and their past interactions. Our system obtains F1 approx. 0.9 using only 10 features and 5K training instances.Comment: This is a long paper version of the extended abstract titled "A Personalized Global Filter To Predict Retweets", of the same authors, which was published in the 25th ACM UMAP conference in Bratislava, Slovakia, in July 201

Tandem Solar Cell Concept Using Black Silicon for Enhanced Infrared Absorption

AbstractIn this work we present a novel tandem solar cell concept that is based on enhanced below band gap infrared absorption. The solar cell structure is based on silicon and infrared activated Black Silicon. Infrared active Black Silicon is produced by exposing silicon to fs-laser pulses. It features an enhanced IR absorption, when processed under a sulfur-containing atmosphere. Then sulfur is incorporated into the silicon lattice during laser processing providing energy states in the band gap. This silicon based tandem cell thus absorbs light with wavelengths beyond 1.1μm. This can potentially increase the overall efficiency. In this paper we present the first experimental realization of this concept. We use a standard aluminium-back-surface-field (Al-BSF) silicon solar cell and implement a Black Silicon solar cell on its rear side for enhanced IR absorption. Current and voltage measurements show the feasibility of our concept

An atom fiber for guiding cold neutral atoms

We present an omnidirectional matter wave guide on an atom chip. The rotational symmetry of the guide is maintained by a combination of two current carrying wires and a bias field pointing perpendicular to the chip surface. We demonstrate guiding of thermal atoms around more than two complete turns along a spiral shaped 25mm long curved path (curve radii down to 200$\mu$m) at various atom--surface distances (35-450$\mu$m). An extension of the scheme for the guiding of Bose-Einstein condensates is outlined

A HMM POS Tagger for Micro-blogging Type Texts

The high volume of communication via micro-blogging type messages has created an increased demand for text processing tools customised the unstructured text genre. The available text processing tools developed on structured texts has been shown to deteriorate significantly when used on unstructured, micro-blogging type texts. In this paper, we present the results of testing a HMM based POS (Part-Of-Speech) tagging model customized for unstructured texts. We also evaluated the tagger against published CRF based state-of-the-art POS tagging models customized for Tweet messages using three publicly available Tweet corpora. Finally, we did cross-validation tests with both the taggers by training them on one Tweet corpus and testing them on another one

Tailoring the Absorption Properties of Black Silicon

AbstractSamples of crystalline silicon for use as solar cell material are structured and hyperdoped with sulfur by irradiation with femtosecond laser pulses under a sulfur hexafluoride atmosphere. The sulfur creates energy levels in the silicon band gap, allowing light absorption in the infrared wavelength regime, which offers the potential of a significant efficiency increase. This Black Silicon is a potential candidate for impurity or intermediate band photovoltaics. In this paper we determine the laser processed sulfur energy levels by deep-level transient spectroscopy (DLTS). We present how the number of laser pulses per sample spot influence the sulfur energy levels and hence the DLTS spectra. Further we show that changing the laser pulse by splitting it with a Michelson interferometer setup results in altered absorption which is most likely due to altered sulfur energy levels. This contribution focuses on the possibility of controlling the sulfur in Black Silicon through manipulating the laser pulse shape. As a first step samples of microstructured silicon are fabricated with doubled laser pulses at two different laser pulse distances and the absorption spectra by integrating sphere measurements are compared

Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction

We explore a new Bayesian model for probabilistic grammars, a family of distributions over discrete structures that includes hidden Markov models and probabilistic context-free grammars. Our model extends the correlated topic model framework to probabilistic grammars, exploiting the logistic normal distribution as a prior over the grammar parameters. We derive a variational EM algorithm for that model, and then experiment with the task of unsupervised grammar induction for natural language dependency parsing. We show that our model achieves superior results over previous models that use different priors.

Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1

From unlabelled tweets to Twitter-specific opinion words

In this article, we propose a word-level classification model for automatically generating a Twitter-specific opinion lexicon from a corpus of unlabelled tweets. The tweets from the corpus are represented by two vectors: a bag-of-words vector and a semantic vector based on word-clusters. We propose a distributional representation for words by treating them as the centroids of the tweet vectors in which they appear. The lexicon generation is conducted by training a word-level classifier using these centroids to form the instance space and a seed lexicon to label the training instances. Experimental results show that the two types of tweet vectors complement each other in a statistically significant manner and that our generated lexicon produces significant improvements for tweet-level polarity classification

Feasibility of detecting single atoms using photonic bandgap cavities

We propose an atom-cavity chip that combines laser cooling and trapping of neutral atoms with magnetic microtraps and waveguides to deliver a cold atom to the mode of a fiber taper coupled photonic bandgap (PBG) cavity. The feasibility of this device for detecting single atoms is analyzed using both a semi-classical treatment and an unconditional master equation approach. Single-atom detection seems achievable in an initial experiment involving the non-deterministic delivery of weakly trapped atoms into the mode of the PBG cavity.Comment: 11 pages, 5 figure

"Super" Cocktails for Heavy Ion Testing

The 4.5 MeV/nucleon heavy ion cocktail at the 88-Inch Cyclotron has been expanded by incorporating beams from solid material to fill in the linear energy transfer curve. This supercocktail is available by special request and is useful when only normal incidence between the beam and the device under test is possible or desirable