Extracting Atoms on Demand with Lasers

We propose a scheme that allows to coherently extract cold atoms from a reservoir in a deterministic way. The transfer is achieved by means of radiation pulses coupling two atomic states which are object to different trapping conditions. A particular realization is proposed, where one state has zero magnetic moment and is confined by a dipole trap, whereas the other state with non-vanishing magnetic moment is confined by a steep microtrap potential. We show that in this setup a predetermined number of atoms can be transferred from a reservoir, a Bose-Einstein condensate, into the collective quantum state of the steep trap with high efficiency in the parameter regime of present experiments.Comment: 11 pages, 8 figure

United we fall, divided we stand: A study of query segmentation and PRF for patent prior art search

Previous research in patent search has shown that reducing queries by extracting a few key terms is ineffective primarily because of the vocabulary mismatch between patent applications used as queries and existing patent documents. This finding has led to the use of full patent applications as queries in patent prior art search. In addition, standard information retrieval (IR) techniques such as query expansion (QE) do not work effectively with patent queries, principally because of the presence of noise terms in the massive queries. In this study, we take a new approach to QE for patent search. Text segmentation is used to decompose a patent query into selfcoherent sub-topic blocks. Each of these much shorted sub-topic blocks which is representative of a specific aspect or facet of the invention, is then used as a query to retrieve documents. Documents retrieved using the different resulting sub-queries or query streams are interleaved to construct a final ranked list. This technique can exploit the potential benefit of QE since the segmented queries are generally more focused and less ambiguous than the full patent query. Experiments on the CLEF-2010 IP prior-art search task show that the proposed method outperforms the retrieval effectiveness achieved when using a single full patent application text as the query, and also demonstrates the potential benefits of QE to alleviate the vocabulary mismatch problem in patent search

Multiple Volume Reflection from Different Planes Inside One Bent Crystal

It is shown that multiple volume reflections from different planes of one bent crystal becomes possible when particles move at a small angle with respect to a crystal axis. Such a Multiple Volume Reflection makes it possible to increase the particle deflection angle inside one crystal by more than four times and can be used to increase the efficiency of beam extraction and collimation at the LHC and many other accelerators.Comment: 17 pages, 6 figure

Space-charge compensation experiments at IOTA ring

Space-charge effects belong to the category of the most long-standing issues in beam physics, and even today, after several decades of very active exploration and development of counter-measures, they still pose the most profound limitations on performance of high intensity proton accelerators. We briefly consider past experience in active compensation of these effects and present in detail the progress towards experimental studies of novel schemes of space-charge compensation at the Fermilab's IOTA ring.Comment: 5 p

Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks

We participated in three of the protein-protein interaction subtasks of the Second BioCreative Challenge: classification of abstracts relevant for protein-protein interaction (IAS), discovery of protein pairs (IPS) and text passages characterizing protein interaction (ISS) in full text documents. We approached the abstract classification task with a novel, lightweight linear model inspired by spam-detection techniques, as well as an uncertainty-based integration scheme. We also used a Support Vector Machine and the Singular Value Decomposition on the same features for comparison purposes. Our approach to the full text subtasks (protein pair and passage identification) includes a feature expansion method based on word-proximity networks. Our approach to the abstract classification task (IAS) was among the top submissions for this task in terms of the measures of performance used in the challenge evaluation (accuracy, F-score and AUC). We also report on a web-tool we produced using our approach: the Protein Interaction Abstract Relevance Evaluator (PIARE). Our approach to the full text tasks resulted in one of the highest recall rates as well as mean reciprocal rank of correct passages. Our approach to abstract classification shows that a simple linear model, using relatively few features, is capable of generalizing and uncovering the conceptual nature of protein-protein interaction from the bibliome. Since the novel approach is based on a very lightweight linear model, it can be easily ported and applied to similar problems. In full text problems, the expansion of word features with word-proximity networks is shown to be useful, though the need for some improvements is discussed

Bridging the Gap Between Retrieval and Summarization

Information Retrieval is, at its core, a field focused on providing information to users to fulfill an information need. One of the most common use cases of Information Retrieval is document-level retrieval, which seeks to provide a collection of documents to the user that addresses their needs. In contrast to this, single document retrieval seeks to instead provide the user with a single document comprised of all required information. We seek to extend single document retrieval to single document generation, in which we use multiple source documents to create a new document which directly addresses the information need