Efficient storage and decoding of SURF feature points

Practical use of SURF feature points in large-scale indexing and retrieval engines requires an efficient means for storing and decoding these features. This paper investigates several methods for compression and storage of SURF feature points, considering both storage consumption and disk-read efficiency. We compare each scheme with a baseline plain-text encoding scheme as used by many existing SURF implementations. Our final proposed scheme significantly reduces both the time required to load and decode feature points, and the space required to store them on disk

An investigation of term weighting approaches for microblog retrieval

The use of effective term frequency weighting and document length normalisation strategies have been shown over a number of decades to have a significant positive effect for document retrieval. When dealing with much shorter documents, such as those obtained from microblogs, it would seem intuitive that these would have less benefit. In this paper we investigate their effect on microblog retrieval performance using the Tweets2011 collection from the TREC 2011 Microblog Track

Respiratory challenge MRI: practical aspects

Respiratory challenge MRI is the modification of arterial oxygen (PaO2) and/or carbon dioxide (PaCO2) concentration to induce a change in cerebral function or metabolism which is then measured by MRI. Alterations in arterial gas concentrations can lead to profound changes in cerebral haemodynamics which can be studied using a variety of MRI sequences. Whilst such experiments may provide a wealth of information, conducting them can be complex and challenging. In this paper we review the rationale for respiratory challenge MRI including the effects of oxygen and carbon dioxide on the cerebral circulation. We also discuss the planning, equipment, monitoring and techniques that have been used to undertake these experiments. We finally propose some recommendations in this evolving area for conducting these experiments to enhance data quality and comparison between techniques

CLARITY at the TREC 2011 microblog track

For the first year of the TREC Microblog Track the CLARITY group concentrated on a number of areas, investigating the underlying term weighting scheme for ranking tweets, incorporating query expansion to introduce new terms into the query, as well as introducing an element of temporal re-weighting based on the temporal distribution of assumed relevant microblogs

Vulnerabilities in first-generation RFID-enabled credit cards

Credit cards ; Radio frequency identification systems

K-Space at TRECVid 2008

In this paper we describe K-Space’s participation in TRECVid 2008 in the interactive search task. For 2008 the K-Space group performed one of the largest interactive video information retrieval experiments conducted in a laboratory setting. We had three institutions participating in a multi-site multi-system experiment. In total 36 users participated, 12 each from Dublin City University (DCU, Ireland), University of Glasgow (GU, Scotland) and Centrum Wiskunde & Informatica (CWI, the Netherlands). Three user interfaces were developed, two from DCU which were also used in 2007 as well as an interface from GU. All interfaces leveraged the same search service. Using a latin squares arrangement, each user conducted 12 topics, leading in total to 6 runs per site, 18 in total. We officially submitted for evaluation 3 of these runs to NIST with an additional expert run using a 4th system. Our submitted runs performed around the median. In this paper we will present an overview of the search system utilized, the experimental setup and a preliminary analysis of our results

Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.</p

The emergence of a climate change signal in long-term Irish meteorological observations

Detecting the emergence of a forced anthropogenic climate change signal from observations is critical for informing adaptation responses. By regressing local variations in climate onto annual Global Mean Surface Temperature (GMST), we track the emergence of an anthropogenic signal in long-term quality assured observations of temperature and precipitation for the island of Ireland, a sentinel location on the western European Atlantic seaboard. Analysis of station based observations, together with island scale composite series is undertaken for annual and seasonal means, together with 16 indices of extremes, with the derived signal-to-noise ratio classified as normal, unusual or unfamiliar relative to early industrial climate. More than half of indices show the emergence of at least unusual conditions relative to early industrial climate. The increase in annual mean temperature has led to the emergence of unfamiliar climate at six of eleven stations. Warming at the island scale is estimated at 0.88 °C per degree warming in GMST. While many stations show the emergence of unusual climate for spring, summer and autumn mean temperature, no forced signal of change is found for winter mean temperature. Changes in cool/warm days and nights are unfamiliar relative to early industrial climate. However, no anthropogenic signal is found for the hottest day annually or in summer – an extreme often associated with climate change in public consciousness. Increases in annual precipitation totals have emerged as unusual for western stations with large increases in winter totals per degree warming in GMST (e.g., 25.2% and 19.7% at Malin Head and Markree, respectively), indicating heightened flood risk with continued warming. By contrast, summer precipitation shows no significant relationship with GMST. Increases in rainfall intensity have emerged as unusual for 30% of stations, with increases consistent with the Clausius-Clapeyron relationship. Our analysis shows that an emerging climate change signal is discernible for Ireland, a location strongly influenced by climate variability

Striatal neuropeptides enhance selection and rejection of sequential actions

The striatum is the primary input nucleus for the basal ganglia, and receives glutamatergic afferents from the cortex. Under the hypothesis that basal ganglia perform action selection, these cortical afferents encode potential “action requests.” Previous studies have suggested the striatum may utilize a mutually inhibitory network of medium spiny neurons (MSNs) to filter these requests so that only those of high salience are selected. However, the mechanisms enabling the striatum to perform clean, rapid switching between distinct actions that form part of a learned action sequence are still poorly understood. Substance P (SP) and enkephalin are neuropeptides co-released with GABA in MSNs preferentially expressing D1 or D2 dopamine receptors respectively. SP has a facilitatory effect on subsequent glutamatergic inputs to target MSNs, while enkephalin has an inhibitory effect. Blocking the action of SP in the striatum is also known to affect behavioral transitions. We constructed phenomenological models of the effects of SP and enkephalin, and integrated these into a hybrid model of basal ganglia comprising a spiking striatal microcircuit and rate–coded populations representing other major structures. We demonstrated that diffuse neuropeptide connectivity enhanced the selection of unordered action requests, and that for true action sequences, where action semantics define a fixed structure, a patterning of the SP connectivity reflecting this ordering enhanced selection of actions presented in the correct sequential order and suppressed incorrect ordering. We also showed that selective pruning of SP connections allowed context–sensitive inhibition of specific undesirable requests that otherwise interfered with selection of an action group. Our model suggests that the interaction of SP and enkephalin enhances the contrast between selection and rejection of action requests, and that patterned SP connectivity in the striatum allows the “chunking” of actions and improves selection of sequences. Efficient execution of action sequences may therefore result from a combination of ordered cortical inputs and patterned neuropeptide connectivity within striatum