Towards Egocentric Person Re-identification and Social Pattern Analysis

Wearable cameras capture a first-person view of the daily activities of the camera wearer, offering a visual diary of the user behaviour. Detection of the appearance of people the camera user interacts with for social interactions analysis is of high interest. Generally speaking, social events, lifestyle and health are highly correlated, but there is a lack of tools to monitor and analyse them. We consider that egocentric vision provides a tool to obtain information and understand users social interactions. We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams. Given sets of egocentric images, we detect the appearance of faces within the days of the camera wearer, and rely on clustering algorithms to group their feature descriptors in order to re-identify persons. Recurrence of detected faces within photostreams allows us to shape an idea of the social pattern of behaviour of the user. We validated our model over several weeks recorded by different camera wearers. Our findings indicate that social profiles are potentially useful for social behaviour interpretation

Are black holes about information?

Information theory is increasingly invoked by physicists concerned with fundamental physics, including black hole physics. But to what extent is the application of information theory in those contexts legitimate? Using the case of black hole thermodynamics and Bekenstein's celebrated argument for the entropy of black holes, I will argue that information-theoretic notions are problematic in the present case. Bekenstein's original argument, as suggestive as it may appear, thus fails. This example is particularly pertinent to the theme of the present collection because the Bekenstein-Hawking formula for black hole entropy is widely accepted as 'empirical data' in notoriously empirically deprived quantum gravity, even though the laws of black hole thermodynamics have so far evaded empirical confirmation.Comment: 20 pages; forthcoming in Richard Dawid, Radin Dardashti, and Karim Th\'ebault (eds.), Epistemology of Fundamental Physics, Cambridge University Press; minor changes and additions of reference

Explicit diversification of event aspects for temporal summarization

During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness

A review of self-processing biases in cognition

When cues in the environment are associated with self (e.g., one’s own name, face, or coffee cup), these items trigger processing biases such as increased attentional focus, perceptual prioritization and memorial support. This paper reviews the existing literature on self-processing biases before introducing a series of studies that provide new insight into the influence of the self on cognition. In particular, the studies examine affective and memorial biases for self-relevant stimuli, and their flexible application in response to different task demands. We conclude that self-processing biases function to ensure that self-relevant information is attended to and preferentially processed because this is a perpetual goal of the self-system. However, contrary task-demands or priming can have an attenuating effect on their influence, speaking to the complexity and dynamism of the self-processing system in cognition

Machine Learning of Generic and User-Focused Summarization

A key problem in text summarization is finding a salience function which determines what information in the source should be included in the summary. This paper describes the use of machine learning on a training corpus of documents and their abstracts to discover salience functions which describe what combination of features is optimal for a given summarization task. The method addresses both "generic" and user-focused summaries.Comment: In Proceedings of the Fifteenth National Conference on AI (AAAI-98), p. 821-82

Joint Multi-Pitch Detection Using Harmonic Envelope Estimation for Polyphonic Music Transcription

In this paper, a method for automatic transcription of music signals based on joint multiple-F0 estimation is proposed. As a time-frequency representation, the constant-Q resonator time-frequency image is employed, while a novel noise suppression technique based on pink noise assumption is applied in a preprocessing step. In the multiple-F0 estimation stage, the optimal tuning and inharmonicity parameters are computed and a salience function is proposed in order to select pitch candidates. For each pitch candidate combination, an overlapping partial treatment procedure is used, which is based on a novel spectral envelope estimation procedure for the log-frequency domain, in order to compute the harmonic envelope of candidate pitches. In order to select the optimal pitch combination for each time frame, a score function is proposed which combines spectral and temporal characteristics of the candidate pitches and also aims to suppress harmonic errors. For postprocessing, hidden Markov models (HMMs) and conditional random fields (CRFs) trained on MIDI data are employed, in order to boost transcription accuracy. The system was trained on isolated piano sounds from the MAPS database and was tested on classic and jazz recordings from the RWC database, as well as on recordings from a Disklavier piano. A comparison with several state-of-the-art systems is provided using a variety of error metrics, where encouraging results are indicated

Heavy social drinkers score higher on implicit wanting and liking for alcohol than alcohol-dependent patients and light social drinkers

Abstract Background and objectives Automatic hedonic ("liking") and incentive ("wanting") processes are assumed to play an important role in addiction. Whereas some neurobiological theories suggest that these processes become dissociated when drug use develops into an addiction (i.e. "liking" becomes weaker, whereas "wanting" becomes exaggerated; e.g. Robinson & Berridge, 1993), other theories suggest that there is a linear relationship between these two processes (i.e. both "liking" and "wanting" increase equally; e.g. Koob & Le Moal, 1997). Our aim was to examine "wanting" and "liking" in three groups of participants: alcohol-dependent patients, heavy social drinkers, and light social drinkers. Methods Participants performed two different single target implicit association tests (ST-IATs; e.g. Bluemke & Friese, 2007) and explicit ratings that were designed to measure "liking" and "wanting" for alcohol. Results Our results are in sharp contrast with the theories of both Robinson and Berridge and Koob and Le Moal: heavy drinkers had higher scores than light drinkers and alcohol-dependent patients on both the wanting ST-IAT and the liking ST-IAT. There were no differences between alcohol-dependent patients and light drinkers. Explicit ratings mirrored these results. Limitations These findings suggest that our ST-IATs are not valid measures of "wanting" and "liking". Instead, they might assess more complex knowledge regarding participants' experiences and goals. Conclusions These findings suggest that the relationship between drug consumption and appetitive drug associations is not linear, highlighting the importance of testing both sub-clinical and clinical samples in future research.SCOPUS: ar.jinfo:eu-repo/semantics/publishe