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Wearable cameras can gather large a\-mounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a Late Fusion Ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85\%, outperforming state of the art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average.

This paper proposes a system for automatic social pattern characterization using a wearable photo-camera. The proposed pipeline consists of three major steps. First, detection of people with whom the camera wearer interacts and, second, categorization of the detected social interactions into formal and informal. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task, and a LSTM network is employed for time-series classification. In the last step, recurrences of the same person across the whole set of social interactions are clustered to achieve a comprehensive understanding of the diversity and frequency of the social relations of the user. Experiments over a dataset acquired by a user wearing a photo-camera during a month show promising results on the task of social pattern characterization from egocentric photo-streams., Comment: International Conference on Computer Vision (ICCV). Workshop on Egocentric Percetion, Interaction and Computing

One of the most common critical factors directly related to the cause of a chronic disease is unhealthy diet consumption. In this sense, building an automatic system for food analysis could allow a better understanding of the nutritional information with respect to the food eaten and thus it could help in taking corrective actions in order to consume a better diet. The Computer Vision community has focused its efforts on several areas involved in the visual food analysis such as: food detection, food recognition, food localization, portion estimation, among others. For food detection, the best results evidenced in the state of the art were obtained using Convolutional Neural Network. However, the results of all these different approaches were gotten on different datasets and therefore are not directly comparable. This article proposes an overview of the last advances on food detection and an optimal model based on GoogLeNet Convolutional Neural Network method, principal component analysis, and a support vector machine that outperforms the state of the art on two public food/non-food datasets.

With the arrival of convolutional neural networks, the complex problem of food recognition has experienced an important improvement in recent years. The best results have been obtained using methods based on very deep convolutional neural networks, which show that the deeper the model,the better the classification accuracy will be obtain. However, very deep neural networks may suffer from the overfitting problem. In this paper, we propose a combination of multiple classifiers based on different convolutional models that complement each other and thus, achieve an improvement in performance. The evaluation of our approach is done on two public datasets: Food-101 as a dataset with a wide variety of fine-grained dishes, and Food-11 as a dataset of high-level food categories, where our approach outperforms the independent CNN models.

Recently, there has been a growing interest in analyzing human daily activities from data collected by wearable cameras. Since the hands are involved in a vast set of daily tasks, detecting hands in egocentric images is an important step towards the recognition of a variety of egocentric actions. However, besides extreme illumination changes in egocentric images, hand detection is not a trivial task because of the intrinsic large variability of hand appearance. We propose a hand detector that exploits skin modeling for fast hand proposal generation and Convolutional Neural Networks for hand recognition. We tested our method on UNIGE-HANDS dataset and we showed that the proposed approach achieves competitive hand detection results.

Following the increasingly popular trend of social interaction analysis in egocentric vision, this manuscript presents a comprehensive study for automatic social pattern characterization of a wearable photo-camera user, by relying on the visual analysis of egocentric photo-streams. The proposed framework consists of three major steps. The first step is to detect social interactions of the user where the impact of several social signals on the task is explored. The detected social events are inspected in the second step for categorization into different social meetings. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task, and LSTM is employed to classify the time-series. The last step of the framework is to characterize social patterns, which is essentially to infer the diversity and frequency of the social relations of the user through discovery of recurrences of the same people across the whole set of social events of the user. Experimental evaluation over a dataset acquired by 9 users demonstrates promising results on the task of social pattern characterization from egocentric photo-streams., Comment: 42 pages, 14 figures. Submitted to Elsevier, Computer Vision and Image Understanding (Under Review)

Activity recognition from long unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring and frailty detection, just to name a few. However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames. In consequence, important discriminatory low-level features from motion such as optical flow cannot be estimated. In this paper, we present a batch-driven approach for training a deep learning architecture that strongly rely on Long short-term units to tackle this problem. We propose two different implementations of the same approach that process a photo-stream sequence using batches of fixed size with the goal of capturing the temporal evolution of high-level features. The main difference between these implementations is that one explicitly models consecutive batches by overlapping them. Experimental results over a public dataset acquired by three users demonstrate the validity of the proposed architectures to exploit the temporal evolution of convolutional features over time without relying on event boundaries., Comment: 8 pages, 7 figures, 1 table. To appear at the ICCV 2017 workshop on Egocentric Perception, Interaction and Computing

Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet. We tackle the problem of food ingredients recognition as a multi-label learning problem. We propose a method for adapting a highly performing state of the art CNN in order to act as a multi-label predictor for learning recipes in terms of their list of ingredients. We prove that our model is able to, given a picture, predict its list of ingredients, even if the recipe corresponding to the picture has never been seen by the model. We make public two new datasets suitable for this purpose. Furthermore, we prove that a model trained with a high variability of recipes and ingredients is able to generalize better on new data, and visualize how it specializes each of its neurons to different ingredients., Comment: 8 pages

Recognizing Activities of Daily Living (ADLs) has a large number of health applications, such as characterize lifestyle for habit improvement, nursing and rehabilitation services. Wearable cameras can daily gather large amounts of image data that provide rich visual information about ADLs than using other wearable sensors. In this paper, we explore the classification of ADLs from images captured by low temporal resolution wearable camera (2fpm) by using a Convolutional Neural Networks (CNN) approach. We show that the classification accuracy of a CNN largely improves when its output is combined, through a random decision forest, with contextual information from a fully connected layer. The proposed method was tested on a subset of the NTCIR-12 egocentric dataset, consisting of 18,674 images and achieved an overall accuracy of 86% activity recognition on 21 classes., Comment: To appear in the Proceedings of IbPRIA 2017

In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate both techniques in an energy-minimization framework that serves to disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames descriptors. We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods.

In our society and century, clothing is not anymore used only as a means for body protection. Our paper builds upon the evidence, studied within the social sciences, that clothing brings a clear communicative message in terms of social signals, influencing the impression and behaviour of others towards a person. In fact, clothing correlates with personality traits, both in terms of self-assessment and assessments that unacquainted people give to an individual. The consequences of these facts are important: the influence of clothing on the decision making of individuals has been investigated in the literature, showing that it represents a discriminative factor to differentiate among diverse groups of people. Unfortunately, this has been observed after cumbersome and expensive manual annotations, on very restricted populations, limiting the scope of the resulting claims. With this position paper, we want to sketch the main steps of the very first systematic analysis, driven by social signal processing techniques, of the relationship between clothing and social signals, both sent and perceived. Thanks to human parsing technologies, which exhibit high robustness owing to deep learning architectures, we are now capable to isolate visual patterns characterising a large types of garments. These algorithms will be used to capture statistical relations on a large corpus of evidence to confirm the sociological findings and to go beyond the state of the art., Comment: To appear in the 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017)

Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures. In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also publish the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Furthermore, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description., Comment: 19 pages, 10 figures, 3 tables. Submitted to Journal of Visual Communication and Image Representation

Lifelogging is a process of collecting rich source of information about daily life of people. In this paper, we introduce the problem of sentiment analysis in egocentric events focusing on the moments that compose the images recalling positive, neutral or negative feelings to the observer. We propose a method for the classification of the sentiments in egocentric pictures based on global and semantic image features extracted by Convolutional Neural Networks. We carried out experiments on an egocentric dataset, which we organized in 3 classes on the basis of the sentiment that is recalled to the user (positive, negative or neutral).

Given an unconstrained stream of images captured by a wearable photo-camera (2fpm), we propose an unsupervised bottom-up approach for automatic clustering appearing faces into the individual identities present in these data. The problem is challenging since images are acquired under real world conditions; hence the visible appearance of the people in the images undergoes intensive variations. Our proposed pipeline consists of first arranging the photo-stream into events, later, localizing the appearance of multiple people in them, and finally, grouping various appearances of the same person across different events. Experimental results performed on a dataset acquired by wearing a photo-camera during one month, demonstrate the effectiveness of the proposed approach for the considered purpose., Comment: 5 pages, 3 figures, accepted in IEEE International Conference on Image Processing (ICIP 2017)

In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed., Comment: Submitted to IbPRIA'17, 8 pages, 3 figures, 1 table

Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient's behaviour, allowing specialists to discover unhealthy food patterns and understand the user's lifestyle. With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediterranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29\%, and top-5 of 97.07\%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07\%, and top-5 of 89.53\%, for a total of 115+101 food classes., Comment: 9 pages, 6 figures, 6 tables

We describe an application of machine learning to the problem of predicting preterm birth. We conduct a secondary analysis on a clinical trial dataset collected by the National In- stitute of Child Health and Human Development (NICHD) while focusing our attention on predicting different classes of preterm birth. We compare three approaches for deriving predictive models: a support vector machine (SVM) approach with linear and non-linear kernels, logistic regression with different model selection along with a model based on decision rules prescribed by physician experts for prediction of preterm birth. Our approach highlights the pre-processing methods applied to handle the inherent dynamics, noise and gaps in the data and describe techniques used to handle skewed class distributions. Empirical experiments demonstrate significant improvement in predicting preterm birth compared to past work., Comment: Presented at 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles, CA. In this revision, we updated page 4 by adding the reference Vovsha et al. (2013) (incorrectly referenced as XXX in the previous version due to double blind reviewing). The bibtex entry is now added to the references

The interpretation and analysis of the wireless capsule endoscopy recording is a complex task which requires sophisticated computer aided decision (CAD) systems in order to help physicians with the video screening and, finally, with the diagnosis. Most of the CAD systems in the capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, new CAD system has to be designed from scratch. This characteristic makes the design of new CAD systems a very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which avoids the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed by using state of the art hand-crafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase).

CVSO 30 is a unique young low-mass system, because, for the first time, a close-in transiting and a wide directly imaged planet candidates are found around a common host star. The inner companion, CVSO 30 b, is the first possible young transiting planet orbiting a previously known weak-lined T-Tauri star. With five telescopes of the 'Young Exoplanet Transit Initiative' (YETI) located in Asia, Europe and South America we monitored CVSO 30 over three years in a total of 144 nights and detected 33 fading events. In two more seasons we carried out follow-up observations with three telescopes. We can confirm that there is a change in the shape of the fading event between different observations and that the fading event even disappears and reappears. A total of 38 fading event light curves were simultaneously modelled. We derived the planetary, stellar, and geometrical properties of the system and found them slightly smaller but in agreement with the values from the discovery paper. The period of the fading event was found to be 1.36 s shorter and 100 times more precise than the previous published value. If CVSO 30 b would be a giant planet on a precessing orbit, which we cannot confirm, yet, the precession period may be shorter than previously thought. But if confirmed as a planet it would be the youngest transiting planet ever detected and will provide important constraints on planet formation and migration time-scales., Comment: 14 pages (20 with appendix), 7 figures (16 with appendix), 6 tables (7 with appendix)

Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams., Comment: 6 pages, 9 figures, accepted and presented in International Conference on Pattern Recognition (ICPR 2016)

The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, second, recognize each of the food types or food-related objects present in each bounding box. We demonstrate that our proposal, compared to the most similar problem nowadays - object localization, is able to obtain high precision and reasonable recall levels with only a few bounding boxes. Furthermore, we show that it is applicable to both conventional and egocentric images., Comment: 6 pages, 6 figures, 2 tables. International Conference on Pattern Recognition (ICPR) 2016 (in press)

Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames., Comment: 8 pages, 3 figures, 1 table, Submitted to International Conference on Artificial Neural Networks (ICANN)

While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods., Comment: 23 pages, 10 figures, 2 tables. In Press in Computer Vision and Image Understanding Journal

With the rapid increase of users of wearable cameras in recent years and of the amount of data they produce, there is a strong need for automatic retrieval and summarization techniques. This work addresses the problem of automatically summarizing egocentric photo streams captured through a wearable camera by taking an image retrieval perspective. After removing non-informative images by a new CNN-based filter, images are ranked by relevance to ensure semantic diversity and finally re-ranked by a novelty criterion to reduce redundancy. To assess the results, a new evaluation metric is proposed which takes into account the non-uniqueness of the solution. Experimental results applied on a database of 7,110 images from 6 different subjects and evaluated by experts gave 95.74% of experts satisfaction and a Mean Opinion Score of 4.57 out of 5.0. Source code is available at https://github.com/imatge-upc/egocentric-2017-lta, Comment: Oral paper at the ACM Multimedia 2017 Workshop on Lifelogging Tools and Applications (LTA), Mountain View, California USA. http://lta2017.computing.dcu.ie/

Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging., Comment: 16 pages, 11 figures, Submitted to IEEE Transactions on Human-Machine Systems

Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in it. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness., Comment: 27 pages, 18 figures, submitted to computer vision and image understanding journal

We introduce the study of the ant colony house-hunting problem from a distributed computing perspective. When an ant colony's nest becomes unsuitable due to size constraints or damage, the colony must relocate to a new nest. The task of identifying and evaluating the quality of potential new nests is distributed among all ants. The ants must additionally reach consensus on a final nest choice and the full colony must be transported to this single new nest. Our goal is to use tools and techniques from distributed computing theory in order to gain insight into the house-hunting process. We develop a formal model for the house-hunting problem inspired by the behavior of the Temnothorax genus of ants. We then show a \Omega(log n) lower bound on the time for all n ants to agree on one of k candidate nests. We also present two algorithms that solve the house-hunting problem in our model. The first algorithm solves the problem in optimal O(log n) time but exhibits some features not characteristic of natural ant behavior. The second algorithm runs in O(k log n) time and uses an extremely simple and natural rule for each ant to decide on the new nest., Comment: To appear in PODC 2015

Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted by means of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the summaries., Comment: Paper accepted in the IEEE First International Workshop on Wearable and Ego-vision Systems for Augmented Experience (WEsAX). Turin, Italy. July 3, 2015

Lifelogging devices are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. Given an egocentric video/images sequence acquired by the camera, our algorithm uses both the appearance extracted by means of a convolutional neural network and an object refill methodology that allows to discover objects even in case of small amount of object appearance in the collection of images. An SVM filtering strategy is applied to deal with the great part of the False Positive object candidates found by most of the state of the art object detectors. We validate our method on a new egocentric dataset of 4912 daily images acquired by 4 persons as well as on both PASCAL 2012 and MSRC datasets. We obtain for all of them results that largely outperform the state of the art approach. We make public both the EDUB dataset and the algorithm code., Comment: 9 pages, 13 figures, Submitted to: Image and Vision Computing

Results from UBVRI photometric observations of the pre-main sequence star GM Cep obtained in the period April 2011 - August 2014 are reported in the paper. Presented data are a continuation of our photometric monitoring of the star started in 2008. GM Cep is located in the field of the young open cluster Trumpler 37 and over the past years it has been an object of intense photometric and spectral studies. The star shows a strong photometric variability interpreted as a possible outburst from EXor type in previous studies. Our photometric data for a period of over six years show a large amplitude variability (Delta V ~ 2.3 mag.) and several deep minimums in brightness are observed. The analysis of the collected multicolor photometric data shows the typical of UX Ori variables a color reversal during the minimums in brightness. The observed decreases in brightness have a different shape, and evidences of periodicity are not detected. At the same time, high amplitude rapid variations in brightness typical for the classical T Tauri stars also present on the light curve of GM Cep. The spectrum of GM Cep shows the typical of classical T Tauri stars wide H/alpha emission line and absorption lines of some metals. We calculate the outer radius of the H/alpha emitting region as 10.4 +/-0.5 Rsun and the accretion rate as 1.8 x 10 E-7 Msun/yr., Comment: 9 pages, 4 figures, accepted for publication in PASA. arXiv admin note: text overlap with arXiv:1111.2166

We consider the ANTS problem [Feinerman et al.] in which a group of agents collaboratively search for a target in a two-dimensional plane. Because this problem is inspired by the behavior of biological species, we argue that in addition to studying the {\em time complexity} of solutions it is also important to study the {\em selection complexity}, a measure of how likely a given algorithmic strategy is to arise in nature due to selective pressures. In more detail, we propose a new selection complexity metric $\chi$, defined for algorithm ${\cal A}$ such that $\chi({\cal A}) = b + \log \ell$, where $b$ is the number of memory bits used by each agent and $\ell$ bounds the fineness of available probabilities (agents use probabilities of at least $1/2^\ell$). In this paper, we study the trade-off between the standard performance metric of speed-up, which measures how the expected time to find the target improves with $n$, and our new selection metric. In particular, consider $n$ agents searching for a treasure located at (unknown) distance $D$ from the origin (where $n$ is sub-exponential in $D$). For this problem, we identify $\log \log D$ as a crucial threshold for our selection complexity metric. We first prove a new upper bound that achieves a near-optimal speed-up of $(D^2/n +D) \cdot 2^{O(\ell)}$ for $\chi({\cal A}) \leq 3 \log \log D + O(1)$. In particular, for $\ell \in O(1)$, the speed-up is asymptotically optimal. By comparison, the existing results for this problem [Feinerman et al.] that achieve similar speed-up require $\chi({\cal A}) = \Omega(\log D)$. We then show that this threshold is tight by describing a lower bound showing that if $\chi({\cal A}) < \log \log D - \omega(1)$, then with high probability the target is not found within $D^{2-o(1)}$ moves per agent. Hence, there is a sizable gap to the straightforward $\Omega(D^2/n + D)$ lower bound in this setting., Comment: appears in PODC 2014

We report our investigation of the first transiting planet candidate from the YETI project in the young (~4 Myr old) open cluster Trumpler 37. The transit-like signal detected in the lightcurve of the F8V star 2M21385603+5711345 repeats every 1.364894+/-0.000015 days, and has a depth of 54.5+/-0.8 mmag in R. Membership to the cluster is supported by its mean radial velocity and location in the color-magnitude diagram, while the Li diagnostic and proper motion are inconclusive in this regard. Follow-up photometric monitoring and adaptive optics imaging allow us to rule out many possible blend scenarios, but our radial-velocity measurements show it to be an eclipsing single-lined spectroscopic binary with a late-type (mid-M) stellar companion, rather than one of planetary nature. The estimated mass of the companion is 0.15-0.44 solar masses. The search for planets around very young stars such as those targeted by the YETI survey remains of critical importance to understand the early stages of planet formation and evolution., Comment: 12 pages, 8 figures

We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well.

The New York Public Library is participating in the Chronicling America initiative to develop an online searchable database of historically significant newspaper articles. Microfilm copies of the newspapers are scanned and high resolution Optical Character Recognition (OCR) software is run on them. The text from the OCR provides a wealth of data and opinion for researchers and historians. However, categorization of articles provided by the OCR engine is rudimentary and a large number of the articles are labeled editorial without further grouping. Manually sorting articles into fine-grained categories is time consuming if not impossible given the size of the corpus. This paper studies techniques for automatic categorization of newspaper articles so as to enhance search and retrieval on the archive. We explore unsupervised (e.g. KMeans) and semi-supervised (e.g. constrained clustering) learning algorithms to develop article categorization schemes geared towards the needs of end-users. A pilot study was designed to understand whether there was unanimous agreement amongst patrons regarding how articles can be categorized. It was found that the task was very subjective and consequently automated algorithms that could deal with subjective labels were used. While the small scale pilot study was extremely helpful in designing machine learning algorithms, a much larger system needs to be developed to collect annotations from users of the archive. The "BODHI" system currently being developed is a step in that direction, allowing users to correct wrongly scanned OCR and providing keywords and tags for newspaper articles used frequently. On successful implementation of the beta version of this system, we hope that it can be integrated with existing software being developed for the Chronicling America project.

The paper presents a sample of newly detected eclipsing binaries from the public Kepler data. Orbits and fundamental parameters of 20 unknown eclipsing binaries were determined by modeling of their photometric data. Most of them are well-detached, high-eccentric binaries. We established that the target KID8552719 satisfied all widespread criteria for a planetary candidate. Fitting its light curve we obtained radius R_p=0.9 R_Nept, distance to the host star a = 42.58 Rsun = 0.198 AU and equilibrium temperatute T_p= 489 K. These values imply a Neptune-size object out of the habitable zone of the host star., Comment: 16 pages, 8 figures, 3 tables. This study was presented at the 7th National Conference of the Bulgarian Astronomical Society, June 08-10, 2012, Dimitrovgrad, Bulgaria. This manuscript will be published in Bulgarian Astronomical Journal, volume 19

We present new results from optical photometric and spectroscopic observations of the eruptive pre-main sequence star V2493 Cyg (HBC 722). The object has continued to undergo significant brightness variations over the past few months and is an ideal target for follow-up observations. We carried out CCD BVRI photometric observations in the field of V2493 Cyg ("Gulf of Mexico") from August 1994 to April 2012, i.e. at the pre-outburst states and during the phases of the outburst. We acquired high, medium, and low resolution spectroscopy of V2493 Cyg during the outburst. To study the pre-outburst variability of the target and construct its historical light curve, we searched for archival observations in photographic plate collections. Both CCD and photographic observations were analyzed using 15 comparison stars in the field of V2493 Cyg. The pre-outburst photographic and CCD photometric observations of V2493 Cyg show low-amplitude light variations typical of T Tauri stars. The recent photometric data show a slow light decrease from October 2010 to June 2011 followed by an increase in brightness that continued until early 2012. The spectral observations of V2493 Cyg are typical of FU Orionis stars absorption spectra with strong P Cyg profiles of H alpha and Na I D lines. On the basis of photometric monitoring performed over the past two years, the spectral properties at the maximal light, as well as the shape of long-term light curves, we confirm that the observed outburst of V2493 Cyg is of FU Orionis type., Comment: 7 pages, 6 figures, accepted for publication in A&A

We present the Young Exoplanet Transit Initiative (YETI), in which we use several 0.2 to 2.6m telescopes around the world to monitor continuously young (< 100 Myr), nearby (< 1 kpc) stellar clusters mainly to detect young transiting planets (and to study other variability phenomena on time-scales from minutes to years). The telescope network enables us to observe the targets continuously for several days in order not to miss any transit. The runs are typically one to two weeks long, about three runs per year per cluster in two or three subsequent years for about ten clusters. There are thousands of stars detectable in each field with several hundred known cluster members, e.g. in the first cluster observed, Tr-37, a typical cluster for the YETI survey, there are at least 469 known young stars detected in YETI data down to R=16.5 mag with sufficient precision of 50 milli-mag rms (5 mmag rms down to R=14.5 mag) to detect transits, so that we can expect at least about one young transiting object in this cluster. If we observe 10 similar clusters, we can expect to detect approximately 10 young transiting planets with radius determinations. The precision given above is for a typical telescope of the YETI network, namely the 60/90-cm Jena telescope (similar brightness limit, namely within +/-1 mag, for the others) so that planetary transits can be detected. For planets with mass and radius determinations, we can calculate the mean density and probe the internal structure. We aim to constrain planet formation models and their time-scales by discovering planets younger than 100 Myr and determining not only their orbital parameters, but also measuring their true masses and radii, which is possible so far only by the transit method. Here, we present an overview and first results. (Abstract shortened), Comment: 15 pages, 10 figures, AN accepted 2011 June 15

We report measurements of eight native (i.e., released directly from the comet nucleus) volatiles (H2O, HCN, CH4, C2H2, C2H6, CO, H2CO, and CH3OH) in comet 8P/Tuttle using NIRSPEC at Keck 2. Comet Tuttle reveals a truly unusual composition, distinct from that of any comet observed to date at infrared wavelengths. The prominent enrichment of methanol relative to water contrasts the depletions of other molecules, especially C2H2 and HCN. We suggest that the nucleus of 8P/Tuttle may contain two cometesimals characterized by distinct volatile composition. The relative abundances C2/CN, C2/OH, and CN/OH in 8P/Tuttle (measured at optical/near-UV wavelengths) differ substantially from the mixing ratios of their potential parents (C2H2/HCN, C2H2/H2O, and HCN/H2O) found in this work. Based on this comparison, our results do not support C2H2 and HCN being the principal precursors for respectively C2 and CN in Tuttle. The peculiar native composition observed in 8P/Tuttle (compared to other comets) provides new strong evidence for chemical diversity in the volatile materials stored in comet nuclei. We discuss the implications of this diversity for expected variations in the deuterium enrichment of water among comets., Comment: Accepted for Astrophysical Journal Letters

We present R-band observations of comet C2004B1 obtained in the period June, 21 - August 20, 2006. The data have been reduced to surface brightness maps, light curves, and mean radial profiles of the coma. In two cases a decrease of the brightness was recorded, which lasted for several days. The brightness decrease was accompanied by morphological changes in the coma., Comment: 6 pages, 6 figures, gzipped tar file. To appear in Proceeding of the 1st conference of BAS (Bulgarian Astronomical Society): "25 years National Astronomical Observatory"

We present the calibration and background model for the Proportional Counter Array (PCA) aboard the Rossi X-ray Timing Explorer (RXTE). The energy calibration is systematics limited below 10 keV with deviations from a power-law fit to the Crab nebula plus pulsar less than 1%. Unmodelled variations in the instrument background amount to less than 2% of the observed background below 10 keV and less than 1% between 10 and 20 keV. Individual photon arrival times are accurate to 4.4 micro-seconds at all times during the mission and to 2.5 micro-seconds after 29 April 1997. The peak pointing direction of the five collimators is known to a precision of a few arc-seconds, Comment: 77 pages, 34 figures. accepted by Ap J Supplement for April 2006 issue. This paper and additional calibration information are available from http://universe.gsfc.nasa.gov/xrays/programs/rxte/pca/ pcapage.html. Revised version contains minor changes to match accepted version