INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 932 authors who have published 2618 publications receiving 37658 citations.

Go with the flow: advances and trends in magnetic flow cytometry

Abstract: The growing need for biological information at the single cell level has driven the development of improved cytometry technologies. Flow cytometry is a particularly powerful method that has evolved over the past few decades. Flow cytometers have become essential instruments in biomedical research and routine clinical tests for disease diagnosis, prognosis, and treatment monitoring. However, the increasing number of cellular parameters unveiled by genomic, proteomic, and metabolomic data platforms demands an augmented multiplexability. Also, the need for identification and quantification of relevant biomarkers at low levels requires outstanding analytical sensitivity and reliability. In addition, growing awareness of the advantages associated with miniaturization of analytical devices is pushing forward the progress in integrated and compact, microfluidic-based devices at the point-of-care. In this context, novel types of flow cytometers are emerging during the search to tackle these challenges. Notwithstanding the relevance of other promising alternatives to standard optical flow cytometry (e.g., mass cytometry, various optical and electrical microcytometers), this report focuses on a recent microcytometric technology based on magnetic sensors and magnetic particles integrated into microfluidic structures for dynamic bioanalysis of fluid samples-magnetic flow cytometry. Its concept, main developments, targeted applications, as well as the challenges and trends behind this technology are presented and discussed. Graphical abstract ᅟ "Kindly advise whether there is online abstract figure for this paper. If so, kindly resupply.The graphical abstract is correctly supplied.

Efficient behavior learning in human–robot collaboration

Abstract: We present a novel method for a robot to interactively learn, while executing, a joint human–robot task. We consider collaborative tasks realized by a team of a human operator and a robot helper that adapts to the human’s task execution preferences. Different human operators can have different abilities, experiences, and personal preferences so that a particular allocation of activities in the team is preferred over another. Our main goal is to have the robot learn the task and the preferences of the user to provide a more efficient and acceptable joint task execution. We cast concurrent multi-agent collaboration as a semi-Markov decision process and show how to model the team behavior and learn the expected robot behavior. We further propose an interactive learning framework and we evaluate it both in simulation and on a real robotic setup to show the system can effectively learn and adapt to human expectations.

The LECTRA Corpus - Classroom Lecture Transcriptions in European Portuguese

Abstract: This paper describes the corpus of university lectures that has been recorded in European Portuguese, and some of the recognition experiments we have done with it. The highly specific topic domain and the spontaneous speech nature of the lectures are two of the most challenging problems. Lexical and language model adaptation proved difficult given the scarcity of domain material in Portuguese, but improvements can be achieved with unsupervised acoustic model adaptation. From the point of view of the study of spontaneous speech characteristics, namely disflluencies, the LECTRA corpus has also proved a very valuable resource.

ReEnact: Sketch based Choreographic Design from Archival Dance Footage

Abstract: We describe a novel system for synthesising video choreography using sketched visual storyboards comprising human poses (stick men) and action labels. First, we describe an algorithm for searching archival dance footage using sketched pose. We match using an implicit representation of pose parsed from a mix of challenging low and high fidelity footage. In a training pre-process we learn a mapping between a set of exemplar sketches and corresponding pose representations parsed from the video, which are generalized at query-time to enable retrieval over previously unseen frames, and over additional unseen videos. Second, we describe how a storyboard of sketched poses, interspersed with labels indicating connecting actions, may be used to drive the synthesis of novel video choreography from the archival footage.We demonstrate both our retrieval and synthesis algorithms over both low fidelity PAL footage from the UK Digital Dance Archives (DDA) repository of contemporary dance, circa 1970, and over higher-definition studio captured footage.

Computing MMSE Estimates and Residual Uncertainty Directly in the Feature Domain of ASR using STFT Domain Speech Distortion Models

Abstract: In this paper we demonstrate how uncertainty propagation allows the computation of minimum mean square error (MMSE) estimates in the feature domain for various feature extraction methods using short-time Fourier transform (STFT) domain distortion models. In addition to this, a measure of estimate reliability is also attained which allows either feature re-estimation or the dynamic compensation of automatic speech recognition (ASR) models. The proposed method transforms the posterior distribution associated to a Wiener filter through the feature extraction using the STFT Uncertainty Propagation formulas. It is also shown that non-linear estimators in the STFT domain like the Ephraim-Malah filters can be seen as special cases of a propagation of the Wiener posterior. The method is illustrated by developing two MMSE-Mel-frequency Cepstral Coefficient (MFCC) estimators and combining them with observation uncertainty techniques. We discuss similarities with other MMSE-MFCC estimators and show how the proposed approach outperforms conventional MMSE estimators in the STFT domain on the AURORA4 robust ASR task.