Low-Spin Spectroscopy of 50Mn

The data on low spin states in the odd-odd nucleus 50Mn investigated with the 50Cr(p,ngamma)50Mn fusion evaporation reaction at the FN-TANDEM accelerator in Cologne are reported. Shell model and collective rotational model interpretations of the data are given.Comment: 7 pages, 2 figures, to be published in the proceedings of the "Bologna 2000 - Structure of the Nucleus at the Dawn of the Century" Conference, (Bologna, Italy, May 29 - June 3, 2000

Evolution and Classification of Myosins, a Paneukaryotic Whole-Genome Approach

notes: PubMed ID: 24443438© The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.Myosins are key components of the eukaryotic cytoskeleton, providing motility for a broad diversity of cargoes. Therefore, understanding the origin and evolutionary history of myosin classes is crucial to address the evolution of eukaryote cell biology. Here, we revise the classification of myosins using an updated taxon sampling that includes newly or recently sequenced genomes and transcriptomes from key taxa. We performed a survey of eukaryotic genomes and phylogenetic analyses of the myosin gene family, reconstructing the myosin toolkit at different key nodes in the eukaryotic tree of life. We also identified the phylogenetic distribution of myosin diversity in terms of number of genes, associated protein domains and number of classes in each taxa. Our analyses show that new classes (i.e., paralogs) and domain architectures were continuously generated throughout eukaryote evolution, with a significant expansion of myosin abundance and domain architectural diversity at the stem of Holozoa, predating the origin of animal multicellularity. Indeed, single-celled holozoans have the most complex myosin complement among eukaryotes, with paralogs of most myosins previously considered animal specific. We recover a dynamic evolutionary history, with several lineage-specific expansions (e.g., the myosin III-like gene family diversification in choanoflagellates), convergence in protein domain architectures (e.g., fungal and animal chitin synthase myosins), and important secondary losses. Overall, our evolutionary scheme demonstrates that the ancestral eukaryote likely had a complex myosin repertoire that included six genes with different protein domain architectures. Finally, we provide an integrative and robust classification, useful for future genomic and functional studies on this crucial eukaryotic gene family.LeverhulmeBBSRCERCMINECONERCGordon and Betty Moore Foundatio

Attribute-Preserving Face Dataset Anonymization via Latent Code Optimization

This work addresses the problem of anonymizing the identity of faces in a dataset of images, such that the privacy of those depicted is not violated, while at the same time the dataset is useful for downstream task such as for training machine learning models. To the best of our knowledge, we are the first to explicitly address this issue and deal with two major drawbacks of the existing state-of-the-art approaches, namely that they (i) require the costly training of additional, purpose-trained neural networks, and/or (ii) fail to retain the facial attributes of the original images in the anonymized counterparts, the preservation of which is of paramount importance for their use in downstream tasks. We accordingly present a task-agnostic anonymization procedure that directly optimizes the images' latent representation in the latent space of a pretrained GAN. By optimizing the latent codes directly, we ensure both that the identity is of a desired distance away from the original (with an identity obfuscation loss), whilst preserving the facial attributes (using a novel feature-matching loss in FaRL's [48] deep feature space). We demonstrate through a series of both qualitative and quantitative experiments that our method is capable of anonymizing the identity of the images whilst-crucially-better-preserving the facial attributes. We make the code and the pretrained models publicly available at: https://github.com/chi0tzp/FALCO

AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups

IEEE We present AMIGOS-- A dataset for Multimodal research of affect, personality traits and mood on Individuals and GrOupS. Different to other databases, we elicited affect using both short and long videos in two social contexts, one with individual viewers and one with groups of viewers. The database allows the multimodal study of people's affective responses, by means of neuro-physiological signals, and their relation with personality, mood, social context and stimuli duration. The data is collected in two experimental settings. In the first one, 40 participants watched 16 short emotional videos. In the second one, they watched 4 long videos, some of them alone and the rest in groups. The participants' signals, namely, Electroencephalogram (EEG), Electrocardiogram (ECG) and Galvanic Skin Response (GSR), were recorded using wearable sensors. Participants' frontal HD video and both RGB and depth full body videos were also recorded. Participants emotions have been annotated with both self-assessment of affective levels (valence, arousal, control, familiarity, liking and basic emotions) and external-assessment of valence and arousal. We present a detailed correlation analysis of the different dimensions as well as baseline methods and results for single-trial classification of valence, arousal, personality traits, mood and social context. The database is publicly available

Improving Fairness using Vision-Language Driven Image Augmentation

Fairness is crucial when training a deep-learning discriminative model, especially in the facial domain. Models tend to correlate specific characteristics (such as age and skin color) with unrelated attributes (downstream tasks), resulting in biases which do not correspond to reality. It is common knowledge that these correlations are present in the data and are then transferred to the models during training (e.g., [35]). This paper proposes a method to mitigate these correlations to improve fairness. To do so, we learn interpretable and meaningful paths lying in the se- mantic space of a pre-trained diffusion model (DiffAE) [27] – such paths being supervised by contrastive text dipoles. That is, we learn to edit protected characteristics (age and skin color). These paths are then applied to augment images to improve the fairness of a given dataset. We test the proposed method on CelebA-HQ and UTKFace on several downstream tasks with age and skin color as protected characteristics. As a proxy for fairness, we compute the difference in accuracy with respect to the protected characteristics. Quantitative results show how the augmented images help the model improve the overall accuracy, the aforementioned metric, and the disparity of equal opportunity. Code is available at: https://github.com/Moreno98/Vision-Language-Bias-Control

Realtime Video Classification Using Dense HOF/HOG

ABSTRACT The current state-of-the-art in Video Classification is based on Bag-of-Words using local visual descriptors. Most commonly these are Histogram of Oriented Gradient (HOG) and Histogram of Optical Flow (HOF) descriptors. While such system is very powerful for classification, it is also computationally expensive. This paper addresses the problem of computational efficiency. Specifically: (1) We propose several speed-ups for densely sampled HOG and HOF descriptors and release Matlab code. (2) We investigate the trade-off between accuracy and computational efficiency of descriptors in terms of frame sampling rate and type of Optical Flow method. (3) We investigate the trade-off between accuracy and computational efficiency for the video representation, using either a k-means or hierarchical k-means based visual vocabulary, a Random Forest based vocabulary or the Fisher kernel

What does touch tell us about emotions in touchscreen-based gameplay?

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACM. It is posted here by permission of ACM for your personal use. Not for redistribution.Nowadays, more and more people play games on touch-screen mobile phones. This phenomenon raises a very interesting question: does touch behaviour reflect the player’s emotional state? If possible, this would not only be a valuable evaluation indicator for game designers, but also for real-time personalization of the game experience. Psychology studies on acted touch behaviour show the existence of discriminative affective profiles. In this paper, finger-stroke features during gameplay on an iPod were extracted and their discriminative power analysed. Based on touch-behaviour, machine learning algorithms were used to build systems for automatically discriminating between four emotional states (Excited, Relaxed, Frustrated, Bored), two levels of arousal and two levels of valence. The results were very interesting reaching between 69% and 77% of correct discrimination between the four emotional states. Higher results (~89%) were obtained for discriminating between two levels of arousal and two levels of valence

A Broad Genomic Survey Reveals Multiple Origins and Frequent Losses in the Evolution of Respiratory Hemerythrins and Hemocyanins

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly citedHemerythrins and hemocyanins are respiratory proteins present in some of the most ecologically diverse animal lineages; however, the precise evolutionary history of their enzymatic domains (hemerythrin, hemocyanin M, and tyrosinase) is still not well understood. We survey a wide dataset of prokaryote and eukaryote genomes and RNAseq data to reconstruct the phylogenetic origins of these proteins. We identify new species with hemerythrin, hemocyanin M, and tyrosinase domains in their genomes, particularly within animals, and demonstrate that the current distribution of respiratory proteins is due to several events of lateral gene transfer and/or massive gene loss. We conclude that the last common metazoan ancestor had at least two hemerythrin domains, one hemocyanin M domain, and six tyrosinase domains. The patchy distribution of these proteins among animal lineages can be partially explained by physiological adaptations, making these genes good targets for investigations into the interplay between genomic evolution and physiological constraints. © The Author(s) 2013.This work was funded by the Sars International Centre for Marine Molecular Biology to J.M.M.-D. and A.H., and an ICREA contract, an European Research Council Starting Grant (ERC-2007-StG-206883), and a grant (BFU2011-23434) from Ministerio de Economía y Competitividad (MINECO) to I.R.-T. A.S.-P.’s salary was supported by a pregraduate FPU grant and A.d.M.’s salary from a FPI grant, both from MICINN.Peer Reviewe