Microscopic description of Gamow-Teller transitions in middle pf--shell nuclei by a realistic shell model calculation

GT transitions in $N=28\sim 30$ nuclei are studied in terms of a large-scale realistic shell-model calculation, by using Towner's microscopic parameters. $B({\rm GT})$ values to low-lying final states are reproduced with a reasonable accuracy. Several gross properties with respect to the GT transitions are investigated with this set of the wavefunctions and the operator. While the calculated total GT$^-$ strengths show no apparent disagreement with the measured ones, the calculated total GT$^+$ strengths are somewhat larger than those obtained from charge-exchange experiments. Concerning the Ikeda sum-rule, the proportionality of $S_{\rm GT}$ to $(N-Z)$ persists to an excellent approximation, with a quenching factor of 0.68. For the relative GT$^-$ strengths among possible isospin components, the lowest isospin component gathers greater fraction than expected by the squared CG coefficients of the isospin coupling. It turns out that these relative strengths are insensitive to the size of model space. Systematics of the summed $B({\rm GT})$ values are discussed for each isospin component.Comment: IOP-LaTeX 23 pages, to appear in J. Phys. G., 5 Postscript figures available upon reques

ELVIS: Entertainment-led video summaries

© ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Multimedia Computing, Communications, and Applications, 6(3): Article no. 17 (2010) http://doi.acm.org/10.1145/1823746.1823751Video summaries present the user with a condensed and succinct representation of the content of a video stream. Usually this is achieved by attaching degrees of importance to low-level image, audio and text features. However, video content elicits strong and measurable physiological responses in the user, which are potentially rich indicators of what video content is memorable to or emotionally engaging for an individual user. This article proposes a technique that exploits such physiological responses to a given video stream by a given user to produce Entertainment-Led VIdeo Summaries (ELVIS). ELVIS is made up of five analysis phases which correspond to the analyses of five physiological response measures: electro-dermal response (EDR), heart rate (HR), blood volume pulse (BVP), respiration rate (RR), and respiration amplitude (RA). Through these analyses, the temporal locations of the most entertaining video subsegments, as they occur within the video stream as a whole, are automatically identified. The effectiveness of the ELVIS technique is verified through a statistical analysis of data collected during a set of user trials. Our results show that ELVIS is more consistent than RANDOM, EDR, HR, BVP, RR and RA selections in identifying the most entertaining video subsegments for content in the comedy, horror/comedy, and horror genres. Subjective user reports also reveal that ELVIS video summaries are comparatively easy to understand, enjoyable, and informative

Evolution of communication signals and information during species radiation

Communicating species identity is a key component of many animal signals. However, whether selection for species recognition systematically increases signal diversity during clade radiation remains debated. Here we show that in woodpecker drumming, a rhythmic signal used during mating and territorial defense, the amount of species identity information encoded remained stable during woodpeckers’ radiation. Acoustic analyses and evolutionary reconstructions show interchange among six main drumming types despite strong phylogenetic contingencies, suggesting evolutionary tinkering of drumming structure within a constrained acoustic space. Playback experiments and quantification of species discriminability demonstrate sufficient signal differentiation to support species recognition in local communities. Finally, we only find character displacement in the rare cases where sympatric species are also closely related. Overall, our results illustrate how historical contingencies and ecological interactions can promote conservatism in signals during a clade radiation without impairing the effectiveness of information transfer relevant to inter-specific discrimination

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

Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night

Deep learning techniques have enabled the emergence of state-of-the-art models to address object detection tasks. However, these techniques are data-driven, delegating the accuracy to the training dataset which must resemble the images in the target task. The acquisition of a dataset involves annotating images, an arduous and expensive process, generally requiring time and manual effort. Thus, a challenging scenario arises when the target domain of application has no annotated dataset available, making tasks in such situation to lean on a training dataset of a different domain. Sharing this issue, object detection is a vital task for autonomous vehicles where the large amount of driving scenarios yields several domains of application requiring annotated data for the training process. In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented. For that, a model based on Generative Adversarial Networks (GANs) is explored to enable the generation of an artificial dataset with its respective annotations. The artificial dataset (fake dataset) is created translating images from day-time domain to night-time domain. The fake dataset, which comprises annotated images of only the target domain (night images), is then used to train the car detector model. Experimental results showed that the proposed method achieved significant and consistent improvements, including the increasing by more than 10% of the detection performance when compared to the training with only the available annotated data (i.e., day images).Comment: 8 pages, 8 figures, https://github.com/viniciusarruda/cross-domain-car-detection and accepted at IJCNN 201

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image samples of the problem (i.e., real world) for proper training. The acquisition of such real-world data sets is not always possible in the autonomous driving context, and sometimes their annotation is not feasible (e.g., takes too long or is too expensive). Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. It turns out that traffic sign detection is a problem in which these three issues are seen altogether. In this work, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the domain of interest, and (ii) templates of the traffic signs, i.e., templates synthetically created to illustrate the appearance of the category of a traffic sign. The effortlessly generated training database is shown to be effective for the training of a deep detector (such as Faster R-CNN) on German traffic signs, achieving 95.66% of mAP on average. In addition, the proposed method is able to detect traffic signs with an average precision, recall and F1-score of about 94%, 91% and 93%, respectively. The experiments surprisingly show that detectors can be trained with simple data generation methods and without problem domain data for the background, which is in the opposite direction of the common sense for deep learning

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

The synergistic effects of omega-3 fatty acids against 5-fluorouracil-induced mucosal impairment in mice

Background: Anti-cancer pharmaceuticals frequently have adverse side effects on patients such as gastrointestinal involvement limiting their clinical applications. These effects may be controlled by nutritional interventions, however, there are few studies that have shown any mechanistic effects. In this study, we examined effects of diet enhanced with eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) on 5-fluorouracil (5-FU)-induced intestinal impairment and immunity in mice. Methods: C57Bl6 mice were randomized to control diet, control diet + EPA, control + DHA, control + fish oil, or diet enchanced with DHA/EPA. After seven days of each respective diet, mice, excluding those in the sham group, were treated with 10 mg/kg/day 5-FU for 7 days. The effects of 5-FU-induced impairment in the small intestine were assessed using cytokine concentrations in serum and tissue, secretory immunoglobulin (Ig) A, diamine oxidase (DAO) activity, the length of the small intestine, and the expression of apoptosis signaling genes. Results: The EPA/DHA-enhanced diet resulted in the most beneficial, synergystic and protective effect against 5-FU induced weight loss. Protection against inflammation, impaired intestinal function, and immunity of the small intestine were also observed. Individually, a DHA-enriched diet demonstrated a protective effect against 5-FU damage with longer small intestine mucosal and crypt lengths, greater DAO activity, and higher IgA concentrations, whereas the EPA-enriched diet resulted in decreased inflammatory cytokine concentrations in both plasma and small intestine and expression of apoptosis target genes. Conclusions: In conclusion, a diet enhanced with EPA and DHA results in synergism protecting against the detrimental effects of 5-FU and limiting chemotherapy induced mucosal impairment