J Regularization Improves Imbalanced Multiclass Segmentation

We propose a new loss formulation to further advance the multiclass segmentation of cluttered cells under weakly supervised conditions. When adding a Youden's J statistic regularization term to the cross entropy loss we improve the separation of touching and immediate cells, obtaining sharp segmentation boundaries with high adequacy. This regularization intrinsically supports class imbalance thus eliminating the necessity of explicitly using weights to balance training. Simulations demonstrate this capability and show how the regularization leads to correct results by helping advancing the optimization when cross entropy stagnates. We build upon our previous work on multiclass segmentation by adding yet another training class representing gaps between adjacent cells. This addition helps the classifier identify narrow gaps as background and no longer as touching regions. We present results of our methods for 2D and 3D images, from bright field images to confocal stacks containing different types of cells, and we show that they accurately segment individual cells after training with a limited number of images, some of which are poorly annotated

FERAtt: Facial Expression Recognition with Attention Net

We present a new end-to-end network architecture for facial expression recognition with an attention model. It focuses attention in the human face and uses a Gaussian space representation for expression recognition. We devise this architecture based on two fundamental complementary components: (1) facial image correction and attention and (2) facial expression representation and classification. The first component uses an encoder-decoder style network and a convolutional feature extractor that are pixel-wise multiplied to obtain a feature attention map. The second component is responsible for obtaining an embedded representation and classification of the facial expression. We propose a loss function that creates a Gaussian structure on the representation space. To demonstrate the proposed method, we create two larger and more comprehensive synthetic datasets using the traditional BU3DFE and CK+ facial datasets. We compared results with the PreActResNet18 baseline. Our experiments on these datasets have shown the superiority of our approach in recognizing facial expressions

Risk factor for footpad dermatitis and hock burns in broiler chickens

Footpad dermatitis (FPD) and hock burn (HB) are a major welfare concern in broiler chicken farming. In general, foot lesions are linked to poor environmental conditions. Ulcers caused by advanced lesions can negatively affect the gait of the birds, with effects on the welfare of animals, including, in the worst cases, inability to reach the feed or water. FPD and HB score data were collected manually at two broiler farms across Europe, during welfare assessments performed within the EU-PLF (Precision Livestock Farming) project, which is supported by the European Commission. This ongoing project aims to create "added value" for the farmer through the application of sensors and information technology at farm level. On those broiler farms, a number of variables such as temperature, relative humidity, ventilation rate, bird weight, light schedule, and feed and water consumption rates are measured automatically. The welfare of the chickens was assessed three times per cycle (at week 3, 4 and 5), scoring FPD, HB, gait score, cleanliness of the birds and litter quality. Data analysis was performed by combining data from the welfare assessments with environmental data collected by the automatic monitoring systems. The analysis showed that FPD and HB were more frequent when the flock was exposed to poor environmental conditions for prolonged periods of time. As environmental conditions can be measured continuously, and the risk factor for FPD and HB increases with poor environmental conditions, there is potential to develop a detection and control system for foot and hock lesions.</p

Influence of culinary process on free and bound (poly)phenolic compounds and antioxidant capacity of artichoke

Artichokes are an important source of (poly)phenolic compounds, mainly caffeoylquinic acids, which consumption has been associated with health benefits. However, heat treatments have shown to affect the amounts of these bioactive food compounds. In the present study the influence of culinary techniques (boiling, griddling, and frying) on the total (poly)phenolic content of artichokes (Cynara Scolymus cv. Blanca de Tudela) was evaluated by LC-MS/MS. Additionally, the antioxidant capacity of cooked artichokes was evaluated by spectrophotometric methods. A total of 31 (poly)phenols were identified and quantified, being caffeoylquinic acids the most abundant compounds in raw artichokes accounting for more than 95% of total (poly)phenolic compounds. With the different culinary techniques, these compounds suffered degradation but also redistribution, probably due to isomerization and hydrolysis reactions. Frying and griddling showed the lowest content of (poly)phenolic compounds and antioxidant capacity suggesting thermal degradation. Boiling also provoked losses, which were mainly due to leaching of phenolic compounds into the water. However, it was the heat treatment that best preserved (poly)phenolic compounds in artichokes

J Regularization Improves Imbalanced Multiclass Segmentation

We propose a new loss formulation to further advance the multiclass segmentation of cluttered cells under weakly supervised conditions. When adding a Youden's J statistic regularization term to the cross entropy loss we improve the separation of touching and immediate cells, obtaining sharp segmentation boundaries with high adequacy. This regularization intrinsically supports class imbalance thus eliminating the necessity of explicitly using weights to balance training. Simulations demonstrate this capability and show how the regularization leads to correct results by helping advancing the optimization when cross entropy stagnates. We build upon our previous work on multiclass segmentation by adding yet another training class representing gaps between adjacent cells. This addition helps the classifier identify narrow gaps as background and no longer as touching regions. We present results of our methods for 2D and 3D images, from bright field images to confocal stacks containing different types of cells, and we show that they accurately segment individual cells after training with a limited number of images, some of which are poorly annotated

Multi-site observations of Delta Scuti stars 7 Aql and 8 Aql (a new Delta Scuti variable): The twelfth STEPHI campaign in 2003

We present an analysis of the pulsation behaviour of the Delta Scuti stars 7 Aql (HD 174532) and 8 Aql (HD 174589) -- a new variable star -- observed in the framework of STEPHI XII campaign during 2003 June--July. 183 hours of high precision photometry were acquired by using four-channel photometers at three sites on three continents during 21 days. The light curves and amplitude spectra were obtained following a classical scheme of multi-channel photometry. Observations in different filters were also obtained and analyzed. Six and three frequencies have been unambiguously detected above a 99% confidence level in the range 0.090 mHz--0.300 mHz and 0.100 mHz-- 0.145 mHz in 7 Aql and 8 Aql respectively. A comparison of observed and theoretical frequencies shows that 7 Aql and 8 Aql may oscillate with p modes of low radial orders, typical among Delta Scuti stars. In terms of radial oscillations the range of 8 Aql goes from n=1 to n=3 while for 7 Aql the range spans from n=4 to n=7. Non-radial oscillations have to be present in both stars as well. The expected range of excited modes according to a non adiabatic analysis goes from n=1 to n=6 in both stars.Comment: 8 pages, 7 fugures, 5 tables, accepted for publication in Astronomical Journa