Miracle-dz/UniFAR-FTUs huggingface dataset:
Jointly Learning Convolutional Representations to Compress Radiological Images and Classify Thoracic Diseases in the Compressed Domain

Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain. Generally, very high dimensional medical images are down-sampled by using interpolation techniques before feeding them to deep learning models that are ImageNet compliant and accept only low-resolution images of size 224 x 224 px. This popular technique may lead to the loss of key information thus hampering the classification. Significant pathological features in medical images typically being small sized and highly affected. To combat this problem, we introduce a convolutional neural network (CNN) based classification approach which learns to reduce the resolution of the image using an autoencoder and at the same time classify it using another network, while both the tasks are trained jointly. This algorithm guides the model to learn essential representations from high-resolution images for classification along with reconstruction. We have used the publicly available dataset of chest x-rays to evaluate this approach and have outperformed state-of-the-art on test data. Besides, we have experimented with the effects of different augmentation approaches in this dataset and report baselines using some well known ImageNet class of CNNs.
Temporal Cricket Stroke Localization from Untrimmed Highlight Videos

The problem of temporal Cricket stroke localization is important and is the first step towards creation of a domain-specific sports activity dataset. Having a pre-annotated dataset of Cricket activities would be beneficial for training deep neural networks and for recognition of Cricket activities from the telecast videos. Taking Cricket as our domain of interest, we collect 26 Highlight videos of a single T20 tournament and hand-label the Cricket stroke segments in them. Our contribution is two-fold, apart from the Cricket stroke dataset creation, we also model the temporal Cricket stroke localization problem by training a recurrent neural network (RNN) and predicting the localized stroke segments using a sliding-window approach. Two types of features, motion (dense optical flow) and frame-level (HoG of centre crops), were considered as input to the RNN and a detailed experimental evaluation is presented for both. Our experiments are based on the hypothesis that the action-of-interest have an inherent temporal dependence and, thus, can be better learned using a sequence learning approach. We do not consider temporal localization based on 3D CNNs, which has been done previously. Input sequence length, granularity of the feature and minimum predicted segment size (as a post-processing step) are some of the hyperparameters that need tuning, apart from that of the model. The best result that we achieve is a Temporal Intersection over Union (TIoU) of 0.689 on the test set.
Leveraging information from imperfect examples: Common action sequence mining from a mix of incorrect performances

As much as good representation and theory are needed to explain human actions, so are the action videos used for learning good segmentation techniques. To accurately model complex actions such as diving, figure skating, and yoga practices, videos depicting action by human experts are required. Lack of experts in any domain leads to reduced number of videos and hence an improper learning. In this work we attempt to utilize imperfect amateur performances to get more confident representations of human action sequences. We introduce a novel Community Detection based unsupervised framework that provides mechanisms to interpret video data and address its limitations to produce better action representation. Human actions are composed of distinguishable key poses which form dense communities in graph structures. Anomalous poses performed for a longer duration can also form such dense communities but can be identified based on their rare occurrence across action videos and be rejected. Further, we propose a technique to learn the temporal order of these key poses from these imperfect videos, where the inter community links help reduce the search space of many possible pose sequences. Our framework is seen to improve the segmentation performance of complex human actions with the help of some imperfect performances. The efficacy of our approach has been illustrated over two complex action datasets - Sun Salutation and Warm-up exercise, that have been developed using random executions from amateur performers.
An Integrated Optimization Approach for Depth Map Enhancement on Special Riemannian Manifold

Depth images captured by consumer depth sensors like ToF Cameras or Microsoft Kinect are often noisy and incomplete. Most existing methods recover missing depth values from low quality measurements using information in the corresponding color images. However, the performance of such methods is susceptible when color image is noisy or correlation between RGB-D is weak. This paper presents a depth map enhancement algorithm based on Riemannian Geometry that performs depth map de-noising and completion simultaneously. The algorithm is based on the observation that similar RGB-D patches lie in a very low-dimensional subspace over the Riemannian quotient manifold of varying-rank matrices. The similar RGB-D patches are assembled into a matrix and optimization is performed on the search space of this quotient manifold with Kronecker product trace norm penalty. The proposed convex optimization problem on a special quotient manifold essentially captures the underlying structure in the color and depth patches. This enables robust depth refinement against noise or weak correlation between RGB-D data. This non-Euclidean approach with Kronecker product trace-norm constraints and cones in the non-linear matrix spaces provide a proper geometric framework to perform optimization. This formulates depth map enhancement as a matrix completion problem in the product space of Riemannian manifolds. This Riemannian submersion automatically handles ranks that change over matrices, and ensures guaranteed convergence over constructed manifold. The experiments on public benchmarks RGB-D images show that proposed method can effectively enhance depth maps.
What's all that noise: analysis and detection of propaganda on Twitter

For many, social networks have become the primary source of news, although the correctness of the provided information and its trustworthiness are often unclear. The investigations of the 2016 US presidential elections have brought the existence of external campaigns to light aiming at affecting the general political public opinion. In this paper, we investigate whether a similar influence on political elections can be observed in Europe as well. To this end, we use the past German federal election as an indicator and inspect the propaganda on Twitter, based on data from a period of 268 days. We find that 79 trolls from the US campaign have also acted upon the German federal election spreading right-wing views. Moreover, we develop a detector for finding automated behavior that enables us to identify 2,414 previously unknown suspicious accounts.
Application of Cluster Analysis in Stock Selection in United States Stock Market

Quantitative trading plays a significant role in stock selection due to its great flexibility and operability. A stock selection strategy was introduced based on the K-means clustering model in machine learning. Some technical indicators, such as MA, KDJ, and MACD with short and long periods, were taken into consideration in our strategy. The United States market stocks were divided into several clusters. And stocks close to the center of the best cluster were chosen to construct a portfolio. Experimental results showed that the investment strategy has a higher excess return rate during the bull market and decreases synchronously with the market trend during the bear market. This strategy, however, is superior to the performance of the S&P500 index at any time. This paper proposes a feasible strategy, which could get a considerable rate of return, to solve the problem of US stock selection.
Osmotic adjustment in wheat in relation to grain yield under water deficit environments

Six greenhouse and three field experiments were conducted at the International Center for Improvement of Maize and Wheat (CIM-MYT), Mexico, on bread wheat (Triticum aestivum L.) genotypes to ascertain the role of osmotic adjustment (OA) in sustaining grain yield and its stability under water deficit conditions. Under simulated water deficit conditions of the greenhouse and field, the genotypes differed considerably regarding OA, ranging from 0.31 to 0.86 MPa and from 0.60 to 0.99 MPa, respectively. When the mean values of OA across the six greenhouse experiments were regressed against those of grain yield obtained at different moisture levels of the line source, the correlation coefficient value increased with the increase in moisture stress, turning positively significant (P < 0.001) at the highest water deficit level. Similarly, osmotic potential at full turgor (OP100) and turgor potential (TP), measured in the greenhouse, were positively correlated (P < 0.05) with grain yield at the highest line source water deficit level. Osmotic adjustment and grain yield, both measured in the simulated water deficit condition in the field, were also positively correlated (P < 0.05). Besides, OA maintained yield stability through maintenance of turgor under water deficit during reproductive period of crop growth. The heritability of OA, OP100, and TP, computed by pooled analysis of variance across the six greenhouse experiments, was 0.74, 0.73, and 0.79, respectively. The results indicated that OA, as well as OP100 and TP, could be used as screening tools for drought-resistant bread wheat genotypes in the greenhouse. This study also demonstrated the appropriate greenhouse screening methodology in this regard.
Autopilot: workload autoscaling at Google

In many public and private Cloud systems, users need to specify a limit for the amount of resources (CPU cores and RAM) to provision for their workloads. A job that exceeds its limits might be throttled or killed, resulting in delaying or dropping end-user requests, so human operators naturally err on the side of caution and request a larger limit than the job needs. At scale, this results in massive aggregate resource wastage. To address this, Google uses Autopilot to configure resources automatically, adjusting both the number of concurrent tasks in a job (horizontal scaling) and the CPU/memory limits for individual tasks (vertical scaling). Autopilot walks the same fine line as human operators: its primary goal is to reduce slack - the difference between the limit and the actual resource usage - while minimizing the risk that a task is killed with an out-of-memory (OOM) error or its performance degraded because of CPU throttling. Autopilot uses machine learning algorithms applied to historical data about prior executions of a job, plus a set of finely-tuned heuristics, to walk this line. In practice, Autopiloted jobs have a slack of just 23%, compared with 46% for manually-managed jobs. Additionally, Autopilot reduces the number of jobs severely impacted by OOMs by a factor of 10. Despite its advantages, ensuring that Autopilot was widely adopted took significant effort, including making potential recommendations easily visible to customers who had yet to opt in, automatically migrating certain categories of jobs, and adding support for custom recommenders. At the time of writing, Autopiloted jobs account for over 48% of Google's fleet-wide resource usage.
SpoofNET: Resolving Facial Makeup based Spoofs

Face Recognition (FR) under adversarial conditions has been a big challenge for researchers in the Computer Vision and Machine Learning communities in the recent past. Most of state-of-the-art face recognition systems have been designed to overcome degradations in a face due to variations in pose, illumination, contrast, resolution, along with blur. However, interestingly none have addressed the fascinating issue of makeup as a spoof attack, which drastically changes the appearance of a face, making it difficult for even humans to detect and identify the impostor. In this paper, we propose a novel multi-component deep convolutional neural network (CNN) based architecture which performs the complex task of makeup removal from a disguised face, to reveal the original mugshot image of the impostor (i.e. without makeup). The proposed network also performs the hard tasks of FR on a disguised face in addition to recognition of identity and generation of the face of the spoofed target, by minimizing a novel multi-component objective function. Comparison of performance with a few recent state-of-the-art methods of FR over three benchmark datasets reveals the superiority of our proposed method for both synthesis as well as recognition (FR) tasks.
A Bottom-Up and Top-Down Approach for Image Captioning using Transformer

Describing the contents of an image automatically has been a fundamental problem in the field of artificial intelligence and computer vision. Existing approaches are either top-down, which start from a simple representation of an image and convert it into a textual description; or bottom-up, which come up with attributes describing numerous aspects of an image to form the caption or a combination of both. Recurrent neural networks (RNN) enhanced by Long Short-Term Memory networks (LSTM) have become a dominant component of several frameworks designed for solving the image captioning task. Despite their ability to reduce the vanishing gradient problem, and capture dependencies, they are inherently sequential across time. In this work, we propose two novel approaches, a top-down and a bottom-up approach independently, which dispenses the recurrence entirely by incorporating the use of a Transformer, a network architecture for generating sequences relying entirely on the mechanism of attention. Adaptive positional encodings for the spatial locations in an image and a new regularization cost during training is introduced. The ability of our model to focus on salient regions in the image automatically is demonstrated visually. Experimental evaluation of the proposed architecture on the MS-COCO dataset is performed to exhibit the superiority of our method.
Corn Syrup Solids and Their Saccharide Fractions Affect Crystallization of Amorphous Sucrose

The effects of corn syrup saccharides on the glass transition (T g ) and crystallization of freeze-dried sucrose were studied using differential scanning calorimetry. Corn syrup (CS) solids were fractionated into samples with differing ranges and types of glucose polymers. T g of the sucrose/CS additive mixtures depended on number-average molecular weight, rather than weight-average molecular weight, of CS material. Additive levels of 10% and 20% (w/w) of CS solids and their fractions interfered with crystallization of amorphous sucrose, whereas levels ≥50% prevented crystallization. The mechanism by which CS saccharides interfered with crystallization of amorphous sucrose depended not only on T g but was also influenced by specific interactions between molecules.
A new dwarf forest chameleon (Sauria: Rhampholeon Günther 1874) from Malawi, central Africa

The African genus Rhampholeon Gunther 1874 comprises a small group of basically similar, short-tailed, dwarf chameleons distributed through central and east Africa with the epicentre of speciation in Tanzania. Three species are currently known from Malawi, one of which is endemic. In this paper a further new endemic taxon from southern Malawi is described. Recent literature on the genus is discussed in relation to subgeneric grouping, and an updated key to the genus given.
Post‐mortem studies of Northern elephant seal pups

Thirty-four pups of the Northern elephant seal, Mirounga angustirostris, collected from Mexican and Californian coastal islands were studied to determine causes of death, growth of certain organs, and development of the supernumerary bones of the occiput. Dead pups from An Nuevo Island off central California from 1965 through 1969 appear to have died due to crushing by adults, probably the large breeding bulls. Recent deaths through the 1972 breeding season indicate that some deaths are attributable to bites inflicted by agitated cows. Organ weights were taken on several pups and compared with weights recorded for pups of the Southern elephant seal. Some organs appear to grow proportional to body weight, and other organs show disproportionate growth. The supernumerary bones of the occiput called “extraoccipitalia” were found to be embryonically preformed in cartilage. Ossification centres were shown to be developing in late term foetuses. These extraoccipitalia evidently allow rapid growth of the neurocranium during the first few months postpartum and later fuse with adjacent cranial bones at about one year of age. The rising elephant seal population in relation to the types of mortality is discussed in relation to available breeding grounds.
A Systematic Review into Factors Influencing Sketch Map Quality

Spatial perception is formed throughout our entire lives. Its quality depends on our individual differences and the characteristics of the environment. A sketch map is one way of visualising an individual’s spatial perception. It can be evaluated like a real map, in terms of its positional accuracy, content frequency and choice of cartographic methods. Moreover, the factors influencing the sketch map and/or its individual parameters can be identified. These factors should be of interest to geographers, cartographers and/or (geography) educators. The aim of this paper is to identify and describe the factors that clearly affect sketch map quality, by conducting a systematic review of 90 empirical studies published since 1960. Results show that most empirical studies focus on individual differences more than on environmental characteristics or information sources, even though the importance of these overlooked factors, especially source map characteristics and geographical education, has been proven in most analysed studies. Therefore, further research is needed in the field of sketch map quality parameters, especially in the use of cartographic methods. This paper could serve as a framework for such research.
Kinetics and mechanism of the reaction between maleic anhydride and fatty acid esters and the structure of the products

Alkenyl succinic anhydrides (ASA) were obtained by reaction between maleic anhydride and high‐oleic sunflower oil (HOSO) esters. A kinetics study of the maleinization of alkyl esters indicated that the maleinization reaction was second order overall and first order with respect to the individual reactants, and the activation energy was 77.2 ± 3.3 kJ/mol in the investigated temperature range (185–225 °C). These results showed that the cis configuration and the central position of the double bond in HOSO esters facilitate the maleinization of the latter. On the contrary, the length of the linear ester moiety had no influence on the course of the maleinization reaction. Moreover, new evidence demonstrates that there are two different reaction mechanisms: ene‐reaction and addition in allylic position with a 2 : 1 ratio, respectively. This ratio was constant throughout the reaction, thus indicating that these mechanisms are independent.
Morphometrics of Canis taxa in eastern North Carolina

Abstract We describe the external morphological characters of red wolves, coyotes, and their hybrids from North Carolina and assess if morphology could be an accurate discriminator among the 3 canid taxa. We used body measurements from 171 red wolves (Canis rufus), 134 coyotes (Canis latrans), and 47 hybrids in a polytomous logistic regression analysis to assess if they could be used to identify canids as red wolves, coyotes, or hybrids. Polytomous logistic regression analysis of 7 morphometric variables was able to correctly allocate 86% of canids to their a priori taxa groups. Using Akaike's information criterion, we judged hind-foot length, body mass, width of head, and tail length as variables to best separate taxa. Among the 3 sympatric Canis taxa in eastern North Carolina, red wolves are clearly the larger canid with hybrids intermediate to coyotes and red wolves in body size. Our results suggest that red wolves represent a unique Canis phenotype in the southeastern United States.
Environmental Control of Food Web Structure

The structure of 40 real food webs, representing aquatic and terrestrial communities from all latitudes, is found to be markedly affected by the degree of variability of the physical environment. In particular, food webs in fluctuating ecosystems are characterized by a significantly lower connectance than webs representative of more constant systems. This is interpreted within the context of stability theory as a means to optimize feeding in the face of increasing disturbance. It is shown further than the nature of the habitat itself imposes additional constraints on food web structure in intertidal, pelagic, estuarine, and forest ecosystems. See full-text article at JSTOR
Multitemporal Analysis of Gully Erosion in Olive Groves by Means of Digital Elevation Models Obtained with Aerial Photogrammetric and LiDAR Data

Gully erosion is one of the main processes of soil degradation, representing 50%–90% of total erosion at basin scales. Thus, its precise characterization has received growing attention in recent years. Geomatics techniques, mainly photogrammetry and LiDAR, can support the quantitative analysis of gully development. This paper deals with the application of these techniques using aerial photographs and airborne LiDAR data available from public database servers to identify and quantify gully erosion through a long period (1980–2016) in an area of 7.5 km2 in olive groves. Several historical flights (1980, 1996, 2001, 2005, 2009, 2011, 2013 and 2016) were aligned in a common coordinate reference system with the LiDAR point cloud, and then, digital surface models (DSMs) and orthophotographs were obtained. Next, the analysis of the DSM of differences (DoDs) allowed the identification of gullies, the calculation of the affected areas as well as the estimation of height differences and volumes between models. These analyses result in an average depletion of 0.50 m and volume loss of 85000 m3 in the gully area, with some periods (2009–2011 and 2011–2013) showing rates of 10,000–20,000 m3/year (20–40 t/ha*year). The manual edition of DSMs in order to obtain digital elevation models (DTMs) in a detailed sector has facilitated an analysis of the influence of this operation on the erosion calculations, finding that it is not significant except in gully areas with a very steep shape.
Relations Between Net Nitrogen Mineralization and Soil Characteristics Within an Arable Field

Within-field variations in plant-available soil nitrogen (N) are likely to be affected by differences in soil characteristics. To study this, a 3- year field investigation was conducted during 1998-2000 on a 15 ha arable field in Sweden with considerable within-field soil texture variability. In 34 plots soil N uptake by crops, net nitrogen mineralization (Nm) during the growing season and soil mineral N in spring and shortly after harvest were determined. Beside these parameters, topography, soil organic matter content (SOM), clay content, pH(H 2 O) and grain yield were recorded. The variations in Nm were considerably large both within the field and between years. The within-field variation in Nm could partly be explained by the variation in SOM and clay content (adjusted coefficient of determination = 0.23, P <0.001). The pattern in Nm differed between years, partly because of seasonal variations in soil moisture. For these reasons, the pattern of Nm is difficult to predict without seasonal adjustments.