Miracle-dz/UniFAR-FTUs huggingface dataset:
Polyphonic sound event detection based on convolutional recurrent neural networks with semi-supervised loss function for DCASE challenge 2020 task 4

This report proposes a polyphonic sound event detection (SED) method for the DCASE 2020 Challenge Task 4. The proposed SED method is based on semi-supervised learning to deal with the different combination of training datasets such as weakly labeled dataset, unlabeled dataset, and strongly labeled synthetic dataset. Especially, the target label of each audio clip from weakly labeled or unlabeled dataset is first predicted by using the mean teacher model that is the DCASE 2020 baseline. The data with predicted labels are used for training the proposed SED model, which consists of CNNs with skip connections and self-attention mechanism, followed by RNNs. In order to compensate for the erroneous prediction of weakly labeled and unlabeled data, a semi-supervised loss function is employed for the proposed SED model. In this work, several versions of the proposed SED model are implemented and evaluated on the validation set according to the different parameter setting for the semi-supervised loss function, and then an ensemble model that combines five-fold validation models is finally selected as our final model.
A Comparative Study to Assess Problem of Inverted Nipple and Its Relationship to Successful Breast Feeding Among Antenatal Mothers

Background: The first feed is a profoundly important experience to the mother and her baby. Breast feeding is the ideal form of feeding in the neonate. Artificial feeding exposes the infant to infection and results in over a million deaths annually worldwide due to ill effects. Most of primi mothers are not able to breastfeed their babies properly due to inverted nipples. The poor breastfeeding may not be always the outcome of illiteracy; it can also be due to flat or inverted nipples. Material and Methods: The comparative research approach with two group posttest only experimental design was used. The independent variable was the cut disposable syringe technique in treating inverted nipple and dependent variable was the successful breastfeeding (LATCH). Study was conducted on 60 mothers admitted in maternity ward of Krishna Hospital, Karad, by using purposive sample technique. The data were collected, tabulated and analyzed by using descriptive and inferential statistics. Result: Demographic variables in both groups In group I 27(45%) and 29(48%) mothers from group II belonged to 1825 years of age. The difference in proportions was not significant (Fisher exact P = 0.6120). In group I 24(40%) and 22(37%) mothers from group II were educated up to primary and secondary. The difference was not significant (P = 0.7611). In group I 28(47%) and 29(48%) mothers from group II were housewives. The difference was not significant (P= 1.0000). In group I 26(43%) and 26(43%) mothers from group II had their monthly income more than 5000 Rs. The difference was not significant (P =1.0000). In group I 30(50%) and 27(45%) mothers from group II belonged to joint family. The difference was not significant (P = 0.2373). In group I 27(45%) and 30(50%) mothers from group II were Hindu by religion. The difference was not significant (P = 0.2373). Supportive and teaching needs There was no significant difference found in the mean score of supportive needs and teaching needs between group I and group II (P=0.1595 and t=1.425). Successful breastfeeding according to LATCH score In both groups all the babies had LATCH score 7 and above and they were achieved successful breastfeeding. Weight loss of the babies on 4th day The mean weight of the babies on 4th day was 2.4906 + 0.2409 Kg. in group I, 2.4671 + 0.3080 Kg. in group II the difference was not significant as Unpaired t=0.3268, P=0.7450. Problem of inverted and normal nipples None of the mother was developed problem with inverted and normal. Conclusion: The correction of inverted nipples with cut disposable syringe technique may be effective in achievement of successful breastfeeding with inverted nipples. Shilpa Umesh Bagal et al. A Comparative Study to Assess Problem of Inverted Nipple and Its Relationship to Successful Breast Feeding Among Antenatal Mothers International Journal of Health Sciences & Research (www.ijhsr.org) 281 Vol.7; Issue: 4; April 2017
Dietary preferences of juvenile green turtles (Chelonia mydas) on a tropical reef flat

We offer the first published description of the feeding choices made by juvenile green turtles on a tropical feeding ground, in this case a reef flat environment. We collected 85 lavage samples from 76 turtles and compared the food eaten to the food resources available. Resampling of some individuals enabled us to gain preliminary insights into diet switching by juvenile turtles. The area of the reef flat at Green Island, Queensland, Australia, had similar proportions of coverage by seagrasses (52%) and by algae (48%). Seven species of seagrass and at least 26 species of algae were identified. The dominant seagrasses, on an area basis, were Cymodocea sp. (29.7%), Halodule sp. (11.1%), Thalassia sp. (6.4%) and Syringodium sp. (4.5%). The most dominant algae were Halimeda spp. (10.2%). and Galaxaura sp. (7.25%). Most juvenile green turtles ate primarily seagrass, but some individuals ate predominantly algae. The turtles showed clear preferences for the seagrass Thalassia hemprichii and the algae Gracilaria spp. , Gelidiella sp. , Hypnea spp. despite their low abundance in many cases. Ways to improve our understanding of preferences and possible diet switching, and potential factors affecting them, are discussed.
Estimating Blink Probability for Highlight Detection in Figure Skating Videos

Highlight detection in sports videos has a broad viewership and huge commercial potential. It is thus imperative to detect highlight scenes more suitably for human interest with high temporal accuracy. Since people instinctively suppress blinks during attention-grabbing events and synchronously generate blinks at attention break points in videos, the instantaneous blink rate can be utilized as a highly accurate temporal indicator of human interest. Therefore, in this study, we propose a novel, automatic highlight detection method based on the blink rate. The method trains a one-dimensional convolution network (1D-CNN) to assess blink rates at each video frame from the spatio-temporal pose features of figure skating videos. Experiments show that the method successfully estimates the blink rate in 94% of the video clips and predicts the temporal change in the blink rate around a jump event with high accuracy. Moreover, the method detects not only the representative athletic action, but also the distinctive artistic expression of figure skating performance as key frames. This suggests that the blink-rate-based supervised learning approach enables high-accuracy highlight detection that more closely matches human sensibility.
A fast and robust algorithm for general defocusing particle tracking

The increasing use of microfluidics in industrial, biomedical, and clinical applications requires a more and more precise control of the microfluidic flows and suspended particles or cells. This leads to higher demands in three-dimensional and automated particle tracking methods, e.g. for use in feedback-control systems. General defocusing particle tracking (GDPT) is a 3D particle tracking method based on defocused particle images which is easy to use and requires standard laboratory equipment. In this work, we describe in detail a fast and robust algorithm for performing GDPT, which is suitable for automatized and real-time applications. Its key feature is a fast, segmentation-free approach to identify particles and estimate their 3D position. This detection step is followed by a refinement and iteration step to improve accuracy and identification of overlapping particles. We show that the algorithm is versatile and can be applied to different types of images (darkfield and brightfield). We use synthetic image sets of varying particle concentration to evaluate the performance of the algorithm in terms of detected depth coordinate uncertainty, particle detection rate, and processing time. The algorithm is applied and validated on experimental images showing that it is robust towards background or illumination fluctuations. Finally, to test the algorithm on real-time applications, we use synthetic images to set up a simulation framework with experimentally-relevant parameters and where the true particle positions are known.
Multicommodity Flows in Planar Graphs with Demands on Faces

We consider the problem of multicommodity flows in planar graphs. Seymour showed that if the union of supply and demand graphs is planar, then the cut condition is sufficient for routing demands. Okamura-Seymour showed that if all demands are incident on one face, then again cut condition is sufficient for routing demands. We consider a common generalization of these settings where the end points of each demand are on the same face of the planar graph. We show that if the source sink pairs on each face of the graph are such that sources and sinks appear contiguously on the cycle bounding the face, then the flow cut gap is at most 3. We come up with a notion of approximating demands on a face by convex combination of laminar demands to prove this result.
Weakly Supervised Segmentation with Multi-scale Adversarial Attention Gates

Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to obtain, particularly in medical imaging, where annotations also require expert knowledge. Weakly-supervised learning can train models by relying on weaker forms of annotation, such as scribbles. Here, we learn to segment using scribble annotations in an adversarial game. With unpaired segmentation masks, we train a multi-scale GAN to generate realistic segmentation masks at multiple resolutions, while we use scribbles to learn the correct position in the image. Central to the model's success is a novel attention gating mechanism, which we condition with adversarial signals to act as a shape prior, resulting in better object localization at multiple scales. We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks. We also demonstrate extensions in a variety of settings: semi-supervised learning; combining multiple scribble sources (a crowdsourcing scenario) and multi-task learning (combining scribble and mask supervision). We will release expert-made scribble annotations for the ACDC dataset, and the code used for the experiments, at this https URL.
Bayesian Coresets: An Optimization Perspective

Bayesian coresets have emerged as a promising approach for implementing scalable Bayesian inference. The Bayesian coreset problem involves selecting a (weighted) subset of the data samples, such that posterior inference using the selected subset closely approximates posterior inference using the full dataset. This manuscript revisits Bayesian coresets through the lens of sparsity constrained optimization. Leveraging recent advances in accelerated optimization methods, we propose and analyze a novel algorithm for coreset selection. We provide explicit convergence rate guarantees and present an empirical evaluation on a variety of benchmark datasets to highlight our proposed algorithm's superior performance compared to state of the art on speed and accuracy.
Instagram and Eating Disorders: An Empirical Study of the Effects of Instagram on Disordered Eating Habits Among Young Girls

Scholars have studied the relationship between body dissatisfaction and social media use, particularly focusing on young women as vulnerable consumers. Many studies concentrate on the amount of media consumed, rather than the specific activities and behaviors associated with feelings of low self-esteem or poor body image. It is important to determine exactly what behaviors and social media engagements contribute to disordered relationships with food, assessing a user’s pre-existing weight/body concerns in relation to the amount and type of media they consume. Instagram in particular is included in this study, as it is an image-based social networking site where users can engage with peers and celebrities, recently emerging as an advantageous advertising site for companies. This study uses the Tripartite Model of Influence to determine the correlation between Instagram usage and self-esteem, as family, friends and media contribute to the growth and development of a young woman’s self-esteem and relationship with food. It is imperative that these measures be taken into account to further understand social media’s influence on consumers, particularly young women who, through the Social Comparison Theory, are at risk for developing eating disorders and body dysmorphia. Through a comprehensive questionnaire about individual activities, experiences and engagements on Instagram, this study of 187 media users was able to correlate many disordered behaviors and beliefs with Instagram usage.
Stabilization of $866$ nm laser with Pound-Drever-Hall (PDH) technique for quantum manipulation of Ca+ ion in Paul trap

This article provides an introduction to one of the most efficient laser stabilization techniques - the Pound-Drever-Hall (PDH) method. The aim of the experiment is to build the PDH stabilization apparatus from scratch. I describe all the necessary theoretical treatment to formulate the technique and then describe it as I have experimentally implemented in the lab. All major sources of noise are discussed and noise tackling methods are implemented. All the equipments used are also described in detail for their principle and working.
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models

Multimodal learning for generative models often refers to the learning of abstract concepts from the commonality of information in multiple modalities, such as vision and language. While it has proven effective for learning generalisable representations, the training of such models often requires a large amount of "related" multimodal data that shares commonality, which can be expensive to come by. To mitigate this, we develop a novel contrastive framework for generative model learning, allowing us to train the model not just by the commonality between modalities, but by the distinction between "related" and "unrelated" multimodal data. We show in experiments that our method enables data-efficient multimodal learning on challenging datasets for various multimodal VAE models. We also show that under our proposed framework, the generative model can accurately identify related samples from unrelated ones, making it possible to make use of the plentiful unlabeled, unpaired multimodal data.
Proofs of Useless Work - Positive and Negative Results for Wasteless Mining Systems

Many blockchain systems today, including Bitcoin, rely on Proof of Work (PoW). Proof of work is crucial to the liveness and security of cryptocurrencies. The assumption when using PoW is that a lot of trial and error is required on average before a valid block is generated. One of the main concerns raised with regard to this kind of system is the inherent need to "waste" energy on "meaningless" problems. In fact, the Bitcoin system is believed to consume more electricity than several small countries [5]. In this work we formally define three properties that are necessary for wasteless PoW systems: (1) solve "meaningful" problems (2) solve them efficiently and (3) be secure against double-spend attacks. We analyze these properties and deduce constraints that impose on PoW systems. In particular, we conclude that under realistic assumptions, the set of allowed functions for mining must be preimage resistant functions. Finally, we propose a modification to the Bitcoin consensus rule that allows users to upload a certain subset of preimage resistant problems and let the mining process solve them. We prove security against Double-Spend attacks identical to the existing security guarantee in Bitcoin today.
Strong lensing as a giant telescope to localize the host galaxy of gravitational wave event

Standard siren cosmology of gravitational wave (GW) merger events relies on the identification of host galaxies and their redshifts. But this can be highly challenging due to numerous candidates of galaxies in the GW localization area. We point out that the number of candidates can be reduced by orders of magnitude for strongly lensed GW events, due to extra observational constraints. For the next-generation GW detectors like Einstein Telescope (ET), we estimate that this number is usually significantly less than one, as long as the GW localization uncertainty is better than $\sim 10\, \rm deg^2$. This implies that the unique identification of the host galaxy of lensed GW event detected by ET and Cosmic Explorer (CE) is possible. This provides us a promising opportunity to measure the redshift of the GW event and facilitate the standard siren cosmology. We also discuss its potential applications in understanding the evolution process and environment of the GW event.
The European Union summary report on trends and sources of zoonoses, zoonotic agents and food‐borne outbreaks in 2017

Abstract This report of the European Food Safety Authority and the European Centre for Disease Prevention and Control presents the results of zoonoses monitoring activities carried out in 2017 in 37 European countries (28 Member States (MS) and nine non‐MS). Campylobacteriosis was the commonest reported zoonosis and its EU trend for confirmed human cases increasing since 2008 stabilised during 2013–2017. The decreasing EU trend for confirmed human salmonellosis cases since 2008 ended during 2013–2017, and the proportion of human Salmonella Enteritidis cases increased, mostly due to one MS starting to report serotype data. Sixteen MS met all Salmonella reduction targets for poultry, whereas 12 MS failed meeting at least one. The EU flock prevalence of target Salmonella serovars in breeding hens, laying hens, broilers and fattening turkeys decreased or remained stable compared to 2016, and slightly increased in breeding turkeys. Salmonella results on pig carcases and target Salmonella serovar results for poultry from competent authorities tended to be generally higher compared to those from food business operators. The notification rate of human listeriosis further increased in 2017, despite Listeria seldom exceeding the EU food safety limit in ready‐to‐eat food. The decreasing EU trend for confirmed yersiniosis cases since 2008 stabilised during 2013–2017. The number of confirmed shiga toxin‐producing Escherichia coli (STEC) infections in humans was stable. A total of 5,079 food‐borne (including waterborne) outbreaks were reported. Salmonella was the commonest detected agent with S. Enteritidis causing one out of seven outbreaks, followed by other bacteria, bacterial toxins and viruses. The agent was unknown in 37.6% of all outbreaks. Salmonella in eggs and Salmonella in meat and meat products were the highest risk agent/food pairs. The report further summarises trends and sources for bovine tuberculosis, Brucella, Trichinella, Echinococcus, Toxoplasma, rabies, Coxiella burnetii (Q fever), West Nile virus and tularaemia.
LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens

Premise Obtaining phenotypic data from herbarium specimens can provide important insights into plant evolution and ecology but requires significant manual effort and time. Here, we present LeafMachine, an application designed to autonomously measure leaves from digitized herbarium specimens or leaf images using an ensemble of machine learning algorithms. Methods and Results We trained LeafMachine on 2685 randomly sampled specimens from 138 herbaria and evaluated its performance on specimens spanning 20 diverse families and varying widely in resolution, quality, and layout. LeafMachine successfully extracted at least one leaf measurement from 82.0% and 60.8% of high‐ and low‐resolution images, respectively. Of the unmeasured specimens, only 0.9% and 2.1% of high‐ and low‐resolution images, respectively, were visually judged to have measurable leaves. Conclusions This flexible autonomous tool has the potential to vastly increase available trait information from herbarium specimens, and inform a multitude of evolutionary and ecological studies.
Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum)

Premise Equisetum is a distinctive vascular plant genus with 15 extant species worldwide. Species identification is complicated by morphological plasticity and frequent hybridization events, leading to a disproportionately high number of misidentified specimens. These may be correctly identified by applying appropriate computer vision tools. Methods We hypothesize that aerial stem nodes can provide enough information to distinguish among Equisetum hyemale, E. laevigatum, and E . ×ferrissii, the latter being a hybrid between the other two. An object detector was trained to find nodes on a given image and to distinguish E. hyemale nodes from those of E. laevigatum. A classifier then took statistics from the detection results and classified the given image into one of the three taxa. Both detector and classifier were trained and tested on expert manually annotated images. Results In our exploratory test set of 30 images, our detector/classifier combination identified all 10 E. laevigatum images correctly, as well as nine out of 10 E. hyemale images, and eight out of 10 E. ×ferrissii images, for a 90% classification accuracy. Discussion Our results support the notion that computer vision may help with the identification of herbarium specimens once enough manual annotations become available.