Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Action Coding System and Induced Compassion Facial Action Coding System and Induced Compassion

The present study investigated the differential effects of a brief compassion meditation compared to a brief mindfulness meditation on felt and facially expressed compassion while viewing images of suffering. Participants (N = 82) were randomly assigned to one of two meditation conditions designed to promote compassion and relaxation, or relaxation alone. Participants then filmed themselves as they watched a two-minute compassion-inducing video that depicted suffering from around the world. These participant videos were later coded using three-distinct facial coding schemes: Complex FACS, Simplified FACS, and intuition rating. Finally, participants responded to a battery of self-report items about the level of compassion and sadness they experienced during the stimulus video, their trait emotional expressivity, and demographic questions including prior experience with meditation. Results found no difference in felt or facially expressed compassion between participants who completed the compassion meditation and those who completed the mindfulness meditation. Complex and Simplified FACS coding schemes were highly correlated, and both only weakly associated with intuition ratings. However, all three facial coding schemes failed to be even moderately associated with self-reported compassion. Intuition was the most vulnerable facial coding method to the influence of individual differences in gender and trait emotional expressivity. The current study found that FACS was unable to measure participant compassion, however, further research should be conducted using FACS in combination with other indicators of compassion.
Investigating modularity in the analysis of process algebra models of biochemical systems

Compositionality is a key feature of process algebras which is often cited as one of their advantages as a modelling technique. It is certainly true that in biochemical systems, as in many other systems, model construction is made easier in a formalism which allows the problem to be tackled compositionally. In this paper we consider the extent to which the compositional structure which is inherent in process algebra models of biochemical systems can be exploited during model solution. In essence this means using the compositional structure to guide decomposed solution and analysis. Unfortunately the dynamic behaviour of biochemical systems exhibits strong interdependencies between the components of the model making decomposed solution a difficult task. Nevertheless we believe that if such decomposition based on process algebras could be established it would demonstrate substantial benefits for systems biology modelling. In this paper we present our preliminary investigations based on a case study of the pheromone pathway in yeast, modelling in the stochastic process algebra Bio-PEPA.
Measuring Patellar Instability using Deep Learning Predicted Caton-Deschamps Index

In this paper, we aim to predict patellar height parameters from lateral x-ray images using convolutional neural networks. Our dataset consists of 304 pre-treatment lateral x-rays. We explore various data augmentation techniques to increase the size of our training dataset. We explore various state of the art models for image recognition tasks, ex-periment with many combinations of hyperparameters, and provide a comparison of their performances. We obtain an average key point distance of 2 pixels on our test dataset and a CDI error of 0.19 after training with a VGG16 network, pre-trained on the ImageNet dataset. We also design a baseline model that is small enough so that it can train quickly and compare its performance to other state of the art networks on our dataset. Our work provides a solid base for predicting patellar instability using convolutional neural networks.
Tractable Probabilistic Multimodal Learning

Given M modalities or views of a data-point in the form of X 1 , X 2 , . , X M , we aim to learn a joint distribution p θ ( X 1 , X 2 , . . . , X M ) that allows for exact inference queries. This is useful as it allows us to sample from a conditional model which allows us to do things like text guided image synthesis and image captioning from the same model itself (with image, text as the modalities). We can also train using missing data, a common issue in problems with multiple modalities, by training marginals rather than the joint. It is known that probabilistic circuits or sum-product networks [15] as a generative model allow for tractable evaluation of the above inference queries. We train independent auto-encoders for each modality and train a tractable generative model on the joint latent space which is simply the concatenation of the latent spaces of individual modalities. We show that our method generates 1.5 times better results (both quantitative and qualitative) on a variety of datasets such as (MNIST-SVHN, CelebA attributes, CUB-Captions) as compared to prior VAE based methods such as MMVAE [19] and MVAE [25] which cannot compute inference queries and only rely on variational approximations for the same.
Deep Learning to Automate Identification and Characterization of Rib Fractures on Chest Computed Tomography Scans

Rib fractures are among the most common traumatic injuries in the United States. Characterizing detailed rib fractures are critical to inform clinical decisions, yet in-depth characterization is rarely performed due to the manual burden required for radiologists to annotate these injuries on computed tomography scans. We aimed to develop a deep learning model that can automate identification and characterization of rib fractures on chest computed tomography scans and guide real-time clinical decision-making. Our dataset comprised 5,000 annotated rib fractures from 660 chest computed tomography scans within the publicly-available RibFrac dataset. Each image (slice CT scan) was labeled by radiologists as containing no, undis-placed, displaced, segmental (2 fractures in one bone), or buckle (partly displaced) fractures. We trained a 2D convolutional UNet model that was trained using loss functions that combine cross-entropy loss and DICE scores, and weighted classes to account for data imbalance. We also calculated the percent displacement for each rib fracture by using a Breadth First Search algorithm and linear regression to compute angular offset between two bones. We achieved binary DICE score of 0.88 and macro F1 of 0.26 on our validation set for discriminating rib fractures. However, our model had challenges classifying rib fractures into specific subtypes. In the absence of ground truth labels for percent rib displacement, we qualitatively confirmed that our model also accurately quantifies percent displacement for fractures with less than and greater than 100% displacement.
Comparative Analysis of Cryptographic Algorithms in Context of Communication: A Systematic Review

- Currently, there are billions of IoT devices connected to each other through the internet, manufacturers and IT engineers uses modern cryptographic tools to protect the networks and the devices from attacks. Communication is an important domain in this modern digital era. Nowadays, fast and reliable communication without breaking continuity between the IoT devices is a big challenge, because there are many cryptographic algorithms have been invented. So, it is difficult to determine which one is better and compatible for the concerned device and useful for communication purposes. We tried in this research paper to fill this gap by making a comparative analysis of frequently used cryptographic algorithms. In this research, we compared 23 algorithms with parameters, such as; key size, message size and execution time.
Unsupervised Aggregation of Deep Models

Ensemble methods are a group of algorithms focusing on aggregating multiple base learners to outperform each individual learner. In the context of classification problems, an ensemble algorithm receives the labels generated by multiple classifiers, and outputs one final aggregated label. Many ensemble methods work in a supervised setting, where we have access to a reasonable amount of labeled training data. However, with the labeled data being expensive and difficult to acquire in many applications, it is a reasonable approach to investigate this problem in an unsupervised setting, where we only have a set of unlabeled data. Another aspect of the aggregation process in ensemble methods is to make decisions based on the structure and performance of each base classifier. As a simple example, if we have 3 classifiers and we know one of them has a 99 percent accuracy while the other two are slightly better than random guessing, we obviously give much more weight to the label produced by the first classifier compared to those of the second and the third classifier. Moreover, our information about the structure and weights of each base classifier can result in a better aggregation. For example, if we have two SVMs, we might be able to use each classifier’s weight matrix to come up with a new aggregated set of weights that outperforms each of the base classifiers. However, this is not the case in many applications where we do not have access to anything that lies within the base models. This, for example, can be due to confidentiality of the models, where the model owner will not provide us with the inner structure of their model. Also, the base classifiers may have totally different structures such that even if we know what lies inside them, we can not use the information to improve the aggregation. In these scenarios, we assume a black-box structure for each base classifier, and only rely on the labels that they generate. Combining the unsupervised setting with the blackbox structure, we seem to be quite in the dark to make any useful aggregation. Note that since we do not have access to labeled data, we cannot evaluate the performance of the base classifiers, and it seems that the best we can do is to perform the simple majority voting, and produce the aggregated label as the label supported by the majority of the base classifiers. However, quite surprisingly, with not very strict assumptions, we propose algorithms to outperform the majority vote in this setting.
Swimming pool detection from Aerial imagery

State-of-the-art object detection methods are in two major groups: one stage like YOLOs and double stage with region proposal based methods like Faster R-CNN. The goal of this project is to detect the swimming pool from aerial imagery using Deep Learning. We study different techniques to detect the swimming pools in the aerial imagery. We implement FasterRCNN as well as other algorithms like Cor-nerNet, which detects an object bounding box as a pair of keypoints, and DynamicHead which aims to unify object detection heads with attentions. These models have demonstrated very good performance on common benchmark like COCO [9]. We experiment on a custom dataset publicly available on the Kaggle platform. We show that CornerNet and DynamicHead models are still better than FasterRCNN model with our dataset focused on the detection of swimming pools.
Diet Selective-Backprop: Accelerating Training in Deep Learning by Pruning Examples

In this paper, we investigated multiple methods of data pruning for training deep neural networks on the CIFAR-100 dataset. Selective-Backprop samples data based on the training loss and drops examples that are unlikely to contribute significantly to the gradient updates; the EL2N score uses early training performance across multiple model runs to prune data points that are easy for the model to learn. We observed that both methods find a similar set of data points that are not “useful” for the training process, and both methods are able to effectively prune data without much loss in accuracy. Next, we showed that Random-Backprop, where we randomly select data to backprop, performs comparatively with Selective-Backprop in most cases and uses much less compute. In addition, we combined Selective-Backprop and EL2N to further reduce computational cost while maintaining similar model performance.
Towards a Notional Machine for Runtime Stacks and Scope: When Stacks Don’t Stack Up

Background and Context. Modern programming is replete with features like closures, callbacks, generators, asynchronous functions, and so on. These features can be subtle in their behavior and interaction with the rest of the language, so students need notional machines that are both accurate and manageable. We specifically focus on stacks and their associated environments, which are key to understanding these features. Objectives. What conceptions do students have of stacks and environments on entry to a tertiary, upper-level programming languages course? What impact does coursework around an interpreter-based approach to programming languages education have on their understanding? What is the value of the tooling we create for students to express notional machine states? Method. Our studies were conducted at two different institutions in a tertiary, upper-level course on programming languages. The intermediate interventions were different, but both used a pre-/post-test format conducted at the beginning and near the end of the semester. We also created a Snap! -based tool to assist in notional machine description. Findings. We found that students have a relatively weak understanding of stacks and environments at entry, and exhibited various misconceptions. At one institution, which primarily relied on interpreters, we found numerous problems persisted at the end of the semester. At the other, which in addition to interpreters also used direct instruction and the Snap! -based tool, students did much better—but still not on aspects that we would have liked to have transferred from interpreters. Implications. Our findings suggest that it is important for other educators to also assess their students’ understanding of stacks and environments, especially in light of modern programming concepts. Assuming similar misconceptions are widespread, we believe the community needs to invest much more effort into notional machines, so that students can better understand the features and programs they are working with. In particular, the “standard” stack presentation is insufficient and in some cases even “harmful” (since it reinforces some misconceptions), and hence must be revised. Finally, our study forces a reconsideration of the learning objectives met by interpreters in programming languages education.
Integration of Business Process Architectures within Enterprise Architecture Approaches: A Literature Review

Abstract Business Process Architecture (BPA) and Enterprise Architecture (EA) require integration to serve as effective management tools. Prior literature reviews have not focused on EA-BPA integration. This work aims to fill this gap by conducting a literature review analyzing and comparing how EA approaches include and consider BPAs. We found most related research that analyzed EA approaches recognize the need of integrating business processes with multiple organizational aspects but do not provide a standalone BPA viewpoint nor offer specific integration guidelines. The article concludes by identifying criteria for adopting an EA approach and highlighting research gaps that suggest the EA-BPA integration field remains largely unexplored and open to further inquiry.
Inertia Estimation of Wind Power Plants Based on the Swing Equation and Phasor Measurement Units

High penetration of wind power plants may have an adverse impact on power systems’ stability by reducing the inertia, and problems like frequency stability could appear due to total inertia in the system being reduced, making the power system more vulnerable to disturbances. However, most recent grid codes include an emulation inertia requirement for wind power plants, because modern wind turbines are capable of providing virtual inertia through power electronic converter controls to improve frequency stability issues. Because of this, it is necessary that the inertia estimation analyze and quantify the impact of the inertia reduction in power systems. In this paper, an implementation of a methodology for the inertia estimation of wind power plants is presented. It is evaluated through synchrophasor measurements obtained from a Real-Time Digital Simulator (RTDS) implementation, using industrial Phasor Measurement Units (PMUs). This methodology is based on the swing equation. Furthermore, a comparison of the results obtained between two professional tools RSCAD and DIgSILENT PowerFactory is performed, in order to evaluate the accuracy and the robustness of the methodology. This methodology is applied for the inertia estimation of an equivalent of the southeast zone of the Mexican power system, where there is a large-scale penetration of wind power plants. The results demonstrate that this methodology can be applied in real power systems using PMUs.
NBER WORKING PAPER SERIES SHAREHOLDER POWER AND THE DECLINE OF LABOR

Shareholder power in the US grew over recent decades due to a steep rise in concentrated institutional ownership. Using establishment-level data from the US Census Bureau’s Longitudinal Business Database for 1982-2015, this paper examines the impact of increases in concentrated institutional ownership on employment, wages, shareholder returns, and labor productivity. Consistent with theory of the firm based on conflicts of interests between shareholders and stakeholders, we find that establishments of firms that experience an increase in ownership by larger and more concentrated institutional shareholders have lower employment and wages. This result holds in both panel regressions with establishment fixed effects and a difference-in-differences design that exploits large increases in concentrated institutional ownership, and is robust to controls for industry and local shocks. The result is more pronounced in industries where labor is relatively less unionized, in more monopsonistic local labor markets, and for dedicated and activist institutional shareholders. The labor losses are accompanied by higher shareholder returns but no improvements in labor productivity, suggesting that shareholder power mainly reallocates rents away from workers. Our results imply that the rise in concentrated institutional ownership could explain about a quarter of the secular decline in the aggregate labor share. Antonio Falato Federal Reserve Board Mail Stop #89 20th Street & Constitution Ave. NW Washington, DC 20551 antonio.falato@frb.gov Hyunseob Kim Federal Reserve Bank of Chicago 230 South LaSalle St. Chicago, IL 60604 United States hyunseob.kim@chi.frb. org Till M. von Wachter Department of Economics University of California, Los Angeles 8283 Bunche Hall MC 147703 Los Angeles, CA 90095 and NBER tvwachter@econ.ucla. edu
Fungal species that cause powdery mildew in greenhouse-grown cucumber and melon in Paraná State, Brazil - doi: 10.4025/actasciagron.v34i3.13999

The powdery mildew caused by Oidium spp. is an important disease for several crops of the Cucurbitaceae family. Although the teleomorphs, Podosphaera xanthii and Golovinomyces cichoracearum, currently have already been described as the causal agents of powdery mildew in Brazil, only P. xanthii is considered the main causal agent of powdery mildew field epidemics. The objective of this work was to identify and determine the prevalence of the species causing powdery mildew in cucumber (Cucumis sativus) and melon (Cucumis melo var. reticulatus) grown in greenhouses in the State of Parana in Brazil. The morphological traits of the conidial stages, such as the presence of fibrosin bodies and a germinative tube, were used to identify the species. Leaves exhibiting high severity of powdery mildew were collected from plants of 13 plastic greenhouses during different seasons in 2003/2004 and in different regions of Parana State. In all environments, a significant prevalence of P. xanthii (80-100%) was observed affecting parthenocarpic or ordinary cucumber and melon. Golovinomyces cichoracearum was observed in six greenhouses, with up to 20% of conidia of this species on the samples.
First-principles calculations of the pressure dependence on the structural and electronic properties of GaN/CrN superlattice

This work provides first-principle calculations to investigate how pressure affects the electronic and magnetic properties of GaN/CrN superlattice. A method based on Full-Potential Linearized Augmented Plane Waves (FP-LAPW) is used exactly as implemented in code Wien2k. It was found that the most favorable phase for GaN/CrN superlattice is the hexagonal wurtzite type, and also that, due to pressure , the superlattice can reach the NaCl phase, with a transition pressure P T1 = 13,5 GPa. Additionally, in the most favorable phase, it was observed that the magnetic moment changes from 0 to 2,1 µ β for a transition pressure P T2 = 25,50 GPa. From the calculations of the density states, it can be stated that the superlattice exhibits a half-metallic behavior at equilibrium pressure. Moreover, at high pressures P > P T2 , the superlattice exhibits a metallic behavior. The evidence indicates that the superlattice may be used in spintronics.
AN ELECTRONICALLY TUNABLE DUAL-BAND FILTERING POWER DIVIDER WITH TUNING DIODES SHARING TECHNIQUE

This paper presents an electronically tunable dual-band filtering power divider (TDFPD) with tuning diodes sharing technique. Two dual-mode tunable resonators (DMTRs) are embedded into a conventional power divider to achieve dual-band tunable bandpass filtering response. The two bands of the proposed TDFPD can be tuned independently. Tuning diodes sharing technique is utilized to reduce the number of tunable diodes. A prototype has been designed and fabricated to validate the proposed design as shown by the good agreement between the measured and simulated results. The measurement shows that the center frequencies of the lower and upper bands can be independently tuned from 1.31 to 1.62 GHz and 2.92 to 3.30 GHz, respectively. Within the passbands, isolation between the two output ports is higher than 16 dB with small phase and magnitude imbalance.
Predicting Li-ion Battery Cycle Life with LSTM RNN

Efficient and accurate remaining useful life prediction is a key factor for reliable and safe usage of lithium-ion batteries. This work trains a long short-term memory recurrent neural network model to learn from sequential data of discharge capacities at various cycles and voltages and to work as a cycle life predictor for battery cells cycled under different conditions. Using experimental data of first 60 - 80 cycles, our model can achieve promising prediction accuracy on test sets of around 80 samples.
The Halo Bias inside Cosmic Voids

The bias of dark matter halos and galaxies is a crucial quantity in many cosmological analyses. In this work, using large cosmological simulations, we explore the halo mass function and halo bias within cosmic voids. For the first time to date, we show that they are scale dependent along the void profile, and provide a predictive theoretical model of both the halo mass function and halo bias inside voids, recovering for the latter a 1% accuracy against simulated data. These findings may help shed light on the dynamics of halo formation within voids and improve the analysis of several void statistics from ongoing and upcoming galaxy surveys.