Miracle-dz/UniFAR-FTUs huggingface dataset:
A Privacy Preserved Image-to-Image Translation Model in MRI: Distributed Learning of WGANs

In this project, we introduce a distributed training approach for Generative Adversarial Networks (GANs) on Magnetic Resonance Imaging (MRI) tasks. In our distributed framework, we have n discrimnator and a single generator. We first generate fake images via the generator, which are fed to the discriminator. In addition to the fake images, we uniformly distribute the real images into n discriminators. Each discriminator first computes a gradient using the local data revealed to itself. Then, we update a global parameter via the average of the computed gradients. With this approach, since we distribute both the data and processing to several discriminators, we reduce the computational complexity and storage demands for each discriminator. Moreover, we preserve the privacy thanks to our novel training aproach that only needs local data. 1 Motivation & Related Work In recent years, Generative Adversarial Networks (GAN) have been widely used in Magnetic Resonance Imaging (MRI) tasks such as image-to-image translation and image reconstruction. GANs are a class of methods to learn generative models based on a game theoretical point of view [1]. Particularly, in these networks, we have a generator that generates sample from the data distribution and a discriminator that distinguishes the generated samples from the real ones. Even though GANs have shown considerable performance improvement in several computer vision and medical imaging tasks [2, 3], their training is difficult due to the mode collapse issue where the generator overfits to the data during training. To circumvent this issue, Wasserstein GANs (WGANs) are introduced [4]. WGANs mitigate the mode collapse issue of GANs, and offer improved training stability, and therefore are widely used in many machine learning tasks [4]. However, even WGANs might suffer from training problems since all GAN architectures, including WGANs, demand a large amount of high dimensional samples to be adequately trained, which causes complexity and storage issues [5, 6]. This problem is even more exacerbated in medical imaging applications, where the data dimensionality and the number of samples is extremely high. Furthermore, medical data privacy regulations inhibit utilizing patient data in a centralised manner. To mitigate such problems, we employ a distributed approach, where we distribute the training samples to different processing units with low storage and processing capabilities. After training a relatively small number of samples in each unit separately, we collect and merge the training results to obtain a final result. Therefore, we are able to process MRI images without suffering from complexity and storage issues. Furthermore, since data is held locally in different units, we will also preserve data privacy. Our goal in this work is to achieve a distributed approach that has the following advantages: • Privacy preservation • Low computational complexity • Low storage demand
Recurrent Neural Physics Engine

The humans brain possesses intuitive understandings of physics [1] learned during infanthood [2], allowing them to reason about the world using mental simulations [3]. For example, when given tasks to predict the final position of an object given its initial state, humans simulate possible trajectories that might occur given the environment and starting configurations [4, 5]. Understanding the neural architectures and computations that may lead to accurate physical simulations that reflect the processing of the human brain.
Typeface Semantic Attribute Prediction from Rasterized Font Representations

Typefaces comprise an important component of the aesthetic quality of any written work. With the rise of printers and digital word processors has come a tremendous diversity of typefaces, each slightly different in visual effect and design. With this growing number of choices, there has been an increasing need to intelligently categorize and characterize typefaces for searching, grouping, pairing, and more. Most approaches thus far have categorized typefaces by their font styles—for example, ‘bold’ or ‘italic’—as well as other typographic attributes, such as ‘all-capitals’ or ‘cursive’. However, while these characteristics can be descriptive for some fonts, these aspects fail to capture some of the more aesthetic qualities—what we call semantic attributes—that we as humans associate with typefaces in abstract senses, such as ‘artistic’, ‘boring’, or ‘gentle’, which may be desirable when one is looking for typefaces that have particular semantic attributes. We are thus interested in applying machine learning to build on existing work for generalizing the representation and characterization of typeface semantic attributes beyond existing semantic attribute datasets.
Sensor-based Dog Abnormal Behavior Detection

The number of dog-owning households has been increasing in the recent years. Although owners give attention and care to their companions, many of them are forced to leave them alone at home for most of the day. This ends up negatively affecting the dogs’ behavior, leading them eventually to separation anxiety. Hence, detecting the abnormal behavior of freely moving dogs alone at home is necessary. In this paper, we propose a time series data based system that automatically detects dogs abnormal behaviors using LSTM. The time-series data are collected from a wearable device with tri-axial accelerometer at a frequency of 50Hz. The extracted features from each sequence are fed as input to an LSTM network that classifies the dog’s abnormal behaviors. The experimental results show that our approach achieves a good performance of about 0.96 in precision, recall and f-1 score.
Selecting an Application-centric Unified Monitoring solution

Application-centric Unified Monitoring is an excellent strategy for achieving comprehensive, intelligent visibility into the software that drives the enterprise’s critical business transactions. This strategy focuses on discovering, monitoring and managing these essential transactions end to end from the end-user client app to the individual software applications and supporting infrastructure that constitute the entire software portfolio. This makes it much easier to identify and correct issues even before they start, and to also quickly determine the root cause of the application performance problems. The result is smoother application operations and more satisfied users.
Brown University at TREC Deep Learning 2019

This paper describes Brown University's submission to the TREC 2019 Deep Learning track. We followed a 2-phase method for producing a ranking of passages for a given input query: In the the first phase, the user's query is expanded by appending 3 queries generated by a transformer model which was trained to rephrase an input query into semantically similar queries. The expanded query can exhibit greater similarity in surface form and vocabulary overlap with the passages of interest and can therefore serve as enriched input to any downstream information retrieval method. In the second phase, we use a BERT-based model pre-trained for language modeling but fine-tuned for query - document relevance prediction to compute relevance scores for a set of 1000 candidate passages per query and subsequently obtain a ranking of passages by sorting them based on the predicted relevance scores. According to the results published in the official Overview of the TREC Deep Learning Track 2019, our team ranked 3rd in the passage retrieval task (including full ranking and re-ranking), and 2nd when considering only re-ranking submissions.
Simulation of terrestrial laser scanning errors occurring during deformation monitoring

This study estimates range and spatial distribution of terrestrial laser scanning (TLS) errors occurring at deformation monitoring. Simulations of various deformation monitoring scenarios were developed and conducted. To model realistic monitoring conditions, the simulated spatial data are distorted by introduced noise. Also factors influencing TLS results such as environment, surface properties etc. affecting the accuracy of terrestrial laser scanning are also accounted for. The simulation results are approbated with a practical case study, where vertical deformations at a static bridge load tests were determined by actual TLS survey, verified also with precise conventional point-wise surveying techniques. The modelled accuracy was estimated to be ±3.82 mm and ±2.99 mm at two simulated scanning locations, which are slightly larger than that achieved from the actual deformation monitoring results.
Compositional Event Detection Using Weak Supervision

Despite recent advances in computer vision, detecting domain-specific events in videos remains a challenging task due to the amount of labelled data required for building models to detect such events. Rekall [1] is a system which detects compositional events by using off-the-shelf object and face detectors to generate spatiotemporal knowledge of the entities present in videos. The task of detecting abstract events then reduces to writing queries over the generated spatiotemporal knowledge. We investigate whether the outputs of Rekall queries can be supplied as weak supervision to build adequate, fresh machine learning models. Our deep learning experiments involve transfer learning through fine-tuning pre-trained ResNet-50 models. Increasing the amount of weak supervision data supplied will eventually produce models that outperform those trained on lesser amounts of ground-truth labelled (i.e. human labelled) data. We also apply classical ML techniques like GDA and SVM to this problem. Unsurprisingly, we find that these techniques are not able to perform as well as deep learning models, although do perform relatively well. Furthermore, the models converged relatively quickly and increasing the amount of weak supervision data supplied to these algorithms had no material difference on model performance.
Survey on Current Trends and Technologies of Semantic Web Mining

: Semantic Web is a product of Web 2.0 that is supported with automated semantic agents for processing user data to help the user on ease of use and personalization of services. Web Mining is an application of data mining which focuses on discovering patterns from Web logs and data. The semantic structure can be built with the pattern or relation results discovered via Web Mining. The integration of the two fast-developing scientific research areas Semantic Web and Web Mining is known as Semantic Web Mining. The huge increase in the amount of Semantic Web Data became a perfect target for many researchers to apply Data Mining techniques on it. The objective of this paper is to provide an outline of Semantic Web and Web mining technologies and to give a survey on current researches in the area of Semantic Web Mining.
A Distributed Sentiment Analysis Development Environment

This document describes a work-in-progress development of a distributed sentiment analysis system being developed for industrial usage. This project seeks to leverage advances in open source development in natural language processing and understanding, distributed databases, and distributed processing frameworks, as much as possible to advance the development process and to utilize the most recent technology. This paper describes the tools, technologies, and data sources we’ve utilized as well as issues we’ve encountered in their integration.
RFT-based Analysis of Mobility Performance of Hopping Rover on Soft Soil for Planetary Exploration

Future planetary exploration requires rovers to exhibit advanced surface mobility. Various remote observations reveal that a number of scientifically significant sites are located on rough terrain such as cliff, steep slopes, or moon hole. Instead of improving wheel’s performance, this paper studies the performance of a contact-based hopping mechanism on soft soil terrain. In order to design the concept of a jumping mechanism for challenging planetary environments, the movement of rovers on loose regolith is studied based on Resistive Force Theory (RFT). Particularly, this paper analyzes the effect of stepper angles against the ground plane through RFT-based simulation and experiments. Vertical jumping tests were performed on silica sand. The trend of simulation and experiment is consistent, while small numerical errors can be observed. The results show that RFT is effective to get trend of mobility performance on soft soil.
Lifestyle of the Octoradiate Eoandromeda in the Ediacaran

Abstract. The octoradiate Eoandromeda Tang et al. from the Ediacaran of South China and South Australia is poorly understood and there are different interpretations of its morphology and paleoecology. The carbonaceous compression known as Eoandromeda, which is collected in black shales of the upper Doushantuo Formation in northeastern Guizhou, South China, shows various patterns and complex structures. Eoandromeda is interpreted as an umbrella-shaped metazoan, with a dome-shaped polar structure on the top of its body, eight dextrally spiraling arms and tapering skirts. The spiral arms are differentiated into a main segment consisting of rigid and thick masses and a distal segment consisting of flexible and thin masses. Thus, we consider that the spiral arms may have consisted of gelatinous masses and primarily grew in their distal segments (approximately the tapering skirts). The numerous feather-like structures on the platy spiral arms are regularly arranged into two longitudinal rows. We believe that Eoandromeda lived in a primitive “Seabed Grassland” with an abundance and diversity of macroalgae and was capable of swimming in the water column by flapping its feather-like structures. Based on measurements of the maximum diameter of the disk-shaped compression and the maximum width of the spiral arms, Eoandromeda can be divided into a juvenile stage (< 10 mm diameter) that has not been found, an adult stage (10–30 mm diameter) with a high growth rate in the width of the spiral arms, and a senescent stage (> 30 mm diameter) with a slow growth rate in the width of the spiral arms. Fully grown Eoandromeda, with thick and rigid spiral arms, may have mostly stayed on the sediment surface, temporarily swimming to seek new habitat or prey. The juveniles, better swimmers, may have had a soft body with soft and thin arms, unlikely to be preserved, and may have been easily transported by water currents.
Complementary description of Africoseius lativentris and placement of Africoseius in Podocinidae (Acari, Mesostigmata) based on molecular and morphological evidences

Abstract Morphological details of specimens collected from several localities in Brazil and identified as Africoseius lativentris (Karg 1982) are provided. The taxonomic position of Africoseius Krantz, 1962 has been debated over the years, with repeated changes in its familial placement. A phylogenetic study based on 18S and 28S rDNA sequences of the Brazilian population of A. lativentris collected at Jaboticabal, São Paulo State, and similar data of 70 taxa representing 11 families of Gamasina indicated that Africoseius is a member of the well-supported Podocinidae (sensu Lindquist et al. 2009) clade. The main morphological similarities and differences between Africoseius and the group of species until now placed in Podocinidae sensu Lindquist et al. (2009) are listed. Apomorphic characteristics uniting those taxa include the hypotrichous condition of tibia I (eight and nine setae instead of ten or more in other free-living Gamasina) and the insertions of av2 and pv2, considerably more distal on tarsi II and III than in the majority of the free-living Gamasina. Within Podocinidae, a new subfamily, Africoseiinae, is proposed, based on uniquely apomorphic characteristics of the setae ad4 and pd4 (sensu Evans 1969) of tarsi II–IV absent, and setae av4 and pv4 of same basitarsi long, incurved and close to each other and to a posterior longitudinal extension of the peripodomeric suture and on the attenuated form of the lateral (rather than the medial) hypostomatic setae. This subfamily is currently represented by Africoseius areolatus Krantz and Africoseius lativentris (Karg 1982), while all other presently known species of the family are now placed in the subfamily Podocininae.
Early perfusion changes in multiple sclerosis patients as assessed by MRI using arterial spin labeling

Background Gadolinium-perfusion magnetic resonance (MR) identifies gray matter abnormalities in early multiple sclerosis (MS), even in the absence of structural differences. These perfusion changes could be related to the cognitive disability of these patients, especially in the working memory. Arterial spin labeling (ASL) is a relatively recent perfusion technique that does not require intravenous contrast, making the technique especially attractive for clinical research. Purpose To verify the perfusion alterations in early MS, even in the absence of cerebral volume changes. To introduce the ASL sequence as a suitable non-invasive method in the monitoring of these patients. Material and Methods Nineteen healthy controls and 28 patients were included. The neuropsychological test EDSS and SDMT were evaluated. Cerebral blood flow and bolus arrival time were collected from the ASL study. Cerebral volume and cortical thickness were obtained from the volumetric T1 sequence. Spearman’s correlation analyzed the correlation between EDSS and SDMT tests and perfusion data. Differences were considered significant at a level of P < 0.05. Results Reduction of the cerebral blood flow and an increase in the bolus arrival time were found in patients compared to controls. A negative correlation between EDSS and thalamus transit time, and between EDSS and cerebral blood flow in the frontal cortex, was found. Conclusion ASL perfusion might detect changes in MS patients even in absent structural volumetric changes. More longitudinal studies are needed, but perfusion parameters could be biomarkers for monitoring these patients.
Duo in a Mystical Realm—Nuclear Lipid Droplets and the Inner Nuclear Membrane

The lipid droplet (LD) is a cytoplasmic organelle, but it also exists in the nucleus under some conditions or in some cell types. New studies have revealed that nuclear LDs do not occur by haphazard entry of cytoplasmic LDs. Instead, they are generated by specific mechanisms that are increasingly understood. The inner nuclear membrane (INM) plays a critical role in nuclear LD formation in both mammalian hepatocytes and budding yeast, although in significantly different ways. Hepatocyte nuclear LDs derive from precursors of very low-density lipoprotein lacking apolipoprotein B-100, which form in the endoplasmic reticulum lumen and accumulate in intranuclear extensions of the perinuclear space called type I nucleoplasmic reticulum. In contrast, nuclear LDs in yeast are generated by triglyceride synthesized in the INM. Nuclear LDs in hepatocytes and budding yeast are both instrumental in the regulation of phospholipid synthesis; however, again they function in different ways. As the full functional importance is as yet unknown, the close relationship of nuclear LDs and the INM is an attractive target of research from both physiological and pathological perspectives.
Low cogging torque design and simplified model-free predictive current control for radial-flux dual three-phase permanent magnet electric machines

This study presents two improved designs of eccentric-shaped permanent magnets and teeth-shaped stators in radial-flux dual three-phase permanent magnet electric machines to reduce cogging torque and torque ripple. The finite element analysis (ANSYS Electromagnetics) has been adopted in simulation, and real radial-flux dual three-phase permanent magnet electric machines have been fabricated in experiment to verify the study. Using the radial-flux dual three-phase permanent magnet electric machines in electric machine systems can improve the reliability and obtain higher output torque. In electrical drives and control, a simplified model-free predictive current control method has been proposed and implemented to drive the radial-flux dual three-phase permanent magnet electric machines, and the control law has been achieved by a TMS320F28377S microcontroller of Texas Instruments. The simplified model-free predictive current control method is senseless to parameter variations and back electromotive force of the permanent magnet electric machines, and only needs current sensors to detect six-phase current. The optimal one has been chosen by 14 various switching modes, which has the minimum cost function, and then the converter can be directly driven and controlled in the next sampling period. The features of the simplified model-free predictive current control method can reduce the algorithm calculation and avoid the defect of conventional model-based predictive current control scheme. A proportional-integral speed controller has also been designed to achieve the speed response of the fixed-speed tracking effect. Finally, the feasibility and effectiveness of the proposed simplified model-free predictive current control method for the dual three-phase permanent magnet electric machines can be verified in the experimental and quantitative results.
Cultivating Innovative Pragmatic Cluster-Randomized Registry Trials Embedded in Hemodialysis Care: Workshop Proceedings From 2018

Hemodialysis is a life-sustaining treatment for persons with kidney failure. However, those on hemodialysis still face a poor quality of life and a short life expectancy. High-quality research evidence from large randomized controlled trials is needed to identify interventions that improve the experiences, outcomes, and health care of persons receiving hemodialysis. With the support of the Canadian Institutes of Health Research and its Strategy for Patient-Oriented Research, the Innovative Clinical Trials in Hemodialysis Centers initiative brought together Canadian and international kidney researchers, patients, health care providers, and health administrators to participate in a workshop held in Toronto, Canada, on June 2 and 3, 2018. The workshop served to increase knowledge and awareness about the conduct of innovative, pragmatic, cluster-randomized registry trials embedded into routine hemodialysis care and provided an opportunity to discuss and build support for new trial ideas. The workshop content included structured presentations, facilitated group discussions, and expert panel feedback. Partnerships and promising trial ideas borne out of the workshop will continue to be developed to support the implementation of future large-scale trials.