Miracle-dz/UniFAR-FTUs huggingface dataset:
Active multi-task learning with uncertainty weighted loss for coronary calcium scoring.

PURPOSE The coronary artery calcium (CAC) score is an independent marker for the risk of cardiovascular events. Automatic methods for quantifying CAC could reduce workload and assist radiologists in clinical decision making. However, large annotated datasets are needed for training to achieve very good model performance, which is an expensive process and requires expert knowledge. The number of training data required can be reduced in an active learning scenario, which requires only the most informative samples to be labeled. Multi-task learning techniques can improve model performance by joint learning of multiple related tasks and extraction of shared informative features. METHODS We propose an uncertainty-weighted multi-task learning model for coronary calcium scoring in ECG-gated, non-contrast enhanced cardiac calcium scoring CT. The model was trained to solve the two tasks of coronary artery region segmentation (weak labels) and coronary artery calcification segmentation (strong labels) simultaneously in an active learning scenario to improve model performance and reduce the number of samples needed for training. We compared our model with a single-task U-Net and a sequential-task model as well as other state-of-the-art methods. The model was evaluated on 1275 individual patients in three different datasets (DISCHARGE, CADMAN, orCaScore), and the relationship between model performance and various influencing factors (image noise, metal artifacts, motion artifacts, image quality) was analyzed. RESULTS Joint learning of multiclass coronary artery region segmentation and binary coronary calcium segmentation improved calcium scoring performance. Since shared information can be learned from both tasks for complementary purposes, the model reached optimal performance with only 12% of the training data and one-third of the labeling time in an active learning scenario. We identified image noise as one of the most important factors influencing model performance along with anatomical abnormalities and metal artifacts CONCLUSIONS: Our multi-task learning approach with uncertainty-weighted loss improves calcium scoring performance by joint learning of shared features and reduces labeling costs when trained in an active learning scenario. This article is protected by copyright. All rights reserved.
Emotion Recognition of Individuals with Autism Spectrum Disorder Through Machine Learning and Smart Devices

- This paper proposes a system that analyses the needs of individuals having autism spectrum disorder, and to enable the caretaker to recognize the current emotion in them in order to decrease certain behavioral issues. This can be achieved through smart devices. The smart watch system monitors the emotion through the galvanic skin response and EEG, which measures the Electrical Conductivity of the skin and electrical signals from the scalp of the brain. These signals are digitized. These appropriate emotions are classified through the Support Vector Machine Algorithm. The intensity and the type of emotion are flashed on the caretaker's communication device.
Decentralization Analysis based on Blockchain Technology Related Projects

With the appearance of Bitcoin, the blockchain technology behind it has attracted attention from banks, companies, and entrepreneurs from different fields. Many new blockchain technology related projects and companies are started in financial service and others , such as data storage, supply chain, internet of things (IoT) and copyright protection as the valuable features including decentralization, transparency, security, and immutability. The work of blockchain technology can be used to reduce the costs associated with making decision and improve the efficiency of implementation by means of decentralized autonomous organizations (DAOs) which are the products of decentralization management. This paper is to analyze the pros and cons of decentralization management based on blockchain technology with an empirical study of blockchain projects and companies in different industries for a better understanding of decentralization management, and find better ways of decentralization management in blockchain projects and managing decentralized autonomous organizations.
Investigating the Properties of Higher-Generation Black-Hole Mergers

In this research we investigate further the ﬁxed point convergence of black hole remnant mass retained and remnant spin magnitude for higher generation mergers done by Gálvez Ghersi and Stein [1]. We use the NRSur7dq4Remnant surrogate model to predict the properties of a population of merging binary black hole remnants via the surfinBH a Python package. Many combinations of diﬀerent initial distribution relationships in black hole mass and spin magnitude such as power, uniform, Gaussian, and delta functions were used to test the ﬁxed point convergences. We ﬁnd that aligning the spins –meaning the spins were pointed along the orbital angular momentum in each binary direction– changes the ﬁxed point convergences in remnant mass retained, remnant spin magnitude, and kick velocity.
Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem

Bandits with knapsacks (BwK) is an influential model of sequential decision-making under uncertainty that incorporates resource consumption constraints. In each round, the decision-maker observes an outcome consisting of a reward and a vector of nonnegative resource consumptions, and the budget of each resource is decremented by its consumption. In this paper we introduce a natural generalization of the stochastic BwK problem that allows non-monotonic resource utilization. In each round, the decision-maker observes an outcome consisting of a reward and a vector of resource drifts that can be positive, negative or zero, and the budget of each resource is incremented by its drift. Our main result is a Markov decision process (MDP) policy that has constant regret against a linear programming (LP) relaxation when the decision-maker knows the true outcome distributions. We build upon this to develop a learning algorithm that has logarithmic regret against the same LP relaxation when the decision-maker does not know the true outcome distributions. We also present a reduction from BwK to our model that shows our regret bound matches existing results.
Biologically-Inspired Deep Predictive Learning for Episodic Memory Event Segmentation

Computational modeling for event segmentation in the literature has been directed towards detailing individual regions of the brain, mainly the hippocampus, prefrontal cortex and substantia nigra and the role they play in episodic memory formation. Modern predictive recurrent neural networks, which have been shown to perform well on naturalistic video frame prediction, have the potential to explain critical properties of neuronal responses and perception during event segmentation. However, they have not been examined in this light. This thesis explores the ability of state-of-the-art deep predictive learning approaches to explain properties of event segmentation. We identify similar artificial neural responses in our model and biological neural responses from Intracranial Electroencephalography data. These responses are compared in the context of detecting separation between different events in naturalistic video data input. Finding such parallels could provide insight for improving the biological plausibility of deep learning networks. Furthermore, such computational models can serve as a biological proxy and a testing ground for mechanistic hypotheses that bridge neural computation to observable behavior.
A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output

This paper presents a fine-grained test suite for the language pair German–English. The test suite is based on a number of linguistically motivated categories and phenomena and the semi-automatic evaluation is carried out with regular expressions. We describe the creation and implementation of the test suite in detail, providing a full list of all categories and phenomena. Furthermore, we present various exemplary applications of our test suite that have been implemented in the past years, like contributions to the Conference of Machine Translation, the usage of the test suite and MT outputs for quality estimation, and the expansion of the test suite to the language pair Portuguese–English. We describe how we tracked the development of the performance of various systems MT systems over the years with the help of the test suite and which categories and phenomena are prone to resulting in MT errors. For the first time, we also make a large part of our test suite publicly available to the research community.
mwetoolkit-lib: Adaptation of the mwetoolkit as a Python Library and an Application to MWE-based Document Clustering

This paper introduces the mwetoolkit-lib, an adaptation of the mwetoolkit as a python library. The original toolkit performs the extraction and identification of multiword expressions (MWEs) in large text bases through the command line. One of the contributions of our work is the adaptation of the MWE extraction pipeline from the mwetoolkit, allowing its usage in python development environments and integration in larger pipelines. The other contribution is the execution of a pilot experiment aiming to show the impact of MWE discovery in data professionals’ work. This experiment found that the addition of MWE knowledge to the Term Frequency-Inverse Document Frequency (TF-IDF) vectorization altered the word relevance order, improving the linguistic quality of the clusters returned by k-means method.
RU-ADEPT: Russian Anonymized Dataset with Eight Personality Traits

Social media has provided a platform for many individuals to easily express themselves naturally and publicly, and researchers have had the opportunity to utilize large quantities of this data to improve author trait analysis techniques and to improve author trait profiling systems. The majority of the work in this area, however, has been narrowly spent on English and other Western European languages, and generally focuses on a single social network at a time, despite the large quantity of data now available across languages and differences that have been found across platforms. This paper introduces RU-ADEPT, a dataset of Russian authors’ personality trait scores–Big Five and Dark Triad, demographic information (e.g. age, gender), with associated corpus of the authors’ cross-contributions to (up to) four different social media platforms–VKontakte (VK), LiveJournal, Blogger, and Moi Mir. We believe this to be the first publicly-available dataset associating demographic and personality trait data with Russian-language social media content, the first paper to describe the collection of Dark Triad scores with texts across multiple Russian-language social media platforms, and to a limited extent, the first publicly-available dataset of personality traits to author content across several different social media sites.
Bistatic ISAR Sparse Aperture Maneuvering Target Translational Compensation Imaging Algorithm

For bistatic inverse synthetic aperture radar (Bi-ISAR), the non-uniform motion state of maneuvering target and the time-varying bistatic angle make the traditional imaging method of moving target face the problem of translation compensation, and the traditional translation compensation method is not suitable for the return wave in the case of sparse aperture. In this paper, a compensation imaging method combining two-dimension joint linearized Bregman iteration and image contrast search is proposed. The translation compensation problem can be transformed into two-dimension joint compressed sensing sparse reconstruction and moving target motion parameter estimation. The proposed algorithm makes use of the gain of echo two-dimension compression, greatly improves the accuracy of translation compensation and the quality of target image and has stronger robustness to noise. The processing results of simulation data verify the effectiveness and superi-ority of the algorithm.
Database Optimizers in the Era of Learning

In this tutorial, we review advances made recently in a decades-old problem, namely query optimization. Along with the traditional optimization techniques many of which are still being used successfully in production, various techniques inspired by AI, such as genetic algorithms, had been explored as early as in the '90s as potential solutions, without gaining at that time much traction, especially in commercial offerings. More recently, with the rapid progress on learning, several approaches have brought this technology within the core of a database management system (DBMS) aiming at developing scalable, learning solutions to all challenging components of the system optimizer. We present the early efforts in this area, describe advancements, limitations and open issues, and discuss future research directions.
Developing tools for audio-visual speech recognition

Audio-visual speech recognition combines automatic speech recognition with lip reading to improve recognition performance. This project focused on the lip reading aspect, which is a challenging task due to many sources of variation found in real-world environments. The experiments investigated how well a pre-trained lip reading model can generalise to a new dataset. The model, based on the transformer architecture, was evaluated on the LRS2 and LRS3-TED datasets. Further training using LRS3-TED was performed to ﬁne-tune the model, experimenting with different combinations of batch sizes and learning rates. The best ﬁne-tuned model achieved a small improvement in Word Error Rate (WER) on the LRS3-TED test dataset, achieving 78.0% WER. Future work is suggested to further improve performance, including using a TED language model and training for more epochs.
Localized growth drives spongy 1 mesophyll morphogenesis 2

The spongy mesophyll is a complex, porous tissue found in plant leaves that enables 17 carbon capture and provides mechanical stability. Unlike many other biological tissues, which 18 remain conﬂuent throughout development, the spongy mesophyll must develop from an initially 19 conﬂuent tissue into a tortuous network of cells with a large proportion of intercellular airspace. 20 How the airspace in the spongy mesophyll develops while the cells remain mechanically stable 21 remains unknown. Here, we used computer simulations of deformable particles to develop a 22 purely mechanical model for the development of the spongy mesophyll tissue. By stipulating that 23 (1) cell perimeter grows only near voids, (2) cells both form and break adhesive bonds, and (3) the 24 tissue pressure remains constant, the computational model was able to recapitulate the 25 developmental trajectory of the microstructure of the spongy mesophyll observed in Arabidopsis 26 thaliana leaves. Robust generation of pore space in the spongy mesophyll requires a balance of 27 cell growth, adhesion, stiffness and tissue pressure to ensure cell networks remain both porous 28 yet mechanically robust. The success of this mechanical model of tissue growth and porosity 29 evolution suggests that simple physical principles can coordinate and drive the development of 30 complex plant tissues like the spongy mesophyll. 31
Semantic Relations between Text Segments for Semantic Storytelling: Annotation Tool - Dataset - Evaluation

Semantic Storytelling describes the goal to automatically and semi-automatically generate stories based on extracted, processed, classified and annotated information from large content resources. Essential is the automated processing of text segments extracted from different content resources by identifying the relevance of a text segment to a topic and its semantic relation to other text segments. In this paper we present an approach to create an automatic classifier for semantic relations between extracted text segments from different news articles. We devise custom annotation guidelines based on various discourse structure theories and annotate a dataset of 2,501 sentence pairs extracted from 2,638 Wikinews articles. For the annotation, we developed a dedicated annotation tool. Based on the constructed dataset, we perform initial experiments with Transformer language models that are trained for the automatic classification of semantic relations. Our results with promising high accuracy scores suggest the validity and applicability of our approach for future Semantic Storytelling solutions.
Finnish Hate-Speech Detection on Social Media Using CNN and FinBERT

There has been a lot of research in identifying hate posts from social media because of their detrimental effects on both individuals and society. The majority of this research has concentrated on English, although one notices the emergence of multilingual detection tools such as multilingual-BERT (mBERT). However, there is a lack of hate speech datasets compared to English, and a multilingual pre-trained model often contains fewer tokens for other languages. This paper attempts to contribute to hate speech identification in Finnish by constructing a new hate speech dataset that is collected from a popular forum (Suomi24). Furthermore, we have experimented with FinBERT pre-trained model performance for Finnish hate speech detection compared to state-of-the-art mBERT and other practices. In addition, we tested the performance of FinBERT compared to fastText as embedding, which employed with Convolution Neural Network (CNN). Our results showed that FinBERT yields a 91.7% accuracy and 90.8% F1 score value, which outperforms all state-of-art models, including multilingual-BERT and CNN.
Challenges and Design Opportunities in Data Analysis for ML-Empowered Scientific Inquiry - Insights from a Teacher Professional Development Study

: The increasing impact of machine learning (ML) on real-world scientific investigations demands a shift to teaching ML-empowered scientific inquiry to familiarize younger students with authentic science practices. Evidence from previous studies has suggested that glyph-based data visualization is promising for making multidimensional data analysis accessible for novice learners. Little research, however, has examined the contextualized challenges of applying glyphs in K-12 ML-empowered scientific inquiry. Since teachers play a critical role at the frontier of technology integration for learning, as an initial attempt to address this research gap, we investigated the pattern recognition and interpretation behaviors of 18 K-12 STEM teachers when engaged in ML-empowered scientific inquiry during a professional development workshop. Findings confirm that teachers identified and interpreted ML-revealed patterns for scientific hypothesis formulation while it is challenging for novices to analyze and synthesize different patterns conceptually and extensively for more sophisticated inference. Underlying design opportunities and implications are discussed.
This paper is included in the Proceedings of the 2022 USENIX Annual Technical Conference. PilotFish: Harvesting Free Cycles of Cloud Gaming with Deep Learning Training

Cloud gaming services have become important workloads in cloud datacenter. However, our investigation shows that a cloud gaming service cannot saturate the modern cloud GPUs. One way to improve the GPU utilization is to co-locate multiple workloads within one GPU, which is challenging for cloud gaming due to its highly fluctuated and unpredictable GPU usage pattern. In this paper, we present PilotFish, a high-performance system that harvests the free GPU cycles of cloud gaming with deep learning (DL) training, while incur-ring almost zero interference to cloud gaming. We co-locate DL training jobs with cloud gaming, because they have stable and predictable workloads and have no strict latency requirement. In more detail, PilotFish captures the idle periods of the game’s GPU usage with its low-overhead instrumentation to graphic libraries in sub-millisecond granularity. To avoid the potential interference to cloud gaming, PilotFish schedules training computation kernels only when they can finish before the idle GPU periods, and preempts straggler kernels running longer than expected. Our evaluation on popular cloud games and DL models shows PilotFish can harvest up to 85.1% of the idle GPU time from cloud gaming with no interference.
Transformers and Graph Neural Networks for Spell Checking

Spell checking is a general term for methods that detect or correct spelling errors in natural language text. Such methods can not only help with correcting human written text, they can also improve the performance and robustness of natural language processing systems dealing with misspelled text, when applied as a preprocessing or postprocessing step. We study the usage of Transformers and graph neural networks for spelling error detection on sequence and word level, as well as spelling error correction. We show that open vocabulary sequence-to-sequence Transformers can perform well for spelling correction. We also experiment with ways to represent text not as sequences of tokens, but rather as word graphs to be processed with graph neural networks. As a simple way to boost spelling error detection performance we propose to enrich misspelled input texts with additional word features. Our models perform better than most of the strong baselines for both artificially generated benchmarks and benchmarks built from real data. They achieve word accuracies of over 98% for word-level spelling error detection and F1 scores of over 80% for spelling error correction, while running fast enough to be used in practice.
Expression of Cold-Inducible Proteins in Rat Spinal Cord Subjected to Systemic Hypothermia

Introduction: Traumatic spinal cord injury is the main cause of motor disability in developed and underdeveloped countries, being a priority interest to the WHO. The effect of hypothermia on the expression of CIRBP (cold-activated protein) in the anterior grey column of 60-day-old male albino Sprague-Dawley rats was studied at the structural and biochemical levels and proposed as a possible therapeutic approach. Materials and Methods: 24 rats were randomly divided into two groups; normothermia (n = 6), at 24º C, and hypothermia, (n = 18) at 8º C for 180 minutes and euthanized at 12, 24, and 48 h post-treatment. Western blot and immunohistochemistry for CIRBP were used. Results: A progressive increase in the expression of CIRBP was observed from 12 to 48 hours, with statistically significant values after 24 and 48 hours compared to controls. Conclusion: This experimental model demonstrated efficacy, accessibility, and economy to generate systemic hypothermia, which provides a novel range of therapeutic strategies. The increase in the expression of cold-inducible proteins in the rats’ spinal cords allows us to study the benefit of hypothermia at the molecular level for the first time, being of utmost importance for therapeutic studies in spinal cord injuries. Se dividió a 24 ratas en dos grupos: normo-termia a 24 ºC (n = 6) e hipotermia a 8 ºC (n = 18), durante 180 min, sacrificadas a las 12, 24 y 48 h después del tratamiento. Se utilizó Western blot e inmunohistoquímica para la CIRBP. Resultados: Se observó un aumento progresivo de la expresión de la CIRBP de 12 a 48 h en las motoneuronas del asta anterior. Los valores fueron estadísticamente significativos entre los grupos de 24 h y 48 h comparados con los de los controles. Conclusiones: Este modelo experimental resultó eficaz, accesible y económico para generar hipotermia sistémica y abre un abanico de estrategias terapéuticas. El aumento en la expresión de las proteínas inducibles por frío en la médula espinal de ratas permite, por primera vez, estudiar el beneficio que aporta la hipotermia a nivel molecular, lo que resulta de suma importancia para estudios de terapéuticas en las lesiones medulares.