Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of cartilage and bone morphological models of the ankle joint derived from different medical imaging technologies.

Background Accurate geometrical models of bones and cartilage are necessary in biomechanical modelling of human joints, and in planning and designing of joint replacements. Image-based subject-specific model development requires image segmentation, spatial filtering and 3-dimensional rendering. This is usually based on computed tomography (CT) for bone models, on magnetic resonance imaging (MRI) for cartilage models. This process has been reported extensively in the past, but no studies have ever compared the accuracy and quality of these models when obtained also by merging different imaging modalities. The scope of the present work is to provide this comparative analysis in order to identify optimal imaging modality and registration techniques for producing 3-dimensional bone and cartilage models of the ankle joint. Methods One cadaveric leg was instrumented with multimodal markers and scanned using five different imaging modalities: a standard, a dual-energy and a cone-beam CT (CBCT) device, and a 1.5 and 3.0 Tesla MRI devices. Bone, cartilage, and combined bone and cartilage models were produced from each of these imaging modalities, and registered in space according to matching model surfaces or to corresponding marker centres. To assess the quality in overall model reconstruction, distance map analyses were performed and the difference between model surfaces obtained from the different imaging modalities and registration techniques was measured. Results The registration between models worked better with model surface matching than corresponding marker positions, particularly with MRI. The best bone models were obtained with the CBCT. Models with cartilage were defined better with the 3.0 Tesla than the 1.5 Tesla. For the combined bone and cartilage models, the colour maps and the numerical results from distance map analysis (DMA) showed that the smallest distances and the largest homogeneity were obtained from the CBCT and the 3.0 T MRI via model surface registration. Conclusions These observations are important in producing accurate bone and cartilage models from medical imaging and relevant for applications such as designing of custom-made ankle replacements or, more in general, of implants for total as well as focal joint replacements.
Analysis and Determination of Partial Discharge Type using Statistical Techniques and Back Propagation Method of Artificial Neural Network for Phase-Resolved Data

-Partial discharge (PD) patterns are significant tool for the diagnosis of high voltage (HV) insulation systems. Human experts can discover possible insulation defects in various representations of the PD data. One of the most widely used representations is phase-resolved PD (PRPD) patterns. In order to ensure reliable operation of HV equipment, it is crucial to relate the observable statistical characteristics of PDs to the properties of the defect and ultimately to determine the type of the defect. In present work, we have obtained and analysed combined use of PRPD patterns (φ-q), (φ-n) and (n-q) using statistical parameters such as skewness and kurtosis for two patterns viz. (φ-q) and (φ-n) along with mean, standard deviation, variance, skewness and kurtosis for (n-q) to detect type of PD and we have verified the obtained results by providing obtained statistical parameters as an input for training of artificial neural network (ANN) in MATLAB tools.
Investigation into rule-based inferential modelling and prediction with application in healthcare

Sepsis is a serious disease that can cause death. It is important to evaluate patientsâ€™ sepsis risk during diagnostic decisions within the early stages after the detection of the presence of symptoms that suggest sepsis. The conventional approach to sepsis diagnosis is blood culture, which may takes several days. The approaches based on statistics and machine learning for sepsis diagnosis can be cheap, fast, and non-invasive. There are a wide variety of approaches based on statistics and machine learning that can be used for sepsis diagnosis, but these approaches have some issues, e.g. interpretability and overfitting, which may affect their performance in sepsis diagnosis. To address some of the issues in the popular approaches to disease diagnosis, we proposed a new approach, i.e., the rule-based inferential modelling and prediction. This approach integrates statistical analysis, belief rule-base inference, and maximum likelihood prediction, and machine learning. The referential-value-based data discretisation technique used in this approach is closer to reality and better at reducing information loss and distortion, as well as better at presenting the characteristics of the data, compared to other data-processing techniques. We can use the belief rule-base inference to clearly analyse the relationship between system inputs and outputs. An interdependence index is used in this approach to quantify the interdependence between input variables. An adapted genetic algorithm is used in this approach for the bilevel optimisation of models. The stopping criteria for the training process of the models used in this approach help us find the optimal structure of the models, which generally achieves balance between accuracy and complexity. Compared to the complex classifiers for disease diagnosis, e.g., ensemble, ANN, and random forest, the classifier based on the maximum likelihood evidential reasoning (MAKER) framework established by the rule-based inferential modelling and prediction approach is more interpretable. The performance of the MAKER-based classifiers constructed by this approach for sepsis diagnosis is generally better than the majority of alternative models for sepsis diagnosis, and similar to the performance of ensemble: bagged trees, which is a complex model. The MAKER-based classifier is an outstanding classifier for classical data sets: the Banana data set, Habermanâ€™s survival data set, and the Iris data set, and it generally performs better than other interpretable classifiers, e.g., complex tree, logistic regression, and naA¯ve Bayes.
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function

We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss function can change arbitrarily between episodes. The transition function is fixed but unknown to the learner, and the learner only observes bandit feedback (not the entire loss function). For this problem we develop no-regret algorithms that perform asymptotically as well as the best stationary policy in hindsight. Assuming that all states are reachable with probability $\beta > 0$ under any policy, we give a regret bound of $\tilde{O} ( L|X|\sqrt{|A|T} / \beta )$, where $T$ is the number of episodes, $X$ is the state space, $A$ is the action space, and $L$ is the length of each episode. When this assumption is removed we give a regret bound of $\tilde{O} ( L^{3/2} |X| |A|^{1/4} T^{3/4})$, that holds for an arbitrary transition function. To our knowledge these are the first algorithms that in our setting handle both bandit feedback and an unknown transition function.
Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering

We propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) that (a) maximizes the relevance of the selected sentences, (b) minimizes the overlap between the selected facts, and (c) maximizes the coverage of both question and answer. This unsupervised sentence selection can be coupled with any supervised QA model. We show that the sentences selected by our method improve the performance of a state-of-the-art supervised QA model on two multi-hop QA datasets: AI2’s Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC). We obtain new state-of-the-art performance on both datasets among systems that do not use external resources for training the QA system: 56.82% F1 on ARC (41.24% on Challenge and 64.49% on Easy) and 26.1% EM0 on MultiRC. Our justification sentences have higher quality than the justifications selected by a strong information retrieval baseline, e.g., by 5.4% F1 in MultiRC. We also show that our unsupervised selection of justification sentences is more stable across domains than a state-of-the-art supervised sentence selection method.
Nonparametric Density Estimation&Convergence Rates for GANs under Besov IPM Losses

We study the problem of estimating a nonparametric probability density under a large family of losses called Besov IPMs, which include, for example, $\mathcal{L}^p$ distances, total variation distance, and generalizations of both Wasserstein and Kolmogorov-Smirnov distances. For a wide variety of settings, we provide both lower and upper bounds, identifying precisely how the choice of loss function and assumptions on the data interact to determine the minimax optimal convergence rate. We also show that linear distribution estimates, such as the empirical distribution or kernel density estimator, often fail to converge at the optimal rate. Our bounds generalize, unify, or improve several recent and classical results. Moreover, IPMs can be used to formalize a statistical model of generative adversarial networks (GANs). Thus, we show how our results imply bounds on the statistical error of a GAN, showing, for example, that GANs can strictly outperform the best linear estimator.
MACHINE LEARNING CONTENT ADAPTIVE FILTERS FOR IMAGE DE-BLURRING

This paper proposes a novel algorithm to recover blindly a sharp image from its degraded form by pixel pattern classification and filtering by using the deconvolution filters trained for the corresponding pixel pattern. Noise amplification is a well-known phenomenon that arises due to an ill-posed nature of the deconvolution, which is tackled better with the pattern classification, thereby state-of-the-art image de-blurring quality is demonstrated, yet at the very low complexity. For the computational efficiency, the pixel pattern classes are learnt in a multi-layer structure consisting of the coarser and the finer patterns, reducing the pattern search complexity in the dictionary by a factor of 6 without losing any quality. As compared to deep-learning based methods, the proposed approach provides a smaller memory footprint and lightweight implementation. For lightweight and real-time implementation, the paper also validates a simple class matching metric of l 1 -norm. The proposed method is suitable for embedded applications such as camera, TV systems, etc.
Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback

We propose computationally efficient algorithms for \textit{online linear optimization with bandit feedback}, in which a player chooses an \textit{action vector} from a given (possibly infinite) set $\mathcal{A} \subseteq \mathbb{R}^d$, and then suffers a loss that can be expressed as a linear function in action vectors. Although existing algorithms achieve an optimal regret bound of $\tilde{O}(\sqrt{T})$ for $T$ rounds (ignoring factors of $\mathrm{poly} (d, \log T)$), computationally efficient ways of implementing them have not yet been specified, in particular when $|\mathcal{A}|$ is not bounded by a polynomial size in $d$. A standard way to pursue computational efficiency is to assume that we have an efficient algorithm referred to as \textit{oracle} that solves (offline) linear optimization problems over $\mathcal{A}$. Under this assumption, the computational efficiency of a bandit algorithm can then be measured in terms of \textit{oracle complexity}, i.e., the number of oracle calls. Our contribution is to propose algorithms that offer optimal regret bounds of $\tilde{O}(\sqrt{T})$ as well as low oracle complexity for both \textit{non-stochastic settings} and \textit{stochastic settings}. Our algorithm for non-stochastic settings has an oracle complexity of $\tilde{O}( T )$ and is the first algorithm that achieves both a regret bound of $\tilde{O}( \sqrt{T} )$ and an oracle complexity of $\tilde{O} ( \mathrm{poly} ( T ) )$, given only linear optimization oracles. Our algorithm for stochastic settings calls the oracle only $O( \mathrm{poly} (d, \log T))$ times, which is smaller than the current best oracle complexity of $O( T )$ if $T$ is sufficiently large.
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning

The multiple relation extraction task tries to extract all relational facts from a sentence. Existing works didn’t consider the extraction order of relational facts in a sentence. In this paper we argue that the extraction order is important in this task. To take the extraction order into consideration, we apply the reinforcement learning into a sequence-to-sequence model. The proposed model could generate relational facts freely. Widely conducted experiments on two public datasets demonstrate the efficacy of the proposed method.
Measure Country-Level Socio-Economic Indicators with Streaming News: An Empirical Study

Socio-economic conditions are difficult to measure. For example, the U.S. Bureau of Labor Statistics needs to conduct large-scale household surveys regularly to track the unemployment rate, an indicator widely used by economists and policymakers. We argue that events reported in streaming news can be used as “micro-sensors” for measuring socio-economic conditions. Similar to collecting surveys and then counting answers, it is possible to measure a socio-economic indicator by counting related events. In this paper, we propose Event-Centric Indicator Measure (ECIM), a novel approach to measure socio-economic indicators with events. We empirically demonstrate strong correlation between ECIM values to several representative indicators in socio-economic research.
Outlier-Suppressed Triplet Loss with Adaptive Class-Aware Margins for Facial Expression Recognition

Triplet loss has been proposed to increase the inter-class distance and decrease the intra-class distance for various tasks of image recognition. However, for facial expression recognition (FER) problem, the fixed margin parameter does not fit the diversity of scales between different expressions. Meanwhile, the strategy of selecting the hardest triplets can introduce noisy guidance information since various persons may present significantly different expressions. In this work, we propose a new triplet loss based on class-aware margins and outlier-suppressed triplet for FER, where each pair of expressions, e.g. ’happy’ and ’fear’, is assigned with an adaptive margin parameter and the abnormal hard triplets are discarded according to the feature distance distribution. Experimental results of the proposed triplet loss on the FER2013 and CK+ expression databases show that the proposed network achieves much better accuracy than the original triplet loss and the network without using the proposed strategies, and competitive performance compared with the state-of-the-art algorithms.
Image Decolorization Based on Information Theory

In this paper we introduce a novel non-linear mapping technique to effectively decolorize images. Designed in a multi-scale fusion fashion, we first derive three input images represented by the color channels R, G and B. In order to transfer to the decolorized image only the relevant features of the derived inputs, we define two weight maps based on information theoretic approaches. The first weight map extracts visually salient regions based on a information maximization strategy while the second weight map filters the amount of local variation of each derived input computing local entropy per patch. Finally, to reduce the local distortions that might be introduced by the weight maps discontinuities, our decolorization strategy is designed in a multi-scale fusion. We also introduce a blind measure to accurately evaluate image decolorization methods. Our comprehensive qualitative and quantitative validation demonstrates that our method yields very competitive results.
Finite-Sample Analysis for SARSA with Linear Function Approximation

SARSA is an on-policy algorithm to learn a Markov decision process policy in reinforcement learning. We investigate the SARSA algorithm with linear function approximation under the non-i.i. d. \ setting, where a single sample trajectory is available. With a Lipschitz continuous policy improvement operator that is smooth enough, SARSA has been shown to converge asymptotically. However, its non-asymptotic analysis is challenging and remains unsolved due to the non-i.i. d. samples, and the fact that the behavior policy changes dynamically with time. In this paper, we develop a novel technique to explicitly characterize the stochastic bias of a type of stochastic approximation procedures with time-varying Markov transition kernels. Our approach enables non-asymptotic convergence analyses of this type of stochastic approximation algorithms, which may be of independent interest. Using our bias characterization technique and a gradient descent type of analysis, we further provide the finite-sample analysis on the mean square error of the SARSA algorithm. In the end, we present a fitted SARSA algorithm, which includes the original SARSA algorithm and its variant as special cases. This fitted SARSA algorithm provides a framework for \textit{iterative} on-policy fitted policy iteration, which is more memory and computationally efficient. For this fitted SARSA algorithm, we also present its finite-sample analysis.
Voice Identification Using Classification Algorithms

This article discusses the classification algorithms for the problem of personality identification by voice using machine learning methods. We used the MFCC algorithm in the speech preprocessing process. To solve the problem, a comparative analysis of five classification algorithms was carried out. In the first experiment, the support vector method was determined — 0.90 and multilayer perceptron — 0.83, that showed the best results. In the second experiment, a multilayer perceptron with an accuracy of 0.93 was proposed using the Robust scaler method for personal identification. Therefore, to solve this problem, it is possible to use a multi-layer perceptron, taking into account the specifics of the speech signal.
PyTorch: An Imperative Style, High-Performance Deep Learning Library

Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks.
Pareto Multi-Task Learning

Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously. However, it is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other. Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization. In this paper, we generalize this idea and propose a novel Pareto multi-task learning algorithm (Pareto MTL) to find a set of well-distributed Pareto solutions which can represent different trade-offs among different tasks. The proposed algorithm first formulates a multi-task learning problem as a multiobjective optimization problem, and then decomposes the multiobjective optimization problem into a set of constrained subproblems with different trade-off preferences. By solving these subproblems in parallel, Pareto MTL can find a set of well-representative Pareto optimal solutions with different trade-off among all tasks. Practitioners can easily select their preferred solution from these Pareto solutions, or use different trade-off solutions for different situations. Experimental results confirm that the proposed algorithm can generate well-representative solutions and outperform some state-of-the-art algorithms on many multi-task learning applications.
An Experimental Study of the Attention-based View of Idea Integration: The Need for a Multi-level Dependent Variable

Decision making involves creating a rationale for supporting a choice. Groups make many decisions that require individual members to interact and collaborate with one another. High-quality decisions require that group members pay attention to different perspectives on the decision topic, process diverse or even opposing ideas, and combine (i.e., integrate) those ideas into coherent arguments. Despite the availability of information systems (IS), such as electronic brainstorming systems (EBS), to support group decision making, researchers have relatively understudied their role in idea integration. In this paper, we focus on the role of IS user interface design in idea integration. Applying an attention-based view of idea integration, we present a model and subsequent experimental study to explore the interaction between idea visibility, prioritization, and idea integration and the moderating effects of information diversity and group size. While our results generally support the attention-based view, they also identify the need to refine the dependent variable and distinguish between different types of idea integration. The findings have implications for electronically enabled brainstorming and group decision making.
Scalable logic defined static analysis

Logic languages such as Datalog have been proposed as a method for specifying flexible and customisable static analysers. Using Datalog, various classes of static analyses can be expressed precisely and succinctly, requiring fewer lines of code than hand-crafted analysers. In this paradigm, a static analysis specification is encoded by a set of declarative logic rules and an off-the-shelf solver is used to compute the result of the static analysis. Unfortunately, when large-scale analyses are employed, Datalog-based tools currently fail to scale in comparison to hand-crafted static analysers. As a result, Datalog-based analysers have largely remained an academic curiosity, rather than industrially respectful tools. This thesis outlines our efforts in understanding the sources of performance limitations in Datalog-based tools. We propose a novel evaluation technique that is predicated on the fact that in the case of static analysis, the logical specification is a design time artefact and hence does not change during evaluation. Thus, instead of directly evaluating Datalog rules, our approach leverages partial evaluation to synthesise a specialised static analyser from these rules. This approach enables a novel indexing optimisations that automatically selects an optimal set of indexes to speedup and minimise memory usage in the Datalog computation. Lastly, we explore the case of more expressive logics, namely, constrained Horn clause and their use in proving the correctness of programs. We identify a bottleneck in various symbolic evaluation algorithms that centre around Craig interpolation. We propose a method of improving these evaluation algorithms by a proposing a method of guiding theorem provers to discover relevant interpolants with respect to the input logic specification.