Miracle-dz/UniFAR-FTUs huggingface dataset:
The minimum effort maximum output principle applied to Multiple Kernel Learning

The Multiple Kernel Learning (MKL) paradigm aims at learning the representation from data reducing the eﬀort devoted to the choice of the kernel’s hyperparameters. Typically, the resulting kernel is obtained as the maximal margin combination of a set of base kernels. When too expressive base kernels are provided to the MKL algorithm, the solution found by these algorithms can overﬁt data. In this paper, we propose a novel MKL algorithm which takes into consideration the expressiveness of the obtained representation in its objective function in such a way that a trade-oﬀ between large margins and simple hypothesis spaces can be found. Moreover, an empirical comparison against hard baselines and state-of-the-art MKL methods on several real-world datasets is presented showing the merits of the proposed algorithm especially with respect to the robustness to overﬁtting.
Multi-vector feature space based on pseudo-euclidean space and oblique basis for similarity searches of images

Investigators have tried to increase the precision of similarity searches of images by using distance functions that reflect the similarity of features. When the quadratic-form distance is used, however, dissimilar images can be judged to be similar. We therefore propose that the similarity of images be evaluated using a measure of distance in a multi-vector feature space based on pseudo-Euclidean space and an oblique basis (MVPO). In this space an image is represented by a set of vectors each of which represents each feature. And we propose a distance (called D-distance) between two sets of vectors. Roughly speaking, it is the distance between solids.Another representative distance used in similarity searches is the Earth Mover's Distance (EMD). It can be formalized using MVPO, and that explains well why EMD outperforms quad-ratic-form distance. The main difference between EMD and D-distance is that EMD is based on partial matching and D-distance is based on total matching.We also discuss performance issues of MPVO and D-distance to address practical use of them.
Firms’s Internal Factors that Influence Firm Values: Empirical Evidences on State-Owned Firms in Indonesia

Firm value is an important factor to be researched because it is one of the indicators how market evaluates the firm as a whole. The objective of this research is to examine the influence of firm’s internal factors on firm values of Indonesia’s state-owned firms listed on the Indonesia Stock Exchange. The internal factors are: Dividend Payout Ratio, Firm Size, Return on Equity and Price Earning Ratio. This research uses quantitative approach and multiple linear regression method. The population of this research consist of Indonesia state-owned firms that are listed on Indonesia Stock Exchange. Samples were determined by purposive sampling method with a total number of 15 samples of Indonesia state-owned firms. This research uses secondary data by collecting information from 45 financial data sources. This research concludes that partially, Dividend Payout Ratio (X1), Firm Size (X2), Return on Equity (X3), Price Earning Ratio (X4) has significant influence to firm values. Keywords—dividend payout ratio; firm size; firm value; price earning ratio; return on equity
Ouroboros-BFT: A Simple Byzantine Fault Tolerant Consensus Protocol

We present a simple, deterministic protocol for ledger consensus that tolerates Byzantine faults. The protocol is executed by n servers over a synchronous network and can tolerate any number t of Byzantine faults with t < n/3. Furthermore, the protocol can offer (i.) transaction processing at full network speed in the optimistic case where no faults occur, (ii.) instant confirmation: the client can be assured in a single round-trip time that a submitted transaction will be settled, (iii.) instant proof of settlement: the client can obtain a receipt that a submitted transaction will be settled. An equally simple binary consensus protocol can be easily derived as well. We also analyze the protocol in case of network splits and temporary loss of synchrony, arguing the safety of the protocol when synchrony is restored. Finally, we examine the covert adversarial model showing that Byzantine resilience is increased to t < n/2.
Semi-supervised topic models applied to mathematical document classification

Our objective is to build a mathematical document classifier: a machine which for a given mathematical document $\mathbf{x}$, determines the mathematical subject area $\cc$. In particular, we wish to construct the function $f$ such that $f(\mathbf{x}, \TTheta) = \cc$ where $f$ requires the possibly unknown parameters $\TTheta$ which may be estimated using an existing corpus of labelled documents. The novelty here is that our proposed classifiers will observe a mathematical document over dual vocabularies. In particular, as a collection of both words and mathematical symbols. In this thesis, we predominantly review the claims made in \cite{Watt}: mathematical document classification is possible via symbol frequency analysis. In particular, we investigate whether this claim is justified: \cite{Watt} contains no experimental evidence which supports this. Furthermore, we extend this research further and investigate whether the inclusion of mathematical notational information improves classification accuracy over the existing single vocabulary approaches. To do so, we review a selection of machine learning methods for document classification and refine and extend these models to incorporate mathematical notational information and investigate whether these models yield higher classification performance over existing word only versions. In this research, we develop the novel mathematical document models ``Dual Latent Dirichlet Allocation'' and ``Dual Pachinko Allocation'' which are extensions to the existing topic models ``Latent Dirichlet Allocation'' and ``Pachinko Allocation'' respectively. Our proposed models observe mathematical documents over two separate vocabularies (words and mathematical symbols). Furthermore, we present Online Variational Bayes for Pachinko Allocation and our proposed models to allow for fast parameter estimation over a single pass of the data. We perform systematic analysis on these models, and we verify the claims made in \cite{Watt}, and furthermore, we observe that the inclusion of symbol data via Dual Pachinko Allocation only yields in an increase of classification performance over the single vocabulary variants and the prior art in this field.
NOISE-ADAPTIVE DEEP NEURAL NETWORK FOR SINGLE-CHANNEL SPEECH ENHANCEMENT

We introduce a noise-adaptive feed-forward deep neural network (DNN) for single-channel speech enhancement. The goal is to better exploit individual noise characteristics while training a spectral mapping DNN. To this end, we employ noise-dependent adaptation vectors, which are obtained based on the output of an auxiliary noise classification DNN, to adjust the weights and biases of the spectral mapping DNN. The parameters of the spectral mapping DNN, noise classification DNN and adaptation vectors are estimated jointly during the training stage. During the enhancement stage, we combine a classical unsupervised speech enhancement algorithm with the proposed DNN-based approach to further improve the enhanced speech quality. Experiments show that the proposed method provides better enhancement performance than the selected benchmark algorithms.
Multi-omics data integration using cross-modal neural networks

Successful integration of multi-omics data for prediction tasks can bring signiﬁcant advantages to precision medicine and to understanding molecular systems. This paper introduces a novel neural network architecture for exploring and integrating modalities in omics datasets, especially in scenarios with a limited number of training examples available. The proposed cross-modal neural network achieves up to 99% accuracy on omics datasets. Moreover, we show how analysis of the weights and activations in the network can give us biological insights into understanding which genes are most relevant for the decision process and how diﬀerent types of omics inﬂuence each other.
K-spectral centroid: extension and optimizations

In this work, we address the problem of unsupervised classification of large time series datasets. We focus on K-Spectral Centroid (KSC), a k-means-like model, devised for time series clustering. KSC relies on a custom dissimilarity measure between time series, which is invariant to time shifting and Y-scaling. KSC has two downsides: firstly its dissimilarity measure only makes sense for non negative time series. Secondly the KSC algorithm is relatively demanding in terms of computation time. In this paper, we present a natural extension of the KSC dissimilarity measure to time series of arbitrary signs. We show that this new measure is a metric distance. We propose to speed up this extended KSC (EKSC) thanks to four exact optimizations. Finally, we compare EKSC to a similar model, K-Shape, on real world datasets.
Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization

Limbo (LIbrary for Model-Based Optimization) is an open-source C++11 library for Gaussian Processes and data-efficient optimization (e.g., Bayesian optimization, see (Shahriari et al. 2016)) that is designed to be both highly flexible and very fast. It can be used as a state-of-the-art optimization library or to experiment with novel algorithms with "plugin" components. Limbo is currently mostly used for data-efficient policy search in robot learning (Lizotte et al. 2007) and online adaptation because computation time matters when using the low-power embedded computers of robots. For example, Limbo was the key library to develop a new algorithm that allows a legged robot to learn a new gait after a mechanical damage in about 10-15 trials (2 minutes) (Cully et al. 2015), and a 4-DOF manipulator to learn neural networks policies for goal reaching in about 5 trials (Chatzilygeroudis et al. 2017). The implementation of Limbo follows a policy-based design (Alexandrescu 2001) that leverages C++ templates: this allows it to be highly flexible without the cost induced by classic object-oriented designs (Driesen and Holzle 1996) (cost of virtual functions). The regression benchmarks 1 show that the query time of Limbo's Gaussian processes is several orders of magnitude better than the one of GPy (a state-of-the-art Python library for Gaussian processes 2) for a similar accuracy (the learning time highly depends on the optimization algorithm chosen to optimize the hyper-parameters). The black-box optimization benchmarks 3 demonstrate that Limbo is about 2 times faster than BayesOpt (a C++ library for data-efficient optimization, (Martinez-Cantin 2014)) for a similar accuracy and data-efficiency. In practice, changing one of the components of the algorithms in Limbo (e.g., changing the acquisition function) usually requires changing only a template definition in the source code. This design allows users to rapidly experiment and test new ideas while keeping the software as fast as specialized code. Limbo takes advantage of multi-core architectures to parallelize the internal optimization processes (optimization of the acquisition function, optimization of the hyper-parameters of a Gaussian process) and it vectorizes many of the linear algebra operations (via the Eigen 3 library 4 and optional bindings to Intel's MKL).
Towards Cost Effective Privacy Provision for Typed Resources in IoT Environment (S)

We present privacy resources in IoT as data, information, and knowledge. We construct a privacy protection architecture on our previously proposed DIKW graphs: Data Graph, Information Graph, and Knowledge Graph. On this architecture, we search privacy protection target resources both as they appear explicitly in their original types and as they appear implicitly which means that they are expressed not in their original types. For a single privacy protection target, it may have various concrete compositions in various layers of DIKW Graph. It becomes more complex since the implementation of a privacy target might also be intertwined with the implementation of other privacy targets. We propose to protect target resources according to their types by either isolating the elements comprising an implementation, or weakening relationships among elements comprising an implement.To optimize among several choices of implementing a protection in a business environment, we introduced the tradeoff between customers’ expectations/investment and privacy providers’ expectation. Thereafter we proposed to prioritize implementation according to their ratio of cost/benefit. Keywords—Internet of Things; typed resources; privacy provision; Knowledge Graph
Novel Preparation of Monodisperse Microbubbles by Integrating Oscillating Electric Fields with Microfluidics

Microbubbles generated by microfluidic techniques have gained substantial interest in various industries such as cosmetics, food engineering, and the biomedical field. The microfluidic T-junction provides exquisite control over processing parameters, however, it relies on pressure driven flows only; therefore, bubble size variation is limited especially for viscous solutions. A novel set-up to superimpose an alternating current (AC) oscillation onto a direct current (DC) field is invented in this work, capitalising on the possibility to excite bubble resonance phenomenon and properties, and introducing relevant parameters such as frequency, AC voltage, and waveform to further control bubble size. A capillary embedded T-junction microfluidic device fitted with a stainless-steel capillary was utilised for microbubble formation. Furthermore, a numerical model of the T-junction was developed by integrating the volume of fluid (VOF) method with the electric module; simulation results were attained for the formation of the microbubbles with a particular focus on the flow fields along the detachment of the emerging bubble. Two main types of experiments were conducted in this framework: the first was to test the effect of applied AC voltage magnitude and the second was to vary the applied frequency. Experimental results indicated that higher frequencies have a pronounced effect on the bubble diameter within the 100 Hz and 2.2 kHz range, whereas elevated AC voltages tend to promote bubble elongation and growth. Computational results suggest there is a uniform velocity field distribution along the bubble upon application of a superimposed field and that microbubble detachment is facilitated by the recirculation of the dispersed phase. Furthermore, an ideal range of parameters exists to tailor monodisperse bubble size for specific applications.
Latent representations of transient candidates from an astronomical image difference pipeline using Variational Autoencoders

The Chilean Automatic Supernovae SEarch (CHASE) is a survey designed to detect early Supernovae. In this paper we explore deep autoencoders to obtain a compressed latent space for a large transient candidate database from the CHASE image diﬀerence pipeline. Compared to conventional methods, the latent variables obtained with variational au-toencoders preserve more information and are more discriminative towards real astronomical transients.
Root coverage by modified coronally advanced flap with and without platelet-rich fibrin: A clinical study

Background: Marginal gingival recession can cause major functional and esthetic problems. Advanced flaps are the simplest, yet unpredictable procedures for managing these conditions. The predictability of root coverage can be increased by combining coronally advanced flap (CAF) or its modified approach with other regenerative techniques. Objective: To ascertain the potential benefits of platelet-rich fibrin (PRF) on modified CAF for the treatment of gingival recession. Materials and Methods: Study comprised of 12 patients with Millers' class I and class II gingival recession in two non-adjacent anterior teeth having a minimum 3 mm width of attached gingiva. Following split-mouth design, one tooth with gingival recession was subjected to modified CAF, while another was treated by CAF with PRF. The clinical parameters, i.e., plaque index, modified sulcular bleeding index, vertical gingival recession depth (VGRD), gingival recession width (GRW), clinical attachment level (CAL), and gingival thickness (GT) were recorded at baseline, 1 month, 3 months, and 6 months. Results: VGRD, GRW, CAL, and GT improved significantly from baseline to 1 month in both test and control groups. However, change in these parameters from 1 month to 3 months and 3 months to 6 months were statistically nonsignificant in both groups. On intergroup comparison, only the change in GT was found to be statistically significant (P < 0.05) at all three post-treatment visits. Conclusion: Benefits of the combined technique in terms of increased GT appear to justify the use of PRF along with modified CAF for the treatment of mild to moderate gingival recessions.
Edge points-based stereo matching approach for omnidirectional images

Abstract. We address the problem of matching stereo-omnidirectional images acquired by a catadioptric stereo system. The proposed approach deals with omnidirectional stereo image sequences, which allows taking advantage of the temporal information in both rectification and stereo matching steps. First, spherical edge points detection is performed on the spherical images in order to compute the disparity map only on significant features, i.e., edge points. Second, a spherical rectification is performed on the three omnidirectional pairs that are involved in the disparity map computation at the current frame, such as the current pair, the current left image, and its preceding one, and the current right image and its preceding one. Third, dynamic programming-based stereo matching is applied in each of the three stereo pairs. The disparity maps computed at the preceding frame are exploited to deduce the disparity range that will be used by the dynamic programming. Fourth, the three disparity maps provided by the last step are combined together into one 360-deg disparity map. The proposed method has been tested on a high-resolution omnidirectional images captured with a stereo pair of catadioptric cameras, and the obtained results are satisfactory.
Natural products show diverse mechanisms of action against Clostridium difficile

To investigate the mechanisms of action of natural products with bactericidal (cinnamon root powder, peppermint oil, trans‐cinnamaldehyde, menthol and zingerone) or bacteriostatic (fresh garlic bulb extract, garlic clove powder, Leptospermum honey and allicin) activity against two Clostridium difficile strains.
Japanese sentiment analysis using simple alignment sentence classification

Recurrent and convolutional neural networks have been used to learn contextual information in many natural-language processing studies. In particular, they are the most successful methods for English-language text analysis. In the sentiment analysis of English-language text, recurrent neural networks with an attention mechanism have been found to perform well. We might assume that context would be less important in Japanese-language sentiment analysis. To examine this assumption, we apply a simple alignment sentence-classification model to Japanese sentiment analysis.
Performance Comparison of Knowledge-Based Dose Prediction Techniques Based on Limited Patient Data

Purpose: The accuracy of dose prediction is essential for knowledge-based planning and automated planning techniques. We compare the dose prediction accuracy of 3 prediction methods including statistical voxel dose learning, spectral regression, and support vector regression based on limited patient training data. Methods: Statistical voxel dose learning, spectral regression, and support vector regression were used to predict the dose of noncoplanar intensity-modulated radiation therapy (4π) and volumetric-modulated arc therapy head and neck, 4π lung, and volumetric-modulated arc therapy prostate plans. Twenty cases of each site were used for k-fold cross-validation, with k = 4. Statistical voxel dose learning bins voxels according to their Euclidean distance to the planning target volume and uses the median to predict the dose of new voxels. Distance to the planning target volume, polynomial combinations of the distance components, planning target volume, and organ at risk volume were used as features for spectral regression and support vector regression. A total of 28 features were included. Principal component analysis was performed on the input features to test the effect of dimension reduction. For the coplanar volumetric-modulated arc therapy plans, separate models were trained for voxels within the same axial slice as planning target volume voxels and voxels outside the primary beam. The effect of training separate models for each organ at risk compared to all voxels collectively was also tested. The mean squared error was calculated to evaluate the voxel dose prediction accuracy. Results: Statistical voxel dose learning using separate models for each organ at risk had the lowest root mean squared error for all sites and modalities: 3.91 Gy (head and neck 4π), 3.21 Gy (head and neck volumetric-modulated arc therapy), 2.49 Gy (lung 4π), and 2.35 Gy (prostate volumetric-modulated arc therapy). Compared to using the original features, principal component analysis reduced the 4π prediction error for head and neck spectral regression (−43.9%) and support vector regression (−42.8%) and lung support vector regression (−24.4%) predictions. Principal component analysis was more effective in using all/most of the possible principal components. Separate organ at risk models were more accurate than training on all organ at risk voxels in all cases. Conclusion: Compared with more sophisticated parametric machine learning methods with dimension reduction, statistical voxel dose learning is more robust to patient variability and provides the most accurate dose prediction method.
Boundary connectedness-based salient video object segmentation

Abstract. One of the most challenging tasks in computer vision is to emulate human cognitive ability to extract the salient object in a scene. We tackle the task of unsupervised salient video object segmentation using boundary connectedness and space-time salient regions. First, boundary prior measure is used to separate salient regions detected in both space and time. Then, background-foreground regions connectedness is computed and combined with appearance model via an iterative energy minimization framework to segment the salient moving object. For temporal consistency, the segmentation result of the current frame is used in addition to the optical flow and the boundary prior to segmenting the next frame. The experiments show a good performance of our algorithm for salient video object segmentation on benchmark datasets even in the presence of different challenges.