Miracle-dz/UniFAR-FTUs huggingface dataset:
Language lateralization in healthy people with auditory verbal hallucinations : A pilot study

Language lateralization in schizophrenia has been reported to be decreased, which is due to increased language activity of the right hemisphere. Furthermore increased right hemisphere language activity is correlated to the severity of auditory verbal hallucinations (AVH) in schizophrenia, and could possibly be a predisposing factor for AVH. However, schizophrenia is a complex syndrome consisting of psychotic, cognitive and negative symptoms. In order to learn whether decreased lateralization plays a causal role in the pathophysiology of AVH, the isolated form of AVH should be investigated, which can be found in healthy subjects with AVH. Language lateralization was measured in 10 healthy subjects with AVH and compared to 10 schizophrenia patients and 10 healthy controls matched for age, handedness and education. Subjects were scanned while covertly performing a paced letter fluency task. A significant main effect for group was revealed with respect to the right hemisphere. This was due to increased activation in the right hemisphere of the schizophrenia patients relative to the healthy controls. Differences in language lateralization between schizophrenia patients, healthy subjects with AVH and healthy controls did not reach significance. However, mean lateralization indices were lower in both hallucinating groups. This difference is likely to become significant when sample size increases
A Lightweight Distributed Software Agent for Automatic Demand—Supply Calculation in Smart Grids

—The number of renewable energy sources participating in the world-wide energy mix is increasing. However, they come with different characteristics than the traditional power sources. Energy generation happens on a smaller scale and is more distributed, often because the location of such a power generator cannot be freely chosen. Also, some sources like wind or solar power depend on the weather, which is not controllable. This poses more difﬁcult challenges on every grid management. We propose a distributed, self-adjusting agent-based solution for smart grids. Based on a lightweight protocol, this distributed software will dynamically and pro-actively calculate supply and demand within the smart grid.
MIDI Database and Representation Manager for Deep Learning

One of the most important decisions made when training neural networks, is how to represent the data. Despite the large number of possible representations, the piano roll dominates recent literature on modelling music [2, 3]. Furthermore, previous work suggests that modelling simpler conditional probability distributions such as p(pitch|rhythm), may be an advantageous approach to the complex task of modelling music [5]. However, many alternate representations are more difficult to implement than the piano roll. Motivated by these factors, we propose an accessible framework for symbolic musical data storage, and dataset construction, which supports a variety of representations.
A novel calibration approach of MODIS AOD data to predict PM<sub>2.5</sub> concentrations

Abstract. Epidemiological studies investigating the human health effects of PM2.5 are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM2.5 monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing our ability to estimate location- or subject-specific exposures to PM2.5, but some have reported poor predictive power. A new methodology was developed to calibrate aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Subsequently, this method was used to predict ground daily PM2.5 concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM2.5 concentrations measured at 26 US Environmental Protection Agency (EPA) PM2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-to-day variability in daily PM2.5-AOD relationships was used to predict location-specific PM2.5 levels. PM2.5 concentrations measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the observed and predicted concentrations suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM2.5 concentrations. Furthermore, the estimated PM2.5 levels within the study domain were examined in relation to air pollution sources. Our approach made it possible to investigate the spatial patterns of PM2.5 concentrations within the study domain.
Bootstrapping the Performance of Webly Supervised Semantic Segmentation

Fully supervised methods for semantic segmentation require pixel-level class masks to train, the creation of which is expensive in terms of manual labour and time. In this work, we focus on weak supervision, developing a method for training a high-quality pixel-level classifier for semantic segmentation, using only image-level class labels as the provided ground-truth. Our method is formulated as a two-stage approach in which we first aim to create accurate pixel-level masks for the training images via a bootstrapping process, and then use these now-accurately segmented images as a proxy ground-truth in a more standard supervised setting. The key driver for our work is that in the target dataset we typically have reliable ground-truth image-level labels, while data crawled from the web may have unreliable labels, but can be filtered to comprise only easy images to segment, therefore having reliable boundaries. These two forms of information are complementary and we use this observation to build a novel bi-directional transfer learning framework. This framework transfers knowledge between two domains, target domain and web domain, bootstrapping the performance of weakly supervised semantic segmentation. Conducting experiments on the popular benchmark dataset PASCAL VOC 2012 based on both a VGG16 network and on ResNet50, we reach state-of-the-art performance with scores of 60.2% IoU and 63.9% IoU respectively1.
Photovoltaic Deployment Experience and Technical Potential in Indonesia ’ s Java-Madura-Bali Electricity Grid

The largest and most established electricity grid in Indonesia, the Java-Madura-Bali (JAMALI) interconnected system, accounts for around 60% of the country’s total electricity demand. Given continuing high demand growth and the slow addition of major generators in recent years, energy users served by the JAMALI grid are experiencing inadequate and unreliable electricity supply. There is growing interest in the potential of renewable energy to help mitigate the risks of continued fossil fuel dependence, improve system reserve margins, and diversify energy sources to enhance supply security, as well as improve environmental outcomes. The Indonesian government’s vision to increase the renewable energy contribution to Indonesia’s energy mix to 23% by 2025, raises the question of what role utility-scale photovoltaic (PV) generation might play in meeting these challenges and opportunities. Indonesia has an excellent solar resource, while PV costs have fallen markedly over recent years. Still, the JAMALI’s present generation mix is dominated by low cost and relatively abundant coal and gas. This paper reviews the current status and future potential of utility PV in the JAMALI area. Given limited existing studies on solar irradiation and PV mapping for the Indonesian context, we first present a detailed spatial mapping of PV output potential for the JAMALI region including hourly temporal behavior – a key factor for assessing PV integration challenges and opportunities. This proposed spatial PV output mapping is provided at a 5 km2 resolution, using weather data derived from the NASA MERRA-2 satellite database and the Global Solar Energy Estimator model. The mapping provides a preliminary indication of appropriate utility-scale PV locations in the JAMALI context. An open-source generation mix model, NEMO, is then used to assess the potential role of utility PV in least cost future generation portfolios for JAMALI. Results suggest that PV could play a useful role, depending on future technology, fuel and carbon prices, and demand growth. On this basis, current policies and regulations relevant to deployment of large scale PV are described and a number of key challenges for PV deployment and possible opportunities for reform are identified.
Experimental evaluation of emotional development programmes for teachers in Secondary Education

espanolLa inteligencia emocional es un factor predictivo de bienestar entre los docentes. Por lo tanto, el debate se centra en la pertinencia y la necesidad de desarrollar competencias en profesores de educacion secundaria. Esto implica el diseno, implementacion y evaluacion de dos programas de capacitacion vinculados a la gestion de conflictos y la promocion de las competencias emocionales. La eficacia de los programas de capacitacion se estudia mediante la adopcion de un diseno de investigacion de tipo preexperimental con medidas previas y posteriores a la prueba. Los resultados obtenidos tras la aplicacion de ambos programas muestran cambios estadisticamente significativos (p EnglishEmotional intelligence is a predictive factor of wellbeing among teachers. The discussion therefore focuses on the pertinence of, and need for, developing competences in Secondary Education teachers. This involves the design, implementation and evaluation of two training programmes linked to conflict management and the promotion of emotional competences. The efficacy of the training programmes is studied through the adoption of a pre-experimental type research design with pre/post-test measures. The results obtained following the application of both these programmes show statistically significant changes (p
Proceedings of the 14 th Sound and Music Computing Conference , July 5-8 , Espoo , Finland SMC 2017-110

This paper presents an adaptive karaoke system that can extract accompaniment sounds from music audio signals in an online manner and play those sounds synchronously with users’ singing voices. This system enables a user to expressively sing an arbitrary song by dynamically changing the tempo of the user’s singing voices. A key advantage of this systems is that users can immediately enjoy karaoke without preparing musical scores (MIDI files). To achieve this, we use online methods of singing voice separation and audio-to-audio alignment that can be executed in parallel. More specifically, music audio signals are separated into singing voices and accompaniment sounds from the beginning using an online extension of robust nonnegative matrix factorization. The separated singing voices are then aligned with a user’s singing voices using online dynamic time warping. The separated accompaniment sounds are played back according to the estimated warping path. The quantitative and subjective experimental results showed that although there is room for improving the computational efficiency and alignment accuracy, the system has a great potential for offering a new singing experience
Stochastic Optimization for Machine Learning

Numerical optimization has played an important role in the evolution of machine learning, touching almost every aspect of the discipline. Stochastic approximation has evolved and expanded as one of the main streams of research in mathematical optimzation. This survey provides a review and summary on the stochastic optimization algorithms in the context of machine learning applications. The stochastic gradient descent (SGD) method has been widely viewed as an ideal approach for large-scale machine learning problems while the conventional batch gradient method typically falters. Despite its flexibility and scalability, the stochastic gradient is associated with high variance which impedes training. Based on this viewpoint, we provide a comprehensive theoretical and practical discussion on the SGD, and then we investigate a new spectrum of incremental gradient methods that suppress the noise in a smart way, leading to improved convergence results. We further present review on methods that integrate the stochastic gradients into the alternating direction method of multipliers (ADMM), which has been recently advocated as an efficient optimization tool for a wider variety of models. Last but not least, we also presents some stochastic optimization techniques for the deep neural network training, including momentum methods and algorithms with adaptive learning rates.
Interpreting Decision-Making in Interactive Visual Dialogue

Dialogue systems that involve long-term planning can strongly benefit from a high-level notion of dialogue strategy and can avoid making poor decisions early in the game and opt for broadly successful strategies instead. A strategy-signal can additionally be used as a conditioning input on the dialogue generation mechanism allowing better training and generalization over a vastly smaller generation space. In this work, we first analyze the human game-play strategy, using regular expression-based strategy labels, to understand the selection and time-evolution of human-strategy in an object-discovery setup. Subsequently, we examine variational inference-based controlled text generation models as natural language generation models to generate language with precisely controlled semantic features and examine their applicability to generating questions with designated semantics. Finally, we propose a strategy-conditional framework that generates interpretable conditioning signals for a multi-modal question generation mechanism in a GuessWhat? game. We explore variants with conditioning on gold and generated strategy-conditioning labels. These signals improve the performance of the question generation module with stronger conditioning while additionally enforcing a stricter compliance with human game-play strategy. The discrete nature of these labels provides an interpretable readout into the model's current questioning strategy. As a part of this conditioning setup, we also introduce a strategy-prediction module trained on a multi-task learning setup that can generate these conditioning signals autonomously and ahead-of-time allowing the encoder to generate conditioning signals for the decoder through an intermediate transformation to a strategy-label. We also present a detailed examination of optimization policies, strategy-predictor architectures and gradient propagation depth on the training of such models and show that while strategy conditioning improves model performance, it degrades generation ability due to weak strategy prediction accuracy.
LMPIT-Inspired Tests for Detecting a Cyclostationary Signal in Noise With Spatio–Temporal Structure

In spectrum sensing for cognitive radio, the presence of a primary user can be detected by making use of the cyclostationarity property of digital communication signals. For the general scenario of a cyclostationary signal in temporally colored and spatially correlated noise, it has previously been shown that an asymptotic generalized likelihood ratio test (GLRT) and locally most powerful invariant test (LMPIT) exist. In this paper, we derive detectors for the presence of a cyclostationary signal in various scenarios with structured noise. In particular, we consider noise that is temporally white and/or spatially uncorrelated. Detectors that make use of this additional information about the noise process have enhanced performance. We have previously derived GLRTs for these specific scenarios; here, we examine the existence of LMPITs. We show that these exist only for detecting the presence of a cyclostationary signal in spatially uncorrelated noise. For white noise, an LMPIT does not exist. Instead, we propose tests that approximate the LMPIT, and they are shown to perform well in simulations. Finally, if the noise structure is not known in advance, we also present hypothesis tests using our framework.
WEIXI, YUNNAN PROVINCE, CHINA

Identifying the pollinators of Rhododendron species is of great interest due to potential conservation threats in the native range of the genus, but the pollinators of species in Rhododendron subgenus Hymenanthes section Pontica subsection Neriiflora are unknown. Bees (Hymenoptera; family Apidae) are thought to be the pollinators of many Rhododendron species; however, species in subsection Neriiflora have ornithophilous floral morphology. We studied R. floccigerum (subsection Neriiflora) to determine the identities of visiting, potentially pollinating, and robbing species through in-person and time lapsed camera trap observations. We compared floral morphological characteristics of R. floccigerum with visitor morphological measurements to determine if visitors could fit inside the corolla. Thirteen species were observed visiting R. floccigerum (two insects, two mammals, and nine birds) and this study provides the first empirical evidence of both bird and mammal visitors to Rhododendron species. We determined that the following species are potential pollinators: Bombus sp. (an insect genus), Aethopyga gouldiae, Garrulax affinis, Heterophasia melanoleuca, and Yuhina diademata (all bird species), and we suspect that Apis sp. (an insect genus), Dremomys pernyi, Tamiops swinhoei (two mammal species), Minla ignotincta, M. strigula, Parus major, and Phylloscopus affinis (four bird species) likely rob R. floccigerum. All visitors were able to fit their heads/bodies into the corolla. We also found that though predation is frequent, the number of robbers and variety of robbing methods is unlikely to contribute to floral morphological evolution or speciation. Further understanding of the pollination biology of species in subgenus Hymenanthes will allow for effective conservation.
Isolation and characterization of bacterial strains with a hydrolytic profile with potential use in bioconversion of agroindustial by-products and waste

There is a trend towards the use of novel technologies nowadays, mainly focused on biological processes, for recycling and the efficient utilization of organic residues that can be metabolized by different microorganisms as a source of energy. In the present study the isolation of bacterial strains from six different agro-industrial by-products and waste was performed with the objective of evaluating their hydrolytic capacities and suitability for use in bioconversion of specific substrates. The 34 isolated strains were screened in specific culture media for the production of various hydrolytic enzymes (lipase, protease, cellulase, and amylase). It was found that 28 strains exhibited proteolytic activity, 18 had lipolytic activity, 13 had caseinolytic activity, 15 had amylolytic activity, and 11 strains exhibited cellulolytic activity. The strains that showed the highest hydrolytic capacities with biotechnological potential were selected, characterized genotipically, and identified as Bacillus, Serratia, Enterococcus, Klebsiella, Stenotrophomonas, Lactococcus, and Escherichia genera. It was concluded that the strain isolates have a high potential for use in the bioconversion of agro-industrial waste, both as a pure culture and as a microbial consortium.
A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems

Abstract. A neural-network-based method, quantile regression neural networks (QRNNs), is proposed as a novel approach to estimating the a posteriori distribution of Bayesian remote sensing retrievals. The advantage of QRNNs over conventional neural network retrievals is that they learn to predict not only a single retrieval value but also the associated, case-specific uncertainties. In this study, the retrieval performance of QRNNs is characterized and compared to that of other state-of-the-art retrieval methods. A synthetic retrieval scenario is presented and used as a validation case for the application of QRNNs to Bayesian retrieval problems. The QRNN retrieval performance is evaluated against Markov chain Monte Carlo simulation and another Bayesian method based on Monte Carlo integration over a retrieval database. The scenario is also used to investigate how different hyperparameter configurations and training set sizes affect the retrieval performance. In the second part of the study, QRNNs are applied to the retrieval of cloud top pressure from observations by the Moderate Resolution Imaging Spectroradiometer (MODIS). It is shown that QRNNs are not only capable of achieving similar accuracy to standard neural network retrievals but also provide statistically consistent uncertainty estimates for non-Gaussian retrieval errors. The results presented in this work show that QRNNs are able to combine the flexibility and computational efficiency of the machine learning approach with the theoretically sound handling of uncertainties of the Bayesian framework. Together with this article, a Python implementation of QRNNs is released through a public repository to make the method available to the scientific community.
PARALLEL AND ADAPTIVE UNIFORM-DISTRIBUTED REGISTRATION METHOD FOR CHANG’E-1 LUNAR REMOTE SENSED IMAGERY

On the October 24 th 2007, China launched its first Lunar Probe Satellite "Chang'E I". After the 494 days travelling, the probe vehicle landed accurately at its predetermined landing site on the moon at 52.36 degrees east longitude and 1.5 degrees south latitude. It sent back the first imagery of the lunar surface on 26 November 2007 and accomplished all the scheduled scientific tasks successfully. As the first lunar Probe Satellite, the major goal of Chang'E I mission is to obtain three-dimensional images of the landforms and geological structures of the lunar surface, so as to provide a reference for planned future soft landings. However, due to the dramatic change of the radiation information of the CE-1imagery, the traditional methods that are based on the gray and line characters show the limitation achieving a satisfied result. Moreover, the registration processing between lunar images that cover the whole moon has proved to be very time-consuming. To resolve the above-mentioned registration difficulties, a parallel and adaptive uniform-distributed registration method for CE-1 lunar remote sensed imagery is proposed in this paper. Based on 6 pairs of randomly selected images, both the standard SIFT algorithm and the parallel and adaptive uniform-distributed registration method were executed, the versatility and effectiveness were assessed. The experimental results indicate that: by applying the parallel and adaptive uniform-distributed registration method, the efficiency of CE-1 lunar remote sensed imagery registration were increased dramatically. Therefore, the proposed method in the paper could acquire uniform-distributed registration results more effectively, the registration difficulties including difficult to obtain results, time-consuming, non-uniform distribution could be successfully solved.
PanParser: a Modular Implementation for Efficient Transition-Based Dependency Parsing

Abstract We present PanParser, a Python framework dedicated to transition-based structured prediction, and notably suitable for dependency parsing. On top of providing an easy way to train state-of-the-art parsers, as empirically validated on UD 2.0, PanParser is especially useful for research purposes: its modular architecture enables to implement most state-of-the-art transition-based methods under the same unified framework (out of which several are already built-in), which facilitates fair benchmarking and allows for an exhaustive exploration of slight variants of those methods. PanParser additionally includes a number of fine-grained evaluation utilities, which have already been successfully leveraged in several past studies, to perform extensive error analysis of monolingual as well as cross-lingual parsing.