Miracle-dz/UniFAR-FTUs huggingface dataset:
pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution

In this study, we present a new module built for users interested in a programming language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE) distribution. The module is an extension to the open-source program JAGS by which a Gibbs sampler can be applied without requiring the derivation of complete conditionals and the subsequent implementation of strategies to draw samples from unknown distributions. The PE distribution is widely used in the fields of survival analysis and reliability. Currently, it can only be implemented in JAGS through methods to indirectly specify the likelihood based on the Poisson or Bernoulli probabilities. Our module provides a more straightforward implementation and is thus more attractive to the researchers aiming to spend more time exploring the results from the Bayesian inference rather than implementing the Markov Chain Monte Carlo (MCMC) algorithm. For those interested in extending JAGS, this work can be seen as a tutorial including important information not well investigated or organized in other materials. Here, we describe how to use the module taking advantage of the interface between R and JAGS. A short simulation study is developed to ensure that the module behaves well and a real illustration, involving two PE models, exhibits a context where the module can be used in practice.
Endowing Empathetic Conversational Models with Personas

Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy. In addition, our empirical analysis also suggests that persona plays an important role in empathetic conversations. To this end, we propose a new task towards persona-based empathetic conversations and present the first empirical study on the impacts of persona on empathetic responding. Specifically, we first present a novel large-scale multi-domain dataset for persona-based empathetic conversations. We then propose CoBERT, an efficient BERT-based response selection model that obtains the state-of-the-art performance on our dataset. Finally, we conduct extensive experiments to investigate the impacts of persona on empathetic responding. Notably, our results show that persona improves empathetic responding more when CoBERT is trained on empathetic conversations than non-empathetic ones, establishing an empirical link between persona and empathy in human conversations.
Lexically Constrained Neural Machine Translation with Levenshtein Transformer

This paper proposes a simple and effective algorithm for incorporating lexical constraints in neural machine translation. Previous work either required re-training existing models with the lexical constraints or incorporating them during beam search decoding with significantly higher computational overheads. Leveraging the flexibility and speed of a recently proposed Levenshtein Transformer model (Gu et al., 2019), our method injects terminology constraints at inference time without any impact on decoding speed. Our method does not require any modification to the training procedure and can be easily applied at runtime with custom dictionaries. Experiments on English-German WMT datasets show that our approach improves an unconstrained baseline and previous approaches.
A Bayesian dose–response meta-analysis model: A simulations study and application

Dose–response models express the effect of different dose or exposure levels on a specific outcome. In meta-analysis, where aggregated-level data is available, dose–response evidence is synthesized using either one-stage or two-stage models in a frequentist setting. We propose a hierarchical dose–response model implemented in a Bayesian framework. We develop our model assuming normal or binomial likelihood and accounting for exposures grouped in clusters. To allow maximum flexibility, the dose–response association is modelled using restricted cubic splines. We implement these models in R using JAGS and we compare our approach to the one-stage dose–response meta-analysis model in a simulation study. We found that the Bayesian dose–response model with binomial likelihood has lower bias than the Bayesian model with normal likelihood and the frequentist one-stage model when studies have small sample size. When the true underlying shape is log–log or half-sigmoid, the performance of all models depends on choosing an appropriate location for the knots. In all other examined situations, all models perform very well and give practically identical results. We also re-analyze the data from 60 randomized controlled trials (15,984 participants) examining the efficacy (response) of various doses of serotonin-specific reuptake inhibitor (SSRI) antidepressant drugs. All models suggest that the dose–response curve increases between zero dose and 30–40 mg of fluoxetine-equivalent dose, and thereafter shows small decline. We draw the same conclusion when we take into account the fact that five different antidepressants have been studied in the included trials. We show that implementation of the hierarchical model in Bayesian framework has similar performance to, but overcomes some of the limitations of the frequentist approach and offers maximum flexibility to accommodate features of the data.
Federated Transfer Learning for EEG Signal Classification

The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets. Privacy concerns associated with EEG signals limit the possibility of constructing a large EEG-BCI dataset by the conglomeration of multiple small ones for jointly training machine learning models. Hence, in this paper, we propose a novel privacy-preserving DL architecture named federated transfer learning (FTL) for EEG classification that is based on the federated learning framework. Working with the single-trial covariance matrix, the proposed architecture extracts common discriminative information from multi-subject EEG data with the help of domain adaptation techniques. We evaluate the performance of the proposed architecture on the PhysioNet dataset for 2-class motor imagery classification. While avoiding the actual data sharing, our FTL approach achieves 2% higher classification accuracy in a subject-adaptive analysis. Also, in the absence of multi-subject data, our architecture provides 6% better accuracy compared to other state-of-the-art DL architectures.
AutoHR: A Strong End-to-End Baseline for Remote Heart Rate Measurement With Neural Searching

Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e.g., remote healthcare). Existing end-to-end rPPG and heart rate (HR) measurement methods from facial videos are vulnerable to the less-constrained scenarios (e.g., with head movement and bad illumination). In this letter, we explore the reason why existing end-to-end networks perform poorly in challenging conditions and establish a strong end-to-end baseline (AutoHR) for remote HR measurement with neural architecture search (NAS). The proposed method includes three parts: 1) a powerful searched backbone with novel Temporal Difference Convolution (TDC), intending to capture intrinsic rPPG-aware clues between frames; 2) a hybrid loss function considering constraints from both time and frequency domains; and 3) spatio-temporal data augmentation strategies for better representation learning. Comprehensive experiments are performed on three benchmark datasets, and we achieved superior performance on both intra- and cross-dataset testings.
Bulk viscosity of resonating fermions revisited: Kubo formula, sum rule, and the dimer and high-temperature limits

The bulk viscosity of two-component fermions with a zero-range interaction is revisited both in two and three dimensions. We first point out that the ``standard'' Kubo formula employed in recent studies has flaws to give rise to an unphysical divergent bulk viscosity even in a limit where it is supposed to vanish. The corrected Kubo formula as well as the sum rule is, then, carefully rederived so as to confirm that the bulk viscosity, indeed, vanishes in the free, unitarity, and dimer limits. We also discuss that the recently found discrepancy between the Kubo formalism and the kinetic theory for the bulk viscosity is attributed to the fact that the quasiparticle approximation assumed by the latter breaks down even in the high-temperature limit.
Distributed Monitoring of Robot Swarms with Swarm Signal Temporal Logic

In this paper, we develop a distributed monitoring framework for robot swarms so that the agents can monitor whether the executions of robot swarms satisfy Swarm Signal Temporal Logic (SwarmSTL) formulas. We define generalized moments (GMs) to represent swarm features. A dynamic generalized moments consensus algorithm (GMCA) with Kalman filter (KF) is proposed so that each agent can estimate the GMs. Also, we obtain an upper bound for the error between an agent's estimate and the actual GMs. This bound is independent of the motion of the agents. We also propose rules for monitoring SwarmSTL temporal and logical operators. As a result, the agents can monitor whether the swarm satisfies SwarmSTL formulas with a certain confidence level using these rules and the bound of the estimation error. The distributed monitoring framework is applied to a swarm transporting supplies example, where we also show the efficacy of the Kalman filter in the dynamic generalized moments consensus process.
Depthwise Separable Convolutional ResNet with Squeeze-and-Excitation Blocks for Small-footprint Keyword Spotting

One difficult problem of keyword spotting is how to miniaturize its memory footprint while maintain a high precision. Although convolutional neural networks have shown to be effective to the small-footprint keyword spotting problem, they still need hundreds of thousands of parameters to achieve good performance. In this paper, we propose an efficient model based on depthwise separable convolution layers and squeeze-and-excitation blocks. Specifically, we replace the standard convolution by the depthwise separable convolution, which reduces the number of the parameters of the standard convolution without significant performance degradation. We further improve the performance of the depthwise separable convolution by reweighting the output feature maps of the first convolution layer with a so-called squeeze-and-excitation block. We compared the proposed method with five representative models on two experimental settings of the Google Speech Commands dataset. Experimental results show that the proposed method achieves the state-of-the-art performance. For example, it achieves a classification error rate of 3.29% with a number of parameters of 72K in the first experiment, which significantly outperforms the comparison methods given a similar model size. It achieves an error rate of 3.97% with a number of parameters of 10K, which is also slightly better than the state-of-the-art comparison method given a similar model size.
Diversity of unicellular aquatic organisms scaled to body size

The local species diversity found in aquatic habitats is clearly related to body size. Whereas few species of large organisms (e.g. fish) can be found in single lakes or streams, one can find a notable diversity of unicellular eukaryotes (e.g. diatoms or ciliates) in every small-scale sample (FENCHEL et al. 1997, HILLEBRAND et al. 2001). On the other hand, remote anciem lakes have a very high percentage of endemic species among fish and wobemhos, whereas only few percent of microalgae are regarded to be endemic (CocQUIT 2000). These relationships between diversity and body size reflect (l) the high individual number, (2) the high transponability, and (3) the short generation times of small compared to large organisms. Body size is thus a proximate variable for different traits which lead to high local species richness and low global species richness of unicellular organisms (FIN! AY et al. 1996, FENCHEL et al. 1997, HILLEBRAND et al. 2001). A! though the degree of ubiquity is still under debate (Fü! SSNER 1999), there is increasing evidence of strong local and weak regional impacts on unicellular diversity. AuEN et al. (1999), in a survey of North-American lakes, found a significam relationship between area and species richness for al! metazoan taxa, but not for diatoms. In a similar analysis from the Arctic, including a range of areas from a few em' to km', the slope of the species richness-area relationship increased with increasing body size (AzovsKY 2000). If dispersal is higher and iflocal factors control the local richness of small organisms, large-scale patterns of diversity should be absem for aquatic organisms such as protowans and microalgae. In the presem study, this hypothesis is tested for one of the most prominent patterns in ecology, the latitudinal decline of species richness.
BLEU Neighbors: A Reference-less Approach to Automatic Evaluation

Evaluation is a bottleneck in the development of natural language generation (NLG) models. Automatic metrics such as BLEU rely on references, but for tasks such as open-ended generation, there are no references to draw upon. Although language diversity can be estimated using statistical measures such as perplexity, measuring language quality requires human evaluation. However, because human evaluation at scale is slow and expensive, it is used sparingly; it cannot be used to rapidly iterate on NLG models, in the way BLEU is used for machine translation. To this end, we propose BLEU Neighbors, a nearest neighbors model for estimating language quality by using the BLEU score as a kernel function. On existing datasets for chitchat dialogue and open-ended sentence generation, we find that – on average – the quality estimation from a BLEU Neighbors model has a lower mean squared error and higher Spearman correlation with the ground truth than individual human annotators. Despite its simplicity, BLEU Neighbors even outperforms state-of-the-art models on automatically grading essays, including models that have access to a gold-standard reference essay.
Hierarchical Clustering of World Cuisines

Cultures across the world have evolved to have unique patterns despite shared ingredients and cooking techniques. Using data obtained from RecipeDB, an online resource for recipes, we extract patterns in 26 world cuisines and further probe for their inter-relatedness. By application of frequent itemset mining and ingredient authenticity we characterize the quintessential patterns in the cuisines and build a hierarchical tree of the world cuisines. This tree provides interesting insights into the evolution of cuisines and their geographical as well as historical relatedness.
Deep convolutional neural networks for face and iris presentation attack detection: Survey and case study

Biometric presentation attack detection (PAD) is gaining increasing attention. Users of mobile devices find it more convenient to unlock their smart applications with finger, face, or iris recognition instead of passwords. In this study, the authors survey the approaches presented in the recent literature to detect face and iris presentation attacks. Specifically, they investigate the effectiveness of fine-tuning very deep convolutional neural networks to the task of face and iris antispoofing. They compare two different fine-tuning approaches on six publicly available benchmark datasets. Results show the effectiveness of these deep models in learning discriminative features that can tell apart real from fake biometric images with a very low error rate. Cross-dataset evaluation on face PAD showed better generalisation than state-of-the-art. They also performed cross-dataset testing on iris PAD datasets in terms of equal error rate, which was not reported in the literature before. Additionally, they propose the use of a single deep network trained to detect both face and iris attacks. They have not noticed accuracy degradation compared to networks trained for only one biometric separately. Finally, they analysed the learned features by the network, in correlation with the image frequency components, to justify its prediction decision.
Distribución de Eufausiáceos (Crustacea: Malacostraca) en el Océano Pacífico Colombiano durante el Periodo 02 a 27 de Septiembre de 2005

Se estudio la distribucion de los eufausia- ceos durante el periodo 02 al 27 de septiembre de 2005 en el Oceano Pacifico Colombiano (OPC). Se realizaron arrastres oblicuos hasta una profundidad media de 165 m empleando una red bongo de 60 cm de diametro para analizar las muestras de la malla con poro de 294 µm. La temperatura, la salinidad, el oxigeno disuelto y la clorofila a, y las amplitudes de la capa de mezcla y la termoclina, indicaron ausencia de eventos El Nino o La Nina. Los adultos y las larvas calyptopis y furcilia se encontraron en casi todo el OPC, aunque la densidad larval en general fue mas alta, sugiriendo una reproduccion permanente. Al parecer, la mayor concentracion de organismos hacia la parte costera, en especial norte, se relaciono con la mayor amplitud de la termoclina y la consecuente disponibilidad de alimento mas alta. Se identificaron individuos de 11 especies, con Euphausia distinguenda y Euphausia diomedeae dominando en las muestras (1584 y 264 ind./100 m3 , respectivamente) y exhibiendo distribucion espacial amplia. Los multiples procesos hidrodinamicos que caracterizan el OPC probablemente influenciaron la distribucion y densidad de los eufausiaceos. Uno de ellos es la entrada de agua del Pacifico central, demostrada por la presencia de Euphausia brevis, Euphausia mutica, Nematobrachion flexipes y Stylocheiron carinatum . Aunque la predominancia nocturna de los eufausiaceos sugiere migraciones verticales, este aspecto no fue concluyente, debido a la metodologia de muestreo y los datos limitados, lo cual pudo incidir en las asociaciones estadisticas muy bajas obtenidas entre las densidades de los eufausia- ceos y las variables abioticas.
Measurement-induced quantum criticality under continuous monitoring

We investigate entanglement phase transitions from volume-law to area-law entanglement in a quantum many-body state under continuous position measurement on the basis of the quantum trajectory approach. We find the signatures of the transitions as peak structures in the mutual information as a function of measurement strength, as previously reported for random unitary circuits with projective measurements. At the transition points, the entanglement entropy scales logarithmically and various physical quantities scale algebraically, implying emergent conformal criticality, for both integrable and nonintegrable one-dimensional interacting Hamiltonians; however, such transitions have been argued to be absent in noninteracting regimes in some previous studies. With the aid of $U(1)$ symmetry in our model, the measurement-induced criticality exhibits a spectral signature resembling a Tomonaga-Luttinger liquid theory from symmetry-resolved entanglement. These intriguing critical phenomena are unique to steady-state regimes of the conditional dynamics at the single-trajectory level, and are absent in the unconditional dynamics obeying the Lindblad master equation, in which the system ends up with the featureless, infinite-temperature mixed state. We also propose a possible experimental setup to test the predicted entanglement transition based on the subsystem particle-number fluctuations. This quantity should readily be measured by the current techniques of quantum gas microscopy and is in practice easier to obtain than the entanglement entropy itself.
Resolving the Soft X-Ray Ultrafast Outflow in PDS 456

Past X-ray observations of the nearby luminous quasar PDS 456 (at z = 0.184) have revealed a wide-angle accretion disk wind with an outflow velocity of ∼−0.25c, as observed through observations of its blueshifted iron K-shell absorption line profile. Here we present three new XMM-Newton observations of PDS 456: one in 2018 September where the quasar was bright and featureless and two in 2019 September, 22 days apart, occurring when the quasar was five times fainter and where strong blueshifted lines from the wind were present. During the second 2019 September observation, three broad (σ = 3000 km s−1) absorption lines were resolved in the high-resolution Reflection Grating Spectrometer spectrum that are identified with blueshifted O viii Lyα, Ne ix Heα, and Ne x Lyα. The outflow velocity of this soft X-ray absorber was found to be v/c = −0.258 ± 0.003, fully consistent with an iron K absorber with v/c = −0.261 ± 0.007. The ionization parameter and column density of the soft X-ray component (log ξ = 3.4, NH = 2 × 1021 cm−2) outflow was lower by about 2 orders of magnitude when compared to the high-ionization wind at iron K (log ξ = 5, NH = 7 × 1023 cm−2). Substantial variability was seen in the soft X-ray absorber between the 2019 observations, declining from NH = 1023 to 1021 cm−2 over 20 days, while the iron K component was remarkably stable. We conclude that the soft X-ray wind may originate from an inhomogeneous wind streamline passing across the line of sight that, due to its lower ionization, is located further from the black hole, on parsec scales, than the innermost disk wind.
Hybrid Neuro-Evolutionary Method for Predicting Wind Turbine Power Output

Reliable wind turbine power prediction is imperative to the planning, scheduling and control of wind energy farms for stable power production. In recent years Machine Learning (ML) methods have been successfully applied in a wide range of domains, including renewable energy. However, due to the challenging nature of power prediction in wind farms, current models are far short of the accuracy required by industry. In this paper, we deploy a composite ML approach--namely a hybrid neuro-evolutionary algorithm--for accurate forecasting of the power output in wind-turbine farms. We use historical data in the supervisory control and data acquisition (SCADA) systems as input to estimate the power output from an onshore wind farm in Sweden. At the beginning stage, the k-means clustering method and an Autoencoder are employed, respectively, to detect and filter noise in the SCADA measurements. Next, with the prior knowledge that the underlying wind patterns are highly non-linear and diverse, we combine a self-adaptive differential evolution (SaDE) algorithm as a hyper-parameter optimizer, and a recurrent neural network (RNN) called Long Short-term memory (LSTM) to model the power curve of a wind turbine in a farm. Two short time forecasting horizons, including ten-minutes ahead and one-hour ahead, are considered in our experiments. We show that our approach outperforms its counterparts.