Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Nonlinear Autoregressive with Exogenous Input (Narx) Neural Network in Macroeconomic Forecasting, National Goal Setting and Global Competitiveness Assessment

This paper selects the NARX neural network as the method through literature review, and constructs specific NARX neural networks under application scenarios involving macroeconomic forecasting, national goal setting and global competitiveness assessment. Through case studies on China, US and Eurozone, this study explores how those limited & partial exogenous inputs or abundant & comprehensive exogenous inputs, a small set of most relevant exogenous inputs or a large set of exogenous inputs covering all major aspects of the macro economy, whole area related exogenous inputs or both whole area and subdivision area related exogenous inputs specifically affect the forecasting performance of NARX neural networks for specific macroeconomic indicators or indices. Through the case study on Russia this paper explores how the limited & most relevant exogenous inputs set or the abundant & comprehensive exogenous inputs set specifically influences the prediction performance of those specific NARX neural networks for national goal setting. Finally, comparative studies on the application of NARX neural networks for the forecasts of Global Competitiveness Indices (GCIs) of various economies are conducted, in order to explore whether the specific NARX neural network trained on the basis of the GCI related data of some economies can make sufficiently accurate predictions about GCIs of other economies, and whether the specific NARX neural network trained on the basis of the data of some type of economies can give more accurate predictions about GCIs of the same type of economies than those of different type of economies. Based on all of the above successful application, this paper provides policy recommendations on applying fully trained NARX neural networks that are assessed as qualified to assist or even replace the deductive and inductive abilities of the human brain in a variety of appropriate tasks.
Studies on the resistance of grapevine to powdery mildew

Studies of 68 cultivars of Vitis vinifera and two interspecific hybrids inoculated with four naturally occurring isolates of Uncinula necator demonstrated variation for pathogenicity between isolates and variation in resistance between cultivars. There was no specific interaction between the cultivars and the isolates. The progenies produced either by selling or hybridization of cultivars of V. vinifera always contained some resistant individuals regardless of the parental resistance ratings. However, the proportion of resistant plants was positively correlated to the level of resistance of the parent(s). The results indicate that ‘minor’ resistance genes exist in the cultivars of V. vinifera, and that resistance to U. necator could be improved by recurrent selection.
Pleiades or Not? Resolving the Status of the Lithium-rich M Dwarfs HHJ 339 and HHJ 430

Oppenheimer et al. discovered two M5 dwarfs in the Pleiades with nearly primordial lithium. These stars are not low enough in mass to represent the leading edge of the lithium depletion boundary at Pleiades age (∼125 Myr). A possible explanation for the enhanced lithium in these stars is that they are actually not members of the Pleiades but instead are members of a younger moving group seen in projection toward the Pleiades. We have used data from Gaia DR2 to confirm that these two stars, HHJ 339 and HHJ 430, are indeed not members of the Pleiades. Based on their space motions, parallaxes, and positions in a Gaia-based color–magnitude diagram, it is probable that these two stars are about 40 parsecs foreground to the Pleiades and have ages of ∼25 Myr. Kinematically they are best matched to the 32 Ori moving group.
Modal decompositions of the kinematics of Crevalle jack and the fluid–caudal fin interaction

To understand the governing mechanisms of bio-inspired swimming has always been challenging due to intense interactions between flexible bodies of natural aquatic species and water around them. Advanced modal decomposition techniques provide us with tools to develop more in-depth understating about these complex dynamical systems. In this paper, we employ proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) techniques to extract energetically strongest spatio-temporal orthonormal components of complex kinematics of a Crevalle jack (Caranx hippos) fish. Then, we present a computational framework for handling fluid–structure interaction related problems in order to investigate their contributions towards the overall dynamics of highly nonlinear systems. We find that the undulating motion of this fish can be described by only two standing-wave like spatially orthonormal modes. Constructing the data set from our numerical simulations for flows over the membranous caudal fin of the jack fish, our modal analyses reveal that only the first few modes receive energy from both the fluid and structure, but the contribution of the structure in the remaining modes is minimal. For the viscous and transitional flow conditions considered here, both spatially and temporally orthonormal modes show strikingly similar coherent flow structures. Our investigations are expected to assist in developing data-driven reduced-order mathematical models to examine the dynamics of bio-inspired swimming robots and develop new and effective control strategies to bring their performance closer to real fish species.
Particle creation by a massless spin-1/2 field in a warped cosmological braneworld scenario

Energy momentum tensor of a quantised massless bulk spin-1/2 field in five dimensional warped cosmological spacetimes is studied. The four dimensional part of our model represents a warped cosmological thick brane and the scale of the extra dimension is time-dependent. We use a simple ansatz to solve the Dirac equation in the bulk that helps us to compare our results with the known four dimensional case. Renormalisation of the components of the energy momentum tensor is achieved using adiabatic regularization method. We compute the leading order finite contribution to the stress-energy tensor which is of adiabatic order six. The resulting energy and pressure densities explicitly show the effects of the so-called warping factor and the dynamic extra dimension on the created matter. We show how the energy density produced is localised to form `3-branes' along the extra dimension.
Anomaly Detection in Cloud Components

Cloud platforms, under the hood, consist of a complex inter-connected stack of hardware and software components. Each of these components can fail which may lead to an outage. Our goal is to improve the quality of Cloud services through early detection of such failures by analyzing resource utilization metrics. We tested Gated-Recurrent-Unit-based autoencoder with a likelihood function to detect anomalies in various multidimensional time series and achieved high performance.
Situação epidemiológica da brucelose bovina no Estado de São Paulo

A study to characterize the epidemiological situation of bovine brucellosis in the State of Sao Paulo was carried out. The state was stratified in seven cattle production regions and 150 herds with reproductive activity were randomly selected within each one. A total of 1,073 herds were sampled. In a second stage, 10 or 15 cows older than 24 month age (in herds with <100 cows and herds ≥100 cows, respectively) were bled at random. A total of 8,761 animals were bled. Sera were tested using a serial testing procedure with the Rose Bengal test as the screening method and the Complement Fixation as the confirmatory method. The estimated prevalence of herds with at least one positive animal was 9.7% [7.8-11.6%], whereas the estimated prevalence of positive cattle was 3.8% [0.7-6.9%] in Sao Paulo State. An epidemiological questionnaire was applied in each farm, allowing the evaluation of the association of selected risk factors with the disease. The characteristics associated with the brucellosis infected herds were farms with more than 87 bovines (OR= 2.25) and introduction of breeding cattle (OR= 1.56).
SciANN: A Keras wrapper for scientific computations and physics-informed deep learning using artificial neural networks

In this paper, we introduce SciANN, a Python package for scientific computing and physics-informed deep learning using artificial neural networks. SciANN uses the widely used deep-learning packages Tensorflow and Keras to build deep neural networks and optimization models, thus inheriting many of Keras's functionalities, such as batch optimization and model reuse for transfer learning. SciANN is designed to abstract neural network construction for scientific computations and solution and discovery of partial differential equations (PDE) using the physics-informed neural networks (PINN) architecture, therefore providing the flexibility to set up complex functional forms. We illustrate, in a series of examples, how the framework can be used for curve fitting on discrete data, and for solution and discovery of PDEs in strong and weak forms. We summarize the features currently available in SciANN, and also outline ongoing and future developments.
Deep Lighting Environment Map Estimation from Spherical Panoramas

Estimating a scene's lighting is a very important task when compositing synthetic content within real environments, with applications in mixed reality and postproduction. In this work we present a data-driven model that estimates an HDR lighting environment map from a single LDR monocular spherical panorama. In addition to being a challenging and ill-posed problem, the lighting estimation task also suffers from a lack of facile illumination ground truth data, a fact that hinders the applicability of data-driven methods. We approach this problem differently, exploiting the availability of surface geometry to employ image-based relighting as a data generator and supervision mechanism. This relies on a global Lambertian assumption that helps us overcome issues related to pre-baked lighting. We relight our training data and complement the model’s supervision with a photometric loss, enabled by a differentiable image-based relighting technique. Finally, since we predict spherical spectral coefficients, we show that by imposing a distribution prior on the predicted coefficients, we can greatly boost performance. Code and models available at vcl3d.github. io/DeepPanoramaLighting
Sequential Sentence Matching Network for Multi-turn Response Selection in Retrieval-based Chatbots

Recently, open domain multi-turn chatbots have attracted much interest from lots of researchers in both academia and industry. The dominant retrieval-based methods use context-response matching mechanisms for multi-turn response selection. Specifically, the state-of-the-art methods perform the context-response matching by word or segment similarity. However, these models lack a full exploitation of the sentence-level semantic information, and make simple mistakes that humans can easily avoid. In this work, we propose a matching network, called sequential sentence matching network (S2M), to use the sentence-level semantic information to address the problem. Firstly and most importantly, we find that by using the sentence-level semantic information, the network successfully addresses the problem and gets a significant improvement on matching, resulting in a state-of-the-art performance. Furthermore, we integrate the sentence matching we introduced here and the usual word similarity matching reported in the current literature, to match at different semantic levels. Experiments on three public data sets show that such integration further improves the model performance.
Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T-cell memory formation after mild COVID-19 infection

COVID-19 is a global pandemic caused by the SARS-CoV-2 coronavirus. T cells play a key role in the adaptive antiviral immune response by killing infected cells and facilitating the selection of virus-specific antibodies. However neither the dynamics and cross-reactivity of the SARS-CoV-2-specific T cell response nor the diversity of resulting immune memory are well understood. In this study we use longitudinal high-throughput T cell receptor sequencing to track changes in the T cell repertoire following two mild cases of COVID-19 infection. In both donors we identified SARS-CoV-2-responding CD4+ and CD8+ T cell clones. We describe characteristic motifs in TCR sequences of COVID-19-reactive clones, suggesting the existence of immunodominant epitopes. We show that in both donors the majority of infection-reactive clonotypes acquire memory phenotypes. Certain T cell clones were detected in the memory fraction at the pre-infection timepoint, suggesting participation of pre-existing cross-reactive memory T cells in the immune response to SARS-CoV-2.
Building BROOK: A Multi-modal and Facial Video Database for Human-Vehicle Interaction Research

With the growing popularity of Autonomous Vehicles, more opportunities have bloomed in the context of Human-Vehicle Interactions. However, the lack of comprehensive and concrete database support for such specific use case limits relevant studies in the whole design spaces. In this paper, we present our work-in-progress BROOK, a public multi-modal database with facial video records, which could be used to characterise drivers' affective states and driving styles. We first explain how we over-engineer such database in details, and what we have gained through a ten-month study. Then we showcase a Neural Network-based predictor, leveraging BROOK, which supports multi-modal prediction (including physiological data of heart rate and skin conductance and driving status data of speed) through facial videos. Finally we discuss related issues when building such a database and our future directions in the context of BROOK. We believe BROOK is an essential building block for future Human-Vehicle Interaction Research. More details and updates about the project BROOK is online at https: //unnc-idl-ucc.github. io/BROOK/.
Aspects on Effective Theories and the QCD Transition

We review recent advances in the understanding of the Quantum Chromodynamics (QCD) transition and its nature, paying special attention to the analysis of chiral symmetry restoration within different approaches based on effective theories. After presenting some of the main aspects of the current knowledge of the phase diagram from the theoretical, experimental and lattice sides, we discuss some recent problems where approaches relying on effective theories have been particularly useful. In particular, the combination of ideas such as Chiral Perturbation Theory, unitarity and Ward Identities allows us to describe successfully several observables of interest. This is particularly relevant for quantities expected to be dominated by the light meson components of the hadron gas such as the scalar and topological susceptibilities. In addition, ward identities and effective Lagrangians provide systematic results regarding chiral and U ( 1 ) A partner degeneration properties which are of great importance for the interplay between those two transitions and the nature of chiral symmetry restoration. Special attention is paid to the connection of this theoretical framework with lattice simulations.
Remote State Estimation With Smart Sensors Over Markov Fading Channels

We consider a fundamental remote state estimation problem of discrete-time linear time-invariant (LTI) systems. A smart sensor forwards its local state estimate to a remote estimator over a time-correlated multistate Markov fading channel, where the packet drop probability is time-varying and depends on the current fading channel state. We establish a necessary and sufficient condition for mean-square stability of the remote estimation error covariance in terms of the state transition matrix of the LTI system, the packet drop probabilities in different channel states, and the transition probability matrix of the Markov channel states. To derive this result, we propose a novel estimation-cycle based approach and provide new elementwise bounds of matrix powers. The stability condition is verified by numerical results and is shown more effective than existing sufficient conditions in the literature. We observe that the stability region in terms of the packet drop probabilities in different channel states can either be convex or nonconvex depending on the transition probability matrix of the Markov channel states. Our numerical results suggest that the stability conditions for remote estimation may coincide for setups with a smart sensor and with a conventional one (which sends raw measurements to the remote estimator) though the smart sensor setup achieves a better estimation performance.
A Spontaneous Driver Emotion Facial Expression (DEFE) Dataset for Intelligent Vehicles: Emotions Triggered by Video-Audio Clips in Driving Scenarios

In this article, a new dataset, the driver emotion facial expression (DEFE) dataset for drivers’ spontaneous emotions analysis is introduced. The dataset includes facial expression recordings from 60 participants during driving. After watching a selected video-audio clip to elicit a speciﬁc emotion, each participant completed the driving tasks in the same driving scenario and rated his/her emotional responses during the driving processes from the aspects of dimensional emotion method and discrete emotion method. The study also conducted classiﬁcation experiments to recognize the scales of arousal, valence, dominance, as well as the emotion category and intensity to establish baseline results for the proposed dataset. Furthermore, this paper compared emotion recognition results difference through facial expressions between dynamic driving and static life scenarios. The results showed that dynamic driving and static life datasets were different in emotion recognition results. To further explore the reasons for the difference in emotion recognition results, the analysis from the AU (action unit) presence perspective was studied. The results showed significant differences in the AUs presence of facial expressions between dynamic driving and static life scenarios, indicating that drivers’ facial expressions may be affected by the driving task to influence the recognition of drivers’ emotions through facial expressions. Therefore, to accurately recognize the drivers’ emotions to establish a reliable emotion-aware human-machine interaction system, thereby improving driving safety and comfort, publishing a human emotion dataset speciﬁcally for the driver is necessary. The proposed dataset will be publicly available so that researchers worldwide can use it to develop and examine their driver emotion analysis methods. To the best of our knowledge, this is currently the only public driver facial expression dataset.
Circadian Protection against Reactive Oxygen Species Involves Changes in Daily Levels of the Manganese- and Iron-Containing Superoxide Dismutase Isoforms in Lingulodinium polyedrum

A circadian rhythm in the total activity of superoxide dismutase (SOD; EC 1.15.1.1) from the unicellular alga Lingulodinium polyedrum is shown to be attributable to the mitochondrial MnSOD and chloroplastic FeSOD isoforms. Activity gels and labelling with polyclonal antibodies against pure CuZnSOD, MnSOD and FeSOD revealed a distinct circadian pattern in the abundance of the latter two isoforms, with peak values in early photophase 5 times greater than at the dark phase. However, no such changes were detected for the CuZnSOD isoform, which remained at constant levels over the 24-h light/dark cycle. These SOD isoforms might provide protection against damage from photochemically generated oxygen radicals, thus preventing subcellular oxidative stress.
Dynamics of reallocation within India’s income distribution

It is well known that inequality has been rising in India in the recent past, but the assumption has been that while the rich benefit more than proportionally from economic growth, the poor are also better off than before. Our modelled outcomes (using the RGBM framework) cast doubt on this proposition. We find that the income share dynamics are consistent with a negative reallocation since the early 2000s, i.e., the Indian income distribution possibly entered a regime of perverse redistribution of resources from the poor to the rich. Our model suggests that the historically low-income shares of the bottom decile (~ 1%) and bottom percentile (~ 0.03%) are possibly due to a decline in real incomes in the 2000s. We find qualified support for these theoretical predictions using income distribution data. We characterize these findings in the context of increasing informalization of the workforce in the formal manufacturing and service sectors as well as the growing economic insecurity of the agricultural workforce in India. Significant structural changes will be required to address this phenomenon.
Single-participant structural connectivity matrices lead to greater accuracy in classification of participants than function in autism in MRI

In this work, we introduce a technique of deriving symmetric connectivity matrices from regional histograms of grey-matter volume estimated from T1-weighted MRIs. We then validated the technique by inputting the connectivity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey-matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs. Our results gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural connectivity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural and functional connectivities gave an AUROC of 0.7354 (69.40% accuracy). Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural connectivity. This work provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models.
Characterising quantum correlations of fixed dimension

We give a converging semidefinite programming hierarchy of outer approximations for the set of quantum correlations of fixed dimension. Starting from the Navascues-Pironio-Acin (NPA) hierarchy for general quantum correlations, we identify additional semidefinite constraints for any fixed dimension, which lead to analytical bounds on the convergence speed of the resulting hierarchy. In particular, we give an algorithm to compute additive $\epsilon$-approximations on the value of two-player free games with $|T|\times|T|$-dimensional quantum assistance in time $\exp(\mathcal{O}\big(|T|^8/\epsilon^2(\log^2|A||T|+\log|Q|\log|A||T|)\big))$, where $|A|$ and $|Q|$ denote the numbers of answers and questions, respectively. For fixed $|T|$, this scales polynomially in $|Q|$ and quasi-polynomially in $|A|$, thereby improving on previously known approximation algorithms for which worst case run-time guarantees were at best exponential in $|Q||A|$. For the proof, we make a connection to the quantum separability problem and employ as our main technical tool improved multipartite quantum de Finetti theorems with linear constraints.