Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulating the Effects of Social Presence on Trust, Privacy Concerns & Usage Intentions in Automated Bots for Finance

FinBots are chatbots built on automated decision technology, aimed to facilitate accessible banking and to support customers in making financial decisions. Chatbots are increasing in prevalence, sometimes even equipped to mimic human social rules, expectations and norms, decreasing the necessity for human-to-human interaction. As banks and financial advisory platforms move towards creating bots that enhance the current state of consumer trust and adoption rates, we investigated the effects of chatbot vignettes with and without socio-emotional features on intention to use the chatbot for financial support purposes. We conducted a between-subject online experiment with N = 410 participants. Participants in the control group were provided with a vignette describing a secure and reliable chatbot called XRO23, whereas participants in the experimental group were presented with a vignette describing a secure and reliable chatbot that is more human-like and named Emma. We found that Vignette Emma did not increase participants’ trust levels nor lowered their privacy concerns even though it increased perception of social presence. However, we found that intention to use the presented chatbot for financial support was positively influenced by perceived humanness and trust in the bot. Participants were also more willing to share financially-sensitive information such as account number, sort code and payments information to XRO23 compared to Emma - revealing a preference for a technical and mechanical FinBot in information sharing. Overall, this research contributes to our understanding of the intention to use chatbots with different features as financial technology, in particular that socio-emotional support may not be favoured when designed independently of financial function.
Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition

Neural rendering techniques promise efficient photo-realistic image synthesis while at the same time providing rich control over scene parameters by learning the physical image formation process. While several supervised methods have been proposed for this task, acquiring a dataset of images with accurately aligned 3D models is very difficult. The main contribution of this work is to lift this restriction by training a neural rendering algorithm from unpaired data. More specifically, we propose an autoencoder for joint generation of realistic images from synthetic 3D models while simultaneously decomposing real images into their intrinsic shape and appearance properties. In contrast to a traditional graphics pipeline, our approach does not require to specify all scene properties, such as material parameters and lighting by hand. Instead, we learn photo-realistic deferred rendering from a small set of 3D models and a larger set of unaligned real images, both of which are easy to acquire in practice. Simultaneously, we obtain accurate intrinsic decompositions of real images while not requiring paired ground truth. Our experiments confirm that a joint treatment of rendering and decomposition is indeed beneficial and that our approach outperforms state-of-the-art image-to-image translation baselines both qualitatively and quantitatively.
Identification and Mapping Quantitative Trait Loci for Stem Reserve Mobilisation in Wheat (Triticum aestivum L.)

Quantitative trait loci (QTL) analysis was carried out with a set of 114 recombinant inbred lines (RILs) from the International Triticeae Mapping Initiative (ITMI) population of ‘W7984’ × ‘Opata 85’ to identify genomic regions controlling traits related to post-anthesis drought tolerance of wheat ( Triticum aestivum L.). In two experiments performed in Gatersleben in 2001 and 2003, the amount stem reserves mobilisation was estimated by measuring of changes in 1000-grain weight after chemical desiccation treatment. QTLs for stem reserves mobilisation (Srm) were mapped on chromosomes 2D, 5D and 7D. The mapping positions obtained in the present investigation are discussed with respect to studies on drought tolerance performed in wheat previously. QTLs for drought tolerance preferentially appeared in homoeologous regions at distal parts of the group 7 chromosomes.
Isoprene synthase activity and its relation to isoprene emission in Quercus robur L. leaves

Pedunculate oak (Quercus robur L.) is known as a strong isoprene (2-methyl-1,3-butadiene) emitter. Diurnal changes in isoprene emission were determined by branch enclosure measurements. In contrast to the diurnal cycle in emission rates, specific isoprene synthase activity in the leaves remained unchanged. Based on in vitro enzyme activity and its temperature dependency, an isoprene synthesis capacity at specific leaf temperatures was calculated. The comparison of these 'leaf temperature-dependent enzyme capacities' and the measured emission rates revealed that the enzyme activity of isoprene synthase is comparable to the observed isoprene emission rates. In addition, variation in the isoprene synthase activity of the leaves due to changes in light intensity during leaf development was investigated. A 50% reduction of light intensity by shading of single branches reduced isoprene synthase activity by [almost equal to] 60% compared with full sunlight. The calculation of isoprene synthesis capacities based on enzymatic data obtained under optimum reaction conditions, corrected for actual leaf temperature and related to leaf surface area, provides a sound basis for predicting the isoprene emission potential of plants.
Offline Handwritten Chinese Text Recognition with Convolutional Neural Networks

Deep learning based methods have been dominating the text recognition tasks in different and multilingual scenarios. The offline handwritten Chinese text recognition (HCTR) is one of the most challenging tasks because it involves thousands of characters, variant writing styles and complex data collection process. Recently, the recurrent-free architectures for text recognition appears to be competitive as its highly parallelism and comparable results. In this paper, we build the models using only the convolutional neural networks and use CTC as the loss function. To reduce the overfitting, we apply dropout after each max-pooling layer and with extreme high rate on the last one before the linear layer. The CASIA-HWDB database is selected to tune and evaluate the proposed models. With the existing text samples as templates, we randomly choose isolated character samples to synthesis more text samples for training. We finally achieve 6.81% character error rate (CER) on the ICDAR 2013 competition set, which is the best published result without language model correction.
Faecal near infrared reflectance spectroscopy estimates of diet quality and responses to nitrogen supplements by cattle grazing Bothriochloa pertusa pastures

A grazing experiment in the seasonally dry tropics of north Queensland examined the diet selected and the growth responses of Bos indicus steers to urea supplement over two dry seasons and one wet season, from August 2001 to January 2003. There were two groups of 10 steers (control and urea-supplemented) and each group comprised two age cohorts, A and B, of five steers each with an age difference of ~1 year. In June 2002, cohort A steers were replaced with steers 2 years younger (cohort C). The steers grazed Bothriochloa pertusa pastures on a low fertility Red Chromosol soil. The groups were switched between two adjoining paddocks at fortnightly intervals when they were weighed and faecal samples were collected for faecal near infrared reflectance spectroscopy (F.NIRS) estimates of diet quality and growth rate. Rainfall and diet quality followed the expected seasonal pattern, but the 2001–02 wet season was very short (November–January) with only 65% of the long-term average rainfall. There was no rain during the 2001 dry season (DS-1) from August to mid November, and no effective rain (17 mm) between February and December 2002. Non-grass (i.e. herbaceous dicot plants) made only a small contribution to the diet, averaging 13%. In DS-1 and in the dry season of 2002 (DS-2) diet crude protein (CP) averaged 2.5% and 2.9%, and DMD/CP (ratio of dry matter digestibility to CP) averaged 18.6 and 17.1, respectively. Liveweight (LW) loss in control steers during DS-1 averaged 32 kg. Urea supplement reduced LW loss by 18 kg (P < 0.001), but most of the benefit was lost during the following growing season. During the extended dry period in 2002, average LW losses of control steers were 85 and 47 kg in the older and younger cohorts, respectively, and supplementation with urea reduced weight losses by 53 and 31 kg, respectively (P < 0.001). F.NIRS predicted the cumulative LW of the unsupplemented steers in cohorts A and B with acceptable accuracy, the difference between the observed and predicted final LW being 6 kg for cohort A after 10 months, 12 kg for cohort B after 17 months, and 27 kg for cohort C after 7 months. The results demonstrated that F.NIRS can be effectively used to monitor dietary CP and DMD levels in grazing cattle, to help develop an understanding of cattle responses to urea supplement relative to the quality of the basal forage diet, and to provide useful decision support information for the nutritional management of grazing cattle.
ANALYSIS OF FUSION AND COMBINING FOR WIRELESS SOURCE DETECTION

In wireless detection scenarios such as sensor networks or cognitive radio, “hidden nodes” can occur due to channel fading, which can be overcome by collaborative sensing. This work considers the performance of collaboration with combining and fusion methods for both static and fading environments, where each node employs energy detection for sensing an unknown source. The case of combined Rayleigh fading and shadowing is also considered, showing that AND fusion can have even lower performance than a single sensor. Our results show that choosing a fusion rule without considering the statistics of the fading environment can lead to very poor performance. It also proposes a new quantized equal gain combining (QEGC) method that strikes a good balance between performance and transmission overhead among the collaborating nodes.
Spectral Diversities of Gamma-Ray Bursts in High-energy Bands: Hints from Turbulent Cascade

We statistically examine the gamma-ray burst (GRB) photon indices obtained by the Fermi-GBM and Fermi-LAT observations and compare the LAT GRB photon indices to the GBM GRB photon indices. We apply the jitter radiation to explain the GRB spectral diversities in the high-energy bands. In our model, the jitter radiative spectral index is determined by the spectral index of the turbulence. We classify GRBs into three classes depending on the shape of the GRB high-energy spectrum when we compare the GBM and LAT detections: the GRB spectrum is concave (GRBs turn out to be softer and are labeled as S-GRBs), the GRB spectrum is convex (GRBs turn out to be harder and are labeled as H-GRBs), and the GRBs have no strong spectral changes (labeled as N-GRBs). A universal Kolmogorov index 7/3 in the turbulent cascade is consistent with the photon index of the N-GRBs. The S-GRB spectra can be explained by the turbulent cascade due to the kinetic magnetic reconnection with the spectral index range of the turbulence from 8/3 to 3.0. The H-GRB spectra originate from the inverse turbulent cascade with the spectral index range of the turbulence from 2.0 to 3.5 that occurred during the large lengthscale magnetic reconnection. Thus, the GRB radiative spectra are diversified because the turbulent cascade modifies the turbulent energy spectrum. More observational samples are expected in the future to further identify our suggestions.
Simultaneous determination of the cosmic birefringence and miscalibrated polarization angles II: Including cross-frequency spectra

We develop a strategy to determine the cosmic birefringence and miscalibrated polarization angles simultaneously using the observed $EB$ polarization power spectra of the cosmic microwave background and the Galactic foreground emission. We extend the methodology of Y. Minami et al. (Prog. Theor. Exp. Phys. $\mathbf{2019}$, 083E02, 2019), which was developed for auto frequency power spectra, by including cross frequency spectra. By fitting one global birefringence angle and independent miscalibration angles at different frequency bands, we determine both angles with significantly smaller uncertainties (by more than a factor of two) compared to the auto spectra.
Propagation for Dynamic Continuous Time Chain Event Graphs

Chain Event Graphs (CEGs) are a family of event-based graphical models that represent context-specific conditional independences typically exhibited by asymmetric state space problems. The class of continuous time dynamic CEGs (CT-DCEGs) provides a factored representation of longitudinally evolving trajectories of a process in continuous time. Temporal evidence in a CT-DCEG introduces dependence between its transition and holding time distributions. We present a tractable exact inferential scheme analogous to the scheme in Kjaerulff (1992) for discrete Dynamic Bayesian Networks (DBNs) which employs standard junction tree inference by "unrolling" the DBN. To enable this scheme, we present an extension of the standard CEG propagation algorithm (Thwaites et al., 2008). Interestingly, the CT-DCEG benefits from simplification of its graph on observing compatible evidence while preserving the still relevant symmetries within the asymmetric network. Our results indicate that the CT-DCEG is preferred to DBNs and continuous time BNs under contexts involving significant asymmetry and a natural total ordering of the process evolution.
Towards hybrid primary intersubjectivity: a neural robotics library for human science

Human-robot interaction is becoming an interesting area of research in cognitive science, notably, for the study of social cognition. Interaction theorists consider primary intersubjectivity a non-mentalist, pre-theoretical, non-conceptual sort of processes that ground a certain level of communication and understanding, and provide support to higher-level cognitive skills. We argue this sort of low level cognitive interaction, where control is shared in dyadic encounters, is susceptible of study with neural robots. Hence, in this work we pursue three main objectives. Firstly, from the concept of active inference we study primary intersubjectivity as a second person perspective experience characterized by predictive engagement, where perception, cognition, and action are accounted for an hermeneutic circle in dyadic interaction. Secondly, we propose an open-source methodology named \textit{neural robotics library} (NRL) for experimental human-robot interaction, and a demonstration program for interacting in real-time with a virtual Cartesian robot (VCBot). Lastly, through a study case, we discuss some ways human-robot (hybrid) intersubjectivity can contribute to human science research, such as to the fields of developmental psychology, educational technology, and cognitive rehabilitation.
A Distributionally Robust Optimization Approach to the NASA Langley Uncertainty Quantification Challenge

We study a methodology to tackle the NASA Langley Uncertainty Quantification Challenge problem, based on an integration of robust optimization, more specifically a recent line of research known as distributionally robust optimization, and importance sampling in Monte Carlo simulation. The main computation machinery in this integrated methodology boils down to solving sampled linear programs. We will illustrate both our numerical performances and theoretical statistical guarantees obtained via connections to nonparametric hypothesis testing.
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees

Bayesian optimization is a sequential decision making framework for optimizing expensive-to-evaluate black-box functions. Computing a full lookahead policy amounts to solving a highly intractable stochastic dynamic program. Myopic approaches, such as expected improvement, are often adopted in practice, but they ignore the long-term impact of the immediate decision. Existing nonmyopic approaches are mostly heuristic and/or computationally expensive. In this paper, we provide the first efficient implementation of general multi-step lookahead Bayesian optimization, formulated as a sequence of nested optimization problems within a multi-step scenario tree. Instead of solving these problems in a nested way, we equivalently optimize all decision variables in the full tree jointly, in a ``one-shot'' fashion. Combining this with an efficient method for implementing multi-step Gaussian process ``fantasization,'' we demonstrate that multi-step expected improvement is computationally tractable and exhibits performance superior to existing methods on a wide range of benchmarks.
Cluster-Based Partitioning of Convolutional Neural Networks, A Solution for Computational Energy and Complexity Reduction

In this paper, we propose a novel solution to reduce the computational complexity of convolutional neural network models used for many class image classification. Our proposed model breaks the classification task into three stages: 1) general feature extraction, 2) Mid-level clustering, and 3) hyper-class classification. Steps 1 and 2 could be repeated to build larger hierarchical models. We illustrate that our proposed classifier can reach the level of accuracy reported by the best in class classification models with far less computational complexity (Flop Count) by only activating parts of the model that are needed for the image classification.
Extracting Latent State Representations with Linear Dynamics from Rich Observations

Recently, many reinforcement learning techniques were shown to have provable guarantees in the simple case of linear dynamics, especially in problems like linear quadratic regulators. However, in practice, many reinforcement learning problems try to learn a policy directly from rich, high dimensional representations such as images. Even if there is an underlying dynamics that is linear in the correct latent representations (such as position and velocity), the rich representation is likely to be nonlinear and can contain irrelevant features. In this work we study a model where there is a hidden linear subspace in which the dynamics is linear. For such a model we give an efficient algorithm for extracting the linear subspace with linear dynamics. We then extend our idea to extracting a nonlinear mapping, and empirically verify the effectiveness of our approach in simple settings with rich observations.
Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems

We develop a novel variance-reduced algorithm to solve a stochastic nonconvex-concave minimax problem which has various applications in different fields. This problem has several computational challenges due to its nonsmoothness, nonconvexity, nonlinearity, and non-separability of the objective functions. Our approach relies on a novel combination of recent ideas, including smoothing and hybrid stochastic variance-reduced techniques. Our algorithm and its variants can achieve $\mathcal{O}(T^{-2/3})$-convergence rate in $T$, and the best-known oracle complexity under standard assumptions. They have several computational advantages compared to existing methods. They can also work with both single sample or mini-batch on derivative estimators, with constant or diminishing step-sizes. We demonstrate the benefits of our algorithms over existing methods through two numerical examples.
SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the different possible approaches, methods based on convolutional neural networks (CNNs) have recently shown to reach state-of-the-art performance for SAR image restoration. CNN training requires good training data: many pairs of speckle-free/speckle-corrupted images. This is an issue in SAR applications, given the inherent scarcity of speckle-free images. To handle this problem, this paper analyzes different strategies one can adopt, depending on the speckle removal task one wishes to perform and the availability of multitemporal stacks of SAR data. The first strategy applies a CNN model, trained to remove additive white Gaussian noise from natural images, to a recently proposed SAR speckle removal framework: MuLoG (MUlti-channel LOgarithm with Gaussian denoising). No training on SAR images is performed, the network is readily applied to speckle reduction tasks. The second strategy considers a novel approach to construct a reliable dataset of speckle-free SAR images necessary to train a CNN model. Finally, a hybrid approach is also analyzed: the CNN used to remove additive white Gaussian noise is trained on speckle-free SAR images. The proposed methods are compared to other state-of-the-art speckle removal filters, to evaluate the quality of denoising and to discuss the pros and cons of the different strategies. Along with the paper, we make available the weights of the trained network to allow its usage by other researchers.
Uncovering socioeconomic gaps in mobility reduction during the COVID-19 pandemic using location data

Using smartphone location data from Colombia, Mexico, and Indonesia, we investigate how non-pharmaceutical policy interventions intended to mitigate the spread of the COVID-19 pandemic impact human mobility. In all three countries, we find that following the implementation of mobility restriction measures, human movement decreased substantially. Importantly, we also uncover large and persistent differences in mobility reduction between wealth groups: on average, users in the top decile of wealth reduced their mobility up to twice as much as users in the bottom decile. For decision-makers seeking to efficiently allocate resources to response efforts, these findings highlight that smartphone location data can be leveraged to tailor policies to the needs of specific socioeconomic groups, especially the most vulnerable.
Easing cosmic tensions with an open and hotter universe

Despite the great observational success of the standard cosmological model some discrepancies in the inferred parameter constraints have manifested among a number of cosmological data sets. These include a tension between the expansion rate of our Cosmos as inferred from the cosmic microwave background (CMB) and as found from local measurements, the preference for an enhanced amplitude of CMB lensing, a somewhat low quadrupole moment of the CMB fluctuations as well as a preference for a lower amplitude of matter fluctuations in large-scale structure surveys than inferred from the CMB. We analyse these observational tensions under the addition of spatial curvature and a free CMB background temperature that may deviate from its locally measured value. With inclusion of these parameters, we observe a trend in the parameter constraints from CMB and baryon acoustic oscillation data towards an open and hotter universe with larger current expansion rate, standard CMB lensing amplitudes, lower amplitude of matter fluctuations, and lower CMB quadrupole moment, consistently reducing the individual tensions among the cosmological data sets. Combining this data with local distance measurements, we find a preference for an open and hotter universe beyond the 99.7\% confidence level. Finally, we briefly discuss a local void as a possible source for a deviation of the locally measured CMB temperature from its background value and as mimic of negative spatial curvature for CMB photons.