Miracle-dz/UniFAR-FTUs huggingface dataset:
Does AlphaGo actually play Go? Concerning the State Space of Artificial Intelligence

The overarching goal of this paper is to develop a general model of the state space of AI. Given the breathtaking progress in AI research and technologies in recent years, such conceptual work is of substantial theoretical interest. The present AI hype is mainly driven by the triumph of deep learning neural networks. As the distinguishing feature of such networks is the ability to self-learn, self-learning is identified as one important dimension of the AI state space. Another main dimension lies in the possibility to go over from specific to more general types of problems. The third main dimension is provided by semantic grounding. Since this is a philosophically complex and controversial dimension, a larger part of the paper is devoted to it. We take a fresh look at known foundational arguments in the philosophy of mind and cognition that are gaining new relevance in view of the recent AI developments including the blockhead objection, the Turing test, the symbol grounding problem, the Chinese room argument, and general use-theoretic considerations of meaning. Finally, the AI state space, spanned by the main dimensions generalization, grounding and "selfx-ness", possessing self-x properties such as self-learning, is outlined.
Coupled Elastic-Acoustic Modelling for Quantitative Photoacoustic Tomography

Quantitative photoacoustic tomography (qPAT) is an imaging technique aimed at estimating chromophore concentrations inside tissues from photoacoustic images, which are formed by combining optical information and ultrasonic propagation. The application of qPAT as a transcranial imaging modality is complicated by shear waves that can be produced when ultrasound waves travel from soft tissue to bone. Because of this, the estimation of chromophores distributions near the skull can be problematic. In this paper, we take steps towards compensating for aberrations of the recorded photoacoustic signals caused by elastic wave propagation. With photoacoustic data simulated in a coupled elastic-acoustic domain, we conduct inversions in a purely acoustic domain. Estimation of the posterior density of the initial pressure is achieved by inversion under the Bayesian framework. We utilize the Bayesian approximation error approach to compensate for the modelling errors arising from approximating a coupled elastic-acoustic domain with a purely fluid domain. The resulting reconstructions and corresponding uncertainty estimates are then used to evaluate the posterior density of the optical absorption parameter. In the sense of the posterior uncertainty, the results show that the Bayesian approximation error approach yields a more feasible estimate for the posterior model of the initial pressure which, in turn, yields a more feasible estimate for the posterior model of the absorption coefficient.
Flow transitions and mapping for undulating swimmers

Natural swimmers usually perform undulations to propel themselves and perform a range of maneuvers. These include various biological species ranging from micro-sized organisms to large-sized fishes that undulate at typical kinematic patterns. In this paper, we consider anguilliform and carangiform swimming modes to perform numerical simulations using an immersed-boundary methods based computational solver at various Reynolds number regimes. We carry out thorough studies using wavelength and Strouhal frequency as the governing parameters for the hydrodynamic performance of undulating swimmers. Our analysis shows that the anguilliform kinematics achieves better hydrodynamic efficiency for viscous flow regime, whereas for flows with higher Reynolds number, the wavelength of the wavy motion dictates which kinematics will outperform the other. We find that the constructive interference between vortices produced at anterior parts of the bodies and co-rotating vortices present at the posterior parts plays an important role in reversing the direction of Benard-von Karman vortex street. Since most of the thrust producing conditions appear to cause wake deflection; a critical factor responsible for degrading the hydrodynamic efficiency of a swimmer, we discuss the underlying mechanics that would trigger this phenomenon. We demonstrate that the choice of kinematic and flow conditions may be restricted for the natural swimmers due to their morphological structures, but our findings provide a guideline on their selection for bio-inspired underwater vehicles.
Uncertainty-sensitive Learning and Planning with Ensembles

We propose a reinforcement learning framework for discrete environments in which an agent makes both strategic and tactical decisions. The former manifests itself through the use of value function, while the latter is powered by a tree search planner. These tools complement each other. The planning module performs a local \textit{what-if} analysis, which allows to avoid tactical pitfalls and boost backups of the value function. The value function, being global in nature, compensates for inherent locality of the planner. In order to further solidify this synergy, we introduce an exploration mechanism with two distinctive components: uncertainty modelling and risk measurement. To model the uncertainty we use value function ensembles, and to reflect risk we use propose several functionals that summarize the implied by the ensemble. We show that our method performs well on hard exploration environments: Deep-sea, toy Montezuma's Revenge, and Sokoban. In all the cases, we obtain speed-up in learning and boost in performance.
Morphy: A Datamorphic Software Test Automation Tool

This paper presents an automated tool called Morphy for datamorphic testing. It classifies software test artefacts into test entities and test morphisms, which are mappings on testing entities. In addition to datamorphisms, metamorphisms and seed test case makers, Morphy also employs a set of other test morphisms including test case metrics and filters, test set metrics and filters, test result analysers and test executers to realise test automation. In particular, basic testing activities can be automated by invoking test morphisms. Test strategies can be realised as complex combinations of test morphisms. Test processes can be automated by recording, editing and playing test scripts that invoke test morphisms and strategies. Three types of test strategies have been implemented in Morphy: datamorphism combination strategies, cluster border exploration strategies and strategies for test set optimisation via genetic algorithms. This paper focuses on the datamorphism combination strategies by giving their definitions and implementation algorithms. The paper also illustrates their uses for testing both traditional software and AI applications with three case studies.
A Fair Comparison of Graph Neural Networks for Graph Classification

Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve the quality of the field. Recently, the graph representation learning field has attracted the attention of a wide research community, which resulted in a large stream of works. As such, several Graph Neural Network models have been developed to effectively tackle graph classification. However, experimental procedures often lack rigorousness and are hardly reproducible. Motivated by this, we provide an overview of common practices that should be avoided to fairly compare with the state of the art. To counter this troubling trend, we ran more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks. Moreover, by comparing GNNs with structure-agnostic baselines we provide convincing evidence that, on some datasets, structural information has not been exploited yet. We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.
Properties of OB star−black hole systems derived from detailed binary evolution models

Context. The recent gravitational wave measurements have demonstrated the existence of stellar mass black hole binaries. It is essential for our understanding of massive star evolution to identify the contribution of binary evolution to the formation of double black holes. Aims. A promising way to progress is investigating the progenitors of double black hole systems and comparing predictions with local massive star samples, such as the population in 30 Doradus in the Large Magellanic Cloud (LMC). Methods. With this purpose in mind, we analysed a large grid of detailed binary evolution models at LMC metallicity with initial primary masses between 10 and 40 M⊙, and identified the model systems that potentially evolve into a binary consisting of a black hole and a massive main-sequence star. We then derived the observable properties of such systems, as well as peculiarities of the OB star component. Results. We find that ∼3% of the LMC late-O and early-B stars in binaries are expected to possess a black hole companion when stars with a final helium core mass above 6.6 M⊙ are assumed to form black holes. While the vast majority of them may be X-ray quiet, our models suggest that these black holes may be identified in spectroscopic binaries, either by large amplitude radial velocity variations (≳50 km s−1) and simultaneous nitrogen surface enrichment, or through a moderate radial velocity (≳10 km s−1) and simultaneous rapid rotation of the OB star. The predicted mass ratios are such that main-sequence companions can be excluded in most cases. A comparison to the observed OB+WR binaries in the LMC, Be and X-ray binaries, and known massive black hole binaries supports our conclusion. Conclusions. We expect spectroscopic observations to be able to test key assumptions in our models, with important implications for massive star evolution in general and for the formation of double black hole mergers in particular.
Spatio-Temporal Correlation of Interference in MANET Under Spatially Correlated Shadowing Environment

Correlation of interference affects spatio-temporal aspects of various wireless mobile systems, such as retransmission, multiple antennas and cooperative relaying. In this paper, we study the spatial and temporal correlation of interference in mobile ad-hoc networks under a correlated shadowing environment. By modeling the node locations as a Poisson point process with an i.i. d. mobility model and considering Gudmundson (1991)'s spatially correlated shadowing model, we theoretically analyze the relationship between the correlation distance of log-normal shadowing and the spatial and temporal correlation coefficients of interference. Since the exact expressions of the correlation coefficients are intractable, we obtain their simple asymptotic expressions as the variance of log-normal shadowing increases. We found in our numerical examples that the asymptotic expansions can be used as tight approximate formulas and useful for modeling general wireless systems under spatially correlated shadowing.
Dust moments: towards a new modeling of the galactic dust emission for CMB B-modes analysis

The characterization of the spectral energy distribution (SED) of dust emission has become a critical issue in the quest for primordial B-modes. The dust SED is often approximated by a modified black body (MBB) emission law but the extent to which this is accurate is unclear. This paper addresses this question, expanding the dust SED at the power spectrum level. The expansion is performed by means of moments around the MBB law, related to derivatives with respect to the dust spectral index. We present the mathematical formalism and apply it to simulations and Planck total intensity data, from 143 to 857 GHz, because no polarized data are yet available that provide the required sensitivity to perform this analysis. With simulations, we demonstrate the ability of high-order moments to account for spatial variations in MBB parameters. Neglecting these moments leads to poor fits and a bias in the recovered dust spectral index. We identify the main moments that are required to fit the Planck data. The comparison with simulations helps us to disentangle the respective contributions from dust and the cosmic infrared background to the high-order moments, but the simulations give an insufficient description of the actual Planck data. Extending our model to cosmic microwave background B-mode analyses within a simplified framework, we find that ignoring the dust SED distortions, or trying to model them with a single decorrelation parameter, could lead to biases that are larger than the targeted sensitivity for the next generation of CMB B-mode experiments.
The evolution of radio jets across cosmic time

We present predictions for the evolution of radio emission from Active Galactic Nuclei (AGNs). We use a model that follows the evolution of Supermassive Black Hole (SMBH) masses and spins, within the latest version of the GALFORM semi-analytic model of galaxy formation. We use a Blandford-Znajek type model to calculate the power of the relativistic jets produced by black hole accretion discs, and a scaling model to calculate radio luminosities. First, we present the predicted evolution of the jet power distribution, finding that this is dominated by objects fuelled by hot halo accretion and an ADAF accretion state for jet powers above $10^{32}\mathrm{W}$ at $z=0$, with the contribution from objects fuelled by starbursts and in a thin disc accretion state being more important for lower jet powers at $z=0$ and at all jet powers at high redshifts ($z\geq3$). We then present the evolution of the jet power density from the model. The model is consistent with current observational estimates of jet powers from radio luminosities, once we allow for the significant uncertainties in these observational estimates. Next, we calibrate the model for radio emission to a range of observational estimates of the $z=0$ radio luminosity function. We compare the evolution of the model radio luminosity function to observational estimates for $0<z<6$, finding that the predicted evolution is similar to that observed. Finally, we explore recalibrating the model to reproduce luminosity functions of core radio emission, finding that the model is in approximate agreement with the observations.
Soft eSkin: distributed touch sensing with harmonized energy and computing

Inspired by biology, significant advances have been made in the field of electronic skin (eSkin) or tactile skin. Many of these advances have come through mimicking the morphology of human skin and by distributing few touch sensors in an area. However, the complexity of human skin goes beyond mimicking few morphological features or using few sensors. For example, embedded computing (e.g. processing of tactile data at the point of contact) is centric to the human skin as some neuroscience studies show. Likewise, distributed cell or molecular energy is a key feature of human skin. The eSkin with such features, along with distributed and embedded sensors/electronics on soft substrates, is an interesting topic to explore. These features also make eSkin significantly different from conventional computing. For example, unlike conventional centralized computing enabled by miniaturized chips, the eSkin could be seen as a flexible and wearable large area computer with distributed sensors and harmonized energy. This paper discusses these advanced features in eSkin, particularly the distributed sensing harmoniously integrated with energy harvesters, storage devices and distributed computing to read and locally process the tactile sensory data. Rapid advances in neuromorphic hardware, flexible energy generation, energy-conscious electronics, flexible and printed electronics are also discussed. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.
Neural network design for energy-autonomous artificial intelligence applications using temporal encoding

Neural networks (NNs) are steering a new generation of artificial intelligence (AI) applications at the micro-edge. Examples include wireless sensors, wearables and cybernetic systems that collect data and process them to support real-world decisions and controls. For energy autonomy, these applications are typically powered by energy harvesters. As harvesters and other power sources which provide energy autonomy inevitably have power variations, the circuits need to robustly operate over a dynamic power envelope. In other words, the NN hardware needs to be able to function correctly under unpredictable and variable supply voltages. In this paper, we propose a novel NN design approach using the principle of pulse width modulation (PWM). PWM signals represent information with their duty cycle values which may be made independent of the voltages and frequencies of the carrier signals. We design a PWM-based perceptron which can serve as the fundamental building block for NNs, by using an entirely new method of realizing arithmetic in the PWM domain. We analyse the proposed approach building from a 3 × 3 perceptron circuit to a complex multi-layer NN. Using handwritten character recognition as an exemplar of AI applications, we demonstrate the power elasticity, resilience and efficiency of the proposed NN design in the presence of functional and parametric variations including large voltage variations in the power supply. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.
A Systematic Review of the Incremental Costs of Implementing a New Vaccine in the Expanded Program of Immunization in Sub-Saharan Africa

Background. The World Health Organization is planning a pilot introduction of a new malaria vaccine in three sub-Saharan African countries. To inform considerations about including a new vaccine in the vaccination program of those and other countries, estimates from the scientific literature of the incremental costs of doing so are important. Methods. A systematic review of scientific studies reporting the costs of recent vaccine programs in sub-Saharan countries was performed. The focus was to obtain from each study an estimate of the cost per dose of vaccine administered excluding the acquisition cost of the vaccine and wastage. Studies published between 2000 and 2018 and indexed on PubMed could be included and results were standardized to 2015 US dollars (US$). Results. After successive screening of 2119 titles, and 941 abstracts, 58 studies with 80 data points (combinations of country, vaccine type, and vaccination approach–routine v. campaign) were retained. Most studies used the so-called ingredients approach as costing method combining field data collection with documented unit prices per cost item. The categorization of cost items and the extent of detailed reporting varied widely. Across the studies, the mean and median cost per dose administered was US$1.68 and US$0.88 with an interquartile range of US$0.54 to US$2.31. Routine vaccination was more costly than campaigns, with mean cost per dose of US$1.99 and US$0.88, respectively. Conclusion. Across the studies, there was huge variation in the cost per dose delivered, between and within countries, even in studies using consistent data collection tools and analysis methods, and including many health facilities. For planning purposes, the interquartile range of US$0.54 to US$2.31 may be a sufficiently precise estimate.
Comparative Genomics Reveals Accelerated Evolution of Fright Reaction Genes in Ostariophysan Fishes

The ostariophysian fishes are the most species-rich clade in freshwaters. This diversification has been suggested to be associated with the fright reaction presented in most ostariophysians. However, the genetic forces that underlie fright reaction remains poorly understood. In the present study, through integrating behavioral, physiological, transcriptomic, and evolutionary genomic analyses, we found that the fright reaction has a broad impact on zebrafish at multiple levels, including changes in swimming behaviors, cortisol levels, and gene expression patterns. In total, 1,555 and 1,599 differentially expressed genes were identified in olfactory mucosae and brain of zebrafish, respectively, with a greater number upregulated after the fright reaction. Functional annotation showed that response to stress and signal transduction were strongly represented, which is directly associated with the fright reaction. These differentially expressed genes were shown to be evolved accelerated under the influence of positive selection, indicating that protein-coding evolution has played a major role in fright reaction. We found the basal vomeronasal type 2 receptors (v2r) gene, v2rl1, displayed significantly decrease expression after fright reaction, which suggests that v2rs may be important to detect the alarm substance and induce the fright reaction. Collectively, based on our transcriptome and evolutionary genomics analyses, we suggest that transcriptional plasticity of gene may play an important role in fright reaction in ostariophysian fishes.
Comparative Skull Osteology of Oreomylodon wegneri (Xenarthra, Mylodontinae): Defining the Taxonomic Status of the Ecuadorian Endemic Mylodontid

ABSTRACT The diversity reached by the mylodontids during the Pleistocene has been underestimated in the past. Oreomylodon wegneri, the endemic mylodontid of Ecuador, has been considered both a species of Glossotherium and a synonym of Glossotherium robustum. In this work, we provide a detailed description of abundant O. wegneri material and compare it with material of G. robustum from Argentina and other mylodontids. The study presented here shows that O. wegneri was a mylodontid with a singular cranial morphology, especially in the palate and nasal region and is possibly closer evolutionarily to Paramylodon harlani than to the more southern G. robustum.