Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning and Generalisation of Primitives Skills Towards Robust Dual-arm Manipulation

Robots are becoming a vital ingredient in society. Some of their daily tasks require dual-arm manipulation skills in the rapidly changing, dynamic and unpredictable real-world environments where they have to operate. Given the expertise of humans in conducting these activities, it is natural to study humans' motions to use the resulting knowledge in robotic control. With this in mind, this work leverages human knowledge to formulate a more general, real-time, and less task-specific framework for dual-arm manipulation. The proposed framework is evaluated on the iCub humanoid robot and several synthetic experiments, by conducting a dual-arm pick-and-place task of a parcel in the presence of unexpected obstacles. Results suggest the suitability of the method towards robust and generalisable dual-arm manipulation.
The interstellar medium: the key component in galactic evolution and modern cosmology

The gases of the interstellar medium (ISM) possess orders of magnitude more mass than those of all the stars combined and are thus the prime component of the baryonic Universe. With L-band surface sensitivity even better than the planned phase one of the Square Kilometre Array (SKA1), the Five-hundred-meter Aperture Spherical radio Telescope (FAST) promises unprecedented insights into two of the primary components of ISM, namely, atomic hydrogen (HI) and the hydroxyl molecule (OH). Here, we discuss the evolving landscape of our understanding of ISM, particularly, its complex phases, the magnetic fields within, the so-called dark molecular gas (DMG), high velocity clouds and the connection between local and distant ISM. We lay out, in broad strokes, several expected FAST projects, including an all northern-sky high-resolution HI survey (22 000 deg2, 3′ FWHM beam, 0.2 km s−1), targeted OH mapping, searching for absorption and maser signals, etc. Currently under commissioning, the commensal observing mode of FAST will be capable of simultaneously obtaining HI and pulsar data streams, making large-scale surveys in both science areas more efficient.
Ghost imaging with engineered quantum states by Hong–Ou–Mandel interference

Traditional ghost imaging experiments exploit position correlations between correlated states of light. These correlations occur directly in spontaneous parametric down-conversion, and in such a scenario, the two-photon state usually used for ghost imaging is symmetric. Here we perform ghost imaging using an anti-symmetric state, engineering the two-photon state symmetry by means of Hong–Ou–Mandel interference. We use both symmetric and anti-symmetric states and show that the ghost imaging setup configuration results in object-image rotations depending on the state selected. Further, the object and imaging arms employ spatial light modulators for the all-digital control of the projections, being able to dynamically change the measuring technique and the spatial properties of the states under study. Finally, we provide a detailed theory that explains the reported observations.
Joining Bits and Pieces of Reionization History.

Cosmic microwave background (CMB) temperature and polarization anisotropies from Planck have estimated a lower value of the optical depth to reionization (τ) compared to WMAP. A significant period in the reionization history would then fall within 6<redshift(z)<10, where detection of galaxies with Hubble frontier fields program and independent estimation of neutral hydrogen in the inter galactic medium by Lyman-α observations are also available. This overlap allows an analysis of cosmic reionization which utilizes a direct combination of CMB and these astrophysical measurements and potentially breaks degeneracies in parameters describing the physics of reionization. For the first time we reconstruct reionization histories by assuming photoionization and recombination rates to be free-form and by allowing underlying cosmological parameters to vary with CMB (temperature and polarization anisotropies and lensing) data from Planck 2018 release and a compilation of astrophysical data. We find an excellent agreement between the low-ℓ Planck 2018 High Frequency Instrument polarization likelihood and astrophysical data in determining the integrated optical depth. By combining both data, we report for a minimal reconstruction τ=0. 051_{-0.0012-0.002}^{+0.001+0.002} at 68% and 95% C.L. , which, for the errors in the current astrophysical measurements quoted in the literature, is nearly twice better than the projected cosmic variance limited CMB measurements. For the duration of reionization, redshift interval between 10%, and complete ionization, we get 2. 9_{-0.16-0.26}^{+0.12+0.29} at 68% and 95% C.L. , which improves significantly on the corresponding result obtained by using Planck 2015 data. By a Bayesian analysis of the combined results we do not find evidence beyond monotonic reionization histories, therefore a multiphase reionization scenario such as a first burst of reionization followed by recombination plateau and thereafter complete reionization is disfavored compared to minimal alternatives.
Achieving Direct Electrical Connection to Glucose Oxidase Using Aligned Single Walled Carbon Nanotube Arrays

Direct electron transfer between an electrode and the redox active centre of glucose oxidase, flavin adenine dinucleotide (FAD), is probed using carbon nanotube modified gold electrodes. Gold electrodes are first modified with a self-assembled monolayer of cysteamine and then shortened single walled carbon nanotubes (SWNT) are aligned normal to the electrode surface by self-assembly. The electrochemistry of these aligned nanotube electrode arrays is initially investigated using potassium ferricyanide which showed SWNT act as nanoelectrodes with the ends of the tubes more electrochemically active than the walls. Subsequently the nanotubes are plugged into the enzymes in one of two ways. In the first method, native glucose oxidase is covalently attached to the ends of the aligned tubes which allowed close approach to FAD and direct electron transfer to be observed with a rate constant of 0.3 s−1. In the second strategy, FAD was attached to the ends of the tubes and the enzyme reconstituted around the surface immobilized FAD. This latter approach allowed more efficient electron transfer to the FAD with a rate constant of 9 s−1.
Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs

Lipschitz continuity recently becomes popular in generative adversarial networks (GANs). It was observed that the Lipschitz regularized discriminator leads to improved training stability and sample quality. The mainstream implementations of Lipschitz continuity include gradient penalty and spectral normalization. In this paper, we demonstrate that gradient penalty introduces undesired bias, while spectral normalization might be over restrictive. We accordingly propose a new method which is efficient and unbiased. Our experiments verify our analysis and show that the proposed method is able to achieve successful training in various situations where gradient penalty and spectral normalization fail.
Implications of symmetries in the scalar sector

Symmetries play a very important rôle in Particle Physics. In extended scalar sectors, the existence of symmetries may permit the models to comply with the experimental constraints in a natural way, and at the same time reduce the number of free parameters. There is a strong interplay among internal symmetries of the scalar potential, its CP properties and mass degeneracies of the physical scalars. Some of these aspects were discussed in this talk.
Peak Alignment of GC-MS Data with Deep Learning

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatogram-Mass Spectrometry (GC-MS) data. GC-MS is regarded as a gold standard in analysis of chemical composition in samples. However, due to the complexity of the instrument, a substance's retention time (RT) may not stay fixed across multiple GC-MS chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules, consequently, they are unable to handle the complexity of GC-MS data from human breath. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of various complexities and show the model has very good true position rates (up to 99% for easy data sets and up to 92% for very complex data sets). We compared our model with the popular correlation optimized warping (COW) and show our model has much better overall performance. This method can easily be adapted to other similar data such as those from liquid chromatography.
Non-Rigid Point Set Registration Networks

Point set registration is defined as a process to determine the spatial transformation from the source point set to the target one. Existing methods often iteratively search for the optimal geometric transformation to register a given pair of point sets, driven by minimizing a predefined alignment loss function. In contrast, the proposed point registration neural network (PR-Net) actively learns the registration pattern as a parametric function from a training dataset, consequently predict the desired geometric transformation to align a pair of point sets. PR-Net can transfer the learned knowledge (i.e. registration pattern) from registering training pairs to testing ones without additional iterative optimization. Specifically, in this paper, we develop novel techniques to learn shape descriptors from point sets that help formulate a clear correlation between source and target point sets. With the defined correlation, PR-Net tends to predict the transformation so that the source and target point sets can be statistically aligned, which in turn leads to an optimal spatial geometric registration. PR-Net achieves robust and superior performance for non-rigid registration of point sets, even in presence of Gaussian noise, outliers, and missing points, but requires much less time for registering large number of pairs. More importantly, for a new pair of point sets, PR-Net is able to directly predict the desired transformation using the learned model without repetitive iterative optimization routine. Our code is available at this https URL.
Infant-Prints: Fingerprints for Reducing Infant Mortality

In developing countries around the world, a multitude of infants continue to suffer and die from vaccine-preventable diseases, and malnutrition. Lamentably, the lack of any official identification documentation makes it exceedingly difficult to prevent these infant deaths. To solve this global crisis, we propose Infant-Prints which is comprised of (i) a custom, compact, low-cost (85 USD), high-resolution (1,900 ppi) fingerprint reader, (ii) a high-resolution fingerprint matcher, and (iii) a mobile application for search and verification for the infant fingerprint. Using Infant-Prints, we have collected a longitudinal database of infant fingerprints and demonstrate its ability to perform accurate and reliable recognition of infants enrolled at the ages 0-3 months, in time for effective delivery of critical vaccinations and nutritional supplements (TAR=90% @ FAR = 0.1% for infants older than 8 weeks).
Redesenho gerencial do processo educativo da equipe de enfermagem em hospital pediátrico de nível terciário

A systematic process for training new professionals that helps with educational health needs related to patient safety. This study aims to describe the experience of redesigning an educational program for nursing professionals entering a pediatric teaching hospital at a tertiary level in the city of São Paulo. As well as to present considerations on the results of the experience regarding pre and post-test to verify if the new training favors the learning proposals. This research is an experience report based on Nonaka’s knowledge management concepts. The professionals pointed out satisfaction with the differentiated reception and reported a safety feeling to start the activities in the units to provide quality care in pediatrics. There were signifi cant differences in the average scores between the pre and post-tests. The redesign of the training program indicated the effectiveness and greater involvement of the new professionals.
Fast, accurate, and transferable many-body interatomic potentials by genetic programming

The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine learning algorithms to develop fast and accurate interatomic potential models, but it remains a challenge to develop models that generalize well and are fast enough to be used at extreme time and length scales. To address this challenge, we have developed a machine learning algorithm based on genetic programming that is capable of discovering accurate, computationally efficient many-body potential models. The key to our approach is to explore a hypothesis space of models based on fundamental physical principles and select models within this hypothesis space based on their accuracy, speed, and simplicity. The focus on simplicity reduces the risk of overfitting the training data and increases the chances of discovering a model that generalizes well. Our algorithm was validated by rediscovering an exact Lennard Jones potential and a Sutton Chen embedded atom method potential from training data generated using these models. By using training data generated from density functional theory calculations, we found potential models for elemental copper that are simple, as fast as embedded atom models, and capable of accurately predicting properties outside of their training set. Our approach requires relatively small sets of training data, making it possible to generate training data using highly accurate methods at a reasonable computational cost. We present our approach, the forms of the discovered models, and assessments of their transferability, accuracy and speed.
Spectral gap in the group of affine transformations over prime fields

We study random walks on the semi-direct product of F_p^d and SL_d(F_p). We estimate the spectral gap in terms of the spectral gap of the projection to the linear part SL_d(F_p). This problem is motivated by an analogue in the isometry group of Euclidean space, which have application to smoothness of self-similar measures.
Sizing and Dynamic Modeling of a Power System for the MUN Explorer Autonomous Underwater Vehicle Using a Fuel Cell and Batteries

The combination of a fuel cell and batteries has promising potential for powering autonomous vehicles. The MUN Explorer Autonomous Underwater Vehicle (AUV) is built to do mapping-type missions of seabeds as well as survey missions. These missions require a great deal of power to reach underwater depths (i.e., 3000 meters). The MUN Explorer uses 11 rechargeable Lithium-ion (Li-ion) batteries as the main power source with a total capacity of 14.6 kWh to 17.952 kWh, and the vehicle can run for 10 hours. The drawbacks of operating the existing power system of the MUN Explorer, which was done by the researcher at the Holyrood management facility, include mobilization costs, logistics and transport, and facility access, all of which should be taken into consideration. Recharging the batteries for at least 8 hours is also very challenging and time consuming. To overcome these challenges and run the MUN Explorer for a long time, it is essential to integrate a fuel cell into an existing power system (i.e., battery bank). The integration of the fuel cell not only will increase the system power, but will also reduce the number of batteries needed as suggested by HOMER software. In this paper, an integrated fuel cell is designed to be added into the MUN Explorer AUV along with a battery bank system to increase its power system. The system sizing is performed using HOMER software. The results from HOMER software show that a 1-kW fuel cell and 8 Li-ion batteries can increase the power system capacity to 68 kWh. The dynamic model is then built in MATLAB/Simulink environment to provide a better understanding of the system behavior. The 1-kW fuel cell is connected to a DC/DC Boost Converter to increase the output voltage from 24 V to 48 V as required by the battery and DC motor. A hydrogen gas tank is also included in the model. The advantage of installing the hydrogen and oxygen tanks beside the batteries is that it helps the buoyancy force in underwater depths. The design of this system is based on MUN Explorer data sheets and system dynamic simulation results.
Utilization of Freshwater Bream for Canned Fish Ball Manufacture

Freshwater bream (Abramis brama) minced fillets and trimmings were used to prepare fish balls canned in tomato sauce. Before canning, fish balls were boiled in 1.5% brine and/or fried in corn oil after flouring. Tomato sauce packs were heated at 115, 120, and 125°C for the time required to achieve an Fo=7.5 min. Texture measurements and sensory evaluation showed that good quality fish balls could be made from freshwater fish flesh. Sensory characteristics were influenced by pretreatment and processing conditions. Frying can lead to the production of fish balls with better flavor and odor, but tomato sauce was the major determinant of fish ball flavor and odor. Texture scores determined by sensory evaluation correlated well with instrumental readings, and tenderness and juiciness scores can be predicted from instrumental data. Panelists described canned fish balls as somewhat firm to firm, and tenderness and juiciness scores were in good agreement and highly correlated. Texture force significantly (p < 0.0.
Bayesian Optimization for Policy Search via Online-Offline Experimentation

Online field experiments are the gold-standard way of evaluating changes to real-world interactive machine learning systems. Yet our ability to explore complex, multi-dimensional policy spaces - such as those found in recommendation and ranking problems - is often constrained by the limited number of experiments that can be run simultaneously. To alleviate these constraints, we augment online experiments with an offline simulator and apply multi-task Bayesian optimization to tune live machine learning systems. We describe practical issues that arise in these types of applications, including biases that arise from using a simulator and assumptions for the multi-task kernel. We measure empirical learning curves which show substantial gains from including data from biased offline experiments, and show how these learning curves are consistent with theoretical results for multi-task Gaussian process generalization. We find that improved kernel inference is a significant driver of multi-task generalization. Finally, we show several examples of Bayesian optimization efficiently tuning a live machine learning system by combining offline and online experiments.
Extreme hydrodynamic losses of Earth-like atmospheres in the habitable zones of very active stars

Aims. In this Letter, we calculate for the first time the full transonic hydrodynamic escape of an Earth-like atmosphere. We consider the case of an Earth-mass planet with an atmospheric composition identical to that of the current Earth orbiting at 1 AU around a young and very active solar mass star. Methods. To model the upper atmosphere, we used the Kompot Code, which is a first-principles model that calculates the physical structures of the upper atmospheres of planets, taking into account hydrodynamics and the main chemical and thermal processes taking place in the upper atmosphere of a planet. This model enabled us to calculate the 1D vertical structure of the atmosphere using as input the high-energy spectrum of a young and active Sun. Results. The atmosphere has the form of a transonic hydrodynamic Parker wind, which has an outflow velocity at the upper boundary of our computational domain that exceeds the escape velocity. The outflowing gas is dominated by atomic nitrogen and oxygen and their ion equivalents and has a maximum ionization fraction of 20%. The mass outflow rate is found to be 1.8 × 109 g s−1, which would erode the modern Earth’s atmosphere in less than 0.1 Myr. Conclusions. This extreme mass loss rate suggests that an Earth-like atmosphere cannot form when the planet is orbiting within the habitable zone of a very active star. Instead, such an atmosphere can only form after the activity of the star has decreased to a much lower level. This happened in the early atmosphere of the Earth, which was likely dominated by other gases such as CO2. Since the time it takes for the activity of a star to decay is highly dependent on its mass, this is important for understanding possible formation timescales for planets orbiting low-mass stars.
Why ResNet Works? Residuals Generalize

Residual connections significantly boost the performance of deep neural networks. However, few theoretical results address the influence of residuals on the hypothesis complexity and the generalization ability of deep neural networks. This article studies the influence of residual connections on the hypothesis complexity of the neural network in terms of the covering number of its hypothesis space. We first present an upper bound of the covering number of networks with residual connections. This bound shares a similar structure with that of neural networks without residual connections. This result suggests that moving a weight matrix or nonlinear activation from the bone to a vine would not increase the hypothesis space. Afterward, an $\mathcal O(1 / \sqrt {N})$ margin-based multiclass generalization bound is obtained for ResNet, as an exemplary case of any deep neural network with residual connections. Generalization guarantees for similar state-of-the-art neural network architectures, such as DenseNet and ResNeXt, are straightforward. According to the obtained generalization bound, we should introduce regularization terms to control the magnitude of the norms of weight matrices not to increase too much, in practice, to ensure a good generalization ability, which justifies the technique of weight decay.
Exploring the Mass Segregation Effect of X-Ray Sources in Globular Clusters: The Case of 47 Tucanae

Using archival Chandra observations with a total exposure of 510 ks, we present an updated catalog of point sources for globular cluster (GC) 47 Tucanae. Our study covers an area of ∼176. ′7 2 (i.e., with R ≲ 7.′5) with 537 X-ray sources. We show that the surface density distribution of X-ray sources in 47 Tuc is highly peaked in the cluster center, rapidly decreases at intermediate radii, and finally rises again at larger radii, with two distribution dips at R ∼ 100″ and R ∼ 170″ for the faint (LX ≲ 5.0 × 1030 erg s−1) and bright (LX ≳ 5.0 × 1030 erg s−1) groups of X-ray sources, respectively. These distribution features are similar to those of blue straggler stars (BSSs), where the distribution dip is located at R ∼ 200″. By fitting the radial distribution of each group of sources with a “generalized King model,” we estimated an average mass of 1.51 ± 0.17 M⊙, 1.44 ± 0.15 M⊙ and 1.16 ± 0.06 M⊙ for the BSSs, bright X-ray sources, and faint X-ray sources, respectively. These results are consistent with the mass segregation effect of heavy objects in GCs, where more massive objects drop to the cluster center faster and their distribution dip propagates outward further. Besides, the peculiar distribution profiles of X-ray sources and BSSs are also consistent with the mass segregation model of binaries in GCs, which suggests that, in addition to the dynamical formation channel, primordial binaries are also a significant contributor to the X-ray source population in GCs.