Miracle-dz/UniFAR-FTUs huggingface dataset:
Spherically symmetric loop quantum gravity: analysis of improved dynamics

We study the ‘improved dynamics’ for the treatment of spherically symmetric space-times in loop quantum gravity introduced by Chiou et al in analogy with the one that has been constructed by Ashtekar, Pawlowski and Singh for the homogeneous space-times. In this dynamics the polymerization parameter is a well motivated function of the dynamical variables, reflecting the fact that the quantum of area depends on them. Contrary to the homogeneous case, its implementation does not trigger undesirable physical properties. We identify semiclassical physical states in the quantum theory and derive the corresponding effective semiclassical metrics. We then discuss some of their properties. Concretely, the space-time approaches sufficiently fast the Schwarzschild geometry at low curvatures. Besides, regions where the singularity is in the classical theory get replaced by a regular but discrete effective geometry with finite and Planck order curvature, regardless of the mass of the black hole. This circumvents trans-Planckian curvatures that appeared for astrophysical black holes in the quantization scheme without the improvement. It makes the resolution of the singularity more in line with the one observed in models that use the isometry of the interior of a Schwarzschild black hole with the Kantowski–Sachs loop quantum cosmologies. One can observe the emergence of effective violations of the null energy condition in the interior of the black hole as part of the mechanism of the elimination of the singularity.
Propagules of marine macroalgae: Structure and development

The propagules of macroalgae consist principally of the unicellular products of sexual or asexual processes such as zygotes, parthenogenetic gametes, and spores formed as a result of meiosis and/or mitosis. They have several essential functions in the life history of a species, being the means of dispersal, settlement, attachment, survival and the initiation of new individuals. All major structural characters of macroalgal propagules can be related to one or more of these functions. This paper reviews the structural diversity and functional significance of propagules in marine macroalgae with special reference to cell size, flagella, endomembrane systems, mucilage, chloroplasts, energy reserves, and physodes (in brown algae). Multicellular vegetative propagules are not considered.
Mechanics of browsing in dense food patches: effects of plant and animal morphology on intake rate

There appear to be two dominant forms of the functional response of vertebrate herbivores, corresponding to animals foraging in spatially concentrated or spatially dispersed food patches. We examine the factors contributing to the functional response of herbivores feeding on spatially concentrated browses. Specifically, we tested the hypothesis that the instantaneous intake rate of four boreal browsers (moose, Alces alces, woodland caribou, Rangifer tarandus caribou, white-tailed deer, Odocoileus virginianus, and snowshoe hares, Lepus americanus) feeding on red maple (Acer rubrum) browse is an asymptotic function of the size of bite obtainable by the herbivore. Based on this functional response model, we also hypothesized that (i) cropping rate of the herbivores would decline curvilinearly with bite size, (ii) intake rate would decline with increasing fibrousness (measured, as the neutral detergent fiber of the foods consumed), and (iii) maximum intake rate would scale as the 2/3 power of herbivore body m.
Cofilin is required for polarization of tension in stress fiber networks during migration

ABSTRACT Cell migration is associated with the establishment of defined leading and trailing edges, which in turn requires polarization of contractile forces. While the actomyosin stress fiber (SF) network plays a critical role in enforcing this polarity, precisely how this asymmetry is established remains unclear. Here, we provide evidence for a model in which the actin-severing protein cofilin (specifically cofilin-1) participates in symmetry breakage by removing low-tension actomyosin filaments during transverse arc assembly. Cofilin knockdown (KD) produces a non-polarized SF architecture that cannot be rescued with chemokines or asymmetric matrix patterns. Whereas cofilin KD increases whole-cell prestress, it decreases prestress within single SFs, implying an accumulation of low-tension SFs. This notion is supported by time-lapse imaging, which reveals weakly contractile and incompletely fused transverse arcs. Confocal and super-resolution imaging further associate this failed fusion with the presence of crosslinker-rich, tropomyosin-devoid nodes at the junctions of multiple transverse arc fragments and dorsal SFs. These results support a model in which cofilin facilitates the formation of high-tension transverse arcs, thereby promoting mechanical asymmetry. Summary: Mechanical polarization is established in stress fiber networks during migration through cofilin disassembling actin to facilitate the formation of highly prestressed stress fibers needed to break tensional symmetry.
Fast and automated biomarker detection in breath samples with machine learning

Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis that is time-consuming, subjective and may introduce errors. We propose a machine learning-based system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs directly from raw data, thus bypassing expert-led processing. We evaluate this new approach on clinical samples and with four types of convolutional neural networks (CNNs): VGG16, VGG-like, densely connected and residual CNNs. The proposed machine learning methods showed to outperform the expert-led analysis by detecting a significantly higher number of VOCs in just a fraction of time while maintaining high specificity. These results suggest that the proposed novel approach can help the large-scale deployment of breath-based diagnosis by reducing time and cost, and increasing accuracy and consistency.
Deep Learning in Target Space

Deep learning uses neural networks which are parameterised by their weights. The neural networks are usually trained by tuning the weights to directly minimise a given loss function. In this paper we propose to reparameterise the weights into targets for the firing strengths of the individual nodes in the network. Given a set of targets, it is possible to calculate the weights which make the firing strengths best meet those targets. It is argued that using targets for training addresses the problem of exploding gradients, by a process which we call cascade untangling, and makes the loss-function surface smoother to traverse, and so leads to easier, faster training, and also potentially better generalisation, of the neural network. It also allows for easier learning of deeper and recurrent network structures. The necessary conversion of targets to weights comes at an extra computational expense, which is in many cases manageable. Learning in target space can be combined with existing neural-network optimisers, for extra gain. Experimental results show the speed of using target space, and examples of improved generalisation, for fully-connected networks and convolutional networks, and the ability to recall and process long time sequences and perform natural-language processing with recurrent networks.
AT 2017gbl: a dust obscured TDE candidate in a luminous infrared galaxy

We present the discovery with Keck of the extremely infrared (IR) luminous transient AT 2017gbl, coincident with the Northern nucleus of the luminous infrared galaxy (LIRG) IRAS 23436+5257. Our extensive multiwavelength follow-up spans ∼900 d, including photometry and spectroscopy in the optical and IR, and (very long baseline interferometry) radio and X-ray observations. Radiative transfer modelling of the host galaxy spectral energy distribution and long-term pre-outburst variability in the mid-IR indicate the presence of a hitherto undetected dust obscured active galactic nucleus (AGN). The optical and near-IR spectra show broad ∼2000 km s−1 hydrogen, He i, and O i emission features that decrease in flux over time. Radio imaging shows a fast evolving compact source of synchrotron emission spatially coincident with AT 2017gbl. We infer a lower limit for the radiated energy of 7.3 × 1050 erg from the IR photometry. An extremely energetic supernova would satisfy this budget, but is ruled out by the radio counterpart evolution. Instead, we propose AT 2017gbl is related to an accretion event by the central supermassive black hole, where the spectral signatures originate in the AGN broad line region and the IR photometry is consistent with re-radiation by polar dust. Given the fast evolution of AT 2017gbl, we deem a tidal disruption event (TDE) of a star a more plausible scenario than a dramatic change in the AGN accretion rate. This makes AT 2017gbl the third TDE candidate to be hosted by a LIRG, in contrast to the so far considered TDE population discovered at optical wavelengths and hosted preferably by post-starburst galaxies.
Improved scalability under heavy tails, without strong convexity

Real-world data is laden with outlying values. The challenge for machine learning is that the learner typically has no prior knowledge of whether the feedback it receives (losses, gradients, etc.) will be heavy-tailed or not. In this work, we study a simple algorithmic strategy that can be leveraged when both losses and gradients can be heavy-tailed. The core technique introduces a simple robust validation sub-routine, which is used to boost the confidence of inexpensive gradient-based sub-processes. Compared with recent robust gradient descent methods from the literature, dimension dependence (both risk bounds and cost) is substantially improved, without relying upon strong convexity or expensive per-step robustification. Empirically, we also show that under heavy-tailed losses, the proposed procedure cannot simply be replaced with naive cross-validation. Taken together, we have a scalable method with transparent guarantees, which performs well without prior knowledge of how "convenient" the feedback it receives will be.
Activity River: Visualizing Planned and Logged Personal Activities for Reflection

We present Activity River, a personal visualization tool which enables individuals to plan, log, and reflect on their self-defined activities. We are interested in supporting this type of reflective practice as prior work has shown that reflection can help people plan and manage their time effectively. Hence, we designed Activity River based on five design goals (visualize historical and contextual data, facilitate comparison of goals and achievements, engage viewers with delightful visuals, support authorship, and enable flexible planning and logging) which we distilled from the Information Visualization and Human-Computer Interaction literature. To explore our approach's strengths and limitations, we conducted a qualitative study of Activity River using a role-playing method. Through this qualitative exploration, we illustrate how our participants envisioned using our visualization to perform dynamic and continuous reflection on their activities. We observed that they were able to assess their progress towards their plans and adapt to unforeseen circumstances using our tool.
Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data

This paper presents the system used in our submission to the IWPT 2020 Shared Task. Our system is a graph-based parser with second-order inference. For the low-resource Tamil corpora, we specially mixed the training data of Tamil with other languages and significantly improved the performance of Tamil. Due to our misunderstanding of the submission requirements, we submitted graphs that are not connected, which makes our system only rank 6th over 10 teams. However, after we fixed this problem, our system is 0.6 ELAS higher than the team that ranked 1st in the official results.
Accurate face rig approximation with deep differential subspace reconstruction

To be suitable for film-quality animation, rigs for character deformation must fulfill a broad set of requirements. They must be able to create highly stylized deformation, allow a wide variety of controls to permit artistic freedom, and accurately reflect the design intent. Facial deformation is especially challenging due to its nonlinearity with respect to the animation controls and its additional precision requirements, which often leads to highly complex face rigs that are not generalizable to other characters. This lack of generality creates a need for approximation methods that encode the deformation in simpler structures. We propose a rig approximation method that addresses these issues by learning localized shape information in differential coordinates and, separately, a subspace for mesh reconstruction. The use of differential coordinates produces a smooth distribution of errors in the resulting deformed surface, while the learned subspace provides constraints that reduce the low frequency error in the reconstruction. Our method can reconstruct both face and body deformations with high fidelity and does not require a set of well-posed animation examples, as we demonstrate with a variety of production characters.
Learning Opinion Dynamics From Social Traces

Opinion dynamics the research field dealing with how people's opinions form and evolve in a social context? traditionally uses agent-based models to validate the implications of sociological theories. These models encode the causal mechanism that drives the opinion formation process, and have the advantage of being easy to interpret. However, as they do not exploit the availability of data, their predictive power is limited. Moreover, parameter calibration and model selection are manual and difficult tasks. In this work we propose an inference mechanism for fitting a generative, agent-like model of opinion dynamics to real-world social traces. Given a set of observables (e.g., actions and interactions between agents), our model can recover the most-likely latent opinion trajectories that are compatible with the assumptions about the process dynamics. This type of model retains the benefits of agent-based ones (i.e., causal interpretation), while adding the ability to perform model selection and hypothesis testing on real data. We showcase our proposal by translating a classical agent-based model of opinion dynamics into its generative counterpart. We then design an inference algorithm based on online expectation maximization to learn the latent parameters of the model. Such algorithm can recover the latent opinion trajectories from traces generated by the classical agent-based model. In addition, it can identify the most likely set of macro parameters used to generate a data trace, thus allowing testing of sociological hypotheses. Finally, we apply our model to real-world data from Reddit to explore the long-standing question about the impact of the backfire effect. Our results suggest a low prominence of the effect in Reddit's political conversation.
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking

Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs. However, most of them have limitations to efficiently exploit relevant context due to the lack of a powerful mechanism for modeling interactions between the slot and the dialogue history. Besides, existing methods usually ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots and eventually hurts overall performance. In this paper, we propose to enhance the DST through employing a contextual hierarchical attention network to not only discern relevant information at both word level and turn level but also learn contextual representations. We further propose an adaptive objective to alleviate the slot imbalance problem by dynamically adjust weights of different slots during training. Experimental results show that our approach reaches 52.68% and 58.55% joint accuracy on MultiWOZ 2.0 and MultiWOZ 2.1 datasets respectively and achieves new state-of-the-art performance with considerable improvements (+1.24% and +5.98%).
Mpemba effect in molecular gases under nonlinear drag

We look into the Mpemba effect---the initially hotter sample cools sooner---in a molecular gas with nonlinear viscous drag. Specifically, the gas particles interact among them via elastic collisions and also with a background fluid at equilibrium. Thus, within the framework of kinetic theory, our gas is described by an Enskog--Fokker--Planck equation. The analysis is carried out in the first Sonine approximation, in which the evolution of the temperature is coupled to that of the excess kurtosis. This coupling leads to the emergence of the Mpemba effect, which is observed in an early stage of the relaxation and when the initial temperatures of the two samples are close enough. This allows for the development of a simple theory, linearizing the temperature evolution around a reference temperature---namely the initial temperature closer to the asymptotic equilibrium value. The linear theory provides a semiquantitative description of the effect, including expressions for the crossover time and the maximum temperature difference. We also discuss the limitations of our linearized theory.
The evolution of accuracy in natural history illustration: reversal of printed illustrations of snails and crabs in pre-Linnaean works suggests indifference to morphological detail

Most natural history illustrations published prior to about 1700 were engraved as drawn and then printed backwards. This is noticeable, however, only for some groups of organisms, such as snails and some crabs, that are not bilaterally symmetrical. The most likely hypothesis for this is that most pre-eighteenth-century engravers, artists, and authors were indifferent to whether such illustrations were presented backwards or forwards. Illustrations of snail shells were not universally printed rightway- forward until the early to mid-eighteenth century, when standards of accuracy in natural history illustration improved as a result of the combined effects of a number of changes in scientific practice, including increasing collecting, publishing, and encyclopedism.
Dynamical systems on hypergraphs

Networks are a widely used and efficient paradigm to model real-world systems where basic units interact pairwise. Many body interactions are often at play, and cannot be modelled by resorting to binary exchanges. In this work, we consider a general class of dynamical systems anchored on hypergraphs. Hyperedges of arbitrary size ideally encircle individual units so as to account for multiple, simultaneous interactions. These latter are mediated by a combinatorial Laplacian, that is here introduced and characterised. The formalism of the master stability function is adapted to the present setting. Turing patterns and the synchronisation of non linear (regular and chaotic) oscillators are studied, for a general class of systems evolving on hypergraphs. The response to externally imposed perturbations bears the imprint of the higher order nature of the interactions.
Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

Combinatorial optimization has found applications in numerous fields, from aerospace to transportation planning and economics. The goal is to find an optimal solution among a finite set of possibilities. The well-known challenge one faces with combinatorial optimization is the state-space explosion problem: the number of possibilities grows exponentially with the problem size, which makes solving intractable for large problems. In the last years, deep reinforcement learning (DRL) has shown its promise for designing good heuristics dedicated to solve NP-hard combinatorial optimization problems. However, current approaches have an important shortcoming: they only provide an approximate solution with no systematic ways to improve it or to prove optimality. In another context, constraint programming (CP) is a generic tool to solve combinatorial optimization problems. Based on a complete search procedure, it will always find the optimal solution if we allow an execution time large enough. A critical design choice, that makes CP non-trivial to use in practice, is the branching decision, directing how the search space is explored. In this work, we propose a general and hybrid approach, based on DRL and CP, for solving combinatorial optimization problems. The core of our approach is based on a dynamic programming formulation, that acts as a bridge between both techniques. We experimentally show that our solver is efficient to solve three challenging problems: the traveling salesman problem with time windows, the 4-moments portfolio optimization problem, and the 0-1 knapsack problem. Results obtained show that the framework introduced outperforms the stand-alone RL and CP solutions, while being competitive with industrial solvers.
The Ritva Blockchain: Enabling Confidential Transactions at Scale

The distributed ledger technology has been widely hailed as the break-through technology. It has realised a great number of application scenarios, and improved workflow of many domains. Nonetheless, there remain a few major concerns in adopting and deploying the distributed ledger technology at scale. In this white paper, we tackle two of them, namely the throughput scalability and confidentiality protection for transactions. We learn from the existing body of research, and build a scale-out blockchain platform that champions privacy called RVChain. RVChain takes advantage of trusted execution environment to offer confidentiality protection for transactions, and scales the throughput of the network in proportion with the number of network participants by supporting parallel shadow chains.
A Mixture of Mesenchymal Stem Cells from Perinatal Sources Supports Consistent Ex-vivo

Umbilical cord blood transplantation has limitations due to lesser number of Hematopoietic Stem Cells (HSCs) in the graft, needed for efficient hematopoietic reconstitution. Besides that, problems in getting an appropriate HLA match and incidence of severe GvHD are inherent issues. Ex-vivo CD34 + HSC expansion is one of the key strategies to increase HSC numbers to meet the recommended dosage requirement. Recent reports have demonstrated the plasticity of CD34 + HSCs that helps differentiate them into various cell types including renal, cardiac, liver, neural, mesenchymal and muscle cells. This makes CD34 + cells yet another novel cell type for regular use in regenerative medicine applications. Objective: The objective of this research was to establish a cost effective ex-vivo expansion of HSCs using perinatal MSCs as a feeder layer while maintaining stemness and functionality. Method: We used a mixture of Mesenchymal Stem Cells derived from three perinatal sources (Umbilical cord tissue, placenta and amniotic sac) as a feeder layer for Ex-vivo expansion of CD34 + HSCs pooled from multiple cord blood units. The media used contained different concentrations of cytokines. A mix of different MSCs release abundant growth factors and cytokines much required for the maintenance of phenotype and functionality of CD34 + HSCs during expansion. Results: We obtained average 30 fold expansion of CD34 + HSCs. It was a cost effective process. The functional characterization confirmed the stemness of the expanded cells. Conclusion: MSCs from perinatal sources is a good option as a feeder layer for the Ex-vivo expansion of CD34 + HSCs. This source of MSC is ontogenetically naive and can produce more amounts of cytokines for sustainable HSC expansion.