Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Reasoning Strategies in End-to-End Differentiable Proving

Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theorem Provers (NTPs). These neuro-symbolic models can induce interpretable rules and learn representations from data via back-propagation, while providing logical explanations for their predictions. However, they are restricted by their computational complexity, as they need to consider all possible proof paths for explaining a goal, thus rendering them unfit for large-scale applications. We present Conditional Theorem Provers (CTPs), an extension to NTPs that learns an optimal rule selection strategy via gradient-based optimisation. We show that CTPs are scalable and yield state-of-the-art results on the CLUTRR dataset, which tests systematic generalisation of neural models by learning to reason over smaller graphs and evaluating on larger ones. Finally, CTPs show better link prediction results on standard benchmarks in comparison with other neural-symbolic models, while being explainable. All source code and datasets are available online. (At https://github.com/uclnlp/ctp)
The Hα Emission Line Variations of HR 6819

The star system HR 6819 was recently proposed by Rivinius et al. as the site of the nearest example of a stellar-mass black hole. Their spectra show evidence of two components: a B3 III star in a 40 day orbit and a stationary B-emission line star. Based upon the orbital mass function and the lack of evidence of a spectral component with reflex orbital motion, Rivinius et al. suggested that HR 6819 is a triple system consisting of an inner B3 III star plus black hole binary orbited by a distant Be star. Here we present an alternative model based upon an examination of the Hα emission line in their spectra. We show that the emission line displays the small reflex motion expected for the companion in the inner orbit and shows variations in profile shape related to orbital phase. This indicates that HR 6819 is a binary system consisting of a massive Be star and a low-mass companion that is the stripped down remnant of a former mass donor star in a mass transfer binary.
Counts-in-cells of subhaloes in the IllustrisTNG simulations: the role of baryonic physics

We present an analysis of the Counts-in-Cells (CiC) statistics of subhaloes in the publicly available IllustrisTNG cosmological simulations (TNG100-1, TNG100-3 and TNG300-3), considering their full and dark-only versions, in redshifts ranging from $z = 0$ to $z=5$, and different cell sizes. We evaluated two CiC models: the gravitational quasi-equilibrium distribution (GQED) and the negative binomial distribution (NBD), both presenting good fits, with small detectable differences in the presence of baryons. Scaling and time dependencies of the best-fit parameters showed similar trends compared with the literature. We derived a matter density-in-cells probability distribution function (PDF), associated with the GQED, which was compared to the PDF given in Uhlemann et al. (2016), for the IllustrisTNG 100-3-Dark run at $z=0$. Our results indicate that the simplest gravithermodynamical assumptions of the GQED model hold in the presence of baryonic dissipation. Interestingly, the smoothed (density-in-cells) version of the GQED is also adequate for describing the dark matter one-point statistics of subhaloes and converges, to subpercentage levels (for an interval of parameters), to the Uhlemann et al. PDF in the high density range.
Search for helium in the upper atmosphere of the hot Jupiter WASP-127 b using Gemini/Phoenix

Large-scale exoplanet search surveys have shown evidence that atmospheric escape is a ubiquitous process that shapes the evolution and demographics of planets. However, we lack a detailed understanding of this process because very few exoplanets that have been discovered to date could be probed for signatures of atmospheric escape. Recently, the metastable helium triplet at 1.083 μm has been shown to be a viable window for the presence of He-rich escaping envelopes around short-period exoplanets. Our objective is to use, for the first time, the Phoenix spectrograph to search for helium in the upper atmosphere of the inflated hot Jupiter WASP-127 b. We observed one transit and reduced the data manually since no pipeline is available. We did not find a significant in-transit absorption signal indicative of the presence of helium around WASP-127 b, and we set a 90% confidence upper limit for excess absorption at 0.87% in a 0.75 Å passband covering the He triplet. Given the large scale height of this planet, the lack of a detectable feature is likely due to unfavorable photoionization conditions for populating the metastable He I triplet. This conclusion is supported by the inferred low coronal and chromospheric activity of the host star and the old age of the system, which result in a relatively mild high-energy environment around the planet.
Physics successfully implements Lagrange multiplier optimization

Significance All through human civilization, optimization has played a major role, from aerodynamics to airline scheduling, delivery routing, and telecommunications decoding. Optimization is receiving increasing attention, since it is central to today’s artificial intelligence. All of these optimization problems are among the hardest for human or machine to solve. It has been overlooked that physics itself does optimization in the normal evolution of dynamical systems, such as seeking out the minimum energy state. We show that among such physics principles, the idea of minimum power dissipation, also called the Principle of Minimum Entropy Generation, appears to be the most useful, since it can be readily implemented in electrical or optical circuits. Optimization is a major part of human effort. While being mathematical, optimization is also built into physics. For example, physics has the Principle of Least Action; the Principle of Minimum Power Dissipation, also called Minimum Entropy Generation; and the Variational Principle. Physics also has Physical Annealing, which, of course, preceded computational Simulated Annealing. Physics has the Adiabatic Principle, which, in its quantum form, is called Quantum Annealing. Thus, physical machines can solve the mathematical problem of optimization, including constraints. Binary constraints can be built into the physical optimization. In that case, the machines are digital in the same sense that a flip–flop is digital. A wide variety of machines have had recent success at optimizing the Ising magnetic energy. We demonstrate in this paper that almost all those machines perform optimization according to the Principle of Minimum Power Dissipation as put forth by Onsager. Further, we show that this optimization is in fact equivalent to Lagrange multiplier optimization for constrained problems. We find that the physical gain coefficients that drive those systems actually play the role of the corresponding Lagrange multipliers.
Instabilities driven by diffusiophoretic flow on catalytic surfaces

Abstract We theoretically and numerically investigate the instabilities driven by diffusiophoretic flow, caused by a solutal concentration gradient along a reacting surface. The important control parameters are the Péclet number $Pe$, which quantifies the ratio of the solutal advection rate to the diffusion rate, and the Schmidt number $Sc$, which is the ratio of viscosity and diffusivity. First, we study the diffusiophoretic flow on a catalytic plane in two dimensions. From a linear stability analysis, we obtain that for $Pe$ larger than $8{\rm \pi}$ mass transport by convection overtakes that by diffusion, and a symmetry-breaking mode arises, which is consistent with numerical results. For even larger $Pe$, nonlinear terms become important. For $Pe > 16{\rm \pi}$, multiple concentration plumes are emitted from the catalytic plane, which eventually merge into a single larger one. When $Pe$ is even larger ($Pe \gtrsim 603$ for Schmidt number $Sc=1$), there are continuous emissions and merging events of the concentration plumes. The newly found flow states have different flow structures for different $Sc$: for $Sc\geqslant 1$, we observe the chaotic emission of plumes, but the fluctuations of concentration are only present in the region near the catalytic plane. In contrast, for $Sc<1$, chaotic flow motion occurs also in the bulk. In the second part of the paper, we conduct three-dimensional simulations for spherical catalytic particles, and beyond a critical Péclet number again find continuous plume emission and plume merging, now leading to a chaotic motion of the phoretic particle. Our results thus help us to understand the experimentally observed chaotic motion of catalytic particles in the high $Pe$ regime.
IllumiNet: Transferring Illumination from Planar Surfaces to Virtual Objects in Augmented Reality

This paper presents an illumination estimation method for virtual objects in real environment by learning. While previous works tackled this problem by reconstructing high dynamic range (HDR) environment maps or the corresponding spherical harmonics, we do not seek to recover the lighting environment of the entire scene. Given a single RGB image, our method directly infers the relit virtual object by transferring the illumination features extracted from planar surfaces in the scene to the desired geometries. Compared to previous works, our approach is more robust as it works in both indoor and outdoor environments with spatially-varying illumination. Experiments and evaluation results show that our approach outperforms the state-of-the-art quantitatively and qualitatively, achieving realistic augmented experience.
Physical and mechanical wood properties of ring-shaken chestnut (Castanea sativa) trees

Ring shake is a defect that strongly affects chestnut (Castanea sativa Mill.) use and its occurrence is known to be mainly related to mechanical stress within a tree; however, few investigations have compared the physico-mechanical properties of healthy and shaken trees. Hence, the aim of this study is to compare the density, compression strength, bending strength (MOR), and shrinkages between healthy and shaken trees in coppice stands. The investigation was carried out in the Lazio Region in central Italy in trees with a cambial age between 6 and 25 years. The results showed that shaken trees had lower mechanical strength and shrinkages than healthy ones and that the physico-mechanical parameters might be used to predict ring shake occurrence in a specific geographic area. Geographical location strongly affected the physico-mechanical properties of the chestnut wood, and this factor influenced ring shake occurrence. MOR value was assumed to be the parameter that could be applied at almost all study sites.
Optimal structure and development of uneven-aged Norway spruce forests

Optimal harvesting of Norway spruce (Picea abies (L.) Karst. ) forests is studied applying an individual-tree model for uneven-aged management. Optimization is carried out by gradient-based, large-scale interior point methods. Assuming volume maximization and natural regeneration, it is optimal to apply uneven-aged management. Under artificial regeneration, the result is the reverse. Economically optimal solutions with a 20-year harvesting interval produce an annual sawn timber output of 4.4–2.4 m3·ha–1 depending on thermal zone and interest rate. Before harvest basal area varies between 18 and 12 m2·ha–1 and the diameter of harvested trees between 15 and 33 cm. In contrast with the classic inverted J-structure, optimal steady-state size structure resembles a serrate form. Profitability of even- and uneven-aged management is compared assuming that the initial stand state represents an optimal uneven-aged steady state. A switch to even-aged management is optimal given the most favorable growth conditions an.
The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results

Code-switching (CS) is a common phenomenon and recognizing CS speech is challenging. But CS speech data is scarce and there' s no common testbed in relevant research. This paper describes the design and main outcomes of the ASRU 2019 Mandarin-English code-switching speech recognition challenge, which aims to improve the ASR performance in Mandarin-English code-switching situation. 500 hours Mandarin speech data and 240 hours Mandarin-English intra-sentencial CS data are released to the participants. Three tracks were set for advancing the AM and LM part in traditional DNN-HMM ASR system, as well as exploring the E2E models' performance. The paper then presents an overview of the results and system performance in the three tracks. It turns out that traditional ASR system benefits from pronunciation lexicon, CS text generating and data augmentation. In E2E track, however, the results highlight the importance of using language identification, building-up a rational set of modeling units and spec-augment. The other details in model training and method comparsion are discussed.
On mixing enhancement for baroclinic accelerated stretching in shock bubble interaction

Considering variable density effect on mixing enhancement is inherently existed in application of scramjet, the intrinsic coupling mechanism between variable density characteristic and mixing is still unknown. In this paper, a canonical variable density (VD) mixing protocol that is shock bubble interaction (SBI) abstracted from supersonic streamwise vortex, is studied by comparing with a counterpart of passive scalar (PS) mixing. A concrete vortex is formed for both VD and PS case expect that mixing of maximum concentration from vortical stretching decays much faster in VD than in PS case regardless of the shock Mach number concerned. By investigating the azimuthal velocity that stretches the bubble, besides the quasi-Lamb-Oseen type velocity distribution in PS case, a local accelerated stretching relating to secondary barcolinic vorticity in VD SBI explains the faster mixing enhancement and the increase of local mixing rate. Based on secondary baroclinic vorticity production, the fundamental mechanism of the additional accelerated stretching is modelled by inertial velocity difference between shocked light gas bubble and shocked heavy ambient air.Through combining the baroclinic accelerated stretching model and initial compression from shock, a new defined mixing time of VD SBI, $\widetilde{t}_m^*$, is theoretically proposed based on solving advection-diffusion equation under the local accelerated azimuthal velocity. The proposed mixing time further shows well prediction of mixing enhancement behavior in the range of $Re=3000\sim10^5$ and $Pe=1500\sim5\times10^4$. A density stratification distribution is raised accordingly from density inertial effect to control the mixing time behavior which implies the novel method for mixing enhancement of supersonic streamwise vortex.
Efficient Labeling for Reachability in Digraphs

We consider labeling nodes of a directed graph for reachability queries. A reachability labeling scheme for such a graph assigns a binary string, called a label, to each node. Then, given the labels of nodes $u$ and $v$ and no other information about the underlying graph, it should be possible to determine whether there exists a directed path from $u$ to $v$. By a simple information theoretical argument and invoking the bound on the number of partial orders, in any scheme some labels need to consist of at least $n/4$ bits, where $n$ is the number of nodes. On the other hand, it is not hard to design a scheme with labels consisting of $n/2+O(\log n)$ bits. In the classical centralised setting, Munro and Nicholson designed a data structure for reachability queries consisting of $n^2/4+o(n^2)$ bits (which is optimal, up to the lower order term). We extend their approach to obtain a scheme with labels consisting of $n/3+o(n)$ bits.
Availability of demographic cues can negatively impact creativity in dynamic social networks

As the world braces itself for a pandemic-induced surge in automation and a consequent (accelerated) shift in the nature of jobs, it is essential now more than ever to understand how people's creative performances are impacted by their interactions with peers in a social network. However, when it comes to creative ideation, it is unclear how the demographic cues of one's peers can influence the network dynamics and the associated performance outcomes of people. In this paper, we ask: (1) Given the task of creative idea generation, how do social network connectivities adapt to people's demographic cues? (2) How are creative outcomes influenced by such demography-informed network dynamics? We find that link formations in creativity-centric networks are primarily guided by the creative performances of one's peers. However, in the presence of demographic information, the odds of same-gender links to persist increase by 82.03%, after controlling for merit-based link persistence. In essence, homophily-guided link persistence takes place when demographic cues are available. We further find that the semantic similarities between socially stimulated idea-sets increase significantly in the presence of demographic cues (P<1e-4), which is counter-productive for the purposes of divergent creativity. This result can partly be explained by the observation that people's ideas tend to be more homogeneous within demographic groups than between demographic groups (P<1e-7). Therefore, choosing to maintain connections based on demographic similarity can negatively impact one's creative inspiration sources by taking away potential diversity bonuses. Our results can inform intelligent intervention possibilities towards maximizing a social system's creative outcomes.
Representation Learning via Adversarially-Contrastive Optimal Transport

In this paper, we study the problem of learning compact (low-dimensional) representations for sequential data that captures its implicit spatio-temporal cues. To maximize extraction of such informative cues from the data, we set the problem within the context of contrastive representation learning and to that end propose a novel objective via optimal transport. Specifically, our formulation seeks a low-dimensional subspace representation of the data that jointly (i) maximizes the distance of the data (embedded in this subspace) from an adversarial data distribution under the optimal transport, a.k. a. the Wasserstein distance, (ii) captures the temporal order, and (iii) minimizes the data distortion. To generate the adversarial distribution, we propose a novel framework connecting Wasserstein GANs with a classifier, allowing a principled mechanism for producing good negative distributions for contrastive learning, which is currently a challenging problem. Our full objective is cast as a subspace learning problem on the Grassmann manifold and solved via Riemannian optimization. To empirically study our formulation, we provide experiments on the task of human action recognition in video sequences. Our results demonstrate competitive performance against challenging baselines.
Learning latent stochastic differential equations with variational auto-encoders

We present a method for learning latent stochastic differential equations (SDEs) from high dimensional time series data. Given a time series generated from a lower dimensional Ito process, the proposed method uncovers the relevant parameters of the SDE through a self-supervised learning approach. Using the framework of variational autoencoders (VAEs), we consider a conditional generative model for the data based on the Euler-Maruyama approximation of SDE solutions. Furthermore, we use recent results on identifiability of semi-supervised learning to show that our model can recover not only the underlying SDE parameters, but also the original latent space, up to an isometry, in the limit of infinite data. We validate the model through a series of different simulated video processing tasks where the underlying SDE is known. Our results suggest that the proposed method effectively learns the underlying SDE, as predicted by the theory.
Potential of a trap-fishery for deep-water rock lobster Palinurus delagoae off South Africa

An experiment to investigate the potential of a trap-fishery for deep-water rock lobster Palinurus delagoae was conducted off the east coast of South Africa between 1994 and 1996. The 75–425 m depth interval between 27°S and 32°S was stratified according to depth and latitude, and three regions (North, Central and South) were sampled systematically over the three years. Sampling by commercial vessels operating long-lines and traps included an experimental phase (fixed fishing positions) and a commercial phase (no restriction on fishing location). Generalized linear models were used to investigate the influence of year, region, sampling phase, month and soak time. Sampling phase was significant, with traps set during the commercial phase catching more than those set in the experimental phase. The effect of soak time on catch rates was not significant. The almost-50% decrease in the combined abundance index combined with a marked decrease in mean lobster size over the study period suggests a relatively low fisheries potential for this species in South African waters; this may be attributable to the relative scarcity of suitable habitat in the area.
Dissecting and modelling galaxy assembly bias

Understanding the galaxy-halo connection is fundamental for contemporary models of galaxy clustering. The extent to which the haloes' assembly history and environment impact galaxy clustering (a.k.a. galaxy assembly bias; GAB), remains a complex and challenging problem. Using a semi-analytic galaxy formation model, we study the individual contributions of different secondary halo properties to the GAB signal. These are obtained by comparing the clustering of stellar-mass selected samples to that of shuffled samples where the galaxies are randomly reassigned to haloes of fixed mass and a specified secondary halo property. We explore a large range of internal halo properties and environmental measures. We find that commonly-used properties like the halo age or concentration amount to only 20-30% of the signal, while the smoothed matter density or the tidal anisotropy can explain the full level of GAB (though care should be given to the specific definition). For the ``successful'' measures, we examine the occupancy variations and the associated changes in the halo occupation function parameters. These are used to create mock catalogues which reproduce the full level of GAB. Finally, we propose a practical modification of the standard halo occupation distribution model, which can be tuned to any level of assembly bias. Fitting the parameters to our semi-analytic model, we demonstrate that the corresponding mock catalogue recovers the target level of GAB as well as the occupancy variations. Our results enable producing realistic mock catalogues and directly inform theoretical modelling of assembly bias and attempts to detect it in the Universe.