Miracle-dz/UniFAR-FTUs huggingface dataset:
VAPORIZATION OF THE EARTH: APPLICATION TO EXOPLANET ATMOSPHERES

Currently, there are about three dozen known super-Earths (M < 10 M⊕), of which eight are transiting planets suitable for atmospheric follow-up observations. Some of the planets are exposed to extreme temperatures as they orbit close to their host stars, e.g., CoRot-7b, and all of these planets have equilibrium temperatures significantly hotter than the Earth. Such planets can develop atmospheres through (partial) vaporization of their crustal and/or mantle silicates. We investigated the chemical equilibrium composition of such heated systems from 500 to 4000 K and total pressures from 10−6 to 10+2 bars. The major gases are H2O and CO2 over broad temperature and pressure ranges, and Na, K, O2, SiO, and O at high temperatures and low pressures. We discuss the differences in atmospheric composition arising from vaporization of SiO2-rich (i.e., felsic) silicates (like Earth's continental crust) and MgO-, FeO-rich (i.e., mafic) silicates (like the bulk silicate Earth). The computational results will be useful in planning spectroscopic studies of the atmospheres of Earth-like exoplanets.
The origin of physical variations in the star formation law

Observations of external galaxies and of local star-forming clouds in the Milky Way have suggested a variety of star formation laws, i.e., simple direct relations between the column density of star formation (Sigma_SFR: the amount of gas forming stars per unit area and time) and the column density of available gas (Sigma_gas). Extending previous studies, we show that these different, sometimes contradictory relations for Milky Way clouds, nearby galaxies, and high-redshift discs and starbursts can be combined in one universal star formation law in which Sigma_SFR is about 1% of the local gas collapse rate, Sigma_gas/t_ff, but a significant scatter remains in this relation. Using computer simulations and theoretical models, we find that the observed scatter may be primarily controlled by physical variations in the Mach number of the turbulence and by differences in the star formation efficiency. Secondary variations can be induced by changes in the virial parameter, turbulent driving and magnetic field. The predictions of our models are testable with observations that constrain both the Mach number and the star formation efficiency in Milky Way clouds, external disc and starburst galaxies at low and high redshift. We also find that reduced telescope resolution does not strongly affect such measurements when Sigma_SFR is plotted against Sigma_gas/t_ff.
LayoutEnhancer: Generating Good Indoor Layouts from Imperfect Data

We address the problem of indoor layout synthesis, which is a topic of continuing research interest in computer graphics. The newest works made significant progress using data-driven generative methods; however, these approaches rely on suitable datasets. In practice, desirable layout properties may not exist in a dataset, for instance, specific expert knowledge can be missing in the data. We propose a method that combines expert knowledge, for example, knowledge about ergonomics, with a data-driven generator based on the popular Transformer architecture. The knowledge is given as differentiable scalar functions, which can be used both as weights or as additional terms in the loss function. Using this knowledge, the synthesized layouts can be biased to exhibit desirable properties, even if these properties are not present in the dataset. Our approach can also alleviate problems of lack of data and imperfections in the data. Our work aims to improve generative machine learning for modeling and provide novel tools for designers and amateurs for the problem of interior layout creation.
“No, They Did Not”: Dialogue Response Dynamics in Pre-trained Language Models

A critical component of competence in language is being able to identify relevant components of an utterance and reply appropriately. In this paper we examine the extent of such dialogue response sensitivity in pre-trained language models, conducting a series of experiments with a particular focus on sensitivity to dynamics involving phenomena of at-issueness and ellipsis. We find that models show clear sensitivity to a distinctive role of embedded clauses, and a general preference for responses that target main clause content of prior utterances. However, the results indicate mixed and generally weak trends with respect to capturing the full range of dynamics involved in targeting at-issue versus not-at-issue content. Additionally, models show fundamental limitations in grasp of the dynamics governing ellipsis, and response selections show clear interference from superficial factors that outweigh the influence of principled discourse constraints.
Sparse Approximation Over the Cube

This paper presents an anlysis of the NP-hard minimization problem $\min \{\|b - Ax\|_2: \ x \in [0,1]^n, | \text{supp}(x) | \leq \sigma\}$, where $\text{supp}(x) = \{i \in [n]: x_i \neq 0\}$ and $\sigma$ is a positive integer. The object of investigation is a natural relaxation where we replace $| \text{supp}(x) | \leq \sigma$ by $\sum_i x_i \leq \sigma$. Our analysis includes a probabilistic view on when the relaxation is exact. We also consider the problem from a deterministic point of view and provide a bound on the distance between the images of optimal solutions of the original problem and its relaxation under $A$. This leads to an algorithm for generic matrices $A \in \mathbb{Z}^{m \times n}$ and achieves a polynomial running time provided that $m$ and $\|A\|_{\infty}$ are fixed.
Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction

Conventional frequentist learning is known to yield poorly calibrated models that fail to reliably quantify the uncertainty of their decisions. Bayesian learning can improve calibration, but formal guarantees apply only under restrictive assumptions about correct model specification. Conformal prediction (CP) offers a general framework for the design of set predictors with calibration guarantees that hold regardless of the underlying data generation mechanism. However, when training data are limited, CP tends to produce large, and hence uninformative, predicted sets. This paper introduces a novel meta-learning solution that aims at reducing the set prediction size. Unlike prior work, the proposed meta-learning scheme, referred to as meta-XB, i) builds on cross-validation-based CP, rather than the less efficient validation-based CP; and ii) preserves formal per-task calibration guarantees, rather than less stringent task-marginal guarantees. Finally, meta-XB is extended to adaptive non-conformal scores, which are shown empirically to further enhance marginal per-input calibration.
IN SITU MEASUREMENTS OF THE SIZE AND DENSITY OF TITAN AEROSOL ANALOGS

The organic haze produced from complex CH4/N2 chemistry in the atmosphere of Titan plays an important role in processes that occur in the atmosphere and on its surface. The haze particles act as condensation nuclei and are therefore involved in Titan's methane hydrological cycle. They also may behave like sediment on Titan's surface and participate in both fluvial and aeolian processes. Models that seek to understand these processes require information about the physical properties of the particles including their size and density. Although measurements obtained by Cassini–Huygens have placed constraints on the size of the haze particles, their densities remain unknown. We have conducted a series of Titan atmosphere simulation experiments and measured the size, number density, and particle density of Titan aerosol analogs, or tholins, for CH4 concentrations from 0.01% to 10% using two different energy sources, spark discharge and UV. We find that the densities currently in use by many Titan models are higher than the measured densities of our tholins.
Modelling Commonsense Properties Using Pre-Trained Bi-Encoders

Grasping the commonsense properties of everyday concepts is an important prerequisite to language understanding. While contextualised language models are reportedly capable of predicting such commonsense properties with human-level accuracy, we argue that such results have been inflated because of the high similarity between training and test concepts. This means that models which capture concept similarity can perform well, even if they do not capture any knowledge of the commonsense properties themselves. In settings where there is no overlap between the properties that are considered during training and testing, we find that the empirical performance of standard language models drops dramatically. To address this, we study the possibility of fine-tuning language models to explicitly model concepts and their properties. In particular, we train separate concept and property encoders on two types of readily available data: extracted hyponym-hypernym pairs and generic sentences. Our experimental results show that the resulting encoders allow us to predict commonsense properties with much higher accuracy than is possible by directly fine-tuning language models. We also present experimental results for the related task of unsupervised hypernym discovery.
Binding Language Models in Symbolic Languages

Though end-to-end neural approaches have recently been dominating NLP tasks in both performance and ease-of-use, they lack interpretability and robustness. We propose Binder, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) functionalities to a programming language (e.g., SQL, Python) to extend its grammar coverage and thus tackle more diverse questions, (2) adopts an LM as both the program parser and the underlying model called by the API during execution, and (3) requires only a few in-context exemplar annotations. Specifically, we employ GPT-3 Codex as the LM. In the parsing stage, with only a few in-context exemplars, Codex is able to identify the part of the task input that cannot be answerable by the original programming language, correctly generate API calls to prompt Codex to solve the unanswerable part, and identify where to place the API calls while being compatible with the original grammar. In the execution stage, Codex can perform versatile functionalities (e.g., commonsense QA, information extraction) given proper prompts in the API calls. Binder achieves state-of-the-art results on WikiTableQuestions and TabFact datasets, with explicit output programs that benefit human debugging. Note that previous best systems are all finetuned on tens of thousands of task-specific samples, while Binder only uses dozens of annotations as in-context exemplars without any training. Our code is available at https://github.com/HKUNLP/Binder .
AGN-stimulated cooling of hot gas in elliptical galaxies

We study the impact of relatively weak AGN feedback on the interstellar medium of intermediate and massive elliptical galaxies. We find that the AGN activity, while globally heating the ISM, naturally stimulates some degree of hot gas cooling on scales of several kpc. This process generates the persistent presence of a cold ISM phase, with mass ranging between 10$^4$ and $\gtrsim$ 5 $\times$ 10$^7$ M$_\odot$, where the latter value is appropriate for group centered, massive galaxies. Widespread cooling occurs where the ratio of cooling to free-fall time before the activation of the AGN feedback satisfies $t_{cool}/t_{ff} \lesssim 70$, that is we find a less restrictive threshold than commonly quoted in the literature. This process helps explaining the body of observations of cold gas (both ionized and neutral/molecular) in Ellipticals and, perhaps, the residual star formation detected in many early-type galaxies. The amount and distribution of the off-center cold gas vary irregularly with time. The cold ISM velocity field is irregular, initially sharing the (outflowing) turbulent hot gas motion. Typical velocity dispersions of the cold gas lie in the range 100-200 km/s. Freshly generated cold gas often forms a cold outflow and can appear kinematically misaligned with respect to the stars. We also follow the dust evolution in the hot and cold gas. We find that the internally generated cold ISM has a very low dust content, with representative values of the dust-to-gas ratio of 10$^{-4}$- 10$^{-5}$. Therefore, this cold gas can escape detection in the traditional dust-absorption maps.
Impact of baryon physics on dark matter structures: a detailed simulation study of halo density profiles

The back-reaction of baryons on the dark matter halo density profile is of great interest, not least because it is an important systematic uncertainty when attempting to detect the dark matter. Here, we draw on a large suite of high-resolution cosmological hydrodynamical simulations to systematically investigate this process and its dependence on the baryonic physics associated with galaxy formation. The effects of baryons on the dark matter distribution are typically not well described by adiabatic contraction models. In the inner 10 per cent of the virial radius the models are only successful if we allow their parameters to vary with baryonic physics, halo mass and redshift, thereby removing all predictive power. On larger scales the profiles from dark matter only simulations consistently provide better fits than adiabatic contraction models, even when we allow the parameters of the latter models to vary. The inclusion of baryons results in significantly more concentrated density profiles if radiative cooling is efficient and feedback is weak. The dark matter halo concentration can in that case increase by as much as 30 (10) per cent on galaxy (cluster) scales. The most significant effects occur in galaxies at high redshift, where there is a strong anticorrelation between the baryon fraction in the halo centre and the inner slope of both the total and the dark matter density profiles. If feedback is weak, isothermal inner profiles form, in agreement with observations of massive, early-type galaxies. However, we find that active galactic nuclei (AGN) feedback, or extremely efficient feedback from massive stars, is necessary to match observed stellar fractions in groups and clusters, as well as to keep the maximum circular velocity similar to the virial velocity as observed for disc galaxies. These strong feedback models reduce the baryon fraction in galaxies by a factor of 3 relative to the case with no feedback. The AGN is even capable of reducing the baryon fraction by a factor of 2 in the inner region of group and cluster haloes. This in turn results in inner density profiles which are typically shallower than isothermal and the halo concentrations tend to be lower than in the absence of baryons. We therefore conclude that the disagreement between the concentrations inferred from observations of groups of galaxies and predictions from simulations that was identified by Duffy et al. is not alleviated by the inclusion of baryons.
Stable solutions to fractional semilinear equations: uniqueness, classification, and approximation results

We study stable solutions to fractional semilinear equations $(-\Delta)^s u = f(u)$ in $\Omega \subset \mathbb{R}^n$, for convex nonlinearities $f$, and under the Dirichlet exterior condition $u=g$ in $\mathbb{R}^n \setminus \Omega$ with general $g$. We establish a uniqueness and a classification result, and we show that weak (energy) stable solutions can be approximated by a sequence of bounded (and hence regular) stable solutions to similar problems. As an application of our results, we establish the interior regularity of weak (energy) stable solutions to the problem for the half-Laplacian in dimensions $1 \leq n \leq 4$.
Kirkwood-Dirac classical pure states

Kirkwood-Dirac (KD) distribution is a representation of quantum states. Recently, KD distribution has been employed in many scenarios such as quantum metrology, quantum chaos and foundations of quantum theory. KD distribution is a quasiprobability distribution, and negative or nonreal elements may signify quantum advantages in certain tasks. A quantum state is called KD classical if its KD distribution is a probability distribution. Since most quantum information processings use pure states as ideal resources, then a key problem is to determine whether a quantum pure state is KD classical. In this paper, we provide some characterizations for the general structure of KD classical pure states. As an application of our results, we prove a conjecture raised by De Bi\`{e}vre [Phys. Rev. Lett. 127, 190404 (2021)] which finds out all KD classical pure states for discrete Fourier transformation.
Query The Agent: Improving sample efficiency through epistemic uncertainty estimation

Curricula for goal-conditioned reinforcement learning agents typically rely on poor estimates of the agent's epistemic uncertainty or fail to consider the agents' epistemic uncertainty altogether, resulting in poor sample efficiency. We propose a novel algorithm, Query The Agent (QTA), which significantly improves sample efficiency by estimating the agent's epistemic uncertainty throughout the state space and setting goals in highly uncertain areas. Encouraging the agent to collect data in highly uncertain states allows the agent to improve its estimation of the value function rapidly. QTA utilizes a novel technique for estimating epistemic uncertainty, Predictive Uncertainty Networks (PUN), to allow QTA to assess the agent's uncertainty in all previously observed states. We demonstrate that QTA offers decisive sample efficiency improvements over preexisting methods.
Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks

Striking progress has recently been made in understanding human cognition by analyzing how its neuronal underpinnings are engaged in different modes of information processing. Specifically, neural information can be decomposed into synergistic, redundant, and unique features, with synergistic components being particularly aligned with complex cognition. However, two fundamental questions remain unanswered: (a) precisely how and why a cognitive system can become highly synergistic; and (b) how these informational states map onto artificial neural networks in various learning modes. To address these questions, here we employ an information-decomposition framework to investigate the information processing strategies adopted by simple artificial neural networks performing a variety of cognitive tasks in both supervised and reinforcement learning settings. Our results show that synergy increases as neural networks learn multiple diverse tasks. Furthermore, performance in tasks requiring integration of multiple information sources critically relies on synergistic neurons. Finally, randomly turning off neurons during training through dropout increases network redundancy, corresponding to an increase in robustness. Overall, our results suggest that while redundant information is required for robustness to perturbations in the learning process, synergistic information is used to combine information from multiple modalities -- and more generally for flexible and efficient learning. These findings open the door to new ways of investigating how and why learning systems employ specific information-processing strategies, and support the principle that the capacity for general-purpose learning critically relies in the system's information dynamics.
Local analyses of Planck maps with Minkowski functionals

Minkowski Functionals (MF) are excellent tools to investigate the statistical properties of the cosmic background radiation (CMB) maps. Between their notorious advantages is the possibility to use them efficiently in patches of the CMB sphere, which allow studies in masked skies, inclusive analyses of small sky regions. Then, possible deviations from Gaussianity are investigated by comparison with MF obtained from a set of Gaussian isotropic simulated CMB maps to which are applied the same cut-sky masks. These analyses are sensitive enough to detect contaminations of small intensity like primary and secondary CMB anisotropies. Our methodology uses the MF, widely employed to study non-Gaussianities in CMB data, and asserts Gaussian deviations only when all of them points out an exceptional $\chi^2$ value, at more than $2.2 \sigma$ confidence level, in a given sky patch. Following this rigorous procedure, we find 13 regions in the foreground-cleaned Planck maps that evince such high levels of non-Gaussian deviations. According to our results, these non-Gaussian contributions show signatures that can be associated to the presence of hot or cold spots in such regions. Moreover, some of these non-Gaussian deviations signals suggest the presence of foreground residuals in those regions located near the galactic plane. Additionally, we confirm that most of the regions revealed in our analyses, but not all, have been recently reported in studies done by the Planck collaboration. Furthermore, we also investigate whether these non-Gaussian deviations can be possibly sourced by systematics, like inhomogeneous noise and beam effect in the released Planck data, or perhaps due to residual galactic foregrounds.
CLIP model is an Efficient Continual Learner

The continual learning setting aims to learn new tasks over time without forgetting the previous ones. The literature reports several signiﬁcant efforts to tackle this problem with limited or no access to previous task data. Among such efforts, typical solutions offer sophisticated techniques involving memory replay, knowledge distillation, model regularization, and dynamic network expansion. The resulting methods have a retraining cost at each learning task, dedicated memory requirements, and setting-speciﬁc design choices. In this work, we show that a frozen CLIP (Contrastive Language-Image Pretraining) model offers as-tounding continual learning performance without any ﬁne-tuning (zero-shot eval-uation). We evaluate CLIP under a variety of settings including class-incremental, domain-incremental and task-agnostic incremental learning on ﬁve popular benchmarks (ImageNet-100 & 1K, CORe50, CIFAR-100, and TinyImageNet). Without any bells and whistles, the CLIP model outperforms the state-of-the-art continual learning approaches in majority of the settings. We show the effect on CLIP model’s performance by varying text inputs with simple prompt templates. To the best of our knowledge, this is the ﬁrst work to report the CLIP zero-shot performance in a continual setting. We advocate the use of this strong yet embarrass-ingly simple baseline for future comparisons in the continual learning tasks. Code is available at https://github.com/vgthengane/Continual-CLIP .