Miracle-dz/UniFAR-FTUs huggingface dataset:
Assembly bias in the local PNG halo bias and its implication for f NL constraints

We use N-body simulations to study halo assembly bias (i.e., the dependence of halo clustering on properties beyond total mass) in the density and primordial non-Gaussianity (PNG) linear bias parameters b 1 and b ϕ, respectively. We consider concentration, spin and sphericity as secondary halo properties, for which we find a clear detection of assembly bias for b 1 and b ϕ. At fixed total mass, halo spin and sphericity impact b 1 and b ϕ in a similar manner, roughly preserving the shape of the linear b ϕ(b 1) relation satisfied by the global halo population. Halo concentration, however, drives b 1 and b ϕ in opposite directions. This induces significant changes to the b ϕ(b 1) relation, with higher concentration halos having higher amplitude of b ϕ(b 1). For z = 0.5 and b 1 ≈ 2 in particular, the population comprising either all halos, those with the 33% lowest or those with the 33% highest concentrations have a PNG bias of b ϕ ≈ 3, b ϕ ≈ -1 and b ϕ ≈ 9, respectively. Varying the halo concentration can make b ϕ very small and even change its sign. These results have important ramifications for galaxy clustering constraints of the local PNG parameter fNL that assume fixed forms for the b ϕ(b 1) relation. We illustrate the significant impact of halo assembly bias in actual data using the BOSS DR12 galaxy power spectrum: assuming that BOSS galaxies are representative of all halos, the 33% lowest or the 33% highest concentration halos yields σ f NL = 44, 165, 19, respectively. Our results suggest taking host halo concentration into account in galaxy selection strategies to maximize the signal-to-noise on f NL. They also motivate more simulation-based efforts to study the b ϕ(b 1) relation of halos and galaxies.
MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and Report

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, RGB+ToF Depth Completion, one of the five tracks, working on the fusion of RGB sensor and ToF sensor (with spot illumination) is introduced. The participants were provided with a new dataset called TetrasRGBD, which contains 18k pairs of high-quality synthetic RGB+Depth training data and 2.3k pairs of testing data from mixed sources. All the data are collected in an indoor scenario. We require that the running time of all methods should be real-time on desktop GPUs. The final results are evaluated using objective metrics and Mean Opinion Score (MOS) subjectively. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022.
Weak Decays of Strange and Light Quarks

The present report of the RF2 Topical Group to Snowmass 2021 describes the physics case for the studies of weak decays of strange and light quarks. Ongoing and proposed precision measurements of kaon, hyperon, pion and $\eta^{(\prime)}$ meson decays allow for unique tests of the Standard Model (SM). This includes precision measurements of the elements of the CKM quark-mixing matrix leading to stringent unitarity tests; precision symmetry tests including lepton flavor and lepton number conservation; and precision lepton flavor universality tests. In the context of models beyond the SM description, strange and light quark decay experiments provide sensitivity to new physics up to the PeV mass scale, as well as leading sensitivities to scenarios involving feebly interacting hidden sectors below the GeV mass scale.
CommunityLM: Probing Partisan Worldviews from Language Models

As political attitudes have diverged ideologically in the United States, political speech has diverged lingusitically. The ever-widening polarization between the US political parties is accelerated by an erosion of mutual understanding between them. We aim to make these communities more comprehensible to each other with a framework that probes community-specific responses to the same survey questions using community language models CommunityLM. In our framework we identify committed partisan members for each community on Twitter and fine-tune LMs on the tweets authored by them. We then assess the worldviews of the two groups using prompt-based probing of their corresponding LMs, with prompts that elicit opinions about public figures and groups surveyed by the American National Election Studies (ANES) 2020 Exploratory Testing Survey. We compare the responses generated by the LMs to the ANES survey results, and find a level of alignment that greatly exceeds several baseline methods. Our work aims to show that we can use community LMs to query the worldview of any group of people given a sufficiently large sample of their social media discussions or media diet.
Asymptotic Statistical Analysis of f-divergence GAN

Generative Adversarial Networks (GANs) have achieved great success in data generation. However, its statistical properties are not fully understood. In this paper, we consider the statistical behavior of the general $f$-divergence formulation of GAN, which includes the Kullback--Leibler divergence that is closely related to the maximum likelihood principle. We show that for parametric generative models that are correctly specified, all $f$-divergence GANs with the same discriminator classes are asymptotically equivalent under suitable regularity conditions. Moreover, with an appropriately chosen local discriminator, they become equivalent to the maximum likelihood estimate asymptotically. For generative models that are misspecified, GANs with different $f$-divergences {converge to different estimators}, and thus cannot be directly compared. However, it is shown that for some commonly used $f$-divergences, the original $f$-GAN is not optimal in that one can achieve a smaller asymptotic variance when the discriminator training in the original $f$-GAN formulation is replaced by logistic regression. The resulting estimation method is referred to as Adversarial Gradient Estimation (AGE). Empirical studies are provided to support the theory and to demonstrate the advantage of AGE over the original $f$-GANs under model misspecification.
Layerwise Bregman Representation Learning with Applications to Knowledge Distillation

In this work, we propose a novel approach for layerwise representation learning of a trained neural network. In particular, we form a Bregman divergence based on the layer's transfer function and construct an extension of the original Bregman PCA formulation by incorporating a mean vector and normalizing the principal directions with respect to the geometry of the local convex function around the mean. This generalization allows exporting the learned representation as a fixed layer with a non-linearity. As an application to knowledge distillation, we cast the learning problem for the student network as predicting the compression coefficients of the teacher's representations, which are passed as the input to the imported layer. Our empirical findings indicate that our approach is substantially more effective for transferring information between networks than typical teacher-student training using the teacher's penultimate layer representations and soft labels.
The C/N ratio from FUV spectroscopy as a constraint on evolution of the dwarf nova HS 0218+3229

White dwarfs that accrete from non-degenerate companions show anomalous carbon and nitrogen abundances in the photospheres of their stellar components have been postulated to be descendants of supersoft X-ray binaries. Measuring the carbon-to-nitrogen abundance ratio may provide constraints on their past evolution. We fit far-ultraviolet spectroscopy of the cataclysmic variable HS 0218+3229 taken with the Cosmic Origins Spectrograph using Markov Chain Monte Carlo methods, and found the carbon-to-nitrogen ratio is about one tenth of the Solar value ($\mbox{$\log \mathrm{[C/N]}$}=-0.56\pm 0.15$). We also provide estimates of the silicon and aluminium abundances, and upper limits for iron and oxygen. Using the parameters we derived for HS 0218+3229 we reconstruct its past. We calculated a grid of MESA models and implemented Gaussian process fits in order to determine its most likely initial binary configuration. We found that an initial mass of the donor of $M_{\rm donor;i}=0.90-0.98,\mbox{$\mathrm{M}_{\odot }$}$ and an initial orbital period of Porb; i = 2.88 d (Porb; i = 3.12 − 3.16 d) for an assumed initial white dwarf mass of $\mbox{$M_{\mathrm{WD}}$}_\mathrm{;i}=0.83\, \mbox{$\mathrm{M}_{\odot }$}$ ($\mbox{$M_{\mathrm{WD}}$}_{\rm ;i}=0.60\, \mbox{$\mathrm{M}_{\odot }$}$) can replicate the measured parameters. The low mass ratio, $M_{\rm donor;i} / \mbox{$M_{\mathrm{WD}}$}_{\rm ;i} =1.08-1.18\, (1.5-1.63)$, suggests that the system did not go through a phase of hydrogen-burning on the white dwarf’s surface. However, we can not exclude a phase of thermal timescale mass transfer in the past. We predict that HS 0218+3229 will evolve below the ≃ 76.2 ± 1 min period minimum for normal cataclysmic variables.
Matching the mass function of Milky Way satellites in competing dark matter models

Any successful model of dark matter must explain the diversity of observed Milky Way (MW) satellite density profiles, from very dense ultrafaints to low density satellites so large that they could be larger than their inferred dark matter haloes. Predictions for these density profiles are complicated by the limitations of simulation resolution in the stripping of subhaloes by the MW system. We consider cold dark matter (CDM), warm dark matter (WDM, 3.3 keV thermal relic power spectrum), and a self-interacting dark matter model (SIDM) that induces gravothermal collapse in low mass subhaloes. Using N-body simulations combined with a halo stripping algorithm, we find that most CDM and WDM subhaloes of mass >108 ${\, \rm M_\odot }$ are large enough after stripping to fit most satellites; however, the required amount of stripping often requires a stronger tidal field than is available on the subhalo’s orbit. The lower concentrations of WDM subhaloes enable more stripping to take place, even on orbits with large pericentres. SIDM cores offer the best fits to massive, low density satellites at the expense of predicting >109 ${\, \rm M_\odot }$ subhaloes to host low density satellites with no observed analogue. The agreement of the total number of satellites with observations in CDM and WDM depends strongly on the assumptions made to draw the observational estimates. We conclude that an SIDM model must have a very high velocity-dependent cross-section in order to match all satellites, and that WDM offers a marginally better fit than CDM to the MW satellite mass function.
The SATIN project – I. Turbulent multiphase ISM in Milky Way simulations with SNe feedback from stellar clusters

We introduce the star formation and supernova (SN) feedback model of the satin (Simulating AGNs Through ISM with Non-Equilibrium Effects) project to simulate the evolution of the star forming multiphase interstellar medium (ISM) of entire disc galaxies. This galaxy-wide implementation of a successful ISM feedback model tested in small box simulations naturally covers an order of magnitude in gas surface density, shear and radial motions. It is implemented in the adaptive mesh refinement code ramses at a peak resolution of 9 pc. New stars are represented by star cluster (sink) particles with individual SN delay times for massive stars. With SN feedback, cooling, and gravity, the galactic ISM develops a three-phase structure. The star formation rates naturally follow observed scaling relations for the local Milky Way gas surface density. SNe drive additional turbulence in the warm (300 < T < 104 K) gas and increase the kinetic energy of the cold gas, cooling out of the warm phase. The majority of the gas leaving the galactic ISM is warm and hot with mass loading factors of 3 ≤ η ≤ 10 up to h = 5 kpc away from the galaxy. While the hot gas is leaving the system, the warm and cold gas falls back onto the disc in a galactic fountain flow. The inclusion of other stellar feedback processes from massive stars seems to be needed to reduce the rate at which stars form at higher surface densities and to increase/decrease the amount of warm/cold gas.
Out of One, Many: Using Language Models to Simulate Human Samples

Abstract We propose and explore the possibility that language models can be studied as effective proxies for specific human subpopulations in social science research. Practical and research applications of artificial intelligence tools have sometimes been limited by problematic biases (such as racism or sexism), which are often treated as uniform properties of the models. We show that the “algorithmic bias” within one such tool—the GPT-3 language model—is instead both fine-grained and demographically correlated, meaning that proper conditioning will cause it to accurately emulate response distributions from a wide variety of human subgroups. We term this property algorithmic fidelity and explore its extent in GPT-3. We create “silicon samples” by conditioning the model on thousands of sociodemographic backstories from real human participants in multiple large surveys conducted in the United States. We then compare the silicon and human samples to demonstrate that the information contained in GPT-3 goes far beyond surface similarity. It is nuanced, multifaceted, and reflects the complex interplay between ideas, attitudes, and sociocultural context that characterize human attitudes. We suggest that language models with sufficient algorithmic fidelity thus constitute a novel and powerful tool to advance understanding of humans and society across a variety of disciplines.
Comparing Be Star Inclination Angles Determined from Hα Fitting and Gravitational Darkening

Using a sample of 92 Galactic Be stars, we compare inclination angles (the angle between a star’s rotation axis and the line of sight) determined from Hα emission-line profile fitting to those determined by the spectroscopic signature of gravitational darkening. We find good agreement: 70% of the sample (64 out of 92 stars) is consistent with zero difference between the two methods using 1σ errors, and there is a strong linear correlation coefficient between the two methods of r = +0.63 ± 0.05. There is some evidence that the Hα profile fitting method overestimates the inclination angle for i ≲ 25°, perhaps due to the neglect of incoherent electron scattering on the Hα line widths, while the gravitational darkening method underestimates the inclination angle for i ≳ 70°, perhaps due to the neglect of disk radiative transfer effects on the optical spectrum. Overall, it is demonstrated that a single Hα spectrum of modest resolution and SNR can be used to extract a useful estimate for the inclination angle of an individual Be star. This allows equatorial rotation velocities for individual Be stars to be derived from vsini measurements and will allow Be stars to be used to search for correlated spin axes in young, open clusters if unbiased (with respect to inclination) samples of Be stars are used.
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits

We propose a novel algorithm for generalized linear contextual bandits (GLBs) with a regret bound sublinear to the time horizon, the minimum eigenvalue of the covariance of contexts and a lower bound of the variance of rewards. In several identified cases, our result is the first regret bound for generalized linear bandits (GLBs) achieving the regret bound sublinear to the dimension of contexts without discarding the observed rewards. Previous approaches achieve the regret bound sublinear to the dimension of contexts by discarding the observed rewards, whereas our algorithm achieves the bound incorporating contexts from all arms in our double doubly robust (DDR) estimator. The DDR estimator is a subclass of doubly robust estimator but with a tighter error bound. We also provide a logarithmic cumulative regret bound under a probabilistic margin condition. This is the first regret bound under the margin condition for linear models or GLMs when contexts are different for all arms but coefficients are common. We conduct empirical studies using synthetic data and real examples, demonstrating the effectiveness of our algorithm.
Nested $\hat R$: Assessing the convergence of Markov chain Monte Carlo when running many short chains

Recent developments in Markov chain Monte Carlo (MCMC) algorithms allow us to run thousands of chains in parallel almost as quickly as a single chain, using hardware accelerators such as GPUs. While each chain still needs to forget its initial point during a warmup phase, the subsequent sampling phase can be shorter than in classical settings, where we run only a few chains. To determine if the resulting short chains are reliable, we need to assess how close the Markov chains are to their stationary distribution after warmup. The potential scale reduction factor $\widehat R$ is a popular convergence diagnostic but unfortunately can require a long sampling phase to work well. We present a nested design to overcome this challenge and a generalization called nested $\widehat R$. This new diagnostic works under conditions similar to $\widehat R$ and completes the workflow for GPU-friendly samplers. In addition, the proposed nesting provides theoretical insights into the utility of $\widehat R$, in both classical and short-chains regimes.
Principles for Estimating Causal Effects in Observational Settings

To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders and instrumental variables (IVs). Because such approaches are largely characterized by untestable assumptions, analysts must operate under an indefinite paradigm that these methods will work imperfectly. In this tutorial, we formalize a set of general principles and heuristics for estimating causal effects in the two approaches when the assumptions are potentially violated. This crucially requires reframing the process of observational studies as hypothesizing potential scenarios where the estimates from one approach are less inconsistent than the other. While most of our discussion of methodology centers around the linear setting, we touch upon complexities in non-linear settings and flexible procedures such as target minimum loss-based estimation (TMLE) and double machine learning (DML). To demonstrate the application of our principles, we investigate the use of donepezil off-label for mild cognitive impairment (MCI). We compare and contrast results from confounder and IV methods, traditional and flexible, within our analysis and to a similar observational study and clinical trial.
Relation between pole and running masses of heavy quarks using the principle of maximum conformality

The relation of the pole and running heavy quark masses of order O $(\alpha _s^4)$ in perturbative quantum chromodynamics (pQCD) can be obtained using the Principle of Maximum Conformality (PMC), a formalism that provides a rigorous method for eliminating renormalization scale and scheme ambiguities for observables in pQCD. Using PMC, an optimal renormalization scale for the heavy quark mass ratio is determined, independent of the renormalization scale and scheme up to order $\alpha _s^4$. Precise values are then obtained for the PMC pole masses of the heavy quarks $M_b^{\text{PMC}}=4.86^{+0.03}_{-0.02}$ GeV, $M_t^{\text{PMC}}=172.3\pm 0.6$ GeV, and the running mass $\overline{m}_t^{\text{PMC}}=162.6\pm 0.7$ GeV at the PMC scale.
Neural Stochastic Control

Control problems are always challenging since they arise from the real-world systems where stochasticity and randomness are of ubiquitous presence. This naturally and urgently calls for developing efficient neural control policies for stabilizing not only the deterministic equations but the stochastic systems as well. Here, in order to meet this paramount call, we propose two types of controllers, viz., the exponential stabilizer (ES) based on the stochastic Lyapunov theory and the asymptotic stabilizer (AS) based on the stochastic asymptotic stability theory. The ES can render the controlled systems exponentially convergent but it requires a long computational time; conversely, the AS makes the training much faster but it can only assure the asymptotic (not the exponential) attractiveness of the control targets. These two stochastic controllers thus are complementary in applications. We also investigate rigorously the linear controller and the proposed neural stochastic controllers in both convergence time and energy cost and numerically compare them in these two indexes. More significantly, we use several representative physical systems to illustrate the usefulness of the proposed controllers in stabilization of dynamical systems.
SecSkyline: Fast Privacy-Preserving Skyline Queries Over Encrypted Cloud Databases

The well-known benefits of cloud computing have spurred the popularity of database service outsourcing, where one can resort to the cloud to conveniently store and query databases. Coming with such popular trend is the threat to data privacy, as the cloud gains access to the databases and queries which may contain sensitive information, like medical or financial data. A large body of work has been presented for querying encrypted databases, which has been mostly focused on secure keyword search. In this paper, we instead focus on the support for secure skyline query processing over encrypted outsourced databases, where little work has been done. Skyline query is an advanced kind of database query which is important for multi-criteria decision-making systems and applications. We propose SecSkyline, a new system framework building on lightweight cryptography for fast privacy-preserving skyline queries. SecSkyline ambitiously provides strong protection for not only the content confidentiality of the outsourced database, the query, and the result, but also for data patterns that may incur indirect data leakages, such as dominance relationships among data points and search access patterns. Extensive experiments demonstrate that SecSkyline is substantially superior to the state-of-the-art in query latency, with up to <inline-formula><tex-math notation="LaTeX">$813\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>813</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zheng-ieq1-3220595.gif"/></alternatives></inline-formula> improvement.