Miracle-dz/UniFAR-FTUs huggingface dataset:
Snowmass White Paper: Micro- and Macro-Structure of Black Holes

The black-hole information paradox provides a stringent test of would-be theories of quantum gravity. String theory has made significant progress toward a resolution of this paradox, and has led to the fuzzball and microstate geometry programs. The central thesis of these programs is that only string theory has sufficiently many degrees of freedom to resolve black-hole microstructure, and that horizons and singularities are artifacts of attempting to describe gravity using a theory that has too few degrees of freedom to resolve the physics. Fuzzballs and microstate geometries recast black holes within string theory as horizonless and singularity-free objects that not only resolve the paradox but provide new insight into the underlying microstructure. We give an overview of this approach, summarize its current status and describe future prospects and insights that are now within reach.
OneWeb Satellite Brightness -- Characterized From 80,000 Visible Light Magnitudes

The mean apparent magnitude and the mean of magnitudes adjusted to a standard distance are reported. The illumination phase function for OneWeb satellites is determined and it differs strongly from that of VisorSat spacecraft. Brightness flares are characterized and the mean rate of magnitude variation during a pass is determined. Tools for planning observations that minimize interference from bright satellites are illustrated and discussed.
A Comparative Analysis to Deal with Missing Spectral Information Caused by RFI in Cosmological H i 21 cm Observations

We investigate the effect of radio-frequency interference (RFI) excision in estimating the cosmological H i 21 cm power spectrum. Flagging of RFI-contaminated channels results in a nonuniform sampling of the instrumental bandpass response. Hence, the Fourier transformation of visibilities from frequency to delay domain contaminates the higher foreground-free delay modes, and separating the spectrally fluctuating H i signal from spectrally smooth foregrounds becomes challenging. We have done a comparative analysis between two algorithms, one-dimensional CLEAN and least-squares spectral analysis (LSSA), which have been used widely to solve this issue in the literature. We test these algorithms using the simulated SKA-1 Low observations in the presence of different RFI flagging scenarios. We find that, in the presence of random flagging of data, both algorithms perform well and can mitigate the foreground leakage issue. But CLEAN fails to restrict the foreground leakage in the presence of periodic and periodic plus broadband RFI flagging and gives an extra bias to the estimated power spectrum. However, LSSA can restrict the foreground leakage for these RFI flagging scenarios and gives an unbiased estimate of the H i 21 cm power spectrum. We have also applied these algorithms to observations with the upgraded GMRT and found that both CLEAN and LSSA give consistent results in the presence of realistic random flagging scenarios for this observed data set. This comparative analysis demonstrates the effectiveness and robustness of these two algorithms in estimating the H i 21 cm power spectrum from data sets affected by different RFI scenarios.
Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking

Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest. However, existing tracking approaches rely on customized sub-modules and need prior knowledge for architecture selection, hindering the tracking development in a more general system. This paper presents a Simplified Tracking architecture (SimTrack) by leveraging a transformer backbone for joint feature extraction and interaction. Unlike existing Siamese trackers, we serialize the input images and concatenate them directly before the one-branch backbone. Feature interaction in the backbone helps to remove well-designed interaction modules and produce a more efficient and effective framework. To reduce the information loss from down-sampling in vision transformers, we further propose a foveal window strategy, providing more diverse input patches with acceptable computational costs. Our SimTrack improves the baseline with 2.5%/2.6% AUC gains on LaSOT/TNL2K and gets results competitive with other specialized tracking algorithms without bells and whistles.
Tracing Molecular Gas Mass in z ≃ 6 Galaxies with [C ii]

We investigate the fine-structure [C ii] line at 158 μm as a molecular gas tracer by analyzing the relationship between molecular gas mass (M mol) and [C ii] line luminosity (L [C II]) in 11,125 z ≃ 6 star-forming, main-sequence galaxies from the simba simulations, with line emission modeled by the Simulator of Galaxy Millimeter/Submillimeter Emission. Though most (∼50%–100%) of the gas mass in our simulations is ionized, the bulk (>50%) of the [C ii] emission comes from the molecular phase. We find a sublinear (slope 0.78 ± 0.01) logL[CII]–logMmol relation, in contrast with the linear relation derived from observational samples of more massive, metal-rich galaxies at z ≲ 6. We derive a median [C ii]-to-M mol conversion factor of α [C II] ≃ 18 M ⊙/L ⊙. This is lower than the average value of ≃30 M ⊙/L ⊙ derived from observations, which we attribute to lower gas-phase metallicities in our simulations. Thus, a lower, luminosity-dependent conversion factor must be applied when inferring molecular gas masses from [C ii] observations of low-mass galaxies. For our simulations, [C ii] is a better tracer of the molecular gas than CO J = 1–0, especially at the lowest metallicities, where much of the gas is CO-dark. We find that L [C II] is more tightly correlated with M mol than with star formation rate (SFR), and both the logL[CII]–logMmol and logL[CII]–logSFR relations arise from the Kennicutt–Schmidt relation. Our findings suggest that L [C II] is a promising tracer of the molecular gas at the earliest cosmic epochs.
Conics on Gushel-Mukai fourfolds, EPW sextics and Bridgeland moduli spaces

We identify the double dual EPW sextic $\widetilde{Y}_{A^{\perp}}$ and the double EPW sextic $\widetilde{Y}_A$, associated with a very general Gushel-Mukai fourfold $X$, with the Bridgeland moduli spaces of stable objects of character $\Lambda_1$ and $\Lambda_2$ in the Kuznetsov component $\mathcal{K}u(X)$. This provides an affirmative answer to a question of Perry-Pertusi-Zhao. As an application, we prove a conjecture of Kuznetsov-Perry for very general Gushel-Mukai fourfolds.
Novel counterexample to the Nelson-Seiberg theorem

We present a new type of counterexample to the Nelson-Seiberg theorem. It is a generic R-symmetric Wess-Zumino model with nine chiral superﬁelds, including one ﬁeld of R-charge 2 and no R-charge 0 ﬁeld. As in previous counterexamples, the model gives a set of degenerate supersymmetric vacua with a non-zero expectation value for a pair of oppositely R-charged ﬁelds. However, one of these ﬁelds appears quadratically in the superpotential, and many other ﬁelds with non-zero R-charges gain non-zero expectation values at the vacuum, and so this model escapes the suﬃcient condition for counterexamples established in previous literature. Thus there are still open problems in the relation of R-symmetries to supersymmetry breaking in generic models.
Pathwise least-squares estimator for linear SPDEs with additive fractional noise

This paper deals with the drift estimation in linear stochastic evolution equations (with emphasis on linear SPDEs) with additive fractional noise (with Hurst index ranging from 0 to 1) via least-squares procedure. Since the least-squares estimator contains stochastic integrals of divergence type, we address the problem of its pathwise (and robust to observation errors) evaluation by comparison with the pathwise integral of Stratonovich type and using its chain-rule property. The resulting pathwise LSE is then defined implicitly as a solution to a non-linear equation. We study its numerical properties (existence and uniqueness of the solution) as well as statistical properties (strong consistency and the speed of its convergence). The asymptotic properties are obtained assuming fixed time horizon and increasing number of the observed Fourier modes (space asymptotics). We also conjecture the asymptotic normality of the pathwise LSE.
Constraining the nuclear equation of state from rotating neutron stars

We demonstrate how observables of slowly rotating neutron stars can be used to constrain the nuclear equation of state. By building a Bayesian framework we demonstrate how combining different types of neutron star measurements, motivated by the upcoming multi-messenger era, provide informative posterior distributions of commonly used equation of state parametrizations. Exemplarily, we chose the Read et al. four parameter model that is widely used to represent realistic equations of states in neutron star physics. Since future observational campaigns will not only provide mass, radius and rotation rate measurements, but also neutron star oscillation modes, a unified framework to analyze the joint knowledge becomes necessary. While several attempts exist in the literature, the here presented framework explicitly takes into account rotational effects by using state-of-the-art universal relations for their oscillation spectra. To demonstrate its performance we apply it to observables that take into account rotational effects to best possible precision and use our independent model for the reconstruction. Only a few high precision neutron star measurements are required to put tight constraints on the underlying equation of state.
Lee-Yang zeroes of the Curie-Weiss ferromagnet, unitary Hermite polynomials, and the backward heat flow

The backward heat ﬂow on the real line started from the initial condition z n results in the classical n -th Hermite polynomial whose zeroes are distributed according to the Wigner semicircle law in the large n limit. Similarly, the backward heat ﬂow with the periodic initial condition ( sin θ 2 ) n leads to trigonometric or unitary analogues of the Hermite polynomials. These polynomials are closely related to the partition function of the Curie-Weiss model and appeared in the work of Mirabelli on ﬁnite free probability. We relate the n -th unitary Hermite polynomial to the expected characteristic polynomial of a unitary random matrix obtained by running a Brownian motion on the unitary group U ( n ) . We identify the global distribution of zeroes of the unitary Hermite polynomials as the free unitary normal distribution. We also compute the asymptotics of these polynomials or, equivalently, the free energy of the Curie-Weiss model in a complex external ﬁeld. We identify the global distribution of the Lee-Yang zeroes of this model. Finally, we show that the backward heat ﬂow applied to a high-degree real-rooted polynomial (respectively, trigonometric polynomial) induces, on the level of the asymptotic distribution of its roots, a free Brownian motion (respectively, free unitary Brownian motion).
A Tree-Structured Multi-Task Model Recommender

Tree-structured multi-task architectures have been employed to jointly tackle multiple vision tasks in the context of multi-task learning (MTL). The major challenge is to determine where to branch out for each task given a backbone model to optimize for both task accuracy and computation efficiency. To address the challenge, this paper proposes a recommender that, given a set of tasks and a convolutional neural network-based backbone model, automatically suggests tree-structured multi-task architectures that could achieve a high task performance while meeting a user-specified computation budget without performing model training. Extensive evaluations on popular MTL benchmarks show that the recommended architectures could achieve competitive task accuracy and computation efficiency compared with state-of-the-art MTL methods. Our tree-structured multi-task model recommender is open-sourced and available at https://github.com/zhanglijun95/TreeMTL.
Disentangled Multimodal Representation Learning for Recommendation

Many multimodal recommender systems have been proposed to exploit the rich side information associated with users or items (e.g., user reviews and item images) for learning better user and item representations to improve the recommendation performance. Studies from psychology show that users have individual differences in the utilization of various modalities for organizing information. Therefore, for a certain factor of an item (such as appearance or quality), the features of different modalities are of varying importance to a user. However, existing methods ignore the fact that different modalities contribute differently towards a user's preference on various factors of an item. In light of this, in this paper, we propose a novel Disentangled Multimodal Representation Learning (DMRL) recommendation model, which can capture users' attention to different modalities on each factor in user preference modeling. In particular, we employ a disentangled representation technique to ensure the features of different factors in each modality are independent of each other. A multimodal attention mechanism is then designed to capture users' modality preference for each factor. Based on the estimated weights obtained by the attention mechanism, we make recommendations by combining the preference scores of a user's preferences to each factor of the target item over different modalities. Extensive evaluation on five real-world datasets demonstrate the superiority of our method compared with existing methods.
Relative regular Riemann–Hilbert correspondence II

We develop the theory of relative regular holonomic $\mathcal {D}$-modules with a smooth complex manifold $S$ of arbitrary dimension as parameter space, together with their main functorial properties. In particular, we establish in this general setting the relative Riemann–Hilbert correspondence proved in a previous work in the one-dimensional case.
SoK: On the Semantic AI Security in Autonomous Driving

Autonomous Driving (AD) systems rely on AI components to make safety and correct driving decisions. Unfortunately, today's AI algorithms are known to be generally vulnerable to adversarial attacks. However, for such AI component-level vulnerabilities to be semantically impactful at the system level, it needs to address non-trivial semantic gaps both (1) from the system-level attack input spaces to those at AI component level, and (2) from AI component-level attack impacts to those at the system level. In this paper, we define such research space as semantic AI security as opposed to generic AI security. Over the past 5 years, increasingly more research works are performed to tackle such semantic AI security challenges in AD context, which has started to show an exponential growth trend. In this paper, we perform the first systematization of knowledge of such growing semantic AD AI security research space. In total, we collect and analyze 53 such papers, and systematically taxonomize them based on research aspects critical for the security field. We summarize 6 most substantial scientific gaps observed based on quantitative comparisons both vertically among existing AD AI security works and horizontally with security works from closely-related domains. With these, we are able to provide insights and potential future directions not only at the design level, but also at the research goal, methodology, and community levels. To address the most critical scientific methodology-level gap, we take the initiative to develop an open-source, uniform, and extensible system-driven evaluation platform, named PASS, for the semantic AD AI security research community. We also use our implemented platform prototype to showcase the capabilities and benefits of such a platform using representative semantic AD AI attacks.
From dawn till disk: Milky Way’s turbulent youth revealed by the APOGEE+Gaia data

We use accurate estimates of aluminium abundance from the APOGEE Data Release 17 and Gaia Early Data Release 3 astrometry to select a highly pure sample of stars with metallicity −1.5 ≲ [Fe/H] ≲ 0.5 born in-situ in the Milky Way proper. The low-metallicity ([Fe/H]≲ −1.3) in-situ component we dub Aurora is kinematically hot with an approximately isotropic velocity ellipsoid and a modest net rotation. Aurora stars exhibit large scatter in metallicity and in many element abundance ratios. The median tangential velocity of the in-situ stars increases sharply with metallicity between [Fe/H]=−1.3 and −0.9, the transition that we call the spin-up. The observed and theoretically expected age-metallicity correlations imply that this increase reflects a rapid formation of the MW disk over ≈1 − 2 Gyrs. The transformation of the stellar kinematics as a function of [Fe/H] is accompanied by a qualitative change in chemical abundances: the scatter drops sharply once the Galaxy builds up a disk during later epochs corresponding to [Fe/H]>−0.9. Results of galaxy formation models presented in this and other recent studies strongly indicate that the trends observed in the MW reflect generic processes during the early evolution of progenitors of MW-sized galaxies: a period of chaotic pre-disk evolution, when gas is accreted along cold narrow filaments and when stars are born in irregular configurations, and subsequent rapid disk formation. The latter signals formation of a stable hot gaseous halo around the MW progenitor, which changes the mode of gas accretion and allows development of coherently rotating disk.
LineVD: Statement-level Vulnerability Detection using Graph Neural Networks

Current machine-learning based software vulnerability detection methods are primarily conducted at the function-level. However, a key limitation of these methods is that they do not indicate the specific lines of code contributing to vulnerabilities. This limits the ability of developers to efficiently inspect and interpret the predictions from a learnt model, which is crucial for integrating machine-learning based tools into the software development work-flow. Graph-based models have shown promising performance in function-level vulnerability detection, but their capability for statement-level vulnerability detection has not been extensively explored. While interpreting function-level predictions through explainable AI is one promising direction, we herein consider the statement-level software vulnerability detection task from a fully supervised learning perspective. We propose a novel deep learning framework, LineVD, which formulates statement-level vulnerability detection as a node classification task. LineVD leverages control and data dependencies between statements using graph neural networks, and a transformer-based model to encode the raw source code tokens. In particular, by addressing the conflicting outputs between function-level and statement-level information, LineVD significantly improve the prediction performance without vulnerability status for function code. We have conducted extensive experi-ments against a large-scale collection of real-world C/C++ vulnerabilities obtained from multiple real-world projects, and demonstrate an increase of 105% in F1-score over the current state-of-the-art.
Computational methods for adapted optimal transport

Adapted optimal transport (AOT) problems are optimal transport problems for distributions of a time series where couplings are constrained to have a temporal causal structure. In this paper, we develop computational tools for solving AOT problems numerically. First, we show that AOT problems are stable with respect to perturbations in the marginals and thus arbitrary AOT problems can be approximated by sequences of linear programs. We further study entropic methods to solve AOT problems. We show that any entropically regularized AOT problem converges to the corresponding unregularized problem if the regularization parameter goes to zero. The proof is based on a novel method - even in the non-adapted case - to easily obtain smooth approximations of a given coupling with fixed marginals. Finally, we show tractability of the adapted version of Sinkhorn's algorithm. We give explicit solutions for the occurring projections and prove that the procedure converges to the optimizer of the entropic AOT problem.
On the boundedness of non-standard rough singular integral operators

Let Ω be homogeneous of degree zero, have vanishing moment of order one on the unit sphere Sd−1(d ≥ 2). In this paper, our object of investigation is the following rough non-standard singular integral operator TΩ, Af(x) = p. v. ∫ Rd Ω(x− y) |x− y|d+1 ( A(x)−A(y)−∇A(y)(x− y) ) f(y)dy, where A is a function defined on R with derivatives of order one in BMO(R). We show that TΩ, A enjoys the endpoint L logL type estimate and is L bounded if Ω ∈ L(logL)2(Sd−1). These resuts essentially improve the previous known results given by Hofmann [24] for the L boundedness of TΩ, A under the condition Ω ∈ Lq(Sd−1) (q > 1), Hu and Yang [28] for the endpoint weak L logL type estimates when Ω ∈ Lipα(S) for some α ∈ (0, 1]. Quantitative weighted strong and endpoint weak L logL type inequalities are proved whenever Ω ∈ L∞(Sd−1). The analysis of the weighted results relies heavily on two bilinear sparse dominations of TΩ, A established herein.
Electronic correlations and universal long-range scaling in kagome metals

We investigate the real-space profile of effective Coulomb interactions in correlated kagome materials. By particularizing to KV$_3$Sb$_5$, Co$_3$Sn$_2$S$_2$, FeSn, and Ni$_3$In, we analyze representative cases that exhibit a large span of correlation-mediated phenomena, and contrast them to prototypical prevoskite transition metal oxides. From our constrained random phase approximation studies we find that the on-site interaction strength in kagome metals not only depends on the screening processes at high energy, but also on the low-energy hybriziation profile of the electronic density of states. Our results indicate that rescaled by the onsite interaction amplitude, all kagome metals exhibit a universal long-range Coulomb behaviour.
The Magellanic Edges Survey III. Kinematics of the disturbed LMC outskirts

We explore the structural and kinematic properties of the outskirts of the Large Magellanic Cloud (LMC) using data from the Magellanic Edges Survey (MagES) and Gaia EDR3. Even at large galactocentric radii (8◦<R<11◦), we find the north-eastern LMC disk is relatively unperturbed: its kinematics are consistent with a disk of inclination ~36.5◦ and line-of-nodes position angle ~145◦ east of north. In contrast, fields at similar radii in the southern andwestern disk are significantly perturbed from equilibrium, with non-zero radial and vertical velocities, and distances significantly in front of the disk plane implied by our north-eastern fields. We compare our observations to simple dynamical models of the Magellanic/Milky Way system which describe the LMC as a collection of tracer particles within a rigid potential, and the Small Magellanic Cloud (SMC) as a rigid Hernquist potential. A possible SMC crossing of the LMC disk plane ~400 Myr ago, in combination with the LMC’s infall to the Milky Way potential, can qualitatively explain many of the perturbations in the outer disk. Additionally, we find the claw-like and arm-like structures south of the LMC have similar metallicities to the outer LMC disk ([Fe/H]~−1), and are likely comprised of perturbed LMC disk material. The claw-like substructure is particularly disturbed, with out-of-plane velocities >60 km s−1 and apparent counter-rotation relative to the LMC’s disk motion. More detailed N-body models are necessary to elucidate the origin of these southern features, potentially requiring repeated interactions with the SMC prior to ~1 Gyr ago.