Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduced fluctuations for bosons in a double well

We review two recent results on the ground state properties of bosonic systems trapped by a double well external potential. In the limit of a large number of particles and large separation between the wells, we prove that fluctuations in the number of particles occupying each single-well low-energy mode occur at a reduced scale with respect to [Formula: see text], the latter being the typical prediction of the central limit theorem. This signals the breakdown of the independent and uncorrelated particle picture of standard Bose–Einstein condensation and the emergence of an interaction-driven correlated phase in the ground state.
Neural Score Matching for High-Dimensional Causal Inference

Traditional methods for matching in causal inference are impractical for high-dimensional datasets. They suffer from the curse of dimensionality: exact matching and coarsened exact matching find exponentially fewer matches as the input dimension grows, and propensity score matching may match highly unrelated units together. To overcome this problem, we develop theoretical results which motivate the use of neural networks to obtain non-trivial, multivariate balancing scores of a chosen level of coarseness, in contrast to the classical, scalar propensity score. We leverage these balancing scores to perform matching for high-dimensional causal inference and call this procedure neural score matching. We show that our method is competitive against other matching approaches on semi-synthetic high-dimensional datasets, both in terms of treatment effect estimation and reducing imbalance.
Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings

In order to equip NLP systems with ‘selective prediction’ capability, several task-specific approaches have been proposed. However, which approaches work best across tasks or even if they consistently outperform the simplest baseline MaxProb remains to be explored. To this end, we systematically study selective prediction in a large-scale setup of 17 datasets across several NLP tasks. Through comprehensive experiments under in-domain (IID), out-of-domain (OOD), and adversarial (ADV) settings, we show that despite leveraging additional resources (held-out data/computation), none of the existing approaches consistently and considerably outperforms MaxProb in all three settings. Furthermore, their performance does not translate well across tasks. For instance, Monte-Carlo Dropout outperforms all other approaches on Duplicate Detection datasets but does not fare well on NLI datasets, especially in the OOD setting. Thus, we recommend that future selective prediction approaches should be evaluated across tasks and settings for reliable estimation of their capabilities.
Humanoid Path Planning over Rough Terrain using Traversability Assessment

We present a planning framework designed for humanoid navigation over challenging terrain. This framework is designed to plan a traversable, smooth, and collision-free path using a 2.5D height map. The planner is comprised of two stages. The first stage consists of an A* planner which reasons about traversability using terrain features. A novel cost function is presented which encodes the bipedal gait directly into the graph structure, enabling natural paths that are robust to small gaps in traversability. The second stage is an optimization framework which smooths the path while further improving traversability. The planner is tested on a variety of terrains in simulation and is combined with a footstep planner and balance controller to create an integrated navigation framework, which is demonstrated on a DRC Boston Dynamics Atlas robot.
Private Convex Optimization via Exponential Mechanism

In this paper, we study private optimization problems for non-smooth convex functions $F(x)=\mathbb{E}_i f_i(x)$ on $\mathbb{R}^d$. We show that modifying the exponential mechanism by adding an $\ell_2^2$ regularizer to $F(x)$ and sampling from $\pi(x)\propto \exp(-k(F(x)+\mu\|x\|_2^2/2))$ recovers both the known optimal empirical risk and population loss under $(\eps,\delta)$-DP. Furthermore, we show how to implement this mechanism using $\widetilde{O}(n \min(d, n))$ queries to $f_i(x)$ for the DP-SCO where $n$ is the number of samples/users and $d$ is the ambient dimension.We also give a (nearly) matching lower bound $\widetilde{\Omega}(n \min(d, n))$ on the number of evaluation queries. Our results utilize the following tools that are of independent interest:\begin{itemize}\item We prove Gaussian Differential Privacy (GDP) of the exponential mechanism if the loss function is strongly convex and the perturbation is Lipschitz. Our privacy bound is \emph{optimal} as it includes the privacy of Gaussian mechanism as a special case and is proved using the isoperimetric inequality for strongly log-concave measures. \item We show how to sample from $\exp(-F(x)-\mu \|x\|^2_2/2)$ for $G$-Lipschitz $F$ with $\eta$ error in total variation (TV) distance using $\widetilde{O}((G^2/\mu) \log^2(d/\eta))$ unbiased queries to $F(x)$. This is the first sampler whose query complexity has \emph{polylogarithmic dependence} on both dimension $d$ and accuracy $\eta$. \end{itemize}
The intermediate orders of a Coxeter group

We define a class of partial orders on a Coxeter group associated with sets of reflections. In special cases, these lie between the left weak order and the Bruhat order. We prove that these posets are graded by the length function and that the projections on the right parabolic quotients are always order preserving. We also introduce the notion of $k$-Bruhat graph, $k$-absolute length and $k$-absolute order, proposing some related conjectures and problems.
Practical Mitigation of Smart Contract Bugs

In spite of their popularity, developing secure smart contracts remains a challenging task. Existing solutions are either impractical as they do not support many complex real-world contracts or leave the burden to developers for fixing bugs. In this paper, we propose the first practical smart contract compiler, called HCC, which automatically inserts security hardening checks at the source-code level. HCC develops a code property graph (CPG) to model control-flows and data-flows of a given smart contract. Due to the CPG notation, HCC can be applied to various smart contract platforms and programming languages. We demonstrate the effectiveness of this approach on Ethereum's Solidity smart contracts and show that it efficiently mitigates reentrancy and integer bugs. We also show how to integrate HCC within other blockchain platforms such as Hyperledger Fabric. Our evaluation on 10k real-world contracts demonstrates that HCC is highly practical and effective.
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology

Tissue phenotyping is a fundamental task in learning objective characterizations of histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology. However, whole-slide imaging (WSI) is a complex computer vision in which: 1) WSIs have enormous image resolutions with precludes large-scale pixel-level efforts in data curation, and 2) diversity of morphological phenotypes results in inter- and intra-observer variability in tissue labeling. To address these limitations, current efforts have proposed using pretrained image encoders (transfer learning from ImageNet, self-supervised pretraining) in extracting morphological features from pathology, but have not been extensively validated. In this work, we conduct a search for good representations in pathology by training a variety of self-supervised models with validation on a variety of weakly-supervised and patch-level tasks. Our key finding is in discovering that Vision Transformers using DINO-based knowledge distillation are able to learn data-efficient and interpretable features in histology images wherein the different attention heads learn distinct morphological phenotypes. We make evaluation code and pretrained weights publicly-available at: https://github.com/Richarizardd/Self-Supervised-ViT-Path.
Global-Local Regularization Via Distributional Robustness

Despite superior performance in many situations, deep neural networks are often vulnerable to adversarial examples and distribution shifts, limiting model generalization ability in real-world applications. To alleviate these problems, recent approaches leverage distributional robustness optimization (DRO) to find the most challenging distribution, and then minimize loss function over this most challenging distribution. Regardless of achieving some improvements, these DRO approaches have some obvious limitations. First, they purely focus on local regularization to strengthen model robustness, missing a global regularization effect which is useful in many real-world applications (e.g., domain adaptation, domain generalization, and adversarial machine learning). Second, the loss functions in the existing DRO approaches operate in only the most challenging distribution, hence decouple with the original distribution, leading to a restrictive modeling capability. In this paper, we propose a novel regularization technique, following the veins of Wasserstein-based DRO framework. Specifically, we define a particular joint distribution and Wasserstein-based uncertainty, allowing us to couple the original and most challenging distributions for enhancing modeling capability and applying both local and global regularizations. Empirical studies on different learning problems demonstrate that our proposed approach significantly outperforms the existing regularization approaches in various domains: semi-supervised learning, domain adaptation, domain generalization, and adversarial machine learning.
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding

Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds. Self-supervised learning, which operates without any human labeling, is a promising approach to address this issue. We observe in the real world that humans are capable of mapping the visual concepts learnt from 2D images to understand the 3D world. Encouraged by this insight, we propose CrossPoint, a simple cross-modal contrastive learning approach to learn transferable 3D point cloud representations. It enables a 3D-2D correspondence of objects by maximizing agreement between point clouds and the corresponding rendered 2D image in the invariant space, while encouraging invariance to transformations in the point cloud modality. Our joint training objective combines the feature correspondences within and across modalities, thus ensembles a rich learning signal from both 3D point cloud and 2D image modalities in a self-supervised fashion. Experimental results show that our approach outperforms the previous unsupervised learning methods on a diverse range of downstream tasks including 3D object classification and segmentation. Further, the ablation studies validate the potency of our approach for a better point cloud understanding. Code and pretrained models are available at https://github.com/MohamedAfham/CrossPoint.
Flares and their echoes can help distinguish photon rings from black holes with space-Earth very long baseline interferometry

Photon rings near the edge of a black hole shadow is supposed to be a unique tool to validate general relativity and provide reliable measurements of principal black hole parameters: spin and mass. Such measurements are possible though only for nearby supermassive black holes (SMBH) with Space-Earth Very Long Baseline Interferometry (S-VLBI) in the submillimeter wavelength range. For subrings to be distinguished S-EVLBI observations with long baselines at the Lagrangian Sun-Earth L2 libration point are needed. However, the average fluxes of nearby SMBH: Sagittarius A∗ (Sgr A∗) and M87∗ – Fν ∼ 1 Jy, are still insufficient to detect the signal from the photon rings with even such long baselines. We argue that only manifestations of flares in the submillimeter waveband in their accretion disks can reveal observable signals from the photon rings with the S-EVLBI at L2. Such observations will become possible within the planned join program of the Event Horizon Telescope (EHT) and Millimetron Space Observatory (MSO), and within the planned next generation EHT (ngEHT) project. Two different observational tests for photons rings are discussed. The first one involves observations of a time series of responds from subsequent subrings as can be seen in a 1D visibility function within the join EHT-MSO configuration, the second one – measurements of an increase of the angle between subsequent subrings in the 2D VLBI image which can be obtained within the ngEHT project.
Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption

In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain. As an important tool of regularizing the distribution, batch normalization (BN) has been widely used in existing methods. However, they neglect that BN is severely biased to the training domain and inevitably suffers the performance drop if directly generalized without being updated. To tackle this issue, we propose Batch Norm Test-time Adaption (BNTA), a novel re-id framework that applies the self-supervised strategy to update BN parameters adaptively. Specifically, BNTA quickly explores the domain-aware information within unlabeled target data before inference, and accordingly modulates the feature distribution normalized by BN to adapt to the target domain. This is accomplished by two designed self-supervised auxiliary tasks, namely part positioning and part nearest neighbor matching, which help the model mine the domain-aware information with respect to the structure and identity of body parts, respectively. To demonstrate the effectiveness of our method, we conduct extensive experiments on three re-id datasets and confirm the superior performance to the state-of-the-art methods.
Towards IID representation learning and its application on biomedical data

Due to the heterogeneity of real-world data, the widely accepted independent and identically distributed (IID) assumption has been criticized in recent studies on causality. In this paper, we argue that instead of being a questionable assumption, IID is a fundamental task-relevant property that needs to be learned. Consider $k$ independent random vectors $\mathsf{X}^{i = 1, \ldots, k}$, we elaborate on how a variety of different causal questions can be reformulated to learning a task-relevant function $\phi$ that induces IID among $\mathsf{Z}^i := \phi \circ \mathsf{X}^i$, which we term IID representation learning. For proof of concept, we examine the IID representation learning on Out-of-Distribution (OOD) generalization tasks. Concretely, by utilizing the representation obtained via the learned function that induces IID, we conduct prediction of molecular characteristics (molecular prediction) on two biomedical datasets with real-world distribution shifts introduced by a) preanalytical variation and b) sampling protocol. To enable reproducibility and for comparison to the state-of-the-art (SOTA) methods, this is done by following the OOD benchmarking guidelines recommended from WILDS. Compared to the SOTA baselines supported in WILDS, the results confirm the superior performance of IID representation learning on OOD tasks. The code is publicly accessible via https://github.com/CTPLab/IID_representation_learning.
Do Transformers use variable binding?

Increasing the explainability of deep neural networks (DNNs) requires evaluating whether they implement symbolic computation . One central symbolic capacity is variable binding : linking an input value to an abstract variable held in system-internal memory. Prior work on the computational abilities of DNNs has not resolved the question of whether their internal processes involve variable binding. We argue that the reason for this is fundamental, inherent in the way experiments in prior work were designed. We provide the ﬁrst systematic evaluation of the variable binding capacities of the state-of-the-art Transformer networks BERT and RoBERTa . Our experiments are designed such that the model must generalize a rule across disjoint subsets of the input vocabulary, and cannot rely on associative pattern matching alone. The results show a clear discrepancy between classiﬁcation and sequence-to-sequence tasks: BERT and RoBERTa can easily learn to copy or reverse strings even when trained on task-speciﬁc vocabularies that are switched in the test set; but both models completely fail to generalize across vocabularies in similar sequence classiﬁ-cation tasks. These ﬁndings indicate that the eﬀec-tiveness of Transformers in sequence modelling may lie in their extensive use of the input itself as an ex-ternal “memory” rather than network-internal symbolic operations involving variable binding. Therefore, we propose a novel direction for future work: augmenting the inputs available to circumvent the lack of network-internal variable binding.
Magnetic Braking of Accreting T Tauri Stars II: Torque Formulation Spanning Spin-up and Spin-down Regimes

The magnetic interaction between a classical T Tauri star and its surrounding accretion disk is thought to influence its rotational evolution. We use 2.5D magnetohydrodynamic, axisymmetric simulations of star-disk interaction, computed via the PLUTO code, to calculate the net torque acting on these stars. We divide the net torque into three contributions: accretion (spin-up), stellar winds (spin-down), and magnetospheric ejections (MEs) (spin-up or down). In Paper I, we explored interaction regimes in which the stellar magnetosphere truncates the inner disk at a location spinning faster than the star, resulting in a strong net spin-up contribution from accretion and MEs (“steady accretion” regime). In this paper, we investigate interaction regimes in which the truncation radius gets closer to and even exceeds corotation, where it is possible for the disk material to gain angular momentum and be periodically ejected by the centrifugal barrier (“propeller” regime). This reduces the accretion torque, can change the sign of the ME torque, and can result in a net stellar spin-down configuration. These results suggest it is possible to have a net spin-down stellar torque even for truncation radii within the corotation radius (R t ≳ 0.7R co). We fit semi-analytic functions for the truncation radius, and the torque associated with star-disk interaction (i.e., the sum of accretion and ME torques) and stellar wind, allowing for the prediction of the net stellar torque for a parameter regime covering both net spin-up and spin-down configurations, as well as the possibility of investigating rotational evolution via 1D stellar evolution codes.
Explicit Non-Abelian Gerbes with Connections

We define the notion of adjustment for strict Lie 2-groups and provide the complete cocycle description for non-Abelian gerbes with connections whose structure 2-group is an adjusted 2-group. Most importantly, we depart from the common fake-flat connections and employ adjusted connections. This is an important generalisation that is needed for physical applications especially in the context of supergravity. We give a number of explicit examples; in particular, we lift the spin structure on $S^4$, corresponding to an instanton-anti-instanton pair, to a string structure, a 2-group bundle with connection. We also outline how categorified forms of Bogomolny monopoles known as self-dual strings can be obtained via a Penrose-Ward transform of string bundles over twistor space.
Hitting a New Low: The Unique 28 hr Cessation of Accretion in the TESS Light Curve of YY Dra (DO Dra)

We present the Transiting Exoplanet Surveying Satellite light curve of the intermediate polar YY Draconis (YY Dra, also known as DO Dra). The power spectrum indicates that while there is stream-fed accretion for most of the observational period, there is a day-long, flat-bottomed low state at the beginning of 2020 during which the only periodic signal is ellipsoidal variation and there is no appreciable flickering. We interpret this low state to be a complete cessation of accretion, a phenomenon that has been observed only once before in an intermediate polar. Simultaneous ground-based observations of this faint state establish that when accretion is negligible, YY Dra fades to g = 17.37 ± 0.12, which we infer to be the magnitude of the combined photospheric contributions of the white dwarf and its red dwarf companion. Using survey photometry, we identify additional low states in 2018–2019 during which YY Dra repeatedly fades to—but never below—this threshold. This implies relatively frequent cessations in accretion. Spectroscopic observations during future episodes of negligible accretion can be used to directly measure the field strength of the white dwarf by Zeeman splitting. Separately, we search newly available catalogs of variable stars in an attempt to resolve the long-standing dispute over the proper identifier of this system.
How Well Can We Measure Galaxy Dust Attenuation Curves? The Impact of the Assumed Star-dust Geometry Model in Spectral Energy Distribution Fitting

One of the most common methods for inferring galaxy attenuation curves is via spectral energy distribution (SED) modeling, where the dust attenuation properties are modeled simultaneously with other galaxy physical properties. In this paper, we assess the ability of SED modeling to infer these dust attenuation curves from broadband photometry, and suggest a new flexible model that greatly improves the accuracy of attenuation curve derivations. To do this, we fit mock SEDs generated from the simba cosmological simulation with the prospector SED fitting code. We consider the impact of the commonly assumed uniform screen model and introduce a new nonuniform screen model parameterized by the fraction of unobscured stellar light. This nonuniform screen model allows for a nonzero fraction of stellar light to remain unattenuated, resulting in a more flexible attenuation curve shape by decoupling the shape of the UV attenuation curve from the optical attenuation curve. The ability to constrain the dust attenuation curve is significantly improved with the use of a nonuniform screen model, with the median offset in UV attenuation decreasing from −0.30 dex with a uniform screen model to −0.17 dex with the nonuniform screen model. With this increase in dust attenuation modeling accuracy, we also improve the star formation rates (SFRs) inferred with the nonuniform screen model, decreasing the SFR offset on average by 0.12 dex. We discuss the efficacy of this new model, focusing on caveats with modeling star-dust geometries and the constraining power of available SED observations.
When A Conventional Filter Meets Deep Learning: Basis Composition Learning on Image Filters

Image filters are fast, lightweight and effective, which make these conventional wisdoms preferable as basic tools in vision tasks. In practical scenarios, users have to tweak parameters multiple times to obtain satisfied results. This inconvenience heavily discounts the efficiency and user experience. We propose basis composition learning on single image filters to automatically determine their optimal formulas. The feasibility is based on a two-step strategy: first, we build a set of filtered basis (FB) consisting of approximations under selected parameter configurations; second, a dual-branch composition module is proposed to learn how the candidates in FB are combined to better approximate the target image. Our method is simple yet effective in practice; it renders filters to be user-friendly and benefits fundamental low-level vision problems including denoising, deraining and texture removal. Extensive experiments demonstrate that our method achieves an appropriate balance among the performance, time complexity and memory efficiency.