Miracle-dz/UniFAR-FTUs huggingface dataset:
Spin-functional renormalization group for the 
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We use our recently developed functional renormalization group (FRG) approach for quantum spin systems to investigate the phase diagram of the frustrated $J_{1}J_{2}J_{3}$ quantum Heisenberg model on a cubic lattice. From a simple truncation of the hierarchy of FRG flow equations for the irreducible spin-vertices which retains only static spin fluctuations and neglects the flow of the four-spin interaction, we can estimate the critical temperature with a similar accuracy as the numerically more expensive pseudofermion FRG. In the regime where the ground state exhibits either ferromagnetic or antiferromagnetic order, a more sophisticated truncation including the renormalization of the four-spin interaction as well as dynamic spin fluctuations reveals the underlying renormalization group fixed point and yields critical temperatures which deviate from the accepted values by at most 4 %.
Speech Enhancement and Dereverberation With Diffusion-Based Generative Models

In this work, we build upon our previous publication and use diffusion-based generative models for speech enhancement. We present a detailed overview of the diffusion process that is based on a stochastic differential equation and delve into an extensive theoretical examination of its implications. Opposed to usual conditional generation tasks, we do not start the reverse process from pure Gaussian noise but from a mixture of noisy speech and Gaussian noise. This matches our forward process which moves from clean speech to noisy speech by including a drift term. We show that this procedure enables using only 30 diffusion steps to generate high-quality clean speech estimates. By adapting the network architecture, we are able to significantly improve the speech enhancement performance, indicating that the network, rather than the formalism, was the main limitation of our original approach. In an extensive cross-dataset evaluation, we show that the improved method can compete with recent discriminative models and achieves better generalization when evaluating on a different corpus than used for training. We complement the results with an instrumental evaluation using real-world noisy recordings and a listening experiment, in which our proposed method is rated best. Examining different sampler configurations for solving the reverse process allows us to balance the performance and computational speed of the proposed method. Moreover, we show that the proposed method is also suitable for dereverberation and thus not limited to additive background noise removal.
Top Gear or Black Mirror: Inferring Political Leaning From Non-Political Content

Polarization and echo chambers are often studied in the context of explicitly political events such as elections, and little scholarship has examined the mixing of political groups in non-political contexts. A major obstacle to studying political polarization in non-political contexts is that political leaning (i.e., left vs right orientation) is often unknown. Nonetheless, political leaning is known to correlate (sometimes quite strongly) with many lifestyle choices leading to stereotypes such as the “latte-drinking liberal. ” We develop a machine learning classiﬁer to infer political leaning from nonpolitical text and, optionally, the accounts a user follows on social media. We use Voter Advice Application results shared on Twitter as our groundtruth and train and test our classiﬁer on a Twitter dataset comprising the 3,200 most recent tweets of each user after removing any tweets with political text. We correctly classify the political leaning of most users (F1 scores range from 0.70 to 0.85 depending on coverage). We ﬁnd no relationship between the level of political activity and our classiﬁcation results. We apply our classiﬁer to a case study of news sharing in the UK and discover that, in general, the sharing of political news exhibits a distinctive left–right divide while sports news does not.
An Optical and H I Study of NGC 5850: Victim of a High-Speed Encounter?

We present optical CCD surface photometry and VLA H I observations of NGC 5850, one of the largest and brightest barred spirals of the inner ring variety in the sky. The broadband images reveal numerous morphological peculiarities, particularly in the spiral arms. Structural asymmetries are more obvious in H I, the most pronounced being a large-scale displacement of gas to the west and northwest of the nucleus. Most of the (3.3 ± 0.1) × 109 M☉ of atomic hydrogen is concentrated in the prominent optical ring and faint spiral arms, with very low H I surface densities (ΣH I) in the bulge and interarm regions. We detect approximately 2 × 107 M☉ of H I in the southwestern half of the nuclear ring. The H I surface density drops rapidly outside the arms, and we find no evidence for either large-scale tidal features or an extended gas disk above 0.05 M☉ pc-2 (3 σ). Overall, the intensity-weighted H I velocity field appears fairly regular, yet still shows clear deviations from circular rotation that we attribute to a warped oval disk and streaming motions across the arms. Radio continuum emission at 20 cm is dominated by a faint bulge component that peaks at the optical nucleus. The spiral arms are not detected. The absence of extended X-ray emission throughout the NGC 5846 group and the close similarity between NGC 5850's optical and H I morphologies argue against ram pressure stripping through a dense intergalactic medium as the cause of the galaxy's peculiar morphology. We attribute it instead to a high-speed encounter with the massive elliptical galaxy NGC 5846. We identify the peculiar spiral arms as a disrupted outer pseudoring. Star formation in NGC 5850 has not been enhanced relative to other intermediate field spirals, nor have significant gas masses been transported to the nucleus. This may be attributed to the recent (≲200 Myr) nature of the interaction.
Semi-supervised Vision Transformers at Scale

We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this problem, we propose a new SSL pipeline, consisting of first un/self-supervised pre-training, followed by supervised fine-tuning, and finally semi-supervised fine-tuning. At the semi-supervised fine-tuning stage, we adopt an exponential moving average (EMA)-Teacher framework instead of the popular FixMatch, since the former is more stable and delivers higher accuracy for semi-supervised vision transformers. In addition, we propose a probabilistic pseudo mixup mechanism to interpolate unlabeled samples and their pseudo labels for improved regularization, which is important for training ViTs with weak inductive bias. Our proposed method, dubbed Semi-ViT, achieves comparable or better performance than the CNN counterparts in the semi-supervised classification setting. Semi-ViT also enjoys the scalability benefits of ViTs that can be readily scaled up to large-size models with increasing accuracies. For example, Semi-ViT-Huge achieves an impressive 80% top-1 accuracy on ImageNet using only 1% labels, which is comparable with Inception-v4 using 100% ImageNet labels.
FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing

We propose a Few-shot Dynamic Neural Radiance Field (FDNeRF), the first NeRF-based method capable of reconstruction and expression editing of 3D faces based on a small number of dynamic images. Unlike existing dynamic NeRFs that require dense images as input and can only be modeled for a single identity, our method enables face reconstruction across different persons with few-shot inputs. Compared to state-of-the-art few-shot NeRFs designed for modeling static scenes, the proposed FDNeRF accepts view-inconsistent dynamic inputs and supports arbitrary facial expression editing, i.e., producing faces with novel expressions beyond the input ones. To handle the inconsistencies between dynamic inputs, we introduce a well-designed conditional feature warping (CFW) module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. As a result, features of different expressions are transformed into the target ones. We then construct a radiance field based on these view-consistent features and use volumetric rendering to synthesize novel views of the modeled faces. Extensive experiments with quantitative and qualitative evaluation demonstrate that our method outperforms existing dynamic and few-shot NeRFs on both 3D face reconstruction and expression editing tasks. Code is available at https://fdnerf.github. io .
Space Mapping for PDE Constrained Shape Optimization

The space mapping technique is used to efficiently solve complex optimization problems. It combines the accuracy of fine model simulations with the speed of coarse model optimizations to approximate the solution of the fine model optimization problem. In this paper, we propose novel space mapping methods for solving shape optimization problems constrained by partial differential equations (PDEs). We present the methods in a Riemannian setting based on Steklov-Poincar\'e-type metrics and discuss their numerical discretization and implementation. We investigate the numerical performance of the space mapping methods on several model problems. Our numerical results highlight the methods' great efficiency for solving complex shape optimization problems.
Speech Synthesis With Mixed Emotions

Emotional speech synthesis aims to synthesize human voices with various emotional effects. The current studies are mostly focused on imitating an averaged style belonging to a specific emotion type. In this paper, we seek to generate speech with a mixture of emotions at run-time. We propose a novel formulation that measures the relative difference between the speech samples of different emotions. We then incorporate our formulation into a sequence-to-sequence emotional text-to-speech framework. During the training, the framework does not only explicitly characterize emotion styles but also explores the ordinal nature of emotions by quantifying the differences with other emotions. At run-time, we control the model to produce the desired emotion mixture by manually defining an emotion attribute vector. The objective and subjective evaluations have validated the effectiveness of the proposed framework. To our best knowledge, this research is the first study on modelling, synthesizing, and evaluating mixed emotions in speech.
Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials

We study the problem of adaptively identifying patient subpopulations that beneﬁt from a given treatment during a conﬁrmatory clinical trial. This type of adaptive clinical trial, often referred to as adaptive enrichment design , has been thoroughly studied in biostatistics with a focus on a limited number of subgroups (typically two) which make up (sub)populations, and a small number of interim analysis points. In this paper, we aim to relax classical restrictions on such designs and investigate how to incorporate ideas from the recent machine learning literature on adaptive and online experimentation to make trials more ﬂexible and eﬃcient. We ﬁnd that the unique characteristics of the subpopulation selection problem – most importantly that (i) one is usually interested in ﬁnding subpopulations with any treatment beneﬁt (and not necessarily the single subgroup with largest eﬀect) given a limited budget and that (ii) eﬀectiveness only has to be demonstrated across the subpopulation on average – give rise to interesting challenges and new desiderata when designing algorithmic solutions. Building on these ﬁndings, we propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction, which focus on identifying good subgroups and good composite subpopulations, respectively. We empirically investigate their performance across a range of simulation scenarios and derive insights into their (dis)advantages across diﬀerent settings.
Pseudomagnetic fields, particle-hole asymmetry, and microscopic effective continuum Hamiltonians of twisted bilayer graphene

Using the method developed in the companion paper, we construct the effective continuum theories for two different microscopic tight binding models of the twisted bilayer graphene at the twist angle of $1.05^\circ$, one Slater-Koster based and the other ab-initio Wannier based. The energy spectra obtained from the continuum theory -- either for rigid twist or including lattice relaxation -- are found to be in nearly perfect agreement with the spectra from the tight binding models when the gradient expansion is carried out to second order, demonstrating the validity of the method. We also analyze the properties of the Bloch states of the resulting narrow bands, finding non-negligible particle-hole symmetry breaking near the $\Gamma$ point in our continuum theory constructed for the ab-initio based microscopic model due to a term in the continuum theory that was previously overlooked. This reveals the difference with all existing continuum models where the particle-hole symmetry of the narrow band Hilbert space is nearly perfect.
Updated constraints from the effective field theory analysis of the BOSS power spectrum on early dark energy

Analyses of the full shape of BOSS DR12 power spectrum using the one-loop prediction from the Effective Field Theory of Large-Scale Structure (EFTBOSS) have led to new constraints on extensions to the $\Lambda$CDM model, such as Early Dark Energy (EDE) which has been suggested as a resolution to the"Hubble tension". In this paper, we re-assess the constraining power of the EFTBOSS on EDE in light of a correction to the normalization of BOSS window functions. Overall we find that constraints from EFTBOSS on EDE are weakened, and represent a small change compared to constraints from Planck and the conventional BAO/$f\sigma_8$ measurements. The combination of Planck data with EFTBOSS provides a bound on the maximal fractional contribution of EDE $f_{\rm EDE}<0.083$ at 95% C.L. (compared to $<0.054$ with the incorrect normalization, and $<0.088$ without full-shape data) and the Hubble tension is reduced to $2.1\sigma$. However, the more extreme model favored by an analysis with just data from the Atacama Cosmology Telescope is disfavored by the EFTBOSS data. We also show that the updated Pantheon+ Type Ia supernova analysis can slightly increase the constraints on EDE. Yet, the inclusion of the SN1a magnitude calibration by SH0ES strongly increases the preference for EDE to above $5\sigma$, yielding $f_{\rm EDE}\sim 0.12^{+0.03}_{-0.02}$ around the redshift $z_c=4365^{+3000}_{-1100}$. Our results demonstrate that EFTBOSS data (alone or combined with Planck data) do not exclude the EDE resolution of the Hubble tension.
Triangular quantum photonic devices with integrated detectors in silicon carbide

Triangular cross-section SiC photonic devices have been studied as an efficient and scalable route for integration of color centers into quantum hardware. In this work, we explore efficient collection and detection of color center emission in a triangular cross-section SiC waveguide by introducing a photonic crystal mirror on its one side and a superconducting nanowire single photon detector (SNSPD) on the other. Our modeled triangular cross-section devices with a randomly positioned emitter have a maximum coupling efficiency of 89 % into the desired optical mode and a high coupling efficiency (> 75 %) in more than half of the configurations. For the first time, NbTiN thin films were sputtered on 4H-SiC and the electrical and optical properties of the thin films were measured. We found that the transport properties are similar to the case of NbTiN on SiO2 substrates, while the extinction coefficient is up to 50 % higher for 1680 nm wavelength. Finally, we performed Finite-Difference Time-Domain simulations of triangular cross-section waveguide integrated with an SNSPD to identify optimal nanowire geometries for efficient detection of light from TE and TM polarized modes.
Nonphononic spectrum of two-dimensional structural glasses.

The scaling form and system-size dependence of the low-frequency wing of the nonphononic vibrational spectrum of two-dimensional structural glasses have been debated in recent literature. Here we provide numerical evidence that the prefactor $A_{\rm g}$ of the low-frequency nonphononic vibrational spectrum -- the latter grows from zero frequency ($\omega\! =\! 0$) as $A_{\rm g}\omega^\beta$ -- is system-size dependent, and that the exponent $\beta$ exhibits a similar glass-formation-protocol and system-size dependence as seen in three-dimensional structural glasses.
Photoemission "experiments" on holographic lattices

We construct a 2D holographic ionic lattice with hyperscaling-violating infrared geometry and study single-electron spectral functions (“ARPES photoemission curves”) on this background. The spectra typically show a three-peak structure, where the central peak undergoes a crossover from a sharp but not Fermi-liquid-like quasiparticle to a wide incoherent maximum, and the broad side peaks resemble the Hubbard bands. These findings are partially explained by a perturbative near-horizon analysis of the bulk Dirac equation. Comparing the holographic Green functions in imaginary frequency with the Green functions of the Hubbard model obtained from quantum Monte Carlo, we find that the holographic model provides a very good fit to the Hubbard Green function. However, the information loss when transposing the holographic Green functions to imaginary frequencies implies that a deeper connection to Hubbard-like models remains questionable.
New Non-Equivalent (Self-Dual) MDS Codes From Elliptic Curves

It is well known that MDS codes can be constructed as algebraic geometric (AG) codes from elliptic curves. It is always interesting to construct new non-equivalent MDS codes and self-dual MDS codes. In recent years several constructions of new self-dual MDS codes from the generalized twisted Reed-Solomon codes were proposed. In this paper we construct new non-equivalent MDS and almost MDS codes from elliptic curve codes. Moreover there are some non-equivalent MDS elliptic curve codes with the same length and the dimension. The application to MDS entanglement-assisted quantum codes is given.
A Systematic Study of the Short-term X-Ray Variability of Seyfert Galaxies. I. Diversity of the X-Ray rms Spectra

The X-ray variability of active galactic nuclei (AGN) carries crucial information about the X-ray radiation mechanism. We performed a systematic study of the X-ray short-term (1–100 ks timescale) variability for a large sample of 78 Seyferts with 426 deep XMM-Newton observations. In this paper, we present the time-averaged spectra and rms spectra for the entire sample, which show a variety of properties. Based on the spectral shape, we divide the rms spectra into five subtypes and the time-averaged spectra into four subtypes. The most common shape of the rms spectra is concave-down where the rms peaks at ∼1 keV. We find that different sources can show similar time-averaged spectra and rms spectra. However, there is no one-to-one mapping between the subtypes of the time-averaged spectra and rms spectra, as similar time-averaged spectra can be accompanied by different rms spectra, and vice versa. This is likely because different physical mechanisms can produce similar rms spectra. For every subtype of the time-averaged spectra, we report its preferred subtypes of the rms spectra in both low- and high-frequency bands. We also compare the statistical properties for different subtypes, such as the black hole mass and Eddington ratio. Finally, we investigate the rms in the Fe Kα line regime and find that those with a broad and extended red-wing profile tend to show stronger variability than those showing a narrow or relatively symmetric profile. Our results demonstrate the necessity of performing joint spectral and variability modeling in order to understand the mechanism of the X-ray emission in AGN. All of the rms spectra have been made publicly available.
RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild

We describe a data-driven method for inferring the camera viewpoints given multiple images of an arbitrary object. This task is a core component of classic geometric pipelines such as SfM and SLAM, and also serves as a vital pre-processing requirement for contemporary neural approaches (e.g. NeRF) to object reconstruction and view synthesis. In contrast to existing correspondence-driven methods that do not perform well given sparse views, we propose a top-down prediction based approach for estimating camera viewpoints. Our key technical insight is the use of an energy-based formulation for representing distributions over relative camera rotations, thus allowing us to explicitly represent multiple camera modes arising from object symmetries or views. Leveraging these relative predictions, we jointly estimate a consistent set of camera rotations from multiple images. We show that our approach outperforms state-of-the-art SfM and SLAM methods given sparse images on both seen and unseen categories. Further, our probabilistic approach significantly outperforms directly regressing relative poses, suggesting that modeling multimodality is important for coherent joint reconstruction. We demonstrate that our system can be a stepping stone toward in-the-wild reconstruction from multi-view datasets. The project page with code and videos can be found at https://jasonyzhang.com/relpose.
Path-aware Siamese Graph Neural Network for Link Prediction

In this paper, we propose a Path-aware Siamese Graph neural network(PSG) for link prediction tasks. First, PSG captures both nodes and edge features for given two nodes, namely the structure information of k-neighborhoods and relay paths information of the nodes. Furthermore, a novel multi-task GNN framework with self-supervised contrastive learning is proposed for differentiation of positive links and negative links while content and behavior of nodes can be captured simultaneously. We evaluate the proposed algorithm PSG on two link property prediction datasets, ogbl-ddi and ogbl-collab. PSG achieves top 1 performance on ogbl-ddi until submission and top 3 performance on ogbl-collab. The experimental results verify the superiority of our proposed PSG
What Are Those Tiny Things? A First Study of Compact Star Clusters in the SMACS0723 Field with JWST

We use the unprecedented resolution and depth of the JWST NIRCam Early Release Observations at 1–5 μm to study the stellar mass, age, and metallicity of compact star clusters in the neighborhood of the host galaxies in the SMACS J0723.3–7327 galaxy cluster field at z = 0.39. The measured colors of these star clusters show a similar distribution as quiescent galaxies at the same redshift, but are >3 mag fainter than the current depths of wide-field galaxy survey. The star clusters are unresolved in the NIRCam/F150W data suggesting sizes smaller than 50 pc. This is significantly smaller than star-forming clumps or dwarf galaxies in local galaxies. From fitting their photometry with simple stellar population (SSP) models, we find stellar metallicities consistent with 0.2–0.3 Z ⊙ and ages of 1.5−0.5+0.5Gyr . We rule out metallicities <0.2 Z ⊙ and solar/supersolar at 4σ significance. Assuming mass-to-light ratios obtained from the best-fit SSP, we estimate stellar masses of 2.4−1.5+3.0×106M⊙ . These are between average masses of local globular clusters and dwarf galaxies. Our analysis suggests middle-aged globular cluster with relatively recent formation times at z = 0.5–0.7, which could have been subsequently stripped away from their host galaxies due to interactions in the cluster environment or formed in cold flows onto the cluster core. However, we cannot rule out these objects being compact cores of stripped dwarf galaxies.