Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Anisotropy of Upper Critical Fields in Fe$_{1+y}$Te$_{0.6}$Se$_{0.4}$

Studying the upper critical field ($\mu_0$$H$$_{\rm{c2}}$) and its anisotropy of superconductors is of great importance because it can provide an unusual insight into the pair-breaking mechanism. Since Fe$_{1+y}$Te$_{1-x}$Se$_x$ exhibits the high $\mu_0$$H$$_{\rm{c2}}$ and small anisotropic superconductivity, it has attracted considerable attention. However, some issues related to $\mu_0$$H$$_{\rm{c2}}$ are still unknown, including the effect of excess Fe content on $\mu_0$$H$$_{\rm{c2}}$ behavior and the origin of the crossover of the $\mu_0H_{\rm{c2}}^c $ -- $ T$ and $\mu_0H_{\rm{c2}}^{ab}$ -- $T$ curves. In this work, the value of $\mu_0$$H$$_{\rm{c2}}$ of Fe$_{1+y}$Te$_{0.6}$Se$_{0.4}$ single crystals with controlled amounts of excess Fe was obtained by resistivity measurements over a wide range of temperatures down to $\sim$ 1.5 K, and magnetic fields up to $\sim$ 60 T. The crossover of the $\mu_0H_{\rm{c2}}^c $ -- $ T$ and $\mu_0H_{\rm{c2}}^{ab}$ -- $T$ curves was found to be independent of the excess Fe content. The angle dependence of $\mu_0H_{\rm{c2}}$ was also checked. The $\mu_0H_{\rm{c2}}(\theta)$ symmetry at higher temperature near $T_c$ could be fitted by anisotropic G-L model, and novel fourfold symmetry of $\mu_0H_{\rm{c2}}$ at lower temperature was found. Based on our spin-locking pairing model, the crossover behavior originates from the anisotropic spin-paramagnetic effect, and the novel fourfold symmetry of $\mu_0H_{\rm{c2}}$ could be understood by our extended anisotropic G-L model.
The 2022 active state of the AM CVn star NSV 1440

We found an active state lasting for ~200 d in the AM CVn star NSV 1440 in 2022. During this state, the object reached a magnitude of 16.5, 2.0-2.5 mag above quiescence, and showed a number of superposed normal outbursts. Such an active state was probably brought either by an enhanced mass-transfer from the secondary or increased quiescent viscosity of the accretion disk. These possibilities are expected to be distinguished by an observation of the interval to the next superoutburst. We also found that the brightness and the course toward the end of the event were similar to the post-superoutburst fading tail in 2021. The mechanism producing the 2022 active state and post-superoutburst fading tails in AM CVn stars may be the same, and the present finding is expected to clarify the nature of these still poorly understood fading tails in AM CVn stars, and potentially of the corresponding phenomenon in hydrogen-rich WZ Sge stars. We also note that the faint, long"superoutbursts"in long-period AM CVn stars claimed in the past were not true outbursts powered by disk instability, but were more likely phenomena similar to the 2022 active state in NSV 1440.
A Closest Point Method for Surface PDEs with Interior Boundary Conditions for Geometry Processing

Many geometry processing techniques require the solution of partial differential equations (PDEs) on surfaces. Such surface PDEs often involve boundary conditions prescribed on the surface, at points or curves on its interior or along the geometric (exterior) boundary of an open surface. However, input surfaces can take many forms (e.g., meshes, parametric surfaces, point clouds, level sets, neural implicits). One must therefore generate a mesh to apply finite element-type techniques or derive specialized discretization procedures for each surface representation. We propose instead to address such problems through a novel extension of the closest point method (CPM) to handle interior boundary conditions specified at surface points or curves. CPM solves the surface PDE by solving a volumetric PDE defined over the Cartesian embedding space containing the surface; only a closest point function is required to represent the surface. As such, CPM supports surfaces that are open or closed, orientable or not, and of any codimension or even mixed-codimension. To enable support for interior boundary conditions, we develop a method to implicitly partition the embedding space across interior boundaries. CPM's finite difference and interpolation stencils are adapted to respect this partition while preserving second-order accuracy. Furthermore, an efficient sparse-grid implementation and numerical solver is developed that can scale to tens of millions of degrees of freedom, allowing PDEs to be solved on more complex surfaces. We demonstrate our method's convergence behaviour on selected model PDEs. Several geometry processing problems are explored: diffusion curves on surfaces, geodesic distance, tangent vector field design, and harmonic map construction. Our proposed approach thus offers a powerful and flexible new tool for a range of geometry processing tasks on general surface representations.
Can we discover lepton number violation with the LHC far detectors?

Two classes of far detectors have been proposed or are under operation at the LHC. The first class is a series of neutrino detectors that are sensitive to light active neutrinos via either charged-current or neutral-current interactions; exemplary ideas are FASER$\nu$, SND@LHC, and FLArE. Another type aims primarily at looking for displaced decays of long-lived particles (LLPs) into charged final-state particles, including ANUBIS and FASER. In this work, we propose searches for probing lepton number violation associated with a Majorana active/sterile neutrino, for the first time with these experiments, which, if discovered, would be a clear signature of new physics beyond the Standard Model. With Monte-Carlo simulation, we find that while the neutrino detectors, unfortunately, are estimated to have signal-event rates orders of magnitude below $\mathcal{O}(1)$, some LLP far detectors such as ANUBIS, if upgraded, would be most promising for discovering a Majorana sterile neutrino of mass $\mathcal{O}(\text{1})$ GeV in certain so-far unexcluded parameter space. In this exploratory work, we emphasize on the importance of leveraging the LHC far detectors for purposes beyond the planned ones, such as searching for lepton number violation.
Graph Masked Autoencoder for Sequential Recommendation

While some powerful neural network architectures (e.g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios. To address the issue of insufficient labels, Contrastive Learning (CL) has attracted much attention in recent methods to perform data augmentation through embedding contrasting for self-supervision. However, due to the hand-crafted property of their contrastive view generation strategies, existing CL-enhanced models i) can hardly yield consistent performance on diverse sequential recommendation tasks; ii) may not be immune to user behavior data noise. In light of this, we propose a simple yet effective Graph Masked AutoEncoder-enhanced sequential Recommender system (MAERec) that adaptively and dynamically distills global item transitional information for self-supervised augmentation. It naturally avoids the above issue of heavy reliance on constructing high-quality embedding contrastive views. Instead, an adaptive data reconstruction paradigm is designed to be integrated with the long-range item dependency modeling, for informative augmentation in sequential recommendation. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art baseline models and can learn more accurate representations against data noise and sparsity. Our implemented model code is available at https://github.com/HKUDS/MAERec.
Stanford MLab at SemEval-2023 Task 10: Exploring GloVe- and Transformer-Based Methods for the Explainable Detection of Online Sexism

In this paper, we discuss the methods we applied at SemEval-2023 Task 10: Towards the Explainable Detection of Online Sexism. Given an input text, we perform three classification tasks to predict whether the text is sexist and classify the sexist text into subcategories in order to provide an additional explanation as to why the text is sexist. We explored many different types of models, including GloVe embeddings as the baseline approach, transformer-based deep learning models like BERT, RoBERTa, and DeBERTa, ensemble models, and model blending. We explored various data cleaning and augmentation methods to improve model performance. Pre-training transformer models yielded significant improvements in performance, and ensembles and blending slightly improved robustness in the F1 score.
Optically driven spin precession in polariton condensates

External driving of spinor degrees of freedom by magnetic or optical fields in quantum systems underpin many applications ranging from nuclear magnetic resonance to coherent state control in quantum computing. Although spinor polariton condensates are offering a flexible platform for spinoptronic applications, strong inter-particle interactions limit their spin coherence. Here, we introduce an all-optically driven spin precession in microcavity polariton condensates that eliminates depolarisation, through a radio frequency modulation of a spatially rotating, asymmetric exciton reservoir that both confines, and actively replenishes the polariton condensate. We realise several GHz driven spin precession with a macroscopic spin coherence time that is limited only by the extraneous to the condensate, frequency drift of the composite pumping sources. Our observations are supported by mean field modelling and evidence a driven-dissipative quantum fluidic analogue of the nuclear magnetic resonance effect.
ALP dark matter with non-periodic potentials: parametric resonance, halo formation and gravitational signatures

Axion-like particles (ALPs) are leading candidates to explain the dark matter in the universe. Their production via the misalignment mechanism has been extensively studied for cosine potentials characteristic of pseudo-Nambu-Goldstone bosons. In this work we investigate ALPs with non-periodic potentials, which allow for large misalignment of the field from the minimum. As a result, the ALP can match the relic density of dark matter in a large part of the parameter space. Such potentials give rise to self-interactions which can trigger an exponential growth of fluctuations in the ALP field via parametric resonance, leading to the fragmentation of the field. We study these effects with both Floquet analysis and lattice simulations. Using the Press-Schechter formalism, we predict the halo mass function and halo spectrum arising from ALP dark matter. These halos can be dense enough to produce observable gravitational effects such as astrometric lensing, diffraction of gravitational wave signals from black hole mergers, photometric microlensing of highly magnified stars, perturbations of stars in the galactic disk or stellar streams. These effects would provide a probe of dark matter even if it does not couple to the Standard Model. They would not be observable for halos predicted for standard cold dark matter and for ALP dark matter in the standard misalignment mechanism. We determine the relevant regions of parameter space in the (ALP mass, decay constant)-plane and compare predictions in different axion fragmentation models.
Distributed Coordination of Multi-Microgrids in Active Distribution Networks for Provisioning Ancillary Services

We propose a distributed optimization framework that coordinates multiple microgrids in an Active Distribution Network (ADN) for provisioning passive voltage support based ancillary services while satisfying operational constraints. Specifically, we exploit the reactive power support capability of the inverters and the flexibility offered by storage systems available with microgrids for provisioning ancillary service support to the transmission grid. We develop novel mixed integer inequalities to represent the set of feasible active and reactive power exchange with the transmission grid that ensure passive voltage support. The proposed alternating direction method of multipliers (ADMM) based algorithm is fully distributed, and does not require the presence of a centralized entity to achieve coordination among the microgrids. We present detailed numerical results on the IEEE 33-bus distribution test system to demonstrate the effectiveness of the proposed approach and examine convergence behavior of the distributed algorithm for different choice of hyper-parameters.
Aggregation of Variables in Least-Squares Regression

Abstract I consider the properties of the estimator when the true model is y = β1 x 1 + β2 x 2 + u, but the restriction β1 = β2 = β is incorrectly imposed. I show that the probability limit of is a weighted sum of β1 and β2; the weights sum to 1 but do not necessarily lie in the unit interval, so plim need not be bounded by β1 and β2. Sufficient conditions for such bounding are derived. Certain changes in the moments of x 1 and x 2 have “perverse” effects on the weights. I illustrate the consequences of inappropriate aggregation of variables with an empirical example of the effect of research and development investment on productivity.
Behavioural Types for Local-First Software

Peer-to-peer systems are the most resilient form of distributed computing, but the design of robust protocols for their coordination is difficult. This makes it hard to specify and reason about global behaviour of such systems. This paper presents swarm protocols to specify such systems from a global viewpoint. Swarm protocols are projected to machines, that is local specifications of peers. We take inspiration from behavioural types with a key difference: peers communicate through an event notification mechanism rather than through point-to-point message passing. Our goal is to adhere to the principles of local-first software where network devices collaborate on a common task while retaining full autonomy: every participating device can locally make progress at all times, not encumbered by unavailability of other devices or network connections. This coordination-free approach leads to inconsistencies that may emerge during computations. Our main result shows that under suitable well-formedness conditions for swarm protocols consistency is eventually recovered and the locally observable behaviour of conforming machines will eventually match the global specification. The model we propose elaborates on an existing industrial platform and provides the basis for tool support (sketched here and fully described in a companion artifact paper), wherefore we consider this work to be a viable step towards reasoning about local-first and peer-to-peer software systems.
On User-Level Private Convex Optimization

We introduce a new mechanism for stochastic convex optimization (SCO) with user-level differential privacy guarantees. The convergence rates of this mechanism are similar to those in the prior work of Levy et al. (2021); Narayanan et al. (2022), but with two important improvements. Our mechanism does not require any smoothness assumptions on the loss. Furthermore, our bounds are also the first where the minimum number of users needed for user-level privacy has no dependence on the dimension and only a logarithmic dependence on the desired excess error. The main idea underlying the new mechanism is to show that the optimizers of strongly convex losses have low local deletion sensitivity, along with an output perturbation method for functions with low local deletion sensitivity, which could be of independent interest.
Information capacity analysis of fully correlated multilevel amplitude damping channels

The primary objective of quantum Shannon theory is to evaluate the capacity of quantum channels. In spite of the existence of rigorous coding theorems that quantify the transmission of information through quantum channels, superadditivity effects limit our understanding of the channel capacities. In this paper, we mainly focus on a family of channels known as multi-level amplitude damping channels. We investigate some of the information capacities of the simplest member of multi-level Amplitude Damping Channel, a qutrit channel, in the presence of correlations between successive applications of the channel. We find the upper bounds of the single-shot classical capacities and calculate the quantum capacities associated with a specific class of maps after investigating the degradability property of the channels. Additionally, the quantum and classical capacities of the channels have been computed in entanglement-assisted scenarios.
Affine Equivariant Tyler's M-Estimator Applied to Tail Parameter Learning of Elliptical Distributions

We propose estimating the scale parameter (mean of the eigenvalues) of the scatter matrix of an unspecified elliptically symmetric distribution using weights obtained by solving Tyler's M-estimator of the scatter matrix. The proposed Tyler's weights-based estimate (TWE) of scale is then used to construct an affine equivariant Tyler's M-estimator as a weighted sample covariance matrix using normalized Tyler's weights. We then develop a unified framework for estimating the unknown tail parameter of the elliptical distribution (such as the degrees of freedom (d.o.f.) <inline-formula><tex-math notation="LaTeX">$\nu$</tex-math></inline-formula> of the multivariate <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> (MVT) distribution). Using the proposed TWE of scale, a new robust estimate of the d.o. f. parameter of MVT distribution is proposed with excellent performance in heavy-tailed scenarios, outperforming other competing methods. R-package is available that implements the proposed method.
Cross-domain Augmentation Networks for Click-Through Rate Prediction

Data sparsity is an important issue for click-through rate (CTR) prediction, particularly when user-item interactions is too sparse to learn a reliable model. Recently, many works on cross-domain CTR (CDCTR) prediction have been developed in an effort to leverage meaningful data from a related domain. However, most existing CDCTR works have an impractical limitation that requires homogeneous inputs (\textit{i.e.} shared feature fields) across domains, and CDCTR with heterogeneous inputs (\textit{i.e.} varying feature fields) across domains has not been widely explored but is an urgent and important research problem. In this work, we propose a cross-domain augmentation network (CDAnet) being able to perform knowledge transfer between two domains with \textit{heterogeneous inputs}. Specifically, CDAnet contains a designed translation network and an augmentation network which are trained sequentially. The translation network is able to compute features from two domains with heterogeneous inputs separately by designing two independent branches, and then learn meaningful cross-domain knowledge using a designed cross-supervised feature translator. Later the augmentation network encodes the learned cross-domain knowledge via feature translation performed in the latent space and fine-tune the model for final CTR prediction. Through extensive experiments on two public benchmarks and one industrial production dataset, we show CDAnet can learn meaningful translated features and largely improve the performance of CTR prediction. CDAnet has been conducted online A/B test in image2product retrieval at Taobao app over 20days, bringing an absolute \textbf{0.11 point} CTR improvement and a relative \textbf{1.26\%} GMV increase.
$p$-nuclearity of $L^p$-operator crossed products

Let $(X,\mathcal{B},\mu)$ be a measure space and $A$ be a closed subalgebra of $\mathcal{B}(L^p(X,\mu))$, where $p\in [1,\infty)\setminus\{2\}$. Let $(G,A,\alpha)$ be an $L^p$-operator dynamical system, where $G$ is a countable discrete amenable group. We prove that the full $L^p$-operator crossed product $F^p(G,A,\alpha)$ is $p$-nuclear if and only if $A$ is $p$-nuclear {provided the action} $\alpha$ of $G$ on $A$ is $p$-completely isometric. As applications, we show that $L^p$-Cuntz algebras and $L^p$-irrational rotation algebras are both $p$-nuclear for $p\in [1,\infty)\setminus\{2\}$. Our results solve the {$p$-nuclearity problem} for $L^p$-Cuntz algebras raised by N. C. Phillips.
Near-infrared Spectroscopy of Dense Ejecta Knots in the Outer Eastern Area of the Cassiopeia A Supernova Remnant

The Cassiopeia A supernova remnant has a complex structure, manifesting the multidimensional nature of core-collapse supernova explosions. To further understand this, we carried out near-infrared multiobject spectroscopy on the ejecta knots located in the northeastern (NE) jet and Fe K plume regions, which are two distinct features in the outer eastern area of the remnant. Our study reveals that the knots exhibit varying ratios of [S ii] 1.03, [P ii] 1.189, and [Fe ii] 1.257 μm lines depending on their locations within the remnant, suggesting regional differences in elemental composition. Notably, the knots in the NE jet are mostly S-rich with weak or no [P ii] lines, implying that they originated below the explosive Ne-burning layer, consistent with the results of previous studies. We detected no ejecta knots exhibiting only [Fe ii] lines in the NE jet area that are expected in the jet-driven supernova explosion model. Instead, we discovered a dozen Fe-rich knots in the Fe K plume area. We propose that they are dense knots produced by a complete Si burning with α-rich freeze-out in the innermost region of the progenitor and ejected with the diffuse X-ray-emitting Fe ejecta but decoupled after crossing the reverse shock. In addition to these metal-rich ejecta knots, several knots emitting only He i 1.083 μm lines were detected, and their origin remains unclear. We also detected three extended H emission features of circumstellar or interstellar origin in this area and discuss their association with the supernova remnant.
Augmented Datasheets for Speech Datasets and Ethical Decision-Making

Speech datasets are crucial for training Speech Language Technologies (SLT); however, the lack of diversity of the underlying training data can lead to serious limitations in building equitable and robust SLT products, especially along dimensions of language, accent, dialect, variety, and speech impairment—and the intersectionality of speech features with socioeconomic and demographic features. Furthermore, there is often a lack of oversight on the underlying training data—commonly built on massive web-crawling and/or publicly available speech—with regard to the ethics of such data collection. To encourage standardized documentation of such speech data components, we introduce an augmented datasheet for speech datasets1, which can be used in addition to “Datasheets for Datasets” [78]. We then exemplify the importance of each question in our augmented datasheet based on in-depth literature reviews of speech data used in domains such as machine learning, linguistics, and health. Finally, we encourage practitioners—ranging from dataset creators to researchers—to use our augmented datasheet to better define the scope, properties, and limits of speech datasets, while also encouraging consideration of data-subject protection and user community empowerment. Ethical dataset creation is not a one-size-fits-all process, but dataset creators can use our augmented datasheet to reflexively consider the social context of related SLT applications and data sources in order to foster more inclusive SLT products downstream.