Miracle-dz/UniFAR-FTUs huggingface dataset:
Explicit formula of deformation quantization with separation of variables for complex two-dimensional locally symmetric Kahler manifold

We give a complex two-dimensional noncommutative locally symmetric K\"{a}hler manifold via a deformation quantization with separation of variables. We present an explicit formula of its star product by solving the system of recurrence relations given by Hara-Sako. In the two-dimensional case, this system of recurrence relations gives two types of equations corresponding to the two coordinates. From the two types of recurrence relations, symmetrized and antisymmetrized recurrence relations are obtained. The symmetrized one gives the solution of the recurrence relation. From the antisymmetrized one, the identities satisfied by the solution are obtained. The star products for $\mathbb{C}^{2}$ and $\mathbb{C}P^{2}$ are constructed by the method obtained in this study, and we verify that these star products satisfy the identities.
Hierarchical Mask Calibration for Unified Domain Adaptive Panoptic Segmentation

Domain adaptive panoptic segmentation aims to mitigate data annotation challenge by leveraging off-the-shelf annotated data in one or multiple related source domains. However, existing studies employ two networks for instance segmentation and semantic segmentation separately which lead to a large amount of network parameters with complicated and computationally intensive training and inference processes. We design UniDAPS, a Unified Domain Adaptive Panoptic Segmentation network that is simple but capable of achieving domain adaptive instance segmentation and semantic segmentation simultaneously within a single network. UniDAPS introduces Hierarchical Mask Calibration (HMC) that rectifies the predicted pseudo masks, pseudo superpixels and pseudo pixels and performs network re-training via an online self-training process on the fly. It has three unique features: 1) it enables unified domain adaptive panoptic adaptation; 2) it mitigates false predictions and improves domain adaptive panoptic segmentation effectively; 3) it is end-to-end trainable with much less parameters and simpler training and inference pipeline. Extensive experiments over multiple public benchmarks show that UniDAPS achieves superior domain adaptive panoptic segmentation as compared with the state-of-the-art.
A Look at Outbursts of Comet C/2014 UN271 (Bernardinelli–Bernstein) near 20 au

Cometary activity may be driven by ices with very low sublimation temperatures, such as carbon monoxide ice, which can sublimate at distances well beyond 20 au. This point is emphasized by the discovery of the Oort cloud comet C/2014 UN271 (Bernardinelli–Bernstein) and its observed activity out to ∼26 au. Through observations of this comet’s optical brightness and behavior, we can potentially discern the drivers of activity in the outer solar system. We present a study of the activity of comet Bernardinelli–Bernstein with broad-band optical photometry taken at 19–20 au from the Sun (2021 June to 2022 February) as part of the LCO Outbursting Objects Key (LOOK) Project. Our analysis shows that the comet’s optical brightness during this period was initially dominated by cometary outbursts, stochastic events that ejected ∼107 to ∼108 kg of material on short (<1 day) timescales. We present evidence for three such outbursts occurring in 2021 June and September. The nominal nuclear volumes excavated by these events are similar to the 10–100 m pit-shaped voids on the surfaces of short-period comet nuclei, as imaged by spacecraft. Two out of three Oort cloud comets observed at large pre-perihelion distances exhibit outburst behavior near 20 au, suggesting such events may be common in this population. In addition, quiescent CO-driven activity may account for the brightness of the comet in 2022 January to February, but that variations in the cometary active area (i.e., the amount of sublimating ice) with heliocentric distance are also possible.
AFAFed - Protocol analysis

In this paper, we design, analyze the convergence properties and address the implementation aspects of AFAFed. This is a novel Asynchronous Fair Adaptive Federated learning framework for stream-oriented IoT application environments, which are featured by time-varying operating conditions, heterogeneous resource-limited devices (i.e., coworkers), non-i.i. d. local training data and unreliable communication links. The key new of AFAFed is the synergic co-design of: (i) two sets of adaptively tuned tolerance thresholds and fairness coefficients at the coworkers and central server, respectively; and, (ii) a distributed adaptive mechanism, which allows each coworker to adaptively tune own communication rate. The convergence properties of AFAFed under (possibly) non-convex loss functions is guaranteed by a set of new analytical bounds, which formally unveil the impact on the resulting AFAFed convergence rate of a number of Federated Learning (FL) parameters, like, first and second moments of the per-coworker number of consecutive model updates, data skewness, communication packet-loss probability, and maximum/minimum values of the (adaptively tuned) mixing coefficient used for model aggregation.
A new method for ultra-local thermal control of a single living cell

We report a new approach to a controllable thermal stimulation of a single living cell and its compartments. The technique is based on the use of a single polycrystalline diamond particle containing silicon-vacancy (SiV) color centers. Due to the presence of amorphous carbon at its intercrystalline boundaries such a particle is an efficient light absorber and, when illuminated by a laser, becomes a local heat source. Furthermore, the temperature of such a local heater is tracked by the spectral shift of the zero-phonon line of SiV centers [1]. Thus, the diamond particle acts as both a heater and a thermometer, simultaneously. In the current work, we demonstrate the ability of such a Diamond Heater-Thermometer (DHT) to locally alter the temperature, one of the numerous parameters that play a decisive role for the living organisms at the nanoscale. In particular, we show that the local heating of 11-12 °C relative to the ambient temperature (23°C) next to individual HeLa cells and neurons, isolated from the mouse hippocampus, leads to a change in the intracellular distribution of the concentration of free calcium ions. For individual HeLa cells,
Metric Dimension Parameterized by Feedback Vertex Set and Other Structural Parameters

For a graph $G$, a subset $S \subseteq V(G)$ is called a \emph{resolving set} if for any two vertices $u,v \in V(G)$, there exists a vertex $w \in S$ such that $d(w,u) \neq d(w,v)$. The {\sc Metric Dimension} problem takes as input a graph $G$ and a positive integer $k$, and asks whether there exists a resolving set of size at most $k$. This problem was introduced in the 1970s and is known to be \NP-hard~[GT~61 in Garey and Johnson's book]. In the realm of parameterized complexity, Hartung and Nichterlein~[CCC~2013] proved that the problem is \W[2]-hard when parameterized by the natural parameter $k$. They also observed that it is \FPT\ when parameterized by the vertex cover number and asked about its complexity under \emph{smaller} parameters, in particular the feedback vertex set number. We answer this question by proving that {\sc Metric Dimension} is \W[1]-hard when parameterized by the combined parameter feedback vertex set number plus pathwidth. This also improves the result of Bonnet and Purohit~[IPEC 2019] which states that the problem is \W[1]-hard parameterized by the pathwidth. On the positive side, we show that {\sc Metric Dimension} is \FPT\ when parameterized by either the distance to cluster or the distance to co-cluster, both of which are smaller parameters than the vertex cover number.
Exploring Temporally Dynamic Data Augmentation for Video Recognition

Data augmentation has recently emerged as an essential component of modern training recipes for visual recognition tasks. However, data augmentation for video recognition has been rarely explored despite its effectiveness. Few existing augmentation recipes for video recognition naively extend the image augmentation methods by applying the same operations to the whole video frames. Our main idea is that the magnitude of augmentation operations for each frame needs to be changed over time to capture the real-world video's temporal variations. These variations should be generated as diverse as possible using fewer additional hyper-parameters during training. Through this motivation, we propose a simple yet effective video data augmentation framework, DynaAugment. The magnitude of augmentation operations on each frame is changed by an effective mechanism, Fourier Sampling that parameterizes diverse, smooth, and realistic temporal variations. DynaAugment also includes an extended search space suitable for video for automatic data augmentation methods. DynaAugment experimentally demonstrates that there are additional performance rooms to be improved from static augmentations on diverse video models. Specifically, we show the effectiveness of DynaAugment on various video datasets and tasks: large-scale video recognition (Kinetics-400 and Something-Something-v2), small-scale video recognition (UCF- 101 and HMDB-51), fine-grained video recognition (Diving-48 and FineGym), video action segmentation on Breakfast, video action localization on THUMOS'14, and video object detection on MOT17Det. DynaAugment also enables video models to learn more generalized representation to improve the model robustness on the corrupted videos.
Improving the Generalization of Supervised Models

We consider the problem of training a deep neural network on a given classification task, e.g., ImageNet-1K (IN1K), so that it excels at that task as well as at other (future) transfer tasks. These two seemingly contradictory properties impose a trade-off between improving the model’s generalization while maintaining its performance on the original task. Models trained with self-supervised learning (SSL) tend to generalize better than their supervised counterparts for transfer learning; yet, they still lag behind supervised models on IN1K. In this paper, we propose a supervised learning setup that leverages the best of both worlds. We enrich the common supervised training framework using two key components of recent SSL models: multi-scale crops for data augmentation and the use of an expendable projector head. We replace the last layer of class weights with class prototypes computed on the fly using a memory bank. We show that these three improvements lead to a more favorable trade-off between the IN1K training task and 13 transfer tasks. Over all the explored configurations, we single out two models: t-ReX that achieves a new state of the art for transfer learning and outperforms top methods such as DINO and PAWS on IN1K, and t-ReX* that matches the highly optimized RSB-A1 model on IN1K while performing better on transfer tasks.
Twisted bilayer BC$_3$: Valley interlocked anisotropic flat bands

Here we propose BC$_3$, a graphene derivative that has been synthesized, as a platform to realize exotic quantum phases by introducing a moir\'e pattern with mismatched stacking. In twisted bilayer BC$_3$, it is shown that a crossover from two-dimensional to quasi one-dimensional band structure takes place with the twist angle as a control parameter. This is a typical manifestation of the guiding principle in van der Waals stacked systems: the quantum interference between Bloch wave functions in adjacent layers has a striking effect on the effective interlayer tunneling. Interestingly, quasi one-dimensionalization happens in a valley dependent manner. Namely, there is interlocking between the valley index and the quasi-1D directionality, which makes BC$_3$ a plausible candidate for valleytronics devices. In addition, the strongly correlated regime of the valley interlocked quasi-1D state reduces to an interesting variant of the Kugel-Khomskii model where intertwined valley and spin degrees of freedom potentially induces exotic quantum phases. Notably, this variant of the Kugel-Khomskii model cannot be realized in conventional solids due to the three fold valley degeneracy.
Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging?

Previous Part-Of-Speech (POS) induction models usually assume certain independence assumptions (e.g., Markov, unidirectional, local dependency) that do not hold in real languages. For example, the subject-verb agreement can be both long-term and bidirectional. To facilitate flexible dependency modeling, we propose a Masked Part-of-Speech Model (MPoSM), inspired by the recent success of Masked Language Models (MLM). MPoSM can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction. We achieve competitive results on both the English Penn WSJ dataset as well as the universal treebank containing 10 diverse languages. Though modeling the long-term dependency should ideally help this task, our ablation study shows mixed trends in different languages. To better understand this phenomenon, we design a novel synthetic experiment that can specifically diagnose the model’s ability to learn tag agreement. Surprisingly, we find that even strong baselines fail to solve this problem consistently in a very simplified setting: the agreement between adjacent words. Nonetheless, MPoSM achieves overall better performance. Lastly, we conduct a detailed error analysis to shed light on other remaining challenges.
GDA-AM: On the effectiveness of solving minimax optimization via Anderson Acceleration

Many modern machine learning algorithms such as generative adversarial networks (GANs) and adversarial training can be formulated as minimax optimization. Gradient descent ascent (GDA) is the most commonly used algorithm due to its simplicity. However, GDA can converge to non-optimal minimax points. We propose a new minimax optimization framework, GDA-AM, that views the GDAdynamics as a fixed-point iteration and solves it using Anderson Mixing to con-verge to the local minimax. It addresses the diverging issue of simultaneous GDAand accelerates the convergence of alternating GDA. We show theoretically that the algorithm can achieve global convergence for bilinear problems under mild conditions. We also empirically show that GDA-AMsolves a variety of minimax problems and improves GAN training on several datasets
Polarized High-frequency Wave Propagation Beyond the Nonlinear Schrödinger Approximation

This paper studies highly oscillatory solutions to a class of systems of semilinear hyperbolic equations with a small parameter, in a setting that includes Klein--Gordon equations and the Maxwell--Lorentz system. The interest here is in solutions that are polarized in the sense that up to a small error, the oscillations in the solution depend on only one of the frequencies that satisfy the dispersion relation with a given wave vector appearing in the initial wave packet. The construction and analysis of such polarized solutions is done using modulated Fourier expansions. This approach includes higher harmonics and yields approximations to polarized solutions that are of arbitrary order in the small parameter, going well beyond the known first-order approximation via a nonlinear Schr\"odinger equation. The given construction of polarized solutions is explicit, uses in addition a linear Schr\"odinger equation for each further order of approximation, and is accessible to direct numerical approximation.
Padé-parametric FEM approximation for fractional powers of elliptic operators on manifolds

This paper focuses on numerical approximation for fractional powers of elliptic operators on two-dimensional manifolds. Firstly, the parametric finite element method is employed to discretize the original problem. We then approximate fractional powers of the discrete elliptic operator by the product of rational functions, each of which is a diagonal Padé approximant for the corresponding power function. Rigorous error analysis is carried out and sharp error bounds are presented that show that the scheme is robust for $\alpha \rightarrow 0^+$ and $\alpha \rightarrow 1^-$. The cost of the proposed algorithm is solving some elliptic problems. Since the approach is exponentially convergent with respect to the number of solves, it is very efficient. Some numerical tests are given to confirm our theoretical analysis and the robustness of the algorithm.
Equidistributed Closed Geodesics on Closed Finsler and Riemannian Surfaces

In this paper, we prove that for a C ∞ -generic Finsler (symmetric Finsler and Riemannian) metric on a closed surface Σ, there exists a sequence of (nondegenerate) closed geodesics which are equidistributed on Σ. The proof is based on the volume property of the spectral invariants in embedded con- tact homology theory by Cristofaro-Gardiner, Hutchings, Ramos and some transversality arguments, which are inspired by the equidistribution result of Irie for three-dimensional Reeb ﬂows and the analogous result of Marques, Neves and Song for embedded minimal hypersurfaces.
Shifts 2.0: Extending The Dataset of Real Distributional Shifts

Distributional shift, or the mismatch between training and deployment data, is a significant obstacle to the usage of machine learning in high-stakes industrial applications, such as autonomous driving and medicine. This creates a need to be able to assess how robustly ML models generalize as well as the quality of their uncertainty estimates. Standard ML baseline datasets do not allow these properties to be assessed, as the training, validation and test data are often identically distributed. Recently, a range of dedicated benchmarks have appeared, featuring both distributionally matched and shifted data. Among these benchmarks, the Shifts dataset stands out in terms of the diversity of tasks as well as the data modalities it features. While most of the benchmarks are heavily dominated by 2D image classification tasks, Shifts contains tabular weather forecasting, machine translation, and vehicle motion prediction tasks. This enables the robustness properties of models to be assessed on a diverse set of industrial-scale tasks and either universal or directly applicable task-specific conclusions to be reached. In this paper, we extend the Shifts Dataset with two datasets sourced from industrial, high-risk applications of high societal importance. Specifically, we consider the tasks of segmentation of white matter Multiple Sclerosis lesions in 3D magnetic resonance brain images and the estimation of power consumption in marine cargo vessels. Both tasks feature ubiquitous distributional shifts and a strict safety requirement due to the high cost of errors. These new datasets will allow researchers to further explore robust generalization and uncertainty estimation in new situations. In this work, we provide a description of the dataset and baseline results for both tasks.
U-Attention to Textures: Hierarchical Hourglass Vision Transformer for Universal Texture Synthesis

We present a novel U-Attention vision Transformer for universal texture synthesis. We exploit the natural long-range dependencies enabled by the attention mechanism to allow our approach to synthesize diverse textures while preserving their structures in a single inference. We propose a hierarchical hourglass backbone that attends to the global structure and performs patch mapping at varying scales in a coarse-to-fine-to-coarse stream. Completed by skip connection and convolution designs that propagate and fuse information at different scales, our hierarchical U-Attention architecture unifies attention to features from macro structures to micro details, and progressively refines synthesis results at successive stages. Our method achieves stronger 2 × synthesis than previous work on both stochastic and structured textures while generalizing to unseen textures without fine-tuning. Ablation studies demonstrate the effectiveness of each component of our architecture.
Best of Both Worlds Model Selection

We study the problem of model selection in bandit scenarios in the presence of nested policy classes, with the goal of obtaining simultaneous adversarial and stochastic ("best of both worlds") high-probability regret guarantees. Our approach requires that each base learner comes with a candidate regret bound that may or may not hold, while our meta algorithm plays each base learner according to a schedule that keeps the base learner's candidate regret bounds balanced until they are detected to violate their guarantees. We develop careful mis-specification tests specifically designed to blend the above model selection criterion with the ability to leverage the (potentially benign) nature of the environment. We recover the model selection guarantees of the CORRAL algorithm for adversarial environments, but with the additional benefit of achieving high probability regret bounds, specifically in the case of nested adversarial linear bandits. More importantly, our model selection results also hold simultaneously in stochastic environments under gap assumptions. These are the first theoretical results that achieve best of both world (stochastic and adversarial) guarantees while performing model selection in (linear) bandit scenarios.
Understanding Covid-19 emergency social security measures as a from of basic income: Lessons from Australia

This article examines the changes in social security measures introduced by the Australian government during the first wave of Covid-19 lockdowns in 2020. These measures were basic income-like in that they became both more unconditional and adequate for a reasonable standard of living. This was achieved through a significant supplementary payment, suspension of mutual obligation requirements, and the relaxation of eligibility criteria on a range of unemployment-related payments. Through drawing on the results of an online survey, we examine the impacts of these measures and find that they significantly helped to alleviate poverty and improve wellbeing. These gains were not insignificant for the individuals involved, and offer empirical insights into studies of basic income. While seeing the Australian government embrace more generous and basic income-like measures, we also note that during Covid-19 gendered and class inequalities increased. This reminds us that basic income is never a silver bullet and, alongside implementing basic income payments, there also needs to be a concerted effort to restructure economic relations more generally.