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Skyrmions are important topologically non-trivial fields characteristic of models spanning scales from the microscopic to the cosmological. However, the Skyrmion number can only be defined for fields with specific boundary conditions, limiting its use in broader contexts. Here, we address this issue through a generalized notion of the Skyrmion derived from the De Rham cohomology of compactly supported forms. This allows for the definition of an entirely new $\coprod_{i=1}^\infty \mathbb{Z}^i$-valued topological number that assigns a tuple of integers $(a_1, \ldots, a_k)\in \mathbb{Z}^k$ to a field instead of a single number, with no restrictions to its boundary. The notion of the generalized Skyrmion presented in this paper is completely abstract and can be applied to vector fields in any discipline, not unlike index theory within dynamical systems. To demonstrate the power of our new formalism, we focus on the propagation of optical polarization fields and show that our newly defined generalized Skyrmion number significantly increases the dimension of data that can be stored within the field while also demonstrating strong robustness. Our work represents a fundamental paradigm shift away from the study of fields with natural topological character to engineered fields that can be artificially embedded with topological structures.

The decline of Moore's law coupled with the growing dominance of artificial intelligence has recently motivated research into photonic computing as a high-bandwidth, low-power strategy to accelerate digital electronics. However, modern-day photonic computing strategies are predominantly analog, making them susceptible to noise and intrinsically difficult to scale. The optical Skyrmion offers a route to overcoming these limitations through digitization in the form of a discrete topological number that can be assigned to the analog optical field. Apart from an intrinsic robustness against perturbations, optical Skyrmions represent a new dimension that has yet to be exploited for photonic computing, namely spatially varying polarization. Here, we propose a method of performing perturbation-resilient integer arithmetic with this new dimension of optical Skyrmions through passive optical components and present experimental evidence demonstrating its feasibility. To the best of our knowledge, this is the first time such discrete mathematical operations have been directly achieved using optical Skyrmions without external energy input.

Aromatic molecules represent fundamental building blocks in prebiotic chemistry and are contemplated as vital precursors to DNA and RNA nitrogen bases. However, despite the identification of some 300 molecules in extraterrestrial environments, the pathways to pyridine (C5H5N), pyridinyl (C5H4N), and (iso)quinoline (C9H7N) the simplest representative of mono and bicyclic aromatic molecule carrying nitrogen are elusive. Here, we afford compelling evidence on the gas phase formation of methylene amidogen (H2CN) and cyanomethyl (H2CCN) radicals via molecular beam studies and electronic structure calculations. The modeling of the chemistries of Taurus Molecular Cloud (TMC 1) and Titans atmosphere contemplates a complex chain of reactions synthesizing pyridine, pyridinyl, and (iso)quinoline from H2CN and H2CCN at levels of up to 75%. This study affords unique entry points to precursors of DNA and RNA nitrogen bases in hydrocarbon rich extraterrestrial environments thus changing the way we think about the origin of prebiotic molecules in our Galaxy., Comment: Main paper and supplementary information file

The service conditions of wheelset bearings has a direct impact on the safe operation of railway heavy haul freight trains as the key components. However, speed fluctuation of the trains and few fault samples are the two main problems that restrict the accuracy of bearing fault diagnosis. Therefore, a cross-machine transfer diagnosis (pyDSN) network coupled with interpretable modulated differentiable short-time Fourier transform (STFT) and physics-informed balanced spectrum quality metric is proposed to learn domain-invariant and discriminative features under time-varying speeds. Firstly, due to insufficiency in extracting extract frequency components of time-varying speed signals using fixed windows, a modulated differentiable STFT (MDSTFT) that is interpretable with STFT-informed theoretical support, is proposed to extract the robust time-frequency spectrum (TFS). During training process, multiple windows with different lengths dynamically change. Also, in addition to the classification metric and domain discrepancy metric, we creatively introduce a third kind of metric, referred to as the physics-informed metric, to enhance transferable TFS. A physics-informed balanced spectrum quality (BSQ) regularization loss is devised to guide an optimization direction for MDSTFT and model. With it, not only can model acquire high-quality TFS, but also a physics-restricted domain adaptation network can be also acquired, making it learn real-world physics knowledge, ultimately diminish the domain discrepancy across different datasets. The experiment is conducted in the scenario of migrating from the laboratory datasets to the freight train dataset, indicating that the hybrid-driven pyDSN outperforms existing methods and has practical value.

Adaptive optics (AO) is a powerful tool used in a wide range of research areas spanning from aerospace to microscopy. To date, AO has largely been applied to optical phase aberration correction, with recent advances extending to include the vectorial properties of light. However, intensity errors widely exist in optical systems, yet their associated correction methods are still very much in their infancy. Here, we propose a new adaptive optics method that is termed intensity adaptive optics (I-AO), which features a dual-feedback loop for intensity aberration correction that addresses both intensity uniformity and the overall intensity. We demonstrate that I-AO can operate in both sensor-based and sensorless regimes and validate its feasibility by quantitatively analysing the quality of the focus of an aberrated optical system. This technique expands the AO toolkit, broadens its scope of application, and opens a new avenue for next-generation AO innovations.

Deep image hashing aims to map input images into simple binary hash codes via deep neural networks and thus enable effective large-scale image retrieval. Recently, hybrid networks that combine convolution and Transformer have achieved superior performance on various computer tasks and have attracted extensive attention from researchers. Nevertheless, the potential benefits of such hybrid networks in image retrieval still need to be verified. To this end, we propose a hybrid convolutional and self-attention deep hashing method known as HybridHash. Specifically, we propose a backbone network with stage-wise architecture in which the block aggregation function is introduced to achieve the effect of local self-attention and reduce the computational complexity. The interaction module has been elaborately designed to promote the communication of information between image blocks and to enhance the visual representations. We have conducted comprehensive experiments on three widely used datasets: CIFAR-10, NUS-WIDE and IMAGENET. The experimental results demonstrate that the method proposed in this paper has superior performance with respect to state-of-the-art deep hashing methods. Source code is available https://github.com/shuaichaochao/HybridHash., Comment: Accepted by ICMR 2024

Blind deconvolution (BD) has been demonstrated as an efficacious approach for extracting bearing fault-specific features from vibration signals under strong background noise. Despite BD's desirable feature in adaptability and mathematical interpretability, a significant challenge persists: How to effectively integrate BD with fault-diagnosing classifiers? This issue arises because the traditional BD method is solely designed for feature extraction with its own optimizer and objective function. When BD is combined with downstream deep learning classifiers, the different learning objectives will be in conflict. To address this problem, this paper introduces classifier-guided BD (ClassBD) for joint learning of BD-based feature extraction and deep learning-based fault classification. Firstly, we present a time and frequency neural BD that employs neural networks to implement conventional BD, thereby facilitating the seamless integration of BD and the deep learning classifier for co-optimization of model parameters. Subsequently, we develop a unified framework to use a deep learning classifier to guide the learning of BD filters. In addition, we devise a physics-informed loss function composed of kurtosis, $l_2/l_4$ norm, and a cross-entropy loss to jointly optimize the BD filters and deep learning classifier. Consequently, the fault labels provide useful information to direct BD to extract features that distinguish classes amidst strong noise. To the best of our knowledge, this is the first of its kind that BD is successfully applied to bearing fault diagnosis. Experimental results from three datasets demonstrate that ClassBD outperforms other state-of-the-art methods under noisy conditions.

Optical Skyrmions have many important properties that make them ideal units for high-density data applications, including the ability to carry digital information through a discrete topological number and the independence of spatially varying polarization to other dimensions. More importantly, the topological nature of the optical Skyrmion heuristically suggests a strong degree of robustness to perturbations, which is crucial for reliably carrying information in noisy environments. However, the study of the topological robustness of optical Skyrmions is still in its infancy. Here, we quantify this robustness precisely by proving that the topological nature of the Skyrmion arises from its structure on the boundary and, by duality, is therefore resilient to complex perturbations provided they respect the relevant boundary conditions of the unperturbed Skyrmion. We then present experimental evidence validating this robustness in the context of paraxial Skyrmion beams against different polarization aberrations. Our work provides a framework for handling various perturbations of Skyrmion fields and offers guarantees of robustness in a general sense. This, in turn, has implications for applications of the optical Skyrmion where their topological nature is exploited explicitly, and, in particular, provides an underpinning for the use of Skyrmions in optical communications and photonic computing.

Impacts are critical to producing the aqueous environments necessary to stimulate prebiotic chemistry on Titan's surface. Furthermore, organic hazes resting on the surface are a likely feedstock of biomolecules. In this work, we conduct impact experiments on laboratory-produced organic haze particles and haze/sand mixtures and analyze these samples for life's building blocks. Samples of unshocked haze and sand particles are also analyzed to determine the change in biomolecule concentrations and distributions from shocking. Across all samples, we detect seven nucleobases, nine proteinogenic amino acids, and five other biomolecules (e.g., urea) using a blank subtraction procedure to eliminate signals due to contamination. We find that shock pressures of 13 GPa variably degrade nucleobases, amino acids, and a few other organics in haze particles and haze/sand mixtures; however, certain individual biomolecules become enriched or are even produced from these events. Xanthine, threonine, and aspartic acid are enriched or produced in impact experiments containing sand, suggesting these minerals may catalyze the production of these biomolecules. On the other hand, thymine and isoleucine/norleucine are enriched or produced in haze samples containing no sand, suggesting catalytic grains are not necessary for all impact shock syntheses. Uracil, glycine, proline, cysteine, and tyrosine are the most unstable to impact-related processing. These experiments suggest that impacts alter biomolecule distributions on Titan's surface, and that organic hazes co-occurring with fine-grained material on the surface may provide an initial source for further prebiotic chemistry on Titan., Comment: Accepted to PSJ. 13 pages, 3 tables, 7 figures

Over 4 billion years ago, Earth is thought to have been a hazy world akin to Saturn's moon Titan. The organic hazes in the atmosphere at this time could contain a vast inventory of life's building blocks, and thus may have seeded warm little ponds for life. In this work, we produce organic hazes in the lab in atmospheres with high (5%) and low (0.5%) CH4 abundances and analyze the solid particles for nucleobases, amino acids, and a few other organics using GC/MS/MS to obtain their concentrations. We also analyze heated (200 $^{\circ}$C) samples from the high methane organic haze experiment to simulate these particles sitting on an uninhabitable surface. Finally, we use our experimental results and estimates of atmospheric haze production as inputs for a comprehensive numerical pond model to calculate the concentrations of nucleobases from organic hazes in these environments. We find that organic hazes typically provide up to 0.2-6.5 $\mu$M concentrations of nucleobases to warm little ponds for potentially habitable Hadean conditions. However, without seepage, uracil and thymine can reach ~100 $\mu$M concentrations, which is the present lower experimental limit to react these species to form nucleotides. Heating samples leads to partial or complete decay of biomolecules, suggesting that biomolecule stockpiling on the hot surface is unlikely. The ideal conditions for the delivery of life's building blocks from organic hazes would be when the Hadean atmosphere is rich in methane, but not so rich as to create an uninhabitable surface., Comment: Accepted to PSJ, 12 pages, 5 figures, 1 table, 13 supplementary pages

There is a growing demand for customized and expressive 3D characters with the emergence of AI agents and Metaverse, but creating 3D characters using traditional computer graphics tools is a complex and time-consuming task. To address these challenges, we propose a user-friendly framework named Make-A-Character (Mach) to create lifelike 3D avatars from text descriptions. The framework leverages the power of large language and vision models for textual intention understanding and intermediate image generation, followed by a series of human-oriented visual perception and 3D generation modules. Our system offers an intuitive approach for users to craft controllable, realistic, fully-realized 3D characters that meet their expectations within 2 minutes, while also enabling easy integration with existing CG pipeline for dynamic expressiveness. For more information, please visit the project page at https://human3daigc.github.io/MACH/., Comment: Technical Report

Vectorial adaptive optics (V-AO) is a cutting-edge technique extending conventional AO into the vectorial domain encompassing both polarization and phase feedback correction for optical systems. However, previous V-AO approaches focus on point correction. In this letter, we extend this AO approach into the imaging domain. We show how V-AO can benefit an aberrated imaging system to enhance not only scalar imaging but also the quality of vectorial information. Two important criteria, vectorial precision and uniformity are put forward and used in practice to evaluate the performance of the correction. These experimental validations pave the way for real-world imaging for V-AO technology and its applications.

With fewer feature dimensions, filter banks are often used in light-weight full-band speech enhancement models. In order to further enhance the coarse speech in the sub-band domain, it is necessary to apply a post-filtering for harmonic retrieval. The signal processing-based comb filters used in RNNoise and PercepNet have limited performance and may cause speech quality degradation due to inaccurate fundamental frequency estimation. To tackle this problem, we propose a learnable comb filter to enhance harmonics. Based on the sub-band model, we design a DNN-based fundamental frequency estimator to estimate the discrete fundamental frequencies and a comb filter for harmonic enhancement, which are trained via an end-to-end pattern. The experiments show the advantages of our proposed method over PecepNet and DeepFilterNet., Comment: accepted by Interspeech 2023

Coronal mass ejections (CMEs) are violent ejections of magnetized plasma from the Sun, which can trigger geomagnetic storms, endanger satellite operations and destroy electrical infrastructures on the Earth. After systematically searching Sun-as-a-star spectra observed by the Extreme-ultraviolet Variability Experiment (EVE) onboard the Solar Dynamics Observatory (SDO) from May 2010 to May 2022, we identified eight CMEs associated with flares and filament eruptions by analyzing the blue-wing asymmetry of the O III 52.58 nm line profiles. Combined with images simultaneously taken by the 30.4 nm channel of the Atmospheric Imaging Assembly onboard SDO, the full velocity and propagation direction for each of the eight CMEs are derived. We find a strong correlation between geomagnetic indices (Kp and Dst) and the angle between the CME propagation direction and the Sun-Earth line, suggesting that Sun-as-a-star spectroscopic observations at EUV wavelengths can potentially help to improve the prediction accuracy of the geoeffectiveness of CMEs. Moreover, an analysis of synthesized long-exposure Sun-as-a-star spectra implies that it is possible to detect CMEs from other stars through blue-wing asymmetries or blueshifts of spectral lines., Comment: Accepted by ApJ

Skyrmions are topologically protected quasiparticles, originally studied in condensed-matter systems and recently in photonics, with great potential in ultra-high-capacity information storage. Despite the recent attention, most optical solutions require complex and expensive systems yet produce limited topologies. Here we demonstrate an extended family of quasiparticles beyond normal skyrmions, which are controlled in confined photonic gradient-index media, extending to higher-order members such as multiskyrmions and multimerons, with increasingly complex topologies. We introduce new topological numbers to describe these complex photonic quasiparticles and propose how this new zoology of particles could be used in future high-capacity information transfer. Our compact creation system lends integrated and programmable solutions of complex particle textures, with potential impacts on both photonic and condensed-matter systems for revolutionizing topological informatics and logic devices.