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Scale-Dependent Dynamic Alignment (SDDA) in Els\"asser field fluctuations is theorized to suppress nonlinearities and modulate the energy spectrum. Limited empirical evidence exists for SDDA within the solar wind turbulence's inertial range. We analyzed data from the WIND mission to assess the effects of compressibility, intermittency, and imbalance on SDDA. SDDA consistently appears at energy-containing scales, with a trend toward misalignment at inertial scales. Compressible fluctuations show no increased alignment; however, their impact on SDDA's overall behavior is minimal. The alignment angles inversely correlate with field gradient intensity, likely due to "anomalous" or "counterpropagating" wave packet interactions. This suggests that SDDA originates from mutual shearing of Els\"asser fields during imbalanced ($\delta \boldsymbol{z}^{\pm} \gg \delta \boldsymbol{z}^{\mp}$) interactions. Rigorous thresholding on field gradient intensity reveals SDDA signatures across much of the inertial range. The scaling of Els\"asser increments' alignment angle, $\Theta^{z}$, steepens with increasing global Alfv\'enic imbalance, while the angle between magnetic and velocity field increments, $\Theta^{ub}$, becomes shallower. $\Theta^{ub}$ only correlates with global Els\"asser imbalance, steepening as the imbalance increases. Furthermore, increasing alignment in $\Theta^{ub}$ persists deep into the inertial range of balanced intervals but collapses at large scales for imbalanced ones. Simplified theoretical analysis and modeling of high-frequency, low-amplitude noise in the velocity field indicate significant impacts on alignment angle measurements even at very low frequencies, with effects growing as global imbalance increases.

Recent observations of Parker Solar Probe (PSP) from around the Alfv\'en surface have shown that the trace magnetic power spectrum density (PSD) is often characterized by a shallow-inertial double power law, where in the low frequency energy injection range, the power spectrum is shallow (flatter than $1/f$), and in the inertial range the spectrum is steep, with a scaling index of [1.5, 1.67]. Consequently, close to the sun, the majority of the fluctuation energy concentrates in a small frequency range around the low frequency power spectral break. In this work, we conduct a systematic survey of PSP observations for the first 17 encounters to statistically study the energy behaviors of the magnetic fluctuations. Our results show that the center frequency of fluctuation energy systematically drifts to around 3-minute for the most pristine solar wind (smallest solar wind advection time). Moreover, the center frequency rapidly drifts to lower frequency as solar wind advection time increases, as expected for active turbulence. The concentration of fluctuation energy around 3-minutes suggests that Alfv\'enic fluctuations in solar wind might mostly be coming from resonant p-mode oscillations in the photosphere, though other potential sources are discussed.

The Extended Quantum Molecular Dynamics (EQMD) model is one of the few QMD-like transport approaches that can describe the $\alpha$-clustering structure with efficient computational power. However, compared to most QMD-like models, the choice of equation of state (EOS) for nuclear matter is very limited. In this work, a Monte Carlo integral method is employed to calculate the density integration with non-integer exponent. We demonstrate the superiority of our approach by studying the isovector giant dipole resonance (IVGDR). This improvement will be beneficial for the EQMD model to study more valuable effects for heavy ion collisions in the near future., Comment: 10 pages, 8 figures

The extended quantum molecular dynamics (EQMD) model is one of the few quantum molecular dynamics (QMD)-like transport approaches that can be used to study the effective clustering structure as well as heavily deformed nuclei in both ground state nuclei and nuclear reactions. However, there are only two parameter sets that lead to hard incompressibility for long times. The aim of the present work is to obtain a soft equation of state (EoS) in the EQMD model. In this context, we take the isoscalar giant monopole resonance (ISGMR), which is sensitive to the EoS, as an example to check our work. By introducing a kind of standard Skyrme energy density functional with different parameter sets, such as SkP, SkT1, and SKXce, whose incompressibility value ranges from 200 to 268 MeV, the ISGMR of $^{208}$Pb and other nuclei are studied. When the SkP parameter sets are adopted, our new soft interaction in the EQMD model gives reasonable agreement with the experimental data in the heavy ion regime., Comment: 10 pages, 8 figures

We analyze in-situ observations of imbalanced solar wind turbulence to evaluate MHD turbulence models grounded in "Critical Balance" (CB) and "Scale-Dependent Dynamic Alignment" (SDDA). At energy injection scales, both outgoing and ingoing modes exhibit a weak cascade; a simultaneous tightening of SDDA is noted. Outgoing modes persist in a weak cascade across the inertial range, while ingoing modes shift to a strong cascade at $\lambda \approx 3 \times 10^{4} d_i$, with associated spectral scalings deviating from expected behavior due to "anomalous coherence" effects. The inertial range comprises two distinct sub-inertial segments. Beyond $\lambda \gtrsim 100 d_i$, eddies adopt a field-aligned tube topology, with SDDA signatures mainly evident in high amplitude fluctuations. The scaling exponents $\zeta_{n}$ of the $n$-th order conditional structure functions, orthogonal to both the local mean field and fluctuation direction, align with the analytical models of Chandran et al. 2015 and Mallet et al. 2017, indicating "multifractal" statistics and strong intermittency; however, scaling in parallel and displacement components is more concave than predicted, possibly influenced by expansion effects. Below $\lambda \approx 100 d_i$, eddies become increasingly anisotropic, evolving into thin current sheet-like structures. Concurrently, $\zeta_{n}$ scales linearly with order, marking a shift towards "monofractal" statistics. At $\lambda \approx 8 d_i$, the increase in aspect ratio halts, and the eddies become quasi-isotropic. This change may signal tearing instability, leading to reconnection, or result from energy redirection into the ion-cyclotron wave spectrum, aligning with the "helicity barrier". Our analysis utilizes 5-point structure functions, proving more effective than the traditional 2-point method in capturing steep scaling behaviors at smaller scales.

The task of financial analysis primarily encompasses two key areas: stock trend prediction and the corresponding financial question answering. Currently, machine learning and deep learning algorithms (ML&DL) have been widely applied for stock trend predictions, leading to significant progress. However, these methods fail to provide reasons for predictions, lacking interpretability and reasoning processes. Also, they can not integrate textual information such as financial news or reports. Meanwhile, large language models (LLMs) have remarkable textual understanding and generation ability. But due to the scarcity of financial training datasets and limited integration with real-time knowledge, LLMs still suffer from hallucinations and are unable to keep up with the latest information. To tackle these challenges, we first release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. It has a positive impact on training LLMs for completing financial analysis. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task, which integrates retrieval-augmented generation (RAG) techniques. Extensive experiments are conducted to demonstrate the effectiveness of our framework on financial analysis., Comment: COLING 2024. The first three authors contributed equally. Project website: https://github.com/AlphaFin-proj/AlphaFin

Generative query rewrite generates reconstructed query rewrites using the conversation history while rely heavily on gold rewrite pairs that are expensive to obtain. Recently, few-shot learning is gaining increasing popularity for this task, whereas these methods are sensitive to the inherent noise due to limited data size. Besides, both attempts face performance degradation when there exists language style shift between training and testing cases. To this end, we study low-resource generative conversational query rewrite that is robust to both noise and language style shift. The core idea is to utilize massive unlabeled data to make further improvements via a contrastive co-training paradigm. Specifically, we co-train two dual models (namely Rewriter and Simplifier) such that each of them provides extra guidance through pseudo-labeling for enhancing the other in an iterative manner. We also leverage contrastive learning with data augmentation, which enables our model pay more attention on the truly valuable information than the noise. Extensive experiments demonstrate the superiority of our model under both few-shot and zero-shot scenarios. We also verify the better generalization ability of our model when encountering language style shift., Comment: Accepted to COLING 2024

The Aesthetics Assessment of Children's Paintings (AACP) is an important branch of the image aesthetics assessment (IAA), playing a significant role in children's education. This task presents unique challenges, such as limited available data and the requirement for evaluation metrics from multiple perspectives. However, previous approaches have relied on training large datasets and subsequently providing an aesthetics score to the image, which is not applicable to AACP. To solve this problem, we construct an aesthetics assessment dataset of children's paintings and a model based on self-supervised learning. 1) We build a novel dataset composed of two parts: the first part contains more than 20k unlabeled images of children's paintings; the second part contains 1.2k images of children's paintings, and each image contains eight attributes labeled by multiple design experts. 2) We design a pipeline that includes a feature extraction module, perception modules and a disentangled evaluation module. 3) We conduct both qualitative and quantitative experiments to compare our model's performance with five other methods using the AACP dataset. Our experiments reveal that our method can accurately capture aesthetic features and achieve state-of-the-art performance., Comment: AAAI 2024

The heliosphere is permeated with highly structured solar wind originating from the sun. One of the primary science objectives of Parker Solar Probe (PSP) is to determine the structures and dynamics of the plasma and magnetic fields at the sources of the solar wind. However, establishing the connection between in situ measurements and structures and dynamics in the solar atmosphere is challenging: most of the magnetic footpoint mapping techniques have significant uncertainties in the source localization of a plasma parcel observed in situ, and the PSP plasma measurements suffer from a limited field of view. Therefore it lacks a universal tool to self-contextualize the in situ measurements. Here we develop a novel time series visualization method named Jensen-Shannon Scalogram. Utilizing this method, by analyzing the magnetic magnitude data from both PSP and Ulysses, we successfully identify in situ remnants of solar atmospheric and magnetic structures spanning more than seven orders of magnitude, from years to seconds, including polar and mid-latitude coronal holes, as well as structures compatible with super-granulation, ``jetlets'' and very small scale flaring activity. Furthermore, computer simulations of Alfv\'enic turbulence support key features of the observed magnetic magnitude distribution. Building upon these discoveries, the Jensen-Shannon Scalogram therefore not only enables us to reveal the fractal fine structures in the solar wind time series from both PSP and decades-old data archive, but will also serve as a general-purpose data visualization method applicable to all time series.

In this paper, we present DreaMoving, a diffusion-based controllable video generation framework to produce high-quality customized human videos. Specifically, given target identity and posture sequences, DreaMoving can generate a video of the target identity moving or dancing anywhere driven by the posture sequences. To this end, we propose a Video ControlNet for motion-controlling and a Content Guider for identity preserving. The proposed model is easy to use and can be adapted to most stylized diffusion models to generate diverse results. The project page is available at https://dreamoving.github.io/dreamoving, Comment: 5 pages, 5 figures, Tech. Report

Semantic segmentation tasks naturally require high-resolution information for pixel-wise segmentation and global context information for class prediction. While existing vision transformers demonstrate promising performance, they often utilize high resolution context modeling, resulting in a computational bottleneck. In this work, we challenge conventional wisdom and introduce the Low-Resolution Self-Attention (LRSA) mechanism to capture global context at a significantly reduced computational cost. Our approach involves computing self-attention in a fixed low-resolution space regardless of the input image's resolution, with additional 3x3 depth-wise convolutions to capture fine details in the high-resolution space. We demonstrate the effectiveness of our LRSA approach by building the LRFormer, a vision transformer with an encoder-decoder structure. Extensive experiments on the ADE20K, COCO-Stuff, and Cityscapes datasets demonstrate that LRFormer outperforms state-of-the-art models. The code will be made available at https://github.com/yuhuan-wu/LRFormer., Comment: 11 pages, 11 tables, 6 figures

We conduct 3D magnetohydrodynamic (MHD) simulations of decaying turbulence in the solar wind context. To account for the spherical expansion of the solar wind, we implement the expanding box model. The initial turbulence comprises uncorrelated counter-propagating Alfv\'en waves and exhibits an isotropic power spectrum. Our findings reveal the consistent generation of negative residual energy whenever nonlinear interactions are present, independent of the normalized cross helicity $\sigma_c$. The spherical expansion facilitates this process. The resulting residual energy is primarily distributed in the perpendicular direction, with $[S_2(\mathbf{b})-S_2(\mathbf{u})] \propto l_\perp$ or equivalently $-E_r \propto k_\perp^{-2}$. Here $S_2(\mathbf{b})$ and $S_2(\mathbf{u})$ are second-order structure functions of magnetic field and velocity respectively. In most runs, $S_2(\mathbf{b})$ develops a scaling relation $S_2(\mathbf{b}) \propto l_\perp^{1/2}$ ($E_b \propto k_\perp^{-3/2}$). In contrast, $S_2(\mathbf{u})$ is consistently shallower than $S_2(\mathbf{b})$, which aligns with in-situ observations of the solar wind. We observe that the higher-order statistics of the turbulence, which act as a proxy for intermittency, are strongly affected by the expansion effect but have weak dependence on the initial $\sigma_c$. Generally, the intermittency is more pronounced when the expansion effect is present. Finally, we find that in our simulations although the negative residual energy and intermittency grow simultaneously as the turbulence evolves, there is no obvious causal relation between them because they are generated on different scales.

Tuberculosis (TB) is a major global health threat, causing millions of deaths annually. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially in developing countries. Recently, computer-aided tuberculosis diagnosis (CTD) using deep learning has shown promise, but progress is hindered by limited training data. To address this, we establish a large-scale dataset, namely the Tuberculosis X-ray (TBX11K) dataset, which contains 11,200 chest X-ray (CXR) images with corresponding bounding box annotations for TB areas. This dataset enables the training of sophisticated detectors for high-quality CTD. Furthermore, we propose a strong baseline, SymFormer, for simultaneous CXR image classification and TB infection area detection. SymFormer incorporates Symmetric Search Attention (SymAttention) to tackle the bilateral symmetry property of CXR images for learning discriminative features. Since CXR images may not strictly adhere to the bilateral symmetry property, we also propose Symmetric Positional Encoding (SPE) to facilitate SymAttention through feature recalibration. To promote future research on CTD, we build a benchmark by introducing evaluation metrics, evaluating baseline models reformed from existing detectors, and running an online challenge. Experiments show that SymFormer achieves state-of-the-art performance on the TBX11K dataset. The data, code, and models will be released at https://github.com/yun-liu/Tuberculosis., Comment: Accepted by IEEE TPAMI; 17 pages

This Letter reports the first observational estimate of the heating rate in the slowly expanding solar corona. The analysis exploits the simultaneous remote and local observations of the same coronal plasma volume with the Solar Orbiter/Metis and the Parker Solar Probe instruments, respectively, and relies on the basic solar wind magnetohydrodynamic equations. As expected, energy losses are a minor fraction of the solar wind energy flux, since most of the energy dissipation that feeds the heating and acceleration of the coronal flow occurs much closer to the Sun than the heights probed in the present study, which range from 6.3 to 13.3 solar radii. The energy deposited to the supersonic wind is then used to explain the observed slight residual wind acceleration and to maintain the plasma in a non-adiabatic state. As derived in the Wentzel-Kramers-Brillouin limit, the present energy transfer rate estimates provide a lower limit, which can be very useful in refining the turbulence-based modeling of coronal heating and subsequent solar wind acceleration.

Determining the mechanism responsible for the plasma heating and particle acceleration is a fundamental problem in the study of the heliosphere. Due to efficient wave-particle interactions of ion-scale waves with charged particles, these waves are widely believed to be a major contributor to ion energization, and their contribution considerably depends on the wave occurrence rate. By analyzing the radial distribution of quasi-monochromatic ion-scale waves observed by the Parker Solar Probe, this work shows that the wave occurrence rate is significantly enhanced in the near-Sun solar wind, specifically 21%$-$29% below 0.3 au, in comparison to 6%$-$14% beyond 0.3 au. The radial decrease of the wave occurrence rate is not only induced by the sampling effect of a single spacecraft detection, but also by the physics relating to the wave excitation, such as the enhanced ion beam instability in the near-Sun solar wind. This work also shows that the wave normal angle $\theta$, the absolute value of ellipticity $\epsilon$, the wave frequency $f$ normalized by the proton cyclotron frequency $f_{\mathrm{cp}}$, and the wave amplitude $\delta B$ normalized by the local background magnetic field $B_0$ slightly vary with the radial distance. The median values of $\theta$, $|\epsilon|$, $f$, and $\delta B$ are about $9^\circ$, $0.73$, $3f_{\mathrm{cp}}$, and $0.01B_0$, respectively. Furthermore, this study proposes that the wave mode nature of the observed left-handed and right-handed polarized waves corresponds to the Alfv\'en ion cyclotron mode wave and the fast-magnetosonic whistler mode wave, respectively., Comment: Accepted for publication by The Astrophysical Journal (ApJ)

The trace magnetic power spectrum in the solar wind is known to be characterized by a double power law at scales much larger than the proton gyro-radius, with flatter spectral exponents close to -1 found at the lower frequencies below an inertial range with indices closer to $[-1.5,-1.6]$. The origin of the $1/f$ range is still under debate. In this study, we selected 109 magnetically incompressible solar wind intervals ($\delta |\boldsymbol B|/|\boldsymbol B| \ll 1$) from Parker Solar Probe encounters 1 to 13 which display such double power laws, with the aim of understanding the statistics and radial evolution of the low frequency power spectral exponents from Alfv\'en point up to 0.3 AU. New observations from closer to the sun show that in the low frequency range solar wind turbulence can display spectra much shallower than $1/f$, evolving asymptotically to $1/f$ as advection time increases, indicating a dynamic origin for the $1/f$ range formation. We discuss the implications of this result on the Matteini et al. (2018) conjecture for the $1/f$ origin as well as example spectra displaying a triple power law consistent with the model proposed by Chandran et al. (2018), supporting the dynamic role of parametric decay in the young solar wind. Our results provide new constraints on the origin of the $1/f$ spectrum and further show the possibility of the coexistence of multiple formation mechanisms., Comment: Accepted by ApJL

We investigate dispersive and kinetic effects on the evolution of a two-dimensional kinked Alfv\'en wave packet by comparing results from MHD, Hall-MHD and hybrid simulations of a low-$\beta$ plasma. We find that the Hall term determines the overall evolution of the wave packet over a characteristic time $\tau^*=\tau_a\ell/d_i$ in both fluid and hybrid models. Dispersion of the wave packet leads to the conversion of the wave energy into internal plasma energy. When kinetic protons are considered, the proton internal energy increase has contributions from both plasma compressions and phase space mixing. The latter occurs in the direction parallel to the guiding mean magnetic field, due to protons resonating at the Alfv\'en speed with a compressible mode forced by the wave packet. Implications of our results for switchbacks observations and solar wind energetics are discussed., Comment: In press at PoP for the Special Collection on the Plasma Physics of the Sun in Honor of Eugene Parker

Designing an efficient yet deployment-friendly 3D backbone to handle sparse point clouds is a fundamental problem in 3D perception. Compared with the customized sparse convolution, the attention mechanism in Transformers is more appropriate for flexibly modeling long-range relationships and is easier to be deployed in real-world applications. However, due to the sparse characteristics of point clouds, it is non-trivial to apply a standard transformer on sparse points. In this paper, we present Dynamic Sparse Voxel Transformer (DSVT), a single-stride window-based voxel Transformer backbone for outdoor 3D perception. In order to efficiently process sparse points in parallel, we propose Dynamic Sparse Window Attention, which partitions a series of local regions in each window according to its sparsity and then computes the features of all regions in a fully parallel manner. To allow the cross-set connection, we design a rotated set partitioning strategy that alternates between two partitioning configurations in consecutive self-attention layers. To support effective downsampling and better encode geometric information, we also propose an attention-style 3D pooling module on sparse points, which is powerful and deployment-friendly without utilizing any customized CUDA operations. Our model achieves state-of-the-art performance with a broad range of 3D perception tasks. More importantly, DSVT can be easily deployed by TensorRT with real-time inference speed (27Hz). Code will be available at \url{https://github.com/Haiyang-W/DSVT}., Comment: Accepted by CVPR2023

We analyze a merged Parker Solar Probe ($PSP$) and Solar Orbiter ($SO$) dataset covering heliocentric distances $13 \ R_{\odot} \lesssim R \lesssim 220$ $R_{\odot}$ to investigate the radial evolution of power and spectral-index anisotropy in the wavevector space of solar wind turbulence. Our results show that anisotropic signatures of turbulence display a distinct radial evolution when fast, $V_{sw} \geq ~ 400 ~km ~s^{-1}$, and slow, $V_{sw} \leq ~ 400 ~km ~s^{-1}$, wind streams are considered. The anisotropic properties of slow wind in Earth orbit are consistent with a ``critically balanced'' cascade, but both spectral-index anisotropy and power anisotropy diminish with decreasing heliographic distance. Fast streams are observed to roughly retain their near-Sun anisotropic properties, with the observed spectral index and power anisotropies being more consistent with a ``dynamically aligned'' type of cascade, though the lack of extended fast-wind intervals makes it difficult to accurately measure the anisotropic scaling. A high-resolution analysis during the first perihelion of PSP confirms the presence of two sub-ranges within the inertial range, which may be associated with the transition from weak to strong turbulence. The transition occurs at $\kappa d_{i} \approx 6 \times 10^{-2}$, and signifies a shift from -5/3 to -2 and -3/2 to -1.57 scaling in parallel and perpendicular spectra, respectively. Our results provide strong observational constraints for anisotropic theories of MHD turbulence in the solar wind., Comment: Accepted to APJ

The heating and acceleration of the solar wind remains one of the fundamental unsolved problems in heliophysics. It is usually observed that the proton temperature $T_i$ is highly correlated with the solar wind speed $V_{SW}$, while the electron temperature $T_e$ shows anti-correlation or no clear correlation with the solar wind speed. Here we inspect both Parker Solar Probe (PSP) and WIND data and compare the observations with simulation results. PSP observations below 30 solar radii clearly show a positive correlation between proton temperature and wind speed and a negative correlation between electron temperature and wind speed. One year (2019) of WIND data confirm that proton temperature is positively correlated with solar wind speed, but the electron temperature increases with the solar wind speed for slow wind while it decreases with the solar wind speed for fast wind. Using a one-dimensional Alfv\'en-wave-driven solar wind model with different proton and electron temperatures, we for the first time find that if most of the dissipated Alfv\'en wave energy heats the ions instead of electrons, a positive $T_i-V_{SW}$ correlation and a negative $T_e-V_{SW}$ correlation arise naturally. If the electrons gain a small but finite portion of the dissipated wave energy, the $T_e-V_{SW}$ correlation evolves with radial distance to the Sun such that the negative correlation gradually turns positive. The model results show that Alfv\'en waves are one of the possible explanations of the observed evolution of proton and electron temperatures in the solar wind.