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The distributed recursion (DR) algorithm is an effective method for solving the pooling problem that arises in many applications. It is based on the well-known P-formulation of the pooling problem, which involves the flow and quality variables; and it can be seen as a variant of the successive linear programming (SLP) algorithm, where the linear programming (LP) approximation problem can be transformed from the LP approximation problem derived by using the first-order Taylor series expansion technique. In this paper, we first propose a new nonlinear programming (NLP) formulation for the pooling problem involving only the flow variables, and show that the DR algorithm can be seen as a direct application of the SLP algorithm to the newly proposed formulation. With this new useful theoretical insight, we then develop a new variant of DR algorithm, called penalty DR (PDR) algorithm, based on the proposed formulation. The proposed PDR algorithm is a penalty algorithm where violations of the (linearized) nonlinear constraints are penalized in the objective function of the LP approximation problem with the penalty terms increasing when the constraint violations tend to be large. Compared with the LP approximation problem in the classic DR algorithm, the LP approximation problem in the proposed PDR algorithm can return a solution with a better objective value, which makes it more suitable for finding high-quality solutions for the pooling problem. Numerical experiments on benchmark and randomly constructed instances show that the proposed PDR algorithm is more effective than the classic SLP and DR algorithms in terms of finding a better solution for the pooling problem., Comment: 22 pages, 2 figures, submitted for possible publication

In this work, we propose an automatic method for the analysis of experiments that incorporates hierarchical relationships between the experimental variables. We use a modified version of nonnegative garrote method for variable selection which can incorporate hierarchical relationships. The nonnegative garrote method requires a good initial estimate of the regression parameters for it to work well. To obtain the initial estimate, we use generalized ridge regression with the ridge parameters estimated from a Gaussian process prior placed on the underlying input-output relationship. The proposed method, called HiGarrote, is fast, easy to use, and requires no manual tuning. Analysis of several real experiments are presented to demonstrate its benefits over the existing methods.

Recovering a spectrum of diverse policies from a set of expert trajectories is an important research topic in imitation learning. After determining a latent style for a trajectory, previous diverse policies recovering methods usually employ a vanilla behavioral cloning learning objective conditioned on the latent style, treating each state-action pair in the trajectory with equal importance. Based on an observation that in many scenarios, behavioral styles are often highly relevant with only a subset of state-action pairs, this paper presents a new principled method in diverse polices recovery. In particular, after inferring or assigning a latent style for a trajectory, we enhance the vanilla behavioral cloning by incorporating a weighting mechanism based on pointwise mutual information. This additional weighting reflects the significance of each state-action pair's contribution to learning the style, thus allowing our method to focus on state-action pairs most representative of that style. We provide theoretical justifications for our new objective, and extensive empirical evaluations confirm the effectiveness of our method in recovering diverse policies from expert data., Comment: 18 pages, 6 figures

Federated Domain Adaptation (FDA) is a Federated Learning (FL) scenario where models are trained across multiple clients with unique data domains but a shared category space, without transmitting private data. The primary challenge in FDA is data heterogeneity, which causes significant divergences in gradient updates when using conventional averaging-based aggregation methods, reducing the efficacy of the global model. This further undermines both in-domain and out-of-domain performance (within the same federated system but outside the local client). To address this, we propose a novel framework called \textbf{M}ulti-domain \textbf{P}rototype-based \textbf{F}ederated Fine-\textbf{T}uning (MPFT). MPFT fine-tunes a pre-trained model using multi-domain prototypes, i.e., pretrained representations enriched with domain-specific information from category-specific local data. This enables supervised learning on the server to derive a globally optimized adapter that is subsequently distributed to local clients, without the intrusion of data privacy. Empirical results show that MPFT significantly improves both in-domain and out-of-domain accuracy over conventional methods, enhancing knowledge preservation and adaptation in FDA. Notably, MPFT achieves convergence within a single communication round, greatly reducing computation and communication costs. To ensure privacy, MPFT applies differential privacy to protect the prototypes. Additionally, we develop a prototype-based feature space hijacking attack to evaluate robustness, confirming that raw data samples remain unrecoverable even after extensive training epochs. The complete implementation of MPFL is available at \url{https://anonymous.4open.science/r/DomainFL/}.

We present a multi-frequency polarimetric study for the quasar 1604+159. The source was observed at the $L$ band with the American Very Long Baseline Array (VLBA) and the $L$, $X$, and $U$ bands with the Very Large Array (VLA). These observations provide different resolutions from mas to arcsec, enabling us to probe the morphology and magnetic field from tens of parsec to hundreds of kilo-parsec scale. We detect a symmetrical Fanaroff-Riley-Class-I-like structure. The source has several lobes and bulges, forming a cocoon shape. The polarization is normal to the edges of the structure with high fractional polarization up to $\sim 60\%$. Two hotspots are observed at the eastern and western sides of the source, located symmetrically relative to the core. The flux density ratio ($>1.5$) between the two hotspots suggests the Doppler beaming effect exists at a large scale. The polarized emission in the hotspots also shows a symmetrical structure with an oblique direction from the jet direction. In general, the jet propagates in a collimating structure with several bends. Polarization is also detected perpendicular to the local jet from $\sim$100 mas to $\sim$ 1 arcsec. The jet shows strong polarized intensity and high fractional polarization at the bending edges. We discuss the possible origins of the observed structure and magnetic field., Comment: 17 pages, accepted for publication in ApJ

Feature noise and label noise are ubiquitous in practical scenarios, which pose great challenges for training a robust machine learning model. Most previous approaches usually deal with only a single problem of either feature noise or label noise. However, in real-world applications, hybrid noise, which contains both feature noise and label noise, is very common due to the unreliable data collection and annotation processes. Although some results have been achieved by a few representation learning based attempts, this issue is still far from being addressed with promising performance and guaranteed theoretical analyses. To address the challenge, we propose a novel unified learning framework called "Feature and Label Recovery" (FLR) to combat the hybrid noise from the perspective of data recovery, where we concurrently reconstruct both the feature matrix and the label matrix of input data. Specifically, the clean feature matrix is discovered by the low-rank approximation, and the ground-truth label matrix is embedded based on the recovered features with a nuclear norm regularization. Meanwhile, the feature noise and label noise are characterized by their respective adaptive matrix norms to satisfy the corresponding maximum likelihood. As this framework leads to a non-convex optimization problem, we develop the non-convex Alternating Direction Method of Multipliers (ADMM) with the convergence guarantee to solve our learning objective. We also provide the theoretical analysis to show that the generalization error of FLR can be upper-bounded in the presence of hybrid noise. Experimental results on several typical benchmark datasets clearly demonstrate the superiority of our proposed method over the state-of-the-art robust learning approaches for various noises.

This paper presents the design, calibration, and survey strategy of the Fast Radio Burst (FRB) digital backend and its real-time data processing pipeline employed in the Tianlai Cylinder Pathfinder array. The array, consisting of three parallel cylindrical reflectors and equipped with 96 dual-polarization feeds, is a radio interferometer array designed for conducting drift scans of the northern celestial semi-sphere. The FRB digital backend enables the formation of 96 digital beams, effectively covering an area of approximately 40 square degrees with 3 dB beam. Our pipeline demonstrates the capability to make automatic search of FRBs, detecting at quasi-real-time and classify FRB candidates automatically. The current FRB searching pipeline has an overall recall rate of 88\%. During the commissioning phase, we successfully detected signals emitted by four well-known pulsars: PSR B0329+54, B2021+51, B0823+26, and B2020+28. We report the first discovery of an FRB by our array, designated as FRB 20220414A. We also investigate the optimal arrangement for the digitally formed beams to achieve maximum detection rate by numerical simulation., Comment: 27 pages, 21 figures, 7 tables, RAA accepted

In the paper, we consider the competitive facility location problem with limited choice rule (CFLPLCR), which attempts to open a subset of facilities to maximize the net profit of a newcomer company, requiring customers to patronize only a limited number of opening facilities and an outside option. We propose an efficient branch-and-cut (B&C) approach for the CFLPLCR based on newly proposed mixed integer linear programming (MILP) formulations. Specifically, by establishing the submodularity of the probability function, we develop an MILP formulation for the CFLPLCR using the submodular inequalities. For the special case where each customer patronizes at most one open facility and the outside option, we show that the submodular inequalities can characterize the convex hull of the considered set and provide a compact MILP formulation. Moreover, for the general case, we strengthen the submodular inequalities by sequential lifting, resulting in a class of facet-defining inequalities. The proposed lifted submodular inequalities are shown to be stronger than the classic submodular inequalities, enabling to obtain another MILP formulation with a tighter linear programming (LP) relaxation. By extensive numerical experiments, we show that the proposed B&C approach outperforms the state-of-the-art generalized Benders decomposition approach by at least one order of magnitude. Furthermore, it enables to solve CFLPLCR instances with 10000 customers and 2000 facilities., Comment: 29 pages, 2 figures, submitted for possible publication

Federated Learning (FL) is a promising privacy-preserving machine learning paradigm that allows data owners to collaboratively train models while keeping their data localized. Despite its potential, FL faces challenges related to the trustworthiness of both clients and servers, especially in the presence of curious or malicious adversaries. In this paper, we introduce a novel framework named \underline{\textbf{F}}ederated \underline{\textbf{L}}earning with \underline{\textbf{U}}pdate \underline{\textbf{D}}igest (FLUD), which addresses the critical issues of privacy preservation and resistance to Byzantine attacks within distributed learning environments. FLUD utilizes an innovative approach, the $\mathsf{LinfSample}$ method, allowing clients to compute the $l_{\infty}$ norm across sliding windows of updates as an update digest. This digest enables the server to calculate a shared distance matrix, significantly reducing the overhead associated with Secure Multi-Party Computation (SMPC) by three orders of magnitude while effectively distinguishing between benign and malicious updates. Additionally, FLUD integrates a privacy-preserving, voting-based defense mechanism that employs optimized SMPC protocols to minimize communication rounds. Our comprehensive experiments demonstrate FLUD's effectiveness in countering Byzantine adversaries while incurring low communication and runtime overhead. FLUD offers a scalable framework for secure and reliable FL in distributed environments, facilitating its application in scenarios requiring robust data management and security., Comment: 14 pages

We revisit the hard QCD contributions to the pion gravitational form factors (GFFs), in terms of the twist-2,3 pion distribution amplitudes (DA), including novel semi-hard contributions from the instantons. The pion DAs are evaluated in the QCD instanton vacuum, and then properly evolved to higher resolution. The results are compared to our recent results from the QCD instanton vacuum, as well as Bethe-Salpeter calculations and recent lattice data. The interpolated hard and soft contributions to the pion D-form-factor, are used to derive the (gravitational) pressure and shear within the pion, with a clear delineation of their range., Comment: 14 pages, 6 figures

With a growing sample of fast radio bursts (FRBs), we investigate the energy budget of different power sources within the framework of magnetar starquake triggering mechanism. During a starquake, the energy can be released in any form through strain, magnetic, rotational, and gravitational energies. The strain energy can be converted from other three kinds of energy during starquakes. The following findings are revealed: 1. The crust can store free magnetic energy of $\sim10^{46}$ erg by existing toroidal fields, sustaining $10^6$ bursts with frequent starquakes occurring due to crustal instability. 2. The strain energy develops as a rigid object spins down, which can be released during a global starquake accompanied by a glitch. However, it takes a long time to accumulate enough strain energy via spindown. 3. The rotational energy of a magnetar with $P\lesssim0.1\rm\,s$ can match the energy and luminosity budget of FRBs. 4. The budget of the total gravitational energy is high, but the mechanism and efficiency of converting this energy to radiation deserve further exploration., Comment: 11 pages, 3 figures. Accepted. Some intriguing FAST's results are expected!

We analyze the photo-production of $\eta_{c,b}$ off a proton in the threshold region, in terms of C-odd gluonic correlations in the off-forward proton matrix element. Near threshold, the skewness is large leading to a production amplitude that is dominated by four C-odd twist-3 gluon GPDs. We use the QCD instanton vacuum to estimate these C-odd contributions in the proton. The results are used to estimate the differential cross sections for coherent photo-production of $\eta_{c,b}$ in the threshold region, at current electron facilities., Comment: 25 pages, 9 figures

We discuss a general framework for the evaluation of the gluonic form factors in light hadrons at low momentum transfer, in the QCD instanton vacuum. At medium resolution of the order of the inverse mean instanton size, the glue is mostly localized in single or pair of pseudoparticles, and globally constrained by the fluctuations of their topological charges. These pseudoparticles trap light quarks, giving rise to emerging multiflavor 't Hooft interactions. We explicitly evaluate the gluonic scalar, pseudoscalar, energy-momentum tensor (EMT), and the leading C-odd and C-even three gluons hadronic form factors, at next to leading order (NLO) in the instanton density, including molecular clusters of like and unlike instantons. We use the results for the EMT to address the contribution of the gluons in Ji$^\prime$s mass and spin sum rules, at low resolution. When evolved, our results for the mass and spin composition of the nucleon, are shown to be in good agreement with the recently reported lattice results at higher resolution., Comment: 67 pages, 16 figures

Resonant inelastic x-ray scattering (RIXS) experiment was performed at the Mn $L_3$ edge. A 10 $\%$ Mn-doped Sr$_{3}$Ru$_{2}$O$_{7}$ compound, where the Mn$^{3+}$ ions are in the 3$d^4$ state, were probed for $dd$ excitations. The dilute doping concentration allows one to treat the dopant Mn$^{3+}$ ions as effectively free in the host ruthenium compound. The local nature of $dd$ RIXS spectroscopy permits one to use a single-site model to simulate the experimental spectra. The simulated spectra reproduces the in-plane [100] experimental RIXS spectrum. We also predict the intensity for the in-plane [110] direction and the out-of-plane spin orientation configuration [001]. Based on our single-ion model we were able to fit the experimental data to obtain the crystal field parameters, the 10Dq value, and the intra-orbital spin-flip energy 2$\mathcal{J}$(or $3J_{H}$, where $J_{H}$ is the Hund's energy) of the Mn$^{3+}$ ion. Utilizing our computed RIXS quantum transition amplitudes between the various $d$ orbitals of the Mn$^{3+}$ ion, the expression for the Kramers-Heisenberg cross section, and a self-consistent fitting procedure we also identify the energy boundaries of the non-spin-flip and spin-flip $dd$ excitations present in the experimental data. From our fitting procedure we obtain $2\mathcal{J} (3J_{H})=2.06$ eV, a value which is in excellent agreement with that computed from the free ion Racah parameters. We also identified the charge transfer boundary. In addition to predicting the microscopic parameters, we find a quantum spin-flip transition in the non-cross ($\sigma_{in}-\sigma_{out}$, $\pi_{in}-\pi_{out}$) x-ray polarization channels of the $dd$ RIXS spectra. A similar transition, was previously predicted to occur in the $\pi-\pi$ channel of the magnon spectrum in the non-collinear non-coplanar Kagome compound composed of Cu$^{2+}$ 3d$^{9}$ ion., Comment: 12 pages, 7 figures, see PDF text for full abstract info

Stroke is a common disabling neurological condition that affects about one-quarter of the adult population over age 25; more than half of patients still have poor outcomes, such as permanent functional dependence or even death, after the onset of acute stroke. The aim of this study is to investigate the efficacy of diffusion-weighted MRI modalities combining with structured health profile on predicting the functional outcome to facilitate early intervention. A deep fusion learning network is proposed with two-stage training: the first stage focuses on cross-modality representation learning and the second stage on classification. Supervised contrastive learning is exploited to learn discriminative features that separate the two classes of patients from embeddings of individual modalities and from the fused multimodal embedding. The network takes as the input DWI and ADC images, and structured health profile data. The outcome is the prediction of the patient needing long-term care at 3 months after the onset of stroke. Trained and evaluated with a dataset of 3297 patients, our proposed fusion model achieves 0.87, 0.80 and 80.45% for AUC, F1-score and accuracy, respectively, outperforming existing models that consolidate both imaging and structured data in the medical domain. If trained with comprehensive clinical variables, including NIHSS and comorbidities, the gain from images on making accurate prediction is not considered substantial, but significant. However, diffusion-weighted MRI can replace NIHSS to achieve comparable level of accuracy combining with other readily available clinical variables for better generalization., Comment: 12 pages, 5 figures, 5 tables

We study how delays in NIH grant funding affect the career outcomes of research personnel. Using comprehensive earnings and tax records linked to university transaction data along with a difference-in-differences design, we find that a funding interruption of more than 30 days has a substantial effect on job placements for personnel who work in labs with a single NIH R01 research grant, including a 3 percentage point (40%) increase in the probability of not working in the US. Incorporating information from the full 2020 Decennial Census and data on publications, we find that about half of those induced into nonemployment appear to permanently leave the US and are 90% less likely to publish in a given year, with even larger impacts for trainees (postdocs and graduate students). Among personnel who continue to work in the US, we find that interrupted personnel earn 20% less than their continuously-funded peers, with the largest declines concentrated among trainees and other non-faculty personnel (such as staff and undergraduates). Overall, funding delays account for about 5% of US nonemployment in our data, indicating that they have a meaningful effect on the scientific labor force at the national level.

This paper presents an innovative framework that integrates Large Language Models (LLMs) with an external Thinker module to enhance the reasoning capabilities of LLM-based agents. Unlike augmenting LLMs with prompt engineering, Thinker directly harnesses knowledge from databases and employs various optimization techniques. The framework forms a reasoning hierarchy where LLMs handle intuitive System-1 tasks such as natural language processing, while the Thinker focuses on cognitive System-2 tasks that require complex logical analysis and domain-specific knowledge. Our framework is presented using a 9-player Werewolf game that demands dual-system reasoning. We introduce a communication protocol between LLMs and the Thinker, and train the Thinker using data from 18800 human sessions and reinforcement learning. Experiments demonstrate the framework's effectiveness in deductive reasoning, speech generation, and online game evaluation. Additionally, we fine-tune a 6B LLM to surpass GPT4 when integrated with the Thinker. This paper also contributes the largest dataset for social deduction games to date.

We have analyzed the total intensity, spectral index, linear polarization, and RM distributions at pc scale for the quasar 1604+159. The source was observed in 2002 and 2020 with the VLBA. Combining the MOJAVE results, we studied the evolution of the magnetic field. We detected a core-jet structure. The jet extends to a distance of ~25 mas. The jet shape varies slightly with time. We divided the source structure into the central region and the jet region. In the jet region, we find the polarized emission varies with time. The flatter spectral index values and EVPA direction indicate the possible existence of shocks, contributing to the variation. In the central region, the derived core shift index k_r values indicate that the core in 2002 is close to the equipartition case while deviating from it in 2020. The measured magnetic field strength in 2020 is two orders of magnitude lower than that in 2002. We detected transverse RM gradients, evidence of a helical magnetic field, in the core. At 15 GHz, in the place close to the jet base, the polarization direction changes significantly with time from perpendicular to parallel to the jet direction. The evolution of RM and magnetic field structure are potential reasons for the observed polarization change. The core |RM| in 2020 increases with frequency following a power law with index a = 2.7, suggesting a fast electron density fall-off in the medium with distance from the jet base., Comment: 24 pages, 14 figures, accepted for publication in ApJ

The design of research grants has been hypothesized to be a useful tool for influencing researchers and their science. We test this by conducting two thought experiments in a nationally representative survey of academic researchers. First, we offer participants a hypothetical grant with randomized attributes and ask how the grant would influence their research strategy. Longer grants increase researchers' willingness to take risks, but only among tenured professors, which suggests that job security and grant duration are complements. Both longer and larger grants reduce researchers' focus on speed, which suggests a significant amount of racing in science is in pursuit of resources. But along these and other strategic dimensions, the effect of grant design is small. Second, we identify researchers' indifference between the two grant design parameters and find they are very unwilling to trade off the amount of funding a grant provides in order to extend the duration of the grant $\unicode{x2014}$ money is much more valuable than time. Heterogeneity in this preference can be explained with a straightforward model of researchers' utility. Overall, our results suggest that the design of research grants is more relevant to selection effects on the composition of researchers pursuing funding, as opposed to having large treatment effects on the strategies of researchers that receive funding.

We introduce a new survey of professors at roughly 150 of the most research-intensive institutions of higher education in the US. We document seven new features of how research-active professors are compensated, how they spend their time, and how they perceive their research pursuits: (1) there is more inequality in earnings within fields than there is across fields; (2) institutions, ranks, tasks, and sources of earnings can account for roughly half of the total variation in earnings; (3) there is significant variation across fields in the correlations between earnings and different kinds of research output, but these account for a small amount of earnings variation; (4) measuring professors' productivity in terms of output-per-year versus output-per-research-hour can yield substantial differences; (5) professors' beliefs about the riskiness of their research are best predicted by their fundraising intensity, their risk-aversion in their personal lives, and the degree to which their research involves generating new hypotheses; (6) older and younger professors have very different research outputs and time allocations, but their intended audiences are quite similar; (7) personal risk-taking is highly predictive of professors' orientation towards applied, commercially-relevant research.

The growing Synthetic Aperture Radar (SAR) data has the potential to build a foundation model through Self-Supervised Learning (SSL) methods, which can achieve various SAR Automatic Target Recognition (ATR) tasks with pre-training in large-scale unlabeled data and fine-tuning in small labeled samples. SSL aims to construct supervision signals directly from the data, which minimizes the need for expensive expert annotation and maximizes the use of the expanding data pool for a foundational model. This study investigates an effective SSL method for SAR ATR, which can pave the way for a foundation model in SAR ATR. The primary obstacles faced in SSL for SAR ATR are the small targets in remote sensing and speckle noise in SAR images, corresponding to the SSL approach and signals. To overcome these challenges, we present a novel Joint-Embedding Predictive Architecture for SAR ATR (SAR-JEPA), which leverages local masked patches to predict the multi-scale SAR gradient representations of unseen context. The key aspect of SAR-JEPA is integrating SAR domain features to ensure high-quality self-supervised signals as target features. Besides, we employ local masks and multi-scale features to accommodate the various small targets in remote sensing. By fine-tuning and evaluating our framework on three target recognition datasets (vehicle, ship, and aircraft) with four other datasets as pre-training, we demonstrate its outperformance over other SSL methods and its effectiveness with increasing SAR data. This study showcases the potential of SSL for SAR target recognition across diverse targets, scenes, and sensors.Our codes and weights are available in \url{https://github.com/waterdisappear/SAR-JEPA., Comment: 15 pages, 7 figures

Fast radio bursts (FRBs) can present a variety of polarization properties, and some of them have narrow spectra. We study spectral properties from perspectives of intrinsic radiation mechanisms and absorption during the waves propagating in the magnetosphere. The intrinsic radiation mechanisms are considered by invoking quasi-periodic bunch distribution and perturbations on charged bunches moving on curved trajectories. The narrow-band emission likely reflects some quasi-periodic structure on the bulk of bunches, which may be due to quasi-periodically sparking in a ``gap'' or quasi-monochromatic Langmuir waves. A sharp spike would appear at the spectrum if the perturbations can induce a monochromatic oscillation of bunches, however, it is hard to create a narrow spectrum because the Lorentz factor has large fluctuations so that the spike disappears. Both the bunching mechanism and perturbations scenarios share the same polarization properties with a uniformly distributed bulk of bunches. We investigate absorption effects including Landau damping and curvature self-absorption in the magnetosphere, which are significant at low frequencies. Subluminous O-mode photons can not escape from the magnetosphere due to the Landau damping, leading to a height-dependent lower frequency cut-off. Spectra can be narrow when the frequency cut-off is close to the characteristic frequency of curvature radiation, while such conditions can only be met sometimes. The spectral index is 5/3 at low-frequency bands due to the curvature self-absorption but not as steep as the observations. The intrinsic radiation mechanisms are more likely to generate the observed narrow spectra of FRBs rather than the absorption effects., Comment: 19 pages, 11 figures, A&A accepted

Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately, current state-of-the-art video generation methods, primarily focusing on text-to-video generation, tend to produce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text instructions is insufficient and suboptimal for video generation. In this paper, we introduce PixelDance, a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions, setting a new standard for video generation., Comment: 12 pages

Most text-driven human motion generation methods employ sequential modeling approaches, e.g., transformer, to extract sentence-level text representations automatically and implicitly for human motion synthesis. However, these compact text representations may overemphasize the action names at the expense of other important properties and lack fine-grained details to guide the synthesis of subtly distinct motion. In this paper, we propose hierarchical semantic graphs for fine-grained control over motion generation. Specifically, we disentangle motion descriptions into hierarchical semantic graphs including three levels of motions, actions, and specifics. Such global-to-local structures facilitate a comprehensive understanding of motion description and fine-grained control of motion generation. Correspondingly, to leverage the coarse-to-fine topology of hierarchical semantic graphs, we decompose the text-to-motion diffusion process into three semantic levels, which correspond to capturing the overall motion, local actions, and action specifics. Extensive experiments on two benchmark human motion datasets, including HumanML3D and KIT, with superior performances, justify the efficacy of our method. More encouragingly, by modifying the edge weights of hierarchical semantic graphs, our method can continuously refine the generated motion, which may have a far-reaching impact on the community. Code and pre-training weights are available at https://github.com/jpthu17/GraphMotion., Comment: Accepted by NeurIPS 2023

Fast radio bursts (FRBs) are extragalactic radio transients with extremely high brightness temperature, which strongly suggests the presence of coherent emission mechanisms. In this study, we introduce a novel radiation mechanism for FRBs involving coherent Cherenkov radiation (ChR) emitted by bunched particles that may originate within the magnetosphere of a magnetar. We assume that some relativistic particles are emitted from the polar cap of a magnetar and move along magnetic field lines through a charge-separated magnetic plasma, emitting coherent ChR along their trajectory. The crucial condition for ChR to occur is that the refractive index of the plasma medium, denoted as $n_r$, must satisfy the condition $n_r^2 > 1$. We conduct comprehensive calculations to determine various characteristics of ChR, including its characteristic frequency, emission power, required parallel electric field, and coherence factor. Notably, our proposed bunched coherent ChR mechanism has the remarkable advantage of generating a narrower-band spectrum. Furthermore, a frequency downward drifting pattern, and $\sim100\%$ linearly polarized emission can be predicted within the framework of this emission mechanism., Comment: 15 pages, 5 figures, 1 table. Accepted for publication in ApJ