Miracle-dz/UniFAR-FTUs huggingface dataset:
A multigrid-based multiscale numerical scheme for shallow water flows at low froude number

A new multiscale semi-implicit scheme for the computation of low Froude number shallow water flows is presented. Motivated by the needs of atmospheric flow applications, it aims to minimize dispersion and amplitude errors in the computation of long-wave gravity waves. While it correctly balances “slaved” dynamics of shortwave solution components induced by slow forcing, the method eliminates freely propagating compressible short-wave modes, which are under-resolved in time. This is achieved through a multilevel approach borrowing ideas from multigrid schemes for elliptic equations. The scheme is second-order accurate and admits time steps depending essentially on the flow velocity. It incorporates a predictor step using a Godunov-type method for hyperbolic conservation laws and two elliptic corrections for the computation of the numerical fluxes. First, a multilevel method is derived for the one-dimensional linearized shallow water equations. Scale-wise decomposition of the data enables a scale-dependent blending of time integrators with different principal features. To guide the selection of these integrators, the discrete-dispersion relations of some standard second-order schemes are analyzed, and their response to high-wave-number low-frequency source terms is discussed. In particular, the implicit trapezoidal rule and two backward differentiation formula (BDF) schemes are considered. The resulting method essentially consists of the solution of a Helmholtz problem on the original fine grid, where the differencing operator and the right hand side incorporate the multiscale information of the discretization. The performance of the new multilevel method is illustrated on a test case with “multiscale” initial data and a problem with a slowly varying high-wavenumber source term. The scheme for simulating fully nonlinear shallow water flows is a generalization of a projection method for the zero Froude number equations. Therefore, the method described in Vater and Klein (Numer. Math. 113, pp. 123–161, 2009) is extended to account for time dependent bottom topography. Numerical simulations show that the method is well-balanced in that it can reproduce the steady state of a lake at rest with non-trivial bottom topography. Furthermore, the results of two other test cases verify the correct representation of time dependent bottom topography. Finally, the multiscale semi-implicit method for the nonlinear shallow water equations at low Froude numbers is derived. This incorporates two different extensions of the aforementioned projection method by incorporating the local time derivatives
Interhemispheric differences of pyramidal cells in the primary motor cortices of schizophrenia patients investigated postmortem

Abstract Motor disturbances are observed in schizophrenia patients, but the neuroanatomical background is unknown. Our aim was to investigate the pyramidal cells of the primary motor cortex (BA 4) in both hemispheres of postmortem control and schizophrenia subjects—8 subjects in each group—with 2.5–5.5 h postmortem interval. The density and size of the Sternberger monoclonal incorporated antibody 32 (SMI32)-immunostained pyramidal cells in layer 3 and 5 showed no change; however, the proportion of larger pyramidal cells is decreased in layer 5. Giant pyramidal neurons (Betz cells) were investigated distinctively with SMI32- and parvalbumin (PV) immunostainings. In the right hemisphere of schizophrenia subjects, the density of Betz cells was decreased and their PV-immunopositive perisomatic input showed impairment. Part of the Betz cells contained PV in both groups, but the proportion of PV-positive cells has declined with age. The rat model of antipsychotic treatment with haloperidol and olanzapine showed no differences in size and density of SMI32-immunopositive pyramidal cells. Our results suggest that motor impairment of schizophrenia patients may have a morphological basis involving the Betz cells in the right hemisphere. These alterations can have neurodevelopmental and neurodegenerative explanations, but antipsychotic treatment does not explain them.
3D-printed gelatin/sodium alginate/58S bioactive glass scaffolds promote osteogenesis in vitro and in vivo

Three-dimensional (3D)-printed scaffolds are a new strategy to fabricate biomaterials for treating bone defects. Here, using a 3D-printing technique, we fabricated scaffolds consisting of gelatin (Gel), sodium alginate (SA), and 58S bioactive glass (58S BG). To evaluate mechanical properties and biocompatibility of Gel/SA/58S BG scaffolds, the degradation test, compressive strength test, and cytotoxicity test were performed. The effect of the scaffolds on cell proliferation in vitro was determined by 4′,6-diamidino-2-phenylindole (DAPI) staining. To evaluate osteoinductive properties, rBMSCs were cultured on the scaffolds for 7, 14, and 21 days and the expression of osteogenesis-related genes was analyzed using qRT-PCR. To examine the bone healing properties of Gel/SA/58S BG scaffolds in vivo, we used a rat mandibular critical-size defect bone model. The scaffolds were implanted into the defect area of rat mandible and bone regeneration and new tissue formation were assessed using microcomputed tomography (microCT) and hematoxylin and eosin (H&E) staining. The results showed that Gel/SA/58S BG scaffolds had appropriate mechanical strength as a filling material for bone defects. Furthermore, the scaffolds could be compressed within certain limits and then could recover their shape. The extract of the Gel/SA/58S BG scaffold showed no cytotoxicity. In vitro, the expression levels of Bmp2, Runx2, and OCN were increased in rBMSCs cultured on the scaffolds. In vivo, microCT and H&E staining demonstrated that scaffolds induced the formation of new bone at the mandibular defect area. These results indicated that Gel/SA/58S BG scaffolds have excellent mechanical characteristics, biocompatibility, and osteoinductive properties, suggesting that it could be a promising biomaterial for the repair of bone defects.
A Bayesian Analysis for Change Point Problems

A sequence of observations undergoes sudden changes at unknown times. We model the process by supposing that there is an underlying sequence of parameters partitioned into contiguous blocks of equal parameter values; the beginning of each block is said to be a change point. Observations are then assumed to be independent in different blocks given the sequence of parameters. In a Bayesian analysis it is necessary to give probability distributions to both the change points and the parameters. We use product partition models (Barry and Hartigan 1992), which assume that the probability of any partition is proportional to a product of prior cohesions, one for each block in the partition, and that given the blocks the parameters in different blocks have independent prior distributions. Given the observations a new product partition model holds, with posterior cohesions for the blocks and new independent block posterior distributions for parameters. The product model thus provides a convenient machinery for allo.
Flufenamic Acid, a Promising Agent for the Sensitization of Colistin-Resistant Gram-Negative Bacteria to Colistin

Colistin is a last-line antibiotic used for the treatment of MDR Gram-negative bacterial infections. However, increasing resistance to it has been observed during clinical treatment. ABSTRACT The continuous development of multidrug-resistant (MDR) Gram-negative bacteria poses a serious risk to public health on a worldwide scale. Colistin is used as the last-line antibiotic for the treatment of MDR pathogens, and colistin-resistant (COL-R) bacterial emergence thus has the potential to have a severe adverse impact on patient outcomes. In this study, synergistic activity was observed when colistin and flufenamic acid (FFA) were combined and used for the in vitro treatment of clinical COL-R Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, and Acinetobacter baumannii strains, as shown by checkerboard and time-kill assays. Crystal violet staining and scanning electron microscopy revealed the synergistic action of colistin-FFA against biofilms. When used to treat murine RAW264.7 macrophages, this combination did not induce any adverse toxicity. Strikingly, the survival rates of bacterially infected Galleria mellonella larvae were improved by such combination treatment, which was also sufficient to reduce the measured bacterial loads in a murine thigh infection model. Mechanistic propidium iodide (PI) staining analysis further demonstrated the ability of these agents to alter bacterial permeability in a manner that enhanced the efficacy of colistin treatment. Together, these data thus demonstrate that colistin and FFA can be synergistically combined to combat the spread of COL-R Gram-negative bacteria, providing a promising therapeutic tool with the potential to protect against COL-R bacterial infections and improve patient outcomes. IMPORTANCE Colistin is a last-line antibiotic used for the treatment of MDR Gram-negative bacterial infections. However, increasing resistance to it has been observed during clinical treatment. In this work, we assessed the efficacy of the combination of colistin and FFA for the treatment of COL-R bacterial isolates, demonstrating that the combined treatment has effective antibacterial and antibiofilm activities. Due to its low cytotoxicity and good therapeutic effects in vitro, the colistin-FFA combination may be a potential candidate for research into a resistance-modifying agent to combat infections caused by COL-R Gram-negative bacteria.
Quasi-One-Dimensional Nonequilibrium Flow of Partially Ionized Argon with Different Species Temperatures

Three-fluid nonequilibrium equations governing the flows of a partially ionized argon plasma are formulated. The equations are applied to the ionization-recombination nonequilibrium flow behind a strong normal shock wave, and to that in a convergent-divergent nozzle. The main consideration is made on the equipartition of the atom and ion thermal energies. For the flow behind a shock wave T a = T i is confirmed for a wide range of the upstream pressure. For the flow in a nozzle, T a = T i is usually confirmed. However, an example is shown in which T i is in slight excess of T a in the far downstream region from the throat.
Pulsations and Radial Velocity Variations in Pulsating Ap Stars. I. Analysis of γ Equulei

We present precise radial velocity measurements of the pulsational motions on the rapidly oscillating Ap star γ Equulei taken with an iodine absorption cell. These measurements indicate that the pulsational amplitude is higher in those spectral regions dominated by weak lines. A more detailed analysis of the individual lines of Cr, Fe, and Ti indeed shows that the pulsational amplitudes for strong lines are less than 100 m s-1, whereas for weak lines they are above 200 m s-1 and in one case as high as 1000 m s-1. Also, the mean pulsation amplitude derived from chromium and titanium lines is higher than that derived from iron lines. Time series measurements of radial velocity shifts of single lines are a powerful tool for measuring ion distributions on Ap stars, not only as a function of surface location but of depth as well. This kind of measurement will also be useful in checking predictions of diffusion theory since we can now compare how different ions can move up and down along and across magnetic field lines.
Tracking the validity of the quasi-static and sub-horizon approximations in modified gravity

Within the framework of modified gravity, the quasi-static and sub-horizon approximations are widely used in analyses aiming to identify departures from the concordance model at late-times. In general, it is assumed that time derivatives are subdominant with respect to spatial derivatives given that the relevant physical modes are those well inside the Hubble radius. In practice, the perturbation equations under these approximations are reduced to a tractable algebraic system in terms of the gravitational potentials and the perturbations of involved matter fields. Here, in the framework of f(R) theories, we revisit standard results when these approximations are invoked using a new parameterization scheme that allows us to track the relevance of each time-derivative term in the perturbation equations. This new approach unveils correction terms which are neglected in the standard procedure. We assess the relevance of these differences by comparing results from both approaches against full numerical solutions for two well-known toy-models: the designer f(R) model and the Hu-Sawicki model. We find that: i) the sub-horizon approximation can be safely applied to linear perturbation equations for scales 0.06 h/Mpc ≲ k ≲ 0.2 h/Mpc, ii) in this “safety region”, the quasi-static approximation provides a very accurate description of the late-time cosmological dynamics even when dark energy significantly contribute to the cosmic budget, and iii) our new methodology performs better than the standard procedure, even for several orders of magnitude in some cases. Although, the impact of this major improvement on the linear observables is minimal for the studied cases, this does not represent an invalidation for our approach. Instead, our findings indicate that the perturbation expressions derived under these approximations in more general modified gravity theories, such as Horndeski, should be also revisited.
Multi-view knowledge distillation transformer for human action recognition

Recently, Transformer-based methods have been utilized to improve the performance of human action recognition. However, most of these studies assume that multi-view data is complete, which may not always be the case in real-world scenarios. Therefore, this paper presents a novel Multi-view Knowledge Distillation Transformer (MKDT) framework that consists of a teacher network and a student network. This framework aims to handle incomplete human action problems in real-world applications. Specifically, the multi-view knowledge distillation transformer uses a hierarchical vision transformer with shifted windows to capture more spatial-temporal information. Experimental results demonstrate that our framework outperforms the CNN-based method on three public datasets.
A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation

Symmetric bi-manual manipulation is essential for various on-orbit operations due to its potent load capacity. As a result, there exists an emerging research interest in the problem of achieving high operation accuracy while enhancing adaptability and compliance. However, previous works relied on an inefficient algorithm framework that separates motion planning from compliant control. Additionally, the compliant controller lacks robustness due to manually adjusted parameters. This paper proposes a novel Learning-based Adaptive Compliance algorithm (LAC) that improves the efficiency and robustness of symmetric bi-manual manipulation. Specifically, first, the algorithm framework combines desired trajectory generation with impedance-parameter adjustment to improve efficiency and robustness. Second, we introduce a centralized Actor-Critic framework with LSTM networks, enhancing the synchronization of bi-manual manipulation. LSTM networks pre-process the force states obtained by the agents, further ameliorating the performance of compliance operations. When evaluated in the dual-arm cooperative handling and peg-in-hole assembly experiments, our method outperforms baseline algorithms in terms of optimality and robustness.
Conditional Score-Based Reconstructions for Multi-contrast MRI

Magnetic resonance imaging (MRI) exam protocols consist of multiple contrast-weighted images of the same anatomy to emphasize diﬀerent tissue properties. Due to the long acquisition times required to collect fully sampled k-space measurements, it is common to only collect a fraction of k-space for some, or all, of the scans and subsequently solve an inverse problem for each contrast to recover the desired image from sub-sampled measurements. Recently, there has been a push to further accelerate MRI exams using data-driven priors, and generative models in particular, to regularize the ill-posed inverse problem of image reconstruction. These methods have shown promising improvements over classical methods. However, many of the approaches neglect the multi-contrast nature of clinical MRI exams and treat each scan as an independent reconstruction. In this work we show that by learning a joint Bayesian prior over multi-contrast data with a score-based generative model we are able to leverage the underlying structure between multi-contrast images and thus improve image reconstruction ﬁdelity over generative models that only reconstruct images of a single contrast.
Toward Human-Like Social Robot Navigation: A Large-Scale, Multi-Modal, Social Human Navigation Dataset

Humans are well-adept at navigating public spaces shared with others, where current autonomous mobile robots still struggle: while safely and efficiently reaching their goals, humans communicate their intentions and conform to unwritten social norms on a daily basis; conversely, robots become clumsy in those daily social scenarios, getting stuck in dense crowds, surprising nearby pedestrians, or even causing collisions. While recent research on robot learning has shown promises in data-driven social robot navigation, good-quality training data is still difficult to acquire through either trial and error or expert demonstrations. In this work, we propose to utilize the body of rich, widely available, social human navigation data in many natural human-inhabited public spaces for robots to learn similar, human-like, socially compliant navigation behaviors. To be specific, we design an open-source egocentric data collection sensor suite wearable by walking humans to provide multimodal robot perception data; we collect a large-scale (~100 km, 20 hours, 300 trials, 13 humans) dataset in a variety of public spaces which contain numerous natural social navigation interactions; we analyze our dataset, demonstrate its usability, and point out future research directions and use cases.11Website: https://cs.gmu. edu/-xiao/Research/MuSoHu/
A Spatial-Constraint Model for Manipulating Static Visualizations

We propose a spatial-constraint approach for modeling spatial-based interactions and enabling interactive visualizations, which involves the manipulation of visualizations through selection, filtering, navigation, arrangement, and aggregation. We proposes a system that activates static visualizations by adding intelligent interactions, which is achieved by associating static visual objects with forces. Our force-directed technique facilitates smooth animated transitions of the visualizations between different interaction states. We showcase the effectiveness of our technique through usage scenarios that involve activating visualizations in real-world settings.
Vibrarfon of Cracked Rectangular Plates

The present paper deals with vibration problems of cracked rectangular plates. The two opposite edges perpendicular to the line of the crack are assumed to be simply supported in these plates. Therefore, the Levy-Nadai's form of solution is used. Fulfilment of mixed boundary conditions on the line of the crack are formulated by means of the weighted residual methods. Numerical calculations are carried out for three examples; In the first example two opposite edges parallel to the crack are assumed to be simply supported, in the second, free, and in the last example clamped. Results of calculation are compared with the works of other investigators.
Statistical Limit Theorems in Distributionally Robust Optimization

The goal of this paper is to develop a methodology for the systematic analysis of asymptotic statistical properties of data-driven DRO formulations based on their corresponding non-DRO counterparts. We illustrate our approach in various settings, including both phi-divergence and Wasserstein uncertainty sets. Different types of asymptotic behaviors are obtained depending on the rate at which the uncertainty radius decreases to zero as a function of the sample size and the geometry of the uncertainty sets.
Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier

More than a thousand 8″ silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Calorimeter for the CMS experiment at CERN. A deep learning-based algorithm that pre-selects potentially anomalous images of the sensor surface in real time was developed to automate the visual inspection. The anomaly detection is done by an ensemble of independent deep convolutional neural networks: an autoencoder and a classifier. The algorithm was deployed and has been continuously running in production, and data gathered were used to evaluate its performance. The pre-selection reduces the number of images requiring human inspection by 85%, with recall of 97%, and saves 15 person-hours per a batch of a hundred sensors. Data gathered in production can be used for continuous learning to improve the accuracy incrementally.
Design and Control of a Humanoid Equipped with Flight Unit and Wheels for Multimodal Locomotion

—Humanoids are versatile robotic platforms because of their limbs with multiple degrees of freedom. Although humanoids can walk like humans, the speed is relatively slow, and they cannot run over large barriers. To address these problems, we aim to achieve rapid terrestrial locomotion ability and simultaneously expand the domain of locomotion to the air by utilizing thrust for propulsion. In this paper, we ﬁrst describe an optimized construction method of a humanoid robot equipped with wheels and a ﬂight unit to achieve these abilities. Then, we describe the integrated control framework of the proposed ﬂying humanoid for each mode of locomotion: aerial locomotion, leg locomotion, and wheel locomotion. Finally, we achieved multimodal locomotion and aerial manipulation experiments using the robot platform proposed in this work. To the best of our knowledge, it is the ﬁrst time to achieve three different types of locomotion, including ﬂight, by a single humanoid
Guiding AI-Generated Digital Content with Wireless Perception

Recent advances in artificial intelligence (AI), coupled with a surge in training data, have led to the widespread use of AI for digital content generation, with ChatGPT serving as a representative example. Despite the increased efficiency and diversity, the inherent instability of AI models poses a persistent challenge in guiding these models to produce the desired content for users. In this paper, we introduce an integration of wireless perception (WP) with AI-generated content (AIGC) and propose a unified WP-AIGC framework to improve the quality of digital content production. The framework employs a novel multi-scale perception technology to read user's posture, which is difficult to describe accurately in words, and transmits it to the AIGC model as skeleton images. Based on these images and user's service requirements, the AIGC model generates corresponding digital content. Since the production process imposes the user's posture as a constraint on the AIGC model, it makes the generated content more aligned with the user's requirements. Additionally, WP-AIGC can also accept user's feedback, allowing adjustment of computing resources at edge server to improve service quality. Experiments results verify the effectiveness of the WP-AIGC framework, highlighting its potential as a novel approach for guiding AI models in the accurate generation of digital content.