Miracle-dz/UniFAR-FTUs huggingface dataset:
Taking A Closer Look at Visual Relation: Unbiased Video Scene Graph Generation with Decoupled Label Learning

Current video-based scene graph generation (VidSGG) methods have been found to perform poorly on predicting predicates that are less represented due to the inherent biased distribution in the training data. In this paper, we take a closer look at the predicates and identify that most visual relations (e.g. sit_above) involve both actional pattern (sit) and spatial pattern (above), while the distribution bias is much less severe at the pattern level. Based on this insight, we propose a decoupled label learning (DLL) paradigm to address the intractable visual relation prediction from the pattern-level perspective. Specifically, DLL decouples the predicate labels and adopts separate classifiers to learn actional and spatial patterns respectively. The patterns are then combined and mapped back to the predicate. Moreover, we propose a knowledge-level label decoupling method to transfer non-target knowledge from head predicates to tail predicates within the same pattern to calibrate the distribution of tail classes. We validate the effectiveness of DLL on the commonly used VidSGG benchmark, i.e. VidVRD. Extensive experiments demonstrate that the DLL offers a remarkably simple but highly effective solution to the long-tailed problem, achieving the state-of-the-art VidSGG performance.
Position-Guided Point Cloud Panoptic Segmentation Transformer

DEtection TRansformer (DETR) started a trend that uses a group of learnable queries for unified visual perception. This work begins by applying this appealing paradigm to LiDAR-based point cloud segmentation and obtains a simple yet effective baseline. Although the naive adaptation obtains fair results, the instance segmentation performance is noticeably inferior to previous works. By diving into the details, we observe that instances in the sparse point clouds are relatively small to the whole scene and often have similar geometry but lack distinctive appearance for segmentation, which are rare in the image domain. Considering instances in 3D are more featured by their positional information, we emphasize their roles during the modeling and design a robust Mixed-parameterized Positional Embedding (MPE) to guide the segmentation process. It is embedded into backbone features and later guides the mask prediction and query update processes iteratively, leading to Position-Aware Segmentation (PA-Seg) and Masked Focal Attention (MFA). All these designs impel the queries to attend to specific regions and identify various instances. The method, named Position-guided Point cloud Panoptic segmentation transFormer (P3Former), outperforms previous state-of-the-art methods by 3.4% and 1.2% PQ on SemanticKITTI and nuScenes benchmark, respectively. The source code and models are available at https://github.com/SmartBot-PJLab/P3Former .
The least-squares analysis of the moments of the charge distribution in the mean-field models

The nth moment, $R^{(n)}_c$, of the charge distribution is composed of not only the m( ≤ n)th moments, $R^{(m)}_p$, of the point proton distribution, but also the m( ≤ (n − 2))th ones, $R^{(m)}_n$, of the point neutron distribution. The experimental value of $R^{(n)}_c(R^{(n)}_{c,{\rm exp}})$ observed through electromagnetic interaction makes it possible to investigate the point proton and neutron distributions together on the same basis. In order to estimate $R^{(m)}_\tau (\tau =p,n)$ from $R^{(n)}_{c,{\rm exp}}$, however, nuclear models are required. The structure of the least squares analysis(LSA) between $R^{(n)}_c$ and $R^{(m)}_\tau$ is investigated within the mean-field(MF) framework. The LSA reveals constraints inherent in the model-framework through the least squares lines(LSL) and determines the value of $R^{(m)}_\tau$ of $R^{(n)}_{c,{\rm exp}}$ uniquely as a result of the sum rule with respect to the coefficients of the LSL-equations. The n-dependence of the values of $R^{(m)}_\tau$ in the LSA is examined numerically by using the moments calculated up to n = 6 for 40Ca, 48Ca and 208Pb.
Effect of collaborative-filtering-based recommendation algorithms on opinion polarization.

A central role in shaping the experience of users online is played by recommendation algorithms. On the one hand they help retrieving content that best suits users taste, but on the other hand they may give rise to the so-called "filter bubble" effect, favoring the rise of polarization. In the present paper we study how a user-user collaborative-filtering algorithm affects the behavior of a group of agents repeatedly exposed to it. By means of analytical and numerical techniques we show how the system stationary state depends on the strength of the similarity and popularity biases, quantifying respectively the weight given to the most similar users and to the best rated items. In particular, we derive a phase diagram of the model, where we observe three distinct phases: disorder, consensus, and polarization. In the last users spontaneously split into different groups, each focused on a single item. We identify, at the boundary between the disorder and polarization phases, a region where recommendations are nontrivially personalized without leading to filter bubbles. Finally, we show that our model well reproduces the behavior of users on the online music platform last.fm. This analysis paves the way to a systematic analysis of recommendation algorithms by means of statistical physics methods and opens the possibility of devising less polarizing recommendation algorithms.
Cube-Based 3D Denoising Diﬀusion Probabilistic Model for Cone Beam Computed Tomography Reconstruction with Incomplete Data

Deep learning (DL) has been extensively researched in the ﬁeld of computed tomography (CT) reconstruction with incomplete data, particularly in sparse-view CT reconstruction. However, applying DL to sparse-view cone beam CT (CBCT) remains challenging. Many models learn the mapping from sparse-view CT images to ground truth but struggle to achieve satisfactory performance in terms of global artifact removal. Incorporating sinogram data and utilizing dual-domain information can enhance anti-artifact performance, but this requires storing the entire sinogram in memory. This presents a memory issue for high-resolution CBCT sinograms, limiting further research and application. In this paper, we propose a cube-based 3D denoising diﬀusion probabilistic model (DDPM) for CBCT reconstruction using down-sampled data. A DDPM network, trained on cubes extracted from paired fully sampled sinograms and down-sampled sinograms, is employed to inpaint down-sampled sinograms. Our method divides the entire sinogram into over-lapping cubes and processes these cubes in parallel using multiple GPUs, overcoming memory limitations. Experimental results demonstrate that our approach eﬀectively suppresses few-view artifacts while preserving textural details faithfully.
l/sub 1/ state estimation for linear systems using nonlinear observers

In this paper we study the l/sub 1/ optimal estimation problem for linear systems. It has been shown in the literature that in contrast with the state feedback problem it is in general sufficient to consider only linear controllers. However these estimators can have very high complexity. In this paper we study whether by using nonlinear estimators we can obtain estimators of a lower complexity.
Ambient Intelligence for Next-Generation AR

Next-generation augmented reality (AR) promises a high degree of context-awareness - a detailed knowledge of the environmental, user, social and system conditions in which an AR experience takes place. This will facilitate both the closer integration of the real and virtual worlds, and the provision of context-specific content or adaptations. However, environmental awareness in particular is challenging to achieve using AR devices alone; not only are these mobile devices' view of an environment spatially and temporally limited, but the data obtained by onboard sensors is frequently inaccurate and incomplete. This, combined with the fact that many aspects of core AR functionality and user experiences are impacted by properties of the real environment, motivates the use of ambient IoT devices, wireless sensors and actuators placed in the surrounding environment, for the measurement and optimization of environment properties. In this book chapter we categorize and examine the wide variety of ways in which these IoT sensors and actuators can support or enhance AR experiences, including quantitative insights and proof-of-concept systems that will inform the development of future solutions. We outline the challenges and opportunities associated with several important research directions which must be addressed to realize the full potential of next-generation AR.
High precise tracking control for a chain of integrator systems with modelling uncertainties

In this paper, adaptive compensation with a robust integral of the sign of the extended error (RISEE) feedback is developed for high precise tracking control of systems in the presence of simultaneous structured and unstructured uncertainties. To handle various uncertainties existing in the system in one controller, we propose an adaptive RISEE controller (ARISEE), in which an adaptive law based on the improved discontinuous projection method is synthesized to handle parametric uncertainties and the RISEE robust term to attenuate unmodelled disturbances. Moreover, the present controller does not need a priori knowledge on the bounds of the lumped disturbances and the gain of the designed robust control law can itself be tuned. The major feature of the proposed controller is that it can theoretically guarantee global asymptotic tracking performance with a continuous control input, in the presence of parametric uncertainties and unmodelled disturbances via Lyapunov analysis. Comparative numerical results verify the effectiveness of the proposed non-linear controller.
Extra Dimensions and Fuzzy Branes in String-inspired Nonlocal Field Theory

Particle physics models with extra dimensions of space (EDS's) and branes shed new light on electroweak and flavor hierarchies with a rich TeV scale phenomenology. This article highlights new model building issues with EDS's and branes, arising in the framework of weakly nonlocal field theories. It is shown that a brane-localized field is still delocalized in the bulk on a small distance from the brane position: fields localized on such distant fuzzy branes are thus allowed to interact directly with suppressed couplings. Directions for model building are also given: (i) with fuzzy branes, a new realization of split fermions in an EDS is presented, naturally generating flavor hierarchies; (ii) with a warped EDS, the usual warp transmutation of a brane-localized mass term is revisited, where it is shown that the nonlocal scale is also redshifted and provides a smooth UV cutoff for the Higgs boson mass. This framework is expected to have natural UV completions in string theory, but the possibility to embed it in recent UV complete weakly nonlocal quantum field theories is commented.
The potential for skill across the range of the seamless weather‐climate prediction problem: a stimulus for our science

Predictability is considered in the context of the seamless weather‐climate prediction problem, and the notion is developed that there can be predictive power on all time‐scales. On all scales there are phenomena that occur as well as longer time‐scales and external conditions that should combine to give some predictability. To what extent this theoretical predictability may actually be realised and, further, to what extent it may be useful is not clear. However the potential should provide a stimulus to, and high profile for, our science and its application for many years. Copyright © 2012 Royal Meteorological Society
On near resonances and symmetry breaking in forced rotating flows at moderate Rossby number

Numerical simulations are used to study a series of reduced models of homogeneous, rotating flow at moderate Rossby numbers $Ro \,{\approx}\, 0.1$, for which both numerical and physical experiments show the generation of quasi-two-dimensional vortices and symmetry breaking in favour of cyclones. A random force at intermediate scales injects energy at a constant average rate. The nonlinear term of reduced models is restricted to include only a subset of triad interactions in Fourier space. Reduced models of near-resonant, non-resonant and near two-dimensional triad interactions are considered. Only the model of near resonances reproduces all of the important characteristics of the full simulations: (i) efficient energy transfer from three-dimensional forced modes to two-dimensional large-scale modes, (ii) large-scale energy spectra scaling approximately as $k_h^{-3}$, where $k_h$ is the wavenumber in the plane perpendicular to the axis of rotation, and (iii) strong cyclone/anticyclone asymmetry in favour of cyclones. Non-resonances, defined as the complement to near resonances, act to reduce the energy transfer to large scales.
PHOTOINDUCED ELECTRON TRANSFER FROM CONJUGATED POLYMERS TO CDSE NANOCRYSTALS

We study photoinduced electron transfer from derivatives of poly(p-phenylenevinylene) (PPV) and nanocrystals of cadmium selenide via photoluminescence (PL) quenching and photoinduced absorption (PIA) spectroscopy. Using size-dependent quantum confinement to vary the energy levels of the nanocrystal acceptors, and chemical substitution to vary the energy levels of the polymer donors, we present a systematic investigation of charge transfer in these polymer/quantum-dot composites. We observe efficient PL quenching in blends of poly[2-methoxy-5-(${2}^{\ensuremath{'}}$-ethyl-hexyloxy-p-phenylenevinylene)] (MEH-PPV) with nearly monodisperse CdSe samples for nanocrystal diameters from 2.5 to 4.0 nm. The observed PIA peaks, as well as their frequency and temperature dependence, are consistent with the formation of long-lived positive polarons on MEH-PPV following electron transfer to the nanocrystals. Both the PL quenching and the PIA features are insensitive to nanocrystal size. We have also studied blends of CdSe nanocrystals of 2.5\char21{}4.0 nm diameter with two high electron affinity cyano-substituted PPV derivatives. One of these polymers behaves similarly to MEH-PPV; however in the other polymer, which has different alkoxy side chains, we find neither efficient PL quenching nor any PIA features indicative of charge transfer. We explain the insensitivity of the electron transfer process to nanocrystal size in the context of the relevant polymer and nanocrystal energy levels and discuss the influence of the polymer side chains on the charge-transfer process.
A catalog of IRAS observations of large optical galaxies

A catalog is presented of the IRAS observations of 85 galaxies listed in the Second Reference Catalogue of Bright Galaxies with blue-light isophotal diameters greater than 8'. “Total” flux densities at 12, 25, 60, and 100 µm, obtained from spatial maps constructed from co-added IRAS detector data, are reported. Infrared brightness profiles of the detected galaxies and infrared surface brightness contour maps of the galaxies for which structural features were resolved are displayed in an atlas. A far-infrared classification scheme based on the degree of central concentration and spatial structure of the 60 µm emission of the best-resolved galaxies is proposed. The 60 µm and blue-light isophotal diameters of the largest galaxies are compared.
Prioritized constraint handling NMPC using Volterra series models

This paper presents a nonsquare Nonlinear Model Predictive Control (NMPC) formulation that uses prioritized soft output constraints and hard input constraints. The proposed method uses empirical Volterra series models to represent the plant dynamics and is a closed loop tested on multivariable refinery facility simulation. The process exhibits significant open‐loop nonlinearity such as input steady‐state multiplicity and nonlinear dynamics near the nominal operating point. The results show improvement when compared with traditional linear control methods. Copyright © 2010 John Wiley & Sons, Ltd.
ON THE PARTIAL ORDERING OF RANGE HERMITIAN MATRICES

For a given range Hermitian matrix B, conditions are obtained for all matrices A that lie below B (above B) A ≤ B (A ≥ B) to be range Hermitian under a given partial ordering on matrices. As an application, it is shown that the monotonicity of the constitutive operators in linear electro-mechanical systems having the same structure operator is preserved for the corresponding transfer impedances.
Modeling Polarimetric Imaging using DIRSIG

The remote sensing community is beginning to recognize the potential benefit of exploiting polarimetric signatures. The ability to accurately model polarimetric phenomenology in a remote sensing system will assist efforts in system design, algo rithm development, phenomenology studies, and analyst training. This dissertation lays the ground work for enhancing the current Digital Imaging and Remote Sens ing Laboratory's Synthetic Image Generation (DIRSIG) model to include polarimetric phenomenology. The current modeling capabilities are discussed along with the theo retical background required to expand upon the current state of the art. Methods for modeling and estimating polarimetric signatures and phenomenology from start to end in a typical remote sensing system are presented. A series of simple simulations were conducted to assess the performance of the new polarimetric capabilities. Analy sis was performed to characterize the individual models and the collected performance of the models.
Fuzzy filtering for mixed noise removal during image processing

Presents a new nonlinear fuzzy filter for image processing in a mixed noise environment, where both additive Gaussian noise and nonadditive impulsive noise may be present. Averaging filters can effectively remove the Gaussian noise and order statistics filters or median filters can effectively remove the impulsive noise. However, it is difficult to combine these filters to remove mixed noise in an image processing environment without blurring the image details or edges. Trying to distinguish between noise and edge information in the image is an inherently ambiguous problem and naturally leads to the development of a fuzzy filter. We use local statistics to train the membership function of a fuzzy filter for image processing to remove both Gaussian noise and impulsive noise while preserving edges. We show that such a fuzzy filter gives superior results when compared to averaging filters, median filters, and other fuzzy filters. We also demonstrate the robustness of this filtering technique. <<ETX>>
A RATIONALE FOR AN ASYMPTOTIC LOGNORMAL FORM OF WORD‐FREQUENCY DISTRIBUTIONS1

The lognormal distribution has been found to fit word-frequency distributions satisfactorily if account is taken of the relations between populations and samples. A rationale for an asymptotic lognormal distribution is derived by supposing that the probabilities at the nodes of decision trees are symmetrically distributed around . 5 with a certain variance. By the central limit theorem, the logarithms of the continued products of probabilities randomly sampled from such a distribution would have an asymptotically normal distribution. Two mathematical models incorporating this notion are developed and tested; in one, the number of factors in the continued products is assumed to be fixed, while in the other, that number is dependent upon a Poisson distribution. Psycholinguistic processes corresponding to these models are postulated and illustrated with reference to two sets of data: (1) word associations to the stimulus LIGHT, and (2) the Lorge Magazine Count. Reasonable fits to observed data or to underlying lognormal distributions are obtained but there remain certain problems in estimating parameters.
Stellar populations in Active Galactic Nuclei III

In this paper we apply the stellar population synthesis method previously described in Boisson et al. (2000) to five more AGN. The analysis of these new data strengthen our previous conclusions: i) homogeneity of the stellar population within a class of nuclear activity regardless of the morphological type of the host galaxy; ii) populations within the nuclear regions of LINERs and Seyfert 2s are different: LINERs have a very old metal-rich population while in the Seyfert 2s a contribution of a weak burst of star formation is observed together with the old high metallicity component; iii) in the circum-nuclar region (200 pc ≤ D ≤ 1 kpc) of all the active galaxies in our sample, except for NGC 2992, we detect an old burst of star formation (0.2−1 Gyr),which is contrary to what is observed in normal galaxies. We note that the broad OIλ8446 A emission line detected in the spectrum of the nucleus of NGC 2992 confirms its classification as a Seyfert 1.
Effect of inelastic scattering on resonant and sequential tunneling in double barrier heterostructures

We demonstrate that the current through a double barrier heterostructure is independent of whether the electron tunneling mechanism is sequential or Fabry–Perot like. For typical experimental parameters we show that inelastic scattering, which destroys the phase coherence, is an important effect that strongly reduces the probability for coherent tunneling. As a result, the tunneling will be mainly incoherent (sequential). However, in a model calculation where the degree of coherency can be varied, we find that the tunneling current does not depend on which of the two mechanisms dominate as long as the resonant energy is well defined.
The Dark Energy Survey

The Dark Energy Survey (DES) is a proposed 5‐year, 5000 deg 2 optical imaging survey, to 24th magnitude in the SDSS griz bands, that will use a new wide‐field 3 deg2 mosaic CCD camera on the Blanco 4m telescope at the Cerror Tololo Inter‐American Observatory (CTIO). The primary scientific goal of the DES is to probe the nature of dark energy using four complementary methods: galaxy clusters, weak gravitational lensing, galaxy angular correlations, and Type Ia supernovae. For each of these methods, the DES should determine the dark energy equation of state parameter w with a statistical precision of about 5–15%, assuming constant w but without strong priors on other cosmological parameters. By deriving dark energy constraints using four complementary sets of measurements made from the same data set and within a common analysis framework, we will obtain important crosschecks of our systematic errors and thereby make a substantial and robust advance in the precision of dark energy measurements.