Miracle-dz/UniFAR-FTUs huggingface dataset:
Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks

Prediction of dynamic environment is crucial to safe navigation of an autonomous vehicle. Urban traffic scenes are particularly challenging to forecast due to complex interactions between various dynamic agents, such as vehicles and vulnerable road users. Previous approaches have used ego-centric occupancy grid maps to represent and predict dynamic environments. However, these predictions suffer from blurriness, loss of scene structure at turns, and vanishing of agents over longer prediction horizon. In this work, we propose a novel framework to make long-term predictions by representing the traffic scene in a fixed frame, referred as allo-centric occupancy grid. This allows for the static scene to remain fixed and to represent motion of the ego-vehicle on the grid like other agents'. We study the allo-centric grid prediction with different video prediction networks and validate the approach on the real-world Nuscenes dataset. The results demonstrate that the allo-centric grid representation significantly improves scene prediction, in comparison to the conventional ego-centric grid approach.
Predicting Tags For Programming Tasks by Combining Textual And Source Code Data

Competitive programming remains a very popular activity that combines both software engineering and education. In order to prepare and to practice, contestants use extensive archives of problems from past contents available on various competitive programming platforms. One way to make this process more effective is to provide an automatic tag system for the tasks. Prior works do that by either using the tasks' problem statements or the code of their solutions. In this study, we investigate which information source is more valuable for tag prediction. To answer that question, we compare existing approaches of both types on the same dataset and with the same set of tags. Then, we propose a novel approach, which is an ensemble of the Gated Graph Neural Network model for analyzing solutions and the Bidirectional Encoder Representations from Transformers model for processing statements. Our experiments show that our approach outperforms previously proposed models by 0.175 of the PR-AUC metric.
Deep Learning based Multi-Label Image Classification of Protest Activities

With the rise of internet technology amidst increasing rates of urbanization, sharing information has never been easier thanks to globally-adopted platforms for digital communication. The resulting output of massive amounts of user-generated data can be used to enhance our understanding of significant societal issues particularly for urbanizing areas. In order to better analyze protest behavior, we enhanced the GSR dataset and manually labeled all the images. We used deep learning techniques to analyze social media data to detect social unrest through image classification, which performed good in predict multi-attributes, then also used map visualization to display protest behaviors across the country.
Multiple emission components in the Cygnus cocoon detected from Fermi-LAT observations

Star-forming regions may play an important role in the life cycle of Galactic cosmic rays. Gamma-ray observations of Cygnus X have revealed the presence of an excess of hard-spectrum gamma-ray emission, possibly related to a cocoon of freshly accelerated particles. Based on ~13 years of observations with the Fermi-Large Area Telescope (LAT), we performed an improved spectro-morphological characterisation of the residual emission including the cocoon. The best-fit model for the cocoon includes two main emission components: an extended component FCES G78.74+1.56, described by a 2D Gaussian of extension $r_{68} = 4.4^\circ \pm 0.1^\circ\,^{+0.1^\circ}_{-0.1^\circ}$, and a central component FCES G80.00+0.50, traced by the distribution of ionised gas within the borders of the photo-dissociation regions. The two have significantly different spectra. An additional extended emission component FCES G78.83+3.57, located on the edge of the central cavities in Cygnus X and with a spectrum compatible with that of FCES G80.00+0.50, is likely related to the cocoon. For the two main components, spectra and radial-azimuthal profiles of the emission can be accounted for in a diffusion-loss framework involving one single population of non-thermal particles. Particles span the full extent of FCES G78.74+1.56 as a result of diffusion from a central source, and give rise to source FCES G80.00+0.50 by interacting with ionised gas in the innermost region. For this simple diffusion-loss model, viable setups can be very different in terms of energetics, transport conditions, and timescales involved, and both hadronic and leptonic scenarios are possible. The solutions range from long-lasting particle acceleration, possibly in prominent star clusters such as Cyg OB2 and NGC 6910, to a more recent and short-lived release of particles within the last 10-100 kyr, likely from a supernova remnant. (Abridged)
Geometry-biased Transformers for Novel View Synthesis

We tackle the task of synthesizing novel views of an object given a few input images and associated camera viewpoints. Our work is inspired by recent 'geometry-free' approaches where multi-view images are encoded as a (global) set-latent representation, which is then used to predict the color for arbitrary query rays. While this representation yields (coarsely) accurate images corresponding to novel viewpoints, the lack of geometric reasoning limits the quality of these outputs. To overcome this limitation, we propose 'Geometry-biased Transformers' (GBTs) that incorporate geometric inductive biases in the set-latent representation-based inference to encourage multi-view geometric consistency. We induce the geometric bias by augmenting the dot-product attention mechanism to also incorporate 3D distances between rays associated with tokens as a learnable bias. We find that this, along with camera-aware embeddings as input, allows our models to generate significantly more accurate outputs. We validate our approach on the real-world CO3D dataset, where we train our system over 10 categories and evaluate its view-synthesis ability for novel objects as well as unseen categories. We empirically validate the benefits of the proposed geometric biases and show that our approach significantly improves over prior works.
Harvesting L2 Caches in Server Processors

We make three observations in modern processors: (1) LLC capacity is getting larger (up to 1GB); (2) core counts are increasing (up to 128 cores), accumulating a more significant amount of private L2 cache capacity on the chip; and (3) overall processor utilization in the cloud remains very low despite many efforts, leaving many large private caches unused. To enable better use of these beefy processors, we propose to open up a logical path for LLC evictions to unused private caches. In other words, instead of writing LLC evictions back to slow and busy main memory, we send some of them that are still alive up to idle L2 caches to avoid unnecessary long and costly main memory. Our scheme takes the importance of applications (user-facing vs. background), and system load into account to provide each application with a fair share of idle resources. Our results show that we can improve system performance by up to 2X (geomean of 10%) for single-application runs. Also, for mixes of user-facing and background jobs, our scheme improves the P99 latency of user-facing tasks by up to 32% (geomean of 15%), and the IPC of background jobs by up to 50% (geomean of 10%).
Proca-Higgs balls and stars in a UV completion for Proca self-interactions

We consider a Proca-Higgs model wherein a complex vector field gains mass via spontaneous symmetry breaking, by coupling to a real scalar field with a Higgs-type potential. This vector version of the scalar Friedberg-Lee-Sirlin model, can be considered as a UV completion of a complex Proca model with self-interactions. We study the flat spacetime and self-gravitating solitons of the model, that we dub Proca-Higgs balls and stars respectively, exploring the domain of solutions and describing some of their mathematical and physical properties. The stars reduce to the well-known (mini-)Proca stars in some limits. The full model evades the hyperbolicity problems of the self-interacting Proca models, offering novel possibilities for dynamical studies beyond mini-Proca stars.
Optimal Observational Scheduling Framework for Binary and Multiple Stellar Systems

The optimal instant of observation of astrophysical phenomena for objects that vary on human timescales is an important problem, as it bears on the cost-effective use of usually scarce observational facilities. In this paper, we address this problem in the case of tight visual binary systems through a Bayesian framework based on the maximum entropy sampling principle. Our proposed information-driven methodology exploits the periodic structure of binary systems to provide a computationally efficient estimation of the probability distribution of the optimal observation time. We show the optimality of the proposed sampling methodology in the Bayes sense and its effectiveness through direct numerical experiments. We successfully apply our scheme to the study of two visual-spectroscopic binaries and one purely astrometric triple hierarchical system. We note that our methodology can be applied to any time-evolving phenomena, a particularly interesting application in the era of dedicated surveys, where a definition of the cadence of observations can have a crucial impact on achieving the science goals.
Approximation of Estimates of (Co)variance Components with Multiple-Trait Restricted Maximum Likelihood by Multiple Diagonalization for More Than One Random Effect

Abstract Canonical transformation for REML can be applied to models with several random effects by simultaneously diagonalizing (co)variance matrices for all random effects. This procedure is an approximation when matrices cannot be diagonalized completely. The level of the approximation was studied with simulated data by comparing multiple diagonalization and exact REML estimates. For a range of diagonalization levels, the error of the estimates was 2 to 10 times lower than the fraction of non-diagonalizable (co)variances because 57 to 91% of these variances were recovered after the backtransformation step. To determine whether REML by multiple diagonalization is successful with real data, a study used 98,113 records of 44,765 Holstein cows for 14 conformation traits. Effects included in the model were herd classification, animal with unknown parent groups, and permanent environmental effects. Estimates of (co)variance components were on average 5% higher for type compared with estimates from an earlier analysis using only first records, but the estimate for udder cleft increased 2.4 times. Correlations for the permanent environmental effect were within . 1 of genetic correlations. Multitrait REML by multiple diagonalization provides accurate multiple-trait estimates for repeatability models more efficiently than general model REML.
Parabolic Regularity of Spectral Functions

This paper is devoted to the study of the second-order variational analysis of spectral functions. It is well-known that spectral functions can be expressed as a composite function of symmetric functions and eigenvalue functions. We establish several second-order properties of spectral functions when their associated symmetric functions enjoy these properties. Our main attention is given to characterize parabolic regularity for this class of functions. It was observed recently that parabolic regularity can play a central rule in ensuring the validity of important second-order variational properties such as twice epi-differentiability. We demonstrates that for convex spectral functions, their parabolic regularity amounts to that of their symmetric functions. As an important consequence, we calculate the second subderivative of convex spectral functions, which allows us to establish second-order optimality conditions for a class of matrix optimization problems.
Self-consistent thermodynamic potential for magnetized QCD matter

Within the two-flavor Nambu--Jona-Lasinio model, we derive a self-consistent thermodynamic potential $\Omega$ for a QCD matter in an external magnetic field $B$. To be consistent with Schwinger's renormalization spirit, counter terms with vacuum quark mass are introduced into $\Omega$ and then the explicit $B$-dependent parts can be regularized in a cutoff-free way. Following that, explicit expressions of gap equation and magnetization can be consistently obtained according to the standard thermodynamic relations. The formalism is able to reproduce the paramagnetic feature of a QCD matter without ambiguity. For more realistic study, a running coupling constant is also adopted to account for the inverse magnetic catalysis effect. It turns out that the running coupling would greatly suppress magnetization at large $B$ and is important to reproduce the temperature enhancement effect to magnetization. The case with finite baryon chemical potential is also explored: no sign of first-order transition is found by varying $B$ for the running coupling and the de Haas-van Alphen oscillation shows up in the small $B$ region.
Pollution transport by convective clouds in a mesocale model

The effects of cumulus convection on the dispersion of passive tracers are considered in the framework of a mesoscale model, where the subgrid‐scale clouds are parametrized. the parametrization uses a ‘mass‐flux’ scheme, where guidance on vertical fluxes and cloud cover is obtained from a cloud‐resolving large‐eddy model run, and applied to the one‐dimensional time‐dependent vertical transport model. A set of three one‐dimensional schemes is presented, the simplest of which contains approximations which are appropriate to, and commonly used in large‐scale models. On the mesoscale, however, it is postulated that these approximations are no longer valid and a less idealised scheme is necessary.
Decoupling and descattering networks for antennas

The possibilities of connecting a lossless network between input ports and antenna ports such that there is no coupling and scattering between the antennas are discussed. A necessary condition for complete decoupling and descattering is power orthogonality between the patterns of the individual antennas. Numerical and experimental results are presented for monopole antennas.
Variational Bayes Inference for Data Detection in Cell-Free Massive MIMO

Cell-free massive MIMO is a promising technology for beyond-5G networks. Through the deployment of many cooperating access points (AP), the technology can significantly enhance user coverage and spectral efficiency compared to traditional cellular systems. Since the APs are distributed over a large area, the level of favorable propagation in cell-free massive MIMO is less than the one in colocated massive MIMO. As a result, the current linear processing schemes are not close to the optimal ones when the number of AP antennas is not very large. The aim of this paper is to develop nonlinear variational Bayes (VB) methods for data detection in cell-free massive MIMO systems. Contrary to existing work in the literature, which only attained point estimates of the transmit data symbols, the proposed methods aim to obtain the posterior distribution and the Bayes estimate of the data symbols. We develop the VB methods accordingly to the levels of cooperation among the APs. Simulation results show significant performance advantages of the developed VB methods over the linear processing techniques.
AdaPoinTr: Diverse Point Cloud Completion With Adaptive Geometry-Aware Transformers

In this paper, we propose a Transformer encoder-decoder architecture, called PoinTr, which reformulates point cloud completion as a set-to-set translation problem and employs a geometry-aware block to model local geometric relationships explicitly. The migration of Transformers enables our model to better learn structural knowledge and preserve detailed information for point cloud completion. Taking a step towards more complicated and diverse situations, we further propose AdaPoinTr by developing an adaptive query generation mechanism and designing a novel denoising task during completing a point cloud. Coupling these two techniques enables us to train the model efficiently and effectively: we reduce training time (by 15x or more) and improve completion performance (over 20%). Additionally, we propose two more challenging benchmarks with more diverse incomplete point clouds that can better reflect real-world scenarios to promote future research. We also show our method can be extended to the scene-level point cloud completion scenario by designing a new geometry-enhanced semantic scene completion framework. Extensive experiments on the existing and newly-proposed datasets demonstrate the effectiveness of our method, which attains 6.53 CD on PCN, 0.81 CD on ShapeNet-55 and 0.392 MMD on real-world KITTI, surpassing other work by a large margin and establishing new state-of-the-arts on various benchmarks. Most notably, AdaPoinTr can achieve such promising performance with higher throughputs and fewer FLOPs compared with the previous best methods in practice.
Diffusion Models For Stronger Face Morphing Attacks

—Face morphing attacks seek to deceive a Face Recognition (FR) system by presenting a morphed image consisting of the biometric qualities from two different identities with the aim of triggering a false acceptance with one of the two identities, thereby presenting a signiﬁcant threat to biometric systems. The success of a morphing attack is dependent on the ability of the morphed image to represent the biometric characteristics of both identities that were used to create the image. We present a novel morphing attack that uses a Diffusion-based architecture to improve the visual ﬁdelity of the image and improve the ability of the morphing attack to represent characteristics from both identities. We demonstrate the high ﬁdelity of the proposed attack by evaluating its visual ﬁdelity via the Fr´echet Inception Distance. Extensive experiments are conducted to measure the vulnerability of FR systems to the proposed attack. The proposed attack is compared to two state-of-the-art GAN-based morphing attacks along with two Landmark-based attacks. The ability of a morphing attack detector to detect the proposed attack is measured and compared against the other attacks. Additionally, a novel metric to measure the relative strength between morphing attacks is introduced and evaluated.
Quantum backreaction for overspinning BTZ geometries

We examine the semiclassical backreaction of a conformally coupled scalar field on an overspinning BTZ geometry. This extends the work done on a similar problem for ($2+1$)- AdS geometries of the BTZ family with $|M|>|J|$. The overspinning classical solutions corresponds to $|M|<|J|$ and possess a naked singularity at $r=0$. Using the renormalized quantum stress-energy tensor for a conformally coupled scalar field on such a spacetime, we obtain the semiclassical Einstein equations, which we attempt to solve perturbatively. We show that the stress-energy tensor is non-renormalizable in this approach, and consequently the perturbative solution to the semiclassical equations in the overspinning case does not exist. This could be an indication of the fact that the naked singularity at the center of an overspinning geometry is of a more severe nature than the conical singularity found in the same family of BTZ geometries.
Radio Interferometer with Simple antennas

A Radio interferometer comprises several antennas, spared over a large area. Say ALMA(Atacama Large Millimeter/submillimeter Array), VLA(very large array), VLBA(Very Long Baseline Array), GMRT(Giant Metrewave Radio Telescope), MWA( Murchison Widefield Array), EHT(Event Horizon Telescope), and the SKA(Square Kilometer Array), the name itself speaks about square-kilometres of area. Most radio observatories are constructed or constitute giant dish antennas, and few constitute an extensive array of antennas. However, what if a simple antenna like Dipole, Loop or Yagi-UDA is considered an element of an interferometer? Then how does it affect the visibility of the instrument? Yes, it will be less, but how weak? Furthermore, what is the math to reach it? These questions pushed for this work. Here, one can find the detailed derivation starting from a simple Young's double slit experiment to a radio interferometer intensity distribution in terms of the Gain of the antenna element. This literature aided in understanding the interferometer of yagi antennas of Gain 11dBi, resulting in a visibility of 0.0714. This clarity was insignificant in the current work. Hence using this work, one can design and construct a suited interferometer for their requirement.
Elevation Estimation-Driven Building 3-D Reconstruction From Single-View Remote Sensing Imagery

Building 3-D reconstruction from remote sensing images has a wide range of applications in smart cities, photogrammetry, and other fields. Methods for automatic 3-D urban building modeling typically employ multiview images as input to algorithms to recover point clouds and 3-D models of buildings. However, such models rely heavily on multiview images of buildings, which are time-intensive and limit the applicability and practicality of the models. To solve these issues, we focus on designing an efficient DSM estimation-driven reconstruction framework (Building3-D), which aims to reconstruct 3-D building models from the input single-view remote sensing image. Existing DSM estimation networks suffer from the imbalance between local and global features, which leads to oversmooth DSM estimates at instance boundaries. To address this issue, we propose a Semantic Flow Field-guided DSM Estimation (SFFDE) network, which utilizes the proposed concept of elevation semantic flow (ESF) to achieve the registration of local and global features. First, in order to make the network semantics globally aware, we propose an elevation semantic globalization (ESG) module to realize the semantic globalization of instances. Furthermore, in order to alleviate the semantic span of global features and original local features, we propose a local-to-global elevation semantic registration (L2G-ESR) module based on ESF. Our Building3-D is rooted in the SFFDE network for building elevation prediction, synchronized with a building extraction network for building masks, and then sequentially performs point cloud reconstruction and surface reconstruction (or CityGML model reconstruction). On this basis, our Building3-D can optionally generate CityGML models or surface mesh models of the buildings. Extensive experiments on ISPRS Vaihingen and DFC2019 datasets on the DSM estimation task show that our SFFDE significantly improves upon state-of-the-art, and $\delta _{1}$ , $\delta _{2}$ , and $\delta _{3}$ metrics of our SFFDE are improved to 0.595, 0.897, and 0.970. Furthermore, our Building3-D achieves impressive results in the 3-D point cloud and 3-D model reconstruction process.
Photoinduced pairing in Mott insulators

Utilizing time-evolution techniques in (infinite) matrix-product-state representation, we study the non-equilibrium dynamics of driven Mott insulators and demonstrate photoinduced \etaη pairing directly in the thermodynamic limit. Analyzing the time evolution of the corresponding pairing correlations, we determine the optimal laser pump parameters for which long-range \etaη-pairing becomes dominant after pulse irradiation. The time-dependent photoemission spectra for this optimal pump parameter set show clear signatures of the photoinduced insulator-to-metal phase transition related to the formation of \etaη pairs.