Miracle-dz/UniFAR-FTUs huggingface dataset:
S-Graphs+: Real-Time Localization and Mapping Leveraging Hierarchical Representations

In this letter, we present an evolved version of Situational Graphs, which jointly models in a single optimizable factor graph (1) a pose graph, as a set of robot keyframes comprising associated measurements and robot poses, and (2) a 3D scene graph, as a high-level representation of the environment that encodes its different geometric elements with semantic attributes and the relational information between them. Specifically, our S-Graphs+ is a novel four-layered factor graph that includes: (1) A keyframes layer with robot pose estimates, (2) a walls layer representing wall surfaces, (3) a rooms layer encompassing sets of wall planes, and (4) a floors layer gathering the rooms within a given floor level. The above graph is optimized in real-time to obtain a robust and accurate estimate of the robot's pose and its map, simultaneously constructing and leveraging high-level information of the environment. To extract this high-level information, we present novel room and floor segmentation algorithms utilizing the mapped wall planes and free-space clusters. We tested S-Graphs+ on multiple datasets, including simulated and real data of indoor environments from varying construction sites, and on a real public dataset of several indoor office areas. On average over our datasets, S-Graphs+ outperforms the accuracy of the second-best method by a margin of 10.67%, while extending the robot situational awareness by a richer scene model. Moreover, we make the software available as a docker file.
ABODE-Net: An Attention-based Deep Learning Model for Non-intrusive Building Occupancy Detection Using Smart Meter Data

Occupancy information is useful for efficient energy management in the building sector. The massive high-resolution electrical power consumption data collected by smart meters in the advanced metering infrastructure (AMI) network make it possible to infer buildings' occupancy status in a non-intrusive way. In this paper, we propose a deep leaning model called ABODE-Net which employs a novel Parallel Attention (PA) block for building occupancy detection using smart meter data. The PA block combines the temporal, variable, and channel attention modules in a parallel way to signify important features for occupancy detection. We adopt two smart meter datasets widely used for building occupancy detection in our performance evaluation. A set of state-of-the-art shallow machine learning and deep learning models are included for performance comparison. The results show that ABODE-Net significantly outperforms other models in all experimental cases, which proves its validity as a solution for non-intrusive building occupancy detection.
Fully-discrete numerical transfer in diffusive regimes

Abstract We study the behavior of two fully-discrete numerical schemes (the step scheme and the cell-edge even-parity scheme) for the transfer equation in diffusive regimes with coarse meshes. The diffusion boundary conditions for these numerical schemes are derived through a boundary layer analysis that matches the interior discrete diffusion approximation to the boundary data for the discrete transfer equation. Requiring that the resulting discrete diffusion boundary condition to be a good approximation to the diffusion boundary conditions obtained for the transfer equation then gives constraints on the discretization. It is shown that the quadrature set Bn developed in our earlier paper [9] gives accurate extrapolated endpoints, while new discrete one-way flux boundary conditions introduced here that are based on the discrete W-function, give accurate forcing terms. Consequently, as we then demonstrate numerically, these new schemes give more accurate results than traditional schemes for problems conta.
A Heuristic for Multiple Lot Sizing For an Order Under Variable Yield

Abstract Consider a job shop which must completely fill a large make-to-order demand of a product where production yield is highly variable. After a production lot is completed, if the total output of satisfactory units is inadequate to satisfy the demand, then a new run (with associated setup cost) is made. When the output of good units exceeds the demand, then the excess units are scrapped (with possible salvage value). The optimal lot size minimizes the total of production, setup, holding, shortage, and scrap costs. A heuristic is developed based on the incremental cost of increasing the lot size by one unit. The computational ease and excellent cost performance of the heuristic favor its use in place of the mathematically optimal solution obtained by dynamic programming. Real world manufacturing applications and additional properties of the model are also discussed.
Update on the CASTOR mission concept: scientific opportunities in the Euclid and WFIRST era

CASTOR (the Cosmological Advanced Survey Telescope for Optical and uv Research) is a proposed CSA-led mission that would carry out deep, high-resolution imaging at ultraviolet and blue-optical wavelengths. Operating close to the diffraction limit, the 1m CASTOR telescope would have a spatial resolution comparable to the Hubble Space Telescope (HST), but with an instantaneous field of view of 1.2° x 0.6° -- about two hundred times larger than that of the Advanced Camera for Surveys on HST. Imaging would be carried out simultaneously in three non-overlapping bandpasses: UV (0.15-0.3 μm), u′ (0.3-0.4 μm) and g (0.4-0.55 μm). In the blue-optical region, CASTOR imaging would far exceed that from LSST in terms of depth and angular resolution, even after a decade of LSST operations. In this review, we summarize the various technical efforts being carried out in support of the CASTOR mission concept, and describe the potential scientific synergy between the CASTOR, Euclid and WFIRST missions.
Novel model-marginalized cosmological bound on the QCD axion mass

We present model-marginalized limits on mixed hot dark matter scenarios, which consider both thermal neutrinos and thermal QCD axions. A novel aspect of our analyses is the inclusion of small-scale Cosmic Microwave Background (CMB) observations from the Atacama Cosmology Telescope (ACT) and the South Pole Telescope (SPT), together with those from the Planck satellite and Baryon Acoustic Oscillation (BAO) data. After marginalizing over a number of well-motivated non-minimal background cosmologies, the tightest $95\%$ CL upper bound we obtain is $0.21$ eV, both for $\sum m_\nu$ and $m_{\rm a}$, from the combination of ACT, Planck and BAO measurements. Restricting the analyses to the standard $\Lambda$CDM picture, we find $\sum m_\nu<0.16$ eV and $m_{\rm a}<0.18$ eV, both at $95\%$ CL. Interestingly, the best background cosmology is never found within the minimal $\Lambda$CDM plus hot relics, regardless of the data sets exploited in the analyses. The combination of Planck with either BAO, SPT or ACT prefers a universe with a non-zero value of the running in the primordial power spectrum with strong evidence. Small-scale CMB probes, both alone and combined with BAO, either prefer, with substantial evidence, non-flat universes (as in the case of SPT) or a model with a time varying dark energy component (as in the case of ACT).
Effect of solar free oscillations on TianQin’s range acceleration noise

TianQin is a proposed space-based gravitational-wave detector mission to be deployed and operated in high Earth orbits. As a sequel to Zhang et al (2021 Phys. Rev. D 103 062001), we investigate a type of ‘orbital noise’ in TianQin’s range acceleration that is caused by gravitational perturbation associated with solar free oscillations. Frequencies of such oscillations are typically within TianQin’s measurement band of 0.1 mHz–1 Hz, and the disturbance level needs careful assessment. By using high-precision orbit propagation and adding the Sun’s time-variable oblateness J 2 to detailed gravity-field models, we examine the effect in the frequency domain and show that the solar free oscillation noise is expected to be two orders of magnitude lower than the noise requirement on single links and hence has little impact on the mission.
Radiative decays of charmed mesons in a modified relativistic quark model

In this study, we perform systematic estimations of the radiative decays of the charmed mesons in a modified relativistic quark model. Our estimations indicate that the branching ratios of the processes of , , , , and are of the order of , which are sizable to be detected experimentally. Moreover, the branching ratios of some channels, for example, , , and , are estimated to be of the order of , which may be accessible with the accumulation of data in future experiments.
Mind Your Heart: Stealthy Backdoor Attack on Dynamic Deep Neural Network in Edge Computing

Transforming off-the-shelf deep neural network (DNN) models into dynamic multi-exit architectures can achieve inference and transmission efficiency by fragmenting and distributing a large DNN model in edge computing scenarios (e.g., edge devices and cloud servers). In this paper, we propose a novel backdoor attack specifically on the dynamic multi-exit DNN models. Particularly, we inject a backdoor by poisoning one DNN model’s shallow hidden layers targeting not this vanilla DNN model but only its dynamically deployed multi-exit architectures. Our backdoored vanilla model behaves normally on performance and cannot be activated even with the correct trigger. However, the backdoor will be activated when the victims acquire this model and transform it into a dynamic multi-exit architecture at their deployment. We conduct extensive experiments to prove the effectiveness of our attack on three structures (ResNet-56, VGG-16, and MobileNet) with four datasets (CIFAR-10, SVHN, GTSRB, and Tiny-ImageNet) and our backdoor is stealthy to evade multiple state-of-the-art backdoor detection or removal methods.
The USDA Ultraviolet Radiation Monitoring Program

Abstract The U.S. Department of Agriculture's Ultraviolet (UV) Radiation Monitoring Program has been measuring UV radiation since 1994. The initial network of 12 stations employed broadband meters to measure UVB irradiance and included ancillary measurements of temperature, humidity, and irradiance at seven wavelengths in the visible produced by a Multi-Filter Rotating Shadowband Radiometer (MFRSR). Since that beginning the network has expanded to more than 20 stations and the broadband meters have been supplemented with a seven-wavelength Ultraviolet Multi-Filter Rotating Shadowband Radiometer (UV-MFRSR). The network has been designed to include 30 stations, each with a full complement of instrumentation. Annual characterizations of the network's filter radiometers indicate that gradual shifts in instrument response are manageable but must be accounted for to achieve accurate and precise measurements of UV irradiance. The characterization and calibration of the filter instruments is discussed along with .
Vision-Based Environmental Perception for Autonomous Driving

Visual perception plays an important role in autonomous driving. One of the primary tasks is object detection and identification. Since the vision sensor is rich in color and texture information, it can quickly and accurately identify various road information. The commonly used technique is based on extracting and calculating various features of the image. The recent development of deep learning-based method has better reliability and processing speed and has a greater advantage in recognizing complex elements. For depth estimation, vision sensor is also used for ranging due to their small size and low cost. Monocular camera uses image data from a single viewpoint as input to estimate object depth. In contrast, stereo vision is based on parallax and matching feature points of different views, and the application of Deep learning also further improves the accuracy. In addition, Simultaneous Location and Mapping (SLAM) can establish a model of the road environment, thus helping the vehicle perceive the surrounding environment and complete the tasks. In this paper, we introduce and compare various methods of object detection and identification, then explain the development of depth estimation and compare various methods based on monocular, stereo, and RGB-D sensors, next review and compare various methods of SLAM, and finally summarize the current problems and present the future development trends of vision technologies.
A study of the white dwarf luminosity function

The dependence of white dwarf luminosity functions on the assumed age of the Galactic disk is examined, taking into account the relationship between white dwarf mass and progenitor mass, the progenitor lifetime, the mass dependence of cooling curves, and possible variations in the birthrate function in the past. At low luminosities, the resultant distribution in number versus luminosity depends very strongly not only on the shape of the cooling curves for different masses, but also on the spectrum of white dwarf masses that contribute to the distribution. For an age of about 9 Gyr, it is suggested that at luminosities only a quarter of a magnitude fainter than the dimmest white dwarfs yet found, the space density of white dwarfs is larger by a factor of 10 than the space density which the Winget et al. (1987) luminosity function suggests. 43 refs.
Simulation of fermionic and bosonic critical points with emergent SO(5) symmetry

We introduce a model of Dirac fermions in 2+1 dimensions with a semimetallic, a quantum spin-Hall insulating (QSHI), and an s-wave superconducting (SSC) phase. The phase diagram features a multicritical point at which all three phases meet as well as a QSHI-SSC deconfined critical point. The QSHI and SSC orders correspond to mutually anti-commuting mass terms of the Dirac Hamiltonian. Based on this algebraic property, SO(5) symmetric field theories have been put forward to describe both types of critical points. Using quantum Monte Carlo simulations, we directly study the operator that rotates between QSHI and SSC states. The results suggest that it commutes with the low-energy effective Hamiltonian at criticality but has a gap in the ordered phases. This implies an emergent SO(5) symmetry at both the multicritical and the deconfined critical points.
Small time approximation in Wright-Fisher diffusion

Wright-Fisher model has been widely used to represent random variation in allele frequency over time, due to its simple form even though, a closed analytical form for the allele frequency has not been constructed. However, variation in allele frequency allows to represent selection in the evolutionary process. In this work, we present two alternatives of parametric approximating functions: Asymptotic expansion (AE) and Gaussian approximation (GaussA), obtained by means of probabilistic tools and useful for statistical inference, to estimate the allele frequency density for a small $t/2N$ in the interval $[0,1]$. The proposed densities satisfactorily capture the problem of fixation at 0 or 1, unlike the commonly used methods. While Asymptotic Expansion defines a suitable density for the distribution allele frequency (DAF), Gaussian Approximation describes a range of validity for the Gaussian distribution. Through a simulation study and using an adaptive method for density estimation, the proposed densities are compared with the beta and Gaussian distribution with, their corresponding parameters.
Park-and-Ride Facility Location Selection under Nested Logit Demand Function

Park-and-ride (P&R) facilities are car parks where users can transfer to public transportation, by which they reach their ﬁnal destinations. Commuters can use P&R facilities or choose to travel by car to their destinations, and individual choice behavior is assumed to follow a logit model. The P&R facility location problem identiﬁes locations for a ﬁxed number of P&R facilities from among potential locations such that the number of users of the P&R facilities is maximized. This problem has previously been formalized under a multinomial logit (MNL) demand function. However, as it imposes the strong condition of the independence of irrelevant alternatives (IIA), the MNL model is unable to represent the real-world P&R facility location problem exactly. Respecting the nested structure of individual choice behavior, we generalize the MNL model to a nested logit (NL) model and develop two computational methods—neighborhood search and randomized rounding—to solve large-scale P&R facility location problems under the NL demand function. The neighborhood search method ﬁrst ﬁnds one feasible solution and then improves the feasible solution to the next one along an edge of the polyhedron whose vertices are the feasible solutions. The neighborhood search method is randomized to create an adaptive randomized rounding procedure. Computational experiments veriﬁed that the computational methods were able to solve the nonlinear optimization problem under the NL demand function on 1,000 medium-scale instances to exact optimality rapidly. Speciﬁcally, the methods were veriﬁed to solve the MNL model to exact optimality 10,000 times faster than the mixed integer linear programming formulation described in the literature. We performed additional computational experiments to assess the performance of our computational methods on a variety of large-scale instances. Our computational analysis also elucidates the diﬀerence between the MNL and NL models.
Defocus deblurring for catadioptric omnidirectional imaging based on spatially invariant point spread function

The problem of catadioptric omnidirectional imaging defocus blur, which is caused by lens aperture and mirror curvature, becomes more severe when high resolution sensors and large apertures are applied. In order to overcome this problem, a novel method based on computational photography is proposed. Firstly, the defocus blur of catadioptric omnidirectional imaging is analyzed to calculate the point spread function for different scene points. Then, the defocus blur kernel of omnidirectional image is confirmed to be spatially invariant when rotating the focus ring of camera lens during an image’s integration time. Lastly, the deconvolution algorithm using prior sparse derivatives is applied to obtain all-focused/sharp omnidirectional images. Experimental results demonstrate that the proposed method is effective for omnidirectional image deblurring and can be applied to most existing catadioptric omnidirectional imaging systems.
End-to-end AI framework for interpretable prediction of molecular and crystal properties

We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab.
Artistic Arbitrary Style Transfer

Arbitrary Style Transfer is a technique used to produce a new image from two images: a content image, and a style image. The newly produced image is unseen and is generated from the algorithm itself. Balancing the structure and style components has been the major challenge that other state-of-the-art algorithms have tried to solve. Despite all the efforts, it's still a major challenge to apply the artistic style that was originally created on top of the structure of the content image while maintaining consistency. In this work, we solved these problems by using a Deep Learning approach using Convolutional Neural Networks. Our implementation will first extract foreground from the background using the pre-trained Detectron 2 model from the content image, and then apply the Arbitrary Style Transfer technique that is used in SANet. Once we have the two styled images, we will stitch the two chunks of images after the process of style transfer for the complete end piece.
Lattice approach to string theory

We construct a lattice version of dual string theory, which we hope will be useful in systematically analyzing the properties of that theory. Mass renormalization is discussed, and the familiar tachyon problem is seen to arise so long as counterterms are restricted to be local. The dual-loop expansion is discussed and the origin of the critical dimension elucidated. In the interacting theory, the critical dimension is required for Lorentz invariance, but it is also the only dimension for which the coupling-constant renormalization is finite. The choice of bare coupling constant, g/sub 0/ = 1, is seen to be particularly attractive. If we make this choice, the string interaction coupling constant is calculable in terms of the fundamental rest tension T/sub 0/. The instabilities associated with tachyons are discussed, and we propose a method for discovering whether the interactions stabilize the theory. Application of our ideas to the baryon three-string problem is also mentioned.
Quantifying Patterns in Mortuary Practices: An Application of Factor Analysis and Cluster Analysis to Data From the Taosi Site, China

Abstract In Chinese mortuary research, too much reliance on traditional qualitative typological analysis renders quantitative attributes of mortuary practice data ignored. Examining the Taosi cemetery one of the famous cemeteries of Neolithic China (2300–1900 BC), this study discusses the advantages and disadvantages of typology and digital methods. Extant qualitative research has classified the Taosi burials into six vertical categories, representing a pyramidal social hierarchy. However, this approach solely relied on the labor expenditure principle, whose outcome was highly subjective and difficult to verify. This study applies a multivariant analysis. Factor analysis is used to investigate the correlations within the mortuary data. The statistical factor scores quantify the differences between the combination of burial objects in different tombs and allow clarification by cluster analysis to investigate their different social meanings. The results reveal two-axial divisions in the Taosi cemetery: vertical stratification based on hierarchy and horizontal differentiation based on social categories/identities. Compared with the simplistic typological description, such a quantitative method reveals the characteristics of each category more clearly, clarifies the classification criteria, and extracts more detailed information about the society and its mortuary practices.
Production of Very High Magnetic Fields by Implosion

Magnetic fields are produced in the 10–15 megagauss range by use of high explosives which compress the flux obtained from initial fields of approximately a hundred thousand gauss. The fields described here occupy a cylindrical volume and are essentially axial. A typical field might have these general characteristics: Peak field 14 megagauss; 2 μsec duration from 10–14 megagauss; field volume around peak, 6 mm diameter, 50 mm estimated length.