Miracle-dz/UniFAR-FTUs huggingface dataset:
BASS. XXIX. The Near-infrared View of the Broad-line Region (BLR): The Effects of Obscuration in BLR Characterization

Virial black hole (BH) mass (M BH) determination directly involves knowing the broad-line region (BLR) clouds’ velocity distribution, their distance from the central supermassive BH (R BLR), and the virial factor (f). Understanding whether biases arise in M BH estimation with increasing obscuration is possible only by studying a large (N > 100) statistical sample of obscuration-unbiased (hard) X-ray-selected active galactic nuclei (AGNs) in the rest-frame near-infrared (0.8–2.5 μm) since it penetrates deeper into the BLR than the optical. We present a detailed analysis of 65 local Burst Alert Telescope (BAT) selected Seyfert galaxies observed with Magellan/FIRE. Adding these to the near-infrared BAT AGN spectroscopic survey database, we study a total of 314 unique near-infrared spectra. While the FWHMs of Hα and near-infrared broad lines (He i, Paβ, Paα) remain unbiased to either BLR extinction or X-ray obscuration, the Hα broad-line luminosity is suppressed when N H ≳ 1021 cm−2, systematically underestimating M BH by 0.23–0.46 dex. Near-infrared line luminosities should be preferred to Hα until N H < 1022 cm−2, while at higher obscuration a less-biased R BLR proxy should be adopted. We estimate f for Seyfert 1 and 2 using two obscuration-unbiased M BH measurements, i.e., the stellar velocity dispersion and a BH mass prescription based on near-infrared and X-ray, and find that the virial factors do not depend on the redshift or obscuration, but some broad lines show a mild anticorrelation with M BH. Our results show the critical impact obscuration can have on BLR characterization and the importance of the near-infrared and X-rays for a less-biased view of the BLR.
Lessons of L’Aquila for Operational Earthquake Forecasting

Editor’s note: The following is the text of an address given at the Public Policy Luncheon of the Seismological Society of America (SSA) Annual Meeting on 19 April 2012. In light of the manslaughter convictions handed down for six earth scientists and a public safety official in an Italian court on 22 October 2012, SRL is publishing the content of this presentation. The recorded presentation can be accessed at www.seismosoc. org/italy/. See SSA’s statement on the convictions in the News and Notes column in this issue of SRL or at http://www.seismosoc. org/news/newsitem.php? id=i20121011183. The L’Aquila earthquake of 6 April 2009 (magnitude 6.3) killed 309 people, injured more than 1500 people, and left tens of thousands homeless. In June 2010, the vice‐director of the Italian Department of Civil Protection (DPC) and six scientists associated with one of its advisory bodies, the Commission on the Forecasting and Prevention of Major Risk, were indicted on charges of criminal manslaughter. After two judicial reviews, the case was ordered to trial, which began last September. The prosecution was completed a few weeks ago, and the trial has now moved into its defense phase. Meanwhile, on the basis of newly revealed wiretaps, which were recorded before the earthquake, charges are being prepared against the former head of the DPC, Guido Bertolaso. The case quickly became a cause celebre among scientists. When news of the prosecutions broke in June 2010, the reaction was furious. The indictments appeared to blame the scientists for not alerting the local population of an impending earthquake—for failure to predict. It is well known that large earthquakes cannot be accurately predicted in the short term. Why would an Italian court try to punish scientists for not doing something they did not (and still do not) know how to do? > It is well known that …
Classification of integrable boundary equations for integrable quad-graph systems

In the context of integrable systems on quad-graphs, the boundary consistency around a half of a rhombic dodecahedron, as a companion notion to the three-dimensional consistency around a cube, was introduced as a criterion for defining integrable boundary conditions for quad-graph systems with a boundary. In this paper, we formalize the notions of boundary equations as boundary conditions for quad-graph systems, and provide a systematic method for solving the boundary consistency, which results in a classification of integrable boundary equations for quad-graph equations in the Adler-Bobenko-Suris classification. This relies on factorizing, first the quad-graph equations into pairs of dual boundary equations, and then the consistency on a rhombic dodecahedron into two equivalent boundary consistencies. Generalizations of the method to rhombic-symmetric equations are also considered.
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Federated Learning

Federated learning (FL) facilitates the training and deploying AI models on edge devices. Preserving user data privacy in FL introduces several challenges, including expensive communication costs, limited resources, and data heterogeneity. In this paper, we propose SPATL, an FL method that addresses these issues by: (a) introducing a salient parameter selection agent and communicating selected parameters only; (b) splitting a model into a shared encoder and a local predictor, and transferring its knowledge to heterogeneous clients via the locally customized predictor. Additionally, we leverage a gradient control mechanism to further speed up model convergence and increase robustness of training processes. Experiments demonstrate that SPATL reduces communication overhead, accelerates model inference, and enables stable training processes with better results compared to state-of-the-art methods. Our approach reduces communication cost by up to 86.45%, accelerates local inference by reducing up to 39.7% FLOPs on VGG-11, and requires 7.4× less communication overhead when training ResNet-20.11Code is available at: https://github.com/yusx-swapp/SPATL
The language of pre-topology in knowledge spaces

We systematically study some basic properties of the theory of pre-topological spaces, such as, pre-base, subspace, axioms of separation, connectedness, etc. Pre-topology is also known as knowledge space in the theory of knowledge structures. We discuss the language of axioms of separation of pre-topology in the theory of knowledge spaces, the relation of Alexandroff spaces and quasi ordinal spaces, and the applications of the density of pre-topological spaces in primary items for knowledge spaces. In particular, we give a characterization of a skill multimap such that the delineate knowledge structure is a knowledge space, which gives an answer to a problem in \cite{falmagne2011learning} or \cite{XGLJ} whenever each item with finitely many competencies; moreover, we give an algorithm to find the set of atom primary items for any finite knowledge spaces.
Robust and Accurate Superquadric Recovery: a Probabilistic Approach

Interpreting objects with basic geometric primitives has long been studied in computer vision. Among geometric primitives, superquadrics are well known for their ability to represent a wide range of shapes with few parameters. However, as the first and foremost step, recovering superquadrics accurately and robustly from 3D data still remains challenging. The existing methods are subject to local optima and sensitive to noise and outliers in real-world scenarios, resulting in frequent failure in capturing geometric shapes. In this paper, we propose the first probabilistic method to recover superquadrics from point clouds. Our method builds a Gaussian-uniform mixture model (GUM) on the parametric surface of a superquadric, which explicitly models the generation of outliers and noise. The superquadric recovery is formulated as a Maximum Likelihood Estimation (MLE) problem. We propose an algorithm, Expectation, Maximization, and Switching (EMS), to solve this problem, where: (1) outliers are predicted from the posterior perspective; (2) the superquadric parameter is optimized by the trust-region reflective algorithm; and (3) local optima are avoided by globally searching and switching among parameters encoding similar superquadrics. We show that our method can be extended to the multi-superquadrics recovery for complex objects. The proposed method outperforms the state-of-the-art in terms of accuracy, efficiency, and robustness on both synthetic and real-world datasets. The code is at http://github.com/bmlklwx/EMS-superquadric_fitting. git.
A prediction for anisotropies in the nearby Hubble flow

We assess the dominant low-redshift anisotropic signatures in the distance-redshift relation and redshift drift signals. We adopt general-relativistic irrotational dust models allowing for gravitational radiation — the `quiet universe models' — which are extensions of the silent universe models. Using cosmological simulations evolved with numerical relativity, we confirm that the quiet universe model is a good description on scales larger than those of collapsing structures. With this result, we reduce the number of degrees of freedom in the fully general luminosity distance and redshift drift cosmographies by a factor of ∼ 2 and ∼ 2.5, respectively, for the most simplified case. We predict a dominant dipolar signature in the distance-redshift relation for low-redshift data, with direction along the gradient of the large-scale density field. Further, we predict a dominant quadrupole in the anisotropy of the redshift drift signal, which is sourced by the electric Weyl curvature tensor. The signals we predict in this work should be tested with present and near-future cosmological surveys.
Performance of earth-moving machinery cab with hydraulic mounts in low frequency

The purpose of this study is to explore the low-frequency advantages and characteristics of the hydraulic mounts used for vibration isolation of an earth-moving machinery cab. For the feature of the cab’s center of mass being relatively high above the cab’s supporting surface, the pitch and roll vibrations of the cab are prone to generate in the low frequency range. A six-degree-of-freedom (d.f.) model of the cab supported by hydraulic mounts with quadratic damping is set up in this paper. And the simulation which compares performance of the hydraulic mounts and the rubber mounts used in the cab is carried out. It shows that the cab system with quadratic damping hydraulic mounts has remarkable efficiency to mitigate the vibrations and in turn to enhance the cab comfort, but its nonlinear damping characteristic has almost no effect on the natural frequencies of the cab system. A new approach is also proposed, which considers the absolute displacement of the pitch motion of the cab besides the traditional absolute accelerations, to improve the indications of the ride comfort for the suspended cab with a high-positioned mass center in the isolation design of the earth-moving machinery cab.
Factor-augmented tree ensembles

This manuscript proposes to extend the information set of time-series regression trees with latent stationary factors extracted via state-space methods. In doing so, this approach generalises time-series regression trees on two dimensions. First, it allows to handle predictors that exhibit measurement error, non-stationary trends, seasonality and/or irregularities such as missing observations. Second, it gives a transparent way for using domain-specific theory to inform time-series regression trees. Empirically, ensembles of these factor-augmented trees provide a reliable approach for macro-finance problems. This article highlights it focussing on the lead-lag effect between equity volatility and the business cycle in the United States.
A Hierarchy-Aware Pose Representation for Deep Character Animation

Data-driven character animation techniques rely on the existence of a properly established model of motion, capable of describing its rich context. However, commonly used motion representations often fail to accurately encode the full articulation of motion, or present artifacts. In this work, we address the fundamental problem of finding a robust pose representation for motion modeling, suitable for deep character animation, one that can better constrain poses and faithfully capture nuances correlated with skeletal characteristics. Our representation is based on dual quaternions, the mathematical abstractions with well-defined operations, which simultaneously encode rotational and positional orientation, enabling a hierarchy-aware encoding, centered around the root. We demonstrate that our representation overcomes common motion artifacts, and assess its performance compared to other popular representations. We conduct an ablation study to evaluate the impact of various losses that can be incorporated during learning. Lever-aging the fact that our representation implicitly encodes skeletal motion attributes, we train a network on a dataset comprising of skeletons with different proportions, without the need to retarget them first to a universal skeleton, which causes subtle motion elements to be missed. We show that smooth and natural poses can be achieved, paving the way for fascinating applications.
Cross-Task Consistency Learning Framework for Multi-Task Learning

Multi-task learning (MTL) is an active field in deep learning in which we train a model to jointly learn multiple tasks by exploiting relationships between the tasks. It has been shown that MTL helps the model share the learned features between tasks and enhance predictions compared to when learning each task independently. We propose a new learning framework for 2-task MTL problem that uses the predictions of one task as inputs to another network to predict the other task. We define two new loss terms inspired by cycle-consistency loss and contrastive learning, alignment loss and cross-task consistency loss. Both losses are designed to enforce the model to align the predictions of multiple tasks so that the model predicts consistently. We theoretically prove that both losses help the model learn more efficiently and that cross-task consistency loss is better in terms of alignment with the straight-forward predictions. Experimental results also show that our proposed model achieves significant performance on the benchmark Cityscapes and NYU dataset.
Development of real-time system for simultaneous observation of visual discharges and leakage current on contaminated DC insulators

A novel real-time observation system has been developed. It is capable of simultaneously recording visual partial discharge arcs and leakage currents on insulator surfaces under wet-contaminated conditions during a 2-h test. Since this system uses a video recording system uses a video recording system, the discharges and currents can be reproduced repeatedly even at a slowed-down speed. To permit simultaneous recording and accurate data processing of leakage currents, a personal computer is also employed. It was found that the sampling frequency of 1.0 kHz was satisfactory for the recording of the leakage current. Another function of the computer is to exhibit the contours of insulators to be tested in a dark room so that the locations where the arcs stay can be clearly identified. Tests by the clean fog procedure confirmed the good performance of the system at the time of the design. Some experimental results and the applicability of this system are discussed. >
Throughput Maximization for IRS-Aided MIMO FD-WPCN With Non-Linear EH Model

This paper studies an intelligent reflecting surface (IRS)-aided multiple-input-multiple-output (MIMO) full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid access point (HAP) operating in FD broadcasts energy signals to multiple devices for their energy harvesting (EH) in the downlink (DL) and meanwhile receives information signals from devices in the uplink (UL) with the help of an IRS. Taking into account the practical finite self-interference (SI) and the non-linear EH model, we formulate the weighted sum throughput maximization optimization problem by jointly optimizing DL/UL time allocation, precoding matrices at devices, transmit covariance matrices at the HAP, and phase shifts at the IRS. Since the resulting optimization problem is non-convex, there are no standard methods to solve it optimally in general. To tackle this challenge, we first propose an element-wise (EW) based algorithm, where each IRS phase shift is alternately optimized in an iterative manner. To reduce the computational complexity, a minimum mean-square error (MMSE) based algorithm is proposed, where we transform the original problem into an equivalent form based on the MMSE method, which facilities the design of an efficient iterative algorithm. In particular, the IRS phase shift optimization problem is recast as an second-order cone program (SOCP), where all the IRS phase shifts are simultaneously optimized. For comparison, we also study two suboptimal IRS beamforming configurations in simulations, namely partially dynamic IRS beamforming (PDBF) and static IRS beamforming (SBF), which strike a balance between the system performance and practical complexity. Simulation results demonstrate the effectiveness of proposed two algorithms. Besides, the results show the superiority of our proposed scheme over other benchmark schemes and also unveil the importance of the joint design of passive beamforming and resource allocation for achieving energy efficient MIMO FD-WPCNs.
Adaptive Perturbation for Adversarial Attack

In recent years, the security of deep learning models achieves more and more attentions with the rapid development of neural networks, which are vulnerable to adversarial examples. Almost all existing gradient-based attack methods use the sign function in the generation to meet the requirement of perturbation budget on L∞ norm. However, we find that the sign function may be improper for generating adversarial examples since it modifies the exact gradient direction. Instead of using the sign function, we propose to directly utilize the exact gradient direction with a scaling factor for generating adversarial perturbations, which improves the attack success rates of adversarial examples even with fewer perturbations. At the same time, we also theoretically prove that this method can achieve better black-box transferability. Moreover, considering that the best scaling factor varies across different images, we propose an adaptive scaling factor generator to seek an appropriate scaling factor for each image, which avoids the computational cost for manually searching the scaling factor. Our method can be integrated with almost all existing gradient-based attack methods to further improve their attack success rates. Extensive experiments on the CIFAR10 and ImageNet datasets show that our method exhibits higher transferability and outperforms the state-of-the-art methods.
3D Kinematics and Kinetics of the Golf Swing

Three-dimensional kinematics and kinetics for a double pendulum model golf swing were determined for 6 subjects, who were filmed by two phase-locked Photosonics cameras. The film was digitally analyzed. Abdel-Aziz and Karara's (1971) algorithm was used to determine three-dimensional spatial coordinates for the segment endpoints. Linear kinematic and kinetic data showed similarities with previous studies. The orientation of the resultant joint force at the wrists was in the direction of motion of the club center of gravity for most of the downswing. Such an orientation of the force vector would tend to prevent wrist uncocking. Indeterminate peak angular velocities for rotations about the X axis were reported. However, these peaks were due to computational instabilities that occurred when the club was perpendicular to the YZ plane. Furthermore, the motion of the club during the downswing was found to be nonplanar. Wrist uncocking appeared to be associated with the resultant joint torque and not the resultan.
Effect of Asynchronous Earthquake Motion on Complex Bridges. I: Methodology and Input Motion

Based on observed damage patterns from previous earthquakes and a rich history of analytical studies, asynchronous input motion has been identified as a major source of unfavorable response for long-span structures, such as bridges. This study is aimed at quantifying the effect of geometric incoherence and wave arrival delay on complex straight and curved bridges using state-of-the-art methodologies and tools. Using fully parametrized computer codes combining expert geotechnical and earthquake structural engineering knowledge, suites of asynchronous accelerograms are produced for use in inelastic dynamic analysis of the bridge model. Two multi- degree-of-freedom analytical models are analyzed using 2,000 unique synthetic accelerograms with results showing significant response amplification due to asynchronous input motion, demonstrating the importance of considering asynchronous seismic input in complex, irregular bridge design. The paper, Part 1 of a two-paper investigation, presents the development of the input motion sets and the modeling and analysis approach employed, concluding with sample results. Detailed results and implications on seismic assessment are presented in the companion paper: Effect of Asynchronous Motion on Complex Bridges. Part II: Results and Implications on Assessment.
Variable Geometry Mixed Flow Turbine for Turbochargers: An Experimental Study

This paper investigates a variable geometry (VG) mixed flow turbine with a novel, purposely designed pivoting nozzle vane ring. The nozzle vane ring was matched to the 3-dimensional aspect of the mixed flow rotor leading edge with lean stacking. It was found that for a nozzle vane ring in a volute, the vane surface pressure is highly affected by the flow in the volute rather than the adjacent vane surface interactions, especially at closer nozzle positions. The performance of the VG mixed flow turbine has been evaluated experimentally in steady and unsteady flow conditions. The VG mixed flow turbine shows higher peak efficiency and swallowing capacity at various vane angle settings compared to an equivalent nozzleless turbine. Comparison with an equivalent straight vane arrangement shows a higher swallowing capacity but similar efficiencies. The VG turbine unsteady performance was found to deviate substantially from the quasi-steady assumption compared to a nozzleless turbine. This is more evident in the higher vane angle settings (smaller nozzle passage), where there are high possibility of choking during a pulse cycle. The presented steady and unsteady results are expected to be beneficial in the design of variable geometry turbochargers, especially the ones with a mixed flow turbine.