Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast and Robust Non-Rigid Registration Using Accelerated Majorization-Minimization

Non-rigid 3D registration, which deforms a source 3D shape in a non-rigid way to align with a target 3D shape, is a classical problem in computer vision. Such problems can be challenging because of imperfect data (noise, outliers and partial overlap) and high degrees of freedom. Existing methods typically adopt the <inline-formula><tex-math notation="LaTeX">$\ell _{p}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>ℓ</mml:mi><mml:mi>p</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="zhang-ieq1-3247603.gif"/></alternatives></inline-formula> type robust norm to measure the alignment error and regularize the smoothness of deformation, and use a proximal algorithm to solve the resulting non-smooth optimization problem. However, the slow convergence of such algorithms limits their wide applications. In this paper, we propose a formulation for robust non-rigid registration based on a globally smooth robust norm for alignment and regularization, which can effectively handle outliers and partial overlaps. The problem is solved using the majorization-minimization algorithm, which reduces each iteration to a convex quadratic problem with a closed-form solution. We further apply Anderson acceleration to speed up the convergence of the solver, enabling the solver to run efficiently on devices with limited compute capability. Extensive experiments demonstrate the effectiveness of our method for non-rigid alignment between two shapes with outliers and partial overlaps, with quantitative evaluation showing that it outperforms state-of-the-art methods in terms of registration accuracy and computational speed. The source code is available at <uri>https://github.com/yaoyx689/AMM_NRR</uri>.
Allometric scaling in global corporations as a benchmarking approach to assess environmental performance

The largest 6,529 international corporations are accountable for almost 30% of global CO 2 e emissions. A growing awareness of the role of the corporate world in the path toward sustainability has led many shareholders and stakeholders to pursue increasingly stringent and ambitious environmental goals. However, how to assess the corporate environmental performance objectively and eﬃciently remains an open question. This study reveals underlying dynamics and structures that can be used to construct a uniﬁed quantitative picture of the environmental impact of companies. This study shows that the environmental impact (metabolism) of companies CO 2 energy used, water withdrawal and waste production, scales with their size according to a simple power law which is often sublinear, and can be used to derive a sector-speciﬁc, size-dependent benchmark to asses unambiguously a company’s environmental performance. Enforcing such a benchmark would potentially result in a 15% emissions reduction, but a fair and eﬀective environmental policy should consider the size of the corporation and the super or sublinear nature of the allometric relationship.
EMPRESS. V. Metallicity Diagnostics of Galaxies over 12 + log(O/H) ≃ 6.9–8.9 Established by a Local Galaxy Census: Preparing for JWST Spectroscopy

We present optical-line gas metallicity diagnostics established by the combination of local SDSS galaxies and the largest compilation of extremely metal-poor galaxies (EMPGs) including new EMPGs identified by the Subaru EMPRESS survey. A total of 103 EMPGs are included, covering a large parameter space of magnitude (M i = −19 to −7) and Hβ equivalent width (10–600 Å), i.e., wide ranges of stellar mass and star formation rate. Using reliable metallicity measurements from the direct method for these galaxies, we derive the relationships between strong optical-line ratios and gas-phase metallicity over the range of 12+log(O/H) ≃ 6.9–8.9, corresponding to 0.02–2 solar metallicity Z ⊙. We confirm that the R23 index, ([O iii]+[O ii])/Hβ, is the most accurate metallicity indicator with a metallicity uncertainty of 0.14 dex over the range among various popular metallicity indicators. The other metallicity indicators show large scatters in the metal-poor range (≲0.1 Z ⊙). It is explained by our CLOUDY photoionization modeling that, unlike the R23 index, the other metallicity indicators do not use a sum of singly and doubly ionized lines and cannot trace both low- and high-ionization gas. We find that the accuracy of the metallicity indicators is significantly improved if one uses Hβ equivalent width measurements that tightly correlate with ionization states. In this work, we also present the relation of physical properties with the UV-continuum slope β and ionization production rate ξ ion derived with GALEX data for the EMPGs and provide local anchors of galaxy properties together with the optical-line metallicity indicators that are available in the form of a machine-readable table and useful for forthcoming JWST spectroscopic studies.
Deep learning metric detectors in general relativity

We consider conceptual issues of deep learning (DL) for metric detectors using test particle geodesics in curved spacetimes. Advantages of DL metric detectors are emphasized from a view point of general coordinate transformations. Two given metrics (two spacetimes) are defined to be conneted by a DL isometry if their geodesic image data cannot be discriminated by any DL metric detector at any time. The fundamental question of when the DL isometry appears is extensively explored. If the two spacetimes connected by the DL isometry are in superposition of quantum gravity theory, the post-measurement state may be still in the same superposition even after DL metric detectors observe the superposed state. We also demonstrate metric-detection DL's in 2+1 dimensional anti-de Sitter (AdS) spacetimes to estimate the cosmological constants and Brown-Henneaux charges. In the AdS/CFT correspondence dictionary, it may be expected that such metric detectors in the AdS bulk region correspond to quantum measurement devices in the CFT at the AdS boundary.
Learning in Observable POMDPs, without Computationally Intractable Oracles

Much of reinforcement learning theory is built on top of oracles that are computationally hard to implement. Specifically for learning near-optimal policies in Partially Observable Markov Decision Processes (POMDPs), existing algorithms either need to make strong assumptions about the model dynamics (e.g. deterministic transitions) or assume access to an oracle for solving a hard optimistic planning or estimation problem as a subroutine. In this work we develop the first oracle-free learning algorithm for POMDPs under reasonable assumptions. Specifically, we give a quasipolynomial-time end-to-end algorithm for learning in"observable"POMDPs, where observability is the assumption that well-separated distributions over states induce well-separated distributions over observations. Our techniques circumvent the more traditional approach of using the principle of optimism under uncertainty to promote exploration, and instead give a novel application of barycentric spanners to constructing policy covers.
Discussion on Nonlinear Dynamic Behavior of Suspension Based Bridge Model

In this paper we explore the numerical study. of the Nonlinear Behavior of Suspension Bridge Models. The study of suspension bridges is one of the classic problems of mechanical vibrations, one of the most famous collapses of which was that of the Tacoma Narrows Bridge. This paper covers an initial explanation of vibrations in a suspension bridge. To do this, three di ff erent systems are going to be simulated: The first being a asystem where only the vertical vibrations of the bridge deck are taken into account, the second covering the vibrations of the main cable and the roadbed, and lastly, a system that takes both vertical and torsional vibrations into account. A time-frequency analysis will also be done on all systems with temporal response, Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT), plus in a specific case the use of Hilbert-Huang transform (HHT). Poincare´ maps and Lyapunov exponents are used to characterize the dynamics of the system. In particular, in the vertical and torsional system, an explanation of why the Tacoma Bridge oscillations have undergone an abrupt change from vertical to torsional oscillations. Thus, extremely rich dynamic behaviors are studied by numerical simulation in the time and frequency domains.
Economic activity and climate change

In this paper, we survey recent econometric contributions to measure the relationship between economic activity and climate change. Due to the critical relevance of these effects for the well-being of future generations, there is an explosion of publications devoted to measuring this relationship and its main channels. The relation between economic activity and climate change is complex with the possibility of causality running in both directions. Starting from economic activity, the channels that relate economic activity and climate change are energy consumption and the consequent pollution. Hence, we first describe the main econometric contributions about the interactions between economic activity and energy consumption, moving then to describing the contributions on the interactions between economic activity and pollution. Finally, we look at the main results on the relationship between climate change and economic activity. An important consequence of climate change is the increasing occurrence of extreme weather phenomena. Therefore, we also survey contributions on the economic effects of catastrophic climate phenomena.
Inferring Unfairness and Error from Population Statistics in Binary and Multiclass Classification

We propose methods for making inferences on the fairness and accuracy of a given classiﬁer, using only aggregate population statistics. This is necessary when it is impossible to obtain individual classiﬁcation data, for instance when there is no access to the classiﬁer or to a representative individual-level validation set. We study fairness with respect to the equalized odds criterion, which we generalize to multiclass classiﬁcation. We propose a measure of unfairness with respect to this criterion, which quantiﬁes the fraction of the population that is treated unfairly. We then show how inferences on the unfairness and error of a given classiﬁer can be obtained using only aggregate label statistics such as the rate of prediction of each label in each sub-population, as well as the true rate of each label. We derive inference procedures for binary classiﬁers and for multiclass classiﬁers, for the case where confusion matrices in each sub-population are known, and for the signiﬁcantly more challenging case where they are unknown. We report experiments on data sets representing diverse applications, which demonstrate the eﬀectiveness and the wide range of possible uses of the proposed methodology.
Nezha: Deployable and High-Performance Consensus Using Synchronized Clocks

This paper presents a high-performance consensus protocol, Nezha, which can be deployed by cloud tenants without support from cloud providers. Nezha bridges the gap between protocols such as Multi-Paxos and Raft, which can be readily deployed, and protocols such as NOPaxos and Speculative Paxos, that provide better performance, but require access to technologies such as programmable switches and in-network prioritization, which cloud tenants do not have. Nezha uses a new multicast primitive called deadline-ordered multicast (DOM). DOM uses high-accuracy software clock synchronization to synchronize sender and receiver clocks. Senders tag messages with deadlines in synchronized time; receivers process messages in deadline order, on or after their deadline. We compare Nezha with Multi-Paxos, Fast Paxos, Raft, (optimized) NOPaxos, and 2 recent protocols, Domino and TOQ-EPaxos, that use synchronized clocks. In throughput, Nezha outperforms all baselines by a median of 5.4X (range: 1.9--20.9X). In latency, Nezha outperforms five baselines by a median of 2.3X (range: 1.3--4.0X), with one exception: it sacrifices 33% of latency compared with our optimized NOPaxos in one test. We also prototype two applications, a key-value store and a fair-access stock exchange, on top of Nezha to show that Nezha only modestly reduces their performance relative to an unreplicated system.
Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning

Works on learning job title representation are mainly based on \textit{Job-Transition Graph}, built from the working history of talents. However, since these records are usually messy, this graph is very sparse, which affects the quality of the learned representation and hinders further analysis. To address this specific issue, we propose to enrich the graph with additional nodes that improve the quality of job title representation. Specifically, we construct \textit{Job-Transition-Tag Graph}, a heterogeneous graph containing two types of nodes, i.e., job titles and tags (i.e., words related to job responsibilities or functionalities). Along this line, we reformulate job title representation learning as the task of learning node embedding on the \textit{Job-Transition-Tag Graph}. Experiments on two datasets show the interest of our approach.
Recent Advances in Bayesian Optimization

Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the development of new Bayesian optimization algorithms and their applications. Hence, this article attempts to provide a comprehensive and updated survey of recent advances in Bayesian optimization that are mainly based on Gaussian processes and identify challenging open problems. We categorize the existing work on Bayesian optimization into nine main groups according to the motivations and focus of the proposed algorithms. For each category, we present the main advances with respect to the construction of surrogate models and adaptation of the acquisition functions. Finally, we discuss the open questions and suggest promising future research directions, in particular with regard to heterogeneity, privacy preservation, and fairness in distributed and federated optimization systems.
Clinical Outcome in Aortic Regurgitation with Cardiovascular Magnetic Resonance

: Background - Current indications for surgery in patients with significant aortic regurgitation (AR) focus on symptoms and left ventricular (LV) dilation/dysfunction. However, prognosis is already reduced by this stage and earlier identification of patients for surgery could be beneficial. Quantifying the regurgitation may help, but there is limited data on its link with outcome. Cardiovascular magnetic resonance (CMR) can accurately quantify AR, and we examined whether this was associated with the future need for surgery. Methods and Results - 113 patients with echocardiographic moderate or severe AR were monitored for up to 9 years (mean 2.6±2.1 years) following a CMR scan, and the progression to symptoms or other indications for surgery was monitored. AR quantification identified outcome with high accuracy: 85% of the 39 subjects with regurgitant fraction >33% progressed to surgery (mostly within 3 years) compared to 8% of 74 subjects with regurgitant fraction (cid:148) 33% (p<0.0001); area under the curve on receiver operating characteristic analysis 0.93 (p<0.0001). This ability remained strong on time-dependent Kaplan-Meier survival curves. CMR-derived end-diastolic volume (LVEDV) >246ml had good, though lower, discriminatory ability (AUC 0.88) but the combination of this with regurgitant fraction provided the best discriminatory power. Conclusions - High degrees of CMR-quantified AR were associated with the development of symptoms or other indications for surgery. Quantifying AR showed slightly better discriminatory ability than ‘Gold-standard’ CMR ventricular volume assessment. This could provide a new paradigm for the timing of surgical intervention but requires confirmation in a clinical trial. monitored for up to 9 years (mean 2.6±2.1 years) following a CMR scan, and thehe ppprororogrgrgresesessisisiononon tto ymptoms or other indications for surgery was monitored. AR quantification identified outcome withth CCarrdididiooovasascuculalar BiBomomeddicical RResseaarcrh UnUitit offf the RoRoyayl BrBrromooptptonon andd HHaraefieeldld NNHS FFouunundation Trurussst aaandndd Impmpmpeereriaiaialll CoCoolllllegegeee LLLonnddooon. SNSNSN ackckcknonowlwlwlededgggess susuppppoorort t frfrfromom ttthehehe OOOxfxffoorord dd BrBrrititisisih h Hearrtt t FoFoFununundadattiionn CCeenentrtrtre e ofoff RRReeseeaearcrchhh EExExceellllenenncecee. of prrracccticeee. AoAorrrtitiic c rreeggugurgrggittatatioionnn. EnEnEngglgl
GAAF: Searching Activation Functions for Binary Neural Networks through Genetic Algorithm

—Binary neural networks (BNNs) show promising utilization in cost and power-restricted domains such as edge devices and mobile systems. This is due to its signiﬁcantly less computation and storage demand, but at the cost of degraded performance. To close the accuracy gap, in this paper we propose to add a complementary activation function (AF) ahead of the sign based binarization, and rely on the genetic algorithm (GA) to automatically search for the ideal AFs. These AFs can help extract extra information from the input data in the forward pass, while allowing improved gradient approximation in the backward pass. Fifteen novel AFs are identiﬁed through our GA- based search, while most of them show improved performance (up to 2.54% on ImageNet) when testing on different datasets and network models. Our method offers a novel approach for designing general and application-speciﬁc BNN architecture. Our code is available at http://github.com/ﬂying-Yan/GAAF.
Spatial Acoustic Projection for 3D Imaging Sonar Reconstruction

In this work we present a novel method for reconstructing 3D surfaces using a multi-beam imaging sonar. We integrate the intensities measured by the sonar from different viewpoints for fixed cell positions in a 3D grid. For each cell we integrate a feature vector that holds the mean intensity for a discretized range of viewpoints. Based on the feature vectors and independent sparse range measurements that act as ground truth information, we train convolutional neural networks that allow us to predict the signed distance and direction to the nearest surface for each cell. The predicted signed distances can be projected into a truncated signed distance field (TSDF) along the predicted directions. Utilizing the marching cubes algorithm, a polygon mesh can be rendered from the TSDF. Our method allows a dense 3D reconstruction from a limited set of viewpoints and was evaluated on three real-world datasets.
Anisotropic magnetic property of single crystals 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>R</mml:mi><mml:msub><mml:mrow><mml:mi mathvariant="normal">V</mml:mi></mml:mrow><mml:mn>6</mml:mn></mml:msub><mml:msub><mml:mrow><mml:mi>Sn</mml:mi></mml:mrow><mml:mn>6</mml:mn></mml:ms

$R$V${_6}$Sn${_6}$ ($R$ = Y, Gd - Tm, Lu) single crystals are synthesized by Sn-flux method and their physical properties are characterized by magnetization, resistivity, and specific heat measurements. Powder X-ray diffraction patterns of all samples can be well indexed with the hexagonal HfFe$_6$Ge$_6$-type structure, where rare-earth atoms form hexagonal layers and vanadium atoms form Kagome layers. At high temperatures, magnetic susceptibility measurements of moment bearing rare-earths ($R$ = Gd - Tm) follow Curie-Weiss behavior. Effective moments estimated from the polycrystalline average of magnetic susceptibility curves are consistent with the values for free $R^{3+}$ ion. Strong magnetic anisotropy due to crystalline electric field effects is observed for moment bearing rare-earths, except GdV$_6$Sn$_6$. The easy magnetization direction is determined to be $c$-axis for $R$ = Tb - Ho and $ab$-plane for $R$ = Er, and Tm. The vanadium ions in $R$V${_6}$Sn${_6}$ possess no magnetic moment. The compounds for $R$ = Y and Lu exhibit typical characteristics of paramagnetic metals. At low temperatures, the magnetic ordering is confirmed from magnetization, specific heat, and resistivity: the highest $T_{N} = 4.9$~K for GdV$_6$Sn$_6$ and the lowest $T_{N} = 2.3$~K for HoV$_6$Sn$_6$. No magnetic ordering is observed down to 1.8~K for $R$ = Er and Tm. A slight deviation of the magnetic ordering temperature from the de Gennes scaling suggests the dominant Ruderman-Kittel-Kasuya-Yosida (RKKY) exchange interaction between rare-earth moments in metallic $R$V${_6}$Sn${_6}$ compounds.
Efficient Machine Learning, Compilers, and Optimizations for Embedded Systems

The recent development of Deep Neural Networks (DNNs) has made machine learning based smart solutions more relevant and accessible to the general public. We have seen that some DNN technologies have been integrated into our daily applications to provide high-quality inference services, such as image recognition, natural language processing, self-driving cars, and augmented and virtual reality [1, 2, 3, 4], which have made our lives more convenient and our work more efficient. A significant number of these machine learning applications leverage edge devices and need to be deployed onto resource-constrained embedded systems, such as cell phones, cameras, and unmanned aerial vehicles (UAVs). They require not only higher inference accuracy to achieve intelligent responses but also aggressive inference speed, throughput, and energy efficiency to meet real-life demands. As DNNs become more complicated, developing and serving the DNN-enabled applications requires more compute and memory resources, longer latency, and greater energy consumption. For example, the computation demands for DNN training have risen by over 300,000 times between AlexNet [5], the champion model of the 2012 ImageNet competition, and the AlphaGo Zero [6], the AI player proposed
DETR++: Taming Your Multi-Scale Detection Transformer

Convolutional Neural Networks (CNN) have dominated the field of detection ever since the success of AlexNet in ImageNet classification [12]. With the sweeping reform of Transformers [27] in natural language processing, Carion et al. [2] introduce the Transformer-based detection method, i.e., DETR. However, due to the quadratic complexity in the self-attention mechanism in the Transformer, DETR is never able to incorporate multi-scale features as performed in existing CNN-based detectors, leading to inferior results in small object detection. To mitigate this issue and further improve performance of DETR, in this work, we investigate different methods to incorporate multi-scale features and find that a Bi-directional Feature Pyramid (BiFPN) works best with DETR in further raising the detection precision. With this discovery, we propose DETR++, a new architecture that improves detection results by 1.9% AP on MS COCO 2017, 11.5% AP on RICO icon detection, and 9.1% AP on RICO layout extraction over existing baselines.
Patch-based image Super Resolution using generalized Gaussian mixture model

Single Image Super Resolution (SISR) methods aim to recover the clean images in high resolution from low resolution observations.A family of patch-based approaches have received considerable attention and development. The minimum mean square error (MMSE) methodis a powerful image restoration method that uses a probability model on the patches of images. This paper proposes an algorithm to learn a jointgeneralized Gaussian mixture model (GGMM) from a pair of the low resolution patches and the corresponding high resolution patches fromthe reference data. We then reconstruct the high resolution image based on the MMSE method. Our numerical evaluations indicate that theMMSE-GGMM method competes with other state of the art methods.