Miracle-dz/UniFAR-FTUs huggingface dataset:
Capturing the motion of every joint: 3D human pose and shape estimation with independent tokens

In this paper we present a novel method to estimate 3D human pose and shape from monocular videos. This task requires directly recovering pixel-alignment 3D human pose and body shape from monocular images or videos, which is challenging due to its inherent ambiguity. To improve precision, existing methods highly rely on the initialized mean pose and shape as prior estimates and parameter regression with an iterative error feedback manner. In addition, video-based approaches model the overall change over the image-level features to temporally enhance the single-frame feature, but fail to capture the rotational motion at the joint level, and cannot guarantee local temporal consistency. To address these issues, we propose a novel Transformer-based model with a design of independent tokens. First, we introduce three types of tokens independent of the image feature: \textit{joint rotation tokens, shape token, and camera token}. By progressively interacting with image features through Transformer layers, these tokens learn to encode the prior knowledge of human 3D joint rotations, body shape, and position information from large-scale data, and are updated to estimate SMPL parameters conditioned on a given image. Second, benefiting from the proposed token-based representation, we further use a temporal model to focus on capturing the rotational temporal information of each joint, which is empirically conducive to preventing large jitters in local parts. Despite being conceptually simple, the proposed method attains superior performances on the 3DPW and Human3.6M datasets. Using ResNet-50 and Transformer architectures, it obtains 42.0 mm error on the PA-MPJPE metric of the challenging 3DPW, outperforming state-of-the-art counterparts by a large margin. Code will be publicly available at https://github.com/yangsenius/INT_HMR_Model
Efficient Routing Algorithm Design for Large DetNet

Deterministic Networking (DetNet) is a rising technology that offers deterministic delay \&jitter and zero packet loss regardless of failures in large IP networks. In order to support DetNet, we must be able to find a set of low-cost routing paths for a given node pair subject to delay-range constraints. Unfortunately, the \textbf{Delay-Range} Constrained Routing (DRCR) problem is NP-Complete. Existing routing approaches either cannot support the delay-range constraints, or incur extremely high computational complexity. We propose Pulse$+$, a highly scalable and efficient DRCR problem solver. Pulse$+$ adopts a branch-and-bound methodology and optimizes its pruning strategies for higher efficiency. We also integrate Pulse$+$ with a divide-and-conquer approach and propose CoSE-Pulse$+$ to find a pair of active/backup paths that meet DetNet's delay-range and delay-diff constraints. Both Pulse$+$ and CoSE-Pulse$+$ offer optimality guarantee. Notably, although Pulse$+$ and CoSE-Pulse$+$ do not have a polynomial worst-case time complexity, their empirical performance is superior. We evaluate Pulse$+$ and CoSE-Pulse$+$ against the K-Shorst-Path and Lagrangian-dual based algorithms using synthetic test cases generated over networks with thousands of nodes and links. Both Pulse$+$ and CoSE-Pulse$+$ achieve significant speedup. To enable reproduction, we open source our code and test cases at [1].
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning

Recent methods for imitation learning directly learn a $Q$-function using an implicit reward formulation rather than an explicit reward function. However, these methods generally require implicit reward regularization to improve stability and often mistreat absorbing states. Previous works show that a squared norm regularization on the implicit reward function is effective, but do not provide a theoretical analysis of the resulting properties of the algorithms. In this work, we show that using this regularizer under a mixture distribution of the policy and the expert provides a particularly illuminating perspective: the original objective can be understood as squared Bellman error minimization, and the corresponding optimization problem minimizes a bounded $\chi^2$-Divergence between the expert and the mixture distribution. This perspective allows us to address instabilities and properly treat absorbing states. We show that our method, Least Squares Inverse Q-Learning (LS-IQ), outperforms state-of-the-art algorithms, particularly in environments with absorbing states. Finally, we propose to use an inverse dynamics model to learn from observations only. Using this approach, we retain performance in settings where no expert actions are available.
Neural Auctions Compromise Bidder Information

Single-shot auctions are commonly used as a means to sell goods, for example when selling ad space or allocating radio frequencies, however devising mechanisms for auctions with multiple bidders and multiple items can be complicated. It has been shown that neural networks can be used to approximate optimal mechanisms while satisfying the constraints that an auction be strategyproof and individually rational. We show that despite such auctions maximizing revenue, they do so at the cost of revealing private bidder information. While randomness is often used to build in privacy, in this context it comes with complications if done without care. Specifically, it can violate rationality and feasibility constraints, fundamentally change the incentive structure of the mechanism, and/or harm top-level metrics such as revenue and social welfare. We propose a method that employs stochasticity to improve privacy while meeting the requirements for auction mechanisms with only a modest sacrifice in revenue. We analyze the cost to the auction house that comes with introducing varying degrees of privacy in common auction settings. Our results show that despite current neural auctions' ability to approximate optimal mechanisms, the resulting vulnerability that comes with relying on neural networks must be accounted for.
Impact of Galaxy Mergers on Stellar Population Profiles of Early-type Galaxies

We study the impact of galaxy mergers on stellar population profiles/gradients of early-type galaxies (ETGs) using ETGs at z < 0.055 in the Stripe 82 region of the Sloan Digital Sky Survey and MaNGA integral field unit spectroscopic data. Tidal features around ETGs, which are detected from deep coadded images, are regarded as direct observational evidence for recent mergers. We find that ETGs with tidal features have less negative metallicity gradients and more positive age gradients than ETGs without tidal features at M star ≳ 1010.6 M ⊙. Moreover, when integrating all the resolved stellar populations, ETGs with tidal features have lower metallicities by ∼0.07 dex and younger ages by ∼1–2 Gyr than ETGs without tidal features. Analyzing star formation histories, we discover that the mass fraction of young stellar populations with age <5 Gyr is higher in the central regions of ETGs with tidal features than in the same regions of the counterparts without tidal features. Compared to normal ETGs, ETGs with tidal features have a slow metal-enrichment history in the early universe, but they have been accelerating the metal enrichment through recently formed stars over the last few billion years. Many of our results can be explained if the effects of recently occurred mergers are different from those in the early universe, which are more likely to be rich in gas.
The Metal-weak Milky Way Stellar Disk Hidden in the Gaia–Sausage–Enceladus Debris: The APOGEE DR17 View

We have for the first time identified the early stellar disk in the Milky Way by using a combination of elemental abundances and kinematics. Using data from APOGEE DR17 and Gaia we select stars in the Mg–Mn–Al–Fe plane with elemental abundances indicative of an accreted origin and find stars with both halo-like and disk-like kinematics. The stars with halo-like kinematics lie along a lower sequence in [Mg/Fe], while the stars with disk-like kinematics lie along a higher sequence. Combined with astroseismic observations, we determine that the stars with halo-like kinematics are old, 9–11 Gyr, and that the more evolved stellar disk is about 1–2 Gyr younger. We show that the in situ fraction of stars on deeply bound orbits is not small, in fact the inner Galaxy likely harbors a genuine in situ population together with an accreted one. In addition, we show that the selection of the Gaia–Sausage–Enceladus in the E n−L z plane is not very robust. In fact, radically different selection criteria give almost identical elemental abundance signatures for the accreted stars.
Photometric Search for variable stars in the field of two Northern open clusters, DOLIDGE 14 and NGC 1960

The aim of present work is extract and analyses the light curves of the stars in the field of two clusters, NGC 1960 and DOLIDZE 14. The photometric calibration is performed by comprehensive method of secondary standard transformation and differential photometry using two comparison stars per candidate variable star. The resultant light curves for each potential variable star are displayed and their period analyzed by two different methods. The period and classification of 18 discovered short periodic type variable stars of NGC 1960 are discussed, which consist of four known variable stars and fourteen new variable stars. In the case of DOLIDZE 14, four discovered variables consist of one miscellaneous, one rotational, two $binary$ type variable stars. In the case of NGC~1960, the 12 selected comparison stars appear to be likely candidate for long periodic variability and 4 stars may be standard stars. The variation in brightness of other twenty comparison stars is non-pulsating with an irregular pattern. Membership analysis of variable stars is performed using their distance, kinematic probability and location in $(U-B)$ vs $(B-V)$ TCD. C-M diagrams were constructed to confirm the evolutionary state of the new variable stars.
Miyaoka type inequality for terminal threefolds with nef anti-canonical divisors

In this paper, we study the Miyaoka type inequality on Chern classes of terminal projective $3$-folds with nef anti-canonical divisors. Let $X$ be a terminal projective $3$-fold such that $-K_X$ is nef. We show that if $c_1(X)\cdot c_2(X)\neq 0$, then $c_1(X)\cdot c_2(X)\geq \frac{1}{252}$; if further $X$ is not rationally connected, then $c_1(X)\cdot c_2(X)\geq \frac{4}{5}$ and this inequality is sharp. In order to prove this, we give a partial classification of such varieties along with many examples. We also study the nonvanishing of $c_1(X)^{\dim X-2}\cdot c_2(X)$ for terminal weak Fano varieties and prove a Miyaoka--Kawamata type inequality for terminal weak Fano $3$-folds.
On the Optimal Regularity Implied by the Assumptions of Geometry I: Connections on Tangent Bundles

We resolve the problem of optimal regularity and Uhlenbeck compactness for affine connections in General Relativity and Mathematical Physics. First, we prove that any affine connection $\Gamma$, with components $\Gamma \in L^{2p}$ and components of its Riemann curvature ${\rm Riem}(\Gamma)$ in $L^p$, in some coordinate system, can be smoothed by coordinate transformation to optimal regularity, $\Gamma \in W^{1,p}$ (one derivative smoother than the curvature), $p>\max\{n/2,2\}$, dimension $n\geq 2$. For Lorentzian metrics in General Relativity this implies that shock wave solutions of the Einstein-Euler equations are non-singular -- geodesic curves, locally inertial coordinates and the Newtonian limit, all exist in a classical sense, and the Einstein equations hold in the strong sense. The proof is based on an $L^p$ existence theory for the Regularity Transformation (RT) equations, a system of elliptic partial differential equations (introduced by the authors) which determine the Jacobians of the regularizing coordinate transformations. Secondly, this existence theory gives the first extension of Uhlenbeck compactness from Riemannian metrics, to general affine connections bounded in $L^\infty$, with curvature in $L^{p}$, $p>n$, including semi-Riemannian metrics, and Lorentzian metric connections of relativistic Physics. We interpret this as a"geometric"improvement of the generalized Div-Curl Lemma. Our theory shows that Uhlenbeck compactness and optimal regularity are pure logical consequences of the rule which defines how connections transform from one coordinate system to another -- what one could take to be the"starting assumption of geometry".
Collective moderation of hate, toxicity, and extremity in online discussions

How can citizens address hate in online discourse? We analyze a large corpus of more than 130,000 discussions on Twitter over four years. With the help of human annotators, language models and machine learning classifiers, we identify different dimensions of discourse that might be related to the probability of hate speech in subsequent tweets. We use a matching approach and longitudinal statistical analyses to discern the effectiveness of different counter speech strategies on the micro-level (individual tweet pairs), meso-level (discussion trees) and macro-level (days) of discourse. We find that expressing simple opinions, not necessarily supported by facts, but without insults, relates to the least hate in subsequent discussions. Sarcasm can be helpful as well, in particular in the presence of organized extreme groups. Mentioning either outgroups or ingroups is typically related to a deterioration of discourse. A pronounced emotional tone, either negative such as anger or fear, or positive such as enthusiasm and pride, also leads to worse discourse quality. We obtain similar results for other measures of quality of discourse beyond hate speech, including toxicity, extremity of speech, and the presence of extreme speakers. Going beyond one-shot analyses on smaller samples of discourse, our findings have implications for the successful management of online commons through collective civic moderation.
On the Coefficient Problem: a Version of the Kahane–Katznelson–De Leeuw Theorem for Spaces of Matrices☆

It is known that, for every (an)∈l2(Z) there exists a functionF∈C(T) such that |an|⩽|F(n)| for everyn∈Z. We prove a noncommutative version: for every matrixA=(aij) such that supi Verbar;(aij)j‖l2and supj ‖(aij)i‖l2are finite, there exists a matrix (bij) defining a bounded operator onl2, such that |aij|⩽|bij| for everyi, j. We extend this to other norms on matrices and present an abstract version of the coefficient problem.
N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space

Error correction models form an important part of Automatic Speech Recognition (ASR) post-processing to improve the readability and quality of transcriptions. Most prior works use the 1-best ASR hypothesis as input and therefore can only perform correction by leveraging the context within one sentence. In this work, we propose a novel N-best T5 model for this task, which is fine-tuned from a T5 model and utilizes ASR N-best lists as model input. By transferring knowledge from the pre-trained language model and obtaining richer information from the ASR decoding space, the proposed approach outperforms a strong Conformer-Transducer baseline. Another issue with standard error correction is that the generation process is not well-guided. To address this a constrained decoding process, either based on the N-best list or an ASR lattice, is used which allows additional information to be propagated.
Quantum equilibration and measurements -- bounds on speeds, Lyapunov exponents, and transport coefficients obtained from the uncertainty relations and their comparison with experimental data

We discuss our recent study of local quantum mechanical uncertainty relations in quantum many body systems. These lead to fundamental bounds for quantities such as the speed, acceleration, relaxation times, spatial gradients and the Lyapunov exponents. We additionally obtain bounds on various transport coefficients like the viscosity, the diffusion constant, and the thermal conductivity. Some of these bounds are related to earlier conjectures, such as the bound on chaos by Maldacena, Shenker and Stanford while others are new. Our approach is a direct way of obtaining exact bounds in fairly general settings. We employ uncertainty relations for local quantities from which we strip off irrelevant terms as much as possible, thereby removing non-local terms. To gauge the utility of our bounds, we briefly compare their numerical values with typical values available from experimental data. In various cases, approximate simplified variants of the bounds that we obtain can become fairly tight, i.e., comparable to experimental values. These considerations lead to a minimal time for thermal equilibrium to be achieved. Building on a conjectured relation between quantum measurements and equilibration, our bounds, far more speculatively, suggest a minimal time scale for measurements to stabilize to equilibrium values.
Mean-Field Approximate Optimization Algorithm

The Quantum Approximate Optimization Algorithm (QAOA) is suggested as a promising application on early quantum computers. Here, a quantum-inspired classical algorithm, the mean-field Approximate Optimization Algorithm (mean-field AOA), is developed by replacing the quantum evolution of the QAOA with classical spin dynamics through the mean-field approximation. Due to the alternating structure of the QAOA, this classical dynamics can be found exactly for any number of QAOA layers. We benchmark its performance against the QAOA on the Sherrington-Kirkpatrick (SK) model and the partition problem, and find that the mean-field AOA outperforms the QAOA in both cases for most instances. Our algorithm can thus serve as a tool to delineate optimization problems that can be solved classically from those that cannot, i.e. we believe that it will help to identify instances where a true quantum advantage can be expected from the QAOA. To quantify quantum fluctuations around the mean-field trajectories, we solve an effective scattering problem in time, which is characterized by a spectrum of time-dependent Lyapunov exponents. These provide an indicator for the hardness of a given optimization problem relative to the mean-field AOA.
Valid Information Guidance Network for Compressed Video Quality Enhancement

In recent years deep learning methods have shown great superiority in compressed video quality enhancement tasks. Existing methods generally take the raw video as the ground truth and extract practical information from consecutive frames containing various artifacts. However, they do not fully exploit the valid information of compressed and raw videos to guide the quality enhancement for compressed videos. In this paper, we propose a unique Valid Information Guidance scheme (VIG) to enhance the quality of compressed videos by mining valid information from both compressed videos and raw videos. Specifically, we propose an efficient framework, Compressed Redundancy Filtering (CRF) network, to balance speed and enhancement. After removing the redundancy by filtering the information, CRF can use the valid information of the compressed video to reconstruct the texture. Furthermore, we propose a progressive Truth Guidance Distillation (TGD) strategy, which does not need to design additional teacher models and distillation loss functions. By only using the ground truth as input to guide the model to aggregate the correct spatio-temporal correspondence across the raw frames, TGD can significantly improve the enhancement effect without increasing the extra training cost. Extensive experiments show that our method achieves the state-of-the-art performance of compressed video quality enhancement in terms of accuracy and efficiency.
Separation of quadrupole, spin, and charge across the magnetic phases of a one-dimensional interacting spin-1 gas

We study the low-energy collective properties of a 1D spin-1 Bose gas using bosonization. After giving an overview of the technique, emphasizing the physical aspects, we apply it to the $S=1$ Bose-Hubbard Hamiltonian and find a novel separation of the quadrupole-spin-charge sectors, confirmed by time-MPS numerical simulations. Additionally, through the single particle spectrum, we show the existence of the superfluid-Mott insulator transition and the point at which the physics are described by a Heisenberg-like Hamiltonian. The magnetic phase diagrams are found for both the superfluid and insulating regimes; the latter is determined by decomposing the complete Heisenberg bilinear-biquadratic Hamiltonian, which describes the Mott insulator, into simpler, effective Hamiltonians. This allows us to keep our methods flexible and transferable to other interesting interacting condensed matter systems.
Three-dimensional flow around and through a porous screen

We investigate the three-dimensional flow around and through a porous screen for various porosities at high Reynolds number Re = O(10000). Historically, the study of this problem was focused on two-dimensional cases and for a screen spanning completely or partially a channel. Since many recent problems have involved a porous object in a three-dimensional free flow, we present a three-dimensional model for porous screens, initially based on Koo&James (1973) and Steiros&Hultmark (2018), accounting for viscous effects at the vicinity of the screen, from which we can derive velocities, pressure distribution as well as aerodynamic forces. We then characterize experimentally the aerodynamic drag coefficient for a porous square screen, composed of fibers, immersed in a laminar air flow with different angles of attack. We test various fiber diameters to explore the effect of the space between the pores on the drag force. The drag prediction from the model is in good agreement with our experimental results. Our theoretical results, supported by experiments, suggest that for high solidity, a homogeneous porous screens composed of fibers can have a higher drag coefficient than a flat plate with the same dimensions. We also show that for the same solidity the Reynolds number based on the fiber diameter of the porous screen has a strong influence on the drag coefficient. The model, taking into account three-dimensional effects and the shape of the porous screen, may have many applications including the determination of water collection for fog harvesters.
Age of Actuation in a Wireless Power Transfer System

In this paper, we study a model relevant to semantics-aware goal-oriented communications. More specifically, observations from an external process are transmitted through status updates to a battery-powered receiver. From these updates, the receiver is informed about the status of the process and if there is sufficient energy, uses them to perform an actuation to achieve a goal. We consider a wireless power transfer model where the destination receives energy from a dedicated power transmitter and occasionally from the data transmitter. We provide the analysis for the Age of Information (AoI). Furthermore, we propose a new metric, namely the Age of Actuation (AoA) which is relevant when the receiver utilizes the status updates to perform actions in a timely manner. We analytically characterize the AoA and we show that is a more general metric than AoI. We provide the optimization problems for both metrics, and we numerically evaluate our analytical results.