Miracle-dz/UniFAR-FTUs huggingface dataset:
CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds

In this work, we tackle the problem of category-level online pose tracking of objects from point cloud sequences. For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories. Here the 9DoF pose, comprising 6D pose and 3D size, is equivalent to a 3D amodal bounding box representation with free 6D pose. Given the depth point cloud at the current frame and the estimated pose from the last frame, our novel end-to-end pipeline learns to accurately update the pose. Our pipeline is composed of three modules: 1) a pose canonicalization module that normalizes the pose of the input depth point cloud; 2) RotationNet, a module that directly regresses small interframe delta rotations; and 3) CoordinateNet, a module that predicts the normalized coordinates and segmentation, enabling analytical computation of the 3D size and translation. Leveraging the small pose regime in the pose-canonicalized point clouds, our method integrates the best of both worlds by combining dense coordinate prediction and direct rotation regression, thus yielding an end-to-end differentiable pipeline optimized for 9DoF pose accuracy (without using non-differentiable RANSAC). Our extensive experiments demonstrate that our method achieves new state-of-the-art performance on category-level rigid object pose (NOCSREAL275 [29]) and articulated object pose benchmarks (SAPIEN [34], BMVC [18]) at the fastest FPS ∼ 12.
Interacting galaxies in the IllustrisTNG simulations – III. (The rarity of) quenching in post-merger galaxies

Galaxy mergers are traditionally one of the favoured mechanisms for the transformation of spiral galaxies to spheroids and for quenching star formation. To test this paradigm in the context of modern cosmological simulations, we use the IllustrisTNG simulation to investigate the impact of individual merger events on quenching star formation [i.e. star formation rate (SFR) at least 3σ below the star-forming main sequence] within 500 Myr after the coalescence phase. The rate of quenching amongst recently merged galaxies is compared with a control sample that is matched in redshift, stellar mass, SFR, black hole mass, and environment. We find quenching to be uncommon among the descendants of post-merger galaxies, with only ${\sim} 5{{\ \rm per\ cent}}$ of galaxies quenching within 500 Myr after the merger. Despite this low absolute rate, we find that quenching occurs in post-mergers at twice the rate of the control galaxies. The fraction of quenched post-merger descendants 1.5 Gyr after the merger become statistically indistinguishable from that of non-post-mergers, suggesting that mergers could speed up the quenching process in those post-mergers whose progenitors had physical conditions able to sustain effective active galactic nuclei (AGN) kinetic feedback, thus capable of removing gas from galaxies. Our results indicate that although quenching does not commonly occur promptly after coalescence, mergers none the less do promote the cessation of star formation in some post-mergers. We find that, in IllustrisTNG, it is the implementation of the AGN kinetic feedback that is responsible for quenching post-mergers, as well as non-post-merger controls. As a result of the released kinetic energy, galaxies experience gas loss and eventually they will quench. Galaxies with an initially low gas fraction show a preferable pre-disposition towards quenching. The primary distinguishing factor between quenched and star-forming galaxies is gas fraction, with a sharp boundary at fgas ∼ 0.1 in TNG.
Muon anomalous magnetic dipole moment in the μ νSSM

Recently, the Muon g-2 experiment at Fermilab measured the muon anomalous magnetic dipole moment (MDM), , and reported that the new experimental average increases the difference between the experiment and the standard model (SM) prediction to 4. 2σ. In this work, we reanalyze the muon anomalous MDM at the two-loop level in the μ from the ν Supersymmetric Standard Model ( SSM) combined with the updated experimental average. The SSM can explain the current difference between the experimental measurement and the SM theoretical prediction for the muon anomalous MDM, constrained by the 125 GeV Higgs boson mass and decays, the rare decay , and so on. We also investigate the anomalous MDM of the electron and tau lepton, and , at the two-loop level in the SSM. In addition, the decaying of the 125 GeV Higgs boson into a pair of charged leptons in the SSM is analyzed.
Ground state of one-dimensional fermion-phonon systems

A new framework for the reflection positivity, based on order-preserving operator inequalities, is proposed. This framework is utilized to investigate one-dimensional fermion-phonon systems, with a particular focus on the detailed examination of ground state properties. Our analysis reveals that the reflection positivity provides a consistent description of the charge-density-wave (CDW) order in such systems. Additionally, we establish the existence of CDW long-range order in the ground state when the Coulomb interaction is long range.
Flavor Anomalies Accommodated in A Flavor Gauged Two Higgs Doublet Model

The $3.1\sigma$ $R_K$ anomaly after Moriond 2021 and $3.3 \sigma$ $\Delta a_\mu$ from Fermilab Muon g-2 experiment implicate that the lepton flavor universality violation (LFUV) may play a role in the exploration of new physics. A Flavor Gauged Two-Higgs Doublet Model (FG2HDM) is proposed and investigated in this work. To get rid of the the redundancy in Yukawa coupling of 2HDM-III, a specific U(1) flavor symmetry is introduced. The charge difference between two scalar doublet forbid the appearance of pseudoscalar and hence there are only three particles (a charged and neutral heavy scalar together with a neutral gauge boson) adding to SM particle spectrum. The heavy neutral scalar-mediated flavor-changing interactions occur among down-type quarks. With obvious difference from 2HDM-II, the charged Higgs in FG2HDM can naturally explain $R_{D^{(*)}}$ anomaly. The heavy neutral vector boson $Z'$, changing flavor for down-type quark uniquely as well, provides a solution to $R_{K^{(*)}}$. The anomalous magnetic dipole moment (AMDM) of muon and electron, especially the new released $\Delta a_\mu$, can further discriminate $Z'$ parameter space.
SiT: Self-supervised vIsion Transformer

Self-supervised learning methods are gaining increasing traction in computer vision due to their recent success in reducing the gap with supervised learning. In natural language processing (NLP) self-supervised learning and transformers are already the methods of choice. The recent literature suggests that the transformers are becoming increasingly popular also in computer vision. So far, the vision transformers have been shown to work well when pretrained either using a large scale supervised data or with some kind of co-supervision, e.g. in terms of teacher network. These supervised pretrained vision transformers achieve very good results in downstream tasks with minimal changes. In this work we investigate the merits of self-supervised learning for pretraining image/vision transformers and then using them for downstream classification tasks. We propose Self-supervised vIsion Transformers (SiT) and discuss several self-supervised training mechanisms to obtain a pretext model. The architectural flexibility of SiT allows us to use it as an autoencoder and work with multiple self-supervised tasks seamlessly. We show that a pretrained SiT can be finetuned for a downstream classification task on small scale datasets, consisting of a few thousand images rather than several millions. The proposed approach is evaluated on standard datasets using common protocols. The results demonstrate the strength of the transformers and their suitability for self-supervised learning. We outperformed existing self-supervised learning methods by large margin. We also observed that SiT is good for few shot learning and also showed that it is learning useful representation by simply training a linear classifier on top of the learned features from SiT. Pretraining, finetuning, and evaluation codes will be available under: https://github.com/Sara-Ahmed/SiT.
Incentivizing Exploration in Linear Bandits under Information Gap

We study the problem of incentivizing exploration for myopic users in linear bandits, where the users tend to exploit arm with the highest predicted reward instead of exploring. In order to maximize the long-term reward, the system offers compensation to incentivize the users to pull the exploratory arms, with the goal of balancing the trade-off among exploitation, exploration and compensation. We consider a new and practically motivated setting where the context features observed by the user are more informative than those used by the system, e.g., features based on users' private information are not accessible by the system. We propose a new method to incentivize exploration under such information gap, and prove that the method achieves both sublinear regret and sublinear compensation. We theoretical and empirically analyze the added compensation due to the information gap, compared with the case that the system has access to the same context features as the user, i.e., without information gap. We also provide a compensation lower bound of our problem.
TokenPose: Learning Keypoint Tokens for Human Pose Estimation

Human pose estimation deeply relies on visual clues and anatomical constraints between parts to locate keypoints. Most existing CNN-based methods do well in visual representation, however, lacking in the ability to explicitly learn the constraint relationships between keypoints. In this paper, we propose a novel approach based on Token representation for human Pose estimation (TokenPose). In detail, each keypoint is explicitly embedded as a token to simultaneously learn constraint relationships and appearance cues from images. Extensive experiments show that the small and large TokenPose models are on par with state-of-the-art CNN-based counterparts while being more lightweight. Specifically, our TokenPose-S and TokenPose-L achieve 72.5 AP and 75.8 AP on COCO validation dataset respectively, with significant reduction in parameters (↓80.6% ; ↓ 56.8%) and GFLOPs (↓ 75.3%; ↓24.7%). Code is publicly available1.
Advances in Metric Ramsey Theory and its Applications

Metric Ramsey theory is concerned with finding large well-structured subsets of more complex metric spaces. For finite metric spaces this problem was first studies by Bourgain, Figiel and Milman \cite{bfm}, and studied further in depth by Bartal et. al \cite{BLMN03}. In this paper we provide deterministic constructions for this problem via a novel notion of \emph{metric Ramsey decomposition}. This method yields several more applications, reflecting on some basic results in metric embedding theory. The applications include various results in metric Ramsey theory including the first deterministic construction yielding Ramsey theorems with tight bounds, a well as stronger theorems and properties, implying appropriate distance oracle applications. In addition, this decomposition provides the first deterministic Bourgain-type embedding of finite metric spaces into Euclidean space, and an optimal multi-embedding into ultrametrics, thus improving its applications in approximation and online algorithms. The decomposition presented here, the techniques and its consequences have already been used in recent research in the field of metric embedding for various applications.
Role of mid- and far-infrared for improving dehydration efficiency in beef jerky drying

ABSTRACT The dehydration efficiency of beef jerky dried by mid- and far-infrared drying (MFIR, MIR 2.9–3.1 µm, FIR 5.8–6.2 µm, 70°C, 1 m/s) was compared with hot air drying (HA, 70°C, 1 m/s). Results showed that the time and energy consumption, heating time to reach the targeted temperature, and activation energy of MFIR-treated samples decreased to 43, 24, 7, and 91%, respectively, when compared to HA drying. In addition, the average effective moisture diffusivity of MFIR-treated samples was 158.1% higher than that of HA. Results of low-field nuclear magnetic resonance showed that MFIR and HA induced 32.7 and 27.9% of immobilized water being shifted to free water, respectively, when water content of samples were 70%. It was assumed to be the one of the reasons for the increase in moisture diffusivity by MFIR drying. Differential scanning calorimetric and magnetic resonance imaging showed that MFIR could promote myofibrillar protein denaturation and cause more uniformed moisture distribution of samples. Our study showed that MFIR could improve dehydration efficiency of beef jerky by promoting protein denaturation, inducing the shift in immobilized water to free water, and reducing the activation energy. It was suggested that MFIR has high application potential on the dehydration of jerky.
Keck Cosmic Web Imager Observations of He ii Emission in I Zw 18

With a metallicity of 12 + Log(O/H) ≈ 7.1–7.2, I Zw 18 is a canonical low-metallicity blue compact dwarf (BCD) galaxy. A growing number of BCDs, including I Zw 18, have been found to host strong, narrow-lined, nebular He ii (λ4686) emission with enhanced intensities compared to Hβ (e.g., He ii(λ4686)/Hβ > 1%). We present new observations of I Zw 18 using the Keck Cosmic Web Imager. These observations reveal two nebular He ii emission regions (or He iii regions) northwest and southeast of the He iii region in the galaxy’s main body investigated in previous studies. All regions exhibit He ii(λ4686)/Hβ greater than 2%. The two newly resolved He iii regions lie along an axis that intercepts the position of I Zw 18's ultraluminous X-ray (ULX) source. We explore whether the ULX could power the two He iii regions via shock activity and/or beamed X-ray emission. We find no evidence of shocks from the gas kinematics. If the ULX powers the two regions, the X-ray emission would need to be beamed. Another potential explanation is that a class of early-type nitrogen-rich Wolf–Rayet stars with low winds could power the two He iii regions, in which case the alignment with the ULX would be coincidental.
Quantized Distributed Gradient Tracking Algorithm With Linear Convergence in Directed Networks

Communication efficiency is a major bottleneck in the applications of distributed networks. To address the problem, the problem of quantized distributed optimization has attracted a lot of attention. However, most of the existing quantized distributed optimization algorithms can only converge sublinearly. To achieve linear convergence, this article proposes a novel quantized distributed gradient tracking algorithm (Q-DGT) to minimize a finite sum of local objective functions over directed networks. Moreover, we explicitly derive lower bounds for the number of quantization levels, and prove that Q-DGT can converge linearly even when the exchanged variables are respectively quantized with three quantization levels. Numerical results also confirm the efficiency of the proposed algorithm.
Dynamic Object Aware LiDAR SLAM based on Automatic Generation of Training Data

Highly dynamic environments, with moving objects such as cars or humans, can pose a performance challenge for LiDAR SLAM systems that assume largely static scenes. To overcome this challenge and support the deployment of robots in real world scenarios, we propose a complete solution for a dynamic object aware LiDAR SLAM algorithm. This is achieved by leveraging a real-time capable neural network that can detect dynamic objects, thus allowing our system to deal with them explicitly. To efficiently generate the necessary training data which is key to our approach, we present a novel end-to-end occupancy grid based pipeline that can automatically label a wide variety of arbitrary dynamic objects. Our solution can thus generalize to different environments without the need for expensive manual labeling and at the same time avoids assumptions about the presence of a predefined set of known objects in the scene. Using this technique, we automatically label over 12000 LiDAR scans collected in an urban environment with a large amount of pedestrians and use this data to train a neural network, achieving an average segmentation IoU of 0.82. We show that explicitly dealing with dynamic objects can improve the LiDAR SLAM odometry performance by 39.6% while yielding maps which better represent the environments. A supplementary video1 as well as our test data2 are available online.
Probing BERT in Hyperbolic Spaces

Recently, a variety of probing tasks are proposed to discover linguistic properties learned in contextualized word embeddings. Many of these works implicitly assume these embeddings lay in certain metric spaces, typically the Euclidean space. This work considers a family of geometrically special spaces, the hyperbolic spaces, that exhibit better inductive biases for hierarchical structures and may better reveal linguistic hierarchies encoded in contextualized representations. We introduce a Poincare probe, a structural probe projecting these embeddings into a Poincare subspace with explicitly defined hierarchies. We focus on two probing objectives: (a) dependency trees where the hierarchy is defined as head-dependent structures; (b) lexical sentiments where the hierarchy is defined as the polarity of words (positivity and negativity). We argue that a key desideratum of a probe is its sensitivity to the existence of linguistic structures. We apply our probes on BERT, a typical contextualized embedding model. In a syntactic subspace, our probe better recovers tree structures than Euclidean probes, revealing the possibility that the geometry of BERT syntax may not necessarily be Euclidean. In a sentiment subspace, we reveal two possible meta-embeddings for positive and negative sentiments and show how lexically-controlled contextualization would change the geometric localization of embeddings. We demonstrate the findings with our Poincare probe via extensive experiments and visualization. Our results can be reproduced at https://github.com/FranxYao/PoincareProbe.
Conditional Meta-Network for Blind Super-Resolution with Multiple Degradations

—Although the single-image super-resolution (SISR) methods have achieved great success on the single degradation, they still suffer performance drop with multiple degrading effects in real scenarios. Recently, some blind and non-blind models for multiple degradations have been explored. However, these methods usually degrade signiﬁcantly for distribution shifts between the training and test data. Towards this end, we propose a novel conditional meta-network framework (named CMDSR), which helps the SR framework learn how to adapt to changes in the degradation distribution of input. We extract degradation prior at the task-level with the proposed ConditionNet, which will be used to adapt the parameters of the basic SR network (BaseNet). Speciﬁcally, the ConditionNet of our framework ﬁrst learns the degradation prior from a support set, which is composed of a series of degraded image patches from the same task. Then the adaptive BaseNet rapidly shifts its parameters according to the conditional features. Moreover, in order to better extract degradation prior, we propose a task contrastive loss to shorten the inner-task distance and enlarge the cross-task distance between task-level features. Without predeﬁning degradation maps, our blind framework can conduct one single parameter update to yield considerable improvement in SR results. Extensive experiments demonstrate the effectiveness of CMDSR over various blind, and even several non-blind methods. The ﬂexible BaseNet structure also reveals that CMDSR can be a general framework for a large series of SISR models.
Numerics and analysis of Cahn-Hilliard critical points

We explore recent progress and open questions concerning local minima and saddle points of the Cahn–Hilliard energy in d ≥ 2 and the critical parameter regime of large system size and mean value close to −1. We employ the String Method of E, Ren, and Vanden-Eijnden—a numerical algorithm for computing transition pathways in complex systems—in d = 2 to gain additional insight into the properties of the minima and saddle point. Motivated by the numerical observations, we adapt a method of Caffarelli and Spruck to study convexity of level sets in d ≥ 2.
On tuning consistent annealed sampling for denoising score matching

Score-based generative models provide state-of-the-art quality for image and audio synthesis. Sampling from these models is performed iteratively, typically employing a discretized series of noise levels and a predefined scheme. In this note, we first overview three common sampling schemes for models trained with denoising score matching. Next, we focus on one of them, consistent annealed sampling, and study its hyper-parameter boundaries. We then highlight a possible formulation of such hyper-parameter that explicitly considers those boundaries and facilitates tuning when using few or a variable number of steps. Finally, we highlight some connections of the formulation with other sampling schemes.
Modeling Object Dissimilarity for Deep Saliency Prediction

Saliency prediction has made great strides over the past two decades, with current techniques modeling low-level information, such as color, intensity and size contrasts, and high-level ones, such as attention and gaze direction for entire objects. Despite this, these methods fail to account for the dissimilarity between objects, which affects human visual attention. In this paper, we introduce a detection-guided saliency prediction network that explicitly models the differences between multiple objects, such as their appearance and size dissimilarities. Our approach allows us to fuse our object dissimilarities with features extracted by any deep saliency prediction network. As evidenced by our experiments, this consistently boosts the accuracy of the baseline networks, enabling us to outperform the state-of-the-art models on three saliency benchmarks, namely SALICON, MIT300 and CAT2000. Our project page is at https://github.com/IVRL/DisSal.
Stabilization by transport noise and enhanced dissipation in the Kraichnan model

Stabilization and sufficient conditions for mixing by stochastic transport are shown. More precisely, given a second order linear operator with possibly unstable eigenvalues on a smooth compact Riemannian manifold, it is shown that the inclusion of transport noise can imply global asymptotic stability. Moreover, it is shown that an arbitrary large exponential rate of convergence can be reached, implying enhanced dissipation. The sufficient conditions are shown to be satisfied by the so-called Kraichnan model for stochastic transport of passive scalars in turbulent fluids. In addition, an example is given showing that it can be sufficient to force four modes in order to induce stabilization.
Value of Information in Networked Control Systems Subject to Delay

In this paper, we study the trade-off between the transmission cost and the control performance of a networked control system subject to network-induced delay. Within the linear–quadratic–Gaussian (LQG) framework, the joint design of control policy and networking strategy is decomposed into separate optimization problems. Based on the trade-off analysis, a delay-dependent Value-of-Information (VoI) metric which quanties the value of transmitting a data packet is introduced. The VoI enables the decision-makers embedded in subsystems to design the triggering policy. The proposed scalable VoI inherits the task criticality of the existing VoI metric. Additionally, the sensitivity to the system parameters such as information freshness and network delays is directly derivable. The VoI-based scheduling policy is shown to outperform the periodical triggering policy and the Age-of-Information (AoI) based policy for network control systems under transmission delay. The effectiveness of the constructed VoI with arbitrary network delay is validated through numerical simulations.