Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Performance of Learning Precoding Policy with GNN and CNNs

Learning-based precoding has been shown able to be implemented in real-time, jointly optimized with channel acquisition, and robust to imperfect channels. Yet previous works rarely explain the design choices and learning performance, and existing methods either suffer from high training complexity or depend on problem-specific models. In this paper, we address these issues by analyzing the properties of precoding policy and inductive biases of neural networks, noticing that the learning performance can be decomposed into approximation and estimation errors where the former is related to the smoothness of the policy and both depend on the inductive biases of neural networks. To this end, we introduce a graph neural network (GNN) to learn precoding policy and analyze its connection with the commonly used convolutional neural networks (CNNs). By taking a sum rate maximization precoding policy as an example, we explain why the learned precoding policy performs well in the low signal-to-noise ratio regime, in spatially uncorrelated channels, and when the number of users is much fewer than the number of antennas, as well as why GNN is with higher learning efficiency than CNNs. Extensive simulations validate our analyses and evaluate the generalization ability of the GNN.
GRelPose: Generalizable End-to-End Relative Camera Pose Regression

This paper proposes a generalizable, end-to-end deep learning-based method for relative pose regression between two images. Given two images of the same scene captured from different viewpoints, our algorithm predicts the relative rotation and translation between the two respective cameras. Despite recent progress in the ﬁeld, current deep-based methods exhibit only limited generalization to scenes not seen in training. Our approach introduces a network architecture that extracts a grid of coarse features for each input image using the pre-trained LoFTR network. It sub-sequently relates corresponding features in the two images, and ﬁnally uses a convolutional network to recover the relative rotation and translation between the respective cameras. Our experiments indicate that the proposed architecture can generalize to novel scenes, obtaining higher accuracy than existing deep-learning-based methods in various settings and datasets, in particular with limited training data.
Hand-Object Interaction Image Generation

In this work, we are dedicated to a new task, i.e., hand-object interaction image generation, which aims to conditionally generate the hand-object image under the given hand, object and their interaction status. This task is challenging and research-worthy in many potential application scenarios, such as AR/VR games and online shopping, etc. To address this problem, we propose a novel HOGAN framework, which utilizes the expressive model-aware hand-object representation and leverages its inherent topology to build the unified surface space. In this space, we explicitly consider the complex self- and mutual occlusion during interaction. During final image synthesis, we consider different characteristics of hand and object and generate the target image in a split-and-combine manner. For evaluation, we build a comprehensive protocol to access both the fidelity and structure preservation of the generated image. Extensive experiments on two large-scale datasets, i.e., HO3Dv3 and DexYCB, demonstrate the effectiveness and superiority of our framework both quantitatively and qualitatively. The project page is available at https://play-with-hoi-generation.github. io/.
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion

Multi-modality (MM) image fusion aims to render fused images that maintain the merits of different modalities, e.g., functional highlight and detailed textures. To tackle the challenge in modeling cross-modality features and decomposing desirable modality-specific and modality-shared features, we propose a novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network. Firstly, CDDFuse uses Restormer blocks to extract cross-modality shallow features. We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information. A correlation-driven loss is further proposed to make the low-frequency features correlated while the high-frequency features uncorrelated based on the embedded information. Then, the LT-based global fusion and INN-based local fusion layers output the fused image. Extensive experiments demonstrate that our CDDFuse achieves promising results in multiple fusion tasks, including infrared-visible image fusion and medical image fusion. We also show that CDDFuse can boost the performance in downstream infrared-visible semantic segmentation and object detection in a unified benchmark. The code is available at https://github.om/haozixiang1228/MMIF-CDDFuse.
Non-Polar Opposites: Analyzing the Relationship Between Echo Chambers and Hostile Intergroup Interactions on Reddit

Previous research has documented the existence of both online echo chambers and hostile intergroup interactions. In this paper, we explore the relationship between these two phenomena by studying the activity of 5.97M Reddit users and 421M comments posted over 13 years. We examine whether users who are more engaged in echo chambers are more hostile when they comment on other communities. We then create a typology of relationships between political communities based on whether their users are toxic to each other, whether echo chamber-like engagement with these communities has a polarizing effect, and on the communities' political leanings. We observe both the echo chamber and hostile intergroup interaction phenomena, but neither holds universally across communities. Contrary to popular belief, we find that polarizing and toxic speech is more dominant between communities on the same, rather than opposing, sides of the political spectrum, especially on the left; however, this mostly points to the collective targeting of political outgroups.
Glueball spectroscopy in lattice QCD using gradient flow

Removing ultraviolet noise from the gauge fields is necessary for glueball spectroscopy in lattice QCD. It is known that the Yang-Mills gradient flow method is an alternative approach instead of link smearing or link fuzzing in various aspects. In this work we study the application of the gradient flow technique to the construction of the extended glueball operators. We examine a simple application of the gradient flow method, which has some problems in glueball mass calculations at large flow time because of its nature of diffusion in space-time. To avoid this problem, the spatial links are evolved by the ``spatial gradient flow'', that is defined to restrict the diffusion to spatial directions only. We test the spatial gradient flow in calculations of glueball two-point functions and Wilson loops as a new smearing method, and then discuss its efficiency in comparison with the original gradient flow method and the conventional method. Furthermore, to demonstrate the feasibility of our proposed method, we determine the masses of the three lowest-lying glueball states, corresponding to the $0^{++}$, $2^{++}$ and $0^{-+}$ glueballs, in the continuum limit in the pure Yang-Mills theory.
Lifelong Compositional Feature Replays Beat Image Replays in Stream Learning

Our brains extract durable, generalizable knowledge from transient experiences of the world. Artiﬁcial neural networks come nowhere close: when tasked with learning to classify objects by training on non-repeating video frames in temporal order (online stream learning), models that learn well from shufﬂed datasets catastrophically forget old knowledge upon learning new stimuli. We propose a new continual learning algorithm, Compositional Replay Using Memory Blocks (CRUMB), which mitigates forgetting by replaying feature maps reconstructed by recombining generic parts. Just as crumbs together form a loaf of bread, we concatenate trainable and re-usable “memory block” vectors to compositionally reconstruct feature map tensors in convolutional neural networks. CRUMB stores the indices of memory blocks used to reconstruct new stimuli, enabling replay of speciﬁc memories during later tasks. CRUMB’s memory blocks are tuned to enhance replay: a single feature map stored, reconstructed, and replayed by CRUMB mitigates forgetting during video stream learning more effectively than an entire image while occupying only 3.6% of the memory. We stress-tested CRUMB alongside 13 competing methods on 5 challenging datasets, including 3 video stream datasets containing 10, 12, and 14 classes, and 2 image datasets containing 100 and 1000 classes. To address the limited number of existing online stream learning datasets, we introduce 2 new benchmarks by adapting existing datasets for stream learning. With about
Good helper is around you: Attention-driven Masked Image Modeling

It has been witnessed that masked image modeling (MIM) has shown a huge potential in self-supervised learning in the past year. Benefiting from the universal backbone vision transformer, MIM learns self-supervised visual representations through masking a part of patches of the image while attempting to recover the missing pixels. Most previous works mask patches of the image randomly, which underutilizes the semantic information that is beneficial to visual representation learning. On the other hand, due to the large size of the backbone, most previous works have to spend much time on pre-training. In this paper, we propose Attention-driven Masking and Throwing Strategy (AMT), which could solve both problems above. We first leverage the self-attention mechanism to obtain the semantic information of the image during the training process automatically without using any supervised methods. Masking strategy can be guided by that information to mask areas selectively, which is helpful for representation learning. Moreover, a redundant patch throwing strategy is proposed, which makes learning more efficient. As a plug-and-play module for masked image modeling, AMT improves the linear probing accuracy of MAE by 2.9% ~ 5.9% on CIFAR-10/100, STL-10, Tiny ImageNet, and ImageNet-1K, and obtains an improved performance with respect to fine-tuning accuracy of MAE and SimMIM. Moreover, this design also achieves superior performance on downstream detection and segmentation tasks.
Design and test results of different aluminum coating layers on the sCMOS sensors for soft X-ray detection

In recent years, tremendous progress has been made on complementary metal-oxide-semiconductor (CMOS) sensors for applications as X-ray detectors. To shield the visible light in X-ray detection, a blocking filter of aluminum is commonly employed. We designed three types of aluminum coating layers, which are deposited directly on the surface of back-illuminated sCMOS sensors during fabrication. A commercial 2k × 2k sCMOS sensor is used to realize these designs. In this work, we report their performance by comparison with that of an uncoated sCMOS sensor. The optical transmissions at 660 nm and 850 nm are measured, and the results show that the optical transmission reaches a level of about 10-9 for the 200 nm aluminum layer and about 10-4 for the 100 nm aluminum layer. Light leakage is found around the four sides of the sensor. The readout noise, fixed-pattern noise and energy resolution of these Al-coated sCMOS sensors do not show significant changes. The dark currents of these Al-coated sCMOS sensors show a noticeable increase compared with that of the uncoated sCMOS sensor at room temperatures, while no significant difference is found when the sCMOS sensors are cooled down to about -15°C. The aluminum coatings show no visible crack after the thermal cycle and aging tests. Based on these results, an aluminum coating of a larger area on larger sCMOS sensors is proposed for future work.
Progressive Learning without Forgetting

Learning from changing tasks and sequential experience without forgetting the obtained knowledge is a challenging problem for artificial neural networks. In this work, we focus on two challenging problems in the paradigm of Continual Learning (CL) without involving any old data: (i) the accumulation of catastrophic forgetting caused by the gradually fading knowledge space from which the model learns the previous knowledge; (ii) the uncontrolled tug-of-war dynamics to balance the stability and plasticity during the learning of new tasks. In order to tackle these problems, we present Progressive Learning without Forgetting (PLwF) and a credit assignment regime in the optimizer. PLwF densely introduces model functions from previous tasks to construct a knowledge space such that it contains the most reliable knowledge on each task and the distribution information of different tasks, while credit assignment controls the tug-of-war dynamics by removing gradient conflict through projection. Extensive ablative experiments demonstrate the effectiveness of PLwF and credit assignment. In comparison with other CL methods, we report notably better results even without relying on any raw data.
Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration

E ﬃ cient and accurate low-rank approximation (LRA) methods are of great signiﬁcance for large-scale data analysis. Randomized tensor decompositions have emerged as powerful tools to meet this need, but most existing methods perform poorly in the presence of noise interference. Inspired by the remarkable performance of randomized block Krylov iteration (rBKI) in reducing the e ﬀ ect of tail singular values, this work designs an rBKI-based Tucker decomposition (rBKI-TK) for accurate approximation, together with a hierarchical tensor ring decomposition based on rBKI-TK for e ﬃ cient compression of large-scale data. Besides, the error bound between the deterministic LRA and the randomized LRA is studied. Numerical experiences demonstrate the e ﬃ ciency, accuracy and scalability of the proposed methods in both data compression and denoising.
Chandra-detected X-Ray Sources in the Nearby Spiral Scd Galaxy NGC 2403

We present an analysis of a 35.6 ks observation made with the Chandra X-Ray Observatory of the nearby spiral Scd galaxy NGC 2403. Forty-one discrete sources are detected at a significance of 3 σ or greater within the portion of the galaxy sampled by the Chandra detectors. The luminosities of the discrete sources range from ∼1036 through ∼1039 ergs s-1 over the energy range of 0.5–10 keV. We have also analyzed Einstein, ROSAT, and ASCA observations of this galaxy to search for time variability from the detected sources. One luminous X-ray source seen in an Einstein observation of this galaxy is not detected by Chandra or in a separate observation made with ASCA, while a luminous source observed by Chandra was not detected by Einstein or ASCA. For the four most luminous X-ray sources detected by the Chandra observation—including one previously identified as an ultraluminous X-ray source—spectral fits to these sources provide the best broadband CCD spectra to date. We have searched for counterparts at optical and radio wavelengths. Only one of the 35 optically identified supernova remnants (SNRs) in this galaxy, MFBL 31, is clearly associated with an X-ray source in our sample. Three other optically identified SNRs—MFBL 15, MFBL 17, and MFBL 29—may also have X-ray counterparts, but the associations are far less robust. A similar search for X-ray counterparts to known discrete radio sources seen toward NGC 2403 yields a single coincidence between an X-ray source and a candidate radio SNR, TH-2. None of the detected X-ray sources are coincident with either candidate globular clusters or massive star clusters associated with NGC 2403. The only historical supernova in NGC 2403, SN 1954J, is not detected; neither is an X-ray counterpart to the galaxy's optical nucleus detected. Finally, we confirm the diffuse emission results of Fraternali et al.
Non-inferiority of Deep Learning Model to Segment Acute Stroke on Non-contrast CT Compared to Neuroradiologists

Purpose To develop a deep learning model to segment the acute ischemic infarct on non-contrast Computed Tomography (NCCT). Materials and Methods In this retrospective study, 227 Head NCCT examinations from 200 patients enrolled in the multicenter DEFUSE 3 trial were included. Three experienced neuroradiologists (experts A, B and C) independently segmented the acute infarct on each study. The dataset was randomly split into 5 folds with training and validation cases. A 3D deep Convolutional Neural Network (CNN) architecture was optimized for the data set properties and task needs. The input to the model was the NCCT and the output was a segmentation mask. The model was trained and optimized on expert A. The outcome was assessed by a set of volume, overlap and distance metrics. The predicted segmentations of the best model and expert A were compared to experts B and C. Then we used a paired Wilcoxon signed-rank test in a one-sided test procedure for all metrics to test for non-inferiority in terms of bias and precision. Results The best performing model reached a Surface Dice at Tolerance (SDT)5mm of 0.68 ± 0.04. The predictions were non-inferior when compared to independent experts in terms of bias and precision (paired one-sided test procedure for differences in medians and bootstrapped standard deviations with non-inferior boundaries of -0.05, 2ml, and 2mm, p < 0.05, n=200). ar X iv :2 21 1. 15 34 1v 1 [ ee ss . I V ] 2 4 N ov 2 02 2 Conclusion For the segmentation of acute ischemic stroke on NCCT, our 3D CNN trained with the annotations of one neuroradiologist is non-inferior when compared to two independent neuroradiologists. Abbreviations ASD Average Surface Distance, ASPECTS Alberta Stroke Program Early CT Score, AVD Absolute Volume Difference, CNN Convolutional Neural Network, ECASS European Cooperative Acute Stroke Study, HD 95 Hausdorff Distance 95 percentile, NCCT Non-contrast Computed Tomography, nnUNet No New U-Net, SDT Surface Dice at Tolerance, VS Volumetric Similarity
Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss

Broadspread use of medical imaging devices with digital storage has paved the way for curation of substantial data repositories. Fast access to image samples with similar appearance to suspected cases can help establish a consulting system for healthcare professionals, and improve diagnostic procedures while minimizing processing delays. However, manual querying of large data repositories is labor intensive. Content-based image retrieval (CBIR) offers an automated solution based on dense embedding vectors that represent image features to allow quantitative similarity assessments. Triplet learning has emerged as a powerful approach to recover embeddings in CBIR, albeit traditional loss functions ignore the dynamic relationship between opponent image classes. Here, we introduce a triplet-learning method for automated querying of medical image repositories based on a novel Opponent Class Adaptive Margin (OCAM) loss. OCAM uses a variable margin value that is updated continually during the course of training to maintain optimally discriminative representations. CBIR performance of OCAM is compared against state-of-the-art loss functions for representational learning on three public databases (gastrointestinal disease, skin lesion, lung disease). Comprehensive experiments in each application domain demonstrate the superior performance of OCAM against baselines.
Spatially Sparse Precoding in Wideband Hybrid Terahertz Massive MIMO Systems

In terahertz (THz) massive multiple-input multiple-output (MIMO) systems, the combination of huge bandwidth and massive antennas results in severe beam split, thus making the conventional phase-shifter based hybrid precoding architecture ineffective. With the incorporation of true-time-delay (TTD) lines in the hardware implementation of the analog precoders, delay-phase precoding (DPP) emerges as a promising architecture to effectively overcome beam split. However, existing DPP approaches suffer from poor performance, high complexity, and weak robustness in practical THz channels. In this paper, we propose a novel DPP approach in wideband THz massive MIMO systems. First, the matrix decomposition optimization problem is converted into a compressive sensing (CS) form, which can be solved by the proposed extended spatially sparse precoding (SSP) algorithm. To compensate for beam split, frequency-dependent measurement matrices are designed, which can be approximately realized by feasible phase and delay codebooks. Furthermore, several efficient atom selection techniques are developed to further reduce the complexity of the extended SSP algorithm. In simulation, the proposed DPP approach achieves superior performance, complexity, and robustness by using it alone or in combination with existing DPP approaches under various settings.
SliceMatch: Geometry-Guided Aggregation for Cross-View Pose Estimation

This work addresses cross-view camera pose estimation, i.e., determining the 3-Degrees-of-Freedom camera pose of a given ground-level image w.r. t. an aerial image of the local area. We propose SliceMatch, which consists of ground and aerial feature extractors, feature aggregators, and a pose predictor. The feature extractors extract dense features from the ground and aerial images. Given a set of candidate camera poses, the feature aggregators construct a single ground descriptor and a set of pose-dependent aerial descriptors. Notably, our novel aerial feature aggregator has a cross-view attention module for ground-view guided aerial feature selection and utilizes the geometric projection of the ground camera's viewing frustum on the aerial image to pool features. The efficient construction of aerial descriptors is achieved using precomputed masks. SliceMatch is trained using contrastive learning and pose estimation is formulated as a similarity comparison between the ground descriptor and the aerial descriptors. Compared to the state-of-the-art, SliceMatch achieves a 19% lower median localization error on the VIGOR benchmark using the same VGG16 backbone at 150 frames per second, and a 50% lower error when using a ResNet50 backbone.
Reassessment of the scale-determining equation for advanced turbulence models

A comprehensive and critical review of closure approximations for two-equation turbulence models has been made. Particular attention has focused on the scale-determining equation in an attempt to find the optimum choice of dependent variable and closure approximations. Using a combination of singular perturbation methods and numerical computations, this paper demonstrates that: 1) conventional A:-e and A>w formulations generally are inaccurate for boundary layers in adverse pressure gradient; 2) using "wall functions'' tends to mask the shortcomings of such models; and 3) a more suitable choice of dependent variables exists that is much more accurate for adverse pressure gradient. Based on the analysis, a two-equation turbulence model is postulated that is shown to be quite accurate for attached boundary layers in adverse pressure gradient, compressible boundary layers, and free shear flows. With no viscous damping of the model's closure coefficients and without the aid of wall functions, the model equations can be integrated through the viscous sublayer. Surface boundary conditions are presented that permit accurate predictions for flow over rough surfaces and for flows with surface mass addition.