Miracle-dz/UniFAR-FTUs huggingface dataset:
Phantom dark energy from nonlocal infrared modifications of general relativity

We discuss the cosmological consequences of a model based on a nonlocal infrared modification of Einstein equations. We find that the model generates a dynamical dark energy that can account for the presently observed value of ΩDE, without introducing a cosmological constant. Tuning a free mass parameter m to a value m≃0.67H0, we reproduce the observed value ΩDE≃0.68. This leaves us with no free parameter, and we then get a pure prediction for the equation of state parameter of dark energy. Writing wDE(a)=w0+(1−a)wa, we find w0≃−1.04 and wa≃−0.02, consistent with the Planck data and on the phantom side. We also argue that nonlocal equations of the type that we propose must be understood as purely classical effective equations, such as those derived in semiclassical gravity for the in-in matrix elements of the metric. As such, any apparent ghost instability in such equations only affects the classical dynamics, but there is no propagating degree of freedom associated to the ghost and no issue of ghost-induced quantum vacuum decay
Information measures and geometry of the hyperbolic exponential families of Poincar\'e and hyperboloid distributions

We study various information-theoretic measures and the information geometry of the Poincar\'e distributions and the related hyperboloid distributions, and prove that their statistical mixture models are universal density estimators of smooth densities in hyperbolic spaces. The Poincar\'e and the hyperboloid distributions are two types of hyperbolic probability distributions defined using different models of hyperbolic geometry. Namely, the Poincar\'e distributions form a triparametric bivariate exponential family whose sample space is the hyperbolic Poincar\'e upper-half plane and natural parameter space is the open 3D convex cone of two-by-two positive-definite matrices. The family of hyperboloid distributions form another exponential family which has sample space the forward sheet of the two-sheeted unit hyperboloid modeling hyperbolic geometry. In the first part, we prove that all $f$-divergences between Poincar\'e distributions can be expressed using three canonical terms using Eaton's framework of maximal group invariance. We also show that the $f$-divergences between any two Poincar\'e distributions are asymmetric except when those distributions belong to a same leaf of a particular foliation of the parameter space. We report closed-form formula for the Fisher information matrix, the Shannon's differential entropy and the Kullback-Leibler divergence. and Bhattacharyya distances between such distributions using the framework of exponential families. In the second part, we state the corresponding results for the exponential family of hyperboloid distributions by highlighting a parameter correspondence between the Poincar\'e and the hyperboloid distributions. Finally, we describe a random generator to draw variates and present two Monte Carlo methods to stochastically estimate numerically $f$-divergences between hyperbolic distributions.
DALL-EVAL: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models

Recently, DALL-E [45], a multimodal transformer language model, and its variants including diffusion models have shown high-quality text-to-image generation capabilities. However, despite the realistic image generation results, there has not been a detailed analysis of how to evaluate such models. In this work, we investigate the visual reasoning capabilities and social biases of different text-to-image models, covering both multimodal transformer language models and diffusion models. First, we measure three visual reasoning skills: object recognition, object counting, and spatial relation understanding. For this, we propose PaintSkills, a compositional diagnostic evaluation dataset that measures these skills. Despite the high-fidelity image generation capability, a large gap exists between the performance of recent models and the upper bound accuracy in object counting and spatial relation understanding skills. Second, we assess the gender and skin tone biases by measuring the gender/skin tone distribution of generated images across various professions and attributes. We demonstrate that recent text-to-image generation models learn specific biases about gender and skin tone from web image-text pairs. We hope our work will help guide future progress in improving text-to-image generation models on visual reasoning skills and learning socially unbiased representations.1
Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges

With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on a high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and the meaning of information into data processing and transmission, is predicted to become a new core paradigm in 6G. While SemCom is expected to progress beyond the classical Shannon paradigm, several obstacles need to be overcome on the way to a SemCom-enabled smart Internet. In this paper, we first highlight the motivations and compelling reasons for SemCom in 6G. Then, we provide an overview of SemCom-related theory development. After that, we introduce three types of SemCom, i.e., semantic-oriented communication, goal-oriented communication, and semantic-aware communication. Following that, we organize the design of the communication system into three dimensions, i.e., semantic information (SI) extraction, SI transmission, and SI metrics. For each dimension, we review existing techniques and discuss their benefits and limitations, as well as the remaining challenges. Then, we introduce the potential applications of SemCom in 6G and portray the vision of future SemCom-empowered network architecture. Finally, we outline future research opportunities. In a nutshell, this paper provides a holistic review of the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues with insights for further in-depth investigations.
On the Local Equivalence of 2D Color Codes and Surface Codes with Applications

In recent years, there have been many studies on local stabilizer codes. Under the assumption of translation and scale invariance Yoshida classified such codes. His result implies that translation invariant 2D color codes are equivalent to copies of toric codes. Independently, Bombin, Duclos-Cianci, and Poulin showed that a local translation invariant 2D topological stabilizer code is locally equivalent to a finite number of copies of Kitaev's toric code. In this paper we focus on 2D topological color codes and relax the assumption of translation invariance. Using a linear algebraic framework we show that any 2D color code (without boundaries) is locally equivalent to two copies of a related surface code. This surface code is induced by color code. We study the application of this equivalence to the decoding of 2D color codes over the bit flip channel as well as the quantum erasure channel. We report the performance of the color code on the square octagonal lattice over the quantum erasure channel. Further, we provide explicit circuits that perform the transformation between 2D color codes and surface codes. Our circuits do not require any additional ancilla qubits.
Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completion

Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence. Differing from existing deterministic approaches, we advocate generative modeling based unsupervised point cloud completion to explore the missing correspondence. Specifically, we propose a novel framework that performs completion by transforming a partial shape encoding into a complete one using a latent transport module, and it is designed as a latent-space energy-based model (EBM) in an encoder-decoder architecture, aiming to learn a probability distribution conditioned on the partial shape encoding. To train the latent code transport module and the encoder-decoder network jointly, we introduce a residual sampling strategy, where the residual captures the domain gap between partial and complete shape latent spaces. As a generative model-based framework, our method can produce uncertainty maps consistent with human perception, leading to explainable unsupervised point cloud completion. We experimentally show that the proposed method produces high-fidelity completion results, outperforming state-of-the-art models by a significant margin.
Asynchronous progressive iterative approximation method for least-squares fitting

For large-scale data fitting, the least-squares progressive-iterative approximation (LSPIA) methods were proposed by Lin et al. (SIAM Journal on Scientific Computing, 2013, 35(6):A3052-A3068) and Deng et al. (Computer-Aided Design, 2014, 47:32-44), where the constant step sizes were used. In this work, we further accelerate the LSPIA method in the sense of a Chebyshev semi-iterative scheme and present an asynchronous LSPIA (ALSPIA) method to fit data points. The control points in ALSPIA are updated by utilizing an extrapolated variant and an adaptive step size is chosen according to the roots of Chebyshev polynomials. Our convergence analysis reveals that ALSPIA is faster than the original LSPIA method in both cases of singular and nonsingular least-squares fittings. Numerical examples show that the proposed algorithm is feasible and effective.
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature Denoiser

Due to the development of deep learning-based denoisers, the plug-and-play strategy has achieved great success in image restoration problems. However, existing plug-and-play image restoration methods are designed for non-blind Gaussian denoising such as zhang et al (2022), the performance of which visibly deteriorate for unknown noises. To push the limits of plug-and-play image restoration, we propose a novel framework with blind Gaussian prior, which can deal with more complicated image restoration problems in the real world. More specifically, we build up a new image restoration model by regarding the noise level as a variable, which is implemented by a two-stage blind Gaussian denoiser consisting of a noise estimation subnetwork and a denoising subnetwork, where the noise estimation subnetwork provides the noise level to the denoising subnetwork for blind noise removal. We also introduce the curvature map into the encoder-decoder architecture and the supervised attention module to achieve a highly flexible and effective convolutional neural network. The experimental results on image denoising, deblurring and single-image super-resolution are provided to demonstrate the advantages of our deep curvature denoiser and the resulting plug-and-play blind image restoration method over the state-of-the-art model-based and learning-based methods. Our model is shown to be able to recover the fine image details and tiny structures even when the noise level is unknown for different image restoration tasks. The source codes are available at https://github.com/Duanlab123/CurvPnP.
Exploring the Impact of Noise and Degradations on Heart Sound Classification Models

The development of data-driven heart sound classification models has been an active area of research in recent years. To develop such data-driven models in the first place, heart sound signals need to be captured using a signal acquisition device. However, it is almost impossible to capture noise-free heart sound signals due to the presence of internal and external noises in most situations. Such noises and degradations in heart sound signals can potentially reduce the accuracy of data-driven classification models. Although different techniques have been proposed in the literature to address the noise issue, how and to what extent different noise and degradations in heart sound signals impact the accuracy of data-driven classification models remains unexplored. To answer this question, we produced a synthetic heart sound dataset including normal and abnormal heart sounds contaminated with a large variety of noise and degradations. We used this dataset to investigate the impact of noise and degradation in heart sound recordings on the performance of different classification models. The results show different noises and degradations affect the performance of heart sound classification models to a different extent; some are more problematic for classification models, and others are less destructive. Comparing the findings of this study with the results of a survey we previously carried out with a group of clinicians shows noise and degradations that are more detrimental to classification models are also more disruptive to accurate auscultation. The findings of this study can be leveraged to develop targeted heart sound quality enhancement approaches - which adapt the type and aggressiveness of quality enhancement based on the characteristics of noise and degradation in heart sound signals.
High-Resource Methodological Bias in Low-Resource Investigations

The central bottleneck for low-resource NLP is typically regarded to be the quantity of accessible data, overlooking the contribution of data quality. This is particularly seen in the development and evaluation of low-resource systems via down sampling of high-resource language data. In this work we investigate the validity of this approach, and we specifically focus on two well-known NLP tasks for our empirical investigations: POS-tagging and machine translation. We show that down sampling from a high-resource language results in datasets with different properties than the low-resource datasets, impacting the model performance for both POS-tagging and machine translation. Based on these results we conclude that naive down sampling of datasets results in a biased view of how well these systems work in a low-resource scenario.
Drug-target affinity prediction method based on consistent expression of heterogeneous data

The first step in drug discovery is to find drug molecule moieties with medicinal activity against specific targets, so it is crucial to investigate the interaction between drug target proteins and chemical small molecules. However, traditional experimental methods for discovering potential drug small molecules are labor-intensive and time-consuming, and there is currently a lot of interest in building computational models to screen drug small molecules by using drug molecule-related databases. In this paper, we propose a method for predicting drug-target binding affinity using deep learning models. The method uses a modified GRU and GNN to extract features from the drug-target protein sequence and the drug-molecule map, respectively, to obtain their feature vectors, and finally the combined vectors are used as vector representations of drug-target molecule pairs into a fully-connected network to predict drug-target binding affinity. The proposed model demonstrates its effectiveness and accuracy in the task of predicting drug-target binding affinity on DAVIS and KIBA datasets.
Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification

—In recent years, research on hyperspectral image (HSI) classiﬁcation has continuous progress on introducing deep network models, and recently the graph convolutional net- work (GCN) based models have shown impressive performance. However, these deep learning frameworks are based on point estimation suffering from low generalization and inability to quantify the classiﬁcation results uncertainty. On the other hand, simply applying the distribution estimation based Bayesian Neural Network (BNN) to classify the HSI is unable to achieve high classiﬁcation efﬁciency due to the large amount of parameters. In this paper, we design a Bayesian layer as an insertion layer into point estimation based neural networks, and propose a Bayesian layer graph convolutional network (BLGCN) model by combin- ing graph convolution operations, which can effectively extract graph information and estimate the uncertainty of classiﬁcation results. Moreover, a Generative Adversarial Network (GAN) is built to solve the sample imbalance problem of HSI dataset. Finally, we design a dynamic control training strategy based on the conﬁdence interval of the classiﬁcation results, which will terminate the training early when the conﬁdence interval reaches the presented threshold. The experimental results show that our model achieves a balance between high classiﬁcation accuracy and strong generalization. In addition, it can quantify the uncertainty of outputs.
Non-thermal broadening of IRIS Fe XXI line caused by turbulent plasma flows in the magnetic reconnection region during solar eruptions

Magnetic reconnection is the key mechanism for energy release in solar eruptions, where the high-temperature emission is the primary diagnostic for investigating the plasma properties during the reconnection process. Non-thermal broadening of high-temperature lines has been observed in both the reconnection current sheet (CS) and flare loop-top regions by UV spectrometers, but its origin remains unclear. In this work, we use a recently developed three-dimensional magnetohydrodynamic (MHD) simulation to model magnetic reconnection in the standard solar flare geometry and reveal highly dynamic plasma flows in the reconnection regions. We calculate the synthetic profiles of the Fe XXI 1354 Å line observed by the Interface Region Imaging Spectrograph (IRIS) spacecraft by using parameters of the MHD model, including plasma density, temperature, and velocity. Our model shows that the turbulent bulk plasma flows in the CS and flare loop-top regions are responsible for the non-thermal broadening of the Fe XXI emission line. The modeled non-thermal velocity ranges from tens of km s−1 to more than two hundred km s−1, which is consistent with the IRIS observations. Simulated 2D spectral line maps around the reconnection region also reveal highly dynamic downwflow structures where the high non-thermal velocity is large, which is consistent with the observations as well.
DEYO: DETR with YOLO for Step-by-Step Object Detection

Object detection is an important topic in computer vision, with post-processing, an essential part of the typical object detection pipeline, posing a significant bottleneck affecting the performance of traditional object detection models. The detection transformer (DETR), as the first end-to-end target detection model, discards the requirement of manual components like the anchor and non-maximum suppression (NMS), significantly simplifying the target detection process. However, compared with most traditional object detection models, DETR converges very slowly, and a query's meaning is obscure. Thus, inspired by the Step-by-Step concept, this paper proposes a new two-stage object detection model, named DETR with YOLO (DEYO), which relies on a progressive inference to solve the above problems. DEYO is a two-stage architecture comprising a classic target detection model and a DETR-like model as the first and second stages, respectively. Specifically, the first stage provides high-quality query and anchor feeding into the second stage, improving the performance and efficiency of the second stage compared to the original DETR model. Meanwhile, the second stage compensates for the performance degradation caused by the first stage detector's limitations. Extensive experiments demonstrate that DEYO attains 50.6 AP and 52.1 AP in 12 and 36 epochs, respectively, while utilizing ResNet-50 as the backbone and multi-scale features on the COCO dataset. Compared with DINO, an optimal DETR-like model, the developed DEYO model affords a significant performance improvement of 1.6 AP and 1.2 AP in two epoch settings.
Computationally Light Spectrally Normalized Memory Neuron Network based Estimator for GPS-denied operation of Micro-UAV

This paper addresses the problem of position estimation in UAVs operating in a cluttered environment where GPS information is unavailable. A learning-based approach is proposed that takes in the rotor RPMs and past state as input and predicts the one-step-ahead position of the UAV using a novel spectral-normalized memory neural network (SN-MNN). The spectral normalization guarantees stable and reliable prediction performance. The predicted position is transformed to the global coordinate frame (GPS), which is then fused along with the odometry of other peripheral sensors like IMU, barometer, compass, etc., using the onboard extended Kalman filter (EKF) to estimate the states of the UAV. The experimental flight data collected from an RTK-GPS facility using a micro-UAV is used to train the SN-MNN. The PX4-ECL library is used to fuse the predicted data using the SN-MNN, and the estimated position is compared with actual ground truth data. The proposed algorithm doesn't require any additional onboard sensors and is computationally light. The performance of the proposed approach is compared with the current state-of-art GPS-denied algorithms, and it can be seen that the proposed algorithm has the least RMSE for position estimates.
Depth and Representation in Vision Models

Deep learning models develop successive representations of their input in sequential layers, the last of which maps the final representation to the output. Here we investigate the informational content of these representations by observing the ability of convolutional image classification models to autoencode the model's input using embeddings existing in various layers. We find that the deeper the layer, the less accurate that layer's representation of the input is before training. Inaccurate representation results from non-uniqueness in which various distinct inputs give approximately the same embedding. Non-unique representation is a consequence of both exact and approximate non-invertibility of transformations present in the forward pass. Learning to classify natural images leads to an increase in representation clarity for early but not late layers, which instead form abstract images. Rather than simply selecting for features present in the input necessary for classification, deep layer representations are found to transform the input so that it matches representations of the training data such that arbitrary inputs are mapped to manifolds learned during training. This work provides support for the theory that the tasks of image recognition and input generation are inseparable even for models trained exclusively to classify.
Batch-based Model Registration for Fast 3D Sherd Reconstruction

3D reconstruction techniques have widely been used for digital documentation of archaeological fragments. However, efficient digital capture of fragments remains as a challenge. In this work, we aim to develop a portable, high-throughput, and accurate reconstruction system for efficient digitization of fragments excavated in archaeological sites. To realize high-throughput digitization of large numbers of objects, an effective strategy is to perform scanning and reconstruction in batches. However, effective batch-based scanning and reconstruction face two key challenges: 1) how to correlate partial scans of the same object from multiple batch scans, and 2) how to register and reconstruct complete models from partial scans that exhibit only small overlaps. To tackle these two challenges, we develop a new batch-based matching algorithm that pairs the front and back sides of the fragments, and a new Bilateral Boundary ICP algorithm that can register partial scans sharing very narrow overlapping regions. Extensive validation in labs and testing in excavation sites demonstrate that these designs enable efficient batch-based scanning for fragments. We show that such a batch-based scanning and reconstruction pipeline can have immediate applications on digitizing sherds in archaeological excavations. Our project page: https://jiepengwang.github. io/FIRES/.