Miracle-dz/UniFAR-FTUs huggingface dataset:
LGFCTR: Local and Global Feature Convolutional Transformer for Image Matching

Image matching that finding robust and accurate correspondences across images is a challenging task under extreme conditions. Capturing local and global features simultaneously is an important way to mitigate such an issue but recent transformer-based decoders were still stuck in the issues that CNN-based encoders only extract local features and the transformers lack locality. Inspired by the locality and implicit positional encoding of convolutions, a novel convolutional transformer is proposed to capture both local contexts and global structures more sufficiently for detector-free matching. Firstly, a universal FPN-like framework captures global structures in self-encoder as well as cross-decoder by transformers and compensates local contexts as well as implicit positional encoding by convolutions. Secondly, a novel convolutional transformer module explores multi-scale long range dependencies by a novel multi-scale attention and further aggregates local information inside dependencies for enhancing locality. Finally, a novel regression-based sub-pixel refinement module exploits the whole fine-grained window features for fine-level positional deviation regression. The proposed method achieves superior performances on a wide range of benchmarks. The code will be available on https://github.com/zwh0527/LGFCTR.
Velocities of Mesenchymal Cells May be Ill-Defined

The dynamics of single cell migration on flat surfaces is usually modeled by a Langevin-like problem consisting of ballistic motion for short periods and random walk. for long periods. Conversely, recent studies have revealed a previously neglected random motion at very short intervals, what would rule out the possibility of defining the cell instantaneous velocity and a robust measurement procedure. A previous attempt to address this issue considered an anisotropic migration model, which takes into account a polarization orientation along which the velocity is well-defined, and a direction orthogonal to the polarization vector that describes the random walk. Although the numerically and analytically calculated mean square displacement and auto-correlation agree with experimental data for that model, the velocity distribution peaks at zero, which contradicts experimental observations of a constant drift in the polarization direction. Moreover, Potts model simulations indicate that instantaneous velocity cannot be measured for any direction. Here, we consider dynamical equations for cell polarization, which is measurable and introduce a polarization-dependent displacement, circumventing the problem of ill defined instantaneous velocity. Polarization is a well-defined quantity, preserves memory for short intervals, and provides a robust measurement procedure for characterizing cell migration. We consider cell polarization dynamics to follow a modified Langevin equation that yields cell displacement distribution that peaks at positive values, in agreement with experiments and Potts model simulations. Furthermore, displacement autocorrelation functions present two different time scales, improving the agreement between theoretical fits and experiments or simulations.
Distance-coupling as an Approach to Position and Formation Control

Studies of coordinated motion in autonomous vehicle groups have become a significant topic of interest in recent years. In this paper, we study the case when the agents are limited to distance measurements from predetermined reference points, or anchors. A novel approach, referred to as distance-coupling, is proposed for approximating agent positions from anchor distances. By taking the difference between squared distance functions, we cancel out the quadratic term and obtain a linear function of the position for the point of interest. We apply this method to the homing problem and prove Lyapunov stability with and without anchor placement error; defining bounds on the region of attraction when the anchors are linearly transformed from their desired positions. To further increase complexity, we show how the policy can be implemented on the formation control problem for a set of autonomous agents. In this context, we prove the existence of the set of equilibria for the distance-coupled formation controller and make preliminary claims on its convergence. The performance for both controllers are subsequently validated through simulation.
Symbol-LLM: Leverage Language Models for Symbolic System in Visual Human Activity Reasoning

Human reasoning can be understood as a cooperation between the intuitive, associative"System-1"and the deliberative, logical"System-2". For existing System-1-like methods in visual activity understanding, it is crucial to integrate System-2 processing to improve explainability, generalization, and data efficiency. One possible path of activity reasoning is building a symbolic system composed of symbols and rules, where one rule connects multiple symbols, implying human knowledge and reasoning abilities. Previous methods have made progress, but are defective with limited symbols from handcraft and limited rules from visual-based annotations, failing to cover the complex patterns of activities and lacking compositional generalization. To overcome the defects, we propose a new symbolic system with two ideal important properties: broad-coverage symbols and rational rules. Collecting massive human knowledge via manual annotations is expensive to instantiate this symbolic system. Instead, we leverage the recent advancement of LLMs (Large Language Models) as an approximation of the two ideal properties, i.e., Symbols from Large Language Models (Symbol-LLM). Then, given an image, visual contents from the images are extracted and checked as symbols and activity semantics are reasoned out based on rules via fuzzy logic calculation. Our method shows superiority in extensive activity understanding tasks. Code and data are available at https://mvig-rhos.com/symbol_llm.
Use of Interactions between NAO and MJO for the Prediction of Dry and Wet Spell in Monsoon Season

The Indian summer monsoon exhibits prominent 30-40 day fluctuations with “active” periods of heavy rain interrupted by dry periods i.e. “Breaks”. The circulation anomalies associated with active/break monsoon cover the entire Indian Ocean remote tropics and North Pacific Ocean. A prolonged dry/wet period will result in severe drought/flooding, which have profound influences on the south Asia water cycle, agriculture and societal activity of more than one billion people. The atmospheric general circulation models have great difficulty in simulating the Intraseasonal oscillation (ISO). Therefore, it is necessary to study the empirical relationship between various atmospheric processes, which are responsible for the ISO. In this paper, the analysis of North Atlantic Oscillation Index (NAOI) and Madden Julian Oscillation Index (MJOI) on daily scale is carried out in relation to daily Indian summer monsoon rainfall (June-September). The analysis is carried out for the period 1979-2001. Since the potential predictability limit for monsoon break is about 20 days, the 20 days running lag/lead correlation analysis between the NAOI and MJOI is found out for each year. It is observed that 20-day lag relationship between NAO and MJO is inverse and significant (0.1 level) and this relationship remains negative throughout the break monsoon period and in active phase it reverses. This twenty days lag relationship between NAO and MJO is potential predictor for break/ active monsoon conditions over the Indian region. The analysis is verified for major drought year 2002.
Self-Supervised Learning of Whole and Component-Based Semantic Representations for Person Re-Identification

Person Re-Identification (ReID) is a challenging problem, focusing on identifying individuals across diverse settings. However, previous ReID methods primarily concentrated on a single domain or modality, such as Clothes-Changing ReID (CC-ReID) and video ReID. Real-world ReID is not constrained by factors like clothes or input types. Recent approaches emphasize on learning semantics through pre-training to enhance ReID performance but are hindered by coarse granularity, on-clothes focus and pre-defined areas. To address these limitations, we propose a Local Semantic Extraction (LSE) module inspired by Interactive Segmentation Models. The LSE module captures fine-grained, biometric, and flexible local semantics, enhancing ReID accuracy. Additionally, we introduce Semantic ReID (SemReID), a pre-training method that leverages LSE to learn effective semantics for seamless transfer across various ReID domains and modalities. Extensive evaluations across nine ReID datasets demonstrates SemReID's robust performance across multiple domains, including clothes-changing ReID, video ReID, unconstrained ReID, and short-term ReID. Our findings highlight the importance of effective semantics in ReID, as SemReID can achieve great performances without domain-specific designs.
Status of e+e- Higgs factory projects

An electron-positron collider designed for precision studies of the Higgs boson, a so-called Higgs factory is the highest-priority next collider of the particle physics community. This contribution summarises the key physics goals of such a Higgs factory and reviews the status of the various proposed realisations from mature concepts to very recent ideas. The commonalities and special advantages of circular and linear approaches will be discussed, respectively, before highlighting some recent developments regarding the key technologies, the operation scenarios and sustainability aspects for future colliders.
Survey on AI Ethics: A Socio-technical Perspective

The past decade has observed a great advancement in AI with deep learning-based models being deployed in diverse scenarios including safety-critical applications. As these AI systems become deeply embedded in our societal infrastructure, the repercussions of their decisions and actions have significant consequences, making the ethical implications of AI deployment highly relevant and important. The ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact. These principles together form the foundations of ethical AI considerations that concern every stakeholder in the AI system lifecycle. In light of the present ethical and future x-risk concerns, governments have shown increasing interest in establishing guidelines for the ethical deployment of AI. This work unifies the current and future ethical concerns of deploying AI into society. While we acknowledge and appreciate the technical surveys for each of the ethical principles concerned, in this paper, we aim to provide a comprehensive overview that not only addresses each principle from a technical point of view but also discusses them from a social perspective.
PHG-Net: Persistent Homology Guided Medical Image Classification

Modern deep neural networks have achieved great successes in medical image analysis. However, the features captured by convolutional neural networks (CNNs) or Transformers tend to be optimized for pixel intensities and neglect key anatomical structures such as connected components and loops. In this paper, we propose a persistent homology guided approach (PHG-Net) that explores topological features of objects for medical image classification. For an input image, we first compute its cubical persistence diagram and extract topological features into a vector representation using a small neural network (called the PH module). The extracted topological features are then incorporated into the feature map generated by CNN or Transformer for feature fusion. The PH module is lightweight and capable of integrating topological features into any CNN or Transformer architectures in an end-to-end fashion. We evaluate our PHG-Net on three public datasets and demonstrate its considerable improvements on the target classification tasks over state-of-the-art methods.
Hearing the boundary conditions of the one-dimensional Dirac operator

We study the isospectrality problem for a relativistic free quantum particle, described by the Dirac Hamiltonian, confined in a one-dimensional ring with a junction. We analyze all the self-adjoint extensions of the Hamiltonian in terms of the boundary conditions at the junction, characterizing the energy spectrum by means of a spectral function. By determining the symmetries of the latter, we are able to divide the self-adjoint extensions in two classes, identifying all the families of isospectral Hamiltonians, and thus completely characterizing the isospectrality problem.
Influence of the November Arctic Oscillation on the subsequent tropical Pacific sea surface temperature

Previous studies suggested that the variability of boreal spring Arctic Oscillation (AO) can exert a significant influence on the sea surface temperature (SST) anomalies in the Niño‐3.4 region during the following winter. This study further reveals that AO in November can have a pronounced influence on the tropical central‐eastern Pacific SST anomalies during the following spring and summer. When the November AO is in its positive (negative) phase, SST anomalies tend to be positive (negative) during the following spring and summer in the tropical central‐eastern Pacific. The influence of the AO is accomplished by atmospheric circulation anomalies over the subtropical North Pacific through an interaction between the synoptic‐scale eddy and the low‐frequency mean flow. In the positive November AO years, pronounced cyclonic circulation and atmospheric heating anomalies are observed over the subtropical North Pacific. The atmospheric heating anomalies sustain westerly wind anomalies over the tropical western North Pacific through a Gill‐like atmospheric response. The westerly wind anomalies extend eastward subsequently through positive air–sea feedback mechanism, and result in SST warming during the following spring and summer in the tropical central‐eastern Pacific. Results of this study imply that the November AO index can be used as an effective predictor of SST anomalies in the Niño‐3.4 region during the following spring and summer.
Maximum Entropy Model Correction in Reinforcement Learning

We propose and theoretically analyze an approach for planning with an approximate model in reinforcement learning that can reduce the adverse impact of model error. If the model is accurate enough, it accelerates the convergence to the true value function too. One of its key components is the MaxEnt Model Correction (MoCo) procedure that corrects the model's next-state distributions based on a Maximum Entropy density estimation formulation. Based on MoCo, we introduce the Model Correcting Value Iteration (MoCoVI) algorithm, and its sampled-based variant MoCoDyna. We show that MoCoVI and MoCoDyna's convergence can be much faster than the conventional model-free algorithms. Unlike traditional model-based algorithms, MoCoVI and MoCoDyna effectively utilize an approximate model and still converge to the correct value function.
Exceptional Mechanical Performance by Spatial Printing with Continuous Fiber

This work explores a spatial printing method to fabricate continuous fiber-reinforced thermoplastic composites (CFRTPCs), which can achieve exceptional mechanical performance. For models giving complex 3D stress distribution under loads, typical planar-layer based fiber placement usually fails to provide sufficient reinforcement due to their orientations being constrained to planes. The effectiveness of fiber reinforcement could be maximized by using multi-axis additive manufacturing (MAAM) to better control the orientation of continuous fibers in 3D-printed composites. Here, we propose a computational approach to generate 3D toolpaths that satisfy two major reinforcement objectives: 1) following the maximal stress directions in critical regions and 2) connecting multiple load-bearing regions by continuous fibers. Principal stress lines are first extracted in an input solid model to identify critical regions. Curved layers aligned with maximal stresses in these critical regions are generated by computing an optimized scalar field and extracting its iso-surfaces. Then, topological analysis and operations are applied to each curved layer to generate a computational domain that preserves fiber continuity between load-bearing regions. Lastly, continuous fiber toolpaths aligned with maximal stresses are generated on each surface layer by computing an optimized scalar field and extracting its iso-curves. A hardware system with dual robotic arms is employed to conduct the physical MAAM tasks depositing polymer or fiber reinforced polymer composite materials by applying a force normal to the extrusion plane to aid consolidation. When comparing to planar-layer based printing results in tension, up to 644% failure load and 240% stiffness are observed on shapes fabricated by our spatial printing method.
Erasing the Ephemeral: Joint Camera Refinement and Transient Object Removal for Street View Synthesis

Synthesizing novel views for urban environments is crucial for tasks like autonomous driving and virtual tours. Compared to object-level or indoor situations, outdoor settings present unique challenges, such as inconsistency across frames due to moving vehicles and camera pose drift over lengthy sequences. In this paper, we introduce a method that tackles these challenges on view synthesis for outdoor scenarios. We employ a neural point light field scene representation and strategically detect and mask out dynamic objects to reconstruct novel scenes without artifacts. Moreover, we simultaneously optimize camera pose along with the view synthesis process, and thus, we simultaneously refine both elements. Through validation on real-world urban datasets, we demonstrate state-of-the-art results in synthesizing novel views of urban scenes.
Strong consistency of an estimator by the truncated singular value decomposition for an errors-in-variables regression model with collinearity

In this paper, we prove strong consistency of an estimator by the truncated singular value decomposition for a multivariate errors-in-variables linear regression model with collinearity. This result is an extension of Gleser's proof of the strong consistency of total least squares solutions to the case with modern rank constraints. While the usual discussion of consistency in the absence of solution uniqueness deals with the minimal norm solution, the contribution of this study is to develop a theory that shows the strong consistency of a set of solutions. The proof is based on properties of orthogonal projections, specifically properties of the Rayleigh-Ritz procedure for computing eigenvalues. This makes it suitable for targeting problems where some row vectors of the matrices do not contain noise. Therefore, this paper gives a proof for the regression model with the above condition on the row vectors, resulting in a natural generalization of the strong consistency for the standard TLS estimator.
BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality Assessment

Banding, also known as staircase-like contours, frequently occurs in flat areas of images/videos processed by the compression or quantization algorithms. As undesirable artifacts, banding destroys the original image structure, thus degrading users' quality of experience (QoE). In this paper, we systematically investigate the banding image quality assessment (IQA) problem, aiming to detect the image banding artifacts and evaluate their perceptual visual quality. Considering that the existing image banding databases only contain limited content sources and banding generation methods, and lack perceptual quality labels (i.e. mean opinion scores), we first build the largest banding IQA database so far, named Banding Artifact Noticeable Database (BAND-2k), which consists of 2,000 banding images generated by 15 compression and quantization schemes. A total of 23 workers participated in the subjective IQA experiment, yielding over 214,000 patch-level banding class labels and 44,371 reliable image-level quality ratings. Subsequently, we develop an effective no-reference (NR) banding evaluator for banding detection and quality assessment by leveraging frequency characteristics of banding artifacts. A dual convolutional neural network is employed to concurrently learn the feature representation from the high-frequency and low-frequency maps, thereby enhancing the ability to discern banding artifacts. The quality score of a banding image is generated by pooling the banding detection maps masked by the spatial frequency filters. Experiments demonstrate that our banding evaluator achieves a remarkably high accuracy in banding detection and also exhibits high SRCC and PLCC results with the perceptual quality labels. These findings unveil the strong correlations between the intensity of banding artifacts and the perceptual visual quality, thus validating the necessity of banding quality assessment.
Enhancing Answer Selection in Community Question Answering with Pre-trained and Large Language Models

Community Question Answering (CQA) becomes increasingly prevalent in recent years. However, there are a large number of answers, which is difficult for users to select the relevant answers. Therefore, answer selection is a very significant subtask of CQA. In this paper, we first propose the Question-Answer cross attention networks (QAN) with pre-trained models for answer selection and utilize large language model (LLM) to perform answer selection with knowledge augmentation. Specifically, we apply the BERT model as the encoder layer to do pre-training for question subjects, question bodies and answers, respectively, then the cross attention mechanism selects the most relevant answer for different questions. Experiments show that the QAN model achieves state-of-the-art performance on two datasets, SemEval2015 and SemEval2017. Moreover, we use the LLM to generate external knowledge from questions and correct answers to achieve knowledge augmentation for the answer selection task by LLM, while optimizing the prompt of LLM in different aspects. The results show that the introduction of external knowledge can improve the correct answer selection rate of LLM on datasets SemEval2015 and SemEval2017. Meanwhile, LLM can also select the correct answer on more questions by optimized prompt.
Redshifted Iron Emission and Absorption Lines in the Chandra X-Ray Spectrum of Centaurus A

Cen A hosts the closest active galactic nucleus to the Milky Way, which makes it an ideal target for investigating the dynamical processes in the vicinity of accreting supermassive black holes. In this paper, we present 14 Chandra HETGS spectra of the nucleus of Cen A that were observed throughout 2022. We compared them with each other, and contrasted them against the two previous Chandra HETGS spectra from 2001. This enabled an investigation into the spectral changes occurring on timescales of months and 21 years. All Chandra spectra could be well fitted by an absorbed power law with a strong and narrow Fe Kα line, a leaked power-law feature at low energies, and Si and S Kα lines that could not be associated with the central engine. The flux of the continuum varied by a factor of 2.74 ± 0.05 over the course of the observations, whereas the Fe line only varied by 18.8% ± 8.8%. The photon index increased over 21 years, and the hydrogen column density varied significantly within a few months as well. The Fe Kα line was found at a lower energy than expected from the Cen A redshift, amounting to an excess velocity of 326 − 94 + 84 km s − 1 relative to Cen A. We investigated warped accretion disks, bulk motion, and outflows as possible explanations of this shift. The spectra also featured ionized absorption lines from Fe xxv and Fe xxvi, describing a variable inflow.