Miracle-dz/UniFAR-FTUs huggingface dataset:
Node Connectivity Augmentation of Highly Connected Graphs

Node-connectivity augmentation is a fundamental network design problem. We are given a $k$-node connected graph $G$ together with an additional set of links, and the goal is to add a cheap subset of links to $G$ to make it $(k+1)$-node connected. In this work, we characterize completely the computational complexity status of the problem, by showing hardness for all values of $k$ which were not addressed previously in the literature. We then focus on $k$-node connectivity augmentation for $k=n-4$, which corresponds to the highest value of $k$ for which the problem is NP-hard. We improve over the previously best known approximation bounds for this problem, by developing a $\frac{3}{2}$-approximation algorithm for the weighted setting, and a $\frac{4}{3}$-approximation algorithm for the unweighted setting.
Decomposition numbers for unipotent blocks with small $\mathfrak{sl}_2$-weight in finite classical groups

We show that parabolic Kazhdan-Lusztig polynomials of type $A$ compute the decomposition numbers in certain Harish-Chandra series of unipotent characters of finite groups of Lie types $B$, $C$ and $D$ over a field of non-defining characteristic $\ell$. Here, $\ell$ is a ``unitary prime"-- the case that remains open in general. The bipartitions labeling the characters in these series are small with respect to $d$, the order of $q$ mod $\ell$, although they occur in blocks of arbitrarily high defect. Our main technical tool is the categorical action of an affine Lie algebra on the category of unipotent representations, which identifies the branching graph for Harish-Chandra induction with the $\widehat{\mathfrak{sl}}_d$-crystal on a sum of level $2$ Fock spaces. Further key combinatorics has been adapted from Brundan and Stroppel's work on Khovanov arc algebras to obtain the closed formula for the decomposition numbers in a $d$-small Harish-Chandra series.
Self-Supervised Motion Magnification by Backpropagating Through Optical Flow

This paper presents a simple, self-supervised method for magnifying subtle motions in video: given an input video and a magnification factor, we manipulate the video such that its new optical flow is scaled by the desired amount. To train our model, we propose a loss function that estimates the optical flow of the generated video and penalizes how far if deviates from the given magnification factor. Thus, training involves differentiating through a pretrained optical flow network. Since our model is self-supervised, we can further improve its performance through test-time adaptation, by finetuning it on the input video. It can also be easily extended to magnify the motions of only user-selected objects. Our approach avoids the need for synthetic magnification datasets that have been used to train prior learning-based approaches. Instead, it leverages the existing capabilities of off-the-shelf motion estimators. We demonstrate the effectiveness of our method through evaluations of both visual quality and quantitative metrics on a range of real-world and synthetic videos, and we show our method works for both supervised and unsupervised optical flow methods.
Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular, Stereo, and RGB-D Cameras

The integration of neural rendering and the SLAM system recently showed promising results in joint localization and photorealistic view reconstruction. However, existing methods, fully relying on implicit representations, are so resource-hungry that they cannot run on portable devices, which deviates from the original intention of SLAM. In this paper, we present Photo-SLAM, a novel SLAM framework with a hyper primitives map. Specifically, we simultaneously exploit explicit geometric features for localization and learn implicit photometric features to represent the texture information of the observed environment. In addition to actively densifying hyper primitives based on geometric features, we further introduce a Gaussian-Pyramid-based training method to progressively learn multi-level features, enhancing photorealistic mapping performance. The extensive experiments with monocular, stereo, and RGB-D datasets prove that our proposed system Photo-SLAM significantly outperforms current state-of-the-art SLAM systems for online photorealistic mapping, e.g., PSNR is 30% higher and rendering speed is hundreds of times faster in the Replica dataset. Moreover, the Photo-SLAM can run at real-time speed using an embedded platform such as Jetson AGX Orin, showing the potential of robotics applications.
Activity Tendency Prediction of Rainfall Induced Landslides and Debris Flows in the Wenchuan Earthquake Areas

After Wenchuan earthquake,the subsequent rainstorms caused new landslides and debris flow occurrence. To explore the future activity of rainfall induced landslides in the Wenchuan earthquake areas,comparative analysis of the earthquake-induced landslides and debris flow activity trends of the Wenchuan earthquake with the Great Kanto earthquake in Japan and the Chi-Chi earthquake in Taiwan was made,on this basis,the future development of landslides in the Wenchuan earthquake areas was analyzed and predicted. The preliminary study results indicate the Landslide and debris flow activity in the Wenchuan erathquake areas will keep still intense in future 10 years. After that,the geological condition will be gradually stable in the future. A study area of 5.9 km2 was selected in western slope of the Beichuan city. The new landslide area induced by heavy rainfall with 100-year return period will be approximately 166.97×104 m2 ,taking up 28.3% of total study wtershed area. This study also predicted the outflow volume of debris flows with different return period in the future. The result shows the outflow volume of debris flow in the watershed of Weijia gully and Sujia gally were estimated to be 71.0×104 m3 and 49.2 ×104 m3 respectively. This research work can provide a reference for further understanding the role of Wenchuan earthquake follow-up rain-induced landslide and debris flow activity trends.
A unified weighting framework for evaluating nearest neighbour classification

We present the first comprehensive and large-scale evaluation of classical (NN), fuzzy (FNN) and fuzzy rough (FRNN) nearest neighbour classification. We show that existing proposals for nearest neighbour weighting can be standardised in the form of kernel functions, applied to the distance values and/or ranks of the nearest neighbours of a test instance. Furthermore, we identify three commonly used distance functions and four scaling measures. We systematically evaluate these choices on a collection of 85 real-life classification datasets. We find that NN, FNN and FRNN all perform best with Boscovich distance. NN and FRNN perform best with a combination of Samworth rank- and distance weights and scaling by the mean absolute deviation around the median ($r_1$), the standard deviaton ($r_2$) or the interquartile range ($r_{\infty}^*$), while FNN performs best with only Samworth distance-weights and $r_1$- or $r_2$-scaling. We also introduce a new kernel based on fuzzy Yager negation, and show that NN achieves comparable performance with Yager distance-weights, which are simpler to implement than a combination of Samworth distance- and rank-weights. Finally, we demonstrate that FRNN generally outperforms NN, which in turns performs systematically better than FNN.
Symmetry-regularized neural ordinary differential equations

Neural Ordinary Differential Equations (Neural ODEs) is a class of deep neural network models that interpret the hidden state dynamics of neural networks as an ordinary differential equation, thereby capable of capturing system dynamics in a continuous time framework. In this work, I integrate symmetry regularization into Neural ODEs. In particular, I use continuous Lie symmetry of ODEs and PDEs associated with the model to derive conservation laws and add them to the loss function, making it physics-informed. This incorporation of inherent structural properties into the loss function could significantly improve robustness and stability of the model during training. To illustrate this method, I employ a toy model that utilizes a cosine rate of change in the hidden state, showcasing the process of identifying Lie symmetries, deriving conservation laws, and constructing a new loss function.
Helioseismic Investigation of Quasi-biennial Oscillation Source Regions

We studied the temporal evolution of quasi-biennial oscillations (QBOs) using acoustic mode oscillation frequencies from the Global Oscillation Network Group. The data used here span more than 25 yr, covering solar cycles 23 and 24 and the ascending phase of cycle 25. The analysis reveals that QBO-like signals are present in both the cycles, but with different periods. The dominant QBO period in cycle 23 is found to be about 2 yr, while it is about 3 yr in cycle 24. Furthermore, the quasi-biennial oscillatory signals are present only during the ascending and high-activity phases of cycle 23 and quickly weaken around 2005, during the declining phase. In comparison, the QBO signals are present throughout cycle 24, starting from 2009 to 2017. We also explored the depth dependence in QBO signals and obtained a close agreement at all depths, except in the near-surface shear layer. A detailed analysis of the near-surface shear layer suggests that the source region of QBOs is probably within a few thousand kilometers just below the surface.
Utility Fairness in Contextual Dynamic Pricing with Demand Learning

This paper introduces a novel contextual bandit algorithm for personalized pricing under utility fairness constraints in scenarios with uncertain demand, achieving an optimal regret upper bound. Our approach, which incorporates dynamic pricing and demand learning, addresses the critical challenge of fairness in pricing strategies. We first delve into the static full-information setting to formulate an optimal pricing policy as a constrained optimization problem. Here, we propose an approximation algorithm for efficiently and approximately computing the ideal policy. We also use mathematical analysis and computational studies to characterize the structures of optimal contextual pricing policies subject to fairness constraints, deriving simplified policies which lays the foundations of more in-depth research and extensions. Further, we extend our study to dynamic pricing problems with demand learning, establishing a non-standard regret lower bound that highlights the complexity added by fairness constraints. Our research offers a comprehensive analysis of the cost of fairness and its impact on the balance between utility and revenue maximization. This work represents a step towards integrating ethical considerations into algorithmic efficiency in data-driven dynamic pricing.
Spatially Adaptive Cloth Regression with Implicit Neural Representations

The accurate representation of fine-detailed cloth wrinkles poses significant challenges in computer graphics. The inherently non-uniform structure of cloth wrinkles mandates the employment of intricate discretization strategies, which are frequently characterized by high computational demands and complex methodologies. Addressing this, the research introduced in this paper elucidates a novel anisotropic cloth regression technique that capitalizes on the potential of implicit neural representations of surfaces. Our first core contribution is an innovative mesh-free sampling approach, crafted to reduce the reliance on traditional mesh structures, thereby offering greater flexibility and accuracy in capturing fine cloth details. Our second contribution is a novel adversarial training scheme, which is designed meticulously to strike a harmonious balance between the sampling and simulation objectives. The adversarial approach ensures that the wrinkles are represented with high fidelity, while also maintaining computational efficiency. Our results showcase through various cloth-object interaction scenarios that our method, given the same memory constraints, consistently surpasses traditional discrete representations, particularly when modelling highly-detailed localized wrinkles.
SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation

We present a novel approach for digitizing real-world objects by estimating their geometry, material properties, and environmental lighting from a set of posed images with fixed lighting. Our method incorporates into Neural Radiance Field (NeRF) pipelines the split sum approximation used with image-based lighting for real-time physical-based rendering. We propose modeling the scene's lighting with a single scene-specific MLP representing pre-integrated image-based lighting at arbitrary resolutions. We achieve accurate modeling of pre-integrated lighting by exploiting a novel regularizer based on efficient Monte Carlo sampling. Additionally, we propose a new method of supervising self-occlusion predictions by exploiting a similar regularizer based on Monte Carlo sampling. Experimental results demonstrate the efficiency and effectiveness of our approach in estimating scene geometry, material properties, and lighting. Our method is capable of attaining state-of-the-art relighting quality after only ${\sim}1$ hour of training in a single NVIDIA A100 GPU.
Enhancing Item-level Bundle Representation for Bundle Recommendation

Bundle recommendation approaches offer users a set of related items on a particular topic. The current state-of-the-art (SOTA) method utilizes contrastive learning to learn representations at both the bundle and item levels. However, due to the inherent difference between the bundle-level and item-level preferences, the item-level representations may not receive sufficient information from the bundle affiliations to make accurate predictions. In this paper, we propose a novel approach EBRec, short of Enhanced Bundle Recommendation, which incorporates two enhanced modules to explore inherent item-level bundle representations. First, we propose to incorporate the bundle-user-item (B-U-I) high-order correlations to explore more collaborative information, thus to enhance the previous bundle representation that solely relies on the bundle-item affiliation information. Second, we further enhance the B-U-I correlations by augmenting the observed user-item interactions with interactions generated from pre-trained models, thus improving the item-level bundle representations. We conduct extensive experiments on three public datasets, and the results justify the effectiveness of our approach as well as the two core modules. Codes and datasets are available at https://github.com/answermycode/EBRec.
VehicleGAN: Pair-flexible Pose Guided Image Synthesis for Vehicle Re-identification

Vehicle Re-identification (Re-ID) has been broadly studied in the last decade; however, the different camera view angle leading to confused discrimination in the feature subspace for the vehicles of various poses, is still challenging for the Vehicle Re-ID models in the real world. To promote the Vehicle Re-ID models, this paper proposes to synthesize a large number of vehicle images in the target pose, whose idea is to project the vehicles of diverse poses into the unified target pose so as to enhance feature discrimination. Considering that the paired data of the same vehicles in different traffic surveillance cameras might be not available in the real world, we propose the first Pair-flexible Pose Guided Image Synthesis method for Vehicle Re-ID, named as VehicleGAN in this paper, which works for both supervised and unsupervised settings without the knowledge of geometric 3D models. Because of the feature distribution difference between real and synthetic data, simply training a traditional metric learning based Re-ID model with data-level fusion (i.e., data augmentation) is not satisfactory, therefore we propose a new Joint Metric Learning (JML) via effective feature-level fusion from both real and synthetic data. Intensive experimental results on the public VeRi-776 and VehicleID datasets prove the accuracy and effectiveness of our proposed VehicleGAN and JML.
Modeling and Control of a Novel Variable Stiffness Three DoFs Wrist

This study introduces an innovative design for a Variable Stiffness 3 Degrees of Freedom actuated wrist capable of actively and continuously adjusting its overall stiffness by modulating the active length of non-linear elastic elements. This modulation is akin to human muscular cocontraction and is achieved using only four motors. The mechanical configuration employed results in a compact and lightweight device with anthropomorphic characteristics, making it potentially suitable for applications such as prosthetics and humanoid robotics. This design aims to enhance performance in dynamic tasks, improve task adaptability, and ensure safety during interactions with both people and objects. The paper details the first hardware implementation of the proposed design, providing insights into the theoretical model, mechanical and electronic components, as well as the control architecture. System performance is assessed using a motion capture system. The results demonstrate that the prototype offers a broad range of motion ([55, −45]° for flexion/extension, ±48° for radial/ulnar deviation, and ±180° for pronation/supination) while having the capability to triple its stiffness. Furthermore, following proper calibration, the wrist posture can be reconstructed through multivariate linear regression using rotational encoders and the forward kinematic model. This reconstruction achieves an average Root Mean Square Error of 6.6°, with an R2 value of 0.93.
Picogrid: An experimental platform for prosumer microgrids

The Microgrid paradigm is gaining momentum as one of the key pieces of technology for expanding clean energy access and improving energy resilience. Most of the interest in this pertains to distinct entities that either generate electricity or act as loads, i.e., distinct producers and consumers. Remote community microgrids and emerging transactive energy service models with interconnected prosumers do not clearly fit into this paradigm. Notwithstanding various publications that present concepts and simulations, there has been a dearth of experimental platforms to study them, due to practical challenges. This paper presents the ‘Picogrid’ - an experimental platform particularly designed for dc prosumer microgrids. It is a low-power, low-cost hardware platform that enables interconnecting multiple prosumer entities in a bench-top setup. Each prosumer sends data to a cloud dashboard and can receive set points for optimal operation from a remote computer system, lending itself to use in a virtual lab setup. The platform enables implementation of custom power profiles based on real-world generation and demand datasets. Features of the platform are demonstrated using simulation and experimental results.
Twilight SLAM: Navigating Low-Light Environments

This paper presents a detailed examination of low-light visual Simultaneous Localization and Mapping (SLAM) pipelines, focusing on the integration of state-of-the-art (SOTA) low-light image enhancement algorithms with standard and contemporary SLAM frameworks. The primary objective of our work is to address a pivotal question: Does illuminating visual input significantly improve localization accuracy in both semi-dark and dark environments? In contrast to previous works that primarily address partially dim-lit datasets, we comprehensively evaluate various low-light SLAM pipelines across obscurely-lit environments. Employing a meticulous experimental approach, we qualitatively and quantitatively assess different combinations of image enhancers and SLAM frameworks, identifying the best-performing combinations for feature-based visual SLAM. The findings advance low-light SLAM by highlighting the practical implications of enhancing visual input for improved localization accuracy in challenging lighting conditions. This paper also offers valuable insights, encouraging further exploration of visual enhancement strategies for enhanced SLAM performance in real-world scenarios.
Over-Squashing in Graph Neural Networks: A Comprehensive survey

—Graph Neural Networks (GNNs) revolutionize machine learning for graph-structured data, effectively capturing complex relationships. They disseminate information through interconnected nodes, but long-range interactions face challenges known as ”over-squashing”. This survey delves into the challenge of over-squashing in Graph Neural Networks (GNNs), where long-range information dissemination is hindered, impacting tasks reliant on intricate long-distance interactions. It comprehensively explores the causes, consequences, and mitigation strategies for over-squashing. Various methodologies are reviewed, including graph rewiring, novel normalization, spectral analysis, and curvature-based strategies, with a focus on their trade-offs and effectiveness. The survey also discusses the inter-play between over-squashing and other GNN limitations, such as over-smoothing, and provides a taxonomy of models designed to address these issues in node and graph-level tasks. Benchmark datasets for performance evaluation are also detailed, making this survey a valuable resource for researchers and practitioners in the GNN ﬁeld.
The Heavy Metal Survey: Star Formation Constraints and Dynamical Masses of 21 Massive Quiescent Galaxies at z~1.4-2.2

In this paper, we present the Heavy Metal Survey, which obtained ultra-deep medium-resolution spectra of 21 massive quiescent galaxies at $1.4\lesssim z\lesssim 2.2$ with Keck/LRIS and MOSFIRE. With integration times of up to 16 hrs per band per galaxy, we observe numerous Balmer and metal absorption lines in atmospheric windows. We successfully derive spectroscopic redshifts for all 21 galaxies and for 19 we also measure stellar velocity dispersions ($\sigma_v$), ages, and elemental abundances, as detailed in an accompanying paper. Except for one emission-line AGN, all galaxies are confirmed as quiescent through their faint or absent H$\alpha$ emission and evolved stellar spectra. For most galaxies exhibiting faint H$\alpha$, elevated [NII]/H$\alpha$ suggests a non-star-forming origin. We calculate dynamical masses ($M_{\rm dyn}$) by combining $\sigma_v$ with structural parameters obtained from HST/COSMOS(-DASH), and compare them with stellar masses ($M_*$) derived using spectrophotometric modeling, considering various assumptions. For a fixed initial mass function (IMF), we observe a strong correlation between $M_{\rm dyn}/M_*$ and $\sigma_v$. This correlation may suggest that a varying IMF, with high-$\sigma_v$ galaxies being more bottom-heavy, was already in place at $z\sim2$. When implementing the $\sigma_v$-dependent IMF found in the cores of nearby early-type galaxies and correcting for biases in our stellar mass and size measurements, we find a low scatter in $M_{\rm dyn}/M_*$ of 0.14 dex. However, these assumptions result in unphysical stellar masses, which exceed the dynamical masses by 34%. This tension suggests that distant quiescent galaxies do not simply grow inside-out into today's massive early-type galaxies and the evolution is more complicated.