Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Measuring Representational Similarity of Large Language Models

Understanding the similarity of the numerous released large language models (LLMs) has many uses, e.g., simplifying model selection, detecting illegal model reuse, and advancing our understanding of what makes LLMs perform well. In this work, we measure the similarity of representations of a set of LLMs with 7B parameters. Our results suggest that some LLMs are substantially different from others. We identify challenges of using representational similarity measures that suggest the need of careful study of similarity scores to avoid false conclusions.
Deterministic Guidance Diffusion Model for Probabilistic Weather Forecasting

Weather forecasting requires not only accuracy but also the ability to perform probabilistic prediction. However, deterministic weather forecasting methods do not support probabilistic predictions, and conversely, probabilistic models tend to be less accurate. To address these challenges, in this paper, we introduce the \textbf{\textit{D}}eterministic \textbf{\textit{G}}uidance \textbf{\textit{D}}iffusion \textbf{\textit{M}}odel (DGDM) for probabilistic weather forecasting, integrating benefits of both deterministic and probabilistic approaches. During the forward process, both the deterministic and probabilistic models are trained end-to-end. In the reverse process, weather forecasting leverages the predicted result from the deterministic model, using as an intermediate starting point for the probabilistic model. By fusing deterministic models with probabilistic models in this manner, DGDM is capable of providing accurate forecasts while also offering probabilistic predictions. To evaluate DGDM, we assess it on the global weather forecasting dataset (WeatherBench) and the common video frame prediction benchmark (Moving MNIST). We also introduce and evaluate the Pacific Northwest Windstorm (PNW)-Typhoon weather satellite dataset to verify the effectiveness of DGDM in high-resolution regional forecasting. As a result of our experiments, DGDM achieves state-of-the-art results not only in global forecasting but also in regional forecasting. The code is available at: \url{https://github.com/DongGeun-Yoon/DGDM}.
Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors

This paper introduces a novel human pose estimation approach using sparse inertial sensors, addressing the shortcomings of previous methods reliant on synthetic data. It leverages a diverse array of real inertial motion capture data from different skeleton formats to improve motion diversity and model generalization. This method features two innovative components: a pseudo-velocity regression model for dynamic motion capture with inertial sensors, and a part-based model dividing the body and sensor data into three regions, each focusing on their unique characteristics. The approach demonstrates superior performance over state-of-the-art models across five public datasets, notably reducing pose error by 19\% on the DIP-IMU dataset, thus representing a significant improvement in inertial sensor-based human pose estimation. Our codes are available at {\url{https://github.com/dx118/dynaip}}.
A Failure of $\Pi^1_{n+3}$-Reduction in the Presence of $\Sigma^1_{n+3}$-Separation

We show that one can force over $L$ that $\Sigma^1_3$-separation holds, while $\Pi^1_3$-reduction fails, thus separating these two principles for the first time. The construction can be lifted to canonical inner models $M_n$ with $n$-many Woodin cardinals, yielding that assuming the existence of $M_n$, $\Sigma^1_{n+3}$-separation can hold, yet $\Pi^1_{n+3}$-reduction fails.
Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space

We develop a theory of finite-dimensional polyhedral subsets over the Wasserstein space and optimization of functionals over them via first-order methods. Our main application is to the problem of mean-field variational inference, which seeks to approximate a distribution $\pi$ over $\mathbb{R}^d$ by a product measure $\pi^\star$. When $\pi$ is strongly log-concave and log-smooth, we provide (1) approximation rates certifying that $\pi^\star$ is close to the minimizer $\pi^\star_\diamond$ of the KL divergence over a \emph{polyhedral} set $\mathcal{P}_\diamond$, and (2) an algorithm for minimizing $\text{KL}(\cdot\|\pi)$ over $\mathcal{P}_\diamond$ with accelerated complexity $O(\sqrt \kappa \log(\kappa d/\varepsilon^2))$, where $\kappa$ is the condition number of $\pi$.
Azimuthal anisotropy from the Valhall 4C 3D survey

Recording and processing both the converted shear waves and the compressional waves from multicomponent ocean bottom seismic (OBS) data provide additional opportunities to understand the subsurface. The converted waves are generally used to provide complementary images and velocity information to support the conventional seismic model. However, they also provide a new opportunity to measure and understand shear-wave azimuthal anisotropy. This paper describes the results from an azimuthal anisotropy study on 3D 4C OBS data from Valhall Field.
Optimally combined incentive for cooperation among interacting agents in population games

Combined prosocial incentives, integrating reward for cooperators and punishment for defectors, are effective tools to promote cooperation among competing agents in population games. Existing research concentrated on how to adjust reward or punishment, as two mutually exclusive tools, during the evolutionary process to achieve the desired proportion of cooperators in the population, and less attention has been given to exploring a combined incentive-based control policy that can steer the system to the full cooperation state at the lowest cost. In this work we propose a combined incentive scheme in a population of agents whose conflicting interactions are described by the prisoner's dilemma game on complete graphs and regular networks, respectively. By devising an index function for quantifying the implementation cost of the combined incentives, we analytically construct the optimally combined incentive protocol by using optimal control theory. By means of theoretical analysis, we identify the mathematical conditions, under which the optimally combined incentive scheme requires the minimal amount of cost. In addition to numerical calculations, we further perform computer simulations to verify our theoretical results and explore their robustness on different types of network structures.
Heavy mineral stratigraphy of Palaeozoic and Mesozoic sandstones of southwestern Sinai, Egypt: A reassessment

Improved methods of analysis and quantification of heavy mineral assemblages in Cambrian to Early Cretaceous sandstones of southwest Sinai have revealed successive changes in provenance that reflect both rejuvenation of the Arabian Shield and changes in the topographic configuration of the source area. Three mineral units have been identified in the Cambrian succession, at least three in the Carboniferous and three in the Cretaceous. It is predicted that the genetic units defined by these successive changes in mineralogy will be of regional extent and thus assist in elucidating the history of uplift of the Arabian-Nubian Shield and provide a better means of correlating sandstone units into adjacent areas. Variation in the abundance of apatite in the Cambrian succession is independent of provenance signature and is interpreted as reflecting alternating dry and humid climatic conditions.
Diffusion Handles: Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D

Diffusion Handles is a novel approach to enabling 3D object edits on diffusion images. We accomplish these edits using existing pre-trained diffusion models, and 2D image depth estimation, without any fine-tuning or 3D object retrieval. The edited results remain plausible, photo-real, and preserve object identity. Diffusion Handles address a critically missing facet of generative image based creative design, and significantly advance the state-of-the-art in generative image editing. Our key insight is to lift diffusion activations for an object to 3D using a proxy depth, 3D-transform the depth and associated activations, and project them back to image space. The diffusion process applied to the manipulated activations with identity control, produces plausible edited images showing complex 3D occlusion and lighting effects. We evaluate Diffusion Handles: quantitatively, on a large synthetic data benchmark; and qualitatively by a user study, showing our output to be more plausible, and better than prior art at both, 3D editing and identity control. Project Webpage: https://diffusionhandles.github. io/
ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation

We introduce"ImageDream,"an innovative image-prompt, multi-view diffusion model for 3D object generation. ImageDream stands out for its ability to produce 3D models of higher quality compared to existing state-of-the-art, image-conditioned methods. Our approach utilizes a canonical camera coordination for the objects in images, improving visual geometry accuracy. The model is designed with various levels of control at each block inside the diffusion model based on the input image, where global control shapes the overall object layout and local control fine-tunes the image details. The effectiveness of ImageDream is demonstrated through extensive evaluations using a standard prompt list. For more information, visit our project page at https://Image-Dream.github. io.
A Simple and Scalable Representation for Graph Generation

Recently, there has been a surge of interest in employing neural networks for graph generation, a fundamental statistical learning problem with critical applications like molecule design and community analysis. However, most approaches encounter significant limitations when generating large-scale graphs. This is due to their requirement to output the full adjacency matrices whose size grows quadratically with the number of nodes. In response to this challenge, we introduce a new, simple, and scalable graph representation named gap encoded edge list (GEEL) that has a small representation size that aligns with the number of edges. In addition, GEEL significantly reduces the vocabulary size by incorporating the gap encoding and bandwidth restriction schemes. GEEL can be autoregressively generated with the incorporation of node positional encoding, and we further extend GEEL to deal with attributed graphs by designing a new grammar. Our findings reveal that the adoption of this compact representation not only enhances scalability but also bolsters performance by simplifying the graph generation process. We conduct a comprehensive evaluation across ten non-attributed and two molecular graph generation tasks, demonstrating the effectiveness of GEEL.
Giant natural optical rotation from chiral electromagnons in a collinear antiferromagnet

In NiTe$_3$O$_6$ with a chiral crystal structure, we report on a giant natural optical rotation of the lowest-energy magnon. This polarization rotation, as large as 140 deg/mm, corresponds to a path difference between right and left circular polarizations that is comparable to the sample thickness. Natural optical rotation, being a measure of structural chirality, is highly unusual for long-wavelength magnons. The collinear antiferromagnetic order of NiTe$_3$O$_6$ makes this giant effect even more peculiar: Chirality of the crystal structure does not affect the magnetic ground state but is strongly manifested in the lowest excited state. We show that the dynamic magnetoelectric effect, turning this magnon to a magnetic- and electric-dipole active hybrid mode, generates the giant natural optical rotation. In finite magnetic fields, it also leads to a strong optical magnetochiral effect.
ZOOPLANCTON DES EAUX DE BAS-FONDS A GAGNOA, CENTRE-OUEST CÔTE D'IVOIRE

Le peuplement zooplanctonique des eaux de trois bas-fonds, a ete inventorie a Gagnoa. Il est constitue de 31 especes, reparties en 14 Rotiferes, 11 Cladoceres et 6 Copepodes. Ce peuplement ne differe pas fondamentalement, du point de vue composition specifique, des autres peuplements deja connus, des systemes hydrographiques continentaux de Cote d’Ivoire. Les valeurs elevees de la diversite (3,03 a 4 bits) et d’equitabilite (0,74 a 0,83) indiquent un peuplement mâture, homogene et equilibre.Les principales especes rencontrees dans ces milieux (Mesocyclops dussarti, Diaphanosoma excisum, Simocephalus sp., Ceriodaphnia cornuta, Asplanchna brightwelli), se retrouvent souvent dans le bol alimentaire des poissons d’elevage. Mots cles : Zooplancton, composition specifique, diversite, bas-fonds, Gagnoa, Cote d’Ivoire
A Dynamic Network for Efficient Point Cloud Registration

For the point cloud registration task, a significant challenge arises from non-overlapping points that consume extensive computational resources while negatively affecting registration accuracy. In this paper, we introduce a dynamic approach, widely utilized to improve network efficiency in computer vision tasks, to the point cloud registration task. We employ an iterative registration process on point cloud data multiple times to identify regions where matching points cluster, ultimately enabling us to remove noisy points. Specifically, we begin with deep global sampling to perform coarse global registration. Subsequently, we employ the proposed refined node proposal module to further narrow down the registration region and perform local registration. Furthermore, we utilize a spatial consistency-based classifier to evaluate the results of each registration stage. The model terminates once it reaches sufficient confidence, avoiding unnecessary computations. Extended experiments demonstrate that our model significantly reduces time consumption compared to other methods with similar results, achieving a speed improvement of over 41% on indoor dataset (3DMatch) and 33% on outdoor datasets (KITTI) while maintaining competitive registration recall requirements.
TRILOBITES OF THE PSEUDOKOLDINIOIDIA FAUNA (UPPERMOST CAMBRIAN) FROM THE TAEBAEK GROUP, TAEBAEKSAN BASIN, KOREA

Abstract The Pseudokoldinioidia Fauna is a newly documented uppermost Cambrian trilobite assemblage from the Dongjeom Formation of the Taebaek Group, Taebaeksan Basin, Korea. It is characterized by low species diversity comprising six trilobite taxa: Micragnostus chiushuensis, Koldinioidia typicalis, leiostegiid genus and species indeterminate, Pseudokoldinioidia perpetis, Onychopyge borealis, and pilekiid genus and species indeterminate. Of these, special attention has been paid to Pseudokoldinioidia perpetis, which was originally assigned to Missisquoia, an index fossil for the uppermost Cambrian in Laurentia. Pseudokoldinioidia is restricted to eastern Asia, whereas Missisquoia is confined to Laurentia. The appearance of the Pseudokoldinioidia Fauna is interpreted as contemporaneous with the base of the ‘Missisquoia’ perpetis Zone of North China, which in turn is correlated with the base of the Missisquoia typicalis Subzone of Laurentia. The associated Koldinioidia and Onychopyge make it possible to compare the Pseudokoldinioidia Fauna of Korea and North China with the latest Cambrian trilobite assemblages of South China, Australia, South America, and Mexico, and also suggests an interesting biogeographic connection among these areas in the latest Cambrian.
A Comparative Study between M30 and M92: M92 is a Merger Remnant with a Large Helium Enhancement

We perform a comparative study of the ex situ second-parameter pair globular clusters (GCs) M30 and M92, having similar metallicities but different horizontal branch morphologies. We obtain similar mean primordial carbon abundances for both clusters. However, M92 shows a large dispersion in carbon due to a more extended C–N anticorrelation, while M30 exhibits a higher primordial nitrogen abundance, suggesting that they have different chemical enrichment histories. Our new results confirm our previous result that M92 is a metal-complex GC showing a bimodal metallicity distribution. We also find that the metal-rich group of stars in M92 shows a helium enhancement as large as ΔY ∼ 0.05 from the red giant branch bump V magnitudes, which can also be supported by (i) a lack of bright red giant branch stars, (ii) synthetic evolutionary horizontal branch population models and (iii) the more extended spatial distribution due to different degree of the diffusion process from their lower masses. We reinterpret the [Eu/Fe] measurements by others, finding that the two metallicity groups of stars in M92 have significantly different [Eu/Fe] abundances with small scatters. This strongly suggests that they formed independently out of well-mixed interstellar media in different environments. We suggest that M92 is a more complex system than a normal GC, most likely a merger remnant of two GCs or an even more complex system. In the Appendix, we address the problems with the recently developed color–temperature relations and the usage of broadband photometry in the populational taggings.
A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing

Existing state-of-the-art 3D point clouds understanding methods only perform well in a fully supervised manner. To the best of our knowledge, there exists no unified framework which simultaneously solves the downstream high-level understanding tasks, especially when labels are extremely limited. This work presents a general and simple framework to tackle point clouds understanding when labels are limited. We propose a novel unsupervised region expansion based clustering method for generating clusters. More importantly, we innovatively propose to learn to merge the over-divided clusters based on the local low-level geometric property similarities and the learned high-level feature similarities supervised by weak labels. Hence, the true weak labels guide pseudo labels merging taking both geometric and semantic feature correlations into consideration. Finally, the self-supervised reconstruction and data augmentation optimization modules are proposed to guide the propagation of labels among semantically similar points within a scene. Experimental Results demonstrate that our framework has the best performance among the three most important weakly supervised point clouds understanding tasks including semantic segmentation, instance segmentation, and object detection even when limited points are labeled, under the data-efficient settings for the large-scale 3D semantic scene parsing. The developed techniques have postentials to be applied to downstream tasks for better representations in robotic manipulation and robotic autonomous navigation. Codes and models are publicly available at: https://github.com/KangchengLiu.
Simplifying Neural Network Training Under Class Imbalance

Real-world datasets are often highly class-imbalanced, which can adversely impact the performance of deep learning models. The majority of research on training neural networks under class imbalance has focused on specialized loss functions, sampling techniques, or two-stage training procedures. Notably, we demonstrate that simply tuning existing components of standard deep learning pipelines, such as the batch size, data augmentation, optimizer, and label smoothing, can achieve state-of-the-art performance without any such specialized class imbalance methods. We also provide key prescriptions and considerations for training under class imbalance, and an understanding of why imbalance methods succeed or fail.
Evaluating the Convergence Limit of Quantum Neural Tangent Kernel

Quantum variational algorithms have been one of major applications of quantum computing with current quantum devices. There are recent attempts to establish the foundation for these algorithms. A possible approach is to characterize the training dynamics with quantum neural tangent kernel. In this work, we construct the kernel for two models, Quantun Ensemble and Quantum Neural Network, and show the convergence of these models in the limit of infinitely many qubits. We also show applications of the kernel limit in regression tasks.
Class-Discriminative Attention Maps for Vision Transformers

Interpretability methods are critical components for examining and exploring deep neural networks (DNN), as well as increasing our understanding of and trust in them. Vision transformers (ViT), which can be trained to state-of-the-art performance with a self-supervised learning (SSL) training method, provide built-in attention maps (AM). While AMs can provide high-quality semantic segmentation of input images, they do not account for any signal coming from a downstream classifier. We introduce class-discriminative attention maps (CDAM), a novel post-hoc explanation method that is highly sensitive to the target class. Our method essentially scales attention scores by how relevant the corresponding tokens are for the predictions of a classifier head. Alternative to classifier outputs, CDAM can also explain a user-defined concept by targeting similarity measures in the latent space of the ViT. This allows for explanations of arbitrary concepts, defined by the user through a few sample images. We investigate the operating characteristics of CDAM in comparison with relevance propagation (RP) and token ablation maps (TAM), an alternative to pixel occlusion methods. CDAM is highly class-discriminative and semantically relevant, while providing implicit regularization of relevance scores. PyTorch implementation: \url{https://github.com/lenbrocki/CDAM} Web live demo: \url{https://cdam.informatism. com/}
Veer: Verifying Equivalence of Dataflow Versions in Iterative Data Analytics (Extended Version)

Data analytics using GUI-based dataflows is an iterative process in which an analyst makes many iterations of changes to refine the dataflow, generating a different version at each iteration. In many cases, the result of executing a dataflow version is equivalent to a result of a prior executed version. Identifying such equivalence between the execution results of different dataflow versions is important for optimizing the performance of a dataflow by reusing results from a previous run. The size of the dataflows and the complexity of their operators often make existing equivalence verifiers (EVs) not able to solve the problem. In this paper, we present"Veer,"which leverages the fact that two dataflow versions can be very similar except for a few changes. The solution divides the dataflow version pair into small parts, called windows, and verifies the equivalence within each window by using an existing EV as a black box. We develop solutions to efficiently generate windows and verify the equivalence within each window. Our thorough experiments on real dataflows show that Veer is able to not only verify the equivalence of dataflows that cannot be supported by existing EVs but also do the verification efficiently.