Miracle-dz/UniFAR-FTUs huggingface dataset:
Paleobotany and Palynology of the Rio Bonito Formation (Lower Permian, Paraná Basin, Brazil) at the Quitéria Outcrop

Abstract. The sedimentary section at the Quitéria outcrop (Pantano Grande, central portion of Rio Grande do Sul State, Brazil) carries diverse and abundant fossil associations of plants and palynomorphs. This section includes coal deposits of the Early Permian Rio Bonito Formation in the Paraná Basin. This contribution presents a taxonomic re-evaluation on paleobotanical records based on an in-depth revision of available data as well as direct analysis from materials deposited in collections (published and unpublished). A taxonomic list is presented, based on the anlysis of all published data. A stratigraphic framework is offered, showing previous and new records in order to better understand the biostratigraphy of the rocks involved. Accordingt to this re-evaluation, the Quitéria Outcrop carries 14 morphospecies of plant macrofossils —including leaves and seeds of Glossopteridales— constituting a typical coal-forming flora. New palynological data derived from six samples taken in this section reveal diversified and abundant associations, including 29 species of spores, 36 of pollen grains and four taxa related to algae and fungi. Quantitative analysis shows that most samples are closely correlated with the previously known “Lundbladispora-Punctatisporites-Portalites Association”, derived from a hygrophilous environment. The associations are compatible with the Glossopteris-Rhodeopteridium Zone and with the Protohaploxypinus goraiensis Subzone (base of the Vittatina costabilis Zone), related to the Cisuralian (Early Permian). Stratigraphical considerations are based on comparision with neighboring sections and on the regional erosional surface recognized in this outcrop.
Exponential Asymptotics of Ricci Flow Neckpinch

In this article we examine the formation of cylindrical neckpinch singularities in Ricci flow in compact manifolds. Rigorous examples of neckpinch sigularity for rotationally symmetric initial data were first constructed by Angenent and Knopf, and examples with given asymptotic behaviour were constructed by them and Isenberg. Here we discuss the asymptotic behavior of the flow under Type-I assumption for general symmetric initial data, and show the previously constructed asymptotic profiles and are the only possibilities.
Uniformly Strict Equilibrium for Repeated Games with Private Monitoring and Communication

Cooperation through repetition is an important theme in game theory. In this regard, various celebrated ``folk theorems'' have been proposed for repeated games in increasingly more complex environments. There has, however, been insufficient attention paid to the robustness of a large set of equilibria that is needed for such folk theorems. Starting with perfect public equilibrium as our starting point, we study uniformly strict equilibria in repeated games with private monitoring and direct communication (cheap talk). We characterize the limit equilibrium payoff set and identify the conditions for the folk theorem to hold with uniformly strict equilibrium.
SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering

We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian Splatting. Gaussian Splatting has recently become very popular as it yields realistic rendering while being significantly faster to train than NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D gaussians as these gaussians tend to be unorganized after optimization and no method has been proposed so far. Our first key contribution is a regularization term that encourages the gaussians to align well with the surface of the scene. We then introduce a method that exploits this alignment to extract a mesh from the Gaussians using Poisson reconstruction, which is fast, scalable, and preserves details, in contrast to the Marching Cubes algorithm usually applied to extract meshes from Neural SDFs. Finally, we introduce an optional refinement strategy that binds gaussians to the surface of the mesh, and jointly optimizes these Gaussians and the mesh through Gaussian splatting rendering. This enables easy editing, sculpting, rigging, animating, compositing and relighting of the Gaussians using traditional softwares by manipulating the mesh instead of the gaussians themselves. Retrieving such an editable mesh for realistic rendering is done within minutes with our method, compared to hours with the state-of-the-art methods on neural SDFs, while providing a better rendering quality. Our project page is the following: https://anttwo.github. io/sugar/
Efficient Domain Adaptation via Generative Prior for 3D Infant Pose Estimation

Although 3D human pose estimation has gained impressive development in recent years, only a few works focus on infants, that have different bone lengths and also have limited data. Directly applying adult pose estimation models typically achieves low performance in the infant domain and suffers from out-of-distribution issues. Moreover, the limitation of infant pose data collection also heavily constrains the efficiency of learning-based models to lift 2D poses to 3D. To deal with the issues of small datasets, domain adaptation and data augmentation are commonly used techniques. Following this paradigm, we take advantage of an optimization-based method that utilizes generative priors to predict 3D infant keypoints from 2D keypoints without the need of large training data. We further apply a guided diffusion model to domain adapt 3D adult pose to infant pose to supplement small datasets. Besides, we also prove that our method, ZeDO-i, could attain efficient domain adaptation, even if only a small number of data is given. Quantitatively, we claim that our model attains state-of-the-art MPJPE performance of 43.6 mm on the SyRIP dataset and 21.2 mm on the MINI-RGBD dataset.
Unifying Corroborative and Contributive Attributions in Large Language Models

As businesses, products, and services spring up around large language models, the trustworthiness of these models hinges on the verifiability of their outputs. However, methods for explaining language model outputs largely fall across two distinct fields of study which both use the term"attribution"to refer to entirely separate techniques: citation generation and training data attribution. In many modern applications, such as legal document generation and medical question answering, both types of attributions are important. In this work, we argue for and present a unified framework of large language model attributions. We show how existing methods of different types of attribution fall under the unified framework. We also use the framework to discuss real-world use cases where one or both types of attributions are required. We believe that this unified framework will guide the use case driven development of systems that leverage both types of attribution, as well as the standardization of their evaluation.
MaskFlow: Object-Aware Motion Estimation

We introduce a novel motion estimation method, MaskFlow, that is capable of estimating accurate motion fields, even in very challenging cases with small objects, large displacements and drastic appearance changes. In addition to lower-level features, that are used in other Deep Neural Network (DNN)-based motion estimation methods, MaskFlow draws from object-level features and segmentations. These features and segmentations are used to approximate the objects' translation motion field. We propose a novel and effective way of incorporating the incomplete translation motion field into a subsequent motion estimation network for refinement and completion. We also produced a new challenging synthetic dataset with motion field ground truth, and also provide extra ground truth for the object-instance matchings and corresponding segmentation masks. We demonstrate that MaskFlow outperforms state of the art methods when evaluated on our new challenging dataset, whilst still producing comparable results on the popular FlyingThings3D benchmark dataset.
Improving Source-Free Target Adaptation with Vision Transformers Leveraging Domain Representation Images

Unsupervised Domain Adaptation (UDA) methods facilitate knowledge transfer from a labeled source domain to an unlabeled target domain, navigating the obstacle of domain shift. While Convolutional Neural Networks (CNNs) are a staple in UDA, the rise of Vision Transformers (ViTs) provides new avenues for domain generalization. This paper presents an innovative method to bolster ViT performance in source-free target adaptation, beginning with an evaluation of how key, query, and value elements affect ViT outcomes. Experiments indicate that altering the key component has negligible effects on Transformer performance. Leveraging this discovery, we introduce Domain Representation Images (DRIs), feeding embeddings through the key element. DRIs act as domain-specific markers, effortlessly merging with the training regimen. To assess our method, we perform target adaptation tests on the Cross Instance DRI source-only (SO) control. We measure the efficacy of target adaptation with and without DRIs, against existing benchmarks like SHOT-B* and adaptations via CDTrans. Findings demonstrate that excluding DRIs offers limited gains over SHOT-B*, while their inclusion in the key segment boosts average precision promoting superior domain generalization. This research underscores the vital role of DRIs in enhancing ViT efficiency in UDA scenarios, setting a precedent for further domain adaptation explorations.
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks

Fine-tuning large pre-trained models has become the de facto strategy for developing both task-specific and general-purpose machine learning systems, including developing models that are safe to deploy. Despite its clear importance, there has been minimal work that explains how fine-tuning alters the underlying capabilities learned by a model during pretraining: does fine-tuning yield entirely novel capabilities or does it just modulate existing ones? We address this question empirically in synthetic, controlled settings where we can use mechanistic interpretability tools (e.g., network pruning and probing) to understand how the model's underlying capabilities are changing. We perform an extensive analysis of the effects of fine-tuning in these settings, and show that: (i) fine-tuning rarely alters the underlying model capabilities; (ii) a minimal transformation, which we call a 'wrapper', is typically learned on top of the underlying model capabilities, creating the illusion that they have been modified; and (iii) further fine-tuning on a task where such hidden capabilities are relevant leads to sample-efficient 'revival' of the capability, i.e., the model begins reusing these capability after only a few gradient steps. This indicates that practitioners can unintentionally remove a model's safety wrapper merely by fine-tuning it on a, e.g., superficially unrelated, downstream task. We additionally perform analysis on language models trained on the TinyStories dataset to support our claims in a more realistic setup.
HandSight: DeCAF & Improved Fisher Vectors to Classify Clothing Color and Texture with a Finger-Mounted Camera

We demonstrate the use of DeCAF and Improved Fisher Vector image features to classify clothing texture. The issue of choosing clothes is a problem for the blind every day. This work attempts to solve the issue with a finger-mounted camera and state-of-the-art classification algorithms. To evaluate our solution, we collected 520 close-up images across 29 pieces of clothing. We contribute (1) the HCTD, an image dataset taken with a NanEyeGS camera, a camera small enough to be mounted on the finger, and (2) evaluations of state-of-the-art recognition algorithms applied to our dataset - achieving an accuracy>95%. Throughout the paper, we will discuss previous work, evaluate the current work, and finally, suggest the project's future direction.
Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI). However, current LLMs are predominantly pretrained on short text snippets, which compromises their effectiveness in processing the long-context prompts that are frequently encountered in practical scenarios. This article offers a comprehensive survey of the recent advancement in Transformer-based LLM architectures aimed at enhancing the long-context capabilities of LLMs throughout the entire model lifecycle, from pre-training through to inference. We first delineate and analyze the problems of handling long-context input and output with the current Transformer-based models. We then provide a taxonomy and the landscape of upgrades on Transformer architecture to solve these problems. Afterwards, we provide an investigation on wildly used evaluation necessities tailored for long-context LLMs, including datasets, metrics, and baseline models, as well as optimization toolkits such as libraries, frameworks, and compilers to boost the efficacy of LLMs across different stages in runtime. Finally, we discuss the challenges and potential avenues for future research. A curated repository of relevant literature, continuously updated, is available at https://github.com/Strivin0311/long-llms-learning.
Spatial Analysis of the Relationship Between Levels of Service Provided by Public Transit and Areas of High Demand in Jefferson County, Kentucky

abstract The responsibility of public transit systems is to provide a mode of transport to individuals who in most cases represent the minority and low-income classes as well was the elderly. Previous studies on the matter have shown unequal distributions of service across different areas, usually to the disadvantage of these vulnerable groups. The goal of this research was to study the relationship between transit system service levels and areas of need in Jefferson County, Kentucky, aiming to identify spatial gaps in public transport provision for people and areas of social disadvantage. A supply index to measure service level was constructed for each census tract, based on the share of the tract within access distance (400 m) to bus stops and route frequencies. A transit needs index was created using socioeconomic and locational variables. Spatial distribution and patterns of public transit supply and needs measures were examined. A needs-gap analysis was performed, particularly focusing on census tracts showing above average and high need but with low or zero transit service (gap areas). This study revealed that most service gap areas were located in the interior of the city close to the central business district where minority and low-income populations tend to concentrate.
Insight into the Formation of β Pic b through the Composition of Its Parent Protoplanetary Disk as Revealed by the β Pic Moving Group Member HD 181327

It has been suggested that β Pic b has a supersolar metallicity and subsolar C/O ratio. Assuming solar carbon and oxygen abundances for the star β Pic and therefore the planet’s parent protoplanetary disk, β Pic b’s C/O ratio suggests that it formed via core accretion between its parent protoplanetary disk’s H2O and CO2 ice lines. However, β Pic b’s high metallicity is difficult to reconcile with its mass M p = 11.7 M Jup. Massive stars can present peculiar photospheric abundances that are unlikely to record the abundances of their former protoplanetary disks. This issue can be overcome for early-type stars in moving groups by inferring the elemental abundances of the FGK stars in the same moving group that formed in the same molecular cloud and presumably share the same composition. We infer the photospheric abundances of the F dwarf HD 181327, a β Pic moving group member that is the best available proxy for the composition of β Pic b’s parent protoplanetary disk. In parallel, we infer updated atmospheric abundances for β Pic b. As expected for a planet of its mass formed via core-accretion beyond its parent protoplanetary disk’s H2O ice line, we find that β Pic b’s atmosphere is consistent with stellar metallicity and confirm that it has superstellar carbon and oxygen abundances with a substellar C/O ratio. We propose that the elemental abundances of FGK dwarfs in moving groups can be used as proxies for the otherwise difficult-to-infer elemental abundances of early-type and late-type members of the same moving groups.
Time-Varying Response of ENSO-Induced Tropical Pacific Rainfall to Global Warming in CMIP5 Models. Part I: Multimodel Ensemble Results

AbstractEl Nino–Southern Oscillation (ENSO) is one of the most important drivers of climatic variability on the global scale. Much of this variability arises in response to ENSO-driven changes in tropical Pacific rainfall. Previous research has shown that the ENSO-driven tropical Pacific rainfall variability can shift east and intensify in response to global warming, even if ENSO-related SST variability remains unchanged. Here, the twenty-first century changes in ENSO-driven tropical Pacific rainfall variability in 32 CMIP5 models forced under the representative concentration pathway 8.5 (RCP8.5) scenario are examined, revealing that the pattern of changes in ENSO-driven rainfall is not only gradually enhanced but also shifts steadily eastward along with the global-mean temperature increase. Using a recently developed moisture budget decomposition method, it is shown that the projected changes in ENSO-driven rainfall variability in the tropical Pacific can be primarily attributed to a projected increase i.
Limitations of measure-first protocols in quantum machine learning

In recent works, much progress has been made with regards to so-called randomized measurement strategies, which include the famous methods of classical shadows and shadow tomography. In such strategies, unknown quantum states are first measured (or ``learned''), to obtain classical data that can be used to later infer (or ``predict'') some desired properties of the quantum states. Even if the used measurement procedure is fixed, surprisingly, estimations of an exponential number of vastly different quantities can be obtained from a polynomial amount of measurement data. This raises the question of just how powerful ``measure-first'' strategies are, and in particular, if all quantum machine learning problems can be solved with a measure-first, analyze-later scheme. This paper explores the potential and limitations of these measure-first protocols in learning from quantum data. We study a natural supervised learning setting where quantum states constitute data points, and the labels stem from an unknown measurement. We examine two types of machine learning protocols: ``measure-first'' protocols, where all the quantum data is first measured using a fixed measurement strategy, and ``fully-quantum'' protocols where the measurements are adapted during the training process. Our main result is a proof of separation. We prove that there exist learning problems that can be efficiently learned by fully-quantum protocols but which require exponential resources for measure-first protocols. Moreover, we show that this separation persists even for quantum data that can be prepared by a polynomial-time quantum process, such as a polynomially-sized quantum circuit. Our proofs combine methods from one-way communication complexity and pseudorandom quantum states. Our result underscores the role of quantum data processing in machine learning and highlights scenarios where quantum advantages appear.
Quantifying the imaginarity of quantum states via Tsallis relative entropy

It is a fundamental question that why quantum mechanics uses complex numbers instead of only real numbers. To address this topic, recently, a rigorous resource theory for the imaginarity of quantum states were established, and several imaginarity measures were proposed. In this work, we propose a new imaginarity measure based on the Tsallis relative entropy. This imaginarity measure has explicit expression, and also, it is computable for bosonic Gaussian states.
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations

Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs). This paper (1) presents resilient control design in presence of adversarial cyber-events, and proposes a novel federated reinforcement learning (Fed-RL) approach to tackle (a) model complexities, unknown dynamical behaviors of IBR devices, (b) privacy issues regarding data sharing in multi-party-owned networked grids, and (2) transfers learned controls from simulation to hardware-in-the-loop test-bed, thereby bridging the gap between simulation and real world. With these multi-prong objectives, first, we formulate a reinforcement learning (RL) training setup generating episodic trajectories with adversaries (attack signal) injected at the primary controllers of the grid forming (GFM) inverters where RL agents (or controllers) are being trained to mitigate the injected attacks. For networked microgrids, the horizontal Fed-RL method involving distinct independent environments is not appropriate, leading us to develop vertical variant Federated Soft Actor-Critic (FedSAC) algorithm to grasp the interconnected dynamics of networked microgrid. Next, utilizing OpenAI Gym interface, we built a custom simulation set-up in GridLAB-D/HELICS co-simulation platform, named Resilient RL Co-simulation (ResRLCoSIM), to train the RL agents with IEEE 123-bus benchmark test systems comprising 3 interconnected microgrids. Finally, the learned policies in simulation world are transferred to the real-time hardware-in-the-loop test-bed set-up developed using high-fidelity Hypersim platform. Experiments show that the simulator-trained RL controllers produce convincing results with the real-time test-bed set-up, validating the minimization of sim-to-real gap.
Geometric Characterization of Many Body Localization

Many body localization (MBL) represents a unique physical phenomenon, providing a testing ground for exploring thermalization, or more precisely its failure. Here we characterize the MBL phase geometrically by the many-body quantum metric (MBQM), defined in the parameter space of twist boundary. We find that MBQM scales linearly as a function of the inverse system length in the MBL phase, and grows faster in the ergodic phase. We validate our theory using the disordered hardcore Bose-Hubbard model, and characterize the ergodic to MBL phase transition via the localization length scale defined from the MBQM. MBQM provides an intuitive and experimentally accessible method to characterize MBL phases.