Miracle-dz/UniFAR-FTUs huggingface dataset:
Singular Riemannian foliations and $\mathcal{I}$-Poisson manifolds

We recall the notion of a singular foliation (SF) on a manifold $M$, viewed as an appropriate submodule of $\mathfrak{X}(M)$, and adapt it to the presence of a Riemannian metric $g$, yielding a module version of a singular Riemannian foliation (SRF). Following Garmendia-Zambon on Hausdorff Morita equivalence of SFs, we define the Morita equivalence of SRFs (both in the module sense as well as in the more traditional geometric one of Molino) and show that the leaf spaces of Morita equivalent SRFs are isomrophic as pseudo-metric spaces. In a second part, we introduce the category of $\mathcal{I}$-Poisson manifolds. Its objects and morphisms generalize Poisson manifolds and morphisms in the presence of appropriate ideals $\mathcal{I}$ of the smooth functions on the manifold such that two conditions are satisfied: $(i)$ The category of Poisson manifolds becomes a full subcategory when choosing $\mathcal{I}=0$ and $(ii)$ there is a reduction functor from this new category to the category of Poisson algebras, which generalizes coistropic reduction to the singular setting. Every SF on $M$ gives rise to an $\mathcal{I}$-Poisson manifold on $T^*M$ and $g$ enhances this to an SRF if and only if the induced Hamiltonian lies in the normalizer of $\mathcal{I}$. This perspective provides, on the one hand, a simple proof of the fact that every module SRF is a geometric SRF and, on the other hand, a construction of an algebraic invariant of singular foliations: Hausdorff Morita equivalent SFs have isomorphic reduced Poisson algebras.
Is Spacetime as Physical as Is Space

Two questions are investigated by looking successively at classical mechanics, special relativity, and relativistic gravity: first, how is space related with spacetime? Our answer is that each given reference fluid, that is a congruence of reference trajectories, defines a physical space. The points of that space are formally defined to be the world lines of the congruence. The second question is: does the geometric structure of the spacetime determine the physics, in particular, does it determine its relativistic or preferred-frame character? We find that it does not.
Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking

Accurate and robust tracking and reconstruction of the surgical scene is a critical enabling technology toward autonomous robotic surgery. Existing algorithms for 3D perception in surgery mainly rely on geometric information, while we propose to also leverage semantic information inferred from the endoscopic video using image segmentation algorithms. In this paper, we present a novel, comprehensive surgical per-ception framework, Semantic-SuPer, that integrates geometric and semantic information to facilitate data association, 3D reconstruction, and tracking of endoscopic scenes, benefiting downstream tasks like surgical navigation. The proposed frame-work is demonstrated on challenging endoscopic data with deforming tissue, showing its advantages over our baseline and several other state-of-the-art approaches. Our code and dataset are available at https://github.com/ucsdarclab/Python-SuPer.
Photon Acceleration in Variable Ultrarelativistic Outflows and High-Energy Spectra of Gamma-Ray Bursts

MeV seed photons produced in shocks in a variable ultrarelativistic outflow gain energy by the Fermi mechanism, because the photons Compton scatter off relativistically colliding shells. The Fermi-modified high-energy photon spectrum has a nonuniversal slope and a universal cutoff. A significant increase in the total radiative efficiency is possible. In some gamma-ray bursts, most of the power might be emitted at the high-energy cutoff for this mechanism, which would be close to 100 MeV for outflows with a mean bulk Lorentz factor of 100.
Mapping Extended Landmarks for Radar SLAM

Simultaneous localization and mapping (SLAM) using automotive radar sensors can provide enhanced sensing capabilities for autonomous systems. In SLAM applications, with a greater requirement for the environment map, information on the extent of landmarks is vital for precise navigation and path planning. Although object extent estimation has been successfully applied in target tracking, its adaption to SLAM remains unaddressed due to the additional uncertainty of the sensor platform, bias in the odometer reading, as well as the measurement non-linearity. In this paper, we propose to incorporate the Bayesian random matrix approach to estimate the extent of landmarks in radar SLAM. We describe the details for implementation of landmark extent initialization, prediction and update. To validate the performance of our proposed approach we compare with the model-free ellipse fitting algorithm with results showing more consistent extent estimation. We also demonstrate that exploiting the landmark extent in the state update can improve localization accuracy.
Random Walk-based Community Key-members Search over Large Graphs

Given a graph $G$, a query node $q$, and an integer $k$, community search (CS) seeks a cohesive subgraph (measured by community models such as $k$-core or $k$-truss) from $G$ that contains $q$. It is difficult for ordinary users with less knowledge of graphs' complexity to set an appropriate $k$. Even if we define quite a large $k$, the community size returned by CS is often too large for users to gain much insight about it. Compared against the entire community, key-members in the community appear more valuable than others. To contend with this, we focus on Community Key-members Search problem (CKS). We turn our perspective to the key-members in the community containing $q$ instead of the entire community. To solve CKS problem, we first propose an exact algorithm based on truss decomposition as a baseline. Then, we present four random walk-based optimized algorithms to achieve a trade-off between effectiveness and efficiency, by carefully considering three important cohesiveness features in the design of transition matrix. As a result, we return key-members according to the stationary distribution when random walk converges. We theoretically analyze the rationality of designing the cohesiveness-aware transition matrix for random walk, through Bayesian theory based on Gaussian Mixture Model with Box-Cox Transformation and Copula Function Fitting. Moreover, we propose a lightweight refinement method following an ``expand-replace"manner to further optimize the result with little overhead, and we extend our method for CKS with multiple query nodes. Comprehensive experimental studies on various real-world datasets demonstrate our method's superiority.
Transmission of a slowly varying Markov signal over memoryless channels

Information rates in certain discrete-time, memoryless, stationary channels with additive non-Gaussian noise and slowly varying input signal are investigated. Under the assumption that the input signal is a stationary Markov chain with rare transitions, it is shown that the information rate is asymptotically equivalent to the entropy of the chain and, therefore, the main term of its asymptotics does not depend on the channel noise.
Control and Simulation of Motion of Constrained Multibody Systems Based on Projection Matrix Formulation

This paper presents a uniﬁed approach for inverse and direct dynamics of constrained multibody systems that can serve as a basis for analysis, simulation, and control. The main advantage of the dynamics formulation is that it does not require the constraint equations be linearly independent. Thus, a simulation may proceed even in the presence of redundant constraints or singular conﬁgurations and a controller does not need to change its structure whenever the mechanical system changes its topology or number of degrees of freedom. A motion control scheme is proposed based on a projected inverse-dynamics scheme which proves to be stable and minimizes the weighted Euclidean norm of the actuation force. The projection-based control scheme is further developed for constrained systems, e.g. parallel manipulators, which have some joints with no actuators (passive joints). This is complemented by the development of constraint force control. A condition on the inertia matrix resulting in a decoupled mechanical system is analytically derived that simpliﬁes the implementation of the force control.
On the Slow Mode of a Simple Air-sea Coupled Model The Sensitivity to the Zonal Phase Difference bet

An eigen analysis of the simple air-sea coupled model is carried out to understand the coupled mechanism with the slow time scale (so called 'slow mode') related to the zonal phase difference between the SST and atmospheric heating anomalies. Both frequency and instability of the slow mode are very sensitive to the zonal phase difference between the SST and atmospheric heating anomalies. When the positive atmospheric heating is located between one-quarter wavelength west and one-quarter wavelength east of the positive SST anomaly, the slow mode becomes the unstable eastward-propagating mode. The further eastward shifting of the atmospheric heating results in damping of the eastward-propagating mode resembling the forced Kelvin mode. On the other hand, the slow mode becomes the unstable westwardpropagating mode when atmospheric heating lies farther west than one-quarter wavelength west from the SST anomaly. This unstable westward-propagating mode resembles the forced Rossby mode. SST changes in the eastward- and westward-propagating slow modes are mainly induced by the changes of the oceanic height and the zonal advection by the current, respectively. The slow mode in the fast-wave limit and the non-rotating system is also discussed.
Atomic Scale Sliding and Rolling of Carbon Nanotubes

A carbon nanotube is an ideal object for understanding the atomic scale aspects of interface interaction and friction. Using molecular statics and dynamics methods different types of motion of nanotubes on a graphite surface are investigated. We found that each nanotube has unique equilibrium orientations with sharp potential energy minima. This leads to atomic scale locking of the nanotube. The effective contact area and the total interaction energy scale with the square root of the radius. Sliding and rolling of nanotubes have different characters. The potential energy barriers for sliding nanotubes are higher than that for perfect rolling. When the nanotube is pushed, we observe a combination of atomic scale spinning and sliding motion. The result is rolling with the friction force comparable to sliding.
Addressing the Impact of Data Truncation and Parameter Uncertainty on Operational Risk Estimates

Typically, operational risk losses are reported above some threshold. This paper studies the impact of ignoring data truncation on the 0.999 quantile of the annual loss distribution for operational risk for a broad range of distribution parameters and truncation levels. Loss frequency and severity are modelled by the Poisson and Lognormal distributions respectively. Two cases of ignoring data truncation are studied: the "naive model" - fitting a Lognormal distribution with support on a positive semi-infinite interval, and "shifted model" - fitting a Lognormal distribution shifted to the truncation level. For all practical cases, the "naive model" leads to underestimation (that can be severe) of the 0.999 quantile. The "shifted model" overestimates the 0.999 quantile except some cases of small underestimation for large truncation levels. Conservative estimation of capital charge is usually acceptable and the use of the "shifted model" can be justified while the "naive model" should not be allowed. However, if parameter uncertainty is taken into account (in practice it is often ignored), the "shifted model" can lead to considerable underestimation of capital charge. This is demonstrated with a practical example.
Algorithmic Enhancements to Data Colocation Grid Frameworks for Big Data Medical Image Processing

Large-scale medical imaging studies to date have predominantly leveraged in-house, laboratory-based or traditional grid computing resources for their computing needs, where the applications often use hierarchical data structures (e.g., Network file system file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance for laboratory-based approaches reveal that performance is impeded by standard network switches since typical processing can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. On the other hand, the grid may be costly to use due to the dedicated resources used to execute the tasks and lack of elasticity. With increasing availability of cloud-based big data frameworks, such as Apache Hadoop, cloud-based services for executing medical imaging studies have shown promise. Despite this promise, our studies have revealed that existing big data frameworks illustrate different performance limitations for medical imaging applications, which calls for new algorithms that optimize their performance and suitability for medical imaging. For instance, Apache HBases data distribution strategy of region split and merge is detrimental to the hierarchical organization of imaging data (e.g., project, subject, session, scan, slice). Big data medical image processing applications involving multi-stage analysis often exhibit significant variability in processing times ranging from a few seconds to several days. Due to the sequential nature of executing the analysis stages by traditional software technologies and platforms, any errors in the pipeline are only detected at the later stages despite the sources of errors predominantly being the highly compute-intensive first stage. This wastes precious computing resources and incurs prohibitively higher costs for re-executing the application. To address these challenges, this research propose a framework Hadoop & HBase for Medical Image Processing (HadoopBase-MIP) which develops a range of performance optimization algorithms and employs a number of system behaviors modeling for data storage, data access and data processing. We also introduce how to build
Level set based Volumetric Anisotropic Diffusion for 3D Image Filtering

We present an anisotropic diffusion model for volumetric image filtering. Our 3D anisotropic diffusion tensor is constructed to enhance 1D features (curves) and 2D features(surfaces). Bilateral prefiltering is employed to construct more accurate anisotropic diffusion tensor. An efficient parallel implementation has been done on a PC cluster, the performance and speed-up are reported also.
Two Centuries of Change in the Native Flora of Franklin County, Massachusetts, U.S.A.

ABSTRACT We examined changes in the native vascular flora of Franklin County, Massachusetts, a largely rural county with a long history of botanical investigation. The historical flora was documented using herbarium specimens and literature records, and the current flora was assessed in 10 years of field work starting in 2010. The county contains 26 towns, and apparent changes were based on town-level presence/absence. In total, 1205 species were recorded. Apparent losses from the historical flora totaled 5.6% of species, and apparent gains totaled 4.2%. Several families exhibited greater declines than the flora as a whole, including the Orchidaceae, Ophioglossaceae, Ranunculaceae, and Violaceae. Species dependent on fungi during at least part of their life cycle declined relative to other species, but were concentrated in a small group of families, including the Orchidaceae and Ophioglossaceae. Species with fleshy fruits increased relative to those with ant-dispersed seeds. Annuals increased relative to biennials and perennials, probably due to the increasing extent of ruderal habitats. Graminoids showed greater apparent increases than forbs, which may reflect undercollecting of the former taxa during the historical period. Species changes were related to habitat, with marked increases in plants of ruderal habitats, but other apparent changes may reflect sampling biases. Northern taxa appear to be declining relative to other species, particularly in towns at lower elevations. This study is one of several that have examined floristic changes in the northeastern United States and is part of a nascent literature suggesting that climate changes have altered the southern New England flora in the past century. Additional documentation of regional floras is needed to facilitate assessments of future floristic changes.
The Five Core Concepts of Biology as a Framework for Promoting Expert-Like Behaviors in Undergraduates Learning How to Read Primary Scientific Literature

A growing body of literature shows that primary scientific literature (PSL) is a valuable and useful tool for science, technology, engineering, and math education. We currently have a relatively limited understanding of how skills relating to reading PSL progress through academic careers, i.e., the process by which expertise in reading PSL develops. ABSTRACT A growing body of literature shows that primary scientific literature (PSL) is a valuable and useful tool for science, technology, engineering, and math education. We currently have a relatively limited understanding of how skills relating to reading PSL progress through academic careers, i.e., the process by which expertise in reading PSL develops. In this study, we built on previous work showing clear differences in strategies that experts use to read PSL that are not often available to or documented with novice PSL readers. Using the five core concepts (5CCs) of biology, outlined in Vision and Change in Undergraduate Biology, as a framework for student engagement with PSL, we investigated whether the 5CCs can be used to (i) increase student engagement with PSL, (ii) provide a context for PSL, and (iii) integrate student prior knowledge when reading PSL. Second, we investigated whether a 5CCs-based, semester-long intervention could shift student reading habits to be more expert-like. As no direct assessment for this exists, we instead measured student motivation for reading PSL, their Biology identity, and their perceived learning gains in science. We found that, through the use of the 5CCs as a framework for reading PSL, students were able to integrate previous knowledge and engaged with PSL constructively. Additionally, we saw positive shifts in student motivation for reading PSL, student Biology identity, and student self-reported learning gains in Biology. Taken together, the 5CCs, as a disciplinary framework, have great potential as a pedagogical tool for increasing student engagement with PSL in Biology classrooms.
Predication of 400kA Aluminium Electrolytic Cell Output Based on the Generative Adversarial Networks

It is difficult to apply accurately in the aluminum reduction industry that aluminum output is mainly based on the material balance and the experienced adjustment according to the performance of electrolytic cell. Dataset is established with data from 10 electrolytic cell in an electrolytic aluminum enterprise during 800 days. The generative adversarial network model inputting a 16-day serial data including the quality of molten aluminum, the condition of electrolytic cell, and the quantity of material, is built to predicate and evaluate aluminum output. In the experiment the models based on GAN to predicate aluminum output are trained by single and multiple datasets, and compared with BP and SVM models in performance. The results indicate that the model trained with combined dataset perform better than the single and the model based on BP and SVM, with the prediction accuracy of 99.4% and the maximum absolute error of 49.7.
Pokémon GO with Social Distancing: Social Media Analysis of Players' Experiences with Location-based Games

Pokémon GO is a popular location-based mobile game that seeks to inspire players to be more active, socialize physically and virtually, and spend more time outside. With the onset of the COVID-19 pandemic, several game mechanics of Pokémon GO were changed to accommodate socially-distanced play. This research aims to understand the impacts of the pandemic and subsequent game adjustments on user perceptions of the game. We used an exploratory mixed-method approach, a machine learning technique (Latent Dirichlet Allocation) for topic modeling, and thematic analysis for qualitative coding of top-level Reddit comments to identify whether and how the social distancing approach changes the players' behaviors. The results demonstrate that players were less physically active, less eager to discover, and more interested in remote social practices. We discuss which players leverage social distancing changes and reflect on key game features that provide a better gaming experience in the age of remote play.
Analysis of the Influence of Extrinsic Motivation in Online Games on Players' Gaming Experience

With the rise of online games, the online game industry has occupied an important position in the national economic system. The economy is the embodiment of value, so the analysis of the influencing factors between the game and the players has certain significance. In this paper, the dimensions of the influence factors of game extrinsic motivation and the influence factors of players' game experience are determined by sorting out the literature. A questionnaire survey was conducted on game players, and the final analysis of the data results obtained the influence of external game motivation on players' game experience. The analysis results are used to understand the game and pave the way for further research.
The Case for Establishing a Collective Perspective to Address the Harms of Platform Personalization

Personalization on digital platforms drives a broad range of harms, including misinformation, manipulation, social polarization, subversion of autonomy, and discrimination. In recent years, policymakers, civil society advocates, and researchers have proposed a wide range of interventions to address these challenges. In this article, we argue that the emerging toolkit reflects an individualistic view of both personal data and data-driven harms that will likely be inadequate to address growing harms in the global data ecosystem. We maintain that interventions must be grounded in an understanding of the fundamentally collective nature of data, wherein platforms leverage complex patterns of behaviors and characteristics observed across a large population to draw inferences and make predictions about individuals. Using the lens of the collective nature of data, we evaluate various approaches to addressing personalization-driven harms currently under consideration. This lens also allows us to frame concrete guidance for future legislation in this space and advocate meaningful transparency that goes far beyond current proposals. We offer a roadmap for what meaningful transparency must constitute: a collective perspective providing a third party with ongoing insight into the information gathered and observed about individuals and how it correlates with any personalized content they receive-across a large, representative population. These insights would enable the third party to understand, identify, quantify, and address cases of personalization-driven harms. We discuss how such transparency can be achieved without sacrificing privacy and provide guidelines for legislation to support the development of this proposal.