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We prove local Calabi and higher order estimates for solutions to the continuity equation introduced by La Nave-Tian and extended to Hermitian metrics by Sherman-Weinkove. We apply the estimates to show that on a compact complex manifold the Chern scalar curvature of a solution must blow up at a finite-time singularity. Additionally, starting from certain classes of initial data on Oeljeklaus-Toma manifolds we prove Gromov-Hausdorff and smooth convergence of the metric to a particular non-negative $(1,1)$-form as $t\to\infty$., 20 pages

"The Art and Science of Data Analysis: Unraveling Insights for Effective Decision Making" explores the interdisciplinary nature of data analysis, combining both scientific methodologies and creative approaches to extract valuable insights. This paper highlights the significance of integrating analytical techniques with intuitive thinking to enable effective decision-making processes. By unraveling the complexities of data analysis, organizations can leverage insights to drive informed decisions and achieve desired outcomes.

General physical scene understanding requires more than simply localizing and recognizing objects -- it requires knowledge that objects can have different latent properties (e.g., mass or elasticity), and that those properties affect the outcome of physical events. While there has been great progress in physical and video prediction models in recent years, benchmarks to test their performance typically do not require an understanding that objects have individual physical properties, or at best test only those properties that are directly observable (e.g., size or color). This work proposes a novel dataset and benchmark, termed Physion++, that rigorously evaluates visual physical prediction in artificial systems under circumstances where those predictions rely on accurate estimates of the latent physical properties of objects in the scene. Specifically, we test scenarios where accurate prediction relies on estimates of properties such as mass, friction, elasticity, and deformability, and where the values of those properties can only be inferred by observing how objects move and interact with other objects or fluids. We evaluate the performance of a number of state-of-the-art prediction models that span a variety of levels of learning vs. built-in knowledge, and compare that performance to a set of human predictions. We find that models that have been trained using standard regimes and datasets do not spontaneously learn to make inferences about latent properties, but also that models that encode objectness and physical states tend to make better predictions. However, there is still a huge gap between all models and human performance, and all models' predictions correlate poorly with those made by humans, suggesting that no state-of-the-art model is learning to make physical predictions in a human-like way. Project page: https://dingmyu.github.io/physion_v2/

Magnetic resonance imaging (MRI) has revolutionized diagnostic imaging in medicine, providingdetailed anatomical and functional information without the use of ionizing radiation. Over theyears, significant advancements in MRI technology have further enhanced its capabilities,allowing for improved image quality, faster scanning times, and expanded clinical applications.This article aims to explore the recent advancements in MRI, their impact on diagnostic imaging,and their role in advancing precision medicine.

Liver transplantation is a life-saving procedure for patients with end-stage liver disease. Thesuccess of liver transplantation depends not only on the surgical expertise but also on theappropriate selection of suitable liver donors. This article provides a comprehensive overview ofthe systems-based approach to liver donor selection, focusing on optimizing organ allocation forthe best possible outcomes. It discusses the evaluation criteria, organ allocation systems, andconsiderations for donor-recipient matching.

Computed Tomography (CT) scan is a widely used imaging modality that plays a crucial role inmodern radiology. It provides detailed cross-sectional images of the body, enabling physicians todiagnose various conditions, plan treatments, and monitor therapeutic interventions. This articleexplores the diverse applications of CT scan in modern radiology, highlighting its role in diagnosis,treatment planning, and patient management.

Emerging Trends in MRI and CT Scan: Innovations in ImagingTechnology for Accurate Diagnosis

Kagome metals are widely recognized as versatile platforms for exploring novel topological properties, unconventional electronic correlations, magnetic frustration, and superconductivity. In the $R$V$_6$Sn$_6$ family of materials ($R$ = Sc, Y, Lu), ScV$_6$Sn$_6$ hosts an unusual charge density wave ground state as well as structural similarities with the $A$V$_3$Sb$_5$ system ($A$ = K, Cs, Rb). In this work, we combine Raman scattering spectroscopy with first-principles lattice dynamics calculations to reveal the charge density wave state in ScV$_6$Sn$_6$. In the low temperature phase, we find a five-fold splitting of the V-containing totally symmetric mode near 240 cm$^{-1}$ suggesting that the density wave acts to mix modes of $P$6/$mmm$ and $R$$\bar{3}$$m$ symmetry - an effect that we quantify by projecting phonons of the high symmetry state onto those of the lower symmetry structure. We also test the stability of the density wave state under compression and find that both physical and chemical pressure act to quench the effect. We discuss these findings in terms of symmetry and the structure-property trends that can be unraveled in this system.

We study the continuity equation of La Nave-Tian, extended to the Hermitian setting by Sherman-Weinkove, on Hopf and Inoue surfaces. We prove a priori estimates for solutions in both cases, and Gromov-Hausdorff convergence of Inoue surfaces to a circle., 15 pages

Transcription factors (TFs) play a key role in regulating gene expression and responses to stimuli. We conducted an integrated analysis of chromatin accessibility and RNA expression across various rat tissues following endurance exercise training (EET) to map epigenomic changes to transcriptional changes and determine key TFs involved. We uncovered tissue-specific changes across both omic layers, including highly correlated differentially accessible regions (DARs) and differentially expressed genes (DEGs). We identified open chromatin regions associated with DEGs (DEGaPs) and found tissue-specific and genomic feature-specific TF motif enrichment patterns among both DARs and DEGaPs. Accessible promoters of up-vs. down-regulated DEGs per tissue showed distinct TF enrichment patterns. Further, some EET-induced TFs in skeletal muscle were either validated at the proteomic level (MEF2C and NUR77) or correlated with exercise-related phenotypic changes. We provide an in-depth analysis of the epigenetic and trans-factor-dependent processes governing gene expression during EET.

In this paper, we propose a coupled system of complex Hessian equations which generalizes the equation for constant scalar curvature K\"ahler (cscK) metrics. We show this system can be realized variationally as the Euler-Lagrange equation of a Hessian version of the Mabuchi K-energy in an infinite dimensional space of $k$-Hessian potentials, which can be seen as an infinite dimensional Riemannian manifold with negative sectional curvature. Finally, we prove an a priori $C^0$-estimate for this system which depends on the Entropy, which generalizes a fundamental result of Chen and Cheng for cscK metrics., Comment: 13 pages

Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis, pushing the state-of-the-art in classification, detection and segmentation tasks. Over the last years, vision transformers (ViTs) have appeared as a competitive alternative to CNNs, yielding impressive levels of performance in the natural image domain, while possessing several interesting properties that could prove beneficial for medical imaging tasks. In this work, we explore the benefits and drawbacks of transformer-based models for medical image classification. We conduct a series of experiments on several standard 2D medical image benchmark datasets and tasks. Our findings show that, while CNNs perform better if trained from scratch, off-the-shelf vision transformers can perform on par with CNNs when pretrained on ImageNet, both in a supervised and self-supervised setting, rendering them as a viable alternative to CNNs., Comment: Extended version of arXiv:2108.09038 originally published at the ICCV 2021 Workshop on Computer Vision for Automated Medical Diagnosis

Many models of intuitive physical reasoning posit some kind of mental simulation mechanism, yet everyday environments frequently contain far more objects than people could plau- sibly represent with their limited cognitive capacity. What determines which objects are actually included in our repre- sentations? We asked participants to predict how a ball will bounce through a complex field of obstacles, and probed work- ing memory for objects in the scene that were more and less likely to be relevant to the ball’s trajectory. We evaluate differ- ent accounts of relevance and find that successful object mem- ory is best predicted by how frequently a ball’s trajectory is expected to contact that object under a probabilistic simulation model. This suggests that people construct representations for mental simulation efficiently and dynamically, on the fly, by adding objects “just in time”: only when they are expected to become relevant for the next stage of simulation.

Kink is an umbrella term for an array of radical erotic and/or sexual interests which is being increasingly recognized as a valid and healthy expression of sexual identity. Approximately 2-10% of the United States population identifies as kinky. Participating in kink activities can convey risk of injury or infection ranging from simple bruising to severe injury and death. Despite the risk of injury and infection, few studies have examined if and how people who participate in kink activities – also self-referred to as kinky people or kinksters – interact with their healthcare providers to mitigate these risks. Kink has historically been stigmatized and criminalized in societal and healthcare settings, resulting in care avoidance and poor health outcomes. Disclosure of other stigmatized identities such as sexual and gender identity to a healthcare provider has been shown to facilitate patient-provider communication, increase delivery of tailored care, and ultimately to improve health outcomes. Factors that promote disclosure of kink identity to a provider are understudied. The current study was conducted to inform the content and delivery of health communication messages that health providers could employ to facilitate disclosure among their kinky patients. The study comprises a qualitative exploration of kinky peoples’ healthcare experiences, thoughts on disclosure of kink identity, and preferences for delivery of pro-disclosure messages from providers. The study also qualitatively explores the personality trait of sensation seeking which has been shown to impact the effectiveness of tailored health communication messages. A second aim uses data from the International Kink Health Study to test constructs from the Theories of Health Behavior and Normative Social Behavior to identify those constructs associated with disclosure intent. Findings from this study will guide future health communication by informing audience segmentation regarding sensation seeking tendency and message content related to common health behavior theories.

If we want to know whether a stack of blocks will fall, we might form our judgment by using mental simulation or simple heuristics. How do we know which strategy to use? Here, we test whether people’s strategy selection is “resource rational,” balancing their utilities and costs. We had participants judge how a balance beam will fall – a task that relies on both simulation and heuristics – and imposed visuospatial cognitive load to increase the cost of mental simulation, expecting the use of simulation to then decrease and the use of heuristics to increase. When cognitive load varied between participants, higher load led to less simulation. However, when load varied across trials for an individual participant, they instead used simulation at about the same rate as high-load-only participants. Thus strategy selection is sensitive to cognitive costs but it is also sticky, not accounting for rapidly fluctuating costs.

Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generating art, ChatGPT, etc. Such a dramatic progress raises the question: how generalizable are neural networks in solving problems that demand broad skills? To answer this question, we propose SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset, for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed specifically for children in the 6--8 age group. Our dataset consists of 101 unique puzzles; each puzzle comprises a picture and a question, and their solution needs a mix of several elementary skills, including arithmetic, algebra, and spatial reasoning, among others. To scale our dataset towards training deep neural networks, we programmatically generate entirely new instances for each puzzle, while retaining their solution algorithm. To benchmark performances on SMART-101, we propose a vision and language meta-learning model using varied state-of-the-art backbones. Our experiments reveal that while powerful deep models offer reasonable performances on puzzles in a supervised setting, they are not better than random accuracy when analyzed for generalization. We also evaluate the recent ChatGPT and other large language models on a subset of SMART-101 and find that while these models show convincing reasoning abilities, the answers are often incorrect., Extended version of CVPR 2023 paper. For the SMART-101 dataset, see http://smartdataset.github.io/smart101

(Neuron 110, 1671–1688; May 18, 2022) It was brought to our attention that there is a clerical mistake in one of the panels of Figure 1 (Figure 1E) in our recent publication. The representative blots for the CNS Stmn2 protein levels in the F 2 generation of Stmn2 mutants included an incorrect duplicated blot for the GAPDH loading control in the brain samples. We have corrected this error and replaced the figure online with one that includes the correct blot for the GAPDH loading control for those samples. We would like to profoundly apologize to the readers for the oversight and honest mistake.[Formula

The world is filled with articulated objects that are difficult to determine how to use from vision alone, e.g., a door might open inwards or outwards. Humans handle these objects with strategic trial-and-error: first pushing a door then pulling if that doesn't work. We enable these capabilities in autonomous agents by proposing "Hypothesize, Simulate, Act, Update, and Repeat" (H-SAUR), a probabilistic generative framework that simultaneously generates a distribution of hypotheses about how objects articulate given input observations, captures certainty over hypotheses over time, and infer plausible actions for exploration and goal-conditioned manipulation. We compare our model with existing work in manipulating objects after a handful of exploration actions, on the PartNet-Mobility dataset. We further propose a novel PuzzleBoxes benchmark that contains locked boxes that require multiple steps to solve. We show that the proposed model significantly outperforms the current state-of-the-art articulated object manipulation framework, despite using zero training data. We further improve the test-time efficiency of H-SAUR by integrating a learned prior from learning-based vision models.

Artifacts on magnetic resonance scans are a serious challenge for both radiologists and computer-aided diagnosis systems. Most commonly, artifacts are caused by motion of the patients, but can also arise from device-specific abnormalities such as noise patterns. Irrespective of the source, artifacts can not only render a scan useless, but can potentially induce misdiagnoses if left unnoticed. For instance, an artifact may masquerade as a tumor or other abnormality. Retrospective artifact correction (RAC) is concerned with removing artifacts after the scan has already been taken. In this work, we propose a method capable of retrospectively removing eight common artifacts found in native-resolution MR imagery. Knowledge of the presence or location of a specific artifact is not assumed and the system is, by design, capable of undoing interactions of multiple artifacts. Our method is realized through the design of a novel volumetric transformer-based neural network that generalizes a \emph{window-centered} approach popularized by the Swin transformer. Unlike Swin, our method is (i) natively volumetric, (ii) geared towards dense prediction tasks instead of classification, and (iii), uses a novel and more global mechanism to enable information exchange between windows. Our experiments show that our reconstructions are considerably better than those attained by ResNet, V-Net, MobileNet-v2, DenseNet, CycleGAN and BicycleGAN. Moreover, we show that the reconstructed images from our model improves the accuracy of FSL BET, a standard skull-stripping method typically applied in diagnostic workflows., BMVC22

Methods

References

Materials

This protocol was developed using the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Extension for Scoping Reviews: Checklist and Explanation http://www.prisma-statement.org/Extensions/ScopingReviews. This protocol will be registered prospectively with the Open Science Framework. This scoping review aims to: Map the extent and level of evidence that exists specific to the effectiveness of surfing’s influence on symptoms of mental health. Identify the limitations of the current evidence specific to the effectiveness of surfing’s influence on symptoms of mental health, and to provide recommendations for future research on the topic.

Methods and Measures

Description of esstential elements

Design and Analysis

Experimental Procedure

A scoping review which aims to identify and explore scientific literature pertaining to surfing equipment design. This review looks to identify themes and knowledge gaps within the sport of surfing.

This scoping review was conducted to provide a comprehensive overview of the current literature relevant to aquatic exercise and its effect on mental health and overall well-being amongst a wide range of populations. It also aims to a) clarify the intervention type, volume, measurement tools used, and populations best served in relation to mental health improvements and b) to identify any gaps in the literature so recommendations can be made for future research.

This scoping review seeks to assess and analyse the body of scientific literature specific to wrist worn fitness trackers to measure cardiovascular parameters.

This scoping review aims to identify physiological, musculoskeletal and anthropometric characteristics that differentiate competitive surfers from recreational surfers. Secondly, it also aims to outline the most appropriate battery of physiological and musculoskeletal tests that could be used to evaluate training interventions and/or athlete screening.

The primary objectives of this scoping review were to (1) summarise the volume and type of scientific literature specific to skin cancer and sun protective measures in water-based sports, (2) examine the current levels of compliance and adherence to sun protective behaviours in water-based sports and (3) identify any knowledge gaps to inform future research or skin health guidelines.

This project investigates the influence of trait anxiety,depression and autism on Theory of Mind. We intend to investigate 1) how traits influence the type of errors that are being made when evaluating social situations 2) what visual information people are focusing on when they are making these errors.

R and ggplot2 are tools for making impactful data visualizations. Some may fear the “code” learning curve, but with ggplot2’s “grammar of graphics” the syntax becomes intuitive and builds on itself. This workshop aims to introduce beginners to practical hands-on examples that can be applied to one’s own data analyses. Experienced users will have the opportunity to test their knowledge and learn new tricks and technique. Improve your research exploration and communication skills.

In this project, we present a computational model that explains when and why effects like the conjunction fallacy are compatible with simulation accounts of physical reasoning.

People make fast and reasonable predictions about the physical behavior of everyday objects. To do so, people may be using principled approximations, similar to models developed by engineers for the purposes of real-time physical simulations. We hypothesize that people use simplified object approximations for tracking and action (the "body" representation), as opposed to fine-grained forms for recognition (the "shape" representation). We used three classic psychophysical tasks (causality perception, collision detection, and change detection) in novel settings that dissociate body and shape. People's behavior across tasks indicates that they rely on approximate bodies for physical reasoning, and that this approximation lies between convex hulls and fine-grained shapes.

This scoping review is aiming to explore the predominate modes of physical activity (PA) within regions of the highest concentrations of centenarians living past the age of 100 years. Knowledge gaps within certain regions will be addressed and the modes of PA that lead to longevous, independent, and robust lives will be uncovered.

A scoping review on the use of topical and oral magnesium in the alleviation of acute musculoskeletal pain