Miracle-dz/UniFAR-FTUs huggingface dataset:
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings

Machine learning models often learn latent embedding representations that capture the domain semantics of their training data. These embedding representations are valuable for interpreting trained models, building new models, and analyzing new datasets. However, interpreting and using embeddings can be challenging due to their opaqueness, high dimensionality, and the large size of modern datasets. To tackle these challenges, we present WizMap, an interactive visualization tool to help researchers and practitioners easily explore large embeddings. With a novel multi-resolution embedding summarization method and a familiar map-like interaction design, WizMap enables users to navigate and interpret embedding spaces with ease. Leveraging modern web technologies such as WebGL and Web Workers, WizMap scales to millions of embedding points directly in users’ web browsers and computational notebooks without the need for dedicated backend servers. WizMap is open-source and available at the following public demo link: https://poloclub.github. io/wizmap.
SIGHT: A Large Annotated Dataset on Student Insights Gathered from Higher Education Transcripts

Lectures are a learning experience for both students and teachers. Students learn from teachers about the subject material, while teachers learn from students about how to refine their instruction. Unfortunately, online student feedback is unstructured and abundant, making it challenging for teachers to learn and improve. We take a step towards tackling this challenge. First, we contribute a dataset for studying this problem: SIGHT is a large dataset of 288 math lecture transcripts and 15,784 comments collected from the Massachusetts Institute of Technology OpenCourseWare (MIT OCW) YouTube channel. Second, we develop a rubric for categorizing feedback types using qualitative analysis. Qualitative analysis methods are powerful in uncovering domain-specific insights, however they are costly to apply to large data sources. To overcome this challenge, we propose a set of best practices for using large language models (LLMs) to cheaply classify the comments at scale. We observe a striking correlation between the model’s and humans’ annotation: Categories with consistent human annotations (0.9 inter-rater reliability, IRR) also display higher human-model agreement (0.7), while categories with less consistent human annotations (0.7-0.8 IRR) correspondingly demonstrate lower human-model agreement (0.3-0.5). These techniques uncover useful student feedback from thousands of comments, costing around $0.002 per comment. We conclude by discussing exciting future directions on using online student feedback and improving automated annotation techniques for qualitative research.
Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers

Human perception of surroundings is often guided by the various poses present within the environment. Many computer vision tasks, such as human action recognition and robot imitation learning, rely on pose-based entities like human skeletons or robotic arms. However, conventional Vision Transformer (ViT) models uniformly process all patches, neglecting valuable pose priors in input videos. We argue that incorporating poses into RGB data is advantageous for learning fine-grained and viewpoint-agnostic representations. Consequently, we introduce two strategies for learning pose-aware representations in ViTs. The first method, called Pose-aware Attention Block (PAAB), is a plug-and-play ViT block that performs localized attention on pose regions within videos. The second method, dubbed Pose-Aware Auxiliary Task (PAAT), presents an auxiliary pose prediction task optimized jointly with the primary ViT task. Although their functionalities differ, both methods succeed in learning pose-aware representations, enhancing performance in multiple diverse downstream tasks. Our experiments, conducted across seven datasets, reveal the efficacy of both pose-aware methods on three video analysis tasks, with PAAT holding a slight edge over PAAB. Both PAAT and PAAB surpass their respective backbone Transformers by up to 9.8% in real-world action recognition and 21.8% in multi-view robotic video alignment. Code is available at https://github.com/dominickrei/PoseAwareVT.
Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking

In schema-guided dialogue state tracking models estimate the current state of a conversation using natural language descriptions of the service schema for generalization to unseen services. Prior generative approaches which decode slot values sequentially do not generalize well to variations in schema, while discriminative approaches separately encode history and schema and fail to account for inter-slot and intent-slot dependencies. We introduce SPLAT, a novel architecture which achieves better generalization and efficiency than prior approaches by constraining outputs to a limited prediction space. At the same time, our model allows for rich attention among descriptions and history while keeping computation costs constrained by incorporating linear-time attention. We demonstrate the effectiveness of our model on the Schema-Guided Dialogue (SGD) and MultiWOZ datasets. Our approach significantly improves upon existing models achieving 85.3 JGA on the SGD dataset. Further, we show increased robustness on the SGD-X benchmark: our model outperforms the more than 30x larger D3ST-XXL model by 5.0 points.
A composite guidance law with enhanced anti-disturbance capability for Mars pinpoint landing

The landing safety and accuracy of a Mars lander will be seriously degraded due to multiple unknown disturbances or perturbations in the powered descent phase. A novel composite guidance algorithm is proposed to improve the landing performance in this paper. The presented guidance algorithm, with a composite hierarchical framework, is developed by the combination of disturbance observer-based control and multiple sliding surfaces guidance theory. The major disturbance owing to the Mars wind could be estimated through a disturbance observer and incorporated in the feed-forward compensation in the inner loop, other disturbances or perturbations could be attenuated by the multiple sliding surfaces technique in the outer loop. The composite guidance algorithm could be utilized without a pre-computed reference trajectory and it has enhanced anti-disturbance capability compared with the multiple sliding surfaces guidance law. Its global stability is verified using a Lyapunov-based approach. Monte Carlo simulation results show that the composite guidance law has a better performance on guiding a Mars lander from the point of engine ignition to the desired landing point in the presence of disturbances and perturbations.
Semiparametric estimation of (constrained) ultrametric trees

This paper is concerned with the semi-parametric estimation of Ultrametric treerepresentations of subjects' paired comparisons of stimuli, and captures subject heterogeneity using a finite mixture formulation. In many other approaches to the analysis of subjects decision processes, such finite mixture models have been gainfully applied. A new likelihood based estimation methodology is presented for Ultrametric tree structures that accommodates the Ultrametric constraints. This estimation procedure in addition permits the incorporation of a variety of additional external restrictions on the tree structure. Correlations among the observed dissimilarities are allowed for. The performance of the method to identify Ultrametric trees is investigated on synthetic data and an empirical application to published data from Schiffman, Reynolds, and Young (1981) is provided. The ability to deal with specific constraints on the tree-topology is demonstrated.
Growth and development of the mammary gland in mice—control of the insulin-like growth factor system by hormones and metalloproteases, and putative interference with micro RNAs

https://doi.org/10.1093/af/vfad024 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. © Hoeflich, Galow, Brenmoehl, Hadlich Feature Article Growth and development of the mammary gland in mice—control of the insulin-like growth factor system by hormones and metalloproteases, and putative interference with micro RNAs
Glycated Hemoglobin Improvement After Medical and Surgical Eye Care in American Veterans Involves Multidisciplinary Care

Purpose The effects of diabetes mellitus (DM) on visual function have been extensively studied. Fewer studies evaluate the effect of visual function on DM, and previous small studies have shown mixed results concerning the relationship between glycated hemoglobin (HbA1c) and cataract surgery. We performed a retrospective, observational, single-site study at a Veterans hospital to evaluate this relationship and the relationship between HbA1c and non-surgical eye care. Patients and Methods We compared pre- and post-operative/examination HbA1c in 431 surgical and 431 matched, non-surgical subjects who underwent eye examination at the same institution. Subgroup analysis was performed by age, elevated (≥8) pre-operative/examination HbA1c, and change in diabetic management. We also assessed for a relationship between changes in best-corrected visual acuity (BCVA) and HbA1c. The Minneapolis Veterans Affairs Health Care System Research Administration determined this study to be Institutional Review Board exempt from the requirements of 38 CFR 16 under Category 4 (iii). Results Pairwise comparison of pre- versus post-operative HbA1c trended towards reduction at 3–6 months in all surgical subjects, with a statistically significant reduction in older subjects, and those with higher pre-operative HbA1c. Eye examination subjects experienced a significant HbA1c reduction 3–6 months after eye examination. Reduction in post-operative/examination HbA1c was associated with concurrent change in diabetic management. Conclusion We found an overall reduction in HbA1c in diabetic Veterans who interacted with an ophthalmologist, whether for cataract surgery or eye examination. HbA1c reduction was greatest when ophthalmic care was delivered as part of a multidisciplinary care team. Our findings add new evidence to further support the importance of ophthalmic care in patients with DM and suggest improved visual function may facilitate improved glycemic control.
Propagation of fronts of a reaction-convection-diffusion equation

Received: date / Revised version: date Abstract. We study the speed of propagating fronts of the convection reaction diffusion equation ut + μuux = uxx + f(u) for reaction terms f(u) such that in the non convective case fronts joining two equilibrium states exist. A variational principle for the wave speed is constructed from which upper and lower bounds are obtained. We find that, in general, there is a transition value μc below which advection has no effect on the speed of the travelling front. Results for the more general case ut + μφ(u)x = uxx + f(u) are also given.
Use of a Humanoid Robot in Supporting Dementia Care: A Qualitative Analysis

Introduction Cognitive impairment significantly affects independence in persons with dementia, and consistent supervision is often needed. While interest has arisen in using humanoid robots, such as Pepper, to assist with daily caregiving activities, little is known about the perceptions of using Pepper to assist people with dementia. Objective This study aimed to explore the perceptions of nonhealthcare workers, care partners, and healthcare workers on the use of a Pepper robot in dementia care. Methods This was a secondary qualitative analysis. Data were collected from a pilot study conducted from November 2020 to March 2021 using an online survey. The survey consisted of quantitative and qualitative questions; this study only focused on the qualitative responses. The detailed procedures and the quantitative results were published elsewhere. Participants included nonhealthcare workers, care partners, and healthcare workers. Results A total of 194 participants responded to the open-ended question. Participants described potential benefits of Pepper including assisting with daily activities, monitoring safety and medication use, initiating reminders, and promoting activities and social interactions. Participants had concerns about privacy, cost, poor acceptance/trust, Pepper making mistakes, limitations in environmental navigation and responding to emergencies, misuse of Pepper, and Pepper replacing humans. Participants suggested that Pepper should be tailored to each individual's background, preferences, and functions and recommended improving the logistics of using Pepper, offering more emotional support and responses, and using a more natural appearance and voice. Conclusion Pepper may support dementia care; yet some concerns need to be addressed. Future research should consider incorporating these comments when designing robots for dementia care.
Variation in prescribing for the prevention of postoperative nausea, vomiting, and pain following abdominal surgery: A retrospective study

Adequate postoperative analgesia and prevention of post‐op nausea and vomiting (PONV) are core components of modern day anaesthesia and peri‐operative care. As well as contributing to overall morbidity, postoperative pain and PONV are frequently cited as one of the most unpleasant and distressing aspects of surgery for patients. Variation in healthcare delivery is known to exist but has often been poorly described. A first step to understanding the consequences of variation is to describe the extent of variation. We aimed to assess variation in pharmacological strategies to prevent postoperative pain, nausea and vomiting in patients undergoing elective major abdominal surgery at a tertiary hospital in Perth, Western Australia, over a three‐month period.
Deep Generative Models with Stick-Breaking Priors

Bayesian nonparametric models are attractive for their data-dependent capacity, but their implementation can be problematic due to computational or analytical obstacles. We make progress on this problem by extending Stochastic Gradient Variational Bayes (Kingma & Welling, 2013), a 'black box' method for approximate posterior inference, to stick-breaking priors (Ishwaran & James, 2001). This innovation allows us to define deep generative models (DGMs) with infinite dimensional latent variables. We experimentally demonstrate that DGMs with Dirichlet process priors learn highly discriminative latent representations that are well suited for semi-supervised settings and often outperform the popular Gaussian alternative.
Profilin1 Promotes Renal Tubular Epithelial Cell Apoptosis in Diabetic Nephropathy Through the Hedgehog Signaling Pathway

Background Profilin-1 (PFN1) regulates the dynamic balance of actin and plays an important role in cell functions as a hub protein in signaling molecule interaction networks. Dysregulation of PFN1 is related to pathologic kidney diseases. Diabetic nephropathy (DN) was recently reported as an inflammatory disorder, however, the molecular mechanisms of PFN1 in DN remain unclear. Therefore, the present study was conducted to explore the molecular and bioinformatic characteristics of PFN1 in DN. Methods Bioinformatics analyses were performed on the chip of database in DN kidney tissues. A cellular model of DN was established in human renal tubular epithelial cells (HK-2) induced by high glucose. The PFN1 gene was overexpressed or knocked-down to investigate its function in DN. Flow cytometry was used to detect cell proliferation and apoptosis. PFN1 and proteins in the related signaling pathways were evaluated by Western blotting. Results The expression of PFN1 was significantly increased in DN kidney tissues (P < 0.001) and was correlated with a high apoptosis-associated score (Pearson’s correlation = 0.664) and cellular senescence-associated score (Pearson’s correlation = 0.703). PFN1 protein was mainly located in cytoplasm. Overexpression of PFN1 promoted apoptosis and blocked the proliferation of HK-2 cells treated with high levels of glucose. Knockdown of PFN1 led to the opposite effects. Additionally, we found that PFN1 was correlated with the inactivation of the Hedgehog signaling pathway in HK-2 cells treated with high levels of glucose. Conclusion PFN1 might play an integral role in the regulation of cell proliferation and apoptosis during DN development by activating the Hedgehog signaling pathway. This study provided molecular and bioinformatic characterizations of PFN1, and contributed to the understanding of the molecular mechanisms leading to DN.
Predicting Short-Term Mortality in Older Patients Discharged from Acute Hospitalizations Lasting Less Than 24 Hours

Purpose Over coming decades, a rise in the number of short, acute hospitalizations of older people is to be expected. To help physicians identify high-risk patients prior to discharge, we aimed to develop a model capable of predicting the risk of 30-day mortality for older patients discharged from short, acute hospitalizations and to examine how model performance changed with an increasing amount of information. Methods This registry-based study included acute hospitalizations in Denmark for 2016–2018 lasting ≤24 hours where patients were permanent residents, ≥65 years old, and discharged alive. Utilizing many different predictor variables, we developed random forest models with an increasing amount of information, compared their performance, and examined important variables. Results We included 107,132 patients with a median age of 75 years. Of these, 3.3% (n=3575) died within 30 days of discharge. Model performance improved especially with the addition of laboratory results and information on prior acute admissions (AUROC 0.835), and again with comorbidities and number of prescription drugs (AUROC 0.860). Model performance did not improve with the addition of sociodemographic variables (AUROC 0.861), apart from age and sex. Important variables included age, dementia, number of prescription drugs, C-reactive protein, and eGFR. Conclusion The best model accurately estimated the risk of short-term mortality for older patients following short, acute hospitalizations. Trained on a large and heterogeneous dataset, the model is applicable to most acute clinical settings and could be a useful tool for physicians prior to discharge.
Squamous Cell Carcinoma Arising from Epidermal Inclusion Cyst of Breast: Imaging Findings and Literature Review

Commonly, epidermal inclusion cysts (EICs) are benign cutaneous lesions that are lined with stratified squamous epithelium and may occur in all body parts, including the breasts. EICs in the breast (EICB) are commonly encountered clinically; it may be under-reported because of their mild and non-specific clinical presentation. Malignant transformation of EICs is extremely rare, occurring 0.011%–0.045%. Presently, we report a rare case of squamous cell carcinoma arising from an EICB of a woman with invasive ductal carcinoma.
The Unmet Needs of Breast Cancer Navigation Services: Reconciling Clinical Care With the Emotional and Logistical Challenges Experienced by Younger Women with Breast Cancer in a Healthcare Delivery System

This study explores the experience of the breast cancer journey for younger women receiving patient navigation services in a healthcare delivery system and any remaining challenges that navigation services may leave unaddressed. In this qualitative analysis, we used a purposeful sampling approach to conduct a semistructured in-person interview with 19 younger women (under 50 years at the time of diagnosis) at various stages of breast cancer treatment and receiving care that included some form of patient navigation services/within the Sutter Health system. Thematic analysis was performed using an inductive grounded theory approach. The patient experience revealed that women receiving navigation services throughout their cancer journey had little concern related to clinical decision-making and treatment. Rather, emotional, and logistical challenges dominate their experience and perceptions of the cancer journey. Managing day-to-day life and the emotional aspects of a cancer diagnosis cannot be disentangled from clinical care. Navigating the emotional and logistical aspects of the cancer journey is an ongoing unmet need for women under age 50, and navigation services can potentially be enhanced to help address these specific needs. Women with breast cancer may benefit from navigation programs focused not only on clinically related challenges but also on recognizing the daily needs of younger women and guiding them through family and job-related obstacles encountered while navigating cancer care. Health systems could enhance existing nurse navigation programs and redesign other aspects of care to focus on meeting these needs.
Color image processing by vectorial total variation with gradient channels coupling

We study a regularization method for color images based on the vectorial total variation approach along with channel coupling for color image processing, which facilitates the modeling of inter channel relations in multidimensional image data. We focus on penalizing channel gradient magnitude similarities by using $L^{2}$ differences, which allow us to explicitly couple all the channels along with a vectorial total variation regularization for edge preserving smoothing of multichannel images. By using matched gradients to align edges from different channels we obtain multichannel edge preserving smoothing and decomposition. A detailed mathematical analysis of the vectorial total variation with penalized gradient channels coupling is provided. We characterize some important properties of the minimizers of the model as well as provide geometrical results regarding the regularization parameter. We are interested in applying our model to color image processing and in particular to denoising and decomposition. A fast global minimization based on the dual formulation of the total variation is used and convergence of the iterative scheme is provided. Extensive experiments are given to show that our approach obtains good decomposition and denoising results in natural images. Comparison with previous color image decomposition and denoising methods demonstrate the advantages of our approach.
Nonlinear microscopy and infrared and Raman microspectroscopy for brain tumor analysis

Scope of the neurosurgical management of brain tumors is to remove pathological tissue, preserve normal tissue and brain functions, and collect material for neuropathological diagnosis. A prerequisite is to recognize the tumor margins as precise as possible. Scope of neuropathology is to determine the type and grade of the tumor that is an important indicator for the treatment and prognosis of the patient. In this contribution we present vibrational spectroscopic approaches to complement existing neurosurgical and neuropathological tools. First, Fourier transform infrared (FTIR) imaging is applied to obtain molecular contrast from dried, thin tissue sections. Second, Raman spectroscopic images were collected from the same specimens. Finally, coherent anti-Stokes Raman scattering (CARS) microscopic images were obtained. To demonstrate the complementary nature of the techniques results from a brain metastasis of a lung cancer are discussed. Whereas CARS images could be collected within seconds, exposure times were minutes for FTIR images and hours for Raman images. However, the CARS microscope just probed a single band near 2850 cm-1. FTIR and Raman system probed the full spectral range involving the fingerprint region below 1800 cm-1 and the stretch vibrations between 2800 and 3600 cm-1. Morphological features were resolved in the images such as solid tumor, tumor islets, necrosis and cell nuclei.