Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of COPD Population Screener Questionnaire in Chinese Population: A National Multicenter Study

Introduction: Chronic obstructive pulmonary disease (COPD) is highly prevalent and underdiagnosed worldwide. The validity and reliability of COPD Population Screening (COPD-PS) questionnaire are not properly known in a large-sample Chinese population. Methods: This is a national multicenter prospective study that enrolled 1,824 outpatients from 12 hospital sites in China. Scores of the Chinese version of COPD-PS questionnaire, demographic data, and clinical information were collected. The validity and the test-retest reliability were evaluated. Results: 1,824 participants were involved in this study, and 404 (22.1%) were diagnosed with COPD. The overall area under the curve (AUC) of the receiver operating characteristic (ROC) for COPD-PS questionnaire was 0.761 (95% CI: 0.734–0.787). A cut-off point of 4 was recommended, corresponding to a sensitivity of 74.50% and a specificity of 64.37%. The COPD-PS questionnaire showed an overall Pearson’s correlation of 0.88. Conclusions: The COPD-PS questionnaire can be used in screening COPD patients from the general Chinese population with respiratory symptoms.
Sea Ice and Climate. Part II: Model Climate Stability to Perturbations of the Hydrological Cycle

Abstract The sensitivity of a simple climate model to variations in a global hydrological cycle is studied. The model consists of a zonally averaged single basin, two-hemisphere ocean model coupled to an atmospheric energy balance model and a thermodynamic sea ice model. Land processes are reduced to considerations of the oceanic catchment basins, which serve to amplify or decrease the oceanic freshwater forcing. The model typically admits both symmetric and asymmetric equilibria. For sufficiently strong global freshwater forcing, the symmetric circulation is replaced by asymmetric states. This is accomplished by a supercritical pitchfork bifurcation. It is shown that sea ice enhances climate sensitivity in that larger changes of the hydrological cycle are required to induce instability if sea ice is neglected. This behavior comes from the strong damping of SST anomalies under ice, which is essential for suppressing deep ocean temperature anomalies. Thus, ocean temperature plays an important role in clima.
The ups and downs of birth rate in Switzerland 2020 to 2023 in a historical context

We follow population trends in the birth rate in Switzerland almost up to the present day and place the latest developments during the Covid-19 pandemic in a historical context. The birth rate in 2022 was the lowest it has been since the 1870s, and it seems the trend is continuing in 2023. The latest decline had already begun 1-2 years before Covid-19. Previous pandemics (1890, 1918, 1920, 1957) had each led to a temporary decline in the birth rate around 9 months after the peak of these outbreaks. With Covid-19, this appears more complex. The immediate shock of the global outbreak has not left a nega-tive mark on births in Switzerland. However, during and shortly after the first two pandemic waves and partial shutdowns in 2020, there were more conceptions and thus significantly more births in 2021, in all available subgroups except Italian-speaking Switzerland, and somewhat more pronounced among >30-year-old mothers and second parities. The subsequent decline in births from January 2022 was stronger than the increase in births in 2021. The first part of the 2022 decline falls on conception months in the first half of 2021, when the vaccination campaign started in Switzerland. However, given that the proportion of young people vaccinated by summer was still small, vaccination cannot by itself explain the decline in birth rate. The second part of the 2022 decline is associated with conceptions during the large Omicron wave in winter 2021/2022. The decline appears to continue in 2023, albeit not substantially.
Characterization of the mutational landscape of high-grade gliomas in a Latin American cohort

Background: Glioblastoma, an aggressive form of high-grade glioma, exhibits variations in the incidence and mortality patterns between populations of different ancestry. However, Hispanic and Latino populations remain largely underrepresented in studies of molecular characterization. Here we characterized the mutagenic landscape of high-grade gliomas within a Chilean population focusing on well characterized genetic variants associated with glioblastoma classification, progression, and prognosis. Methods: We conducted a targeted genomic analysis using next-generation sequencing techniques in 70 Chilean patients with high-grade gliomas from a national single-center referral institution. We focused on the most relevant molecular markers such as isocitrate dehydrogenase 1/2 (IDH) mutations, telomerase reverse transcriptase promoter (TERTp) mutations, histone 3 (H3) gene family mutations, TP53 and PTEN mutations, epidermal growth factor receptor (EGFR) gene amplification, and cyclin-dependent kinase inhibitor 2A (CDKN2A) deletions. Survival analyses were performed to assess the clinical relevance of these markers and their impact on patient prognosis. Additionally, due to our interest in the role of proteostasis and glioblastoma, we investigated possible ERN1 variants in our Chilean cohort. Results: Our findings mostly align with other international cohorts of non-Latin-American origin, confirming the importance of established molecular markers in glioblastoma. Notably, we identified novel TP53 and PTEN mutations with a predicted damaging effect, expanding the genetic spectrum of alterations in these brain tumors. Furthermore, a lower-than-expected mutation rate in the NF1 gene was observed, emphasizing a distinctive genetic profile of glioblastoma in this Latin American population. Prognostic assessments based on TERT promoter and IDH mutations mirrored previous studies on non-Latino American and European populations. Of note, we identified the germline ERN1 variant rs139229826 in 11% of our patient cohort, a variant previously unreported in glioblastoma patients. Conclusion: This study underscores the importance of conducting glioblastoma research in underrepresented populations, providing insights into the molecular characteristics of high-grade gliomas within a Latin American context. Our findings contribute to the growing body of evidence suggesting molecular diversity across diverse glioblastoma populations, offering a foundation for future international comparative studies.
SARS-CoV-2 Infection is Associated with an Increase in New Diagnoses of Schizophrenia Spectrum and Psychotic Disorder: A Study Using the US National COVID Cohort Collaborative (N3C)

Amid the ongoing global repercussions of SARS-CoV-2, its crucial to comprehend its potential long-term psychiatric effects. Several recent studies have suggested a link between COVID-19 and subsequent mental health disorders. Our investigation joinsthis exploration, concentrating on Schizophrenia Spectrum and Psychotic Disorders (SSPD). Different from other studies, we took acute respiratory distress syndrome (ARDS) and COVID-19 lab negative cohorts as control groups to accurately gauge the impact of COVID-19 on SSPD. Data from 19,344,698 patients, sourced from the N3C Data Enclave platform, were methodically filtered to create propensity matched cohorts: ARDS (n = 222,337), COVID-positive (n = 219,264), and COVID-negative (n = 213,183). We systematically analyzed the hazard rate of new-onset SSPD across three distinct time intervals: 0-21 days, 22-90 days, and beyond 90 days post-infection. COVID-19 positive patients consistently exhibited a heightened hazard ratio (HR) across all intervals [0-21 days (HR: 4.6; CI: 3.7-5.7), 22-90 days (HR: 2.9; CI: 2.3 -3.8), beyond 90 days (HR: 1.7; CI: 1.5-1.)]. These are notably higher than both ARDS and COVID-19 lab-negative patients. Validations using various tests, including the Cochran Mantel Haenszel Test, Wald Test, and Log-rank Test confirmed these associations. Intriguingly, our data indicated that younger individuals face a heightened risk of SSPD after contracting COVID-19, a trend not observed in the ARDS and COVID-negative groups. These results, aligned with the known neurotropism of SARS-CoV-2 and earlier studies, accentuate the need for vigilant psychiatric assessment and support in the era of Long-COVID, especially among younger populations.
Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models

Novelty detection is a fundamental task of machine learning which aims to detect abnormal ($\textit{i.e.}$ out-of-distribution (OOD)) samples. Since diffusion models have recently emerged as the de facto standard generative framework with surprising generation results, novelty detection via diffusion models has also gained much attention. Recent methods have mainly utilized the reconstruction property of in-distribution samples. However, they often suffer from detecting OOD samples that share similar background information to the in-distribution data. Based on our observation that diffusion models can \emph{project} any sample to an in-distribution sample with similar background information, we propose \emph{Projection Regret (PR)}, an efficient novelty detection method that mitigates the bias of non-semantic information. To be specific, PR computes the perceptual distance between the test image and its diffusion-based projection to detect abnormality. Since the perceptual distance often fails to capture semantic changes when the background information is dominant, we cancel out the background bias by comparing it against recursive projections. Extensive experiments demonstrate that PR outperforms the prior art of generative-model-based novelty detection methods by a significant margin.
DanZero+: Dominating the GuanDan Game through Reinforcement Learning

The utilization of artificial intelligence (AI) in card games has been a well-explored subject within AI research for an extensive period. Recent advancements have propelled AI programs to showcase expertise in intricate card games such as Mahjong, DouDizhu, and Texas Hold'em. In this work, we aim to develop an AI program for an exceptionally complex and popular card game called GuanDan. This game involves four players engaging in both competitive and cooperative play throughout a long process to upgrade their level, posing great challenges for AI due to its expansive state and action space, long episode length, and complex rules. Employing reinforcement learning techniques, specifically Deep Monte Carlo (DMC), and a distributed training framework, we first put forward an AI program named DanZero for this game. Evaluation against baseline AI programs based on heuristic rules highlights the outstanding performance of our bot. Besides, in order to further enhance the AI's capabilities, we apply policy-based reinforcement learning algorithm to GuanDan. To address the challenges arising from the huge action space, which will significantly impact the performance of policy-based algorithms, we adopt the pre-trained model to facilitate the training process and the achieved AI program manages to achieve a superior performance.
On Wintgen ideal submanifolds satisfying some pseudo-symmetry type curvature conditions

Let M be a Wintgen ideal submanifold of dimension n in a real space form R^{n+m}(k) of dimension (n+m) and of constant curvature k, n>3, m = 1 or m>1. Let g, R, Ricc, g /\ Ricc and C be the metric tensor, the Riemann-Christoffel curvature tensor, the Ricci tensor, the Kulkarni-Nomizu product of g and Ricc, and the Weyl conformal curvature tensor of M, respectively. In this paper we study Wintgen ideal submanifolds M in real space forms R^{n+m}(k), n>3, m = 1 or m>3, satisfying the following pseudo-symmetry type curvature conditions: (i) R.C and Q(g,R) (resp., Q(g,C), Q(g,g/\Ricc), Q(Ricc,R) or Q(Ricc,g/\Ricc)) are linearly dependent; (ii) C.R and Q(g,R) (resp., Q(g,C), Q(g,g/\Ricc), Q(Ricc,R) or Q(Ricc,g/\Ricc)) are linearly dependent; (iii) R.C - C.R and Q(g,R) (resp., Q(g,C), Q(g,g/\Ricc), Q(Ricc,R) or Q(Ricc,g/\Ricc)) are linearly dependent.
How Good Is Open Bicycle Infrastructure Data? A Countrywide Case Study of Denmark

Cycling is a key ingredient for a sustainability shift of Denmark's transportation system. To increase cycling rates, a better nationwide network of bicycle infrastructure is required. Planning such a network requires high-quality infrastructure data, however, the quality of bicycle infrastructure data is severely understudied. Here, we compare Denmark's two largest open data sets on dedicated bicycle infrastructure, OpenStreetMap (OSM) and GeoDanmark, in a countrywide data quality assessment, asking whether data is good enough for network-based analysis of cycling conditions. We find that neither of the data sets is of sufficient quality, and that data set conflation is necessary to obtain a complete dataset. Our analysis of the spatial variation of data quality suggests that rural areas are more likely to suffer from problems with data completeness. We demonstrate that the prevalent method of using infrastructure density as a proxy for data completeness is not suitable for bicycle infrastructure data, and that matching of corresponding features thus is necessary to assess data completeness. Based on our data quality assessment we recommend strategic mapping efforts towards data completeness, consistent standards to support comparability between different data sources, and increased focus on data topology to ensure high-quality bicycle network data.
GeNIe: Generative Hard Negative Images Through Diffusion

Data augmentation is crucial in training deep models, preventing them from overfitting to limited data. Recent advances in generative AI, e.g., diffusion models, have enabled more sophisticated augmentation techniques that produce data resembling natural images. We introduce GeNIe a novel augmentation method which leverages a latent diffusion model conditioned on a text prompt to merge contrasting data points (an image from the source category and a text prompt from the target category) to generate challenging samples. To achieve this, inspired by recent diffusion based image editing techniques, we limit the number of diffusion iterations to ensure the generated image retains low-level and background features from the source image while representing the target category, resulting in a hard negative sample for the source category. We further enhance the proposed approach by finding the appropriate noise level adaptively for each image (coined as GeNIe-Ada) leading to further performance improvement. Our extensive experiments, in both few-shot and long-tail distribution settings, demonstrate the effectiveness of our novel augmentation method and its superior performance over the prior art. Our code is available here: https://github.com/UCDvision/GeNIe
Slice3D: Multi-Slice, Occlusion-Revealing, Single View 3D Reconstruction

We introduce multi-slice reasoning, a new notion for single-view 3D reconstruction which challenges the current and prevailing belief that multi-view synthesis is the most natural conduit between single-view and 3D. Our key observation is that object slicing is more advantageous than altering views to reveal occluded structures. Specifically, slicing is more occlusion-revealing since it can peel through any occluders without obstruction. In the limit, i.e., with infinitely many slices, it is guaranteed to unveil all hidden object parts. We realize our idea by developing Slice3D, a novel method for single-view 3D reconstruction which first predicts multi-slice images from a single RGB image and then integrates the slices into a 3D model using a coordinate-based transformer network for signed distance prediction. The slice images can be regressed or generated, both through a U-Net based network. For the former, we inject a learnable slice indicator code to designate each decoded image into a spatial slice location, while the slice generator is a denoising diffusion model operating on the entirety of slice images stacked on the input channels. We conduct extensive evaluation against state-of-the-art alternatives to demonstrate superiority of our method, especially in recovering complex and severely occluded shape structures, amid ambiguities. All Slice3D results were produced by networks trained on a single Nvidia A40 GPU, with an inference time less than 20 seconds.
STEREOFOG - Computational DeFogging via Image-to-Image Translation on a real-world Dataset

Image-to-Image translation (I2I) is a subtype of Machine Learning (ML) that has tremendous potential in applications where two domains of images and the need for translation between the two exist, such as the removal of fog. For example, this could be useful for autonomous vehicles, which currently struggle with adverse weather conditions like fog. However, datasets for I2I tasks are not abundant and typically hard to acquire. Here, we introduce STEREOFOG, a dataset comprised of $10,067$ paired fogged and clear images, captured using a custom-built device, with the purpose of exploring I2I's potential in this domain. It is the only real-world dataset of this kind to the best of our knowledge. Furthermore, we apply and optimize the pix2pix I2I ML framework to this dataset. With the final model achieving an average Complex Wavelet-Structural Similarity (CW-SSIM) score of $0.76$, we prove the technique's suitability for the problem.
Almost-Sure Safety Guarantees of Stochastic Zero-Control Barrier Functions Do Not Hold

The 2021 paper"Control barrier functions for stochastic systems"provides theorems that give almost sure safety guarantees given stochastic zero control barrier function (ZCBF). Unfortunately, both the theorem and its proof is invalid. In this letter, we illustrate on a toy example that the almost sure safety guarantees for stochastic ZCBF do not hold and explain why the proof is flawed. Although stochastic reciprocal barrier functions (RCBF) also uses the same proof technique, we provide a different proof technique that verifies that stochastic RCBFs are indeed safe with probability one. Using the RCBF, we derive a modified ZCBF condition that guarantees safety with probability one. Finally, we provide some discussion on the role of unbounded controls in the almost-sure safety guarantees of RCBFs, and show that the rate of divergence of the ratio of the drift and diffusion is the key for whether a system has almost sure safety guarantees.
Rethinking Adversarial Training with Neural Tangent Kernel

Adversarial training (AT) is an important and attractive topic in deep learning security, exhibiting mysteries and odd properties. Recent studies of neural network training dynamics based on Neural Tangent Kernel (NTK) make it possible to reacquaint AT and deeply analyze its properties. In this paper, we perform an in-depth investigation of AT process and properties with NTK, such as NTK evolution. We uncover three new findings that are missed in previous works. First, we disclose the impact of data normalization on AT and the importance of unbiased estimators in batch normalization layers. Second, we experimentally explore the kernel dynamics and propose more time-saving AT methods. Third, we study the spectrum feature inside the kernel to address the catastrophic overfitting problem. To the best of our knowledge, it is the first work leveraging the observations of kernel dynamics to improve existing AT methods.
LivePhoto: Real Image Animation with Text-guided Motion Control

Despite the recent progress in text-to-video generation, existing studies usually overlook the issue that only spatial contents but not temporal motions in synthesized videos are under the control of text. Towards such a challenge, this work presents a practical system, named LivePhoto, which allows users to animate an image of their interest with text descriptions. We first establish a strong baseline that helps a well-learned text-to-image generator (i.e., Stable Diffusion) take an image as a further input. We then equip the improved generator with a motion module for temporal modeling and propose a carefully designed training pipeline to better link texts and motions. In particular, considering the facts that (1) text can only describe motions roughly (e.g., regardless of the moving speed) and (2) text may include both content and motion descriptions, we introduce a motion intensity estimation module as well as a text re-weighting module to reduce the ambiguity of text-to-motion mapping. Empirical evidence suggests that our approach is capable of well decoding motion-related textual instructions into videos, such as actions, camera movements, or even conjuring new contents from thin air (e.g., pouring water into an empty glass). Interestingly, thanks to the proposed intensity learning mechanism, our system offers users an additional control signal (i.e., the motion intensity) besides text for video customization.
Describing Differences in Image Sets with Natural Language

How do two sets of images differ? Discerning set-level differences is crucial for understanding model behaviors and analyzing datasets, yet manually sifting through thousands of images is impractical. To aid in this discovery process, we explore the task of automatically describing the differences between two $\textbf{sets}$ of images, which we term Set Difference Captioning. This task takes in image sets $D_A$ and $D_B$, and outputs a description that is more often true on $D_A$ than $D_B$. We outline a two-stage approach that first proposes candidate difference descriptions from image sets and then re-ranks the candidates by checking how well they can differentiate the two sets. We introduce VisDiff, which first captions the images and prompts a language model to propose candidate descriptions, then re-ranks these descriptions using CLIP. To evaluate VisDiff, we collect VisDiffBench, a dataset with 187 paired image sets with ground truth difference descriptions. We apply VisDiff to various domains, such as comparing datasets (e.g., ImageNet vs. ImageNetV2), comparing classification models (e.g., zero-shot CLIP vs. supervised ResNet), summarizing model failure modes (supervised ResNet), characterizing differences between generative models (e.g., StableDiffusionV1 and V2), and discovering what makes images memorable. Using VisDiff, we are able to find interesting and previously unknown differences in datasets and models, demonstrating its utility in revealing nuanced insights.
Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network

Model Parsing defines the research task of predicting hyperparameters of the generative model (GM), given a generated image as input. Since a diverse set of hyperparameters is jointly employed by the generative model, and dependencies often exist among them, it is crucial to learn these hyperparameter dependencies for the improved model parsing performance. To explore such important dependencies, we propose a novel model parsing method called Learnable Graph Pooling Network (LGPN). Specifically, we transform model parsing into a graph node classification task, using graph nodes and edges to represent hyperparameters and their dependencies, respectively. Furthermore, LGPN incorporates a learnable pooling-unpooling mechanism tailored to model parsing, which adaptively learns hyperparameter dependencies of GMs used to generate the input image. We also extend our proposed method to CNN-generated image detection and coordinate attacks detection. Empirically, we achieve state-of-the-art results in model parsing and its extended applications, showing the effectiveness of our method. Our source code are available.