Miracle-dz/UniFAR-FTUs huggingface dataset:
BrainZ-BP: A Noninvasive Cuff-Less Blood Pressure Estimation Approach Leveraging Brain Bio-Impedance and Electrocardiogram

Accurate and continuous blood pressure (BP) monitoring is essential to the early prevention of cardiovascular diseases (CVDs). Noninvasive and cuff-less BP estimation algorithm has gained much attention in recent years. Previous studies have demonstrated that brain bio-impedance (BIOZ) is a promising technique for noninvasive intracranial pressure (ICP) monitoring. Clinically, treatment for patients with traumatic brain injuries (TBI) requires monitoring the ICP and BP of patients simultaneously. Estimating BP by brain BIOZ directly can reduce the number of sensors attached to the patients, thus improving their comfort. To address the issues, in this study, we explore the feasibility of leveraging brain BIOZ for BP estimation and propose a novel cuff-less BP estimation approach called BrainZ-BP. Two electrodes are placed on the forehead and occipital bone of the head in the anterior–posterior direction for brain BIOZ measurement. Various features, including pulse transit time (PTT) and morphological features of brain BIOZ, are extracted and fed into four regression models for BP estimation. Results show that the mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient of random forest (RF) regression model are 2.17 mmHg, 3.91 mmHg, and 0.90 for systolic pressure estimation and are 1.71 mmHg, 3.02 mmHg, and 0.89 for diastolic pressure estimation. The presented BrainZ-BP can be applied in the brain BIOZ-based ICP monitoring scenario to monitor BP simultaneously.
Pair-wise Layer Attention with Spatial Masking for Video Prediction

Video prediction yields future frames by employing the historical frames and has exhibited its great potential in many applications, e.g., meteorological prediction, and autonomous driving. Previous works often decode the ultimate high-level semantic features to future frames without texture details, which deteriorates the prediction quality. Motivated by this, we develop a Pair-wise Layer Attention (PLA) module to enhance the layer-wise semantic dependency of the feature maps derived from the U-shape structure in Translator, by coupling low-level visual cues and high-level features. Hence, the texture details of predicted frames are enriched. Moreover, most existing methods capture the spatiotemporal dynamics by Translator, but fail to sufficiently utilize the spatial features of Encoder. This inspires us to design a Spatial Masking (SM) module to mask partial encoding features during pretraining, which adds the visibility of remaining feature pixels by Decoder. To this end, we present a Pair-wise Layer Attention with Spatial Masking (PLA-SM) framework for video prediction to capture the spatiotemporal dynamics, which reflect the motion trend. Extensive experiments and rigorous ablation studies on five benchmarks demonstrate the advantages of the proposed approach. The code is available at GitHub.
Almost complex structure on finite points from bidirected graphs

We show that there is an almost complex structure on a differential calculus on finite points coming from a bidirected finite graph without multiple edges or loops. We concentrate on a polygon as a concrete case. In particular, a `holomorphic structure on the exterior bundle' built from the polygon is studied. Also a positive Hochschild 2-cocycle on the vertex set of the polygon, albeit a trivial one, is shown to arise naturally from the almost complex structure.
Finding the Needle in a Haystack: Detecting Bug Occurrences in Gameplay Videos

The presence of bugs in video games can bring significant consequences for developers. To avoid these consequences, developers can leverage gameplay videos to identify and fix these bugs. Video hosting websites such as YouTube provide access to millions of game videos, including videos that depict bug occurrences, but the large amount of content can make finding bug instances challenging. We present an automated approach that uses machine learning to predict whether a segment of a gameplay video contains the depiction of a bug. We analyzed 4,412 segments of 198 gameplay videos to predict whether a segment contains an instance of a bug. Additionally, we investigated how our approach performs when applied across different specific genres of video games and on videos from the same game. We also analyzed the videos in the dataset to investigate what characteristics of the visual features might explain the classifier's prediction. Finally, we conducted a user study to examine the benefits of our automated approach against a manual analysis. Our findings indicate that our approach is effective at detecting segments of a video that contain bugs, achieving a high F1 score of 0.88, outperforming the current state-of-the-art technique for bug classification of gameplay video segments.
Classical Stability Margins by PID Control

Proportional-Integral-Derivative (PID) control has been the workhorse of control technology for about a century. Yet to this day, designing and tuning PID controllers relies mostly on either tabulated rules (Ziegler-Nichols) or on classical graphical techniques (Bode). Our goal in this paper is to take a fresh look on PID control in the context of optimizing stability margins for low-order (first- and second-order) linear time-invariant systems. Specifically, we seek to derive explicit expressions for gain and phase margins that are achievable using PID control, and thereby gain insights on the role of unstable poles and nonminimum-phase zeros in attaining robust stability. In particular, stability margins attained by PID control for minimum-phase systems match those obtained by more general control, while for nonminimum-phase systems, PID control achieves margins that are no worse than those of general control modulo a predetermined factor. Furthermore, integral action does not contribute to robust stabilization beyond what can be achieved by PD control alone.
WIP: A Web‐based program for indexing planar features in quartz grains and its usage

Planar deformation features (PDFs) in quartz are the most important diagnostic features that allow the unambiguous identification of impact structures on Earth. In order to confirm that these features (that are characterized by planar character and form along specific crystallographic planes) are indeed PDFs, they need to be properly investigated and indexed. Following universal‐stage measurements, the process of indexing is usually performed manually, using a Wulff stereonet and following a strict procedure, which is time consuming and error prone. In this article, we present WIP, a new Web‐based program for indexing planar deformation features in quartz. The correctness of our program is shown by its application to measurements that had previously been indexed manually. The observed minor differences, especially in the absolute frequency percentage of PDFs, are negligible and not significant enough to influence the estimation of shock pressure that could be calculated from the indexed results. Usability of this program is shown using the spatial relationships between a statistically significant number of 278 quartz grains with 409 sets of PDFs analyzed within the area (~35 mm2) of a single thin section of a meta‐greywacke from the Bosumtwi impact structure. Our program is not only more accurate and faster than the manual (graphical) method but also removes the human error from the plotting process and allows control of several parameters, such as the value of estimated measurement error used in the indexing calculation or method of aggregated error handling. The program also provides information about the angles between the planes of the measured PDF sets present in a grain, which allows determination of the angles between (for example) indexed { 101¯3 } and { 101¯1 } sets.
CastDet: Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning

Object detection in aerial images is a pivotal task for various earth observation applications, whereas current algorithms learn to detect only a pre-defined set of object categories demanding sufficient bounding-box annotated training samples and fail to detect novel object categories. In this paper, we consider open-vocabulary object detection (OVD) in aerial images that enables the characterization of new objects beyond training categories on the earth surface without annotating training images for these new categories. The performance of OVD depends on the quality of class-agnostic region proposals and pseudo-labels that can generalize well to novel object categories. To simultaneously generate high-quality proposals and pseudo-labels, we propose CastDet , a C LIP-a ctivated s tudent-t eacher open-vocabulary object Det ection framework. Our end-to-end framework within the student-teacher mechanism employs the CLIP model as an extra omni-scient teacher of rich knowledge into the student-teacher self-learning process. By doing so, our approach boosts novel object proposals and classification. Furthermore, we design a dynamic label queue technique to maintain high-quality pseudo labels during batch training and mitigate label imbalance. We conduct extensive experiments on multiple existing aerial object detection datasets, which are set up for the OVD task. Experimental results demonstrate our CastDet achieving superior open-vocabulary detection performance, e.g., reaching 40.0 HM (Harmonic Mean), which outperforms previous methods Detic/ViLD by 26.9/21.1 on the VisDroneZSD dataset.
Challenges in data-based geospatial modeling for environmental research and practice

With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research. Accurate geospatial predictions are vital for domain research based on ecosystem monitoring and quality assessment and for policy-making and action planning, considering effective management of natural resources. The accuracy and computation speed of ML has generally proved efficient. However, many questions have yet to be addressed to obtain precise and reproducible results suitable for further use in both research and practice. A better understanding of the ML concepts applicable to geospatial problems enhances the development of data science tools providing transparent information crucial for making decisions on global challenges such as biosphere degradation and climate change. This survey reviews common nuances in geospatial modelling, such as imbalanced data, spatial autocorrelation, prediction errors, model generalisation, domain specificity, and uncertainty estimation. We provide an overview of techniques and popular programming tools to overcome or account for the challenges. We also discuss prospects for geospatial Artificial Intelligence in environmental applications.
A Fast Radio Burst in a Compact Galaxy Group at z ∼ 1

FRB 20220610A is a high-redshift fast radio burst (FRB) that has not been observed to repeat. Here, we present rest-frame UV and optical Hubble Space Telescope observations of the field of FRB 20220610A. The imaging reveals seven extended sources, one of which we identify as the most likely host galaxy with a spectroscopic redshift of z = 1.017. We spectroscopically confirm three additional sources to be at the same redshift and identify the system as a compact galaxy group with possible signs of interaction among group members. We determine the host of FRB 20220610A to be a star-forming galaxy with a stellar mass of ≈109.7 M ⊙, mass-weighted age of ≈2.6 Gyr, and star formation rate (integrated over the last 100 Myr) of ≈1.7 M ⊙ yr−1. These host properties are commensurate with the star-forming field galaxy population at z ∼ 1 and trace their properties analogously to the population of low-z FRB hosts. Based on estimates of the total stellar mass of the galaxy group, we calculate a fiducial contribution to the observed dispersion measure from the intragroup medium of ≈90–182 pc cm−3 (rest frame). This leaves a significant excess of 515 − 272 + 122 pc cm−3 (in the observer frame); further observation will be required to determine the origin of this excess. Given the low occurrence rates of galaxies in compact groups, the discovery of an FRB in one demonstrates a rare, novel environment in which FRBs can occur. As such groups may represent ongoing or future mergers that can trigger star formation, this supports a young stellar progenitor relative to star formation.
Images Connect Us Together: Navigating a COVID-19 Local Outbreak in China Through Social Media Images

Social media images, curated or casual, have become a crucial component of communicating situational information and emotions during health crises. Despite its prevalence and significance in informational dissemination and emotional connection, there lacks a comprehensive understanding of visual crisis communication in the aftermath of a pandemic which is characterized by uncertain local situations and emotional fatigue. To fill this gap, this work collected 345,423 crisis-related posts and 65,376 original images during the Xi'an COVID-19 local outbreak in China, and adopted a mixed-methods approach to understanding themes, goals, and strategies of crisis imagery. Image clustering captured the diversity of visual themes during the outbreak, such as text images embedding authoritative guidelines and ``visual diaries'' recording and sharing the quarantine life. Through text classification of the post that visuals were situated in, we found that different visual themes highly correlated with the informational and emotional goals of the post text, such as adopting text images to convey the latest policies and sharing food images to express anxiety. We further unpacked nuanced strategies of crisis image use through inductive coding, such as signifying authority and triggering empathy. We discuss the opportunities and challenges of crisis imagery and provide design implications to facilitate effective visual crisis communication.
AutoStory: Generating Diverse Storytelling Images with Minimal Human Effort

Story visualization aims to generate a series of images that match the story described in texts, and it requires the generated images to satisfy high quality, alignment with the text description, and consistency in character identities. Given the complexity of story visualization, existing methods drastically simplify the problem by considering only a few specific characters and scenarios, or requiring the users to provide per-image control conditions such as sketches. However, these simplifications render these methods incompetent for real applications. To this end, we propose an automated story visualization system that can effectively generate diverse, high-quality, and consistent sets of story images, with minimal human interactions. Specifically, we utilize the comprehension and planning capabilities of large language models for layout planning, and then leverage large-scale text-to-image models to generate sophisticated story images based on the layout. We empirically find that sparse control conditions, such as bounding boxes, are suitable for layout planning, while dense control conditions, e.g., sketches and keypoints, are suitable for generating high-quality image content. To obtain the best of both worlds, we devise a dense condition generation module to transform simple bounding box layouts into sketch or keypoint control conditions for final image generation, which not only improves the image quality but also allows easy and intuitive user interactions. In addition, we propose a simple yet effective method to generate multi-view consistent character images, eliminating the reliance on human labor to collect or draw character images.
DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model

This paper endeavors to advance the precision of snapshot compressive imaging (SCI) reconstruction for multispectral image (MSI). To achieve this, we integrate the advantageous attributes of established SCI techniques and an image generative model, propose a novel structured zero-shot diffusion model, dubbed DiffSCI. DiffSCI leverages the structural insights from the deep prior and optimization-based methodologies, complemented by the generative capabilities offered by the contemporary denoising diffusion model. Specifically, firstly, we employ a pre-trained diffusion model, which has been trained on a substantial corpus of RGB images, as the generative denoiser within the Plug-and-Play framework for the first time. This integration allows for the successful completion of SCI reconstruction, especially in the case that current methods struggle to address effectively. Secondly, we systematically account for spectral band correlations and introduce a robust methodology to mitigate wavelength mismatch, thus enabling seamless adaptation of the RGB diffusion model to MSIs. Thirdly, an accelerated algorithm is implemented to expedite the resolution of the data subproblem. This augmentation not only accelerates the convergence rate but also elevates the quality of the reconstruction process. We present extensive testing to show that DiffSCI exhibits discernible performance enhancements over prevailing self-supervised and zero-shot approaches, surpassing even supervised transformer counterparts across both simulated and real datasets. Our code will be available.
Catchment‐Scale Permafrost Mapping using Spring Water Characteristics

This study presents a method for mapping the spatial distribution of mountain permafrost based on the chemical‐physical characterisation of spring water in a 36 km2 high‐elevation catchment in the Eastern Italian Alps. Water temperature, electrical conductivity and isotopic composition (δ2H and δ18O) were measured in 45 springs during summer 2007, 2010 and 2012. Existing evidence of permafrost enabled the areas upslope of springs to be classified into two categories of permafrost occurrence (probable permafrost and no permafrost) and used to determine the most suitable tracer for permafrost mapping. Springs from probable permafrost areas have a specific water temperature signature. Spring water temperature was therefore used as a response variable in multiple linear regression, and mean elevation and mean clear sky radiation of spring upslope areas were used as predictors. The multiple regression models were statistically significant and used to map the potential spatial distribution of spring water temperature, which was reclassified into three permafrost categories (probable, possible and improbable). Cross‐validation and independent validation by ground surface temperature data provided evidence that the spring water temperature can be used alone for easy and low‐cost assessment of the catchment‐scale permafrost distribution in similar alpine catchments. Copyright © 2015 John Wiley & Sons, Ltd.
ItsSQL: Intelligent Tutoring System for SQL

SQL is a central component of any database course. Despite the small number of SQL commands, students struggle to practice the concepts. To overcome this challenge, we developed an intelligent tutoring system (ITS) to guide the learning process with a small effort by the lecturer. Other systems often give only basic feedback (correct or incorrect) or require hundreds of instance specific rules defined by a lecturer. In contrast, our system can provide individual feedback based on a semi-automatically/intelligent growing pool of reference solutions, i.e., sensible approaches. Moreover, we introduced the concept of good and bad reference solutions. The system was developed and evaluated in three steps based on Design Science research guidelines. The results of the study demonstrate that providing multiple reference solutions are useful with the support of harmonization to provide individual and real-time feedback and thus improve the learning process for students.
Sensor Networking in Aquatic Environments - Experiences and New Challenges

In this paper we present the design and implemen tation of a small-scale marine sensor network. The network monitors the temperature in the Baltic Sea on different heights from the water surface down to the bottom. Unlike many other wireless sensor networks, this network contains both a wired and a wireless part. One of the major challenges is that the network is hard to access after its deployment and hence both hard- and software must be robust and reliable. We also present the design of an advanced buoy system featuring a diving unit that achieves a better vertical resolution and discuss remaining challenges of sensor networking in aquatic environments.
Religious Competition, Culture and Domestic Violence: Evidence from Colombia

This paper studies how religious competition, as measured by the emergence of religious organizations with innovative worship styles and cultural practices, impacts domestic violence. Using data from Colombia, the study estimates a two-way fixed-effects model and reveals that the establishment of the first non-Catholic church in a predominantly Catholic municipality leads to a significant decrease in reported cases of domestic violence. This effect persists in the long run, indicating that religious competition introduces values and practices that discourage domestic violence, such as household stability and reduced male dominance. Additionally, the effect is more pronounced in municipalities with less clustered social networks, suggesting the diffusion of these values and practices through social connections. This research contributes to the understanding of how culture influences domestic violence, emphasizing the role of religious competition as a catalyst for cultural change.
Overnight atmospheric transport and chemical processing of photochemically aged Houston urban and petrochemical industrial plume

[1] Overnight atmospheric transport and chemical evolution of photochemically aged Houston urban and petrochemical industrial plume were investigated in July 2005. We report here on the 26 July episode in which the aged plume was tagged 1.5 h before sunset with a pair of free-floating controlled meteorological balloons, which guided quasi-Lagrangian aircraft sampling in the plume as it was advected 300 km to the north over 8 h. The aged plume around sunset was well mixed within a 1600 m residual layer, and was characterized by enhanced levels of aerosol, O3, CO, olefins, acetaldehyde, total odd nitrogen compounds (NOy), and relatively small amounts (<1 ppbv) of NOx. The plume experienced appreciable shearing overnight due to the development of a low-altitude nocturnal jet between 300 and 500 m above mean sea level (MSL). However, the plume above 600 m MSL remained largely undiluted even after 8 h of transport due to lack of turbulent mixing above the jet. About 40–60% of the NOx present in the aged plume around sunset was found to be depleted over this 8 h period. A constrained plume modeling analysis of the quasi-Lagrangian aircraft observations suggested that by dawn this NOx was converted to nitric acid, organic nitrates, and peroxy acyl nitrates via reactions of NO3 radicals with enhanced levels of olefins and aldehydes in the plume. Sensitivity of NOx depletion to heterogeneous hydrolysis of N2O5 on aerosols was examined. These results have significant implications for the impacts of urban and industrial pollution on far downwind regions.