Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtual Commons - Bridgewater State University Virtual Commons - Bridgewater State University

This paper shares an exploratory and inquiry-based graphic design project and the resultant pedagogic approach that offers arts and design educators ideas on teaching to instigate positive social change. The author summarizes a year-long fellowship project where he worked as a change agent in service to a partnership of six non-profit, after-school arts programs in Providence, Rhode Island who are organized as the Providence Youth Arts Collaborative (PYAC). The intention of this project was two-fold. The primary effort was to investigate ways graphic design can be strategically used to seed interest and empower youth to make positive choices with how they spend their time after school, such as enrolling in a free PYAC member Arts program. The second aspect was to use design to strengthen the visual identity of PYAC in order to unify their voices and improve their offerings. The design process and the visual outputs are presented as a model for teaching at high school and college levels that cultivate awareness of the social power of visual communication and a civically engaged process. Systems thinking and theoretical models of social change are discussed in support of the project intentions.
Comparison of modularity-based approaches for nodes clustering in binary hypergraphs

We conducted a comparative analysis of the performance of modularity-based methods for clustering nodes in binary hypergraphs. Statistical analysis and node clustering in hypergraphs constitute an emerging topic suffering from a lack of standardization. In contrast to the case of graphs, the concept of nodes’ community in hypergraphs is not unique and encompasses various distinct situations. To address this, we begin by presenting, within a unified framework, the various hypergraph modularity criteria proposed in the literature, emphasizing their differences and respective focuses. Subsequently, we provide an overview of the state-of-the-art codes available to maximize hypergraph modularities for detecting node communities in binary hypergraphs. Through exploration of various simulation settings with controlled ground truth clustering, we offer a comparison of these methods using different quality measures, including true clustering recovery, running time, (local) maximization of the objective, and the number of clusters detected. Our contribution marks the first attempt to clarify the advantages and drawbacks of these newly available methods. This effort lays the foundation for a better understanding of the primary objectives of modularity-based node clustering methods for binary hyper-graphs.
A New Non-linearity Correction Method for Spectrum from GIIRS onboard Fengyun-4 Satellites and its Preliminary Assessments

Non-linearity (NL) correction is a critical procedure to guarantee the calibration accuracy of a spaceborne sensor 10 to approach a good level (i.e. better than 0.5K). Unfortunately, such a NL correction is still unemployed in spectrum calibration of Geostationary Interferometric InfraRed Sounder (GIIRS) onboard Fengyun-4A (FY-4A) satellite. Different from the classical NL correction method where the NL coefficient is estimated from out-band spectral artifacts in an empirical low-frequency region originally with prelaunch results and updated under in-orbit condition, a new NL correction method for a spaceborne Fourier transform spectrometer (including GIIRS) is proposed. Particularly, the NL parameter μ 15 independent of different working conditions (namely the thermal fields from environmental components) can be achieved from laboratory results before launch and directly utilized for in-orbit calibration. Moreover, to overcome the inaccurate linear coefficient from two-point calibration influencing the NL correction, an iteration algorithm is established to make both the linear and the NL coefficients to be converged to their stable values with the relative errors less than 0.5% and 1% respectively, which is universally suitable for NL correction of both infrared and microwave sensors. By using the onboard 20 internal blackbody (BB) which is identical with the in-orbit calibration, the final calibration accuracy for both all the detectors and all the channels with the proposed NL correction method is validated to be around 0.2-0.3K at an ordinary reference temperature
Sclerosis Lesions on 3T MRI Identification of Chronic Active Multiple

BACKGROUND AND PURPOSE: MR imaging–pathologic studies have reported that paramagnetic rims on 7T susceptibility-based MR imaging identify, in vivo, the subset of MS lesions with compartmentalized inﬂammation at the lesion edge and associated remyelination failure. Here, we assessed the reliability of detecting these rims on high-resolution 3T phase images. MATERIALS AND METHODS: High-resolution T2* and phase MR imaging was collected in 20 patients with MS at 3T (3D segmented EPI, 0.65 mm 3 ) and 7T (2D gradient-echo, 0.2 (cid:2) 0.2 (cid:2) 1 mm) MR imaging. In each case, 5 discrete chronic (nonenhancing) MS lesions were selected on T2 FLAIR images for rim evaluation. Five raters experienced in MS imaging contributed to the rim assessment, of whom 3 worked independently on 3T data, and 2, on 7T data. Consensus agreement was reached for both 3T and 7T rim evaluations. Discrepancies between 3T and 7T were discussed, and consensus was reached. RESULTS: Phase rims were seen in 34 lesions at 7T and in 36 lesions at 3T by consensus. Inter-and intrarater reliability were “substantial/ good”bothat3Tand7Tanalysis(Cohen (cid:2) , (cid:3) 0.71). Basedonconsensusagreement,thereliabilityofrimvisualizationat3Tversus7Twas0.78 ( (cid:2) ) with a pair-wise agreement of 90%. More lesions were judged to be false-positive or false-negative at 3T than at 7T. CONCLUSIONS: Nearlyall7Tparamagneticrimscanalsobeseenat3T.Imagingat3Topensthepossibilityofimplementingparamagnetic rims as an outcome measure in multicenter, MR imaging–based clinical trials aimed at treating perilesional persistent inﬂammation and its potential effects on remyelination
Elusive Images: Beyond Coarse Analysis for Fine-Grained Recognition

While the community has seen many advances in recent years to address the challenging problem of Fine-grained Visual Categorization (FGVC), progress seems to be slowing—new state-of-the-art methods often distinguish themselves by improving top-1 accuracy by mere tenths of a percent. However, across all of the now-standard FGVC datasets, there remain sizeable portions of the test data that none of the current state-of-the-art (SOTA) models can successfully predict. This paper provides a framework for identifying and studying the errors that current methods make across diverse ﬁ ne-grained datasets. Three models of dif ﬁ culty— Prediction Overlap , Prediction Rank and Pair-wise Class Confusion —are employed to highlight the most challenging sets of images and classes. Extensive experiments apply a range of standard and SOTA methods, evaluating them on multiple FGVC domains and datasets. In-sights acquired from coupling these dif ﬁ culty paradigms with the careful analysis of experimental results suggest crucial areas for future FGVC research, focusing critically on the set of elusive images that none of the current models can correctly classify. Code is available at catalys1.github. io/elusive-images-fgvc .
A Retrospective Review of 30-Day Hospital Readmission Risk A Retrospective Review of 30-Day Hospital Readmission Risk After Open Heart Surgery in Patients With Atrial Fibrillation After Open Heart Surgery in Patients With Atrial Fibrillation

Introduction Readmission rates after open heart surgery (OHS) remain an important clinical issue. The causes are varied, with identifying risk factors potentially providing valuable information to reduce healthcare costs and the rate of post-operative complications. This study aimed to characterize the reasons for 30-day hospital readmission rates of patients after open heart surgery.
In-Between Visuals and Visible: The Impacts of Text-to-Image Generative AI Tools on Digital Image-making Practices in the Global South

This paper joins the growing body of HCI work on critical AI studies and focuses on the impact of Generative Artificial Intelligence (GAI) tools in Bangladesh. While the West has started to examine the limitations and risks associated with these tools, their impacts on the Global South have remained understudied. Based on our interviews, focus group discussions (FGD), and social media-based qualitative study, this paper reports how popular text-to-image GAI tools (ex., DALL-E, Midjourney, Stable Diffusion, Firefly) are affecting various image-related local creative fields. We report how these tools limit the creative explorations of marginal artists, struggle to understand linguistic nuances, fail to generate local forms of art and architecture, and misrepresent the diversity among citizens in the image production process. Drawing from a rich body of work on critical image theory, postcolonial computing, and design politics, we explain how our findings are pertinent to HCI’s broader interest in social justice, decolonization, and global development.
QuadStream: A Quad-Based Scene Streaming Architecture for Novel Viewpoint Reconstruction

wedecomposethesceneseenfrompositionsina view cell into a series of quad proxies , or view-aligned quads from multiple views. By operating on a raster-ized G-Buffer, our approach is independent of the representation used for the scene itself; the resulting QuadStream is an approximate geometric representation of the scene that can be reconstructed by a thin client to render both the current view and nearby adjacent views. Our technical contributions
UvA-DARE (Digital Academic Repository) A Performance-Adaptive and Time-Monitored Autonomous Ticket Booking Service in Cloud

—With the increasing demand for autonoumous ticket booking services for all kinds of events, user numbers and service performance requirements increase dramatically. Platforms such as BASH, Ticketmaster, Eventbrite, and SeatGeek, often outsource such services to third parties, but the operation cost is often high. Therefore the performance for time-critical requirements is without monitoring. Furthermore, the cloud platform is becoming a popular solution for event opganizing platforms due to its mighty computation power. In order to address these issues, this study presents a cloud-based, performance-adaptive, and time-monitored autonomous ticket booking system, utilizing a paradigm for booking as a service that can be readily included into cloud platforms. Together with the implementation itself, we also provide a variety of flexible service settings that may serve a specified number of concurrent users while consuming the least amount of resources. We carry out thorough implementations to verify our methodology.
Surface splatting

Modern laser range and optical scanners need rendering techniques that can handle millions of points with high resolution textures. This paper describes a point rendering and texture filtering technique called surface splatting which directly renders opaque and transparent surfaces from point clouds without connectivity. It is based on a novel screen space formulation of the Elliptical Weighted Average (EWA) filter. Our rigorous mathematical analysis extends the texture resampling framework of Heckbert to irregularly spaced point samples. To render the points, we develop a surface splat primitive that implements the screen space EWA filter. Moreover, we show how to optimally sample image and procedural textures to irregular point data during pre-processing. We also compare the optimal algorithm with a more efficient view-independent EWA pre-filter. Surface splatting makes the benefits of EWA texture filtering available to point-based rendering. It provides high quality anisotropic texture filtering, hidden surface removal, edge anti-aliasing, and order-independent transparency.
Semantic Image Abstraction using Panoptic Segmentation for Robotic Painting

We propose a comprehensive pipeline for generating adaptable image abstractions from input pictures, tailored explicitly for robotic painting tasks. Our pipeline addresses several key objectives, including the ability to paint from background to foreground, maintain fine details, capture structured regions accurately, and highlight important objects. To achieve this, we employ a panoptic segmentation network to predict the semantic class membership for each pixel in the image. This step provides us with a detailed understanding of the object categories present in the scene. Building upon the semantic segmentation results, we combine them with a color-based image over-segmentation technique. This process partitions the image into monochromatic regions, each corresponding to a specific semantic object. Next, we construct a hierarchical tree based on the segmentation results, which allows us to merge adjacent regions based on their color difference and semantic class. We take care to ensure that shapes belonging to different semantic objects are not merged together. We iteratively perform adjacency merging until no further combinations are possible, resulting in a refined hierarchical shape tree. To obtain the desired image abstraction, we filter the hierarchical shape tree by examining factors such as color differences, relative sizes, and the layering within the hierarchy of each region in relation to their parent regions. By employing this approach, we can preserve fine details, apply local filtering operations, and effectively combine regions with structured shapes. This results in image abstractions well-suited for robotic painting applications and artistic renderings.
a Novel Nordic Hamstring Exercise Performance Test Device: A Reliability and Intervention Study

: There is evidence that a knee ﬂexor exercise, the Nordic hamstring exercise (NHE), prevents hamstring injuries. The purpose of this study was therefore to develop, and to determine the reliability of, a novel NHE test device and, further, to determine the effectiveness of a 10-week low volume NHE program on NHE performance. Twenty female football (soccer) players, aged 16–30 years, participated in this study. From a kneeling position on the device, with the ankles secured under a heavy lifting sling, participants leaned forward in a controlled manner as far as possible (eccentric phase) and then returned to the starting position (concentric phase). A tape measure documented the forward distance achieved by the participants in cm. Participants completed three separate occasions to evaluate test-retest reliability. Additionally, 14 players performed a low volume (1 set of 5 repetitions) NHE program once weekly for 10 weeks. No signiﬁcant test-retest differences in NHE performance were observed. The intra-class correlation coefﬁcient was 0.95 and the coefﬁcient of variation was 3.54% between tests. Mean improvement in the NHE performance test by the players following training was 22% (8.7 cm), p = 0.005. Our test device reliably measured NHE performance and is easy to perform in any setting. Further, NHE performance was improved by a 10-week low volume NHE program. This suggests that even a small dose (1 set of 5 repetitions once weekly) of the NHE may enhance NHE performance.
Query Planning for Robust and Scalable Hybrid Network Telemetry Systems

Network telemetry systems have become hybrid combinations of state-of-the-art stream processors and modern programmable data-plane devices. However, the existing designs of such systems have not focused on ensuring that these systems are also deployable in practice, i.e., able to scale and deal with the dynamics in real-world traffic and query workloads. Unfortunately, efforts to scale these hybrid systems are hampered by severe constraints on available compute resources in the data plane (e.g., memory, ALUs). Similarly, the limited runtime programmability of existing hardware data-plane targets critically affects efforts to make these systems robust. This paper presents the design and implementation of DynaMap, a new hybrid telemetry system that is both robust and scalable. By planning for telemetry queries dynamically, DynaMap allows the remapping of stateful dataflow operators to data-plane registers at runtime. We model the problem of mapping dataflow operators to data-plane targets formally and develop a new heuristic algorithm for solving this problem. We implement our algorithm in prototype and demonstrate its feasibility with existing hardware targets based on Intel Tofino. Using traffic workloads from different real-world production networks, we show that our prototype of DynaMap improves performance on average by 1-2 orders of magnitude over state-of-the-art hybrid systems that use only static query planning.
Can large-scale tree cover change negate climate change impacts on future water availability?

The availability of fresh water over land may become increasingly scarce under climate change, and natural and human-induced tree cover changes can further enhance or negate the water scarcity. Previous studies showed that global tree cover change can have large impacts on water availability under current climate conditions, but did not touch upon the implications of global tree cover change under climate change. Here, we study the hydrological impacts of large-scale tree cover change (climate-induced changes in combination with large-scale afforestation) in a future climate (SSP3-7.0) following an 5 interdisciplinary approach. By combining data from five CMIP6 climate models with a future potential tree cover dataset, six Budyko models, and the UTrack moisture recycling dataset, we can disentangle the impacts of climate change and future tree cover change on evaporation, precipitation, and runoff. We quantify per grid cell and for five selected river basins (Yukon, Mississippi, Amazon, Danube, and Murray-Darling) if tree cover changes enhance or counteract the climate-driven changes in runoff due to their impact on evapotranspiration and moisture recycling. Globally averaged, the impacts of climate change and 10 large-scale tree cover change on runoff are of similar magnitude with opposite signs. While climate change increases the global runoff, the changing tree cover reverses this effect which overall results in a limited net impact on runoff relative to the present climate and current tree cover. Nevertheless, locally the change in runoff due to tree cover change and climate change can be substantial with increases and decreases of more than 100 mmyr − 1 . We show that for approximately 16 % of the land surface, tree cover change can increase the water availability significantly. However, we also find that, for 14 % of the land surface, both 15 tree cover change and climate change might decrease water availability with more than 5 mmyr − 1 . For each of the selected catchments, the direction and magnitude of the impacts of climate change and tree cover change vary, with dominating climate change impacts in all basins except the Mississippi River basin. Our results show that ecosystem restoration projects targeting an altered tree cover should consider the corresponding hydrological impacts to limit unwanted (non-)local reductions in water availability.
Familial influences on the full range of variability in attention and Familial influences on the full range of variability in attention and activity levels during adolescence: A longitudinal twin study activity levels during adolescence: A longitudinal twin study

To investigate familial influences on the full range of variability in attention and activity across adolescence, we collected maternal ratings of 339 twin pairs at ages 12, 14, and 16, and estimated the transmitted and new familial influences on attention and activity as measured by the Strengths and Weaknesses of Attention-Deficit/Hyperactivity Disorder Symptoms and Normal Behavior Scale. Familial influences were substantial for both traits across adolescence: geneticinfluencesaccountedfor54%–73%(attention)and31%–73%(activity)ofthetotalvariance,andsharedenvironmentalinfluencesaccountedfor0%–22% ofthe attentionvariance and 13%–57% ofthe activity variance. The longitudinalstabilityofindividualdifferences inattentionand activity waslargelyaccounted for by familial influences transmitted from previous ages. Innovations over adolescence were also partially attributable to familial influences. Studying the full range of variability in attention and activity may facilitate our understanding of attention-deficit/hyperactivity disorder’s etiology and intervention.
Human-AI Co-Dancing: Evolving Cultural Heritage through Collaborative Choreography with Generative Virtual Characters

This paper presents a novel approach of "human-AI co-dancing" that combines human dancers with virtual dance partners powered by algorithms derived from traditional choreographic knowledge. Specifically, the principles of Thai classical dance "Mae Bot Yai" were translated into computational procedures capable of generating and transforming movements in real-time. A system was developed to allow dancers to explore these traditional vocabularies and principles generatively by improvising alongside the virtual agent. Multiple interaction modalities were devised, including direct voice con-trol, mediated control by a secondary dancer/choreographer, and even audience participation through real-time voting of parameter changes. Preliminary co-creation rehearsals exhibited intriguing artistic results as hybrid movement aesthetics emerged from the synergy and friction between human and machine. This research
ANNALES DE L’INSTITUT FOURIER

— Let Ω ⊂ C n , n (cid:62) 2, be a domain with smooth connected boundary. If Ω is relatively compact, the Hartogs–Bochner theorem ensures that every CR distribution on ∂ Ω has a holomorphic extension to Ω. For unbounded domains this extension property may fail, for example if Ω contains a complex hypersurface. The main result in this paper tells that the extension property holds if and only if the envelope of holomorphy of C n \ Ω is C n . It seems that it is the ﬁrst result in the literature which gives a geometric characterization of unbounded domains in C n for which the Hartogs phenomenon holds. Comparing this to earlier work by the ﬁrst two authors and Z. Słodkowski, one observes that the extension problem changes in character if one restricts to CR functions of higher regularity. Résumé
Soil cation storage as a key control on the timescales of carbon dioxide removal through enhanced weathering

Signiﬁcant interest and capital are currently being channeled into techniques for durable carbon dioxide removal (CDR) from Earth’s atmosphere. A particular class of these approaches — referred to as enhanced weathering (EW) — seeks to modify the surface alkalinity budget to durably store CO2 as dissolved inorganic carbon species. Here, we use SCEPTER — a reaction-transport code designed to simulate EW in managed lands — to evaluate the throughput and storage timescales of anthropogenic alkalinity in agricultural soils. Through a series of alkalinity ﬂux simulations, we explore the main controls on cation storage and export from surface soils in key U.S. agricultural regions. We ﬁnd that lag times between alkalinity modiﬁcation and climate-relevant CDR can span anywhere from years to many decades, with background soil cation exchange capacity, agronomic target pH, and ﬂuid inﬁltration all impacting the timescales of CDR relative to the timing of alkalinity input. There may be scope for optimization of weathering-driven alkalinity transport through variation in land management practice. However, there are tradeoﬀs with total CDR, optimal nutrient use eﬃciencies, and soil nitrous oxide (N2O) ﬂuxes that complicate attempts to perform robust time-resolved analysis of the net radiative impacts of CDR through EW in agricultural systems. Although CDR lag times will be more of an issue in some regions than others, these results have signiﬁcant implications for the technoeconomics of EW and the integration of EW into voluntary carbon markets, as there may often be a large temporal disconnect between deployment of EW and climate-relevant CDR.