Miracle-dz/UniFAR-FTUs huggingface dataset:
REVIEW Current Concepts of Osteomyelitis From Pathologic Mechanisms to Advanced Research Methods

Osteomyelitis is an in ﬂ ammation of the bone and bone marrow that is most commonly caused by a Staphylococcus aureus infection. Much of our understanding of the underlying pathophysiology of osteomyelitis, from the perspective of both host and pathogen, has been revised in recent years, with notable discoveries including the role played by osteocytes in the recruitment of immune cells, the invasion and persistence of S. aureus in submicron channels of cortical bone, and the diagnostic role of polymorphonuclear cells in implant-associated osteomyelitis. Advanced in vitro cell culture models, such as ex vivo culture models or organoids, have also been developed over the past decade
New Zealand place names shared with the Hawaiian Archipelago

A survey of place names shared between New Zealand and East Polynesia found by far the majority in the Hawaiian Archipelago. Given that the immediate origin of Māori is generally thought to lie in the ‘central East Polynesian interaction sphere’, this is surprising, inviting an exploration of other links between these two widely separated corners of the ‘Polynesian Triangle’ in the context of evidence for and against prehistoric contact. A full list of the names found to be shared between these two island groups is given, along with a statistical comparison of New Zealand place names found to occur on other East Polynesian archipelagos. A proposition is made here that, despite the isolation of both the Hawaiian Archipelago and New Zealand at European contact and the immense distance between them, they may have been in direct contact with one another. Un estudio de los nombres de lugares compartidos entre Nueva Zelanda y la Polinesia Oriental descubrio la gran mayoria en el archipielago Hawaiano. Teniendo en cuenta que el origen inmediato de los Māori se piensan generalmente en la ‘esfera de interaccion de la region de central Polinesia oriental’, esta resulta es sorprendente, y la invita una exploracion de otros vinculos entre los dos puntos muy distantes del ‘Triangulo Polinesio’ en el contexto de evidencia para y contra el contacto prehistorico. Una lista completa de los nombres que se corresponden entre estos dos grupos es presentado, junto con una comparacion estadistica de los nombres de lugares de Nueva Zelanda que ocurren en otros archipielagos de Polinesia Oriental. Una propuesta que se presenta aqui, a pesar de aislamiento de los archipielagos de Hawai‘i y Nueva Zelanda durante el tiempo del contacto con los europeos y la inmensa distancia entre ellos, que pueden haber estado en contacto directo.
Burden of Disease of Duchenne Muscular Dystrophy in Denmark – A National Register-Based Study of Individuals with Duchenne Muscular Dystrophy and their Closest Relatives

Background: Duchenne Muscular Dystrophy (DMD) is a progressive genetic disease with a prevalence of 1 per 3,600–6,000 male births. Individuals with DMD are typically diagnosed at age 4–7 years; median survival is 30 years. They require multidisciplinary care, personal assistance, and often special education. Objective: The aim was to assess the burden of disease in DMD in Denmark. This includes incidence, prevalence, use of healthcare services, labour market participation, educational outcomes, and overall attributable costs due to DMD. Impact on the closest relatives (siblings and parents) was also investigated. Methods: The comprehensive Danish national health and administrative registers were used to assess the burden of disease following individuals with DMD and closest relatives from five years before, and up to 20 years after DMD diagnosis. Individuals with DMD (and relatives) from 1994–2021 were included. All outcomes were compared to matched control groups without the disease drawn from the Danish population. Results: 213 unique individuals with DMD were identified. They had lower grades in school, required more special education and more healthcare and home care compared to their control group. The extra costs of special education summed to EUR 180,900 over the course of 11 years elementary school. They had an annual average productivity loss of EUR 20,200 between the age of 18 to 30. The extra healthcare costs of DMD in the 20 years after diagnosis were estimated to EUR 1,524,000. If an individual with DMD lives to be 30, total extra costs sum to EUR 2,365,800. Conclusions: Using national register data this study presented detailed results on the burden of disease of DMD, including impact on closest relatives. With 60 additional hospital admissions and 200 extra outpatient contacts in 20 years healthcare costs, but also costs of home care and special education, increases as disease progresses.
Consistency of electrical source imaging in presurgical evaluation of epilepsy across different vigilance states

Abstract Objective The use of electrical source imaging (ESI) in assessing the source of interictal epileptic discharges (IEDs) is gaining increasing popularity in presurgical work‐up of patients with drug‐resistant focal epilepsy. While vigilance affects the ability to locate IEDs and identify the epileptogenic zone, we know little about its impact on ESI. Methods We studied overnight high‐density electroencephalography recordings in focal drug‐resistant epilepsy. IEDs were marked visually in each vigilance state, and examined in the sensor and source space. ESIs were calculated and compared between all vigilance states and the clinical ground truth. Two conditions were considered within each vigilance state, an unequalized and an equalized number of IEDs. Results The number, amplitude, and duration of IEDs were affected by the vigilance state, with N3 sleep presenting the highest number, amplitude, and duration for both conditions (P < 0.001), while signal‐to‐noise ratio only differed in the unequalized condition (P < 0.001). The vigilance state did not affect channel involvement (P > 0.05). ESI maps showed no differences in distance, quality, extent, or maxima distances compared to the clinical ground truth for both conditions (P > 0.05). Only when an absolute reference (wakefulness) was used, the channel involvement (P < 0.05) and ESI source extent (P < 0.01) were impacted during rapid‐eye‐movement (REM) sleep. Clustering of amplitude‐sensitive and ‐insensitive ESI maps pointed to amplitude rather than the spatial profile as the driver (P < 0.05). Interpretation IED ESI results are stable across vigilance states, including REM sleep, if controlled for amplitude and IED number. ESI is thus stable and invariant to the vigilance state.
The long March: the quest for valid text based indicators of exploration and exploitation

Since the seminal work of March (1991) outlined the importance of balancing exploration and exploitation in organizational learning, an increasing number of studies has analyzed the relationship between this balance and firm performance. While most studies have relied on survey or patent-based indicators to measure firms? exploration and exploitation activities, attention has recently shifted to the use of text based indicators and content analysis, as this holds the promise of generality and universal application. The limited number of studies applying content analysis have used the original terminology and keywords proposed by March to construct indicators of firms? orientation towards exploration or exploitation. In this paper we show 1) that March? s original terminology is unlikely to deliver valid and generally applicable indicators 2) how the construction of representative indicators should be based on a structured series of validity analyses 3) that an inductive approach retrieving keywords from the body of text and based on the validity analyses provides more robust results. Empirically, we analyze more than 100,000 R&D-related press articles (1996-2003) covering 151 R&D intensive firms based in Europe, Japan and the US. As part of our validity analyses, we confirm an inverted-U shaped relationship between firm performance and exploration orientation ? but only if exploration is measured using an inductive approach that departs substantially from March? s original list of keywords.
The use of remotely sensed LiDAR and multispectral imagery for modeling eastern redcedar biomass within North Eastern Kansas

Due in large part to changes in land management practices, eastern redcedar (Juniperus virginiana L.), a native Kansas conifer, is rapidly invading onto valuable rangelands. The suppression of fire and increase of intensive grazing, combined with the rapid growth rate, high reproductive output, and dispersal ability of the species have allowed it to dramatically expand beyond its original range. Based on its abundance and invasive nature there is a growing interest in harvesting this species for use as a biofuel. For economic planning purposes, density and biomass quantities for the trees are needed. Three methods are explored for mapping eastern redcedar and quantifying its biomass in Riley County, Kansas. First a comparison of plotregression versus individual tree based techniques is conducted to determine the optimal approach for characterizing redcedar tree canopy using LiDAR (Light Detection and Ranging). Second a hybrid approach is utilized to characterize redcedar canopy biomass using LiDAR and high-resolution multispectral imagery. Finally, to explore alternative methods of characterizing the three-dimensional structure of redcedar canopy a comparison of “Structure from Motion” photogrammetric techniques and LiDAR is conducted. These methods showed promising results and proved to be useful in the forestry, range management, and bioenergy industries for better understanding the potential of invasive redcedar as a biofuel resource.
Global water vapor variability and trend from the latest 36 year (1979 to 2014) data of ECMWF and NCEP reanalyses, radiosonde, GPS, and microwave satellite

The variability and trend in global precipitable water vapor (PWV) from 1979 to 2014 are analyzed using the PWV data sets from the ERA‐Interim reanalysis of the European Centre for Medium‐Range Weather Forecasts (ECMWF), reanalysis of the National Centers for Environmental Prediction (NCEP), radiosonde, Global Positioning System (GPS), and microwave satellite observations. PWV data from the ECMWF and NCEP have been evaluated by radiosonde, GPS, and microwave satellite observations, showing that ECMWF has higher accuracy than NCEP. Over the oceans, ECMWF has a much better agreement with the microwave satellite than NCEP. An upward trend in the global PWV is evident in all the five PWV data sets over three study periods: 1979–2014, 1992–2014, and 2000–2014. Positive global PWV trends, defined as percentage normalized by annual average, of 0.61 ± 0.33% decade−1, 0.57 ± 0.28% decade−1, and 0.17 ± 0.35% decade−1, have been derived from the NCEP, radiosonde, and ECMWF, respectively, for the period 1979–2014. It is found that ECMWF overestimates the PWV over the ocean prior to 1992. Thus, two more periods, 1992–2014 and 2000–2014, are studied. Increasing PWV trends are observed from all the five data sets in the two periods: 1992–2014 and 2000–2014. The linear relationship between PWV and surface temperature is positive over most oceans and the polar region. Steep positive/negative regression slopes are generally found in regions where large regional moisture flux divergence/convergence occurs.
Projective Averages for Summarizing Redistricting Ensembles

A recurring challenge in the application of redistricting simulation algorithms lies in extracting useful summaries and comparisons from a large ensemble of districting plans. Researchers often compute summary statistics for each district in a plan, and then study their distribution across the plans in the ensemble. This approach discards rich geographic information that is inherent in districting plans. We introduce the projective average, an operation that projects a district-level summary statistic back to the underlying geography and then averages this statistic across plans in the ensemble. Compared to traditional district-level summaries, projective averages are a powerful tool for geographically granular, sub-district analysis of districting plans along a variety of dimensions. However, care must be taken to account for variation within redistricting ensembles, to avoid misleading conclusions. We propose and validate a multiple-testing procedure to control the probability of incorrectly identifying outlier plans or regions when using projective averages.
UAV-borne Mapping Algorithms for Canopy-Level and High-Speed Drone Applications

This article presents a comprehensive review of and analysis of state-of-the-art mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on canopy-level and high-speed scenarios. This article presents a comprehensive exploration of sensor technologies suitable for UAV mapping, assessing their capabilities to provide measurements that meet the requirements of fast UAV mapping. Furthermore, the study conducts extensive experiments in a simulated environment to evaluate the performance of three distinct mapping algorithms: Direct Sparse Odometry (DSO), Stereo DSO (SDSO), and DSO Lite (DSOL). The experiments delve into mapping accuracy and mapping speed, providing valuable insights into the strengths and limitations of each algorithm. The results highlight the versatility and shortcomings of these algorithms in meeting the demands of modern UAV applications. The findings contribute to a nuanced understanding of UAV mapping dynamics, emphasizing their applicability in complex environments and high-speed scenarios. This research not only serves as a benchmark for mapping algorithm comparisons but also offers practical guidance for selecting sensors tailored to specific UAV mapping applications.
AttributionScanner: A Visual Analytics System for Metadata-Free Data-Slicing Based Model Validation

Data slice-finding is an emerging technique for evaluating machine learning models. It works by identifying subgroups within a specified dataset that exhibit poor performance, often defined by distinct feature sets or meta-information. However, in the context of unstructured image data, data slice-finding poses two notable challenges: it requires additional metadata -- a laborious and costly requirement, and also demands non-trivial efforts for interpreting the root causes of the underperformance within data slices. To address these challenges, we introduce AttributionScanner, an innovative human-in-the-loop Visual Analytics (VA) system, designed for data-slicing-based machine learning (ML) model validation. Our approach excels in identifying interpretable data slices, employing explainable features extracted through the lens of Explainable AI (XAI) techniques, and removing the necessity for additional metadata of textual annotations or cross-model embeddings. AttributionScanner demonstrates proficiency in pinpointing critical model issues, including spurious correlations and mislabeled data. Our novel VA interface visually summarizes data slices, enabling users to gather insights into model behavior patterns effortlessly. Furthermore, our framework closes the ML Development Cycle by empowering domain experts to address model issues by using a cutting-edge neural network regularization technique. The efficacy of AttributionScanner is underscored through two prototype use cases, elucidating its substantial effectiveness in model validation for vision-centric tasks. Our approach paves the way for ML researchers and practitioners to drive interpretable model validation in a data-efficient way, ultimately leading to more reliable and accurate models.
Using Natural Language Inference to Improve Persona Extraction from Dialogue in a New Domain

While valuable datasets such as PersonaChat provide a foundation for training persona-grounded dialogue agents, they lack diversity in conversational and narrative settings, primarily existing in the"real"world. To develop dialogue agents with unique personas, models are trained to converse given a specific persona, but hand-crafting these persona can be time-consuming, thus methods exist to automatically extract persona information from existing character-specific dialogue. However, these persona-extraction models are also trained on datasets derived from PersonaChat and struggle to provide high-quality persona information from conversational settings that do not take place in the real world, such as the fantasy-focused dataset, LIGHT. Creating new data to train models on a specific setting is human-intensive, thus prohibitively expensive. To address both these issues, we introduce a natural language inference method for post-hoc adapting a trained persona extraction model to a new setting. We draw inspiration from the literature of dialog natural language inference (NLI), and devise NLI-reranking methods to extract structured persona information from dialogue. Compared to existing persona extraction models, our method returns higher-quality extracted persona and requires less human annotation.
Printed Texture Guided Color Feature Fusion for Impressionism Style Rendering of Oil Paintings Printed Texture Guided Color Feature Fusion for Impressionism Style Rendering of Oil Paintings

: As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses computer algorithms to render a photo into an artistic painting. Recent work has shown that the ex-traction of style information such as stroke texture and color of the target style image is the key to image stylization. Given its stroke texture and color characteristics, a new stroke rendering method is proposed. By fully considering the tonal characteristics and the representative color of the original oil painting, it can ﬁt the tone of the original oil painting image into a stylized image whilst keeping the artist’s creative effect. The experiments have validated the efﬁcacy of the proposed model in comparison to three state-of-the-arts. This method would be more suitable for the works of pointillism painters with a relatively uniform style, especially for natural scenes, otherwise, the results can be less satisfactory. Abstract: As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses computer algorithms to render a photo into an artistic painting. Recent work has shown that the ex-traction of style information such as stroke texture and color of the target style image is the key to image stylization. Given its stroke texture and color characteristics, a new stroke rendering method is proposed. By fully considering the tonal characteristics and the representative color of the original oil painting, it can fit the tone of the original oil painting image into a stylized image whilst keeping the artist's creative effect. The experiments have validated the efficacy of the proposed model in comparison to three state-of-the-arts. This method would be more suitable for the works of pointillism painters with a relatively uniform style, especially for natural scenes, otherwise, the results can be less satisfactory.
Nonconvex Deterministic Matrix Completion by Projected Gradient Descent Methods

We study deterministic matrix completion problem, i.e., recovering a low-rank matrix from a few observed entries where the sampling set is chosen as the edge set of a Ramanujan graph. We first investigate projected gradient descent (PGD) applied to a Burer-Monteiro least-squares problem and show that it converges linearly to the incoherent ground-truth with respect to the condition number \k{appa} of ground-truth under a benign initialization and large samples. We next apply the scaled variant of PGD to deal with the ill-conditioned case when \k{appa} is large, and we show the algorithm converges at a linear rate independent of the condition number \k{appa} under similar conditions. Finally, we provide numerical experiments to corroborate our results.
CNN-DRL for Scalable Actions in Finance

The published MLP-based DRL in finance has difficulties in learning the dynamics of the environment when the action scale increases. If the buying and selling increase to one thousand shares, the MLP agent will not be able to effectively adapt to the environment. To address this, we designed a CNN agent that concatenates the data from the last ninety days of the daily feature vector to create the CNN input matrix. Our extensive experiments demonstrate that the MLP-based agent experiences a loss corresponding to the initial environment setup, while our designed CNN remains stable, effectively learns the environment, and leads to an increase in rewards.
Scanning Spin Probe Based on Magnonic Vortex Quantum Cavities

Performing nanoscale scanning electron paramagnetic resonance (EPR) requires three essential ingredients: First, a static magnetic field together with field gradients to Zeeman split the electronic energy levels with spatial resolution; second, a radio frequency (rf) magnetic field capable of inducing spin transitions; finally, a sensitive detection method to quantify the energy absorbed by spins. This is usually achieved by combining externally applied magnetic fields with inductive coils or cavities, fluorescent defects, or scanning probes. Here, we theoretically propose the realization of an EPR scanning sensor merging all three characteristics into a single device: the vortex core stabilized in ferromagnetic thin-film discs. On one hand, the vortex ground state generates a significant static magnetic field and field gradients. On the other hand, the precessional motion of the vortex core around its equilibrium position produces a circularly polarized oscillating magnetic field, which is enough to produce spin transitions. Finally, the spin–magnon coupling broadens the vortex gyrotropic frequency, suggesting a direct measure of the presence of unpaired electrons. Moreover, the vortex core can be displaced by simply using external magnetic fields of a few mT, enabling EPR scanning microscopy with large spatial resolution. Our numerical simulations show that, by using low damping magnets, it is theoretically possible to detect single spins located on the disc’s surface. Vortex nanocavities could also attain strong coupling to individual spin molecular qubits with potential applications to mediate qubit–qubit interactions or to implement qubit readout protocols.
Drops of volatile binary mixtures on brush-covered substrates

We introduce a mesoscopic hydrodynamic model for drops of binary mixtures of volatile partially wetting liquids on brush-covered substrates. The formulation as gradient dynamics on an underlying free energy functional allows us to systematically account for cross couplings between the six scalar fields needed to describe the two components within the three phases - brush, drop and gas. The energy accounts for concentration- and brush state-dependent capillarity and wettability, miscibility of the two components within drop and brush, and entropy in the gas. Finally, the usage of the model is illustrated by employing numerical time simulations to study the coupled spreading, evaporation, imbibition and partial demixing of a sessile drop on a brush.