Miracle-dz/UniFAR-FTUs huggingface dataset:
The Therapeutic Value of Adjuvant Chemotherapy after Concurrent Chemoradiotherapy for Locally Advanced Cervical Cancer

Objective: The aim of this study was to evaluate the therapeutic value and treatment-related complications of adjuvant chemotherapy after concurrent chemoradiotherapy (CCRT) for locally advanced cervical cancer (LACC). Design: The medical records of LACC patients who underwent CCRT were reviewed retrospectively. Methods: A total of 1,138 patients with LACC who had been treated at our hospital between January 2013 and December 2017 were included in the study and classified into two groups: the CCRT group, comprising 726 patients who had received only CCRT, and the CCRT + adjuvant chemotherapy (ACT) group, comprising 412 patients who had received three cycles of adjuvant chemotherapy after CCRT. 39 patients in the CCRT group and 50 patients in the CCRT + ACT group had undergone lymphadenectomy, which revealed pathology-positive lymph nodes in 22 patients and 35 patients, respectively. Progression-free survival (PFS), overall survival (OS), and adverse events were compared. Results: The median follow-up time was 61 months (range: 2–96 months). No significant differences in PFS and OS were found between the two groups (p > 0.05), but more grade 3–4 acute hematologic toxicities were observed in the CCRT + ACT group than in the CCRT group (24.8% vs. 31.8%, p = 0.01). A subgroup analysis of patients with pathology-positive lymph nodes showed that the 5-year PFS and OS rates were 76.5% and 74.9%, respectively, for the CCRT + ACT group and 45.0% and 49.2%, respectively, for the CCRT group; the differences were statistically significant (p = 0.015 and 0.042, respectively). Limitations: First, the sample size of the subgroup of patients with pathology-positive lymph nodes was too small for a confirmative conclusion. The heterogeneous population and the selection bias resulting from the retrospective design were the other flaws of our study. Conclusion: The application of adjuvant chemotherapy after CCRT may be worth investigating further for women with LACC and pathology-positive lymph nodes, but this approach is associated with an increase in acute hematology toxicities.
Trade-offs in locational choices for care coordination resources in accountable care organizations

Background Care coordination is central to accountable care organizations (ACOs), especially in Medicaid where many patients have complex medical and social needs. Little is known about how to best organize care coordination resources in this context, particularly whether to centralize them. We examined how care coordinators’ location, management, and colocation of both (within ACO headquarters, practice sites, or other organizations) relate to care quality and coordination. Methods We conducted a cross-sectional analysis of surveys administered to a sample of practice sites covering all 17 Medicaid ACOs in Massachusetts (n = 225, response rate = 64%). We applied controlled, cluster-robust regressions, adjusting the significance threshold for the number of ACO clusters, to assess how clinical information sharing across settings, care quality improvement, knowledge of social service referral, and cross-resource coordination (i.e., the ability of multiple resources to work well together) relate to where care coordinators were physically located and/or managed. Results Centralizing care coordinators at ACO headquarters was associated with greater information sharing. Embedding care coordinators in practices was associated with greater care quality improvement. Embedding coordinators at other organizations was associated with less information sharing and care quality improvement. Managing coordinators at practice sites and other organizations were associated with better care quality improvement and cross-resource coordination, respectively. Colocating the two functions showed no significant differences. Practice Implications Choosing care coordinators’ locations may present trade-offs. ACOs may strategically choose embedding care coordinators at practice sites for enhanced care quality versus centralizing them at the ACO to facilitate information sharing.
Adherence to the Dutch recommendation for physical activity: prior to and after primary total hip and knee arthroplasty.

PURPOSE To determine the course of adherence to physical activity (PA) recommendation in hip/knee osteoarthritis patients before and after hip/knee arthroplasty (THA/TKA). Moreover, we explored predictors for non-adherence 12 months postoperatively. MATERIALS AND METHODS Primary THA/TKA were included in a multicenter observational study. Preoperatively and 6/12 months postoperatively, patients reported engagement in moderate-intensity PA in days/week in the past 6 months (PA-recommendation (≥30 min of moderate-intensity ≥5 days/week)). We included predictors stratified by preoperative adherence: sex, age, BMI, comorbidities, smoking, living/working status, season, mental health, HOOS/KOOS subscales before and 6 months postoperatively, and 6-month adherence. RESULTS (1005 THA/972 TKA) Preoperatively, 50% of the population adhered. Adherence increased to 59% at 6 and 12 months. After 12 months, most patients remained at their preoperative PA level, 11% of the preoperative adherers decreased, while 20% of the preoperative non-adherers increased their PA level. In all different groups, adherence to the PA recommendation at 6 months was identified as a predictor (OR-range: 0.16-0.29). In addition, BMI was identified as predictor in the THA adherent (OR = 1.07; 95%CI [1.02-1.15]) and TKA non-adherent groups (OR = 1.08; 95%CI [1.03-1.12]). THA non-adherent group not having paid work (OR = 0.53; 95%CI [0.33-0.85]), and in the TKA adherent group, lower KOOS subscale symptoms (OR = 1.03; 95%CI [1.01-1.05]) were associated with non-adherence. CONCLUSIONS Majority of patients remained at their preoperative PA level. Non-adherence at 6 months was highly predictive for 12-month non-adherence.Implications for rehabilitationPhysical activity (PA) is crucial to decrease mortality risk, especially among patients suffering from osteoarthritis, as these patients have the potential to become more physically active after arthroplasty surgery.We found that physical function and pain improved, but 69% of the patients remained at their preoperative PA level, while 11% decreased and 20% increased their PA levels.Using this information shortly after surgery, orthopedic surgeons and other health care professionals can address patients at risk for decreased PA levels and provide PA advice.
In-line phase tomography using nonlinear phase retrieval

With hard X-rays synchrotron beams, phase contrast for in-line phase tomography can be obtained with the measurement of the Fresnel diffraction intensity patterns associated to a phase shift induced by the object. We have studied the resolution of this inverse problem with an iterative nonlinear method. The phase retrieval algorithm was tested for a 3D Shepp-Logan phantom in the presence of noise. The nonlinear scheme outperforms the linear method. Both the high and low frequency ranges of the phase retrieved are improved and the method is less sensitive to noise. In future work, the method will be tested on experimental data. The method is expected to open new perspectives for the study of biological samples.
A computational study on the nature of meso‐β scale vortex coalescence in a tropical atmosphere

Tropical weather systems commonly contain a variety of mesoscale vortices whose interactions may influence the timing of tropical cyclone formation. Previous computational studies have found that interactions ending in coalescence can either help or hinder development depending on the specific circumstances. Despite the potential importance of mesoscale vortex merger, complications introduced by baroclinic effects and diabatic processes have impeded progress toward elucidating its mechanics. The present study is an effort to advance current understanding by way of idealized numerical experiments with a cloud‐permitting model. Direct comparisons are made between dry adiabatic and moist diabatic simulations of meso‐β (20–200 km) scale vortex merger on a tropical oceanic f‐plane. Consistent with a number of earlier studies, deep cumulus convection is found to accelerate the late stage of surface‐concentrated vortex merger over a warm ocean. On the other hand, incorporating artificial radiation and cloud processes into the numerical model substantially hinders the merger of middle‐tropospheric vortices over a moderately cooler ocean. Such hindering coincides with the partial suppression of shear‐induced vortex misalignments whose amplifications and rotations appear to be essential to the dry adiabatic merger mechanism. The aforementioned dry merger mechanism is examined in detail. Irrotational winds linked to the horizontal divergence field are found to largely control the system‐centric influx of absolute vorticity, in sharp contrast to ideal two‐dimensional hydrodynamics. The consequences of lowering (raising) the sea‐surface temperature under the moist surface‐concentrated (middle‐tropospheric) system are briefly addressed.
Distinctive features of hairy black holes in teleparallel Gauss-Bonnet gravity

We examine the teleparallel formulation of non-minimally coupled scalar Einstein-Gauss-Bonnet gravity. In the teleparallel formulation, gravity is described by torsion instead of curvature, causing the usual Gauss-Bonnet invariant expressed through curvature to decay into two separate invariants built from torsion. Consequently, the teleparallel formulation permits broader possibilities for non-minimal couplings between spacetime geometry and the scalar field. In our teleparallel theory, there are two different branches of equations in spherical symmetry depending on how one solves the antisymmetric part of the field equations, leading to a real and a complex tetrad. We first show that the real tetrad seems to be incompatible with the regularity of the equations at the event horizon, which is a symptom that scalarized black hole solutions beyond the Riemannian Einstein-Gauss-Bonnet theory might not exist. Therefore, we concentrate our study on the complex tetrad. This leads to the emergence of scalarized black hole solutions, where the torsion acts as the scalar field source. Extending our previous work, we study monomial non-minimal couplings of degrees one and two, which are intensively studied in conventional, curvature-based, scalar Einstein-Gauss-Bonnet gravity. We discover that the inclusion of torsion can potentially alter the stability of the resulting scalarized black holes. Specifically, our findings indicate that for a quadratic coupling, which is entirely unstable in the pure curvature formulation, the solutions induced by torsion may exhibit stability within certain regions of the parameter space. In a limiting case, we were also able to find black holes with a strong scalar field close to the horizon but with a vanishing scalar charge.
Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations

Supervised classification algorithms are used to solve a growing number of real-life problems around the globe. Their performance is strictly connected with the quality of labels used in training. Unfortunately, acquiring good-quality annotations for many tasks is infeasible or too expensive to be done in practice. To tackle this challenge, active learning algorithms are commonly employed to select only the most relevant data for labeling. However, this is possible only when the quality and quantity of labels acquired from experts are sufficient. Unfortunately, in many applications, a trade-off between annotating individual samples by multiple annotators to increase label quality vs. annotating new samples to increase the total number of labeled instances is necessary. In this paper, we address the issue of faulty data annotations in the context of active learning. In particular, we propose two novel annotation unification algorithms that utilize unlabeled parts of the sample space. The proposed methods require little to no intersection between samples annotated by different experts. Our experiments on four public datasets indicate the robustness and superiority of the proposed methods in both, the estimation of the annotator's reliability, and the assignment of actual labels, against the state-of-the-art algorithms and the simple majority voting.
MATNilm: Multi-Appliance-Task Non-Intrusive Load Monitoring With Limited Labeled Data

Nonintrusive load monitoring (NILM) identifies the status and power consumption of various household appliances by disaggregating the total power usage signal of an entire house. Efficient and accurate load monitoring facilitates user profile establishment, intelligent household energy management, and peak load shifting. This is beneficial for both the end users and utilities by improving the overall efficiency of a power distribution network. Existing approaches mainly focus on developing an individual model for each appliance. Those approaches typically rely on a large amount of household-labeled data that are hard to collect. In this article, we propose a multi-appliance-task framework with a training-efficient sample augmentation (SA) scheme that boosts the disaggregation performance with limited labeled data. For each appliance, we develop a shared-hierarchical split structure for its regression and classification tasks. In addition, we also propose a 2-D attention mechanism in order to capture spatio-temporal correlations among all appliances. With only one-day training data and limited appliance operation profiles, the proposed SA algorithm can achieve comparable test performance to the case of training with the full dataset. Finally, simulation results show that our proposed approach features a significantly improved performance over many baseline models. The relative errors can be reduced by more than 50% on average.
EyeO: Autocalibrating Gaze Output with Gaze Input

Gaze tracking devices have the potential to greatly expand interactivity, yet miscalibration remains a significant barrier to use. As devices miscalibrate, people tend to compensate by intentionally offsetting their gaze, which makes detecting miscalibration from eye signals difficult. To help address this problem, we propose a novel approach to seamless calibration based on the insight that the system's model of eye gaze can be updated during reading (user does not compensate) to improve calibration for typing (user might compensate). To explore this approach, we built an auto-calibrating gaze typing prototype called EyeO, ran a user study with 20 participants, and conducted a semi-structured interview with 6 ALS community stakeholders. Our user study results suggest that seamless autocalibration can significantly improve typing efficiency and user experience. Findings from the semi-structured interview validate the need for autocalibration, and shed light on the prototype's potential usefulness, desired algorithmic and design improvements for users.
MCPA: Multi-scale Cross Perceptron Attention Network for 2D Medical Image Segmentation

The UNet architecture, based on Convolutional Neural Networks (CNN), has demonstrated its remarkable performance in medical image analysis. However, it faces challenges in capturing long-range dependencies due to the limited receptive fields and inherent bias of convolutional operations. Recently, numerous transformer-based techniques have been incorporated into the UNet architecture to overcome this limitation by effectively capturing global feature correlations. However, the integration of the Transformer modules may result in the loss of local contextual information during the global feature fusion process. To overcome these challenges, we propose a 2D medical image segmentation model called Multi-scale Cross Perceptron Attention Network (MCPA). The MCPA consists of three main components: an encoder, a decoder, and a Cross Perceptron. The Cross Perceptron first captures the local correlations using multiple Multi-scale Cross Perceptron modules, facilitating the fusion of features across scales. The resulting multi-scale feature vectors are then spatially unfolded, concatenated, and fed through a Global Perceptron module to model global dependencies. Furthermore, we introduce a Progressive Dual-branch Structure to address the semantic segmentation of the image involving finer tissue structures. This structure gradually shifts the segmentation focus of MCPA network training from large-scale structural features to more sophisticated pixel-level features. We evaluate our proposed MCPA model on several publicly available medical image datasets from different tasks and devices, including the open large-scale dataset of CT (Synapse), MRI (ACDC), fundus camera (DRIVE, CHASE_DB1, HRF), and OCTA (ROSE). The experimental results show that our MCPA model achieves state-of-the-art performance. The code is available at https://github.com/simonustc/MCPA-for-2D-Medical-Image-Segmentation.
Identifying regime switches through Bayesian wavelet estimation: evidence from flood detection in the Taquari River Valley

Two-component mixture models have proved to be a powerful tool for modeling heterogeneity in several cluster analysis contexts. However, most methods based on these models assume a constant behavior for the mixture weights, which can be restrictive and unsuitable for some applications. In this paper, we relax this assumption and allow the mixture weights to vary according to the index (e.g., time) to make the model more adaptive to a broader range of data sets. We propose an efficient MCMC algorithm to jointly estimate both component parameters and dynamic weights from their posterior samples. We evaluate the method's performance by running Monte Carlo simulation studies under different scenarios for the dynamic weights. In addition, we apply the algorithm to a time series that records the level reached by a river in southern Brazil. The Taquari River is a water body whose frequent flood inundations have caused various damage to riverside communities. Implementing a dynamic mixture model allows us to properly describe the flood regimes for the areas most affected by these phenomena.
Vlasov models for kinetic Weibel-type instabilities

The Weibel instability, driven by a temperature anisotropy, is investigated within different kinetic descriptions based on the semi-Lagrangian full kinetic and relativistic Vlasov–Maxwell model, on the multi-stream approach, which is based on a Hamiltonian reduction technique, and finally, with the full pressure tensor fluid-type description. Dispersion relations of the Weibel instability are derived using the three different models. A qualitatively different regime is observed in Vlasov numerical experiments depending on the excitation of a longitudinal plasma electric field driven initially by the combined action of the stream symmetry breaking and weak relativistic effects, in contrast with the existing theories of the Weibel instability based on their purely transverse characters. The multi-stream model offers an alternate way to simulate easily the coupling with the longitudinal electric field and particularly the nonlinear regime of saturation, making numerical experiments more tractable, when only a few moments of the distribution are considered. Thus a numerical comparison between the reduced Hamiltonian model (the multi-stream model) and full kinetic (relativistic) Vlasov simulations has been investigated in that regime. Although nonlinear simulations of the fluid model, including the dynamics of the pressure tensor, have not been carried out here, the model is strongly relevant even in the three-dimensional case.
Where are the photons in a transmission-line pulse?

We develop a photonic description of short, one-dimensional electromagnetic pulses, specifically in the language of electrical transmission lines. Current practice in quantum technology, using arbitrary waveform generators, can readily produce very short, few-cycle pulses in microwave TEM guided structures (coaxial cables or coplanar waveguides) in a very low noise, low temperature setting. We argue that these systems attain the limit of producing pure coherent quantum states, in which the vacuum has been displaced for a short time, and therefore short spatial extent. When the pulse is bipolar, that is, the integrated voltage of the pulse is zero, then the state can be described by the finite displacement of a single mode. Therefore there is a definite mean number of photons, but which have neither a well defined frequency nor position. Due to the Paley-Wiener theorem, the two-component photon 'wavefunction' of this mode is not strictly bounded in space even if the vacuum displacement that defines it is bounded. This wavefunction's components are, for the case of pulses moving in a specific direction, complex valued, with the real and imaginary parts related by a Hilbert transform. They are thus akin to the 'analytic signals' of communication theory. When the pulse is unipolar no photonic description is possible -- the photon number can be considered to be divergent. We consider properties that photon counters and quantum non-demolition detectors must have to optimally convert and detect the photons in several example pulses, and we discuss some consequence of this optimization for the application of very short pulses in quantum cryptography.
Enhancing Gravity Currents Analysis through Physics-Informed Neural Networks: Insights from Experimental Observations

Gravity currents in oceanic flows require simultaneous measurements of pressure and velocity to assess energy flux, which is crucial for predicting fluid circulation, mixing, and overall energy budget. In this paper, we apply Physics Informed Neural Networks (PINNs) to infer velocity and pressure field from Light Attenuation Technique (LAT) measurements for gravity current induced by lock-exchange. In a PINN model, physical laws are embedded in the loss function of a neural network, such that the model fits the training data but is also constrained to reduce the residuals of the governing equations. PINNs are able to solve ill-posed inverse problems training on sparse and noisy data, and therefore can be applied to real engineering applications. The noise robustness of PINNs and the model parameters are investigated in a 2 dimensions toy case on a lock-exchange configuration , employing synthetic data. Then we train a PINN with experimental LAT measurements and quantitatively compare the velocity fields inferred to PIV measurements performed simultaneously on the same experiment. Finally, we study the energy flux field $J=p \boldsymbol{u}$ derived from the model. The results state that accurate and useful quantities can be derived from a PINN model trained on real experimental data which is encouraging for a better description of gravity currents and improve models of ocean circulation.
Weak (Proxy) Factors Robust Hansen-Jagannathan Distance For Linear Asset Pricing Models

The Hansen-Jagannathan (HJ) distance statistic is one of the most dominant measures of model misspecification. However, the conventional HJ specification test procedure has poor finite sample performance, and we show that it can be size distorted even in large samples when (proxy) factors exhibit small correlations with asset returns. In other words, applied researchers are likely to falsely reject a model even when it is correctly specified. We provide two alternatives for the HJ statistic and two corresponding novel procedures for model specification tests, which are robust against the presence of weak (proxy) factors, and we also offer a novel robust risk premia estimator. Simulation exercises support our theory. Our empirical application documents the non-reliability of the traditional HJ test since it may produce counter-intuitive results when comparing nested models by rejecting a four-factor model but not the reduced three-factor model. At the same time, our proposed methods are practically more appealing and show support for a four-factor model for Fama French portfolios.