Miracle-dz/UniFAR-FTUs huggingface dataset:
Extinction risk patterns in a biodiversity hotspot—The case of Thesium (Santalaceae) in the Greater Cape Floristic Region

Ecologically specialized plants are expected to be at greater risk of extinction than generalists due to climate change. Such risk is greatest in biodiversity hotspots such as the Greater Cape Floristic Region (GCFR), which accommodates both ecological specialists and generalists. Thesium L. , a genus with the highest number of species in Santalaceae and the most diverse in Africa, offers an appropriate system for evaluating both the correlates of range extent and specialization and the relative extinction risks associated with both. We hypothesized that range size, ecological specialization, and consequent climatically modulated extinction risks are all phylogenetically structured, such that climate change will precipitate a disproportionate loss of phylogenetic diversity in the GCFR Thesium. Past and future species distribution ranges were predicted using MaxEnt models based on present‐day occurrences and environmental conditions. Of the 101 Thesium species modeled, 70% have had large range sizes during the Last Glacial Maximum (LGM), 50% currently have a large range size, and future conditions are predicted to allow 40% to obtain large range sizes. Between the LGM and the present, 17% of species are thought to have undergone a contraction of available range space in the present time whereas 37% are expected to expand their ranges into the future, while 51% of species will experience range contractions. Of the 65 species currently ranked as Least Concern in the South African Red List, 24% will likely shift into higher extinction risk categories. Interestingly, 8.5% of ecological specialists, although having experienced a range reduction from the LGM to the present, are predicted to persist in the face of future climate change. However, the range extent, ecological specialization, and extinction risk are phylogenetically random and therefore should have a negligible impact on the phylogenetic diversity of the GCFR Thesium.
Noise-conscious explicit weighting network for robust low-dose CT imaging

Supervised deep learning (DL) methods have been widely developed to remove noise-induced artifacts and promote image quality in the low-dose CT imaging task via good mapping capabilities. These supervised DL methods are usually trained based on a large amount of low- and normal-dose sinogram/image pairs and the reconstruction performance of such supervised DL methods heavily depends on the quality of reference training images. In the CT imaging, it is challenging to collect lots of high quality reference training images in practice due to the risk of high radiation dose to patients. Moreover, the medium quality or even low quality reference training images (i.e., the low quality labeled data with some noise-induced artifacts) might be collected for the supervised DL networks training, which would degrade the reconstruction performance of the network. To address this issue, in this work, we propose an effective noise-conscious explicit weighting network (NEW-Net) for low-dose CT imaging wherein the CT images with noise-induced artifacts are treated as labeled data in the network training. Specifically, the proposed NEW-Net consists of two sub-networks, i.e., noise estimation sub- network, and noise-conscious weighting sub-network. The noise estimation sub-network produces the noise map from the low-quality training data to estimate the noise-conscious weights, which determines the contribution of the label data, i.e., small weights go along with the low quality label data with severe nosie-induced artifacts, and large weights go along with high quality label data with a few noise-induced artifacts. Then the estimated weights are used to condition the training data to train the noise-conscious weighting sub-network to eliminate the effects of low quality label data and promote the reconstruction performance and stability of the proposed NEW-Net method. The Mayo clinic data are utilized to validate and evaluate the reconstruction performance of the proposed NEW-Net method. And the experimental results demonstrate that the proposed NEW-Net method outperforms the other competing methods in the case of low quality training data, in terms of noise-induced artifacts and structure detail preservation both qualitatively and quantitatively.
Seroprevalence and risk factors associated with maedi visna virus in sheep population in southwestern Croatia

The objective of this cross-sectional study was to detect the presence of maedi visna virus (MVV) infections in sheep in three counties in southwestern Croatia: Istria County, Primorje-Gorski Kotar County and Lika-Senj County, and to estimate the influence of geographical location, breed, age and gender as risk factors on the prevalence rate. A total of 460 randomly selected sheep were tested using a commercially available ID Screen® MVV/CAEV Indirect screening test, VISNAS-5P (ID.VET, Grabels France) for detection of antibodies against the Maedi-Visna virus. The prevalence rate for the entire population was 10.00 % (95 % 7.26 - 12.74 %), The highest seroprevalence of MVV was found in Istria County (19.02%, CI 95% 13.35 –24.70%), followed by Lika-Senj County (5.43 %, CI 95 % 2.17- 8.70) and Primorje-Gorski Kotar County (1.07 %, CI 95 % -1.03 – 3.17) (P<0.01). When comparing the seroprevalence among breeds, statistically significant differences were observed. The largest number of positive sheep was found in the following breeds: Istrian Pramenka (22.97%, CI 95% 13.39 - 32.55%), Jezersko-Solcava sheep (23.53%, CI 95% 11.89 - 35.17%%), Pramenka (13.16%, CI 95% 2.41 - 23.91%) and Lika sheep (9.78%, CI 95% 3.71- 15.85%). Prevalence rates in other examined breeds (Krk sheep, Pag sheep and Romanov sheep) were lower than 2 % (P<0.01). No statistically significant differences in seroprevalence between sex and age were found. According to risk estimation, the main risk factors were found to be geographical location and breed. In Istria County, seroprevalence was 4.77 ( CI 95 % 2.67 – 8.54) higher than in other examined areas (OR = 5.66, CI 95 % 2.97-10.79) (P<0.01). As for breeds, significant differences were observed between Jezersko-Solcava sheep and Lika sheep (OR = 2.84, CI 95% 1.13-7.14) and Istrian Pramenka in relation to Pag sheep (OR 22.51,, CI 95% 16.99-119.73), Krk sheep (OR 19.53, CI 95% 5.80-108.27), and Lika sheep (OR 2.35, CI 95% 1.17-6.47). This cross-section study indicates the circulation of MVV in this sheep population in Croatia, and represents the first research into the risk factors for MVV in sheep in southwestern Croatia.
Task-driven CT image quality optimization for low-contrast lesion detectability with tunable neural networks

Low-contrast lesions are difficult to detect in noisy low-dose CT images. Improving CT image quality for this detection task has the potential to improve diagnostic accuracy and patient outcomes. In this work, we use tunable neural networks for CT image restoration with a hyperparameter to control the variance/bias tradeoff. We use clinical images from a super-high-resolution normal-dose CT scan to synthesize low-contrast low-dose CT images for supervised training of deep learning CT reconstruction models. Those models are trained using with multiple noise realizations so that variance and bias can be penalized separately. We use a training loss function with one hyperparameter called the denoising level, which controls the variance/bias tradeoff. Finally, we evaluate the CT image quality to find the optimal denoising level for low-contrast lesion detectability. We evaluate performance using a shallow neural network model classification model to represent a suboptimal image observer. Our results indicate that the optimal networks for low-contrast lesion detectability are those that prioritize bias reduction rather than mean-squared error, which demonstrates the potential clinical benefit of our proposed tunable neural networks.
The June 2012 North American Derecho: A Testbed for Evaluating Regional and Global Climate Modeling Systems at Cloud‐Resolving Scales

In this paper, we introduce a testbed for evaluating and comparing climate modeling systems at cloud resolving scales using hindcasts of the June 2012 North American derecho. To demonstrate its utility for model intercomparison, the testbed is applied to two models: the regionally‐refined Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) at 6.5, 3.25 and 1.625 km grid spacing and the Weather Research and Forecasting (WRF) model with 3.2 and 1.6 km grid spacing. We find the simulation results to be highly sensitive to the initial conditions (ICs), initialization time, and model configurations, with ICs from the Rapid Refresh producing the best simulation. Significant improvement is identified in both models as horizontal grid spacing is refined. While a propagation delay of approximately 2 hr is found in both models, SCREAM at 1.625 km simulates the observed bow echo structure of the derecho well and predicts strong surface gusts that exceed 30 m/s. In comparison, WRF has difficulty producing surface wind over 25 m/s, with wind gusts in WRF 42%–46% lower than in SCREAM. However, WRF has a lower bias in simulating cloud top temperature and extent, but overestimates precipitation intensity. Both models reproduce the observed outgoing longwave radiation spatial patterns well (Pearson correlation >0.88), but, compared with NEXRAD observations, simulate generally larger areas of composite radar reflectivity >40 dBZ and underestimate the precipitating area by ∼47%.
Estimating the variance in coordinate measuring for binary orbit

Abstract The variance of a purely random error - “pure error” - in measuring the relative coordinates during the calculation of the orbits of binary stars, (its "unbiased estimate"), is necessary for each test used in the orbit calculation for double stars, such as the adequacy test for the orbit model, gross-error tests and the like. Since this variance is unknown, in this paper we present the robust PEROBEPE1 method which provides an unbiased variance estimate for the pure error in the coordinate measurements concerning the orbits of double stars. This estimate is independent of the model adequacy for a double-star orbit and thus can be used in any test concerning double stars.
Optimization of MV-kV dual-energy CT imaging for tomographic therapy

Computed tomography (CT) imaging is a key element of effective radiation therapy treatment, providing a threedimensional patient model for treatment planning dose calculations and precise patient positioning at the time of treatment. For radiotherapy, CT images are acquired using either an additional kilovoltage (kV) x-ray source mounted onto the treatment system or the system’s intrinsic megavoltage (MV) source. With beam geometry similar to fan-beam CT, tomographic therapy systems have unique potential for performing MV-kV dual-energy CT (DECT) imaging. This work was undertaken to quantify the prospects of MV-kV DECT. Six MV-kV and three kV-kV spectral combinations were considered. Single ray signal-to-noise ratios (SNRs) were calculated for a two-material object using estimation theory in the context of basis-material decomposition. Maximum-achievable SNR and optimal dose allocation were compared across spectral combinations and object thicknesses. Basis material images of the XCAT phantom were simulated using 140kV-80kV and DetunedMV-80kV spectral pairs, using both optimized and equal dose distributions. Results demonstrated that optimal SNR is achieved with >80% dose allocated to the MV spectrum (1-cm bone, 40-cm tissue object). SNR improved by as much as 31.5% by optimizing the dose allocation. This yielded 26% noise reduction and 23% SNR improvement in the XCAT basis material images. Peak SNR and optimal dose distribution varied considerably for different object thicknesses, suggesting MV-kV DECT protocol should be carefully chosen for a given imaging task. This study demonstrates the potential for enhanced contrast with MV-kV DECT imaging and warrants further investigation into its practical implementation in radiotherapy settings.
Machine Learning to Predict Three Types of Outcomes After Traumatic Brain Injury Using Data at Admission: A Multicenter Study for Development and Validation.

The difficulty of accurately identifying patients who would benefit from promising treatments makes it challenging to prove the efficacy of novel treatments for traumatic brain injury (TBI). Although machine learning is being increasingly applied to this task, existing binary outcome prediction models are insufficient for the effective stratification of TBI patients. The aim of this study was to develop an accurate 3-class outcome prediction model to enable appropriate patient stratification. To this end, retrospective balanced data of 1200 blunt TBI patients admitted to six Japanese hospitals from January 2018 onwards (200 consecutive cases at each institution) were used for model training and validation. We incorporated 21 predictors obtained in the emergency department, including age, sex, six clinical findings, four laboratory parameters, eight CT findings, and an emergency craniotomy. We developed two machine learning models (XGBoost and dense neural network) and logistic regression models to predict 3-class outcomes based on the Glasgow Outcome Scale-Extended (GOSE) at discharge. The prediction models were developed using a training dataset with n = 1000, and their prediction performances were evaluated over two validation rounds on a validation dataset (n = 80) and a test dataset (n = 120) using the bootstrap method. Of the 1200 patients in aggregate, the median patient age was 71 years, 199 (16.7%) exhibited severe TBI, and emergency craniotomy was performed on 104 patients (8.7%). The median length of stay was 13.0 days. The 3-class outcomes were good recovery/moderate disability for 709 patients (59.1%), severe disability/vegetative state in 416 patients (34.7%), and death in 75 patients (6.2%). XGBoost model performed well with 69.5% sensitivity, 82.5% accuracy, and an area under the receiver operating characteristic curve of 0.901 in the final validation. In terms of the receiver operating characteristic curve analysis, the XGBoost outperformed the neural network-based and logistic regression models slightly. In particular, XGBoost outperformed the logistic regression model significantly in predicting severe disability/vegetative state. Although each model predicted favorable outcomes accurately, they tended to miss the mortality prediction. The proposed machine learning model was demonstrated to be capable of accurate prediction of in-hospital outcomes following TBI, even with the three GOSE-based categories. As a result, it is expected to be more impactful in the development of appropriate patient stratification methods in future TBI studies than conventional binary prognostic models. Furthermore, outcomes were predicted based on only clinical data obtained from the emergency department. However, developing a robust model with consistent performance in diverse scenarios remains challenging, and further efforts are needed to improve generalization performance.
Strategic Monitoring of Networked Systems with Heterogeneous Security Levels

We consider a strategic network monitoring problem involving the operator of a networked system and an attacker. The operator aims to randomize the placement of multiple protected sensors to monitor and protect components that are vulnerable to attacks. We account for the heterogeneity in the components' security levels and formulate a large-scale maximin optimization problem. After analyzing its structure, we propose a three-step approach to approximately solve the problem. First, we solve a generalized covering set problem and run a combinatorial algorithm to compute an approximate solution. Then, we compute approximation bounds by solving a nonlinear set packing problem. To evaluate our solution approach, we implement two classical solution methods based on column generation and multiplicative weights updates, and test them on real-world water distribution and power systems. Our numerical analysis shows that our solution method outperforms the classical methods on large-scale networks, as it efficiently generates solutions that achieve a close to optimal performance and that are simple to implement in practice.
Massively Parallel Single-Source SimRanks in o(log n) Rounds

SimRank is one of the most fundamental measures that evaluate the structural similarity between two nodes in a graph and has been applied in a plethora of data management tasks. These tasks often involve single-source SimRank computation that evaluates the SimRank values between a source node $s$ and all other nodes. Due to its high computation complexity, single-source SimRank computation for large graphs is notoriously challenging, and hence recent studies resort to distributed processing. To our surprise, although SimRank has been widely adopted for two decades, theoretical aspects of distributed SimRanks with provable results have rarely been studied. In this paper, we conduct a theoretical study on single-source SimRank computation in the Massive Parallel Computation (MPC) model, which is the standard theoretical framework modeling distributed systems such as MapReduce, Hadoop, or Spark. Existing distributed SimRank algorithms enforce either $\Omega(\log n)$ communication round complexity or $\Omega(n)$ machine space for a graph of $n$ nodes. We overcome this barrier. Particularly, given a graph of $n$ nodes, for any query node $v$ and constant error $\epsilon>\frac{3}{n}$, we show that using $O(\log^2 \log n)$ rounds of communication among machines is almost enough to compute single-source SimRank values with at most $\epsilon$ absolute errors, while each machine only needs a space sub-linear to $n$. To the best of our knowledge, this is the first single-source SimRank algorithm in MPC that can overcome the $\Theta(\log n)$ round complexity barrier with provable result accuracy.
Missing Data Imputation with Graph Laplacian Pyramid Network

Data imputation is a prevalent and important task due to the ubiquitousness of missing data. Many efforts try to first draft a completed data and second refine to derive the imputation results, or"draft-then-refine"for short. In this work, we analyze this widespread practice from the perspective of Dirichlet energy. We find that a rudimentary"draft"imputation will decrease the Dirichlet energy, thus an energy-maintenance"refine"step is in need to recover the overall energy. Since existing"refine"methods such as Graph Convolutional Network (GCN) tend to cause further energy decline, in this work, we propose a novel framework called Graph Laplacian Pyramid Network (GLPN) to preserve Dirichlet energy and improve imputation performance. GLPN consists of a U-shaped autoencoder and residual networks to capture global and local detailed information respectively. By extensive experiments on several real-world datasets, GLPN shows superior performance over state-of-the-art methods under three different missing mechanisms. Our source code is available at https://github.com/liguanlue/GLPN.
Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk

We study the trade-off between expectation and tail risk for regret distribution in the stochastic multi-armed bandit problem. We fully characterize the interplay among three desired properties for policy design: worst-case optimality, instance-dependent consistency, and light-tailed risk. We show how the order of expected regret exactly affects the decaying rate of the regret tail probability for both the worst-case and instance-dependent scenario. A novel policy is proposed to characterize the optimal regret tail probability for any regret threshold. Concretely, for any given $\alpha\in[1/2, 1)$ and $\beta\in[0, \alpha]$, our policy achieves a worst-case expected regret of $\tilde O(T^\alpha)$ (we call it $\alpha$-optimal) and an instance-dependent expected regret of $\tilde O(T^\beta)$ (we call it $\beta$-consistent), while enjoys a probability of incurring an $\tilde O(T^\delta)$ regret ($\delta\geq\alpha$ in the worst-case scenario and $\delta\geq\beta$ in the instance-dependent scenario) that decays exponentially with a polynomial $T$ term. Such decaying rate is proved to be best achievable. Moreover, we discover an intrinsic gap of the optimal tail rate under the instance-dependent scenario between whether the time horizon $T$ is known a priori or not. Interestingly, when it comes to the worst-case scenario, this gap disappears. Finally, we extend our proposed policy design to (1) a stochastic multi-armed bandit setting with non-stationary baseline rewards, and (2) a stochastic linear bandit setting. Our results reveal insights on the trade-off between regret expectation and regret tail risk for both worst-case and instance-dependent scenarios, indicating that more sub-optimality and inconsistency leave space for more light-tailed risk of incurring a large regret, and that knowing the planning horizon in advance can make a difference on alleviating tail risks.
Spectral computation with third-order tensors using the t-product

The tensor t-product, introduced by Kilmer and Martin [26], is a powerful tool for the analysis of and computation with third-order tensors. This paper introduces eigentubes and eigenslices of third-order tensors under the t-product. The eigentubes and eigenslices are analogues of eigenvalues and eigenvectors for matrices. Properties of eigentubes and eigenslices are investigated and numerical methods for their computation are described. The methods include the tensor power method, tensor subspace iteration, and the tensor QR algorithm. Computed examples illustrate the performance of these methods.
RoboPianist: A Benchmark for High-Dimensional Robot Control

—We introduce a new benchmarking suite for high-dimensional control, targeted at testing high spatial and temporal precision, coordination, and planning, all with an underactuated system frequently making-and-breaking contacts. The proposed challenge is mastering the piano through bi-manual dexterity, using a pair of simulated anthropomorphic robot hands. We call it R OBO P IANIST , and the initial version covers a broad set of 150 variable-difﬁculty songs. We investigate both model-free and model-based methods on the benchmark, characterizing their performance envelopes. We observe that while certain existing methods, when well-tuned, can achieve impressive levels of performance in certain aspects, there is signiﬁcant room for improvement. R OBO P IANIST provides a rich quantitative benchmarking environment, with human-interpretable results, high ease of expansion by simply augmenting the repertoire with new songs, and opportunities for further research, including in multi-task learning, zero-shot generalization, multimodal (sound, vision, touch) learning, and imitation. Supplementary information, including videos of our control policies, can be found at https://kzakka.com/robopianist/.