Miracle-dz/UniFAR-FTUs huggingface dataset:
3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge

Segmenting brain tumors is complex due to their diverse appearances and scales. Brain metastases, the most common type of brain tumor, are a frequent complication of cancer. Therefore, an effective segmentation model for brain metastases must adeptly capture local intricacies to delineate small tumor regions while also integrating global context to understand broader scan features. The TransUNet model, which combines Transformer self-attention with U-Net's localized information, emerges as a promising solution for this task. In this report, we address brain metastases segmentation by training the 3D-TransUNet model on the Brain Tumor Segmentation (BraTS-METS) 2023 challenge dataset. Specifically, we explored two architectural configurations: the Encoder-only 3D-TransUNet, employing Transformers solely in the encoder, and the Decoder-only 3D-TransUNet, utilizing Transformers exclusively in the decoder. For Encoder-only 3D-TransUNet, we note that Masked-Autoencoder pre-training is required for a better initialization of the Transformer Encoder and thus accelerates the training process. We identify that the Decoder-only 3D-TransUNet model should offer enhanced efficacy in the segmentation of brain metastases, as indicated by our 5-fold cross-validation on the training set. However, our use of the Encoder-only 3D-TransUNet model already yield notable results, with an average lesion-wise Dice score of 59.8\% on the test set, securing second place in the BraTS-METS 2023 challenge.
Quantitative compositional mapping of mineral phases by electron probe micro-analyser

Abstract Compositional mapping has greatly impacted mineralogical and petrological studies over the past half-century with increasing use of the electron probe micro-analyser. Many technical and analytical developments have benefited from the synergies of physicists and geologists and they have greatly contributed to the success of this analytical technique. Large-area compositional mapping has become routine practice in many laboratories worldwide, improving our ability to measure the compositional variability of minerals in natural geological samples and reducing the operator bias as to where to locate single spot analyses. This chapter aims to provide an overview of existing quantitative techniques for the evaluation of rock and mineral compositions and to present various examples of applications. A new advanced method for compositional map standardization that relies on internal standards and accurately corrects the X-ray intensities for continuum background is also presented. This technique has been implemented into the computer software XMapTools. The improved workflow defines the appropriate practice of accurate standardization and provides data-reporting standards to help improve petrological interpretations.
Unsupervised Learning With Random Forest Predictors

A random forest (RF) predictor is an ensemble of individual tree predictors. As part of their construction, RF predictors naturally lead to a dissimilarity measure between the observations. One can also define an RF dissimilarity measure between unlabeled data: the idea is to construct an RF predictor that distinguishes the “observed” data from suitably generated synthetic data. The observed data are the original unlabeled data and the synthetic data are drawn from a reference distribution. Here we describe the properties of the RF dissimilarity and make recommendations on how to use it in practice. An RF dissimilarity can be attractive because it handles mixed variable types well, is invariant to monotonic transformations of the input variables, and is robust to outlying observations. The RF dissimilarity easily deals with a large number of variables due to its intrinsic variable selection; for example, the Addcl 1 RF dissimilarity weighs the contribution of each variable according to how dependent it is on other variables. We find that the RF dissimilarity is useful for detecting tumor sample clusters on the basis of tumor marker expressions. In this application, biologically meaningful clusters can often be described with simple thresholding rules.
Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding

Safety-critical 3D scene understanding tasks necessitate not only accurate but also confident predictions from 3D perception models. This study introduces Calib3D, a pioneering effort to benchmark and scrutinize the reliability of 3D scene understanding models from an uncertainty estimation viewpoint. We comprehensively evaluate 28 state-of-the-art models across 10 diverse 3D datasets, uncovering insightful phenomena that cope with both the aleatoric and epistemic uncertainties in 3D scene understanding. We discover that despite achieving impressive levels of accuracy, existing models frequently fail to provide reliable uncertainty estimates -- a pitfall that critically undermines their applicability in safety-sensitive contexts. Through extensive analysis of key factors such as network capacity, LiDAR representations, rasterization resolutions, and 3D data augmentation techniques, we correlate these aspects directly with the model calibration efficacy. Furthermore, we introduce DeptS, a novel depth-aware scaling approach aimed at enhancing 3D model calibration. Extensive experiments across a wide range of configurations validate the superiority of our method. We hope this work could serve as a cornerstone for fostering reliable 3D scene understanding. Code and benchmark toolkits are publicly available.
Prognostic factors in agnogenic myeloid metaplasia: a report on 195 cases with a new scoring system.

We studied the survival of 195 patients with agnogenic myeloid metaplasia (AMM) diagnosed between 1962 and 1992 in an attempt to stratify patients into risk groups. Median survival was 42 months. Adverse prognostic factors for survival were age > 60 years, hepatomegaly, weight loss, low hemoglobin level (Hb), low or very high leukocyte count (WBC), high percentage of circulating blasts, male sex, and low platelet count. A new scoring system based on two adverse prognostic factors, namely Hb < 10 g/dL and WBC < 4 or > 30 x 10(9)/L, was able to separate patients in three groups with low (0 factor), intermediate (1 factor), and high (2 factors) risks, associated with a median survival of 93, 26, and 13 months, respectively. An abnormal karyotype (32 cases of 94 tested patients) was associated with a short survival, especially in the low-risk group (median survival of 50 v 112 months in patients with normal karyotype). The prognostic factors for acute conversion were WBC > 30 x 10(9)/L and abnormal karyotype. Thus, hemoglobin level and leukocyte count provide a simple prognostic model for survival in AMM, and the adverse prognostic value of abnormal karyotype may be related to a higher rate of acute conversion.
Simultaneous Retrieval of Surface Roughness Parameters from Combined Active-Passive SMAP Observations

Soil roughness strongly influences processes like erosion, infiltration, moisture and evaporation of soils as well as growth of agricultural plants. An approach to soil roughness based on active-passive microwave covariation is proposed in order to simultaneously retrieve the vertical RMS height (s) and horizontal correlation length (l) of soil surfaces from simultaneously measured radar and radiometer microwave signatures. The approach is based on a retrieval algorithm for active-passive covariation including the improved Integral Equation Method (I2EM). It is tested with the global active-passive microwave observations of NASA’s Soil Moisture Active Passive (SMAP) mission. The developed roughness retrieval algorithm shows independence of permittivity for εs > 10 [-] due to the covariation formalism. Results reveal that s and l can be estimated simultaneously by the proposed approach since surface patterns of nonvegetated areas can be assessed on global scale. In regions with sandy deserts, like the Sahara or the outback in Australia, determined s and l confirm rather smooth to semi-rough surface roughness patterns with most frequent vertical RMS heights smaller 3 cm and corresponding higher horizontal correlation lengths (> 8 cm).
The role of brown adipose tissue in diet-induced thermogenesis

Measurement of energy balance during voluntary overeating in rats unequivocally establishes the quantitative importance of diet-induced thermogenesis in energy balance. Like cold-induced thermogenesis, this form of heat production involves changes in the activity of the sympathetic nervous system and brown adipose tissue which suggest that this tissue may determine metabolic efficiency and resistance to obesity.
Long-Term Coffee Consumption and Risk of Cardiovascular Disease: A Systematic Review and a Dose–Response Meta-Analysis of Prospective Cohort Studies

Background— Considerable controversy exists on the association between coffee consumption and cardiovascular disease (CVD) risk. A meta-analysis was performed to assess the dose–response relationship of long-term coffee consumption with CVD risk. Methods and Results— PubMed and EMBASE were searched for prospective cohort studies of the relationship between coffee consumption and CVD risk, which included coronary heart disease, stroke, heart failure, and CVD mortality. Thirty-six studies were included with 1 279 804 participants and 36 352 CVD cases. A nonlinear relationship of coffee consumption with CVD risk was identified (P for heterogeneity=0.09, P for trend <0.001, P for nonlinearity <0.001). Compared with the lowest category of coffee consumption (median, 0 cups per day), the relative risk of CVD was 0.95 (95% confidence interval, 0.87–1.03) for the highest category (median, 5 cups per day) category, 0.85 (95% confidence interval, 0.80–0.90) for the second highest category (median, 3.5 cups per day), and 0.89 (95% confidence interval, 0.84–0.94) for the third highest category (median, 1.5 cups per day). Looking at separate outcomes, coffee consumption was nonlinearly associated with both coronary heart disease (P for heterogeneity=0.001, P for trend <0.001, P for nonlinearity <0.001) and stroke (P for heterogeneity=0.07, P for trend <0.001, P for nonlinearity <0.001; P for trend differences >0.05) risks. Conclusions— A nonlinear association between coffee consumption and CVD risk was observed in this meta-analysis. Moderate coffee consumption was inversely significantly associated with CVD risk, with the lowest CVD risk at 3 to 5 cups per day, and heavy coffee consumption was not associated with elevated CVD risk.
FH-SSTNet: Forehead Creases based User Verification using Spatio-Spatial Temporal Network

Biometric authentication, which utilizes contactless features, such as forehead patterns, has become increasingly important for identity verification and access management. The proposed method is based on learning a 3D spatio-spatial temporal convolution to create detailed pictures of forehead patterns. We introduce a new CNN model called the Forehead Spatio-Spatial Temporal Network (FH-SSTNet), which utilizes a 3D CNN architecture with triplet loss to capture distinguishing features. We enhance the model's discrimination capability using Arcloss in the network's head. Experimentation on the Forehead Creases version 1 (FH-V1) dataset, containing 247 unique subjects, demonstrates the superior performance of FH-SSTNet compared to existing methods and pre-trained CNNs like ResNet50, especially for forehead-based user verification. The results demonstrate the superior performance of FH-SSTNet for forehead-based user verification, confirming its effectiveness in identity authentication.
Self-Supervised Multi-Frame Neural Scene Flow

Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving. Despite their success, the underlying reasons for their astonishing generalization capabilities remain unclear. Our research addresses this gap by examining the generalization capabilities of NSFP through the lens of uniform stability, revealing that its performance is inversely proportional to the number of input point clouds. This finding sheds light on NSFP's effectiveness in handling large-scale point cloud scene flow estimation tasks. Motivated by such theoretical insights, we further explore the improvement of scene flow estimation by leveraging historical point clouds across multiple frames, which inherently increases the number of point clouds. Consequently, we propose a simple and effective method for multi-frame point cloud scene flow estimation, along with a theoretical evaluation of its generalization abilities. Our analysis confirms that the proposed method maintains a limited generalization error, suggesting that adding multiple frames to the scene flow optimization process does not detract from its generalizability. Extensive experimental results on large-scale autonomous driving Waymo Open and Argoverse lidar datasets demonstrate that the proposed method achieves state-of-the-art performance.
Why do dome workers never participate in training

Despite empirical evidence on the positive effects of training on workers’ wages and employability, a puzzlingly large number of workers have never participated in any training course during their career. Using data from a representative sample of the Dutch labour force, we distinguish between recent participants in training, temporary non-participants, and chronic non-participants to better understand the drivers of training participation. We find a hard core of 17% of the working population who never participated in any training course during their working life. This chronic nonparticipation is related to workers’ intrinsic motivation to learn, low levels of task complexity, and a missing human resource development (HRD) policy in the firm where the worker is employed. However, temporary non-participants in training do not differ from recent participants when it comes to their non-cognitive skills, task complexity, or firm HRD policy. Chronic non-participants appear to earn significantly less than those who recently participated in training, but we do not observe any wage difference between temporary non-participants and recent participants. The latter result suggests that studies that do not distinguish between temporary and chronic non-participants in training underestimate the wage effects of training.
ProIn: Learning to Predict Trajectory Based on Progressive Interactions for Autonomous Driving

Accurate motion prediction of pedestrians, cyclists, and other surrounding vehicles (all called agents) is very important for autonomous driving. Most existing works capture map information through an one-stage interaction with map by vector-based attention, to provide map constraints for social interaction and multi-modal differentiation. However, these methods have to encode all required map rules into the focal agent's feature, so as to retain all possible intentions' paths while at the meantime to adapt to potential social interaction. In this work, a progressive interaction network is proposed to enable the agent's feature to progressively focus on relevant maps, in order to better learn agents' feature representation capturing the relevant map constraints. The network progressively encode the complex influence of map constraints into the agent's feature through graph convolutions at the following three stages: after historical trajectory encoder, after social interaction, and after multi-modal differentiation. In addition, a weight allocation mechanism is proposed for multi-modal training, so that each mode can obtain learning opportunities from a single-mode ground truth. Experiments have validated the superiority of progressive interactions to the existing one-stage interaction, and demonstrate the effectiveness of each component. Encouraging results were obtained in the challenging benchmarks.
Imaging Neuroinflammation in Alzheimer's Disease with Radiolabeled Arachidonic Acid and PET

Incorporation coefficients (K*) of arachidonic acid (AA) in the brain are increased in a rat model of neuroinflammation, as are other markers of AA metabolism. Data also indicate that neuroinflammation contributes to Alzheimer's disease (AD). On the basis of these observations, K* for AA was hypothesized to be elevated in patients with AD. Methods: A total of 8 patients with AD with an average (±SD) Mini-Mental State Examination score of 14.7 ± 8.4 (mean age, 71.7 ± 11.2 y) and 9 controls with a normal Mini-Mental State Examination score (mean age, 68.7 ± 5.6 y) were studied. Each subject received a 15O-water PET scan of regional cerebral blood flow, followed after 15 min by a 1-11C-AA scan of regional K* for AA. Results: In the patients with AD, compared with control subjects, global gray matter K* for AA (corrected or uncorrected for the partial-volume error [PVE]) was significantly elevated, whereas only PVE-uncorrected global cerebral blood flow was reduced significantly (P < 0.05). A false-discovery-rate procedure indicated that PVE-corrected K* for AA was increased in 78 of 90 identified hemispheric gray matter regions. PVE-corrected regional cerebral blood flow, although decreased in 12 regions at P < 0.01 by an unpaired t test, did not survive the false-discovery-rate procedure. The surviving K* increments were widespread in the neocortex but were absent in caudate, pallidum, and thalamic regions. Conclusion: These preliminary results show that K* for AA is widely elevated in the AD brain, particularly in regions reported to have high densities of senile (neuritic) plaques with activated microglia. To the extent that the elevations represent upregulated AA metabolism associated with neuroinflammation, PET with 1-11C-AA could be used to examine neuroinflammation in patients with AD and other brain diseases.
Quasi-randomization tests for network interference

Many classical inferential approaches fail to hold when interference exists among the population units. This amounts to the treatment status of one unit affecting the potential outcome of other units in the population. Testing for such spillover effects in this setting makes the null hypothesis non-sharp. An interesting approach to tackling the non-sharp nature of the null hypothesis in this setup is constructing conditional randomization tests such that the null is sharp on the restricted population. In randomized experiments, conditional randomized tests hold finite sample validity. Such approaches can pose computational challenges as finding these appropriate sub-populations based on experimental design can involve solving an NP-hard problem. In this paper, we view the network amongst the population as a random variable instead of being fixed. We propose a new approach that builds a conditional quasi-randomization test. Our main idea is to build the (non-sharp) null distribution of no spillover effects using random graph null models. We show that our method is exactly valid in finite-samples under mild assumptions. Our method displays enhanced power over other methods, with substantial improvement in complex experimental designs. We highlight that the method reduces to a simple permutation test, making it easy to implement in practice. We conduct a simulation study to verify the finite-sample validity of our approach and illustrate our methodology to test for interference in a weather insurance adoption experiment run in rural China.
Computation of Likelihood Ratios in Fingerprint Identification for Configurations of Three Minutiæ

ABSTRACT: Recent challenges to fingerprint evidence have brought forward the need for peer‐reviewed scientific publications to support the evidential value assessment of fingerprint. This paper proposes some research directions to gather statistical knowledge of the within‐source and between‐sources variability of configurations of three minutiæ on fingermarks and fingerprints. This paper proposes the use of the likelihood ratio (LR) approach to assess the value of fingerprint evidence. The model explores the statistical contribution of configurations of three minutiae using Tippett plots and related measures to assess the quality of the system. Features vectors used for statistical analysis have been obtained following a preprocessing step based on Gabor filtering and image processing to extract minutia position, type, and direction. Spatial relationships have been coded using Delaunay triangulation. The metric, used to assess similarity between two feature vectors is based on an Euclidean distance measure. The within‐source variability has been estimated using a sample of 216 fingerprints from four fingers (two donors). Between‐sources variability takes advantage of a database of 818 ulnar loops from randomly selected males. The results show that the data‐driven approach adopted here is robust. The magnitude of LRs obtained under the prosecution and defense propositions stresses upon the major evidential contribution that small portions of fingermark, containing three minutiæ, can provide regardless of its position on the general pattern.
DHP-Mapping: A Dense Panoptic Mapping System with Hierarchical World Representation and Label Optimization Techniques

Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric and semantic concepts from maps is crucial for robots to make informed and efficient decisions. To comprehensively model the environment and effectively manage the map data structure, we propose DHP-Mapping, a dense mapping system that utilizes multiple Truncated Signed Distance Field (TSDF) submaps and panoptic labels to hierarchically model the environment. The output map is able to maintain both voxel- and submap-level metric and semantic information. Two modules are presented to enhance the mapping efficiency and label consistency: (1) an inter-submaps label fusion strategy to eliminate duplicate points across submaps and (2) a conditional random field (CRF) based approach to enhance panoptic labels through object label comprehension and contextual information. We conducted experiments with two public datasets including indoor and outdoor scenarios. Our system performs comparably to state-of-the-art (SOTA) methods across geometry and label accuracy evaluation metrics. The experiment results highlight the effectiveness and scalability of our system, as it is capable of constructing precise geometry and maintaining consistent panoptic labels. Our code is publicly available at https://github.com/hutslib/DHP-Mapping.
Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble

Recent research underscores the pivotal role of the Out-of-Distribution (OOD) feature representation field scale in determining the efficacy of models in OOD detection. Consequently, the adoption of model ensembles has emerged as a prominent strategy to augment this feature representation field, capitalizing on anticipated model diversity. However, our introduction of novel qualitative and quantitative model ensemble evaluation methods, specifically Loss Basin/Barrier Visualization and the Self-Coupling Index, reveals a critical drawback in existing ensemble methods. We find that these methods incorporate weights that are affine-transformable, exhibiting limited variability and thus failing to achieve the desired diversity in feature representation. To address this limitation, we elevate the dimensions of traditional model ensembles, incorporating various factors such as different weight initializations, data holdout, etc., into distinct supervision tasks. This innovative approach, termed Multi-Comprehension (MC) Ensemble, leverages diverse training tasks to generate distinct comprehensions of the data and labels, thereby extending the feature representation field. Our experimental results demonstrate the superior performance of the MC Ensemble strategy in OOD detection compared to both the naive Deep Ensemble method and a standalone model of comparable size. This underscores the effectiveness of our proposed approach in enhancing the model's capability to detect instances outside its training distribution.