Permutation Method Research Articles

Ultra Low-Latency Block Cipher uLBC

In recent years, there has been a growing interest in low-latency ciphers. Since the first low-latency block cipher PRINCE was proposed at ASIACRYPT 2012, many low-latency primitives sprung up, such as Midori, MANTIS, QARMA and SPEEDY. Some ciphers, like SPEEDY and Orthros, introduce bit permutations to achieve reduced delay. However, this approach poses a challenge in evaluating the resistance against some cryptanalysis, especially differential and linear attacks. SPEEDY-7-192, was fully broken by Boura et.al. using differential attack, for example. In this paper, we manage to propose a novel low-latency block cipher, which guarantees security against differential and linear attacks. Revisiting the permutation technique used in Orthros, we investigate the selection of nibble permutations and propose a method for selecting them systematically rather than relying on random search. Our new nibble permutation method ensures the existence of impossible differential and differential trails for up to 8 rounds, while the nibble permutations for both branches of Orthros may lead to a 9-round impossible differential trail. Furthermore, we introduce a new approach for constructing low-latency coordinate functions for 4-bit S-boxes, which involves a more precise delay computation compared to traditional methods based solely on circuit depth. The new low-latency primitive uLBC we propose, is a family of 128-bit block ciphers, with three different versions of key length, respectively 128-bit and 256-bit key, as well as a 384-bit tweakey version with variable-length key. According to the key length, named uLBC-128, uLBC-256 and uLBC-384t. Our analysis shows that uLBC-128 exhibits lower latency and area requirements compared to ciphers such as QARMA9-128 and Midori128. On performance, uLBC-128 has excellent AT performance, the best performance except SPEEDY-6, and even the best performance in UMC 55nm in our experiments.

Theta-Gamma Decoupling - A neurophysiological marker of impaired reward processing in Parkinson's disease.

Individuals with Parkinson's disease (PD) exhibit altered reward processing, reflected by a decreased amplitude of an event-related potential (ERP) marker called reward positivity (RewP). Most studies have used RewP to investigate reward behavior due to the high temporal resolution of EEG and its high sensitivity. However, traditional single-electrode ERP analyses often overlook the intricate dynamics of non-phase-locked oscillatory activity and the complex interactions within these neural oscillatory patterns. Studying oscillatory activity is crucial as it provides mechanistic insights into the functional, spatial, and temporal aspects of neuronal processing. To address this gap, we employed a data-driven approach to identify EEG-based markers associated with PD reward processing deficits. Using an openly available 64-channel EEG dataset of 28 age- and sex-matched PD and control participants during a reinforcement learning task, we conducted a comprehensive secondary analysis. First, we employed a cluster-based permutation method to extract ERP markers, finding a consistent decrease in reward positivity in PD, regardless of medication status. Additionally, through region of interest (ROI) analysis on time-frequency data, we identified specific oscillatory patterns during reward processing. PD patients exhibited attenuated theta power and increased gamma power compared to healthy controls (HC). Notably, within the PD group, those off medication showed anterior localization of high gamma power, while those on medication displayed higher posterior gamma power. Building upon these findings, we explored phase-amplitude coupling between theta phase and gamma amplitude measured by the modulation index. We observed a trend of decreased theta-gamma coupling in PD patients, with statistically significant differences between on and off medication conditions. These results highlight the potential role of theta-gamma coupling as a neuromodulatory target for improving goal-oriented behavior in PD. Our correlation analyses suggest that high gamma power is linked to longer disease duration, while reduced reward positivity and low theta-gamma coupling may serve as markers of the dopaminergic impact on reward processing. Thus, our study unveils the intricate time-frequency dynamics underlying reward processing deficits in PD, emphasizing the utility of a data-driven approach to elucidate neural mechanisms and to identify potential therapeutic targets.

EFFECT OF CORRUPTION ON NATIONAL SECURITY AND WAY FORWARD

This paper examines the effect of corruption on natural security and way forward in Nigeria. Bribery and corruption have been globally perceived as enemy of national economic development and threatening the security of a nation. Nigeria is of the country that the menace of the corruption said to have gone beyond imagination as the corruption persist from both government and private institutions. However, recent transparency international, 2023 corruption perception index, ranked Nigeria 145th out of 180 countries in the index. Therefore, it is evident that there are several corruption practices on ground in Nigeria to the extent that the practice of the menace has reached an alarming stage as inflation and insecurity are worsening daily. Despite the efforts of the past and present administration to arrest the menace of corruption in Nigeria, through various anti-corruption agencies like ICPC, EFCC and others, yet unfortunately the issue of corruption among political office holders, civil servants, business men and women, contractors, etc. continued to gain ground that something urgently need to be done to curb the phenomena of corruption in Nigeria. The study advanced four objectives to answer the four questions and two hypotheses raised to fund out the effect of corruption on national security in Nigeria. Past empirical research literatures were used and relate it to the work. A survey and descriptive research design is adopted while a secondary data was used as source of data collection. These includes formals, conference papers, online newspaper, whatsapp group members opinion, linkedin friends, telegraph group members opinion, academic and non-academic social media groups, facebook group opinions among others. A purposive sampling technique were used in selecting the study respondents without regards to any demographic affiliation. A sample size of 300 respondents were adopted. The numbers are made up of 50 respondents each from six geopolitical regions zones of Nigeria, such as NE, NC, NW, SW, SE, SS, respectively. The data obtained from the study respondents spread within the six geopolitical zones states were translated in to statistical figures and used statistical method of T-test statistical permutation to distinguish the opinions of the respondents on the effect of corruption on national security and way forward. Discussion, conclusion, and recommendations were made for better Nigeria if properly implemented.

Key feature identification of internal kink mode using machine learning

The internal kink mode is one of the crucial factors affecting the stability of magnetically confined fusion devices. This paper explores the key features influencing the growth rate of internal kink modes using machine learning techniques such as Random Forest, Extreme Gradient Boosting (XGboost), Permutation, and SHapley Additive exPlanations (SHAP). We conduct an in-depth analysis of the significant physical mechanisms by which these key features impact the growth rate of internal kink modes. Numerical simulation data were used to train high-precision machine learning models, namely Random Forest and XGBoost, which achieved coefficients of determination values of 95.07% and 94.57%, respectively, demonstrating their capability to accurately predict the growth rate of internal kink modes. Based on these models, key feature analysis was systematically performed with Permutation and SHAP methods. The results indicate that resistance, pressure at the magnetic axis, viscosity, and plasma rotation are the primary features influencing the growth rate of internal kink modes. Specifically, resistance affects the evolution of internal kink modes by altering current distribution and magnetic field structure; pressure at the magnetic axis impacts the driving force of internal kink modes through the pressure gradient directly related to plasma stability; viscosity modifies the dynamic behavior of internal kink modes by regulating plasma flow; and plasma rotation introduces additional shear forces, affecting the stability and growth rate of internal kink modes. This paper describes the mechanisms by which these four key features influence the growth rate of internal kink modes, providing essential theoretical insights into the behavior of internal kink modes in magnetically confined fusion devices.

Predicting the success of startups using a machine learning approach

Successful investment in early-stage companies has high uncertainty. More specifically, the tools available to investors need to be more robust to reduce the risk and manage the uncertainty of startups. This research aims to use machine learning methods to design a prediction solution to identify successful startups for investors. In order to design the predicting solution and provide policies, classification, and clustering algorithms have been utilized to predict the success of startups and perform feature importance analysis based on the SHAP and permutation methods. Subsequently, the performance of four classification algorithms, such as Random Forest, Gradient Boost, Multilayer Perceptron, Logistic Regression and Support Vector Machine, are compared to predict business success. Meanwhile, Random Forest and Gradient Boosting algorithms showed the best accuracy, which was equal to 82% and 80%, respectively. Based on the feature importance of Random Forest and Gradient Boosting, which is obtained from the SHAP method, indicated that the higher values of “Number of followers on LinkedIn”, “Number of employees on LinkedIn”, “Number of followers on Twitter”, and “Last raised amount” have higher SHAP values and a more significant impact on the model output. Three clustering algorithms including hierarchy, K-means, and DBSCAN were also compared. Among them, the K-means algorithm performs best with 72% silhouette, and K-means was employed to explain each cluster’s characteristics. Finally, an effective artificial intelligence-based prediction solution has been proposed to show the way for investors to apply machine learning concepts to predict the success of startups.

Deep Learning‐Based Precipitation Simulation for Tropical Cyclones, Mesoscale Convective Systems, and Atmospheric Rivers in East Asia

AbstractDifferent types of weather events, including tropical cyclones (TCs), mesoscale convective systems (MCSs), and atmospheric rivers (ARs), significantly impact precipitation patterns in East Asia. This study pioneers the application of deep learning (DL) methods, including convolutional neural network, U‐Net, and Attention U‐Net models, to simulate precipitation associated with these weather events. The spatial permutation method is also used to identify key meteorological variables for accurately generating precipitation in DL models. The DL models trained on all timeslots consistently surpass the performance of state‐of‐the‐art numerical simulations, although their efficacy slightly diminishes during extreme weather events. This outperformance is attributed to the appropriate emphasis on key variables that capture precipitation processes, such as low‐level moisture and mid‐level pressure fields. However, new DL models trained separately for TCs, MCSs, and ARs using clipped precipitation as the output does not exceed the performance of the previous DL models. Among all input features, moisture variables contribute the most to precipitation at low intensity, while the importance of other variables increases for more intense precipitation, although some discrepancies vary across models and event types. The spatial results further reveal the detailed locations of variables that are essential for accurately simulating precipitation related to weather events, such as areas of high specific humidity and strong winds. DL models could also acquire useful information from region remote to the events to improve the simulation. Overall, DL models serve as promising tools for simulating and enhancing our understanding of precipitation patterns associated with various weather events in East Asia.

PyRforest: a comprehensive R package for genomic data analysis featuring scikit-learn Random Forests in R.

Random Forest models are widely used in genomic data analysis and can offer insights into complex biological mechanisms, particularly when features influence the target in interactive, nonlinear, or nonadditive ways. Currently, some of the most efficient Random Forest methods in terms of computational speed are implemented in Python. However, many biologists use R for genomic data analysis, as R offers a unified platform for performing additional statistical analysis and visualization. Here, we present an R package, pyRforest, which integrates Python scikit-learn "RandomForestClassifier" algorithms into the R environment. pyRforest inherits the efficient memory management and parallelization of Python, and is optimized for classification tasks on large genomic datasets, such as those from RNA-seq. pyRforest offers several additional capabilities, including a novel rank-based permutation method for biomarker identification. This method can be used to estimate and visualize P-values for individual features, allowing the researcher to identify a subset of features for which there is robust statistical evidence of an effect. In addition, pyRforest includes methods for the calculation and visualization of SHapley Additive exPlanations values. Finally, pyRforest includes support for comprehensive downstream analysis for gene ontology and pathway enrichment. pyRforest thus improves the implementation and interpretability of Random Forest models for genomic data analysis by merging the strengths of Python with R. pyRforest can be downloaded at: https://www.github.com/tkolisnik/pyRforest with an associated vignette at https://github.com/tkolisnik/pyRforest/blob/main/vignettes/pyRforest-vignette.pdf.

Landscape positioning of Neolithic mustatil stone structures along the margins of the Nefud Desert, Saudi Arabia

Monumental rectangular stone structures called mustatils are an important emerging feature of the Holocene archaeological record of northwestern Arabia. To date, few have been excavated, with available radiocarbon dates suggesting an age range of ca. 5400–4200 BC. Here we present a rigorous spatial analysis to identify the patterning and landscape context of 169 mustatils in the southern and western margins of the Nefud Desert. This included: (1) a systematic survey of satellite imagery to identify mustatils; (2) viewshed analysis to examine location and landscape visibility; (3) a point process model to understand how diverse environmental and landscape variables affect mustatil locations; (4) mark correlation function to assess spatial patterning of mustatils based on their size. Results indicate that mustatil locations are determined most by proximity to water (likely locations of enhanced surface water occurrence under the enhanced humidity of the Mid-Holocene), on east facing slopes, close to rocky areas, at elevations between 880 and 950 masl, and on or near topographic ridges (positive topographic position index). Viewshed analysis showed that mustatils are preferentially located in areas that have good views, but not the best that are available, indicating complex landscape positioning that balances a range of topographic and behavioural factors. Using a rank permutation method with size (length) of mustatils as a proxy for labour mobilization we show that mustatils within clusters are not arranged hierarchically based on size, and were likely built by non-stratified groups of people. Our analyses show that people were choosing multiple factors when deciding where to build mustatils, and that the distribution of mustatils may relate to different groups of people resulting in the construction of complex ritual landscapes. As one of the earliest examples of large-scale monumental stone structure construction in global prehistory, understanding mustatils can enlighten us on human-environment interaction during the Neolithic.

Rapid prediction and visualization of safe moisture content in alfalfa seeds based on multispectral imaging technology

Moisture significantly impacts seed sales, storage, and processing. Traditional moisture testing methods are often slow, labor-intensive, and inadequate for the rapid detection demands of modern agriculture, particularly for non-destructive testing of individual seeds. This study applied multispectral imaging to obtain morphological and spectral data from alfalfa seeds at six moisture levels (4 %, 8 %, 12 %, 16 %, 25 %, and 41 %). By integrating algorithms such as Support Vector Machines (SVM), Random Forests (RF), Linear Discriminant Analysis (LDA), Back Propagation Neural Network (BPNN), and normalized typical discriminant analysis (nCDA) algorithms, classification models were developed to distinguish between safe and unsafe moisture levels. The Results indicated that spectral data alone significantly improved model accuracy and prediction. nCDA visualizations effectively illustrated spatial moisture distribution, highlighting stark color differences between seeds in the safe moisture range (4 %, 8 %, 12 %) and those in the unsafe range (16 %, 25 %, 41 %). BPNN exhibited high model precision, achieving a recognition accuracy rate of 90.1 % for safe and unsafe moisture content. Key wavelengths identified by the Permutation method included 970, 880, 570, and 490 nm. Pearson correlation analysis showed a significant positive correlation between germination indicators and spectral data, which strengthened with longer seed storage. These findings confirm the potential of multispectral imaging for assessing the safe moisture content of alfalfa seeds, supporting the development of detection systems for evaluating moisture content in individual seeds. This advancement enables the rapid removal of high-moisture seeds, preventing deterioration during storage.

Review about the Permutation Approach in Hypothesis Testing

Today, permutation tests represent a powerful and increasingly widespread tool of statistical inference for hypothesis-testing problems. To the best of our knowledge, a review of the application of permutation tests for complex data in practical data analysis for hypothesis testing is missing. In particular, it is essential to review the application of permutation tests in two-sample or multi-sample problems and in regression analysis. The aim of this paper is to consider the main scientific contributions on the subject of permutation methods for hypothesis testing in the mentioned fields. Notes on their use to address the problem of missing data and, in particular, right-censored data, will also be included. This review also tries to highlight the limits and advantages of the works cited with a critical eye and also to provide practical indications to researchers and practitioners who need to identify flexible and distribution-free solutions for the most disparate hypothesis-testing problems.

The Yellow River is the key corridor for Tamarix austromongolica to disperse from Asia inlands to east seashores.

Plants of the Tamarix L. genus (Tamaricaceae) mainly occur in arid inlands of Asia, but a few species occur in the coastal areas of China, and the Yellow River may account for this. This study was conducted to elucidate whether and how the Yellow River affects the pattern and development of the Tamarix genus, involving two critical species of Tamarix austromongolica Nakai and Tamarix chinensis Lour. With geographical distribution data, relationships of T. austromongolica with the Yellow River and the pertaining watershed were examined using the method of random permutation. The base-diameter structures of T. austromongolica populations were investigated and compared between different riparian lands that suffer discriminative water inundation. The nearest distances from T. austromongolica locations to the Yellow River and the pertaining watershed were significantly lower than the theoretical expectations in the condition of random distribution (p < .05). In many riparian lands along the Yellow River, wild T. austromongolica populations occurred with vigorous juveniles, despite frequent human disturbances. In coastal areas near the present estuary of the river, wild T. austromongolica plants were still found. In T. austromongolica populations near the Yellow River and sea, the rates of juvenile plants were significantly higher than in other populations situated farther from the river or sea. These findings suggest that the Yellow River can facilitate the eastward dispersal of Tamarix plants that reasonably caused the evolution from T. austromongolica to T. chinensis in ancient coasts in the China east.

A rank-based adaptive independence test for high-dimensional data

There are lots of methods for complete independence test for high-dimensional random vector. However, it is difficult for practitioners to choose a powerful test because the true alternative hypothesis is unknown. Combining the L 2-type with the L ∞ -type test statistic, we propose a rank-based test method. From a technical point of view, the proposed test is distribution-free and consequently the corresponding critical values or p-values can be obtained by Monte Carlo methods. Compared with permutation or bootstrap test methods, the proposed test method saves calculation cost. Simulation results show that the resulting method has excellent performance with finite sample size. We also provide a real data application to demonstrate the practicality and effectiveness of the proposed test method.

Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks.

Pathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools.

Comparison of the effect of heparin, reteplase, and taurolock in the prevention of thrombosis in hemodialysis catheters.

One of the complications of using catheters is the occurrence of thrombosis, which can be dangerous for patients. The main objective of this study is to compare the effect of heparin, reteplase, and taurolock in the prevention of thrombosis in hemodialysis catheters. The present study is a clinical trial, in which the effect of three drugs, heparin, reteplase, and taurolock, in the prevention of thrombosis in hemodialysis catheters, has been investigated. The research units were studied in two intervention and control groups. The stratified random allocation method was used to assign patients to five groups (control, Heparin 50, Heparin 1000, reteplase, and taurolock), with strata based on the patient's age (20-70 years), gender, and duration of dialysis. Within each stratum, patients were also assigned to groups using the randomized block permutation method and a random number table tool. To prevent bias, this study is triple-blinded. This means that the patient, the thrombosis assessor, and the statistical analyst are unaware of the type of intervention received by the patient. Gender (p < 0.999), age distribution (p = 0.774), and duration of dialysis (p = 0.875) showed no statistically significant relationship with thrombosis. However, significant differences were observed among the five groups regarding thrombosis incidence. The relative risk of thrombosis in the Heparin 50, Heparin 1000, reteplase, and taurolock groups compared to the control group was 92.5%, 92.2%, 98.2%, and 89% lower, respectively. Our study underscores the efficacy of heparin, reteplase, and taurolock in preventing thrombosis in hemodialysis catheters. While all three drugs demonstrated efficacy, the Heparin 50 group exhibited the highest relative risk reduction. These findings suggest that heparin, particularly at a low dose, should be considered a standard prophylactic treatment in hemodialysis patients.

Testing distributional equality for functional random variables

In this article, we present a nonparametric method for the general two-sample problem involving functional random variables modeled as elements of a separable Hilbert space H. First, we present a general recipe based on linear projections to construct a measure of dissimilarity between two probability distributions on H. In particular, we consider a measure based on the energy statistic and present some of its nice theoretical properties. A plug-in estimator of this measure is used as the test statistic to construct a general two-sample test. Large sample distribution of this statistic is derived both under null and alternative hypotheses. However, since the quantiles of the limiting null distribution are analytically intractable, the test is calibrated using the permutation method. We prove the large sample consistency of the resulting permutation test under fairly general assumptions. We also study the efficiency of the proposed test by establishing a new local asymptotic normality result for functional random variables. Using that result, we derive the asymptotic distribution of the permuted test statistic and the asymptotic power of the permutation test under local contiguous alternatives. This establishes that the permutation test is statistically efficient in the Pitman sense. Extensive simulation studies are carried out and a real data set is analyzed to compare the performance of our proposed test with some state-of-the-art methods.

DNA shape features improve prediction of CRISPR/Cas9 activity

The CRISPR/Cas9 genome editing technology has transformed basic and translational research in biology and medicine. However, the advances are hindered by off-target effects and a paucity in the knowledge of the mechanism of the Cas9 protein. Machine learning models have been proposed for the prediction of Cas9 activity at unintended sites, yet feature engineering plays a major role in the outcome of the predictors. This study evaluates the improvement in the performance of similar predictors upon inclusion of epigenetic and DNA shape feature groups in the conventionally used sequence-based Cas9 target and off-target datasets. The approach involved the utilization of neural networks trained on a diverse range of parameters, allowing us to systematically assess the performance increase for the meticulously designed datasets- (i) sequence only, (ii) sequence and epigenetic features, and (iii) sequence, epigenetic and DNA shape feature datasets.The addition of DNA shape information significantly improved predictive performance, evaluated by Akaike and Bayesian information criteria. The evaluation of individual feature importance by permutation and LIME-based methods also indicates that not only sequence features like mismatches and nucleotide composition, but also base pairing parameters like opening and stretch, that are indicative of distortion in the DNA-RNA hybrid in the presence of mismatches, influence model outcomes.

A New Permutation-Based Method for Ranking and Selecting Group Features in Multiclass Classification

The selection of group features is a critical aspect in reducing model complexity by choosing the most essential group features, while eliminating the less significant ones. The existing group feature selection methods select a set of important group features, without providing the relative importance of all group features. Moreover, few methods consider the relative importance of group features in the selection process. This study introduces a permutation-based group feature selection approach specifically designed for high-dimensional multiclass datasets. Initially, the least absolute shrinkage and selection operator (lasso) method was applied to eliminate irrelevant individual features within each group feature. Subsequently, the relative importance of the group features was computed using a random-forest-based permutation method. Accordingly, the process selected the highly significant group features. The performance of the proposed method was evaluated using machine learning algorithms and compared with the performance of other approaches, such as group lasso. We used real-world, high-dimensional, multiclass microarray datasets to demonstrate its effectiveness. The results highlighted the capability of the proposed method, which not only selected significant group features but also provided the relative importance and ranking of all group features. Furthermore, the proposed method outperformed the existing method in terms of accuracy and F1 score.

An MRI radiomics approach to discriminate hemorrhage prone intracranial tumors before stereotactic biopsy.

To explore imaging biomarkers predictive of intratumoral hemorrhage for lesions intended for elective stereotactic biopsy. This study included a retrospective cohort of 143 patients with 175 intracranial lesions intended for stereotactic biopsy. All the lesions were randomly split into a training dataset (n=121) and a test dataset (n=54) at a ratio of 7:3. 34 lesions were defined as "hemorrhage-prone tumors" as hemorrhage occurred between initial diagnostic MRI acquisition and the scheduled biopsy procedure. Radiomics features were extracted from the contrast-enhanced T1WI and T2WI images. Features informative of hemorrhage were then selected by the LASSO algorithm and an SVM model was built with selected features. The SVM model was further simplified by discarding features with low importance calculated using a "permutation importance" method. The model's performance was evaluated with confusion matrix-derived metrics and AUC value on the independent test dataset. Nine radiomics features were selected as hemorrhage related features of intracranial tumors by the LASSO algorithm. The simplified model's sensitivity, specificity, accuracy, and AUC reached 0.909, 0.930, 0.926, and 0.949 (95%CI: 0.865-1.000) on the test dataset in the discrimination of "hemorrhage-prone tumors". The permutation method rated feature "T2_gradient_firstorder_10Percentile" as the most important, the absence of which decreased the model's accuracy by 10.9%. Radiomics features extracted on contrast-enhanced T1WI and T2WI sequences were predictive of future hemorrhage of intracranial tumors with favorable accuracy. This model may assist in the arrangement of biopsy procedures and the selection of target lesions in patients with multiple lesions.

About Latent Roles in Forecasting Players in Team Sports

Forecasting players in sports has grown in popularity due to the potential for a tactical advantage and the applicability of such research to multi-agent interaction systems. Team sports contain a significant social component that influences interactions between teammates and opponents. However, it still needs to be fully exploited. In this work, we hypothesize that each participant has a specific function in each action and that role-based interaction is critical for predicting players’ future moves. We create RolFor, a novel end-to-end model for Role-based Forecasting. RolFor uses a new module we developed called Ordering Neural Networks (OrderNN) to permute the order of the players such that each player is assigned to a latent role. The latent role is then modeled with a RoleGCN. Thanks to its graph representation, it provides a fully learnable adjacency matrix that captures the relationships between roles and is subsequently used to forecast the players’ future trajectories. Extensive experiments on a challenging NBA basketball dataset back up the importance of roles and justify our goal of modeling them using optimizable models. When an oracle provides roles, the proposed RolFor compares favorably to the current state-of-the-art (it ranks first in terms of ADE and second in terms of FDE errors). However, training the end-to-end RolFor incurs the issues of differentiability of permutation methods, which we experimentally review. Finally, this work restates differentiable ranking as a difficult open problem and its great potential in conjunction with graph-based interaction models.

Identification-robust methods for comparing inequality with an application to regional disparities

We propose Fieller-type methods for inference on generalized entropy inequality indices in the context of the two-sample problem which covers testing the statistical significance of the difference in indices, and the construction of a confidence set for this difference. In addition to irregularities arising from thick distributional tails, standard inference procedures are prone to identification problems because of the ratio transformation that defines the considered indices. Simulation results show that our proposed method outperforms existing counterparts including simulation-based permutation methods and results are robust to different assumptions about the shape of the null distributions. Improvements are most notable for indices that put more weight on the right tail of the distribution and for sample sizes that match macroeconomic type inequality analysis. While irregularities arising from the right tail have long been documented, we find that left tail irregularities are equally important in explaining the failure of standard inference methods. We apply our proposed method to analyze income per-capita inequality across U.S. states and non-OECD countries. Empirical results illustrate how Fieller-based confidence sets can: (i) differ consequentially from available ones leading to conflicts in test decisions, and (ii) reveal prohibitive estimation uncertainty in the form of unbounded outcomes which serve as proper warning against flawed interpretations of statistical tests.