Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Jaffe Method and Enzymatic Method at Measuring Serum Creatinine Level, Creatinine Clearance and Estimated Glomerular Filtration Rate

Background: Correct measuring of blood and urine creatinine level is necessary for identification and tracking of chronic kidney disease (CKD). Objective: The aim of this study is a comparison of Jaffe and enzymatic methods for measuring creatinine in serum and in urine, in order to determine whether there are any statistical significant differences between them, and whether they are reflected on creatinine clearance calculation and estimated glomerular filtration rate (eGFR). Methods: Creatinine in serum and urine was measured for the group of patients (N=60; female=34, male=26) from 24 to 69 years of age by using Jaffe’s method on Dimension RxL biochemical analyzer, and enzymatic method on integrated biochemical and immunochemical analyzer Architect ci8200, and obtained levels are used for creatinine clearance calculation and eGFR. Results: The methods correlate well, both in measuring serum creatinine (r 1 = 0.990) and in measuring urine creatinine (r 2 =0.974). There are no statistically significant differences between them (p=0.57). Measuring creatinine using different methods showed no statistically significant differences in the calculated clearances (p=0.93), they significantly correlate (r=0.9722). eGFR, using the MDRD and CKD-EPI formulas, were not statistically significantly different, regardless of the used method. Conclusion: Apart from significant correlations between the used methods, the results of using the Jaffe and enzymatic methods showed no significant differences at measuring serum creatinine level, or creatinine clearance and glomerular filtration rate.
Beyond ℓ1 sparse coding in V1

Growing evidence indicates that only a sparse subset from a pool of sensory neurons is active for the encoding of visual stimuli at any instant in time. Traditionally, to replicate such biological sparsity, generative models have been using the ℓ1 norm as a penalty due to its convexity, which makes it amenable to fast and simple algorithmic solvers. In this work, we use biological vision as a test-bed and show that the soft thresholding operation associated to the use of the ℓ1 norm is highly suboptimal compared to other functions suited to approximating ℓp with 0 ≤ p < 1 (including recently proposed continuous exact relaxations), in terms of performance. We show that ℓ1 sparsity employs a pool with more neurons, i.e. has a higher degree of overcompleteness, in order to maintain the same reconstruction error as the other methods considered. More specifically, at the same sparsity level, the thresholding algorithm using the ℓ1 norm as a penalty requires a dictionary of ten times more units compared to the proposed approach, where a non-convex continuous relaxation of the ℓ0 pseudo-norm is used, to reconstruct the external stimulus equally well. At a fixed sparsity level, both ℓ0- and ℓ1-based regularization develop units with receptive field (RF) shapes similar to biological neurons in V1 (and a subset of neurons in V2), but ℓ0-based regularization shows approximately five times better reconstruction of the stimulus. Our results in conjunction with recent metabolic findings indicate that for V1 to operate efficiently it should follow a coding regime which uses a regularization that is closer to the ℓ0 pseudo-norm rather than the ℓ1 one, and suggests a similar mode of operation for the sensory cortex in general.
The impact of executive compensation incentive on corporate innovation capability: Evidence from agro-based companies in China

This paper aims to examine the impact of executive compensation incentive on corporate innovation capability by dividing executive compensation incentive into short-term monetary incentive and long-term equity incentive. We also investigate the interaction between the two types of executive compensation incentive. Data are collected from China’s agro-based companies during 2012–2019, and multiple regression analysis is utilized. The empirical results show that short-term monetary incentive has no impact on innovation capability, while long-term equity incentive stimulates innovation capability. Regarding company ownership, the impact of long-term equity incentive in state-owned enterprises is greater than that in private-owned enterprises. In addition, the complementary effect between short-term and long-term compensation incentive has a positive impact on innovation capability regardless of company ownership. The findings of this paper could help agribusiness managers to design the reasonable incentive system to incentivize corporate executives and enhance the capability of independent innovation.
Transcriptomic analyses of localized prostate cancers of East Asian and North American men reveal race‐specific luminal‐basal and microenvironmental differences

Dear editor, Prostate cancer (PCa) remains a major healthcare burden in men globally [1]. Most patients present with localized disease, and treatment is recommended based on risk classification systems like theNationalComprehensiveCancer Network (NCCN) [2]. However, these methods are imprecise for estimating metastasis-free survival and prostate cancer-specific mortality and thus biomarkers that can predict tumor aggression are needed [3–5]. Several studies have since characterized the molecular landscape of localized PCa in White [4, 5] and Black/African-American men [6], but data is lacking in Asian men. The Chinese Prostate Cancer Genome and Epigenome Atlas (CPGEA) reported on the genomic and epigenomic landscape of 208 PCa of men from China [7]. Comparative analyses between the CPGEA cohort and data from The Cancer Genome Atlas (TCGA) revealed higher frequencies of Forkhead box A1 (FOXA1) and chromodomain-helicase DNA-binding 1 (CHD1) mutations, and lower frequencies of phosphatase and tensin homolog (PTEN) mutations and transmembrane protease serine 2-E26 transformationspecific related gene (TMPRSS2-ERG) fusion in Chinese compared with White men [7]. These preliminary findings highlight the presence of race-specific differences in molecular phenotypes of PCa. Here, we conducted a study to compare the gene expression profiles across 75 signatures between an East Asian PCa cohort of 181 patients against a propensity
Phenotypic variation and genomic variation in insect virulence traits reveal patterns of intraspecific diversity in a locust‐specific fungal pathogen

Intraspecific pathogen diversity is crucial for understanding the evolution and maintenance of adaptation in host–pathogen interactions. Traits associated with virulence are often a significant source of variation directly impacted by local selection pressures. The specialist fungal entomopathogen, Metarhizium acridum, has been widely implemented as a biological control agent of locust pests in tropical regions of the world. However, few studies have accounted for natural intraspecific phenotypic and genetic variation. Here, we examine the diversity of nine isolates of M. acridum spanning the known geographic distribution, in terms of (1) virulence towards two locust species, (2) growth rates on three diverse nutrient sources, and (3) comparative genomics to uncover genomic variability. Significant variability in patterns of virulence and growth was shown among the isolates, suggesting intraspecific ecological specialization. Different patterns of virulence were shown between the two locust species, indicative of potential host preference. Additionally, a high level of diversity among M. acridum isolates was observed, revealing increased variation in subtilisin‐like proteases from the Pr1 family. These results culminate in the first in‐depth analysis regarding multiple facets of natural variation in M. acridum, offering opportunities to understand critical evolutionary drivers of intraspecific diversity in pathogens.
GSK‐3β activation mediates apolipoprotein E4‐associated cognitive impairment in type 2 diabetes mellitus: A multicenter, cross‐sectional study

Abstract Aim Both the activation of glycogen synthase kinase‐3β (GSK‐3β) and the presence of ApoE ε4 genotype have been found to respectively correlate with cognitive decline in patients with type 2 diabetes mellitus (T2DM), who further show a high incidence of developing Alzheimer's disease. However, the relationship between ApoE ε4 and GSK‐3β in the cognitive impairment of T2DM patients remains unclear. Methods ApoE genotypes and platelet GSK‐3β level were measured in 1139 T2DM patients recruited from five medical centers in Wuhan, China. Cognitive functions were assessed by Mini‐Mental State Examination (MMSE). The association and the relationships among apolipoprotein E (ApoE) genotypes, GSK‐3β activity and cognitive function were analyzed by regression and mediating effect analyses, respectively. Results T2DM patients with ApoE ε4 but not ApoE ε2 haplotype showed poorer cognitive function and elevated platelet GSK‐3β activity, when using ApoE ε3 as reference. The elevation of GSK‐3β activity was positively correlated the diabetes duration, as well as plasma glycated hemoglobin (HbA1c) and glucose levels. Moreover, correlation and regression analysis also revealed significant pairwise correlations among GSK‐3β activity, ApoE gene polymorphism and cognitive function. Lastly, using Baron and Kenny modeling, we unveiled a mediative role of GSK‐3β activity between ApoE ε4 and cognitive impairment. Conclusion We reported here that the upregulation of GSK‐3β activity mediates the exacerbation of cognitive impairment by ApoE ε4‐enhanced cognitive impairment in T2DM patients, suggesting GSK‐3β inhibitors as promising drugs for preserving cognitive function in T2DM patients, especially to those with ApoE ε4 genotype.
Exp[licit]-A Robot modeling Software based on Exponential Maps

$ $Deriving a robot's equation of motion typically requires placing multiple coordinate frames, commonly using the Denavit-Hartenberg convention to express the kinematic and dynamic relationships between segments. This paper presents an alternative using the differential geometric method of Exponential Maps, which reduces the number of coordinate frame choices to two. The traditional and differential geometric methods are compared, and the conceptual and practical differences are detailed. The open-source software, Exp[licit], based on the differential geometric method, is introduced. It is intended for use by researchers and engineers with basic knowledge of geometry and robotics. Code snippets and an example application are provided to demonstrate the benefits of the differential geometric method and assist users to get started with the software.
Online Infinite-Dimensional Regression: Learning Linear Operators

We consider the problem of learning linear operators under squared loss between two infinite-dimensional Hilbert spaces in the online setting. We show that the class of linear operators with uniformly bounded $p$-Schatten norm is online learnable for any $p \in [1, \infty)$. On the other hand, we prove an impossibility result by showing that the class of uniformly bounded linear operators with respect to the operator norm is \textit{not} online learnable. Moreover, we show a separation between sequential uniform convergence and online learnability by identifying a class of bounded linear operators that is online learnable but uniform convergence does not hold. Finally, we prove that the impossibility result and the separation between uniform convergence and learnability also hold in the batch setting.
Distributionally Robust Transfer Learning

Many existing transfer learning methods rely on leveraging information from source data that closely resembles the target data. However, this approach often overlooks valuable knowledge that may be present in different yet potentially related auxiliary samples. When dealing with a limited amount of target data and a diverse range of source models, our paper introduces a novel approach, Distributionally Robust Optimization for Transfer Learning (TransDRO), that breaks free from strict similarity constraints. TransDRO is designed to optimize the most adversarial loss within an uncertainty set, defined as a collection of target populations generated as a convex combination of source distributions that guarantee excellent prediction performances for the target data. TransDRO effectively bridges the realms of transfer learning and distributional robustness prediction models. We establish the identifiability of TransDRO and its interpretation as a weighted average of source models closest to the baseline model. We also show that TransDRO achieves a faster convergence rate than the model fitted with the target data. Our comprehensive numerical studies and analysis of multi-institutional electronic health records data using TransDRO further substantiate the robustness and accuracy of TransDRO, highlighting its potential as a powerful tool in transfer learning applications.
On Kostant's conjecture for components of $V(\rho)\otimes V(\rho)$

For a complex simple Lie algebra $\mathfrak{g}$ or rank $r$, let $\rho$ be the half sum of positive roots and $P(2\rho)\subset \mathbb{R}^r$ be the convex hull of all dominant weights $\lambda$ of the form $\lambda=2\rho-\sum_{i=1}^r a_i\alpha_i$ with $a_i\in \mathbb{Z}_{\geq 0}$ for $1\leq i\leq r$. We show that if $\lambda$ is a vertex of $P(2\rho)$, then $V(\lambda)$ appears in $V(\rho) \otimes V(\rho)$ with multiplicity one, proving partially (for the vertices of $P(2\rho)$) a conjecture of Kostant describing components of $V(\rho)\otimes V(\rho)$. This result allows us to give an alternative proof for a weaker form of the conjecture (up to saturation factor) for any $\mathfrak{g}$. Further, using works of Knutson-Tau on the saturation property of $\mathfrak{sl_{r+1}}$, our results give an alternative proof of Kostant's conjecture in the particular case $\mathfrak{g}=\mathfrak{sl_{r+1}}$.
Bregman Graph Neural Network

Numerous recent research on graph neural networks (GNNs) has focused on formulating GNN architectures as an optimization problem with the smoothness assumption. However, in node classification tasks, the smoothing effect induced by GNNs tends to assimilate representations and over-homogenize labels of connected nodes, leading to adverse effects such as over-smoothing and misclassification. In this paper, we propose a novel bilevel optimization framework for GNNs inspired by the notion of Bregman distance. We demonstrate that the GNN layer proposed accordingly can effectively mitigate the over-smoothing issue by introducing a mechanism reminiscent of the"skip connection". We validate our theoretical results through comprehensive empirical studies in which Bregman-enhanced GNNs outperform their original counterparts in both homophilic and heterophilic graphs. Furthermore, our experiments also show that Bregman GNNs can produce more robust learning accuracy even when the number of layers is high, suggesting the effectiveness of the proposed method in alleviating the over-smoothing issue.
Peculiar Spectral Property of Coherent Radio Emission from a Hot Magnetic Star: The Case of an Extreme Oblique Rotator

We report ultra-wideband (0.4–4.0 GHz) observation of coherent radio emission via electron cyclotron maser emission (ECME) produced by the hot magnetic star HD 142990. With nearly perpendicular rotation and magnetic dipole axes, it represents an extreme case of oblique rotators. The large obliquity is predicted to cause a complex distribution of stellar wind plasma in the magnetosphere. It has been proposed that such a distribution will give rise to a nontrivial frequency dependence of ECME. Indeed we discovered strong frequency dependence of different pulse properties, such as the appearance of secondary pulses, different cutoff frequencies for pulses observed at different rotational phases, etc. But the unique feature that we observed is that while at sub-gigahertz frequencies, the star appears to produce ECME in the extraordinary mode, at gigahertz frequencies, the mode indicated by the pulse property is the ordinary mode. By considering the physical condition needed by such a scenario, we conclude that the required transition of the magnetoionic mode with frequency is unlikely to occur, and the most promising scenario is refraction caused by the complex plasma distribution surrounding the star. This suggests that the conventional way to deduce the magnetoionic mode based on ECME observed at a given frequency is not a reliable method for stars with large misalignment between their rotation and magnetic axes. We also find that ECME exhibits an upper cutoff at ≲3.3 GHz, which is much smaller than the frequency corresponding to the maximum stellar magnetic field strength.
Motion-Bias-Free Feature-Based SLAM

For SLAM to be safely deployed in unstructured real world environments, it must possess several key properties that are not encompassed by conventional benchmarks. In this paper we show that SLAM commutativity, that is, consistency in trajectory estimates on forward and reverse traverses of the same route, is a significant issue for the state of the art. Current pipelines show a significant bias between forward and reverse directions of travel, that is in addition inconsistent regarding which direction of travel exhibits better performance. In this paper we propose several contributions to feature-based SLAM pipelines that remedies the motion bias problem. In a comprehensive evaluation across four datasets, we show that our contributions implemented in ORB-SLAM2 substantially reduce the bias between forward and backward motion and additionally improve the aggregated trajectory error. Removing the SLAM motion bias has significant relevance for the wide range of robotics and computer vision applications where performance consistency is important.
Loading Loss‐Cone Distributions in Particle Simulations

Numerical procedures to generate random variates that follow loss‐cone velocity distributions in particle simulations are presented. We propose a simple summation algorithm for the Ashour‐Abdalla–Kennel‐type loss‐cone distribution, also known as the subtracted Maxwellian. For the Dory‐type loss‐cone distribution, we use a random variate for the gamma distribution. Extending earlier algorithms for the kappa and Dory‐type distributions, we construct a novel algorithm to generate a popular form of a kappa loss‐cone (KLC) distribution. To better express the loss cone, we discuss another family of loss‐cone distributions based on the pitch angle. In addition to the acceptance‐rejection method, we propose two transformation algorithms that convert an isotropic distribution into a loss‐cone distribution. This allows us to generate loss‐cone and KLC distributions from the Maxwell and kappa distributions.
Can humans help BERT gain "confidence"?

The advancements in artificial intelligence over the last decade have opened a multitude of avenues for interdisciplinary research. Since the idea of artificial intelligence was inspired by the working of neurons in the brain, it seems pretty practical to combine the two fields and take the help of cognitive data to train AI models. Not only it will help to get a deeper understanding of the technology, but of the brain as well. In this thesis, I conduct novel experiments to integrate cognitive features from the Zurich Cognitive Corpus (ZuCo) (Hollenstein et al., 2018) with a transformer-based encoder model called BERT. I show how EEG and eye-tracking features from ZuCo can help to increase the performance of the NLP model. I confirm the performance increase with the help of a robustness-checking pipeline and derive a word-EEG lexicon to use in benchmarking on an external dataset that does not have any cognitive features associated with it. Further, I analyze the internal working mechanism of BERT and explore a potential method for model explainability by correlating it with a popular model-agnostic explainability framework called LIME (Ribeiro et al., 2016). Finally, I discuss the possible directions to take this research forward.
Estimation with ultimate quantum precision of the transverse displacement between two photons via two-photon interference sampling measurements

We present a quantum sensing scheme achieving the ultimate quantum sensitivity in the estimation of the transverse displacement between two photons interfering at a balanced beam splitter, based on transverse-momentum sampling measurements at the output. This scheme can possibly lead to enhanced high-precision nanoscopic techniques, such as super-resolved single-molecule localization microscopy with quantum dots, by circumventing the requirements in standard direct imaging of cameras resolution at the diffraction limit, and of highly magnifying objectives. Interestingly, the ultimate spatial precision in nature is achieved irrespectively of the overlap of the two displaced photonic wavepackets. This opens a new research paradigm based on the interface between spatially resolved quantum interference and quantum-enhanced spatial sensitivity.
Adaptive Kalmannet: Data-Driven Kalman Filter with Fast Adaptation

Combining the classical Kalman filter (KF) with a deep neural network (DNN) enables tracking in partially known state space (SS) models. A major limitation of current DNN-aided designs stems from the need to train them to filter data originating from a specific distribution and underlying SS model. Consequently, changes in the model parameters may require lengthy retraining. While the KF adapts through parameter tuning, the black-box nature of DNNs makes identifying tunable components difficult. Hence, we propose Adaptive KalmanNet (AKNet), a DNN-aided KF that can adapt to changes in the SS model without retraining. Inspired by recent advances in large language model fine-tuning paradigms, AKNet uses a compact hypernetwork to generate context-dependent modulation weights. Numerical evaluation shows that AKNet provides consistent state estimation performance across a continuous range of noise distributions, even when trained using data from limited noise settings.
Towards Connecting Control to Perception: High-Performance Whole-Body Collision Avoidance Using Control-Compatible Obstacles

One of the most important aspects of autonomous systems is safety. This includes ensuring safe human-robot and safe robot-environment interaction when autonomously performing complex tasks or in collaborative scenarios. Al-though several methods have been introduced to tackle this, most are unsuitable for real-time applications and require carefully handcrafted obstacle descriptions. In this work, we propose a method combining high-frequency and real-time self and environment collision avoidance of a robotic manipulator with low-frequency, multimodal, and high-resolution environmental perceptions accumulated in a digital twin system. Our method is based on geometric primitives, so-called primitive skeletons. These, in turn, are information-compressed and real-time compatible digital representations of the robot's body and environment, automatically generated from ultra-realistic virtual replicas of the real world provided by the digital twin. Our approach is a key enabler for closing the loop between environment perception and robot control by providing the millisecond real-time control stage with a current and accurate world description, empowering it to react to environmental changes. We evaluate our whole-body collision avoidance on a 9-DOFs robot system through five experiments, demonstrating the functionality and efficiency of our framework.