Optimal Rate of Kernel Regression in Large Dimensions

We perform a study on kernel regression for large-dimensional data (where the sample size $n$ is polynomially depending on the dimension $d$ of the samples, i.e., $n\asymp d^{\gamma}$ for some $\gamma >0$ ). We first build a general tool to characterize the upper bound and the minimax lower bound of kernel regression for large dimensional data through the Mendelson complexity $\varepsilon_{n}^{2}$ and the metric entropy $\bar{\varepsilon}_{n}^{2}$ respectively. When the target function falls into the RKHS associated with a (general) inner product model defined on $\mathbb{S}^{d}$, we utilize the new tool to show that the minimax rate of the excess risk of kernel regression is $n^{-1/2}$ when $n\asymp d^{\gamma}$ for $\gamma =2, 4, 6, 8, \cdots$. We then further determine the optimal rate of the excess risk of kernel regression for all the $\gamma>0$ and find that the curve of optimal rate varying along $\gamma$ exhibits several new phenomena including the {\it multiple descent behavior} and the {\it periodic plateau behavior}. As an application, For the neural tangent kernel (NTK), we also provide a similar explicit description of the curve of optimal rate. As a direct corollary, we know these claims hold for wide neural networks as well

Widespread autogenous mRNA–protein interactions detected by CLIP-seq

Autogenous interactions between mRNAs and the proteins they encode are implicated in cellular feedback-loop regulation, but their extent and mechanistic foundation are unclear. It was recently hypothesized that such interactions may be common, reflecting the role of intrinsic nucleobase–amino acid affinities in shaping the genetic code's structure. Here we analyze a comprehensive set of CLIP-seq experiments involving multiple protocols and report on widespread autogenous interactions across different organisms. Specifically, 230 of 341 (67%) studied RNA-binding proteins (RBPs) interact with their own mRNAs, with a heavy enrichment among high-confidence hits and a preference for coding sequence binding. We account for different confounding variables, including physical (overexpression and proximity during translation), methodological (difference in CLIP protocols, peak callers and cell types) and statistical (treatment of null backgrounds). In particular, we demonstrate a high statistical significance of autogenous interactions by sampling null distributions of fixed-margin interaction matrices. Furthermore, we study the dependence of autogenous binding on the presence of RNA-binding motifs and structured domains in RBPs. Finally, we show that intrinsic nucleobase–amino acid affinities favor co-aligned binding between mRNA coding regions and the proteins they encode. Our results suggest a central role for autogenous interactions in RBP regulation and support the possibility of a fundamental connection between coding and binding

PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation

The Segment Anything Model (SAM) has exhibited outstanding performance in various image segmentation tasks. Despite being trained with over a billion masks, SAM faces challenges in mask prediction quality in numerous scenarios, especially in real-world contexts. In this paper, we introduce a novel prompt-driven adapter into SAM, namely Prompt Adapter Segment Anything Model (PA-SAM), aiming to enhance the segmentation mask quality of the original SAM. By exclusively training the prompt adapter, PA-SAM extracts detailed information from images and optimizes the mask decoder feature at both sparse and dense prompt levels, improving the segmentation performance of SAM to produce high-quality masks. Experimental results demonstrate that our PA-SAM outperforms other SAM-based methods in high-quality, zero-shot, and open-set segmentation. We're making the source code and models available at https://github.com/xzz2/pa-sam.Comment: Code is available at https://github.com/xzz2/pa-sa

Probabilistic Approach for Road-Users Detection

Object detection in autonomous driving applications implies that the detection and tracking of semantic objects are commonly native to urban driving environments, as pedestrians and vehicles. One of the major challenges in state-of-the-art deep-learning based object detection is false positive which occurrences with overconfident scores. This is highly undesirable in autonomous driving and other critical robotic-perception domains because of safety concerns. This paper proposes an approach to alleviate the problem of overconfident predictions by introducing a novel probabilistic layer to deep object detection networks in testing. The suggested approach avoids the traditional Sigmoid or Softmax prediction layer which often produces overconfident predictions. It is demonstrated that the proposed technique reduces overconfidence in the false positives without degrading the performance on the true positives. The approach is validated on the 2D-KITTI objection detection through the YOLOV4 and SECOND (Lidar-based detector). The proposed approach enables enabling interpretable probabilistic predictions without the requirement of re-training the network and therefore is very practical.Comment: This work has been submitted to IEEE T-ITS for review and possible publicatio

Analysis of the anatomic eligibility for transcarotid artery revascularization in Chinese patients who underwent carotid endarterectomy and transfemoral carotid artery stenting

ObjectiveTranscarotid artery revascularization (TCAR) is thought to be a promising technique and instrument for treating carotid stenosis with favorable outcomes. Since there remain several differences in anatomic characteristics among races, this study was conducted to investigate the anatomic eligibility of TCAR in Chinese patients who underwent carotid revascularization.MethodsA retrospective review of patients with carotid stenosis from 2019 to 2021 was conducted. The anatomic eligibility of TCAR was based on the instruction of the ENROUTE Transcarotid Neuroprotection System. The carotid artery characteristics and configuration of the circle of Willis (CoW) were evaluated by CT angiography. The demographic and clinical characteristics and procedure-related complications were recorded. Logistic regression was used to analyze the independent factors for TCAR eligibility.ResultsOf 289 consecutive patients [222 for carotid endarterectomy (CEA) and 67 for transfemoral carotid artery stenting (TF-CAS)] identified, a total of 215 patients (74.4%) met TCAR anatomic eligibility. Specifically, 83.7% had mild common carotid artery (CCA) puncture site plaque, 95.2% had 4–9 mm internal carotid artery diameters, 95.8% had >6 mm CCA diameter, and 98.3% had >5 cm clavicle to carotid bifurcation distance. Those who were female (OR, 5.967; 95% CI: 2.545–13.987; P < 0.001), were of an older age (OR, 1.226; 95% CI: 1.157–1.299; P < 0.001), and higher body mass index (OR, 1.462; 95% CI: 1.260–1.697; P < 0.001) were prone to be associated with TCAR ineligibility. In addition, 71 patients with TCAR eligibility (33.0%) were found to combine with incomplete CoW. A high risk for CEA was found in 29 patients (17.3%) with TCAR eligibility, and a high risk for TF-CAS was noted in nine patients (19.1%) with TCAR eligibility. Overall, cranial nerve injury (CNI) was found in 22 patients after CEA, while 19 of them (11.3%) met TCAR eligibility.ConclusionA significant proportion of Chinese patients meet the anatomic criteria of TCAR, making TCAR a feasible treatment option in China. Anatomic and some demographic factors play key roles in TCAR eligibility. Further analysis indicates a potential reduction of procedure-related complications in patients with high-risk carotid stenosis under the TCAR procedure

Heterogeneous N2O5 reactions on atmospheric aerosols at four Chinese sites : improving model representation of uptake parameters

Heterogeneous reactivity of N2O5 on aerosols is a critical parameter in assessing NOx fate, nitrate production, and particulate chloride activation. Accurate measurement of its uptake coefficient (gamma N2O5) and representation in air quality models are challenging, especially in the polluted environment. With an in situ aerosol flow-tube system, the gamma N2O5 was directly measured on ambient aerosols at two rural sites in northern and southern China. The results were analyzed together with the gamma N2O5 derived from previous field studies in China to obtain a holistic picture of gamma N2O5 uptake and the influencing factors under various climatic and chemical conditions. The field-derived or measured gamma N2O5 was generally promoted by the aerosol water content and suppressed by particle nitrate. Significant discrepancies were found between the measured gamma N2O5 and that estimated from laboratory-determined parameterizations. An observation-based empirical parameterization was derived in the present work, which better reproduced the mean value and variability of the observed gamma N2O5. Incorporating this new parameterization into a regional air quality model (WRF-CMAQ) has improved the simulation of N2O5, nitrogen oxides, and secondary nitrate in the polluted regions of China.Peer reviewe