Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning

The burgeoning navigation services using digital maps provide great convenience to drivers. Nevertheless, the presence of anomalies in lane rendering map images occasionally introduces potential hazards, as such anomalies can be misleading to human drivers and consequently contribute to unsafe driving conditions. In response to this concern and to accurately and effectively detect the anomalies, this paper transforms lane rendering image anomaly detection into a classification problem and proposes a four-phase pipeline consisting of data pre-processing, self-supervised pre-training with the masked image modeling (MiM) method, customized fine-tuning using cross-entropy based loss with label smoothing, and post-processing to tackle it leveraging state-of-the-art deep learning techniques, especially those involving Transformer models. Various experiments verify the effectiveness of the proposed pipeline. Results indicate that the proposed pipeline exhibits superior performance in lane rendering image anomaly detection, and notably, the self-supervised pre-training with MiM can greatly enhance the detection accuracy while significantly reducing the total training time. For instance, employing the Swin Transformer with Uniform Masking as self-supervised pretraining (Swin-Trans-UM) yielded a heightened accuracy at 94.77% and an improved Area Under The Curve (AUC) score of 0.9743 compared with the pure Swin Transformer without pre-training (Swin-Trans) with an accuracy of 94.01% and an AUC of 0.9498. The fine-tuning epochs were dramatically reduced to 41 from the original 280. In conclusion, the proposed pipeline, with its incorporation of self-supervised pre-training using MiM and other advanced deep learning techniques, emerges as a robust solution for enhancing the accuracy and efficiency of lane rendering image anomaly detection in digital navigation systems.
Dynamic landscape of the intracellular termini of acid-sensing ion channel 1a

Acid-sensing ion channels (ASICs) are trimeric proton-gated sodium channels. Recent work has shown that these channels play a role in necroptosis following prolonged acidic exposure like occurs in stroke. The C-terminus of ASIC1a is thought to mediate necroptotic cell death through interaction with receptor interacting serine threonine kinase 1 (RIPK1). This interaction is hypothesized to be inhibited at rest via an interaction between the C- and N-termini which blocks the RIPK1 binding site. Here, we use two transition metal ion FRET methods to investigate the conformational dynamics of the termini at neutral and acidic pH. We do not find evidence that the termini are close enough to be bound while the channel is at rest and find that the termini may modestly move closer together during acidification. At rest, the N-terminus adopts a conformation parallel to the membrane about 10 Å away. The distal end of the C-terminus may also spend time close to the membrane at rest. After acidification, the proximal portion of the N-terminus moves marginally closer to the membrane whereas the distal portion of the C-terminus swings away from the membrane. Together these data suggest that a new hypothesis for RIPK1 binding during stroke is needed.
Evolution of HIV-1 envelope towards reduced neutralization sensitivity, as demonstrated by contemporary HIV-1 subtype B from the United States

Subtype B HIV-1 has been the primary driver of the HIV-1 epidemic in the United States (U.S.) for over forty years and is also a prominent subtype in the Americas, Europe, Australia, the Middle East and North Africa. In this study, the neutralization profiles of contemporary subtype B Envs from the U.S. were assessed to characterize changes in neutralization sensitivities over time. We generated a panel of 30 contemporary pseudoviruses (PSVs) and demonstrated continued diversification of subtype B Env from the 1980s up to 2018. Neutralization sensitivities of the contemporary subtype B PSVs were characterized using 31 neutralizing antibodies (NAbs) and were compared with strains from earlier in the HIV-1 pandemic. A significant reduction in Env neutralization sensitivity was observed for 27 out of 31 NAbs for the contemporary as compared to earlier-decade subtype B PSVs. A decline in neutralization sensitivity was observed across all Env domains; the NAbs that were most potent early in the pandemic suffered the greatest decline in potency over time. A meta-analysis demonstrated this trend across multiple subtypes. As HIV-1 Env diversification continues, changes in Env antigenicity and neutralization sensitivity should continue to be evaluated to inform the development of improved vaccine and antibody products to prevent and treat HIV-1.
Adventures of Trustworthy Vision-Language Models: A Survey

Recently, transformers have become incredibly popular in computer vision and vision-language tasks. This notable rise in their usage can be primarily attributed to the capabilities offered by attention mechanisms and the outstanding ability of transformers to adapt and apply themselves to a variety of tasks and domains. Their versatility and state-of-the-art performance have established them as indispensable tools for a wide array of applications. However, in the constantly changing landscape of machine learning, the assurance of the trustworthiness of transformers holds utmost importance. This paper conducts a thorough examination of vision-language transformers, employing three fundamental principles of responsible AI: Bias, Robustness, and Interpretability. The primary objective of this paper is to delve into the intricacies and complexities associated with the practical use of transformers, with the overarching goal of advancing our comprehension of how to enhance their reliability and accountability.
Estimating the Number of States via the Rodeo Algorithm for Quantum Computation

Our proposal introduces a customization of the rodeo algorithm that enables us to determine the number of states associated with all energy levels of a quantum system without explicitly solving the Schr\"odinger equation. Quantum computers, with their innate ability to address the intricacies of quantum systems, make this approach particularly promising for the study of the thermodynamics of quantum systems. To illustrate the effectiveness of our approach, we apply it to compute the number of states of the 1D transverse-field Ising model and, consequently, its specific heat.
Single Image Reflection Removal with Reflection Intensity Prior Knowledge

Single Image Reflection Removal (SIRR) in real-world images is a challenging task due to diverse image degradations occurring on the glass surface during light transmission and reflection. Many existing methods rely on specific prior assumptions to resolve the problem. In this paper, we propose a general reflection intensity prior that captures the intensity of the reflection phenomenon and demonstrate its effectiveness. To learn the reflection intensity prior, we introduce the Reflection Prior Extraction Network (RPEN). By segmenting images into regional patches, RPEN learns non-uniform reflection prior in an image. We propose Prior-based Reflection Removal Network (PRRN) using a simple transformer U-Net architecture that adapts reflection prior fed from RPEN. Experimental results on real-world benchmarks demonstrate the effectiveness of our approach achieving state-of-the-art accuracy in SIRR.
Improvement of Naive Bayes Text Classifier Based on Ensemble Technology and Feature Engineering

The performance of the Naive Bayes model in text classification is constrained by its assumption of feature independence, which does not hold true for textual data, as well as its reliance solely on word frequency information, disregarding word order and relationships and hindering its ability to capture text semantics effectively. Therefore, this study adopts ensemble learning and feature engineering methods to compensate for these deficiencies of the Naive Bayes model and improve its text classification accuracy. This study proposes a method to improve the performance of a Naive Bayes classifier by combining it with other classifiers, namely Random Forest, Support Vector Machines (SVM), and ensemble learning. The dataset for training and evaluation purposes utilized is the IMDB movie review dataset. The dataset is preprocessed by converting the integer sequences to text and then tokenizing and vectorizing the text using a CountVectorizer. Variousperformance indicators, such as accuracy, precision, and F1-score, are calculated for each classifier and the ensemble model. The results demonstrate that the ensemble model achieves the highest accuracy compared to the individual classifiers. The Naive Bayes classifier achieves an accuracy of 78.19%, Random Forest achieves 81.49%, SVM achieves 84.60%, and the ensemble model achieves an accuracy of 84.89%. These findings highlight the effectiveness of ensemble learning and feature engineering in improving the performance of a Naive Bayes text classifier.
Stop Hiding The Sharp Knives: The WebAssembly Linux Interface

WebAssembly is gaining popularity as a portable binary format targetable from many programming languages. With a well-specified low-level virtual instruction set, minimal memory footprint and many high-performance implementations, it has been successfully adopted for lightweight in-process memory sandboxing in many contexts. Despite these advantages, WebAssembly lacks many standard system interfaces, making it difficult to reuse existing applications. This paper proposes WALI: The WebAssembly Linux Interface, a thin layer over Linux's userspace system calls, creating a new class of virtualization where WebAssembly seamlessly interacts with native processes and the underlying operating system. By virtualizing the lowest level of userspace, WALI offers application portability with little effort and reuses existing compiler backends. With WebAssembly's control flow integrity guarantees, these modules gain an additional level of protection against remote code injection attacks. Furthermore, capability-based APIs can themselves be virtualized and implemented in terms of WALI, improving reuse and robustness through better layering. We present an implementation of WALI in a modern WebAssembly engine and evaluate its performance on a number of applications which we can now compile with mostly trivial effort.
A Theory of Irrotational Contact Fields

We present a framework that enables to write a family of convex approximations of complex contact models. Within this framework, we show that we can incorporate well established and experimentally validated contact models such as the Hunt&Crossley model. Moreover, we show how to incorporate Coulomb's law and the principle of maximum dissipation using a regularized model of friction. Contrary to common wisdom that favors the use of rigid contact models, our convex formulation is robust and performant even at high stiffness values far beyond that of materials such as steel. Therefore, the same formulation enables the modeling of compliant surfaces such as rubber gripper pads or robot feet as well as hard objects. We characterize and evaluate our approximations in a number of tests cases. We report their properties and highlight limitations. Finally, we demonstrate robust simulation of robotic tasks at interactive rates, with accurately resolved stiction and contact transitions, as required for meaningful sim-to-real transfer. Our method is implemented in the open source robotics toolkit Drake.
Effects of CO Effects of CO 22 on Growth Rate, C:N:P, and Fatty Acid Composition on Growth Rate, C:N:P, and Fatty Acid Composition of Seven Marine Phytoplankton Species of Seven Marine Phytoplankton Species

: Carbon dioxide (CO 2 ) is the primary substrate for photosynthesis by the phytoplankton that form the base of the marine food web and mediate biogeochemical cycling of C and nutrient elements. Specific growth rate and elemental composition (C:N:P) were characterized for 7 cosmopolitan coastal and oceanic phytoplankton species (5 diatoms and 2 chlorophytes) using low density, nutrient-replete, semi-continuous culture experiments in which CO 2 was manipulated to 4 levels ranging from post-bloom/glacial maxima (<290 ppm) to geological maxima levels (>2900 ppm). Specific growth rates at high CO 2 were from 19 to 60% higher than in low CO 2 treatments in 4 species and 44% lower in 1 species; there was no significant change in 2 species. Higher CO 2 availability also resulted in elevated C:P and N:P molar ratios in Thalassiosira pseudo-nana (~60 to 90% higher), lower C:P and N:P molar ratios in 3 species (~20 to 50% lower), and no change in 3 species. Carbonate system-driven changes in growth rate did not necessarily result in changes in elemental composition, or vice versa. In a subset of 4 species for which fatty acid composition was examined, elevated CO 2 did not affect the contribution of polyunsaturated fatty acids to total fatty acids significantly. These species show relatively little sensitivity between present day CO 2 and predicted ocean acidification scenarios (year 2100). The results, however, demonstrate that CO 2 availability at environmentally and geologically relevant scales can result in large changes in phytoplankton physiology, with potentially large feedbacks to ocean biogeochemical cycles and ecosystem structure.
Relationships between soil and leaf mineral composition are element‐specific, environment‐dependent and geographically structured in the emerging model Arabidopsis halleri

Summary Leaf mineral composition, the leaf ionome, reflects the complex interaction between a plant and its environment including local soil composition, an influential factor that can limit species distribution and plant productivity. Here we addressed within‐species variation in plant–soil interactions and edaphic adaptation using Arabidopsis halleri, a well‐suited model species as a facultative metallophyte and metal hyperaccumulator. We conducted multi‐element analysis of 1972 paired leaf and soil samples from 165 European populations of A. halleri, at individual resolution to accommodate soil heterogeneity. Results were further confirmed under standardized conditions upon cultivation of 105 field‐collected genotypes on an artificially metal‐contaminated soil in growth chamber experiments. Soil‐independent between‐ and within‐population variation set apart leaf accumulation of zinc, cadmium and lead from all other nutrient and nonessential elements, concurring with differential hypothesized ecological roles in either biotic interaction or nutrition. For these metals, soil–leaf relationships were element‐specific, differed between metalliferous and nonmetalliferous soils and were geographically structured both in the field and under standardized growth conditions, implicating complex scenarios of recent ecological adaptation. Our study provides an example and a reference for future related work and will serve as a basis for the molecular–genetic dissection and ecological analysis of the observed phenotypic variation.
Rampant nuclear–mitochondrial–plastid phylogenomic discordance in globally distributed calcifying microalgae

Summary Incongruent phylogenies have been widely observed between nuclear and plastid or mitochondrial genomes in terrestrial plants and animals. However, few studies have examined these patterns in microalgae or the discordance between the two organelles. Here we investigated the nuclear–mitochondrial–plastid phylogenomic incongruence in Emiliania–Gephyrocapsa, a group of cosmopolitan calcifying phytoplankton with enormous populations and recent speciations. We assembled mitochondrial and plastid genomes of 27 strains from across global oceans and temperature regimes, and analyzed the phylogenomic histories of the three compartments using concatenation and coalescence methods. Six major clades with varying morphology and distribution are well recognized in the nuclear phylogeny, but such relationships are absent in the mitochondrial and plastid phylogenies, which also differ substantially from each other. The rampant phylogenomic discordance is due to a combination of organellar capture (introgression), organellar genome recombination, and incomplete lineage sorting of ancient polymorphic organellar genomes. Hybridization can lead to replacements of whole organellar genomes without introgression of nuclear genes and the two organelles are not inherited as a single cytoplasmic unit. This study illustrates the convoluted evolution and inheritance of organellar genomes in isogamous haplodiplontic microalgae and provides a window into the phylogenomic complexity of marine unicellular eukaryotes.
Conically singular Cayley submanifolds II: Desingularisations

This is the second in a series of three papers working towards constructing fibrations of compact Spin(7) manifolds by Cayley submanifolds. In this paper we show that a conically singular Cayley submanifold in an almost Spin(7)-manifold can be desingularised by gluing in a rescaled asymptotically conical submanifold of $\mathbb{R}^8$ with the same asymptotic cone. We assume that both the conically singular and the asymptotically conical Cayley submanifold are unobstructed in their respective moduli spaces.
Multi-server-based task offloading strategy in mobile edge computing

Edge computing, as a complement to cloud computing, has many unique advantages. In Mobile Edge Computing (MEC) scenarios, tasks on terminal devices can be offloaded to edge servers for computing purposes, reducing the shortage of computing resources on terminal devices. Moreover, the resulting delay is much shorter than uploading the task to the cloud center directly. However, many scenarios in reality show that there are dependencies between tasks, and a task cannot be calculated until all its predecessor tasks have been calculated. In this paper, we summarize a variety of task dependencies. At the same time, we build an efficient task offloading model based on two levels of edge servers with different functions. In particular, the second-level edge server has the routing decision function, which provides us with diversified offloading methods. Finally, we used improved NSGA-Ⅱ for task offloading. Compared with other offloading methods, our algorithm performs better in latency and energy consumption.
Apparatus and method for using effervescent tablets for cosmetic care

An apparatus and method for providing cosmetic treatment to skin comprise one material chemically active in a solid form preferably in a tablet form and a second material which is chemically reactive with the first material so that effervescence is produced during the reaction. The chemically active materials may be activated by a user for providing cosmetic treatment to skin. The activation releases effervescence which urges granules of the reacting materials onto the skin. The size of the granules lowers during the reaction thus providing continuously refining peeling to the skin.
Learning the Dynamics of Future Marine Microgrids Using Temporal Convolutional Neural Network

Medium-voltage direct-current (MVDC) ship-board microgrids (SMGs) are the state-of-the-art architecture for onboard power distribution in navy. These systems are considered to be highly dynamic due to high penetration of power electronic converters and volatile load patterns such as pulsed-power load (PPL) and propulsion motors demand variation. Obtaining the dynamic model of an MVDC SMG is a challenging task due to the confidentiality of system components models and uncertainty in the dynamic models through time. In this paper, a dynamic identification framework based on a temporal convolutional neural network (TCN) is developed to learn the system dynamics from measurement data. Different kinds of testing scenarios are implemented, and the testing results show that this approach achieves an exceptional performance and high generalization ability, thus holding substantial promise for development of advanced data-driven control strategies and stability prediction of the system.
A robust class decomposition-based approach for detecting Alzheimer’s progression

Computer-aided diagnosis of Alzheimer’s disease (AD) is a rapidly growing field with the possibility to be utilized in practice. Deep learning has received much attention in detecting AD from structural magnetic resonance imaging (sMRI). However, training a convolutional neural network from scratch is problematic because it requires a lot of annotated data and additional computational time. Transfer learning can offer a promising and practical solution by transferring information learned from other image recognition tasks to medical image classification. Another issue is the dataset distribution’s irregularities. A common classification issue in datasets is a class imbalance, where the distribution of samples among the classes is biased. For example, a dataset may contain more instances of some classes than others. Class imbalance is challenging because most machine learning algorithms assume that each class should have an equal number of samples. Models consequently perform poorly in prediction. Class decomposition can address this problem by making learning a dataset’s class boundaries easier. Motivated by these approaches, we propose a class decomposition transfer learning (CDTL) approach that employs VGG19, AlexNet, and an entropy-based technique to detect AD from sMRI. This study aims to assess the robustness of the CDTL approach in detecting the cognitive decline of AD using data from various ADNI cohorts to determine whether comparable classification accuracy for the two or more cohorts would be obtained. Furthermore, the proposed model achieved state-of-the-art performance in predicting mild cognitive impairment (MCI)-to-AD conversion with an accuracy of 91.45%.
Adapting Newton's Method to Neural Networks through a Summary of Higher-Order Derivatives

We consider a gradient-based optimization method applied to a function $\mathcal{L}$ of a vector of variables $\boldsymbol{\theta}$, in the case where $\boldsymbol{\theta}$ is represented as a tuple of tensors $(\mathbf{T}_1, \cdots, \mathbf{T}_S)$. This framework encompasses many common use-cases, such as training neural networks by gradient descent. First, we propose a computationally inexpensive technique providing higher-order information on $\mathcal{L}$, especially about the interactions between the tensors $\mathbf{T}_s$, based on automatic differentiation and computational tricks. Second, we use this technique at order 2 to build a second-order optimization method which is suitable, among other things, for training deep neural networks of various architectures. This second-order method leverages the partition structure of $\boldsymbol{\theta}$ into tensors $(\mathbf{T}_1, \cdots, \mathbf{T}_S)$, in such a way that it requires neither the computation of the Hessian of $\mathcal{L}$ according to $\boldsymbol{\theta}$, nor any approximation of it. The key part consists in computing a smaller matrix interpretable as a"Hessian according to the partition", which can be computed exactly and efficiently. In contrast to many existing practical second-order methods used in neural networks, which perform a diagonal or block-diagonal approximation of the Hessian or its inverse, the method we propose does not neglect interactions between layers. Finally, we can tune the coarseness of the partition to recover well-known optimization methods: the coarsest case corresponds to Cauchy's steepest descent method, the finest case corresponds to the usual Newton's method.