A Robust Framework for Multimodal Sentiment Analysis with Noisy Labels Generated from Distributed Data Annotation

Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s attitudes.With the advent of distributed data collection and annotation,we can easily obtain and share such multimodal data.However,due to professional discrepancies among annotators and lax quality control,noisy labels might be introduced.Recent research suggests that deep neural networks(DNNs)will overfit noisy labels,leading to the poor performance of the DNNs.To address this challenging problem,we present a Multimodal Robust Meta Learning framework(MRML)for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously.Specifically,we propose a two-layer fusion net to deeply fuse different modalities and improve the quality of the multimodal data features for label correction and network training.Besides,a multiple meta-learner(label corrector)strategy is proposed to enhance the label correction approach and prevent models from overfitting to noisy labels.We conducted experiments on three popular multimodal datasets to verify the superiority of ourmethod by comparing it with four baselines.

近红外无创血糖浓度的Label Sensitivity算法和支持向量机回归

近红外光谱分析技术在生物医学工程领域具有广阔应用前景。无创且持续性地测量能实时监控人体血糖水平,给糖尿病患者带来极大便利性、提高生存质量、降低糖尿病并发症发生率具有很大的社会效益。无创血糖监测的想法提出较早,但仍然存在预测精度低、预测值与标签值相关性不高等难点,至今没有达到临床要求。近年来,光谱检测技术发展迅猛且机器学习技术在智能信息处理方面具有明显优势,两者结合可以有效提高人体无创血糖医学监测模型的精度和普适性。提出了一种标签敏感度算法(LS),并结合支持向量机方法建立了人体血糖含量预测模型。使用近红外光谱仪采集了4名志愿者食指处动态血液光谱数据(每名志愿者28组数据),并使用多元散射矫正(MSC)方法消除了部分光散射的影响。考虑血糖对不同波长光的吸收有差异,提出了基于血糖浓度标签差的特征波长挑选方法,并构建了标签敏感度支持向量机(LSSVR)预测模型。设计实验,对比该模型与偏最小二乘回归(PLSR)和区分度支持向量机(FSSVR)算法。结果表明,LS算法的最佳特征波长数为32,经特征波长选择后的LSSVR表现最佳,其均方误差降低至0.02 mmol·L^(-1),明显优于全谱段PLSR模型,血糖浓度的预测值与标签值的相关系数提升至99.8%,预测值全部位于可容许误差的克拉克网格A区内。LSSVR模型的优异表现为早日实现血糖的无创监测提供了新思路。

Radio Frequency Fingerprinting Identification Using Semi-Supervised Learning with Meta Labels

Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF identification by leveraging the hardware-level features.However,traditional supervised learning methods require huge labeled training samples.Therefore,how to establish a highperformance supervised learning model with few labels under practical application is still challenging.To address this issue,we in this paper propose a novel RFF semi-supervised learning(RFFSSL)model which can obtain a better performance with few meta labels.Specifically,the proposed RFFSSL model is constituted by a teacher-student network,in which the student network learns from the pseudo label predicted by the teacher.Then,the output of the student model will be exploited to improve the performance of teacher among the labeled data.Furthermore,a comprehensive evaluation on the accuracy is conducted.We derive about 50 GB real long-term evolution(LTE)mobile phone’s raw signal datasets,which is used to evaluate various models.Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97%experimental testing accuracy over a noisy environment only with 10%labeled samples when training samples equal to 2700.

Factors Related to the Reading Nutritional Labels by Consumers

This study aimed to evaluate the features related to consumers’ reading nutritional labels in a city in the interior of the São Paulo State, Brazil. A questionnaire was answered by 100 consumers of a supermarket chain, sociodemographic information and data related to label reading habits were collected. Tables with percentage values and bar graphs were used. Chi-square tests and logistic regression models were performed to verify the association between the variables and the label reading habits. The factors that showed significant associations with the reading labels were gender, ease to understand the labels and access to their information (p 0.10). People who had already read labels reported to have more difficulty to understand the information contained on them, and people who had already received instructions on the labels were three and a half times more likely to read the instructions contained on them than those who hadn’t received any guidance. This study points to the need to expand the disclosure to consumers about the contents present on the labels, through more accessible language, so that the labels fulfill their role to instruct consumers in their choices.

Learning about good nutrition with the 5-color front-of-package label"Nutri-Score":an experimental study

The Nutri-Score is a 5-color front-of-pack nutrition label designed to provide consumers with an easily understandable guideline to the healthiness of food products.The impact that the Nutri-Score may have on consumers'choices is unclear since different experimental paradigms have found vastly different effect sizes.In the present study,we have investigated how student participants change a hypothetical personal 1-daydietary plan after a learning phase during which they learn about the Nutri-Scores of the available food items.Participants were instructed to compose a healthy diet plan in order that the question of whether the NutriScore would improve their ability to compose a healthy dietary plan could be investigated,independent of the question of whether they would apply this knowledge in their ordinary lives.We found a substantial(Cohen's d=0.86)positive impact on nutritional quality(as measured by the Nutrient Profiling System score of the Food Standards Agency)and a medium-sized(Cohen's d=0.43)reduction of energy content.Energy content reduction was larger for participants who had initially composed plans with higher energy content.The results suggest that the Nutri-Score has the potential to guide consumers to healthier food choices.It remains unclear,however,whether this potential will be reflected in real-life dietary choices.

Local saliency consistency-based label inference for weakly supervised salient object detection using scribble annotations

Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information.Therefore,an intuitive idea is to infer annotations that cover more complete object and background regions for training.To this end,a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels.Specifically,k-means clustering algorithm was first performed on both colours and coordinates of original annotations,and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres.Next,the same annotations for pixels with similar colours within each kernel neighbourhood was set further.Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results.

Molecular Mechanisms of Intracellular Delivery of Nanoparticles Monitored by an Enzyme‑Induced Proximity Labeling

Achieving increasingly finely targeted drug delivery to organs,tissues,cells,and even to intracellular biomacromolecules is one of the core goals of nanomedicines.As the delivery destination is refined to cellular and subcellular targets,it is essential to explore the delivery of nanomedicines at the molecular level.However,due to the lack of technical methods,the molecular mechanism of the intracellular delivery of nanomedicines remains unclear to date.Here,we develop an enzyme-induced proximity labeling technology in nanoparticles(nano-EPL)for the real-time monitoring of proteins that interact with intracellular nanomedicines.Poly(lactic-co-glycolic acid)nanoparticles coupled with horseradish peroxidase(HRP)were fabricated as a model(HRP(+)-PNPs)to evaluate the molecular mechanism of nano delivery in macrophages.By adding the labeling probe biotin-phenol and the catalytic substrate H_(2)O_(2)at different time points in cellular delivery,nano-EPL technology was validated for the real-time in situ labeling of proteins interacting with nanoparticles.Nano-EPL achieves the dynamic molecular profiling of 740 proteins to map the intracellular delivery of HRP(+)-PNPs in macrophages over time.Based on dynamic clustering analysis of these proteins,we further discovered that different organelles,including endosomes,lysosomes,the endoplasmic reticulum,and the Golgi apparatus,are involved in delivery with distinct participation timelines.More importantly,the engagement of these organelles differentially affects the drug delivery efficiency,reflecting the spatial–temporal heterogeneity of nano delivery in cells.In summary,these findings highlight a significant methodological advance toward understanding the molecular mechanisms involved in the intracellular delivery of nanomedicines.

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Impaired implicit emotion regulation in patients with panic disorder:An event-related potential study on affect labeling

BACKGROUND Panic disorder(PD)involves emotion dysregulation,but its underlying mechanisms remain poorly understood.Previous research suggests that implicit emotion regulation may play a central role in PD-related emotion dysregulation and symptom maintenance.However,there is a lack of studies exploring the neural mechanisms of implicit emotion regulation in PD using neurophysiological indicators.AIM To study the neural mechanisms of implicit emotion regulation in PD with eventrelated potentials(ERP).METHODS A total of 25 PD patients and 20 healthy controls(HC)underwent clinical evaluations.The study utilized a case-control design with random sampling,selecting participants for the case group from March to December 2018.Participants performed an affect labeling task,using affect labeling as the experimental condition and gender labeling as the control condition.ERP and behavioral data were recorded to compare the late positive potential(LPP)within and between the groups.RESULTS Both PD and HC groups showed longer reaction times and decreased accuracy under the affect labeling.In the HC group,late LPP amplitudes exhibited a dynamic pattern of initial increase followed by decrease.Importantly,a significant group×condition interaction effect was observed.Simple effect analysis revealed a reduction in the differences of late LPP amplitudes between the affect labeling and gender labeling conditions in the PD group compared to the HC group.Furthermore,among PD patients under the affect labeling,the late LPP was negatively correlated with disease severity,symptom frequency,and intensity.CONCLUSION PD patients demonstrate abnormalities in implicit emotion regulation,hampering their ability to mobilize cognitive resources for downregulating negative emotions.The late LPP amplitude in response to affect labeling may serve as a potentially valuable clinical indicator of PD severity.

Challenges with non-descriptive compliance labeling of end-stage renal disease patients in accessibility for renal transplantation

Non-descriptive and convenient labels are uninformative and unfairly project blame onto patients.The language clinicians use in the Electronic Medical Record,research,and clinical settings shapes biases and subsequent behaviors of all providers involved in the enterprise of transplantation.Terminology such as noncompliant and nonadherent serve as a reason for waitlist inactivation and limit access to life-saving transplantation.These labels fail to capture all the circum-stances surrounding a patient’s inability to follow their care regimen,trivialize social determinants of health variables,and bring unsubstantiated subjectivity into decisions regarding organ allocation.Furthermore,insufficient Medicare coverage has forced patients to ration or stop taking medication,leading to allograft failure and their subsequent diagnosis of noncompliant.We argue that perpetuating non-descriptive language adds little substantive information,in-creases subjectivity to the organ allocation process,and plays a major role in reduced access to transplantation.For patients with existing barriers to care,such as racial/ethnic minorities,these effects may be even more drastic.Transplant committees must ensure thorough documentation to correctly encapsulate the entirety of a patient’s position and give voice to an already vulnerable population.

5-Bromo-2'-deoxyuridine labeling:historical perspectives,factors infiuencing the detection,toxicity,and its implications in the neurogenesis

The halopyrimidine 5-bromo-2′-deoxyuridine(BrdU)is an exogenous marker of DNA synthesis.Since the introduction of monoclonal antibodies against BrdU,an increasing number of methodologies have been used for the immunodetection of this synthesized bromine-tagged base analogue into replicating DNA.BrdU labeling is widely used for identifying neuron precursors and following their fate during the embryonic,perinatal,and adult neurogenesis in a variety of vertebrate species including birds,reptiles,and mammals.Due to BrdU toxicity,its incorporation into replicating DNA presents adverse consequences on the generation,survival,and settled patterns of cells.This may lead to false results and misinterpretation in the identification of proliferative neuroblasts.In this review,I will indicate the detrimental effects of this nucleoside during the development of the central nervous system,as well as the reliability of BrdU labeling to detect proliferating neuroblasts.Moreover,it will show factors influencing BrdU immunodetection and the contribution of this nucleoside to the study of prenatal,perinatal,and adult neurogenesis.Human adult neurogenesis will also be discussed.It is my hope that this review serves as a reference for those researchers who focused on detecting cells that are in the synthetic phase of the cell cycle.

基于label-free定量蛋白质组学方法筛选沉默CHAF1B基因后心肌细胞差异表达蛋白及调控网络分析

目的分析沉默染色质装配因子1亚基B(chromatin assembly factor 1 subunit B,CHAF1B)基因后心肌细胞中差异表达蛋白,预测CHAF1B基因调控网络,为寻找促进心肌细胞修复的潜在治疗靶点提供参考。方法采用转染和蛋白质印迹法筛选沉默CHAF1B基因的有效小干扰RNA(small interfering RNA,siRNA)。应用有效siRNA沉默人源心肌AC16细胞CHAF1B基因后,采用细胞活力检测方法检测细胞活力;提取总蛋白质进行定量、还原、烷基化和胰蛋白酶裂解成肽段,利用高效液相串联质谱法鉴定肽段;搜索UniProt蛋白库筛选差异表达的蛋白质进行基因本体(Gene Ontology,GO)富集分析、京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)通路富集和蛋白质互作网络(protein-protein interaction networks,PPI)分析。结果siRNA有效沉默CHAF1B基因后,心肌细胞存活明显受到抑制;label-free定量蛋白质组学方法鉴定结果显示,共有69个差异表达蛋白质,其中50个表达显著上调(差异倍数≥2,P<0.05),19个表达显著下调(差异倍数≤0.5,P<0.05)。GO分析显示,差异表达蛋白质主要参与大分子复合亚基体、细胞组分生物合成和组装等生物学过程,分布在细胞质和囊泡等区域,发挥蛋白质结合等分子功能。KEGG通路富集和PPI分析显示,差异表达蛋白质参与的信号通路包括蛋白酶体、氨酰tRNA生物合成、胞吞、嘧啶代谢和氨基酸生物合成等10条信号途径;表达显著上调的蛋白质如蛋白酶体亚单位A2和B7、26 S蛋白酶体调节亚单位6B和10B参与蛋白酶体途径,丝氨酸、甘氨酸、谷氨酰胺和赖氨酸tRNA合成酶介导氨酰tRNA生物合成;表达显著下调的蛋白质包括骨架相关蛋白2/3复合体亚单位3和热休克70蛋白1样参与胞吞作用,核糖核苷-二磷酸还原酶大亚基介导嘧啶代谢等通路。实时荧光定量聚合酶链式反应结果证实,转染CHAF1B siRNA后心肌细胞中合成骨架相关蛋白2/3复合体亚单位3的基因ARPC3和氨酰tRNA生物合成关键基因QARS1的mRNA水平均显著降低。结论CHAF1B为心肌细胞存活的关键蛋白质,参与调控心肌细胞的胞吞和氨基酸生物合成等多种生物学过程,参考其调控网络可帮助寻找促进心肌细胞修复的干预环节。

Orthogonal Stacked Spectral Coding Labels for Fast Packets Routing over Optical MPLS Network

Multi-protocol label switching（MPLS） has the advantage of high efficiency in the second layer, which improves the performance of data packets routing. In this paper, a new structure to implement optical MPLS is proposed. We construct a code family for spectral-amplitude coding（SAC） labels in the optical MPLS networks. SAC labels are suitable for optical packet switching because they can be constructed and recognized quickly at each router. We use the label stacking to provide hierarchical routing to avoid swapping labels at each forwarding node and reduce system complexity. However, the phase-induced intensity noise（PIIN） appears due to the incoherent property of the light source when the stacked labels set makes the correlation decoding with the local node label,which degrades system performance.

Research on Key Technologies of Electronic Shelf Labels Based on LoRa

The demand for Electronic Shelf Labels(ESL),according to the Internet of Things(IoT)paradigm,is expected to grow considerably in the immediate future.Various wireless communication standards are currently contending to gain an edge over the competition and provide the massive connectivity that will be required by a world in which everyday objects are expected to communicate with each other.Low-Power Wide-Area Networks(LPWANs)are continuously gaining momentum among these standards,mainly thanks to their ability to provide long-range coverage to devices,exploiting license-free frequency bands.The main theme of this work is one of the most prominent LPWAN technologies,LoRa.The purpose of this research is to provide long-range,less intermediate node,less energy dissipation,and a cheaper ESL system.Much research has already been done on designing the LoRaWAN network,not capable to make a reliable network.LoRa is using different gateways to transmit the same data,collision,data jamming,and data repetition are expected.According to the transmission behavior of LoRa,50%of data is lost.In this paper,the Improved Backoff Algorithm with synchronization technique is used to decrease overlapping and data loss.Besides,the improved Adaptive Data Rate algorithm(ADR)avoids the collision in concurrently transmitted data by using different Spreading Factors(SFs).The allocation of SF has the main role in designing LoRa based network to minimize the impact of the intra-interference,cost function,and Euclidean distance.For this purpose,the K-means machine learning algorithm is used for clustering.The data rate model is using an intra-slicing technique based on Maximum Likelihood Estimation(MLE).The data rate model includes three critical communication slices,High Critical Communication(HCC),Medium Critical Communication(MCC),and Low Critical Communication(LCC),having the specified number of End devices(EDs),payload budget delay,and data rate.Finally,different combinations of gateways are used to build ESL for 200 electronic shelf labels.

Eco-labels系统及其对纺织品生产与贸易的影响

文章简要介绍了Eco-labels系统的种类、Eco-labels系统建立的目的要求以及Eco-labels系统在纺织工业中的应用情况,并指出了ko-Tex(or Oeko-tex)labels,EC Eco-labels及Unified Sustainable Textile Standard(2.0的特点。ko-tex standards and EC Eco-label对纺织品中的3种普遍有害物(甲醛、化学残留物、重金属)的限制要求也进行了扼要的阐述,同时摘录了部分EC Eco-label系统对毛纺织品及其生产加工的要求。

Quality Assessment of Training Data with Uncertain Labels for Classification of Subjective Domains

In order to improve the performance of classifiers in subjective domains, this paper defines a metric to measure the quality of the subjectively labelled training data (QoSTD) by means of K-means clustering. Then, the QoSTD is used as a weight of the predicted class scores to adjust the likelihoods of instances. Moreover, two measurements are defined to assess the performance of the classifiers trained by the subjective labelled data. The binary classifiers of Traditional Chinese Medicine (TCM) Zhengs are trained and retrained by the real-world data set, utilizing the support vector machine (SVM) and the discrimination analysis (DA) models, so as to verify the effectiveness of the proposed method. The experimental results show that the consistency of likelihoods of instances with the corresponding observations is increased notable for the classes, especially in the cases with the relatively low QoSTD training data set. The experimental results also indicate the solution how to eliminate the miss-labelled instances from the training data set to re-train the classifiers in the subjective domains.

A rabies virus-based toolkit for efficient retrograde labeling and monosynaptic tracing

Analyzing the structure and function of the brain's neural network is critical for identifying the working principles of the brain and the mechanisms of brain diseases.Recombinant rabies viral vectors allow for the retrograde labeling of projection neurons and cell type-specific trans-monosynaptic tracing,making these vectors powerful candidates for the dissection of synaptic inputs.Although several attenuated rabies viral vectors have been developed,their application in studies of functional networks is hindered by the long preparation cycle and low yield of these vectors.To overcome these limitations,we developed an improved production system for the rapid rescue and preparation of a high-titer CVS-N2c-ΔG virus.Our results showed that the new CVS-N2c-ΔG-based toolkit performed remarkably:(1)N2cG-coated CVS-N2c-ΔG allowed for efficient retrograde access to projection neurons that were unaddressed by rAAV9-Retro,and the efficiency was six times higher than that of rAAV9-Retro;(2)the trans-monosynaptic efficiency of oG-mediated CVS-N2c-ΔG was 2–3 times higher than that of oG-mediated SAD-B19-ΔG;(3)CVS-N2c-ΔG could delivery modified genes for neural activity monitoring,and the time window during which this was maintained was 3 weeks;and(4)CVS-N2c-ΔG could express sufficient recombinases for efficient transgene recombination.These findings demonstrate that new CVS-N2c-ΔG-based toolkit may serve as a versatile tool for structural and functional studies of neural circuits.

Optical Packet Switching Scheme Using Multi-optical-code Labels

Proposed is a novel optical code(OC) label switching scheme in which an optical label is constructed by multiple parallel optical codes.The performances of splitting loss and BER are simulated and analyzed.Simulation results show that the proposed label can be correctly recognized to perform packet switching.Compared with reported schemes using one OC as a label,the splitting loss in our proposal is lowered.

PLDMLT:Multi-Task Learning of Diabetic Retinopathy Using the Pixel-Level Labeled Fundus Images

In the field of medical images,pixel-level labels are time-consuming and expensive to acquire,while image-level labels are relatively easier to obtain.Therefore,it makes sense to learn more information(knowledge)from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training costs.In this paper,using Pixel-Level Labeled Images forMulti-Task Learning(PLDMLT),we focus on grading the severity of fundus images for Diabetic Retinopathy(DR).This is because,for the segmentation task,there is a finely labeled mask,while the severity grading task is without classification labels.To this end,we propose a two-stage multi-label learning weakly supervised algorithm,which generates initial classification pseudo labels in the first stage and visualizes heat maps at all levels of severity using Grad-Cam to further provide medical interpretability for the classification task.A multitask model framework with U-net as the baseline is proposed in the second stage.A label update network is designed to alleviate the gradient balance between the classification and segmentation tasks.Extensive experimental results show that our PLDMLTmethod significantly outperforms other stateof-the-art methods in DR segmentation on two public datasets,achieving up to 98.897%segmentation accuracy.In addition,our method achieves comparable competitiveness with single-task fully supervised learning in the DR severity grading task.