Related Information Research Articles

Research on a Joint Extraction Method of Track Circuit Entities and Relations Integrating Global Pointer and Tensor Learning

To address the issue of efficiently reusing the massive amount of unstructured knowledge generated during the handling of track circuit equipment faults and to automate the construction of knowledge graphs in the railway maintenance domain, it is crucial to leverage knowledge extraction techniques to efficiently extract relational triplets from fault maintenance text data. Given the current lag in joint extraction technology within the railway domain and the inefficiency in resource utilization, this paper proposes a joint extraction model for track circuit entities and relations, integrating Global Pointer and tensor learning. Taking into account the associative characteristics of semantic relations, the nesting of domain-specific terms in the railway sector, and semantic diversity, this research views the relation extraction task as a tensor learning process and the entity recognition task as a span-based Global Pointer search process. First, a multi-layer dilate gated convolutional neural network with residual connections is used to extract key features and fuse the weighted information from the 12 different semantic layers of the RoBERTa-wwm-ext model, fully exploiting the performance of each encoding layer. Next, the Tucker decomposition method is utilized to capture the semantic correlations between relations, and an Efficient Global Pointer is employed to globally predict the start and end positions of subject and object entities, incorporating relative position information through rotary position embedding (RoPE). Finally, comparative experiments with existing mainstream joint extraction models were conducted, and the proposed model’s excellent performance was validated on the English public datasets NYT and WebNLG, the Chinese public dataset DuIE, and a private track circuit dataset. The F1 scores on the NYT, WebNLG, and DuIE public datasets reached 92.1%, 92.7%, and 78.2%, respectively.

Quantum correlations and parameter estimation for two superconducting qubits interacting with a quantized field

In the present manuscript, we introduce a quantum system of two superconducting qubits (S–Qs) interacting with a quantized field under the influence of the Kerr nonlinear medium and Ising interaction. We formulate the Hamiltonian of the quantum model and determine the density operator of whole quantum system as well as quantum subsystems. We examine the dynamics of the quantumness measures for subsequent times including the S–Qs entanglement, S–Qs-field entanglement and quantum Fisher information in relation to the system parameters. Finally, we display the connection among the measures of quantumness during the time evolution.

Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features

Cloud systems, typically comprised of various components ( e.g. , microservices), are susceptible to performance issues, which may cause service-level agreement violations and financial losses. Identifying performance issues is thus of paramount importance for cloud vendors. In current practice, crucial metrics, i.e. , key performance indicators (KPIs), are monitored periodically to provide insight into the operational status of components. Identifying performance issues is often formulated as an anomaly detection problem, which is tackled by analyzing each metric independently. However, this approach overlooks the complex dependencies existing among cloud components. Some graph neural network-based methods take both temporal and relational information into account, however, the correlation violations in the metrics that serve as indicators of underlying performance issues are difficult for them to identify. Furthermore, a large volume of components in a cloud system results in a vast array of noisy metrics. This complexity renders it impractical for engineers to fully comprehend the correlations, making it challenging to identify performance issues accurately. To address these limitations, we propose Identifying Performance Issues based on Relational-Temporal Features (ISOLATE ), a learning-based approach that leverages both the relational and temporal features of metrics to identify performance issues. In particular, it adopts a graph neural network with attention to characterizing the relations among metrics and extracts long-term and multi-scale temporal patterns using a GRU and a convolution network, respectively. The learned graph attention weights can be further used to localize the correlation-violated metrics. Moreover, to relieve the impact of noisy data, ISOLATE utilizes a positive unlabeled learning strategy that tags pseudo labels based on a small portion of confirmed negative examples. Extensive evaluation on both public and industrial datasets shows that ISOLATE outperforms all baseline models with 0.945 F1-score and 0.920 Hit rate@3. The ablation study also proves the effectiveness of the relational-temporal features and the PU-learning strategy. Furthermore, we share the success stories of leveraging ISOLATE to identify performance issues in Huawei Cloud, which demonstrates its superiority in practice.

Is the information provided by large language models valid in educating patients about adolescent idiopathic scoliosis? An evaluation of content, clarity, and empathy : The perspective of the European Spine Study Group.

Large language models (LLM) have the potential to bridge knowledge gaps in patient education and enrich patient-surgeon interactions. This study evaluated three chatbots for delivering empathetic and precise adolescent idiopathic scoliosis (AIS) related information and management advice. Specifically, we assessed the accuracy, clarity, and relevance of the information provided, aiming to determine the effectiveness of LLMs in addressing common patient queries and enhancing their understanding of AIS. We sourced 20 webpages for the top frequently asked questions (FAQs) about AIS and formulated 10 critical questions based on them. Three advanced LLMs-ChatGPT 3.5, ChatGPT 4.0, and Google Bard-were selected to answer these questions, with responses limited to 200 words. The LLMs' responses were evaluated by a blinded group of experienced deformity surgeons(members of the European Spine Study Group) from seven Europeanspine centers. A pre-established 4-level rating system from excellent to unsatisfactory was used with a further rating for clarity, comprehensiveness, and empathy on the 5-point Likert scale. If not rated 'excellent', the raters were asked to report the reasons for their decision for each question. Lastly, raters were asked for their opinion towards AI in healthcare in general in six questions. The responses among all LLMs were 'excellent' in 26% of responses, with ChatGPT-4.0 leading (39%), followed by Bard (17%). ChatGPT-4.0 was rated superior to Bard and ChatGPT 3.5 (p = 0.003). Discrepancies among raters were significant (p < 0.0001), questioning inter-rater reliability. No substantial differences were noted in answer distribution by question (p = 0.43). The answers on diagnosis (Q2) and causes (Q4) of AIS were top-rated. The most dissatisfaction was seen in the answers regarding definitions (Q1) and long-term results (Q7). Exhaustiveness, clarity, empathy, and length of the answers were positively rated (> 3.0 on 5.0) and did not demonstrate any differences among LLMs. However, GPT-3.5 struggled with language suitability and empathy, while Bard's responses were overly detailed and less empathetic. Overall, raters found that 9% of answers were off-topic and 22% contained clear mistakes. Our study offers crucial insights into the strengths and weaknesses of current LLMs in AIS patient and parent education, highlighting the promise of advancements like ChatGPT-4.oand Gemini alongside the need for continuous improvement in empathy, contextual understanding, and language appropriateness.

Enhancing Expert Decision-Making for Wastewater Treatment Plants with Seidel Laplacian Energy and Cosine Similarity Measure in Intuitionistic Fuzzy Graphs

Wastewater treatment facilities’ main goal is to protect the public and environment from the hazardous and poisonous materials found in wastewater. Water treatment facilities were developed to speed up the natural process of cleansing water. A novel cosine similarity measure across intuitionistic fuzzy graphs has been proven to be more effective than certain present ones in group decision-making issues using example verification. This paper provides a unique approach for calculating expert-certified, well-known scores by finding the ambiguous information of intuitionistic fuzzy preference relations as well as the regular cosine similarity grades from one separable intuitionistic fuzzy preference relation to another. The new technique considers both "objective" and "subjective" information provided by experts. Using intuitionistic fuzzy preference relations, we provide workable techniques for judging experts’ eligible reputational ratings. This can be used to raise or decrease the relevance of the stated criteria in an evaluation that takes into account several competing elements. We give a solution to a decisional problem by using two effective methods: the newly constructed cosine similarity measure and the Seidel Laplacian energy (SLe+) of an intuitionistic fuzzy graph. Finally, two working procedures and circumstances are offered to show the effectiveness and superiority of the proposed techniques.

A Unified Model for Chinese Cyber Threat Intelligence Flat Entity and Nested Entity Recognition

In recent years, as cybersecurity threats have become increasingly severe and cyberattacks have occurred frequently, higher requirements have been put forward for cybersecurity protection. Therefore, the Named Entity Recognition (NER) technique, which is the cornerstone of Cyber Threat Intelligence (CTI) analysis, is particularly important. However, most existing NER studies are limited to recognizing single-layer flat entities, ignoring the possible nested entities in CTI. On the other hand, most of the existing studies focus on English CTIs, and the existing models performed poorly in a limited number of Chinese CTI studies. Given the above challenges, we propose in this paper a novel unified model, RBTG, which aims to identify flat and nested entities in Chinese CTI effectively. To overcome the difficult boundary recognition problem and the direction-dependent and distance-dependent properties in Chinese CTI NER, we use Global Pointer as the decoder and TENER as the encoder layer, respectively. Specifically, the Global Pointer layer solves the problem of the insensitivity of general NER methods to entity boundaries by utilizing the relative position information and the multiplicative attention mechanism. The TENER layer adapts to the Chinese CTI NER task by introducing an attention mechanism with direction awareness and distance awareness. Meanwhile, to cope with the complex feature capture of hierarchical structure and dependencies among Chinese CTI nested entities, the TENER layer solves the problem by following the structure of multiple self-attention layers and feed-forward network layers superimposed on each other in the Transformer. In addition, to fill the gap in the Chinese CTI nested entity dataset, we further apply the Large Language Modeling (LLM) technique and domain knowledge to construct a high-quality Chinese CTI nested entity dataset, CDTinee, which consists of six entity types selected from STIX, including nearly 4000 entity types extracted from more than 3000 threatening sentences. In the experimental session, we conduct extensive experiments on multiple datasets, and the results show that the proposed model RBTG outperforms the baseline model in both flat NER and nested NER.

Question Embedding on Weighted Heterogeneous Information Network for Knowledge Tracing

Knowledge Tracing (KT) aims to predict students’ future performance on answering questions based on their historical exercise sequences. To alleviate the problem of data sparsity in KT, recent works have introduced auxiliary information to mine question similarity, resulting in the enhancement of question embeddings. Nonetheless, there remains a gap in developing an approach that effectively incorporates various forms of auxiliary information, including relational information (e.g., question-student , question-skill relation), relationship attributes (e.g., correctness indicating a student's performance on a question), and node attributes (e.g., student ability ). To tackle this challenge, the Similarity-enhanced Question Embedding (SimQE) method for KT is proposed, with its central feature being the utilization of weighted and attributed meta-paths for extracting question similarity. To capture multi-dimensional question similarity semantics by integrating multiple relations, various meta-paths are constructed for learning question embeddings separately. These embeddings, each encoding different similarity semantics, are then fused to serve the task of KT. To capture finer-grained similarity by leveraging the relationship attributes and node attributes on the meta-paths, the biased random walk algorithm is designed. In addition, the auxiliary node generation method is proposed to capture high-order question similarity. Finally, extensive experiments conducted on 6 datasets demonstrate that SimQE performs the best among 10 representative question embedding methods. Furthermore, SimQE proves to be more effective in alleviating the problem of data sparsity.

Peran Pemerintah dalam Pelestarian Kesenian Ludruk di Surabaya

Ludruk is a native East Javanese folk art originating from Jombang, but is also a typical Surabaya cultural mascot. Ludruk is a traditional drama performed by an arts group on a stage based on stories about people's daily lives. Ludruk art is part of East Java's rich and unique cultural heritage. Its preservation is important to maintain local cultural identity which can be a valuable asset for the community, due to the threat of loss and extinction. Ludruk is not only an artistic performance, but is also part of social life and preserving the community's identity can help strengthen a sense of solidarity and pride in local culture. . By choosing this topic, researchers can make a meaningful contribution to efforts to preserve and promote the desirability of ludruk art for the future. This research method is descriptive qualitative by collecting primary data through in-depth interviews and observations from related informants. Based on the research results that: 1. The role of the Surabaya City Culture, Youth and Sports and Tourism Department (Disbudporapar) as a regulator, plays a significant role in supporting arts and cultural activities in the preservation of ludruk art, ensuring that this traditional art remains alive and growing among young generation in ASPK policy.2. The role of the Department of Culture, Youth and Sports and Tourism of the City of Surabaya (Disbudporapar) as a facilitator, currently there is still a lack of support and facilities, allowing regeneration, regeneration of ludruk artists to be slow, competition with modern media, limited training: training programs are often undirected and inconsistent. 3. The role of the Department of Culture, Youth and Sports and Tourism of the City of Surabaya (Disbudporapar) as a dynamist, has been running well, but the current condition is that many ludruk groups in Surabaya have arts registration marks (TDK) and are registered at Disbudporapar. However, many of them have already died and Ludruk performances currently only rely on invitations from the Surabaya City Youth and Sports Culture Service (Disbudporapar).

ChatKG: Visualizing time-series patterns aided by intelligent agents and a knowledge graph

Line-chart visualizations of temporal data enable users to identify interesting patterns for the user to inquire about. Using Intelligent Agents (IA), Visual Analytic tools can automatically uncover explicit knowledge related information to said patterns. Yet, visualizing the association of data, patterns, and knowledge is not straightforward. In this paper, we present ChatKG, a novel visual analytics strategy that allows exploratory data analysis of a Knowledge Graph that associates temporal sequences, the patterns found in each sequence, the temporal overlap between patterns, the related knowledge of each given pattern gathered from a multi-agent IA, and the IA’s suggestions of related datasets for further analysis visualized as annotations. We exemplify and informally evaluate ChatKG by analyzing the world’s life expectancy. For this, we implement an oracle that automatically extracts relevant or interesting patterns, populates the Knowledge Graph to be visualized, and, during user interaction, inquires the multi-agent IA for related information and suggests related datasets to be displayed as visual annotations. Our tests and an interview conducted showed that ChatKG is well suited for temporal analysis of temporal patterns and their related knowledge when applied to history studies.

Learning Cross-Attention Point Transformer with Global Porous Sampling.

In this paper, we propose a point-based cross-attention transformer named CrossPoints with parametric Global Porous Sampling (GPS) strategy. The attention module is crucial to capture the correlations between different tokens for transformers. Most existing point-based transformers design multi-scale self-attention operations with down-sampled point clouds by the widely-used Farthest Point Sampling (FPS) strategy. However, FPS only generates sub-clouds with holistic structures, which fails to fully exploit the flexibility of points to generate diversified tokens for the attention module. To address this, we design a cross-attention module with parametric GPS and Complementary GPS (C-GPS) strategies to generate series of diversified tokens through controllable parameters. We show that FPS is a degenerated case of GPS, and the network learns more abundant relational information of the structure and geometry when we perform consecutive cross-attention over the tokens generated by GPS as well as C-GPS sampled points. More specifically, we set evenly-sampled points as queries and design our cross-attention layers with GPS and C-GPS sampled points as keys and values. In order to further improve the diversity of tokens, we design a deformable operation over points to adaptively adjust the points according to the input. Extensive experimental results on both shape classification and indoor scene segmentation tasks indicate promising boosts over the recent point cloud transformers. We also conduct ablation studies to show the effectiveness of our proposed cross-attention module with GPS strategy.

Medical image segmentation network based on feature filtering with low number of parameters

In recent years, the medical image segmentation method based on hybrid convolutional neural network (CNN) and Vision Transformer (ViT) has made great progress, but it still faces the challenge of unbalanced global and local modeling, and excessive parameters. In addition, ViT repeatedly uses the whole feature map to model the global information, thus generating irrelevant and weakly related information, which will weaken the performance of the model when facing small datasets and segmentation targets. Therefore, this paper proposes a feature screening network based on similarity, named Screening Feature (SF)-MixedNet. Specifically, this paper first proposes a new feature extractor, namely Correlation based Similarity Transformer (CSimFormer). On the basis of parameter pruning, it uses the Screening Feature Multi-head Self Attention (SF-MSA) to establish the remote dependency, and calculates the similarity between local elements through the Location-Sensitive Mechanism (LsM) to obtain the weight matrix. Then, the correlation between regional elements is mined by Region Matching and Selection (RMS) mechanism, and the obtained information is filtered according to the corresponding rules to reduce the side effects of redundant information. Extensive experiments on Synapse dataset, ACDC dataset and SegPC-2021 dataset show that the segmentation accuracy reaches 83.51%, 92.20% and 81.27% respectively. Especially in the Synapse dataset, our method is 6.31% higher than the baseline. The method proposed in this paper effectively improves the segmentation accuracy, provides more detailed information for medical diagnosis and promotes the development of medical artificial intelligence technology.

A Review of Anticancer Potential of Conferone, Diversin and Ferutinin; Which One is Stronger for Cancer Therapy?

One of the growing diseases in today's human societies is cancer, which has become a major challenge, especially in industrialized and developing countries. Cancer treatments are diverse, but they usually use surgery, chemotherapy, and radiotherapy to improve patients. Existing drugs are usually expensive and, in some cases, are not effective due to drug resistance and side effects. Finding compounds of natural origin can be somewhat effective and useful in helping doctors to treat this disease. Ferula plants, which are traditionally used as spices or for medicinal purposes, can be a good source for finding anti-cancer compounds due to their various compounds, such as monoterpenes, sulfide compounds, and polyphenols. Several studies have shown that compounds found in Ferula plants have significant anticancer effects on various types of cancer cells. This article was compiled with the aim of collecting evidence and articles related to the anti-cancer effects of three compounds obtained from these plants, namely Conferone, Diversin, and Ferutinin. This review article was prepared by searching the terms Conferone, Diversin, Ferutinin and cancer and related information was collected through searching electronic databases such as ISI Web of Knowledge, PubMed and Google Scholar until the March of 2024. The results of this review showed that relatively comprehensive studies have been conducted in this field and these studies have shown that these compounds can be used in the design of future anticancer drugs. Among the examined compounds, conferone showed that it has the best effect on cancer cells.

Auxiliary Feature Fusion and Noise Suppression for HOI Detection

In recent years, one-stage HOI (Human–Object Interaction) detection methods tend to divide the original task into multiple sub-tasks by using a multi-branch network structure. However, there is no sufficient attention to information communication between these branches. The inference approach in the cascaded structure is singular, while fully parallel methods will disrupt the associations between different pieces of information. Besides, noise interference may occur during the fusion of different features and thus affect the detection performance. To address these issues, this article proposes a one-stage three-branch parallel HOI detection method, which treats HOI as three separate sub-tasks (human detection, object detection, and interaction detection) and leverages three distinct reasoning relationships to generate richer relational information. Firstly , an auxiliary feature fusion (AFF) module is introduced, which integrates features originally extracted independently to form fused features enriched with supplementary information. This approach strengthens communication between branches in the network while handling the three sub-tasks concurrently, thereby facilitating the exchange of more contextual information. Secondly , to mitigate noise interference generated during the fusion process, a fusion noise suppression (FNS) module is introduced, which effectively suppresses noise and enhances the model’s performance in interaction detection tasks. Finally , experiments are conducted on two major benchmark datasets, and experimental results show that our HOI detection method is superior to previous methods. Also, ablation studies confirm the effectiveness of all the components in our proposed method.

Efficient Semantic Segmentation for Large-Scale Agricultural Nursery Managements via Point Cloud-Based Neural Network

Remote sensing technology has found extensive application in agriculture, providing critical data for analysis. The advancement of semantic segmentation models significantly enhances the utilization of point cloud data, offering innovative technical support for modern horticulture in nursery environments, particularly in the area of plant cultivation. Semantic segmentation results aid in obtaining tree components, like canopies and trunks, and detailed data on tree growth environments. However, obtaining precise semantic segmentation results from large-scale areas can be challenging due to the vast number of points involved. Therefore, this paper introduces an improved model aimed at achieving superior performance for large-scale points. The model incorporates direction angles between points to improve local feature extraction and ensure rotational invariance. It also uses geometric and relative distance information for better adjustment of different neighboring point features. An external attention module extracts global spatial features, and an upsampling feature adjustment strategy integrates features from the encoder and decoder. A specialized dataset was created from real nursery environments for experiments. Results show that the improved model surpasses several point-based models, achieving a Mean Intersection over Union (mIoU) of 87.18%. This enhances the precision of nursery environment analysis and supports the advancement of autonomous nursery managements.

Poverty and resilience impacts of conservation agriculture adoption against climatic-shocks in Eastern Ethiopia

The aims of this study are to analysis adoption and impacts of conservation agriculture on poverty and resilience to drought in Eastern Ethiopia. Multi-stage sampling method was employed and four districts were randomly selected from Eastern Hararghe Zone. Using cross-sectional survey data collected in 2023 from 430 households, multinomial endogenous switching regression was applied in impact evaluation. The result shows farm experience, education, climatic-shocks experience, access to climate information, access to extension, number of oxen, farm size, labor force, livestock and distance to market are determinants of conservation agriculture (CA) adoption in terms of inter-cropping, crop rotation and crop residue retention or mulch practices in its single and combination of practices. Poverty was evaluated based on cost-of-basic-need and resilience to drought is in its index. Accordingly, the food poverty line and total consumption expenditure or total poverty line was found to be 6582.7ETB and 8620.70ETB per Adult/Equivalent per year in the study area. Results of average treatment effect on treated shows adoption is significantly reduced poverty and increased resilience to drought condition in the area. The study also shows importance role of extension service in adoption of CA practices. There is a need to encourage extension facilities and awareness to promote better adoption of CA particularly, in its combination. The combination of practices increased consumption expenditure of adopters by 73.3 percent compared to non-adopters, given its significant impact on households’ poverty and resilience to drought in moisture stress area of eastern Ethiopia. So, it is crucial to more advances farmers and experts’ information on climate shocks and conservation agricultural practices adoption. The policymakers ought to develop and encourage farmers’ asset building plan including livestock, adult education, market linkage facilities, extension and weather related information delivery service to enhance adoption of conservation agricultural practices to combat the current and future drought.

Association of different timepoint and threshold of time-in-target-range and MACEs: insight from the SPRINT study

Abstract Objective To evaluate the prognostic value of different timepoint and threshold of systolic blood pressure (SBP) Time in Target Rang (TTR) in Major Adverse Cardiovascular Events (MACEs). Method This is a post-hoc analysis of the SPIRNT (Systolic Blood Pressure Intervention Trial). Participants with complete record of BP values and no adverse events occurred in the initial 2-years follow-up were enrolled. Different SBP TTR indexes was calculated by different timepoint (3 month,12 month,24 month) and threshold (110-130mmHg;110-140mmHg;110-130mmHg for intensive treatment group and 120-140mmHg for standard treatment group) (Fig 1). The primary outcome was major adverse cardiovascular events (MACEs) occurred 2 years later, defined as the composite of myocardial infarction, stroke, heart failure and cardiovascular death. Results A total of 7134 participants in SPIRNT were enrolled in this study. Those SBP TTR indicators with independent predictive value of MACEs were selected by a multivariable COX model adjusted for traditional cardiovascular risk factors (Fig 2). SBP TTR calculated with 110-140mmHg window in 3 months (3month SBP TTR110-140) was considered to be the best predictor of MACEs occurred 2 years later among selected SBP TTR indexes according to c statistics and relative informativeness (Table1). The restricted cubic spline plot was used to evaluate the nonlinear trend of the TTR indicator. In addition, we obtain the cut-off value by looking for the point in the RCS curve with the largest slope change. 0.65 was considered to be the best truncation for 3month SBP TTR110-140(Fig 3). Patients with 3month TTR110-140 greater than 0.65 had 23.9% lower incidence of MACEs than patients with TTR less than or equal to 0.65 (HR 0.761[0.592-0978], P=0.033).(Table 2) In addition, for patients with early SBP control (3month SBP TTR110-140 &amp;gt;0.65), there was no significant difference in mean SBP between 110-130 mmHg and 130-140 mmHg (Fig4 Group1 Vs Group2). Lasso regression also suggests that TTR110-140 has more informatics advantages than TTR110-130 in cox model at different timepoints (Fig5). Conclusions 3month SBP TTR110-140 was considered to be the best predictor of MACEs occurred 2 years later in different SBP TTR indexes, which indicated the importance of early SBP control. This study also proposed 0.65 as the critical value of 3-month SBP TTR110-140 for prevention of the incident of MACEs. In the future, more cohort studies are needed to expend these findings.

Significant enhancement in the conductivity of highly transparent PEDOT:PSS thin film through Maleic Acid treatment

Abstract The conductivity of poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS, a conducting polymer) thin film is enhanced by simply maleic acid treatment. Here, we have investigated the conductivity enhancement with the variation of maleic acid concentration. The conductivity enhances up to 1.0 M maleic acid concentration and decreases afterward. The optimum conductivity is obtained as 9.35 S cm–1, which is nearly 263 times more compared to the pristine PEDOT:PSS film. The conductivity of the film also depends upon the treating temperature. Therefore, the effect of different treating temperatures on the conductivity enhancement is studied, and the optimum temperature is found to be 140°C. Maleic acid-treated PEDOT:PSS films also exhibit high transmittances, i.e., ≈ 90 – 84 % in the visible region. The mechanism related to the conductivity enhancement and other related information are collected through different spectroscopic and microscopic measurements. Besides, frequency-dependant impedance and electrochemical activity of maleic acid-treated PEDOT:PSS films are also performed. The interaction of maleic acid with PEDOT:PSS promotes the reduction of ionic interaction between PEDOT and PSS chains, resulting in the phase separation between PEDOT and PSS. As a result, the PSS– turn into neutral PSSH and is rinsed away by water, which supports the morphological change and the conductivity enhancement due to the conformational change of coil-like PEDOTs to elongated and better-connected PEDOT chains.&amp;#xD;

PASCL: supervised contrastive learning with perturbative augmentation for particle decay reconstruction

Abstract In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse the hierarchical tree structure, we propose a graph-based deep learning model to infer the tree structure to reconstruct collision events. In particular, we use a compact matrix representation termed as lowest common ancestor generations matrix, to encode the particle decay tree structure. Then, we introduce a perturbative augmentation technique applied to node features, aiming to mimic experimental uncertainties and increase data diversity. We further propose a supervised graph contrastive learning algorithm to utilize the information of inter-particle relations from multiple decay processes. Extensive experiments show that our proposed supervised graph contrastive learning with perturbative augmentation method outperforms state-of-the-art baseline models on an existing physics-based dataset, significantly improving the reconstruction accuracy. This method provides a more effective training strategy for models with the same parameters and makes way for more accurate and efficient high-energy particle physics data analysis.

Fully exploring object relation interaction and hidden state attention for video captioning

Video Captioning (VC) is a challenging task of automatically generating natural language sentences for describing video contents. As a video often contains multiple objects, it is comprehensively crucial to identify multiple objects and model relationships between them. Previous models usually adopt Graph Convolutional Networks (GCN) to infer relational information via object nodes, but there exist uncertainty and over-smoothing issues of relational reasoning. To tackle these issues, we propose a Knowledge Graph based Video Captioning Network (KG-VCN) by fully exploring object relation interaction, hidden state and attention enhancement. In encoding stages, we present a Graph and Convolution Hybrid Encoder (GCHE), which uses an object detector to find visual objects with bounding boxes for Knowledge Graph (KG) and Convolutional Neural Network (CNN). To model intrinsic relations between detected objects, we propose a knowledge graph based Object Relation Graph Interaction (ORGI) module. In ORGI, we design triplets (head, relation, tail) to efficiently mine object relations, and create a global node to enable adequate information flow among all graph nodes for avoiding possibly missed relations. To produce accurate and rich captions, we propose a hidden State and Attention Enhanced Decoder (SAED) by integrating hidden states and dynamically updated attention features. Our SAED accepts both relational and visual features, adopts Long Short-Term Memory (LSTM) to produce hidden states, and dynamically update attention features. Unlike existing methods, we concatenate state and attention features to predict next word sequentially. To demonstrate the effectiveness of our model, we conduct experiments on three well-known datasets (MSVD, MSR-VTT, VaTeX), and our model achieves impressive results significantly outperforming existing state-of-the-art models.

A Patch-Level Region-Aware Module with a Multi-Label Framework for Remote Sensing Image Captioning

Recent Transformer-based works can generate high-quality captions for remote sensing images (RSIs). However, these methods generally feed global or grid visual features to a Transformer-based captioning model for associating cross-modal information, which limits performance. In this work, we investigate unexplored ideas for a remote sensing image captioning task, using a novel patch-level region-aware module with a multi-label framework. Due to an overhead perspective and a significantly larger scale in RSIs, a patch-level region-aware module is designed to filter the redundant information in the RSI scene, which benefits the Transformer-based decoder by attaining improved image perception. Technically, the trainable multi-label classifier capitalizes on semantic features as supplementary to the region-aware features. Moreover, modeling the inner relations of inputs is essential for understanding the RSI. Thus, we introduce region-oriented attention, which associates region features and semantic labels, omits the irrelevant regions to highlight relevant regions, and learns related semantic information. Extensive qualitative and quantitative experimental results show the superiority of our approach on the RSICD, UCM-Captions, and Sydney-Captions. The code for our method will be publicly available.