Patched Line Segment Learning for Vector Road Mapping

This paper presents a novel approach to computing vector road maps from satellite remotely sensed images, building upon a well-defined Patched Line Segment (PaLiS) representation for road graphs that holds geometric significance. Unlike prevailing methods that derive road vector representations from satellite images using binary masks or keypoints, our method employs line segments. These segments not only convey road locations but also capture their orientations, making them a robust choice for representation. More precisely, given an input image, we divide it into non-overlapping patches and predict a suitable line segment within each patch. This strategy enables us to capture spatial and structural cues from these patch-based line segments, simplifying the process of constructing the road network graph without the necessity of additional neural networks for connectivity. In our experiments, we demonstrate how an effective representation of a road graph significantly enhances the performance of vector road mapping on established benchmarks, without requiring extensive modifications to the neural network architecture. Furthermore, our method achieves state-of-the-art performance with just 6 GPU hours of training, leading to a substantial 32-fold reduction in training costs in terms of GPU hours

InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion

This paper addresses a novel task of anticipating 3D human-object interactions (HOIs). Most existing research on HOI synthesis lacks comprehensive whole-body interactions with dynamic objects, e.g., often limited to manipulating small or static objects. Our task is significantly more challenging, as it requires modeling dynamic objects with various shapes, capturing whole-body motion, and ensuring physically valid interactions. To this end, we propose InterDiff, a framework comprising two key steps: (i) interaction diffusion, where we leverage a diffusion model to encode the distribution of future human-object interactions; (ii) interaction correction, where we introduce a physics-informed predictor to correct denoised HOIs in a diffusion step. Our key insight is to inject prior knowledge that the interactions under reference with respect to contact points follow a simple pattern and are easily predictable. Experiments on multiple human-object interaction datasets demonstrate the effectiveness of our method for this task, capable of producing realistic, vivid, and remarkably long-term 3D HOI predictions.Comment: ICCV 2023; Project Page: https://sirui-xu.github.io/InterDiff

The study on the antioxidant activity of polysaccharides isolated from Polygonatum odoratum (Mill.) Druce

The polysaccharides isolated from Polygonatum odoratum (Mill.) Druce (POPs) by water extraction, after precipitation with ethanol were purified through deproteinization, decolorization, dialysis, and column chromatography leading to a purified polysaccharide (POPs-I) content of 90.7 %. The scavenging of oxygen free radicals and inhibition of lipid peroxidation (LPO) by POPs-I were analyzed using a colorimetric method. The results showed that the hydroxyl radical scavenging ability of the polysaccharides was weaker than that of benzoic acid, but stronger than those of ascorbic acid and mannitol, and that the superoxide anion radical scavenging ability was inferior to those of all three. When the concentration was higher than 1.0 mg/mL, the POPs-I could inhibit LPO by superoxide anion radicals to a certain degree. Therefore, this work suggests that POPs-I are potential antioxidant agents in medicine and functional food

Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis

In this paper, we explore a novel solution of constructing a heterogeneous graph for each instance by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect and propose an Interactive Graph Convolutional Networks (InterGCN) model for aspect sentiment analysis. Specifically, an ordinary dependency graph is first constructed for each sentence over the dependency tree. Then we refine the graph by considering the syntactical dependencies between contextual words and aspect-specific words to derive the aspect-focused graph. Subsequently, the aspect-focused graph and the corresponding embedding matrix are fed into the aspect-focused GCN to capture the key aspect and contextual words. Besides, to interactively extract the inter-aspect relations for the specific aspect, an inter-aspect GCN is adopted to model the representations learned by aspect-focused GCN based on the inter-aspect graph which is constructed by the relative dependencies between the aspect words and other aspects. Hence, the model can be aware of the significant contextual and aspect words when interactively learning the sentiment features for a specific aspect. Experimental results on four benchmark datasets illustrate that our proposed model outperforms state-of-the-art methods and substantially boosts the performance in comparison with BERT

Acupuncture Alters Expression of Insulin Signaling Related Molecules and Improves Insulin Resistance in OLETF Rats

To determine effect of acupuncture on insulin resistance in Otsuka Long-Evans Tokushima Fatty (OLETF) rats and to evaluate expression of insulin signaling components. Rats were divided into three groups: Sprague-Dawley (SD) rats, OLETF rats, and acupuncture+OLETF rats. Acupuncture was subcutaneously applied to Neiguan (PC6), Zusanli (ST36), and Sanyinjiao (SP6); in contrast, acupuncture to Shenshu (BL23) was administered perpendicularly. For Neiguan (PC6) and Zusanli (ST36), needles were connected to an electroacupuncture (EA) apparatus. Fasting blood glucose (FPG) was measured by glucose oxidase method. Plasma fasting insulin (FINS) and serum C peptide (C-P) were determined by ELISA. Protein and mRNA expressions of insulin signaling molecules were determined by Western blot and real-time RT-PCR, respectively. OLETF rats exhibit increased levels of FPG, FINS, C-P, and homeostasis model assessment-estimated insulin resistance (HOMA-IR), which were effectively decreased by acupuncture treatment. mRNA expressions of several insulin signaling related molecules IRS1, IRS2, Akt2, aPKCζ, and GLUT4 were decreased in OLETF rats compared to SD controls. Expression of these molecules was restored back to normal levels upon acupuncture administration. PI3K-p85α was increased in OLETF rats; this increase was also reversed by acupuncture treatment. Acupuncture improves insulin resistance in OLETF rats, possibly via regulating expression of key insulin signaling related molecules

Zero-shot stance detection via contrastive learning

Zero-shot stance detection (ZSSD) is challenging as it requires detecting the stance of previously unseen targets during the inference stage. Being able to detect the target-related transferable stance features from the training data is arguably an important step in ZSSD. Generally speaking, stance features can be grouped into targetinvariant and target-specific categories. Target-invariant stance features carry the same stance regardless of the targets they are associated with. On the contrary, target-specific stance features only co-occur with certain targets. As such, it is important to distinguish these two types of stance features when learning stance features of unseen targets. To this end, in this paper, we revisit ZSSD from a novel perspective by developing an effective approach to distinguish the types (target-invariant/-specific) of stance features, so as to better learn transferable stance features. To be specific, inspired by self-supervised learning, we frame the stance-feature-type identification as a pretext task in ZSSD. Furthermore, we devise a novel hierarchical contrastive learning strategy to capture the correlation and difference between target-invariant and -specific features and further among different stance labels. This essentially allows the model to exploit transferable stance features more effectively for representing the stance of previously unseen targets. Extensive experiments on three benchmark datasets show that the proposed framework achieves the state-of-the-art performance in ZSSD

Affective dependency graph for sarcasm detection

Detecting sarcastic expressions could promote the understanding of natural language in social media. In this paper, we revisit sarcasm detection from a novel perspective, so as to account for the longrange literal sentiment inconsistencies. More concretely, we explore a novel scenario of constructing an affective graph and a dependency graph for each sentence based on the affective information retrieved from external affective commonsense knowledge and the syntactical information of the sentence. Based on it, an Affective Dependency Graph Convolutional Network (ADGCN) framework is proposed to draw long-range incongruity patterns and inconsistent expressions over the context for sarcasm detection by means with interactively modeling the affective and dependency information. Experimental results on multiple benchmark datasets show that our proposed approach outperforms the current state-of-the-art methods in sarcasm detection

Aspect-invariant sentiment feature learning : adversarial multi-task learning for aspect-based sentiment analysis

In most previous studies, the aspect-related text is considered an important clue for the Aspect-based Sentiment Analysis (ABSA) task, and thus various attention mechanisms have been proposed to leverage the interactions between aspects and context. However, it is observed that some sentiment expressions carry the same polarity regardless of the aspects they are associated with. In such cases, it is not necessary to incorporate aspect information for ABSA. More observations on the experimental results show that blindly leveraging interactions between aspects and context as features may introduce noises when analyzing those aspect-invariant sentiment expressions, especially when the aspect-related annotated data is insufficient. Hence, in this paper, we propose an Adversarial Multi-task Learning framework to identify the aspect-invariant/dependent sentiment expressions without extra annotations. In addition, we adopt a gating mechanism to control the contribution of representations derived from aspect-invariant and aspect-dependent hidden states when generating the final contextual sentiment representations for the given aspect. This essentially allows the exploitation of aspect-invariant sentiment features for better ABSA results. Experimental results on two benchmark datasets show that extending existing neural models using our proposed framework achieves superior performance. In addition, the aspect-invariant data extracted by the proposed framework can be considered as pivot features for better transfer learning of the ABSA models on unseen aspects

Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation

Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field.Comment: Please feel free to email He-Liang Huang with any comments, questions, suggestions or concern