Intelligent Search Optimized Edge Potential Function (EPF) Approach to Synthetic Aperture Radar (SAR) Scene Matching

Research on synthetic aperture radar (SAR) scene matching in the aircraft end-guidance has a significant value for both research and real-world application. The conventional scene matching methods, however, suffer many disadvantages such as heavy computation burden and low convergence rate so that these methods cannot meet the requirement of end-guidance system in terms of fast and real-time data processing. Furthermore, there are complex noises in the SAR image, which also compromise the effectiveness of using the conventional scene matching methods. To address the above issues, in this paper, the intelligent optimization method, Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration, has been introduced to tackle the SAR scene matching problem. We first establish the effective similarity measurement function for target edge feature matching through introducing the edge potential function (EPF) model. Then, a new method, ADEQFS-EPF, has been proposed for SAR scene matching. In ADEQFS-EPF, the previous studied theoretical model, ADEQFS, is combined with EPF model. We also employed three recent proposed evolutionary algorithms to compare against the proposed method on optical and SAR datasets. The experiments based on Matlab simulation have verified the effectiveness of the application of ADEQFS and EPF model to the field of SAR scene matching

Large Language Models Are Also Good Prototypical Commonsense Reasoners

Commonsense reasoning is a pivotal skill for large language models, yet it presents persistent challenges in specific tasks requiring this competence. Traditional fine-tuning approaches can be resource-intensive and potentially compromise a model's generalization capacity. Furthermore, state-of-the-art language models like GPT-3.5 and Claude are primarily accessible through API calls, which makes fine-tuning models challenging. To address these challenges, we draw inspiration from the outputs of large models for tailored tasks and semi-automatically developed a set of novel prompts from several perspectives, including task-relevance, supportive evidence generation (e.g. chain-of-thought and knowledge), diverse path decoding to aid the model. Experimental results on ProtoQA dataset demonstrate that with better designed prompts we can achieve the new state-of-art(SOTA) on the ProtoQA leaderboard, improving the Max Answer@1 score by 8%, Max Incorrect@1 score by 4% (breakthrough 50% for the first time) compared to the previous SOTA model and achieved an improvement on StrategyQA and CommonsenseQA2.0 (3% and 1%, respectively). Furthermore, with the generated Chain-of-Thought and knowledge, we can improve the interpretability of the model while also surpassing the previous SOTA models. We hope that our work can provide insight for the NLP community to develop better prompts and explore the potential of large language models for more complex reasoning tasks

Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition

In recent years, speech emotion recognition technology is of great significance in industrial applications such as call centers, social robots and health care. The combination of speech recognition and speech emotion recognition can improve the feedback efficiency and the quality of service. Thus, the speech emotion recognition has been attracted much attention in both industry and academic. Since emotions existing in an entire utterance may have varied probabilities, speech emotion is likely to be ambiguous, which poses great challenges to recognition tasks. However, previous studies commonly assigned a single-label or multi-label to each utterance in certain. Therefore, their algorithms result in low accuracies because of the inappropriate representation. Inspired by the optimally interacting theory, we address the ambiguous speech emotions by proposing a novel multi-classifier interactive learning (MCIL) method. In MCIL, multiple different classifiers first mimic several individuals, who have inconsistent cognitions of ambiguous emotions, and construct new ambiguous labels (the emotion probability distribution). Then, they are retrained with the new labels to interact with their cognitions. This procedure enables each classifier to learn better representations of ambiguous data from others, and further improves the recognition ability. The experiments on three benchmark corpora (MAS, IEMOCAP, and FAU-AIBO) demonstrate that MCIL does not only improve each classifier's performance, but also raises their recognition consistency from moderate to substantial.Comment: 10 pages, 4 figure

Study of charge control and gate tunneling in a ferroelectric-oxide-silicon field effect transistor: Comparison with a conventional metal-oxide-silicon structure

It is known that conventional metal-oxide-silicon (MOS) devices will have gate tunneling related problems at very thin oxide thicknesses. Various high-dielectric-constant materials are being examined to suppress the gate currents. In this article we present theoretical results of a charge control and gate tunneling model for a ferroelectric-oxide-silicon field effect transistor and compare them to results for a conventional MOS device. The potential of high polarization charge to induce inversion without doping and high dielectric constant to suppress tunneling current is explored. The model is based on a self-consistent solution of the quantum problem and includes the ferroelectric hysteresis response self-consistently. We show that the polarization charge associated with ferroelectrics can allow greater controllability of the inversion layer charge density. Also the high dielectric constant of ferroelectrics results in greatly suppressed gate current. © 2001 American Institute of Physics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71117/2/JAPIAU-89-3-1856-1.pd

Meta-Learning via Classifier(-free) Guidance

State-of-the-art meta-learning techniques do not optimize for zero-shot adaptation to unseen tasks, a setting in which humans excel. On the contrary, meta-learning algorithms learn hyperparameters and weight initializations that explicitly optimize for few-shot learning performance. In this work, we take inspiration from recent advances in generative modeling and language-conditioned image synthesis to propose meta-learning techniques that use natural language guidance to achieve higher zero-shot performance compared to the state-of-the-art. We do so by recasting the meta-learning problem as a multi-modal generative modeling problem: given a task, we consider its adapted neural network weights and its natural language description as equivalent multi-modal task representations. We first train an unconditional generative hypernetwork model to produce neural network weights; then we train a second "guidance" model that, given a natural language task description, traverses the hypernetwork latent space to find high-performance task-adapted weights in a zero-shot manner. We explore two alternative approaches for latent space guidance: "HyperCLIP"-based classifier guidance and a conditional Hypernetwork Latent Diffusion Model ("HyperLDM"), which we show to benefit from the classifier-free guidance technique common in image generation. Finally, we demonstrate that our approaches outperform existing meta-learning methods with zero-shot learning experiments on our Meta-VQA dataset, which we specifically constructed to reflect the multi-modal meta-learning setting

Hi4D: 4D Instance Segmentation of Close Human Interaction

We propose Hi4D, a method and dataset for the automatic analysis of physically close human-human interaction under prolonged contact. Robustly disentangling several in-contact subjects is a challenging task due to occlusions and complex shapes. Hence, existing multi-view systems typically fuse 3D surfaces of close subjects into a single, connected mesh. To address this issue we leverage i) individually fitted neural implicit avatars; ii) an alternating optimization scheme that refines pose and surface through periods of close proximity; and iii) thus segment the fused raw scans into individual instances. From these instances we compile Hi4D dataset of 4D textured scans of 20 subject pairs, 100 sequences, and a total of more than 11K frames. Hi4D contains rich interaction-centric annotations in 2D and 3D alongside accurately registered parametric body models. We define varied human pose and shape estimation tasks on this dataset and provide results from state-of-the-art methods on these benchmarks.Comment: Project page: https://yifeiyin04.github.io/Hi4D

Antiviral biflavonoids from Radix Wikstroemiae (Liaogewanggen)

<p>Abstract</p> <p>Background</p> <p><it>Radix Wikstroemiae </it>is a common Chinese herbal medicine. The ethyl acetate fraction of the ethanolic extract of <it>W. indica </it>possesses potent <it>in vitro </it>antiviral activity against respiratory syncytial virus (RSV). This study aims to identify the antiviral components of the active fraction.</p> <p>Methods</p> <p>The active fraction of the <it>Radix Wikstroemiae </it>extract was isolated with chromatographic methods such as silica gel, Sephadex LH-20 and semi-preparative high performance liquid chromatography (HPLC) columns. The structures of the isolated compounds were determined based on spectroscopic analyses. The <it>in vitro </it>antiviral activity of the compounds against RSV was tested with the cytopathic effect (CPE) reduction assay and the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method.</p> <p>Results</p> <p>Four biflavonoids, namely neochamaejasmin B, genkwanol B, genkwanol C and stelleranol, were isolated and characterized. Genkwanol B, genkwanol C and stelleranol, which are stereo isomers of spirobiflavonoids, showed potent anti-RSV activity whereas neochamaejasmin B did not.</p> <p>Conclusion</p> <p>Neochamaejasmin B, genkwanol B, genkwanol C and stelleranol were isolated from <it>Radix Wikstroemiae </it>and the complete absolute configurations of five chiral carbons in stelleranol were substantiated for the first time. Furthermore, the anti-RSV activity of genkwanol B, genkwanol C and stelleranol was reported for the first time.</p

Adaptive ankle impedance control for bipedal robotic upright balance

Upright balance control is a fundamental skill of bipedal robots for various tasks that are usually performed by human beings. Conventional robotic control is often realized by developing accurate dynamic models using a series of fixed torque-ankle states, but their success is subject to accurate physical and kinematic models. This can be particularly challenging when external disturbing forces present, but this is common in unstructured robotic working environments, leading to ineffective robotic control. To address such limitation, this paper presents an adaptive ankle impedance control method with the support of the advances of adaptive fuzzy inference systems, by which the desired ankle torques are generated in real time to adaptively meet the dynamic control requirement. In particular, the control method is initialised with specific external disturbing forces first representing a general situation, which then evolves whilst performing in a real-world working environment by acting on the feedback from the control system. This is implemented by initialising a rule base for a typical situation, and then allowing the rule base to evolve to specific robotic working environments. This closed loop feedback and action mechanism timely and effectively configures the control system to meet the dynamic control requirements. The proposed control method was applied to a bipedal robot on a moving vehicle for system validation and evaluation, with robotic loads ranging from 0 to 1.65 kg and external disturbances in terms of vehicle acceleration ranging from 0.5 to 1.5 m/s, leading to robotic swing angles up to 7.6º and anti-disturbance timespans up to 8.5 s. These experimental results demonstrate the power of the proposed upright balance control method in improving the robustness, and thus applicability, of bipedal robots