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#author("2024-12-07T08:28:26+09:00","","") Consequently, the DFRL-controlled robots can achieve efficient adaptive locomotion, to tackle complex terrains with diverse slopes.Existing language models (LMs) represent each word with only a single representation, which is unsuitable for processing words with multiple meanings. This issue has often been compounded by the lack of availability of large-scale data annotated with word meanings. In this paper, we propose a sense-aware framework that can process multi-sense word information without relying on annotated data. In contrast to the existing multi-sense representation models, which handle information in a restricted context, our framework provides context representations encoded without ignoring word order information or long-term dependency. The proposed framework consists of a context representation stage to encode the variable-size context, a sense-labeling stage that involves unsupervised clustering to infer a probable sense for a word in each context, and a multi-sense LM (MSLM) learning stage to learn the multi-sense representations. Particularly for the evaluation of MSLMs with different vocabulary sizes, we propose a new metric, i.e., unigram-normalized perplexity (PPLu), which is also understood as the negated mutual information between a word and its context information. Additionally, there is a theoretical verification of PPLu on the change of vocabulary size. Also, we adopt a method of estimating the number of senses, which does not require further hyperparameter search for an LM performance. For the LMs in our framework, both unidirectional and bidirectional architectures based on long short-term memory (LSTM) and Transformers are adopted. We conduct comprehensive experiments on three language modeling datasets to perform quantitative and qualitative comparisons of various LMs. Our MSLM outperforms single-sense LMs (SSLMs) with the same network architecture and parameters. It also shows better performance on several downstream natural language processing tasks in the General Language Understanding Evaluation (GLUE) and SuperGLUE benchmarks.Attributed graph clustering aims to discover node groups by utilizing both graph structure and node features. Recent studies mostly adopt graph neural networks to learn node embeddings, then apply traditional clustering methods to obtain clusters. However, they usually suffer from the following issues (1) they adopt original graph structure which is unfavorable for clustering due to its noise and sparsity problems; (2) they mainly utilize non-clustering driven losses that cannot well capture the global cluster structure, thus the learned embeddings are not sufficient for the downstream clustering task. In this paper, we propose a spectral embedding network for attributed graph clustering (SENet), which improves graph structure by leveraging the information of shared neighbors, and learns node embeddings with the help of a spectral clustering loss. By combining the original graph structure and shared neighbor based similarity, both the first-order and second-order proximities are encoded into the improved graph structure, thus alleviating the noise and sparsity issues. To make the spectral loss well adapt to attributed graphs, we integrate both structure and feature information into kernel matrix via a higher-order graph convolution. Experiments on benchmark attributed graphs show that SENet achieves superior performance over state-of-the-art methods.To alleviate the shortcomings of target detection in only one aspect and reduce redundant information among adjacent bands, we propose a spectral-spatial target detection (SSTD) framework in deep latent space based on self-spectral learning (SSL) with a spectral generative adversarial network (GAN). The concept of SSL is introduced into hyperspectral feature extraction in an unsupervised fashion with the purpose of background suppression and target saliency. In particular, a novel structure-to-structure selection rule that takes full account of the structure, contrast, and luminance similarity is established to interpret the mapping relationship between the latent spectral feature space and the original spectral band space, to generate the optimal spectral band subset without any prior knowledge. Finally, the comprehensive result is achieved by nonlinearly combining the spatial detection on the fused latent features with the spectral detection on the selected band subset and the corresponding selected target signature. This paper paves a novel self-spectral learning way for hyperspectral target detection and identifies sensitive bands for specific targets in practice. Comparative analyses demonstrate that the proposed SSTD method presents superior detection performance compared with CSCR, ACE, CEM, hCEM, and ECEM.Some individuals with posttraumatic stress disorder (PTSD) are at elevated risk of reexposure to trauma during treatment. Trauma-focused cognitive-behavioral therapies (CBT) are recommended as first-line PTSD treatments but have generally been tested with exclusion criteria related to risk for trauma exposure. Therefore, there is limited knowledge on how to best treat individuals with PTSD under ongoing threat of reexposure. This paper systematically reviewed the effectiveness of CBTs for PTSD in individuals with ongoing threat of reexposure. Literature searches yielded 21 studies across samples at ongoing risk of war-related or community violence (n = 14), domestic violence (n = 5), and work-related traumatic events (n = 2). https://www.selleckchem.com/products/ncb-0846.html Medium to large effects were found from pre to posttreatment and compared with waitlist controls. There were mixed findings for domestic violence samples on long-term outcomes. Treatment adaptations focused on establishing relative safety and differentiating between realistic threat and generalized fear responses. Few studies examined whether ongoing threat influenced treatment outcomes or whether treatments were associated with adverse events. Thus, although the evidence is promising, conclusions cannot be firmly drawn about whether trauma-focused CBTs for PTSD are safe and effective for individuals under ongoing threat. Areas for further inquiry are outlined.
