MOTIVATION: Spatial transcriptomics (ST) enables the measurement of gene expression while preserving the spatial context of tissues. However, the sparsity of ST data leads to poor usage of gene expression and spatial information, resulting in the embeddings that are not well represented and challenging for downstream analyses. RESULTS: Here, we introduced GSG, a generative self-supervised representation learning framework for ST data that leverages a masking mechanism to learn informative representations. For spatial domain identification, GSG consistently outperformed state-of-the-art methods across benchmarking datasets, regardless of sequencing platforms. In addition, we applied GSG to an in-house human fetal heart dataset, revealing anatomically coherent spatial domains and identifying APCDD1 as an endocardial-specific marker potentially involved in congenital heart disease. Our results showcase GSG's superiority and underscore its valuable contributions to advancing ST analysis. AVAILABILITY AND IMPLEMENTATION: Our software package is available at https://github.com/keaml-Guan/GSG.

Parkinson's Disease (PD) is often characterized by abnormal gait patterns, which can be objectively and quantitatively diagnosed using Vertical Ground Reaction Force (VGRF) signals. Previous studies have demonstrated the effectiveness of deep learning in VGRF signal analysis. However, the inherent graph structure of VGRF signals has not been adequately considered, limiting the representation of dynamic gait characteristics. To address this, we propose a Multi-Scale Adaptive Directed Graph Neural Network (MS-ADGNN) approach to distinguish the gaits between Parkinson's patients and healthy controls. This method models the VGRF signal as a multi-scale directed graph, capturing the distribution relationships within the plantar sensors and the dynamic pressure conduction during walking. MS-ADGNN integrates an Adaptive Directed Graph Network (ADGN) unit and a Multi-Scale Temporal Convolutional Network (MSTCN) unit. ADGN extracts spatial features from three scales of the directed graph, effectively capturing local and global connectivity. MSTCN extracts multi-scale temporal features, capturing short to long-term dependencies. The proposed method outperforms existing methods on three widely used datasets. In cross-dataset experiments, the average improvements in terms of accuracy, F1-score, and geometric mean are 2.46$\%$, 1.25$\%$, and 1.11$\%$ respectively. Meanwhile, in 10-fold cross-validation experiments, the improvements are 0.78$\%$, 0.83$\%$, and 0.81$\%$ respectively.

Spatiotemporal video grounding aims to localize target entities in videos based on textual queries. While existing research has made significant progress in exocentric videos, the egocentric setting remains relatively underexplored, despite its growing importance in applications such as augmented reality and robotics. In this work, we conduct a systematic analysis of the discrepancies between egocentric and exocentric videos, revealing key challenges such as shorter object durations, sparser trajectories, smaller object sizes, and larger positional shifts. To address these challenges, we introduce EgoMask, the first pixel-level benchmark for fine-grained spatiotemporal grounding in egocentric videos. It is constructed by our proposed automatic annotation pipeline, which annotates referring expressions and object masks across short-, medium-, and long-term videos. Additionally, we create EgoMask-Train, a large-scale training dataset to facilitate model development. Experiments demonstrate that the state-of-the-art spatiotemporal grounding models perform poorly on our benchmark EgoMask, but fine-tuning on EgoMask-Train yields significant improvements, while preserving performance on exocentric datasets. Our work thus provides essential resources and insights for advancing egocentric video understanding. Our code is available at https://github.com/LaVi-Lab/EgoMask .

We present MetaAI, a novel wireless computing paradigm that integrates neural network computation directly into wireless signal propagation. Unlike traditional approaches that treat wireless channels as mere data conduits, MetaAI transforms them into active computing elements through programmable metasurfaces, enabling concurrent data transmission and neural network processing. By leveraging the inherent linearity of both wireless propagation and neural networks, our design resolves the fundamental mismatch between sequential wireless transmission and parallel neural computation, while supporting efficient multi-sensor late-stage data fusion. We implemented MetaAI using metasurfaces at both dual-band (2.4/5 GHz) and single-band (3.5 GHz) frequencies. Extensive experiments demonstrate robust performance across diverse classification tasks, achieving 82.8% average accuracy (up to 89.8%) even with a simple linear architecture. Multi-sensor fusion further improves accuracy by up to 27.06%. MetaAI represents a fundamental shift in Edge AI architecture, where wireless infrastructure becomes an integral part of the computing pipeline.

Recent advancements in long-context modeling have enhanced language models (LMs) for complex tasks across multiple NLP applications.Despite this progress, we find that these models struggle with multi-hop reasoning and exhibit decreased performance in the presence of noisy contexts.In this paper, we introduce Reasoning with Attributions, a novel approach that prompts LMs to supply attributions for each assertion during their reasoning.We validate our approach through experiments on three multi-hop datasets, employing both proprietary and open-source models, and demonstrate its efficacy and resilience.Furthermore, we explore methods to augment reasoning capabilities via fine-tuning and offer an attribution-annotated dataset and a specialized training strategy.Our fine-tuned model achieves competitive performance on multi-hop reasoning benchmarks, closely paralleling proprietary LMs such as ChatGPT and Claude-instant 1 .

Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research, aiming to extract triplets of the aspect term, its corresponding opinion term, and its associated sentiment polarity from a given sentence. Recently, many neural networks based models with different tagging schemes have been proposed, but almost all of them have their limitations: heavily relying on 1) prior assumption that each word is only associated with a single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level interactions and treating each opinion/aspect as a set of independent words. Hence, they perform poorly on the complex ASTE task, such as a word associated with multiple roles or an aspect/opinion term with multiple words. Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously. To this end, this paper formulates the ASTE task as a multi-class span classification problem. Specifically, STAGE generates more accurate aspect sentiment triplet extractions via exploring span-level information and constraints, which consists of two components, namely, span tagging scheme and greedy inference strategy. The former tag all possible candidate spans based on a newly-defined tagging set. The latter retrieves the aspect/opinion term with the maximum length from the candidate sentiment snippet to output sentiment triplets. Furthermore, we propose a simple but effective model based on the STAGE, which outperforms the state-of-the-arts by a large margin on four widely-used datasets. Moreover, our STAGE can be easily generalized to other pair/triplet extraction tasks, which also demonstrates the superiority of the proposed scheme STAGE.

Vertical Ground Reaction Force (VGRF) signal obtained from foot-worn sensors, also known as plantar data, provides a highly informative and detailed representation of an individual's gait features. Existing methods, such as CNNs, LSTMs and Transformers, have revealed the efficiency of deep learning in Parkinson's Disease (PD) diagnosis using VGRF signal. However, the intrinsic topologic graph and pressure transmission characteristics of plantar data are overlooked in those approaches, which are essential features for gait analysis. In this paper, we propose to construct a plantar directed topologic graph to fully exploit the plantar topology in gait circles. It can facilitate the expression of gait information by representing sensors as nodes and pressure transmissions as directional edges. Accordingly, an Adaptive Spatio-Temporal Directed Graph Neural Network (AST-DGNN) is proposed to extract the connection features of the plantar directed topologic graph. Each AST-DGNN Unit includes an Adaptive Directed Graph Network (ADGN) block and a Temporal Convolutional Network (TCN) block. In order to capture both local and global spatial relationships among sensor nodes and pressure transmission edges, the ADGN block performs message passing on the plantar directed topologic graph in an adaptive manner. To capture the temporal features of sensor nodes and pressure transmission edges, the TCN block defines a temporal feature extraction process for each node and edge in the graph. Moreover, the data augmentation is introduced for plantar data to improve the generalization ability of the AST-DGNN. Experimental results on Ga, Ju, and Si datasets demonstrate that the proposed method outperforms the existing methods under both cross-dataset validation and mixed-data cross-validation. Especially in cross-dataset validation, there is an average improvement of 2.13%, 7.73%, and 12.27% in accuracy, F1 score, and G-mean, respectively.

Abstract The residual belief propagation (RBP) algorithm, which is the most classic informed dynamic scheduling strategy, achieves outstanding performance in error correction and can drastically accelerate convergence speed. However, the greedy algorithmic property of this iterative decoding will inevitably cause loss of decoding performance. To address this, a novel algorithm called the partial average bundle residual belief propagation (PABRBP) is proposed in this paper. According to the construction characteristics of a base matrix of protograph‐LDPC codes, informed dynamic scheduling (IDS) strategies are applied to an edge bundle of base matrices for the first time. This edge bundle of the base matrix can be applied to a corresponding cyclic permutation matrix. Furthermore, the update level of each bundle is determined by the value of the Partially Average Bundle Residual (PABR). Therefore, the edge message with the maximum residual in each bundle is updated in order, and the process of iterative decoding is less likely to become trapped in a local optimum. Additionally, the generation of silent nodes is reduced as much as possible. To further improve the PABRBP decoding performance for medium and long codes over the fading channel, the adjusted compensation term is periodically introduced. Analysis and simulation results show that PABRBP demonstrates a notable convergence quality and decoding performance improvement over the fading channels compared to existing state‐of‐art IDS algorithms.

Abstract The informed dynamic scheduling (IDS) strategies, in which the edge message with the maximum message‐residual is updated preferentially, achieve remarkable error‐correction performance when applied to low‐density parity‐check (LDPC) codes. However, the IDS strategies incur inferior convergence in iterative decoding owing to the greedy problem, which is called the update‐relayed trend here. In order to solve the greediness, two locally informed dynamic scheduling algorithms based on the law of large numbers are proposed. The proposed decoding algorithms use random select of check nodes over a predefined update range (RSPUR) which effectively suppresses the propagation of the update‐relayed trend and accordingly restrains the forming of multi‐update cycles. Moreover, the decoding algorithm is further improved based on random select of check nodes over an adjustable update range (RSCAR). The update ranges are selected based on the law of large numbers. Therefore, the computational resources can be allocated more equitably by increasing iterations. Simulation results show that both the proposed algorithms achieve excellent performance in terms of throughput and convergence with low decoding complexity over the Additive White Gaussian Noise (AWGN) and the fading channels compared to the previous IDS strategies. Hence, the proposed algorithms behave excellently over the wireless channels.

The visualization of evolutionary influence graphs is important for performing many real-life tasks such as citation analysis and social influence analysis. The main challenges include how to summarize large-scale, complex, and time-evolving influence graphs, and how to design effective visual metaphors and dynamic representation methods to illustrate influence patterns over time. In this work, we present Eiffel, an integrated visual analytics system that applies triple summarizations on evolutionary influence graphs in the nodal, relational, and temporal dimensions. In numerical experiments, Eiffel summarization results outperformed those of traditional clustering algorithms with respect to the influence-flow-based objective. Moreover, a flow map representation is proposed and adapted to the case of influence graph summarization, which supports two modes of evolutionary visualization (i.e., flip-book and movie) to expedite the analysis of influence graph dynamics. We conducted two controlled user experiments to evaluate our technique on influence graph summarization and visualization respectively. We also showcased the system in the evolutionary influence analysis of two typical scenarios, the citation influence of scientific papers and the social influence of emerging online events. The evaluation results demonstrate the value of Eiffel in the visual analysis of evolutionary influence graphs.

Many studies were carried out for the construction of low density parity-check (LDPC) codes. They usually focused on introducing the construction methods for good LDPC codes instead of a general method for code optimization. This paper proposes a method with high versatility, called the parallel vector message passing-based edge exchange (PMPE), for optimizing a type of graph-based LDPC codes, without changing the code parameters of mother codes, such as the code length, code rate, and degree distribution. With the approximately nearest codewords searching approach, we find the optimization method can increase the Hamming distance of the LDPC codes. For the quasi-cyclic (QC) LDPC codes, an optimization method, called the parallel vector message passing oriented-to the QC-LDPC codes (QC-PMP), is further suggested, with which the quasi-cyclic characteristics of QC-LDPC codes can remain unchanged in the optimization. To evaluate the performance of the parity-check matrix corresponding to a Tanner graph, a very simple metric, the cycles metric, is introduced to work with the proposed PMPE and QC-PMP algorithms. The experimental results show that the performance of the LDPC codes optimized with the proposed PMPE can be improved significantly at low BER range compared with the mother codes of the random codes, including the regular MacKay code of rate 0.5 and the regular PEG code of rate 0.9. For the case of the regular and irregular QC-LDPC codes with different code lengths and code rates, the optimized LDPC codes with the proposed QC-PMP algorithm significantly outperform the mother codes.