Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has garnered significant research interest recently due to its ability to generalize existing reconfigurable intelligent surface (RIS) architectures and provide enhanced performance through flexible inter-connection among RIS elements. However, current BD-RIS designs often face challenges related to high circuit complexity and computational complexity, and there is limited study on the trade-off between system performance and circuit complexity. To address these issues, in this work, we propose a novel BD-RIS architecture named Q-stem connected RIS that integrates the characteristics of existing single connected, tree connected, and fully connected BD-RIS, facilitating an effective trade-off between system performance and circuit complexity. Additionally, we propose two algorithms to design the RIS scattering matrix for a Q-stem connected RIS aided multiuser broadcast channels, namely, a low-complexity least squares (LS) algorithm and a suboptimal LS-based quasi-Newton algorithm. Simulations show that the proposed architecture is capable of attaining the sum channel gain achieved by fully connected RIS while reducing the circuit complexity. Moreover, the proposed LS-based quasi-Newton algorithm significantly outperforms the baselines, while the LS algorithm provides comparable performance with a substantial reduction in computational complexity.

One challenge of online learning is that students do not necessarily follow the designed learning activities. Thus, how students perceive these activities and regulate their online learning is critical. This study examined the bidirectional relationship between OSRL and perceptions of learning activity design using a cross-lagged model. The Interactive, Constructive, Active, Passive (ICAP) framework was used to classify learning activities. A total of 200 Chinese undergraduates participated in a questionnaire examining OSRL and their perceptions of learning activities twice. The results revealed that OSRL at time point 1 (T1) predicted perceived active activities (but none of the other three types of activity) at time point 2 (T2), while perceiving any type of learning activity at T1 did not predict OSRL at T2. These findings imply that participants did not perceive the learning activities of the same course in the same way; their perceptions of active activities depended on their OSRL.

Rate-splitting multiple access (RSMA) is gaining increasing recognition as a pivotal technology for advancing green communication networks, primarily due to its proficiency in boosting energy efficiency (EE) and lowering power consumption at the transmitter. In this article, we commence by offering a concise overview of the latest advancements in RSMA for green communications. Motivated by the limitations identified in existing studies, we then focus on robust beamforming design of RSMA to optimize the ergodic EE with imperfect channel state information at the transmitter (CSIT). We first introduce an enhanced successive convex approximation (ESCA) algorithm, which expands upon the traditional successive convex approximation (SCA) approach for maximizing EE with perfect CSIT and adapts it to the imperfect CSIT scenario. To further reduce the computational complexity, we develop a novel and efficient beamforming optimization algorithm to tackle the ergodic EE problem. A key feature of our proposed approach is the use of the semi-closed-form optimal beamforming structure identified for the ergodic EE problem. Subsequently, we propose a fixed-point-iteration (FPI)-based algorithm to determine the optimal Lagrange dual variables within the optimal beamforming structure. Numerical results show that both proposed algorithms achieve near-optimal solutions and the efficient semi-closed-form optimization algorithm remarkably reduces the computational complexity. Moreover, this study is the first to present an extensive numerical comparison of the ergodic EE between RSMA and other baseline multiple access techniques under imperfect CSIT. These results further highlight the superior EE gains offered by RSMA, reinforcing its potential as a key enabler for green communication networks.

During tunnel excavation, precise detection of low-velocity anomalies ahead of the tunnel face is crucial for ensuring the smooth construction of the tunnel project. However, current tunnel seismic prediction (TSP) system can only provide reflection interface imaging, which lacks the required accuracy and reliability in seismic data interpretation. In response to these limitations, this study presents a tunnel geological anomaly detection approach by utilizing the developed GEI-TSP system in combination with the multiparameter elastic full waveform inversion (EFWI) technique. This approach enables the retrieval of multiparameter distributions, including P-wave velocity, S-wave velocity, and density, for the geological anomalies ahead of the tunnel face, thereby enhancing the detection reliability. First, we design the tunnel forward model to generate the hybrid seismic data encompassing both body waves and Rayleigh surface waves. Second, we derive gradients and pseudo-Hessian operators using a wave mode decomposition strategy to mitigate crosstalk effects among various model parameters and improve inversion accuracy. With an activity level measurement, we conduct a novel model updating strategy to balance contributions from different wavefield combinations and avoid crosstalk between velocities and density. Experiments with several theoretical tunnel models demonstrate the feasibility of the proposed detection method and highlight its substantial potential for imaging anomalies. Meanwhile, the inversion performance of the proposed detection method is evaluated under different noise levels and observation layouts. Finally, a comprehensive assessment of the computational and storage performance of this method is carried out.

The transmission heterogeneity of an epidemic is associated with a complex mixture of host, pathogen and environmental factors. And it may indicate superspreading events to reduce the efficiency of population-level control measures and to sustain the epidemic over a larger scale and a longer duration. Methods have been proposed to identify significant transmission heterogeneity in historic epidemics based on several data sources, such as contact history, viral genomes and spatial information, which may not be available, and more importantly ignore the temporal trend of transmission heterogeneity. Here we attempted to establish a convenient method to estimate real-time heterogeneity over an epidemic. Within the branching process framework, we introduced an instant-individualheterogenous infectiousness model to jointly characterize the variation in infectiousness both between individuals and among different times. With this model, we could simultaneously estimate the transmission heterogeneity and the reproduction number from incidence time series. We validated the model with data of both simulated and real outbreaks. Our estimates of the overall and real-time heterogeneities of the six epidemics were consistent with those presented in the literature. Additionally, our model is robust to the ubiquitous bias of under-reporting and misspecification of serial interval. By analyzing recent data from South Africa, we found evidence that the Omicron might be of more significant transmission heterogeneity than Delta. Our model based on incidence data was proved to be reliable in estimating the real-time transmission heterogeneity.

This qualitative study reports the results of a phenomenographic analysis of students’ conceptions of Interactive Spherical Video-based Virtual Reality (ISVVR) supported Chinese writing. In the study, six Grade-9 students in Hong Kong participated in an ISVVR supported Chinese writing learning program. Data were collected through semi-structured interviews. Three qualitatively different themes related to ISVVR supported Chinese writing learning were revealed: 1) purpose of learning Chinese writing, 2) use of ISVVR to improve Chinese writing, and 3) motivation of using ISVVR to learn Chinese writing. Different conception categories of the themes form a hierarchy and show a trajectory that writing paradigm shifts from learner-oriented to author-oriented. This study offers an empirical reference for teachers and researchers to have a closer look at students’ conceptions of Chinese writing and ISVVR supported Chinese writing.

This work-in-progress poster discusses our preliminary work on exploring the possibility of harnessing spherical video-based immersive virtual reality (SV-IVR) in learning and teaching of Physical Geography. In particular, it focuses on delineating (i) the rationale behind our initiative, and (ii) the proposed direction of how to adopt SV-IVR in the context of formal schooling in Hong Kong.

Accurate bus trajectory data is the basis of many public transportation applications. However, trajectory data sampled by GPS devices contains notable direction errors. We cannot determine the travelling direction of the bus through trajectory data. To address this problem, we utilize k-nearest neighbor algorithm (K-NN) to determine the direction of the bus trajectory. Meanwhile, the voluminous bus trajectory data accumulated daily need to be process efficiently for further data mining. To meet the scalability and performance requirements, in this paper, we use Map-Reduce programming model for trajectory data direction correcting and projecting the bus GPS point to the road link. Particularly, we compare execution time through setting different amount of reduce to express the extent of running time can be affect. Experimental results indicate that the K-NN algorithm improve the accuracy of the direction field in raw bus trajectory significantly, and parallel processing framework improves the computational efficiency by a factor of 2 at least, which obtained by comparing between reduce quantities.

The ground testing equipment is essential debugging and testing equipment for microwave remote sensor on satellite. The design and realization methods of ground testing equipment bus interface conversion circuit for FY-3 satellite and HY-2 satellite microwave radiometer and microwave scattermeter are expounded in this paper. FPGA technology, MCU technology and DDS technology are adopted in this ground testing equipment. The RS485 interface, RS422 interface, RS232 interface, LVDS bus interface conversion function are achieved.

With the fast growing number of works utilizing link information in enhancing unsupervised document clustering, it is becoming necessary to make a comparative evaluation of the impacts of different link types on document clustering. Various types of links between text documents, including explicit links such as citation links and hyperlinks, implicit links such as coauthorship and cocitation links, and similarity links such as content similarity links, convey topic similarity or topic transferring patterns, which is very useful for document clustering. In this paper, we adopt a clustering algorithm based on Markov random field and relaxation labeling, which employs both content and linkage information, to evaluate the effectiveness of the aforementioned types of links for document clustering on ten data sets. The experimental results show that linkage information is quite effective in improving content-based document clustering. Furthermore, a series of important findings regarding the impacts of different link types on document clustering is discovered through our experiments.

In traditional text clustering methods, documents are represented as "bags of words" without considering the semantic information of each document. For instance, if two documents use different collections of core words to represent the same topic, they may be falsely assigned to different clusters due to the lack of shared core words, although the core words they use are probably synonyms or semantically associated in other forms. The most common way to solve this problem is to enrich document representation with the background knowledge in an ontology. There are two major issues for this approach: (1) the coverage of the ontology is limited, even for WordNet or Mesh, (2) using ontology terms as replacement or additional features may cause information loss, or introduce noise. In this paper, we present a novel text clustering method to address these two issues by enriching document representation with Wikipedia concept and category information. We develop two approaches, exact match and relatedness-match, to map text documents to Wikipedia concepts, and further to Wikipedia categories. Then the text documents are clustered based on a similarity metric which combines document content information, concept information as well as category information. The experimental results using the proposed clustering framework on three datasets (20-newsgroup, TDT2, and LA Times) show that clustering performance improves significantly by enriching document representation with Wikipedia concepts and categories.

We define and study a novel text mining problem for biomedical literature digital library, referred to as the class-attribute mining. Given a collection of biomedical literature from a digital library addressing a set of objects (e.g., proteins) and their descriptions (e.g., protein functions), the tasks of class-attribute mining include: (1) to identify and summarize latent classes in the space of objects, (2) to discover latent attribute themes in the space of object descriptions, and (3) to summarize the commonalities and differences among identified classes along each attribute theme. We approach this mining problem through a mixture language model and estimate the parameters of the model using the EM algorithm. We demonstrate the effectiveness of the model with an application called protein community identification and annotation from Medline, the largest biomedical literature digital library with more than 16 millions abstracts.

Document representation is one of the crucial components that determine the effectiveness of text classification tasks. Traditional document representation approaches typically adopt a popular bag-of-word method as the underlying document representation. Although it’s a simple and efficient method, the major shortcoming of bag-of-word representation is in the independent of word feature assumption. Many researchers have attempted to address this issue by incorporating semantic information into document representation. In this paper, we study the effect of semantic representation on the effectiveness of text classification systems. We employed a novel semantic smoothing technique to derive semantic information in a form of mapping probability between topic signatures and single-word features. Two classifiers, Naïve Bayes and Support Vector Machine, were selected to carry out the classification experiments. Overall, our topic-signature semantic representation approaches significantly outperformed traditional bag-of-word representation in most datasets.

Bayesian text classifiers face a common issue which is referred to as data sparsity problem, especially when the size of training data is very small. The frequently used Laplacian smoothing and corpus-based background smoothing are not effective in handling it. Instead, we propose a novel semantic smoothing method to address the sparse problem. Our method extracts explicit topic signatures (e.g. words, multiword phrases, and ontology-based concepts) from a document and then statistically maps them into single-word features. We conduct comprehensive experiments on three testing collections (OHSUMED, LATimes, and 20NG) to compare semantic smoothing with other approaches. When the size of training documents is small, the bayesian classifier with semantic smoothing not only outperforms the classifiers with background smoothing and Laplacian smoothing, but also beats the state-of-the-art active learning classifiers and SVM classifiers. In this paper, we also compare three types of topic signatures with respect to their effectiveness and efficiency for semantic smoothing.

With a growing number of works utilizing link information in enhancing document clustering, it becomes necessary to make a comparative evaluation of the impacts of different link types on document clustering. Various types of links between text documents, including explicit links such as citation links and hyperlinks, implicit links such as co-authorship links, and pseudo links such as content similarity links, convey topic similarity or topic transferring patterns, which is very useful for document clustering. In this study, we adopt a Relaxation Labeling (RL)-based clustering algorithm, which employs both content and linkage information, to evaluate the effectiveness of the aforementioned types of links for document clustering on eight datasets. The experimental results show that linkage is quite effective in improving content-based document clustering. Furthermore, a series of interesting findings regarding the impacts of different link types on document clustering are discovered through our experiments.

Hidden markov model (HMM) is frequently used for Pinyin-to-Chinese conversion. But it only captures the dependency with the preceding character. Higher order markov models can bring higher accuracy, but are computationally unaffordable to average PC settings. We propose a segment-based hidden markov model (SHMM), which has the same magnitude of complexity as first-order HMM, but generates higher decoding accuracy. SHMM tells a word from a bigram connecting two words, and assigns a reasonable probability to words as a whole. It is more powerful than HMM to decode words containing over two characters. We conduct a comprehensive Pinyin-to-Chinese conversion evaluation on Lancaster corpus. The experiment shows the perfect sentence accuracy is improved from 34.7% (HMM) to 43.3% (SHMM). The one-error sentence accuracy is increased from 72.7% to 78.3%. Furthermore, SHMM can seamlessly integrate with pinyin typing correction, acronym pinyin input, user-defined words, and self-adaptive learning all of which are a must for a commercial Pinyin-to-Chinese conversion product in order to improve the efficiency of pinyin input.

The majority of text retrieval and mining techniques are still based on exact feature (e.g. words) matching and unable to incorporate text semantics. Many researchers believe that the extension with semantic knowledge could improve the results and various methods (most of them are heuristic) have been proposed to account for concept hierarchy, synonymy, and other semantic relationships. However, the results with such semantic extension have been mixed, ranging from slight improvements to decreases in effectiveness, mostly likely due to the lack of a formal framework. Instead, we propose a novel method to address the semantic extension within the framework of language modeling. Our method extracts explicit topic signatures from documents and then statistically maps them into single- word features. The incorporation of semantic knowledge then reduces to the smoothing of unigram language models using semantic knowledge. The dragon toolkit reflects our method and its effectiveness is demonstrated by three tasks, text retrieval, text classification, and text clustering.

Semantic smoothing, which incorporates synonym and sense information into the language models, is effective and potentially significant to improve retrieval performance. Previously implemented semantic smoothing models such as the translation model have shown good experimental results. However, these models are unable to incorporate contextual information. To overcome this limitation, we propose a novel context-sensitive semantic smoothing method that decomposes a document into a set of weighted context-sensitive topic signatures and then maps those topic signatures into query terms. The language model with such a context- sensitive semantic smoothing is referred to as the topic signature language model. In detail, we implement two types of topic signatures, depending on whether ontology exists in the application domain. One is the ontology-based concept and the other is the multiword phrase. The mapping probabilities from each topic signature to individual terms are estimated through the EM algorithm. Document models based on topic signature mapping are then derived. The new smoothing method is evaluated on the TREC 2004/ 2005 Genomics Track with ontology-based concepts, as well as the TREC Ad Hoc Track (Disks 1, 2, and 3) with multiword phrases. Both experiments show significant improvements over the two-stage language model, as well as the language model with context- insensitive semantic smoothing.

This paper discusses an ontology based language modeling text mining approach to the annotation of protein community. Communities appear to play an important role in the functional properties of complex networks. Being able to annotate the identified the community structure in a biological network can help us to understand better the structure and dynamics of biological systems. Traditional method such as gene ontology (GO) provides information about the functionality of gene products, but they are not enough to annotate community as for only limited number of proteins in the database, limited protein properties available for annotation and the inability to annotate a group of gene products as a whole. Thus, we present an ontology based mixture language model approach to annotate protein community. Compared to traditional method, we have the following three advantages. First, biomedical literature mining brings much richer information than existed gene databases. Second, the mixture language model can help "purify" the document by eliminating some background noise. Third, using domain ontology, we extract biological concept and concept pairs from abstracts. Biological concept is more meaningful than word or multi-word phrases. Moreover, using concept pairs can deliver much more information and serve as evidence of annotation results. We test our approach on four communities SAGA-SRB, CCR-NOT, RFC and ARP2/3, detected from dataset of interactions for Saccharomyces cerevisae from the general repository for interaction datasets (GRID). Annotation results provide a very coherent indication of functionality of each community

Electronic Medical Records are important to manage health data and save lives to improve the quality of service in hospitals. Clinical medical records contain a wealth of information, largely in free-text form. This paper proposes a generic framework to semi-automatically extract and mine data from clinical note, automatically learn patterns for each physician’s clinical notes, and automatically populate EMR databases for multi users. In this paper, we also develop a web-based system with a relational database to automatically store data from MEDical Information Extraction (MedIE) system that extracts and mines a variety of patient information with breast complaints from semistructured clinical records.

In this paper, we design and develop a unified system GE-Miner (Gene Expression Miner) to integrate cluster ensemble, text clustering and multi document summarization and provide an environment for comprehensive gene expression data analysis. We present a novel cluster ensemble approach to generate high quality gene cluster. In our text summarization module, given a gene cluster, our Expectation Maximization (EM) based algorithm can automatically identify subtopics and extract most probable terms for each topic. Then, the extracted top k topical terms from each subtopic are combined to form the biological explanation of each gene cluster. Experimental results demonstrate that our system can obtain high quality clusters and provide informative key terms for the gene clusters.

A document is often full of class-independent "general" words and short of class-specific "core " words, which leads to the difficulty of document clustering. We argue that both problems will be relieved after suitable smoothing of document models in agglomerative approaches and of cluster models in partitional approaches, and hence improve clustering quality. To the best of our knowledge, most model-based clustering approaches use Laplacian smoothing to prevent zero probability while most similarity-based approaches employ the heuristic TF*IDF scheme to discount the effect of "general" words. Inspired by a series of statistical translation language model for text retrieval, we propose in this paper a novel smoothing method referred to as context-sensitive semantic smoothing for document clustering purpose. The comparative experiment on three datasets shows that model-based clustering approaches with semantic smoothing is effective in improving cluster quality.

Semantic smoothing, which incorporates synonym and sense information into the language models, is effective and potentially significant to improve retrieval performance. The implemented semantic smoothing models, such as the translation model which statistically maps document terms to query terms, and a number of works that have followed have shown good experimental results. However, these models are unable to incorporate contextual information. Thus, the resulting translation might be mixed and fairly general. To overcome this limitation, we propose a novel context-sensitive semantic smoothing method that decomposes a document or a query into a set of weighted context-sensitive topic signatures and then translate those topic signatures into query terms. In detail, we solve this problem through (1) choosing concept pairs as topic signatures and adopting an ontology-based approach to extract concept pairs; (2) estimating the translation model for each topic signature using the EM algorithm; and (3) expanding document and query models based on topic signature translations. The new smoothing method is evaluated on TREC 2004/05 Genomics Track collections and significant improvements are obtained. The MAP (mean average precision) achieves a 33.6 % maximal gain over the simple language model, as well as a 7.8 % gain over the language model with context-insensitive semantic smoothing.