MOTIVATION: Spatial transcriptomic (ST) technologies, such as GeoMx Digital Spatial Profiler, are increasingly utilized to investigate the role of diverse tumor microenvironment components, particularly in relation to cancer progression, treatment response, and therapeutic resistance. However, in many ST studies, the spatial information obtained from immunofluorescence imaging is primarily used for identifying regions of interest (ROIs) rather than as an integral part of downstream transcriptomic data analysis and interpretation. RESULTS: We developed ROICellTrack, a deep learning-based framework that better integrates cellular imaging with spatial transcriptomic profiling. By analyzing 56 ROIs from urothelial carcinoma of the bladder and upper tract urothelial carcinoma, ROICellTrack identified distinct cancer-immune cell mixtures, characterized by specific transcriptomic and morphological signatures and receptor-ligand interactions linked to tumor content and immune infiltrations. Our findings demonstrate the value of integrating imaging with transcriptomics to analyze spatial omics data, improving our understanding of tumor heterogeneity and its relevance to personalized and targeted therapies. AVAILABILITY AND IMPLEMENTATION: ROICellTrack is publicly available at https://github.com/wanglab1/ROICellTrack.

To investigate the impact of Multiword Expressions (MWEs) on the fine-grained performance of the state-of-the-art metrics for Machine Translation Evaluation (MTE), we conduct experiments on the WMT22 Metrics Shared Task dataset with a preliminary focus on the Chinese-to-English language pair. We further annotate 28 types of Chinese MWEs on the source texts and then examine the performance of 31 MTE metrics on groups of sentences containing different MWEs. We have 3 interesting findings: 1) Machine Translation (MT) systems tend to perform worse on most Chinese MWE categories, confirming the previous claim that MWEs are a bottleneck of MT; 2) automatic metrics tend to overrate the translation of sentences containing MWEs; 3) most neural-network-based metrics perform better than string-overlap-based metrics. It concludes that both MT systems and MTE metrics still suffer from MWEs, suggesting richer annotation of data to facilitate MWE-aware automatic MTE and MT.

In this paper, we present our exploration of annotating Chinese word senses using English WordNet synsets, with examples extracted from OntoNotes Chinese sense inventories. Given a target word along with the example that contains it, the annotators select a WordNet synset that best describes the meaning of the target word in the context. The result demonstrates an inter-annotator agreement of 38% between two annotators. We delve into the instances of disagreement by comparing the two annotated synsets, including their positions within the WordNet hierarchy. The examination reveals intriguing patterns among closely related synsets, shedding light on similar concepts represented within the WordNet structure. The data offers as an indirect linking of Chinese word senses defined in OntoNotes Chinese sense inventories to WordNet sysnets, and thus promotes the value of the OntoNotes corpus. Compared to a direct linking of Chinese word senses to WordNet synsets, the example-based annotation has the merit of not being affected by inaccurate sense definitions and thus offers a new way of mapping WordNets of different languages. At the same time, the annotated data also serves as a valuable linguistic resource for exploring potential lexical differences between English and Chinese, with potential contributions to the broader understanding of cross-linguistic semantic mapping

Triple modular redundancy (TMR) fault tolerance mechanism can provide almost perfect fault-masking, which has the great potential to enhance the reliability of real-time systems. However, multiple copies of a task are executed concurrently, which will lead to a sharp increase in system energy consumption. In this work, the problem of parallel applications using TMR on heterogeneous multi-core platforms to minimize energy consumption is studied. First, the heterogeneous earliest finish time algorithm is improved, and then according to the given application's deadline constraints and reliability requirements, an algorithm to extend the execution time of the copies is designed. Secondly, based on the properties of TMR, an algorithm for minimizing the execution overhead of the third copy (MEOTC) is designed. Finally, considering the actual situation of task execution, an online energy management (OEM) method is proposed. The proposed algorithms were compared with the state-of-the-art AFTSA algorithm, and the results show significant differences in energy consumption. Specifically, for light fault detection, the energy consumption of the MEOTC and OEM algorithms was found to be 80% and 72% respectively, compared with AFTSA. For heavy fault detection, the energy consumption of MEOTC and OEM was measured at 61% and 55% respectively, compared with AFTSA.

In this article, an adaptive neural piecewise implicit inverse control strategy is proposed to effectively compensate for butterfly hysteresis effectively. First, a new butterfly Krasnoselskii–Pokrovskii (BKP) model is developed for the double-loop butterfly hysteresis characteristics. Second, an adaptive neural piecewise implicit inverse control strategy is designed to mitigate the butterfly-like hysteresis without constructing its analytical inverse model. Finally, experimental results on the dielectric elastomer actuator (DEA) motion control platform demonstrate the effectiveness of the adaptive neural piecewise implicit inverse control strategy.

This paper describes a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphological typology.By modeling morphological processes including suffixation, prefixation, infixation, and full and partial reduplication with constrained stem change rules, our system effectively constrains the search space and offers a wide coverage in terms of morphological typology.The system is tested on nine typologically and genetically diverse languages, and shows superior performance over leading systems.We also investigate the effect of an oracle that provides only a handful of bits per language to signal morphological type.

This paper describes a new morphology resource created by Linguistic Data Consortium and the University of Pennsylvania for the DARPA LORELEI Program. The data consists of approximately 2000 tokens annotated for morphological segmentation in each of 9 low resource languages, along with root information for 7 of the languages. The languages annotated show a broad diversity of typological features. A minimal annotation scheme for segmentation was developed such that it could capture the patterns of a wide range of languages and also be performed reliably by non-linguist annotators. The basic annotation guidelines were designed to be language-independent, but included language-specific morphological paradigms and other specifications. The resulting annotated corpus is designed to support and stimulate the development of unsupervised morphological segmenters and analyzers by providing a gold standard for their evaluation on a more typologically diverse set of languages than has previously been available. By providing root annotation, this corpus is also a step toward supporting research in identifying richer morphological structures than simple morpheme boundaries.

This paper describes an unsupervised model for morphological segmentation that exploits the notion of paradigms, which are sets of morphological categories (e.g., suffixes) that can be applied to a homogeneous set of words (e.g., nouns or verbs). Our algorithm identifies statistically reliable paradigms from the morphological segmentation result of a probabilistic model, and chooses reliable suffixes from them. The new suffixes can be fed back iteratively to improve the accuracy of the probabilistic model. Finally, the unreliable paradigms are subjected to pruning to eliminate unreliable morphological relations between words. The paradigm-based algorithm significantly improves segmentation accuracy. Our method achieves start-of-the-art results on experiments using the Morpho-Challenge data, including English, Turkish, and Finnish.

Problems of ordinal regression arise in many fields such as information retrieval, data mining and knowledge management. In this paper, we consider ordinal regression in a semi-supervised scenario, i.e., we try to utilize the ordinal information from the distribution of unlabeled data. Semi-supervised ordinal regression is more applicable than traditional supervised ordinal regression, because nowadays labeled data is expensive and time-consuming as it needs human labor, whereas a large amount of unlabeled data are far accessible with the development of internet technology. We construct a general semi-supervised ordinal regression framework to formulate this problem. Based on the framework, we then propose a semi-supervised ordinal regression method called Semi-supervised Ordinal SVM (SOSVM). Additionally, in order to make our proposed method more applicable to problems with large scaled labeled data, we put forward a kernel based dual coordinate descent algorithm to efficiently solve SOSVM. Both rigorous theoretical analysis and promising experimental evaluations on real world datasets show the great performance and remarkable efficiency of SOSVM.

Term weighting is an important task for text classification. Inverse document frequency (IDF) is one of the most popular methods for this task; however, in some situations, such as supervised learning for text categorization, it doesn 't weight terms properly, because it neglects the category information and assumes that a term that occurs in smaller set of documents should get a higher weight. There have been several term weighting schemes that consider the category information. In this paper, we present a new term weighting scheme that considers more information provided by the term distribution among different categories. The experiments show that our method is more effective than three other popular schemes.