Merge trees are fundamental topological descriptors with broad theoretical and practical significance, yet their computation, particularly for augmented merge trees, incurs substantial computational and memory overhead. We present BiGMT, a highly scalable parallel algorithm for merge tree construction based on regions of the Morse-Smale segmentation. By exploiting boundary vertex characteristics and the dynamic merging of these regions, BiGMT preserves the essential topology of d-dimensional manifolds and represents the structure as a weighted bipartite graph. This formulation decomposes merge tree construction into fine-grained parallel tasks, significantly reducing traversal cost and memory consumption. Furthermore, BiGMT efficiently constructs augmented merge trees using reverse binary lifting search and parallel connection mechanisms, substantially improving augmentation performance. Extensive experiments on medium- to large-scale scalar field datasets demonstrate substantial GPU acceleration. BiGMT consistently outperforms ExTreeM, PPP, and FTM-Tree on RTX4090 and H200, achieving speedups of up to 23.95× and demonstrating superior parallel efficiency and scalability.

With the rapid development of information technology, cybersecurity issues are becoming increasingly prominent. Existing intrusion detection methods are mainly based on centralized machine learning algorithms, which overlook the data privacy issues of edge devices on the Internet of Things. Therefore, federated learning has been proposed by researchers to address the problems of data privacy and leakage in intrusion detection. However, existing intrusion detection algorithms based on federated learning face the following problems: (1) Lack of a standard network traffic feature set; (2) Clients exhibit data heterogeneity, which severely impacts the training of federated learning; (3) Knowledge distillation-based federated learning requires the server to possess a proxy dataset, which may be impractical in certain situations. To address these issues, this paper proposes an intrusion detection model based on improved generative federated distillation learning (FedGen+). Specifically, we use the original network traffic format as the input to the clients. The server learns a generator to integrate the clients’ label information, then the generator is deployed to clients to generate augmented sample feature representations to train local model. Experimental results demonstrate that the FedGen+ outperforms existing federated learning-based intrusion detection algorithms on the IoT benchmark datasets.

Unpaired image-to-image translation aims to translate an image from the source domain to the target domain without paired training data. Some recent works have applied the self-attention to this task and achieved impressive results. Nevertheless, it may generate unsatisfactory results for scenes with complex image content or strong geometric variations between image domains. In this paper, We propose a novel method based on hybrid excitation and adaptive style recombination for unpaired image-to-image translation, which has these main advantages. 1) A hybrid perceptual excitation module is used to capture different ranges of contextual information in complex scenes dynamically. 2) An adaptive cross-attention code recombination module is designed to recombine the content code and style code adaptively according to different positions. 3) We also propose multi-source NCE loss to constrain the generated image content by two aspects: image-level and patch-level. Experiments show that our method achieves more reasonable results than the state-of-the-art methods on several benchmark datasets.

The combination of edge computing and replication technology provides service guarantee for edge applications. However, optimizing replica creation and placement to enhance system performance is challenging due to the limited resources available at the edge. In this context, effective replica management becomes crucial for efficient and reliable edge computing. This study proposes a real-time dynamic replica management model to address the challenges of replica creation and placement in the edge computing environment. Firstly, we design a prediction-based dynamic proactive replica creation algorithm. This algorithm integrates data popularity and node load, utilizing fuzzy membership functions to model data and node states, effectively handling the state uncertainty in certain conditions. It also defines overheating and undercooling similarities to assess the trend of state changes, thereby determining the optimal timing for replica creation. To prevent latency in replica creation, the algorithm employs an Long Short-Term Memory (LSTM) model with a deviation feedback mechanism, which helps prevent lag in replica creation and minimizes unnecessary replica generation. Secondly, we formula replica placement as a multi-objective optimization problem considering the node load and access degree. We use a joint optimization replica placement algorithm that combines Evolutionary Gradient Search (EGS) and Sorting Genetic Algorithm-II to solve the multi-objective replica placement problem. Finally, we conduct extensive experiments on the replica management model. The results demonstrate significant improvements in average response time, effective network utilization rate, storage space utilization rate, and system load balancing, which validate the effectiveness of the proposed method.

Stem cell culture in vitro is essential for research in cell biology, drug toxicity and translational studies. In recent years, there has been a surge in the development of deep learning-based object detection algorithms tailored for image analysis of stem cell culture. However, many of these algorithms fall short in terms of performance and interpretability. To address these challenges, we present StemCellDet, an innovative multimodal-based method for stem cell detection. By harnessing the power of the pretrained CLIP model, StemCellDet uniquely encodes descriptions of stem cell categories into text embeddings, which are then synchronized with image embeddings. This synchronization enhances the model’s ability to identify critical features for accurate stem cell model categorization. Furthermore, by integrating knowledge distillation and introducing our proposed Semantic Fusion Module (SFM), StemCellDet can adeptly identify stem cell culture categories that were not present during its training phase using only their textual descriptions. Our experiments highlight StemCellDet’s robust detection capabilities and its advantages in terms of accuracy and generalizability.

Sparse Matrix-Transpose Vector Product (SMTVP) is a frequently used computation pattern in High Performance Computing applications. It is typically solved by transposition followed by a Sparse Matrix-Vector Product (SMVP) in current linear algebra packages. However, the transposition process can be a serious bottleneck on modern parallel computing platforms. A previous work proposed a relatively complex data structure for efficiently computing SMTVP with multi-core CPUs, but it proved to be inefficient on GPUs. In this work, we show that the Compressed Sparse Row (CSR) based SMVP algorithm can also be efficient for SMTVP computation on modern GPUs. The proposed method exploits atomic operations to perform the reduce operation in the computation of each inner product of a row in the transposed matrix and the vector. Experimental results show that the simple technique can outperform the SMTVP flow of transposition plus SMVP released in the CUSPARSE package by up to 405-fold.

Partition enforcement policy is essential in the cache partition, and its main function is to protect the lines and retain the cache quota of each core. This paper focuses online protection based on its generation time rather than the CPU core ID that it belongs to or the position of the replacement stack, where it is located. The basic idea is that when a line is live, it must be protected and retained in the cache; when the line is “dead,” it needs to be evicted as early as possible. Therefore, the live-time protected counter (LvtP, four bits) is augmented to trace the lines' live time. Moreover, dead blocks are predicted according to the access event sequence. This paper presents a pseudopartition approach-LvtPPP and proposes a two-cascade victim selection mechanism to alleviate dead blocks based on the LRU replacement policy and the LvtP counter. LvtPPP also supports flexible handling of allocation deviation by introducing a parameter λ to adjust the generation time of the line. There is significant improvement of the performance and fairness in LvtPPP over PIPP and UCP according to the evaluation results based on Simics.

Writer adaptive handwriting recognition, which has potential of increasing accuracies for a particular user, is the process of converting a writer-independent recognition system to a writer-dependent one. In this paper, we provide a general incremental learning solution for linear discriminant analysis (LDA) on the basis of previous researches, and propose an Incremental LDA (ILDA) based writer adaptive online handwriting recognition method. The adaptation is performed by modifying both the prototypes and the LDA transformation matrix through ILDA algorithm. It includes: (1) modifying prototypes in original feature space; (2) updating the LDA transformation matrix; (3) projecting the updated prototypes to LDA feature space. Experiments are performed on two datasets, the writer-dependent dataset, in which the writing style is consistent with the incremental training data, and the writer-independent dataset. The results demonstrated that our proposed method can reduce as much as 46.35% error rate on the writer-dependent dataset with only 0.20% accuracy loss on the writer-independent dataset. It indicates that our proposed method can significantly increase the recognition accuracy for a particular writer while has minor effects for general writers.

Abstract Summary: RNATOPS-W is a web server to search sequences for RNA secondary structures including pseudoknots. The server accepts an annotated RNA multiple structural alignment as a structural profile and genomic or other sequences to search. It is built upon RNATOPS, a command line C++software package for the same purpose, in which filters to speed up search are manually selected. RNATOPS-W improves upon RNATOPS by adding the function of automatic selection of a hidden Markov model (HMM) filter and also a friendly user interface for selection of a substructure filter by the user. In addition, RNATOPS-W complements existing RNA secondary structure search web servers that either use built-in structure profiles or are not able to detect pseudoknots. RNATOPS-W inherits the efficiency of RNATOPS in detecting large, complex RNA structures. Availability: The web server RNATOPS-W is available at the web site www.uga.edu/RNA-Informatics/?f=software&p=RNATOPS-w. The underlying search program RNATOPS can be downloaded at www.uga.edu/RNA-Informatics/?f=software&p=RNATOPS. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

This paper focuses on the design and implementation of a domain ontology-based navigation learning system, which can guide the user to learn more efficiently. Aiming at improving users' conceptual understanding of course material, a navigation structure was built among concepts, and each concept is presumed to have at least one concept instance which is provided by a Web answer system based on natural language which was integrated in this system. Then users would browse through the linked resources under those particular concepts as well as related concepts and get a better understanding of the material in the course. The kernel of such function was a domain knowledge base which was composed of concepts and relationship between them. Ontology was introduced as a mechanism to guide the establishment of this base. At last, a method of extracting concepts and relationship between them by using machine learning algorithms were presented.

Abstract Motivation: Searching genomes for non-coding RNAs (ncRNAs) by their secondary structure has become an important goal for bioinformatics. For pseudoknot-free structures, ncRNA search can be effective based on the covariance model and CYK-type dynamic programming. However, the computational difficulty in aligning an RNA sequence to a pseudoknot has prohibited fast and accurate search of arbitrary RNA structures. Our previous work introduced a graph model for RNA pseudoknots and proposed to solve the structure–sequence alignment by graph optimization. Given k candidate regions in the target sequence for each of the n stems in the structure, we could compute a best alignment in time O(ktn) based upon a tree width t decomposition of the structure graph. However, to implement this method to programs that can routinely perform fast yet accurate RNA pseudoknot searches, we need novel heuristics to ensure that, without degrading the accuracy, only a small number of stem candidates need to be examined and a tree decomposition of a small tree width can always be found for the structure graph. Results: The current work builds on the previous one with newly developed preprocessing algorithms to reduce the values for parameters k and t and to implement the search method into a practical program, called RNATOPS, for RNA pseudoknot search. In particular, we introduce techniques, based on probabilistic profiling and distance penalty functions, which can identify for every stem just a small number k (e.g. k ≤ 10) of plausible regions in the target sequence to which the stem needs to align. We also devised a specialized tree decomposition algorithm that can yield tree decomposition of small tree width t (e.g. t ≤ 4) for almost all RNA structure graphs. Our experiments show that with RNATOPS it is possible to routinely search prokaryotic and eukaryotic genomes for specific RNA structures of medium to large sizes, including pseudoknots, with high sensitivity and high specificity, and in a reasonable amount of time. Availability: The source code in C++ for RNATOPS is available at www.uga.edu/RNA-Informatics/software/rnatops/ Contact: [email protected] Supplementary information: The online Supplementary Material contains all illustrative figures and tables referenced by this article.