MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension

The large language models have achieved superior performance on various natural language tasks. One major drawback of such approaches is they are resource-intensive in fine-tuning new datasets. Soft-prompt tuning presents a resource-efficient solution to fine-tune the pre-trained language models (PLMs) while keeping their weight frozen. Existing soft prompt methods mainly focus on designing the input-independent prompts that steer the model to fit the domain of the new dataset. Those methods often ignore the fine-grained information about the task and context of the text. In this paper, we propose a multi-level prompt tuning (MPrompt) method for machine reading comprehension. It utilizes prompts at task-specific, domain-specific, and context-specific levels to enhance the comprehension of input semantics at different granularities. We also propose an independence constraint to steer each domain-specific prompt to focus on information within its domain to avoid redundancy. Moreover, we present a prompt generator that incorporates context-related knowledge in the prompt generation to enhance contextual relevancy. We conducted extensive experiments on 12 benchmarks of various QA formats and achieved an average improvement of 1.94\% over the state-of-the-art methods.Comment: 13 pages, 5 figures, accepted by EMNLP2023-Finding

Perturbation analysis in verification of discrete-time Markov chains

Perturbation analysis in probabilistic verification addresses the robustness and sensitivity problem for verification of stochastic models against qualitative and quantitative properties. We identify two types of perturbation bounds, namely non-asymptotic bounds and asymptotic bounds. Non-asymptotic bounds are exact, pointwise bounds that quantify the upper and lower bounds of the verification result subject to a given perturbation of the model, whereas asymptotic bounds are closed-form bounds that approximate non-asymptotic bounds by assuming that the given perturbation is sufficiently small. We perform perturbation analysis in the setting of Discrete-time Markov Chains. We consider three basic matrix norms to capture the perturbation distance, and focus on the computational aspect. Our main contributions include algorithms and tight complexity bounds for calculating both non-asymptotic bounds and asymptotic bounds with respect to the three perturbation distances. © 2014 Springer-Verlag

Control of Switched Stochastic Systems with Time-Varying Delay

The problems of mean-square exponential stability and robust H ∞ control of switched stochastic systems with time-varying delay are investigated in this paper. Based on the average dwell time method and Gronwall-Bellman inequality, a new mean-square exponential stability criterion of such system is derived in terms of linear matrix inequalities LMIs . Then, H ∞ performance is studied and robust H ∞ controller is designed. Finally, a numerical example is given to illustrate the effectiveness of the proposed approach

Shoulder muscle activation pattern recognition based on sEMG and machine learning algorithms

BACKGROUND AND OBJECTIVE: Surface electromyography (sEMG) has been used for robotic rehabilitation engineering for volitional control of hand prostheses or elbow exoskeleton, however, using sEMG for volitional control of an upper limb exoskeleton has not been perfectly developed. The long-term goal of our study is to process shoulder muscle bio-electrical signals for rehabilitative robotic assistive device motion control. The purposes of this study included: 1) to test the feasibility of machine learning algorithms in shoulder motion pattern recognition using sEMG signals from shoulder and upper limb muscles, 2) to investigate the influence of motion speed, individual variability, EMG recording device, and the amount of EMG datasets on the shoulder motion pattern recognition accuracy. METHODS: A novel convolutional neural network (CNN) structure was constructed to process EMG signals from 12 muscles for the pattern recognition of upper arm motions including resting, drinking, backward-forward motion, and abduction motion. The accuracy of the CNN models for pattern recognition under different motion speeds, among individuals, and by EMG recording devices was statistically analyzed using ANOVA, GLM Univariate analysis, and Chi-square tests. The influence of EMG dataset number used for CNN model training on recognition accuracy was studied by gradually increasing dataset number until the highest accuracy was obtained. RESULTS: Results showed that the accuracy of the normal speed CNN model in motion pattern recognition was 97.57% for normal speed motions and 97.07% for fast speed motions. The accuracy of the cross-subjects CNN model in motion pattern recognition was 79.64%. The accuracy of the cross-device CNN model in motion pattern recognition was 88.93% for normal speed motion and 80.87% for mixed speed. There was a statistical difference in pattern recognition accuracy between different CNN models. CONCLUSION: The EMG signals of shoulder and upper arm muscles from the upper limb motions can be processed using CNN algorithms to recognize the identical motions of the upper limb including drinking, forward/backward, abduction, and resting. A simple CNN model trained by EMG datasets of a designated motion speed accurately detected the motion patterns of the same motion speed, yielding the highest accuracy compared with other mixed CNN models for various speeds of motion pattern recognition. Increase of the number of EMG datasets for CNN model training improved the pattern recognition accuracy

The genetic intractability of Symbiodinium microadriaticum to standard algal transformation methods.

Modern transformation and genome editing techniques have shown great success across a broad variety of organisms. However, no study of successfully applied genome editing has been reported in a dinoflagellate despite the first genetic transformation of Symbiodinium being published about 20 years ago. Using an array of different available transformation techniques, we attempted to transform Symbiodinium microadriaticum (CCMP2467), a dinoflagellate symbiont of reef-building corals, with the view to performing subsequent CRISPR-Cas9 mediated genome editing. Plasmid vectors designed for nuclear transformation containing the chloramphenicol resistance gene under the control of the CaMV p35S promoter as well as several putative endogenous promoters were used to test a variety of transformation techniques including biolistics, electroporation and agitation with silicon carbide whiskers. Chloroplast-targeted transformation was attempted using an engineered Symbiodinium chloroplast minicircle encoding a modified PsbA protein expected to confer atrazine resistance. We report that we have been unable to confer chloramphenicol or atrazine resistance on Symbiodinium microadriaticum strain CCMP2467

FCS-HGNN: Flexible Multi-type Community Search in Heterogeneous Information Networks

Community Search (CS), a crucial task in network science, has attracted considerable interest owing to its prowess in unveiling personalized communities, thereby finding applications across diverse domains. Existing research primarily focuses on traditional homogeneous networks, which cannot be directly applied to heterogeneous information networks (HINs). However, existing research also has some limitations. For instance, either they solely focus on single-type or multi-type community search, which severely lacking flexibility, or they require users to specify meta-paths or predefined community structures, which poses significant challenges for users who are unfamiliar with community search and HINs. In this paper, we propose an innovative method, FCS-HGNN, that can flexibly identify either single-type or multi-type communities in HINs based on user preferences. We propose the heterogeneous information transformer to handle node heterogeneity, and the edge-semantic attention mechanism to address edge heterogeneity. This not only considers the varying contributions of edges when identifying different communities, but also expertly circumvents the challenges presented by meta-paths, thereby elegantly unifying the single-type and multi-type community search problems. Moreover, to enhance the applicability on large-scale graphs, we propose the neighbor sampling and depth-based heuristic search strategies, resulting in LS-FCS-HGNN. This algorithm significantly improves training and query efficiency while maintaining outstanding community effectiveness. We conducted extensive experiments on five real-world large-scale HINs, and the results demonstrated that the effectiveness and efficiency of our proposed method, which significantly outperforms state-of-the-art methods.Comment: 13 page

ESC: Edge-attributed Skyline Community Search in Large-scale Bipartite Graphs

Due to the ability of modeling relationships between two different types of entities, bipartite graphs are naturally employed in many real-world applications. Community Search in bipartite graphs is a fundamental problem and has gained much attention. However, existing studies focus on measuring the structural cohesiveness between two sets of vertices, while either completely ignoring the edge attributes or only considering one-dimensional importance in forming communities. In this paper, we introduce a novel community model, named edge-attributed skyline community (ESC), which not only preserves the structural cohesiveness but unravels the inherent dominance brought about by multi-dimensional attributes on the edges of bipartite graphs. To search the ESCs, we develop an elegant peeling algorithm by iteratively deleting edges with the minimum attribute in each dimension. In addition, we also devise a more efficient expanding algorithm to further reduce the search space and speed up the filtering of unpromising vertices, where a upper bound is proposed and proven. Extensive experiments on real-world large-scale datasets demonstrate the efficiency, effectiveness, and scalability of the proposed ESC search algorithms. A case study was conducted to compare with existing community models, substantiating that our approach facilitates the precision and diversity of results

Accuracy and reliability analysis of a machine learning based segmentation tool for intertrochanteric femoral fracture CT

IntroductionThree-dimensional (3D) reconstruction of fracture fragments on hip Computed tomography (CT) may benefit the injury detail evaluation and preoperative planning of the intertrochanteric femoral fracture (IFF). Manually segmentation of bony structures was tedious and time-consuming. The purpose of this study was to propose an artificial intelligence (AI) segmentation tool to achieve semantic segmentation and precise reconstruction of fracture fragments of IFF on hip CTs.Materials and MethodsA total of 50 labeled CT cases were manually segmented with Slicer 4.11.0. The ratio of training, validation and testing of the 50 labeled dataset was 33:10:7. A simplified V-Net architecture was adopted to build the AI tool named as IFFCT for automatic segmentation of fracture fragments. The Dice score, precision and sensitivity were computed to assess the segmentation performance of IFFCT. The 2D masks of 80 unlabeled CTs segmented by AI tool and human was further assessed to validate the segmentation accuracy. The femoral head diameter (FHD) was measured on 3D models to validate the reliability of 3D reconstruction.ResultsThe average Dice score of IFFCT in the local test dataset for “proximal femur”, “fragment” and “distal femur” were 91.62%, 80.42% and 87.05%, respectively. IFFCT showed similar segmentation performance in cross-dataset, and was comparable to that of human expert in human-computer competition with significantly reduced segmentation time (p < 0.01). Significant differences were observed between 2D masks generated from semantic segmentation and conventional threshold-based segmentation (p < 0.01). The average FHD in the automatic segmentation group was 47.5 ± 4.1 mm (41.29∼56.59 mm), and the average FHD in the manual segmentation group was 45.9 ± 6.1 mm (40.34∼64.93 mm). The mean absolute error of FHDs in the two groups were 3.38 mm and 3.52 mm, respectively. No significant differences of FHD measurements were observed between the two groups (p > 0.05). All ICCs were greater than 0.8.ConclusionThe proposed AI segmentation tool could effectively segment the bony structures from IFF CTs with comparable performance of human experts. The 2D masks and 3D models generated from automatic segmentation were effective and reliable, which could benefit the injury detail evaluation and preoperative planning of IFFs