An interpretability framework for Similar case matching

Similar Case Matching (SCM) plays a pivotal role in the legal system by facilitating the efficient identification of similar cases for legal professionals. While previous research has primarily concentrated on enhancing the performance of SCM models, the aspect of interpretability has been neglected. To bridge the gap, this study proposes an integrated pipeline framework for interpretable SCM. The framework comprises four modules: judicial feature sentence identification, case matching, feature sentence alignment, and conflict resolution. In contrast to current SCM methods, our framework first extracts feature sentences within a legal case that contain essential information. Then it conducts case matching based on these extracted features. Subsequently, our framework aligns the corresponding sentences in two legal cases to provide evidence of similarity. In instances where the results of case matching and feature sentence alignment exhibit conflicts, the conflict resolution module resolves these inconsistencies. The experimental results show the effectiveness of our proposed framework, establishing a new benchmark for interpretable SCM

GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs

Embedding knowledge graphs (KGs) for multi-hop logical reasoning is a challenging problem due to massive and complicated structures in many KGs. Recently, many promising works projected entities and queries into a geometric space to efficiently find answers. However, it remains challenging to model the negation and union operator. The negation operator has no strict boundaries, which generates overlapped embeddings and leads to obtaining ambiguous answers. An additional limitation is that the union operator is non-closure, which undermines the model to handle a series of union operators. To address these problems, we propose a novel probabilistic embedding model, namely Gamma Embeddings (GammaE), for encoding entities and queries to answer different types of FOL queries on KGs. We utilize the linear property and strong boundary support of the Gamma distribution to capture more features of entities and queries, which dramatically reduces model uncertainty. Furthermore, GammaE implements the Gamma mixture method to design the closed union operator. The performance of GammaE is validated on three large logical query datasets. Experimental results show that GammaE significantly outperforms state-of-the-art models on public benchmarks

Modelling and Performance Analysis of the Over-the-Air Computing in Cellular IoT Networks

Ultra-fast wireless data aggregation (WDA) of distributed data has emerged as a critical design challenge in the ultra-densely deployed cellular internet of things network (CITN) due to limited spectral resources. Over-the-air computing (AirComp) has been proposed as an effective solution for ultra-fast WDA by exploiting the superposition property of wireless channels. However, the effect of access radius of access point (AP) on the AirComp performance has not been investigated yet. Therefore, in this work, the mean square error (MSE) performance of AirComp in the ultra-densely deployed CITN is analyzed with the AP access radius. By modelling the spatial locations of internet of things devices as a Poisson point process, the expression of MSE is derived in an analytical form, which is validated by Monte Carlo simulations. Based on the analytical MSE, we investigate the effect of AP access radius on the MSE of AirComp numerically. The results show that there exists an optimal AP access radius for AirComp, which can decrease the MSE by up to 12.7%. It indicates that the AP access radius should be carefully chosen to improve the AirComp performance in the ultra-densely deployed CITN

Choosing the optimal target area for repeated transcranial magnetic stimulation in treating neuropathic pain in spinal cord injury patients: a comparative analysis

ObjectiveThe specific target area of repeated transcranial magnetic stimulation (rTMS) in treating neuropathic pain resulting from spinal cord injury (SCI-NP) remains uncertain.MethodsThirty-four participants with SCI-NP were allocated into three groups, namely, the motor cortex (M1, A) group, the left dorsolateral prefrontal cortex (LDLPFC, B) group, and the control (sham stimulation, C) group. The intervention was administered totally 10 times. Outcome measures assessed pre-(T0) and post-(T1)intervention, including Numerical Rating scale (NRS), anxiety (SAS), depression (SDS), sleep quality (PSQI), brief pain inventory (BPI), and impression of change.ResultsAll outcomes in groups A and B significantly changed after intervention (p < 0.05), and the delta value (T1–T0) also significantly changed than group C (p < 0.05). The delta value of SDS in the group B was better than the group A, and the change of pain degree in the group B was moderately correlated with the change in PSQI (r = 0.575, p < 0.05). Both patients in the groups A and B showed significant impression of change about their received therapy (p < 0.05).ConclusionBoth targets are effective, but LDLPFC is more effective in reducing depression in SCI-NP. Healthcare providers might select the suitable area according to the specific attributes of their patients

Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models

Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. It enables LLMs to excel in various tasks, such as arithmetic reasoning, question answering, summarization, relation extraction, machine translation, and sentiment analysis. Researchers have been actively exploring different prompt engineering strategies, such as Chain of Thought (CoT), Zero-CoT, and In-context learning. However, an unresolved problem arises from the fact that current approaches lack a solid theoretical foundation for determining optimal prompts. To address this issue in prompt engineering, we propose a new and effective approach called Prompt Space. Our methodology utilizes text embeddings to obtain basis vectors by matrix decomposition, and then constructs a space for representing all prompts. Prompt Space significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks. Notably, without the help of the CoT method and the prompt "Let's think step by step", Prompt Space shows superior performance over the few-shot method. Overall, our approach provides a robust and fundamental theoretical framework for selecting simple and effective prompts. This advancement marks a significant step towards improving prompt engineering for a wide variety of applications in LLMs.Comment: Natural language processing (NLP

Quantum coherence and interference of a single moir\'e exciton in nano-fabricated twisted semiconductor heterobilayers

Moir\'e potential acts as periodic quantum confinement for optically generated exciton, generating spatially ordered zero-dimensional quantum system. However, broad emission spectrum arising from inhomogeneity among moir\'e potential hinders the exploration of the intrinsic properties of moir\'e exciton. In this study, we have demonstrated a new method to realize the optical observation of quantum coherence and interference of a single moir\'e exciton in twisted semiconducting heterobilayer beyond the diffraction limit of light. A significant single and sharp photoluminescence peak from a single moir\'e exciton has been demonstrated after nano-fabrication. We present the longer duration of quantum coherence of a single moir\'e exciton, which reaches beyond 10 ps and the accelerated decoherence process with elevating temperature and excitation power density. Moreover, the quantum interference has revealed the coupling between moir\'e excitons in different moir\'e potential minima. The observed quantum coherence and interference of moir\'e exciton will facilitate potential application toward quantum technologies based on moir\'e quantum systems.Comment: 42 pages, 4 figure

MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning

Recently, the astonishing performance of large language models (LLMs) in natural language comprehension and generation tasks triggered lots of exploration of using them as central controllers to build agent systems. Multiple studies focus on bridging the LLMs to external tools to extend the application scenarios. However, the current LLMs' perceiving tool-use ability is limited to a single text query, which may result in ambiguity in understanding the users' real intentions. LLMs are expected to eliminate that by perceiving the visual- or auditory-grounded instructions' information. Therefore, in this paper, we propose MLLM-Tool, a system incorporating open-source LLMs and multi-modal encoders so that the learnt LLMs can be conscious of multi-modal input instruction and then select the function-matched tool correctly. To facilitate the evaluation of the model's capability, we collect a dataset featured by consisting of multi-modal input tools from HuggingFace. Another important feature of our dataset is that our dataset also contains multiple potential choices for the same instruction due to the existence of identical functions and synonymous functions, which provides more potential solutions for the same query. The experiments reveal that our MLLM-Tool is capable of recommending appropriate tools for multi-modal instructions. Codes and data are available at https://github.com/MLLM-Tool/MLLM-Tool.Comment: 21 pages, 9 figures, 10 table

Recent Advances in Soft Biological Tissue Manipulating Technologies

Biological soft tissues manipulation, including conventional (mechanical) and nonconventional (laser, waterjet and ultrasonic) processes, is critically required in most surgical innervations. However, the soft tissues, with their nature of anisotropic and viscoelastic mechanical properties, and high biological and heat sensitivities, are difficult to manipulated. Moreover, the mechanical and thermal induced damage on the surface and surrounding tissue during the surgery can impair the proliferative phase of healing. Thus, understanding the manipulation mechanism and the resulted surface damage is of importance to the community. In recent years, more and more scholars carried out researches on soft biological tissue cutting in order to improve the cutting performance of surgical instruments and reduce the surgery induced tissue damage. However, there is a lack of compressive review that focused on the recent advances in soft biological tissue manipulating technologies. Hence, this review paper attempts to provide an informative literature survey of the state-of-the-art of soft tissue manipulation processes in surgery. This is achieved by exploring and recollecting the different soft tissue manipulation techniques currently used, including mechanical, laser, waterjet and ultrasonic cutting and advanced anastomosis and reconstruction processes, with highlighting their governing removal mechanisms as well as the surface and subsurface damages

Single-cell analysis reveals melanocytes may promote inflammation in chronic wounds through cathepsin G

During acute wound (AW) healing, a series of proper communications will occur between different epidermal cells at precise temporal stages to restore the integrity of the skin. However, it is still unclear what variation happened in epidermal cell interaction in the chronic wound environment. To provide new insights into chronic wound healing, we reconstructed the variations in the epidermal cell-cell communication network that occur in chronic wound healing via single-cell RNA-seq (scRNA-seq) data analysis. We found that the intricate cellular and molecular interactions increased in pressure ulcer (PU) compared to AW, especially the PARs signaling pathways were significantly upregulated. It shows that the PARs signaling pathways’ main source was melanocytes and the CTSG-F2RL1 ligand-receptor pairs were its main contributor. Cathepsin G (CatG or CTSG) is a serine protease mainly with trypsin- and chymotrypsin-like specificity. It is synthesized and secreted by some immune or non-immune cells. Whereas, it has not been reported that melanocytes can synthesize and secrete the CTSG. F2R Like Trypsin Receptor 1 (F2RL1) is a member of proteinase-activated receptors (PARs) that are irreversibly activated by proteolytic cleavage and its stimulation can promote inflammation and inflammatory cell infiltration. In this study, we found that melanocytes increased in pressure ulcers, melanocytes can synthesize and secrete the CTSG and may promote inflammation in chronic wounds through CTSG-F2RL1 pairs, which may be a novel potential target and a therapeutic strategy in the treatment of chronic wounds