Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation

We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation module. The detection module enables a region of interest to come to attention and produces a set of object region candidates which are further used as an attention model. Rather than dealing with the entire volume, the segmentation module distills the information from the potential region. This scheme is an efficient solution for volumetric data as it reduces the influence of the surrounding noise which is especially important for medical data with low signal-to-noise ratio. Experimental results on 3D ultrasound data of the femoral head shows superiority of the proposed method when compared with a standard fully convolutional network like the U-Net

End-to-end detection-segmentation network with ROI convolution

We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object localization first, which is then used as a cue to guide the training of the segmentation network. We test the proposed method on a segmentation task of small objects on a clinical dataset of ultrasound images. We show that by jointly learning for detection and segmentation, the proposed network is able to improve the segmentation accuracy compared to only learning for segmentation. Code is publicly available at https://github.com/vincentzhang/roi-fcn.Comment: ISBI 201

Near 6 GHz Sezawa Mode Surface Acoustic Wave Resonators using AlScN on SiC

Surface Acoustic Wave (SAW) devices featuring Aluminum Scandium Nitride (AlScN) on a 4H-Silicon Carbide (SiC) substrate, offer a unique blend of high sound velocity, low thermal resistance, substantial piezoelectric response, simplified fabrication, as well as suitability for high-temperature and harsh environment operation. This study presents high-frequency SAW resonators employing AlScN thin films on SiC substrates, utilizing the second SAW mode (referred to as the Sezawa mode). The resonators achieve remarkable performance, boasting a K2 value of 5.5% and a maximum Q-factor (Qmax) of 1048 at 4.7 GHz, outperforming previous benchmarks. Additionally, a SAW resonator with a 960 nm wavelength attains 5.9 GHz frequency with record K2 (4.0%) and Qmax (887). Our study underscores the potential of the AlScN on SiC platform for advanced radio-frequency applications.Comment: 19 pages, 5 figures in main text and 3 figures in supplementar

NQE: N-ary Query Embedding for Complex Query Answering over Hyper-relational Knowledge Graphs

Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary relational facts and pay less attention to n-ary facts (n>=2) containing more than two entities, which are more prevalent in the real world. Moreover, previous CQA methods can only make predictions for a few given types of queries and cannot be flexibly extended to more complex logical queries, which significantly limits their applications. To overcome these challenges, in this work, we propose a novel N-ary Query Embedding (NQE) model for CQA over hyper-relational knowledge graphs (HKGs), which include massive n-ary facts. The NQE utilizes a dual-heterogeneous Transformer encoder and fuzzy logic theory to satisfy all n-ary FOL queries, including existential quantifiers, conjunction, disjunction, and negation. We also propose a parallel processing algorithm that can train or predict arbitrary n-ary FOL queries in a single batch, regardless of the kind of each query, with good flexibility and extensibility. In addition, we generate a new CQA dataset WD50K-NFOL, including diverse n-ary FOL queries over WD50K. Experimental results on WD50K-NFOL and other standard CQA datasets show that NQE is the state-of-the-art CQA method over HKGs with good generalization capability. Our code and dataset are publicly available.Comment: Accepted by the 37th AAAI Conference on Artificial Intelligence (AAAI-2023

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models

Knowledge Base Question Answering (KBQA) aims to derive answers to natural language questions over large-scale knowledge bases (KBs), which are generally divided into two research components: knowledge retrieval and semantic parsing. However, three core challenges remain, including inefficient knowledge retrieval, retrieval errors adversely affecting semantic parsing, and the complexity of previous KBQA methods. In the era of large language models (LLMs), we introduce ChatKBQA, a novel generate-then-retrieve KBQA framework built on fine-tuning open-source LLMs such as Llama-2, ChatGLM2 and Baichuan2. ChatKBQA proposes generating the logical form with fine-tuned LLMs first, then retrieving and replacing entities and relations through an unsupervised retrieval method, which improves both generation and retrieval more straightforwardly. Experimental results reveal that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and ComplexWebQuestions (CWQ). This work also provides a new paradigm for combining LLMs with knowledge graphs (KGs) for interpretable and knowledge-required question answering. Our code is publicly available.Comment: Preprin

Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs still significantly relies on manual labor, and n-ary relation extraction still remains at a course-grained level, which is always in a single schema and fixed arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. Experimental results demonstrate that Text2NKG outperforms the previous state-of-the-art model by nearly 20\% points in the $F_1$ scores on the fine-grained n-ary relation extraction benchmark in the hyper-relational schema. Our code and datasets are publicly available.Comment: Preprin

Two-stage robust planning method for distribution network energy storage based on load forecasting

A two-stage robust planning method for energy storage in distribution networks based on load prediction is proposed to address the uncertainty of active load in energy storage planning. First, considering the uncertainty of active load, a short-term load forecasting model combining the mutual information method and BiLSTM is established based on k-means++ clustering. Second, based on the results of load forecasting, a comprehensive norm-constrained uncertainty set is constructed, and a two-stage robust model for distribution network energy storage planning is established. The first stage aims to minimize the annual investment cost of the energy storage system, while the second stage aims to minimize the daily operating cost of the distribution network. At the same time, a second-order cone relaxation transformation model with non-convex constraints is introduced to ultimately achieve the optimal economy of the distribution network in energy storage planning. Finally, the effectiveness of the proposed method and model is validated on the IEEE 33-node distribution network model using the MATLAB platform

Non-linear associations of atherogenic index of plasma with prediabetes and type 2 diabetes mellitus among Chinese adults aged 45 years and above: a cross-sectional study from CHARLS

BackgroundDyslipidemia is strongly associated with the development of prediabetes and type 2 diabetes mellitus (T2DM). The atherogenic index of plasma (AIP), as a comprehensive index for assessing lipid metabolism, has received extensive attention from researchers in recent years. However, there are relatively few studies exploring the relationships between AIP and the risk of prediabetes and T2DM in the Chinese population. This study focuses on exploring the relationships of AIP with the risk of prediabetes and T2DM in the Chinese population.MethodsWe conducted an analysis of the public data from the China Health and Retirement Longitudinal Study (CHARLS), involving a total of 12,060 participants aged 45 years and above in China. The study explored the relationships of AIP with prediabetes and T2DM risk through multivariate logistic regression, subgroup analysis, smooth curve fitting, and threshold effect analysis.ResultsAfter adjusting for potential confounding factors, we observed positive associations between AIP and the risk of prediabetes [odds ratio (OR) = 1.75, 95% confidence interval (CI): 1.49–2.06] and T2DM (OR = 2.91, 95% CI: 2.38–3.57). Participants with higher AIP levels demonstrated a significantly elevated risk of prediabetes (OR = 1.52, 95% CI: 1.33–1.74) and T2DM (OR = 2.28, 95% CI: 1.92–2.71) compared to those with lower AIP levels. AIP showed consistent correlations with prediabetes and T2DM risk in different subgroups. The results showed the non-linear relationships between AIP and risk of prediabetes and T2DM, with inflection points at 0.29 and −0.04, respectively. When AIP > 0.29, there was a positive association between AIP and the risk of prediabetes (OR = 2.24, 95% CI: 1.67–3.00, p < 0.0001). Similarly, when AIP > −0.04, AIP was positively associated with the risk of T2DM (OR = 3.33, 95% CI: 2.67–4.16, p < 0.0001).ConclusionsThis study demonstrated non-linear positive associations of AIP with the risk of prediabetes and T2DM among participants ≥ 45 years of age in China

Association between complete blood count-derived inflammatory markers and the risk of frailty and mortality in middle-aged and older adults

ObjectiveThis study aimed to evaluate the association between six complete blood count (CBC)-derived inflammatory markers [neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammatory index (SII), systemic inflammatory response index (SIRI), and pan-immune inflammation value (PIV)] and the risk of frailty and mortality.MethodsData were obtained from the National Health and Nutrition Examination Survey (NHANES) 1999–2018. Mortality was identified using the National Death Index until December 31, 2019. Multiple logistic regression analysis was conducted to evaluate the association between six CBC-derived inflammatory markers and frailty. The Cox regression model assessed the association between six CBC-derived inflammatory markers and mortality in frail populations. Restricted cubic spline (RCS) was used to visualize the association of the six CBC-derived inflammatory markers with mortality risk. The predictive value of CBC-derived inflammatory markers for mortality was further assessed using a random survival forest (RSF) approach.ResultsThis study analyzed data from a total of 16,705 middle-aged and older participants. Among them, 6,503 participants were frail, with a mortality rate of 41.47%. Multiple logistic regression analysis showed that NLR, MLR, PLR, SII, SIRI, and PIV were positively associated with frailty risk. The Cox regression model revealed that participants in the highest quartile had a significantly increased risk of death compared to those in the lowest quartile: NLR (HR = 1.73, 95% CI:1.54, 1.94), MLR (HR = 1.71, 95% CI:1.51, 1.93), PLR (HR = 1.28, 95%CI: 1.15, 1.43), SII (HR = 1.50, 95%CI:1.34, 1.68), SIRI (HR = 1.88, CI 95%:1.67, 2.12), PIV (HR = 1.55, 95%CI:1.38, 1.73). Random survival forest (RSF) analyses demonstrated that MLR had the highest predictive value for mortality risk middle-aged and older adult frail participants.ConclusionThe results suggest that CBC-derived inflammatory markers are associated with a higher risk of frailty as well as mortality in the middle and old-aged population of the United States

Exploring the Potential of Integrated Optical Sensing and Communication (IOSAC) Systems with Si Waveguides for Future Networks

Advanced silicon photonic technologies enable integrated optical sensing and communication (IOSAC) in real time for the emerging application requirements of simultaneous sensing and communication for next-generation networks. Here, we propose and demonstrate the IOSAC system on the silicon nitride (SiN) photonics platform. The IOSAC devices based on microring resonators are capable of monitoring the variation of analytes, transmitting the information to the terminal along with the modulated optical signal in real-time, and replacing bulk optics in high-precision and high-speed applications. By directly integrating SiN ring resonators with optical communication networks, simultaneous sensing and optical communication are demonstrated by an optical signal transmission experimental system using especially filtering amplified spontaneous emission spectra. The refractive index (RI) sensing ring with a sensitivity of 172 nm/RIU, a figure of merit (FOM) of 1220, and a detection limit (DL) of 8.2*10-6 RIU is demonstrated. Simultaneously, the 1.25 Gbps optical on-off-keying (OOK) signal is transmitted at the concentration of different NaCl solutions, which indicates the bit-error-ratio (BER) decreases with the increase in concentration. The novel IOSAC technology shows the potential to realize high-performance simultaneous biosensing and communication in real time and further accelerate the development of IoT and 6G networks.Comment: 11pages, 5 figutre