Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Enhancement and Feature Purification for Single-stage Joint Entity and Relation Extraction

Joint entity and relation extraction aim to achieve named entity recognition and relation extraction in unstructured text. We use the form of triples (subject, relation, object) to describe entity and relation. Joint entity and relation extraction play an important role in knowledge graph construction, question and answering, data analysis, and other natural language processing domains. Most of the existing works suffered from insufficient interaction between entity features and relation features due to the extraction order. Moreover, there is a prevalence of heterogeneous representations of entities and relations. They not only increase the amount of model design and the complexity of training, but also lead to the problem of exposure bias due to the extraction order. Therefore, in this paper, we propose KFRel, where we enhance the entity and relation representations by encoding text and relation. Then, the features are fused to enhance the entity and relation representations, and adopt a feature purification module, where the features are enhanced by a feature purification module that removes feature information irrelevant to the joint entity and relation extraction while retaining feature information of relevance. In addition, we adopt a single-stage joint entity and relation extraction module to address the issue of overlapping triples. This module aims to help improve the effectiveness of joint entity and relation extraction. Comprehensive experiments are conducted on two widely-used datasets, and the experimental results demonstrate that the proposed method is effective and outperforms the state-of-the-art baselines.
Is questionnaire information valid in the study of a chronic disease such as diabetes? The Nord-Trøndelag diabetes study.

STUDY OBJECTIVE--The aim was to validate information about diabetes mellitus collected by questionnaire in a large epidemiological survey. DESIGN--Questions on diabetes diagnosis, medical treatment for diabetes, diabetes duration, and hypertension treatment were selected from the Nord-Trøndelag health survey questionnaires. One of the municipalities was selected for the validation study. SETTING--The health survey 1984-86 addressed all inhabitants > or = 20 years of age in Nord-Trøndelag county, Norway; 76,885 (90.3%) of the eligible population participated in answering the question on diabetes. PARTICIPANTS--All inhabitants in the municipality answering "yes" to the question on diabetes (n = 169) and the persons with the same sex born closest before and after each diabetic patient and answering "no" to the diabetes question (n = 338) were included. MEASUREMENTS AND MAIN RESULTS--A very thorough search was made in the medical files of the general practitioners in the municipality for corresponding information. Compared to the files, diabetes was verified in 163 out of the 169. The commonest cause of discrepancy was renal glycosuria. One out of the 338 registered non-diabetic persons was found to have diabetes. Diabetic patients tended to overestimate diabetes duration significantly. Insulin treatment was verified in 19/20 (95%) and treatment with oral hypoglycaemic agents in all 44 with an affirmative questionnaire answer. A negative answer on insulin and oral hypoglycaemic agents was verified in 100% and 99% respectively. CONCLUSIONS--The concordance was considerably higher than in a comparable Norwegian study performed 10 years earlier. Patient administered questionnaires may be a very reliable source of information for epidemiological purposes in a well defined chronic disease such as diabetes mellitus.
Comparative Analysis of Boost, Cuk and Zeta PFC Converters in Continuous Current Mode

This paper presents a comparative analysis of boost, Cuk and Zeta converters in power factor correction (PFC) mode of working in continuous current operation. The various parameters of power quality performance and power factor correction for different topologies of converters at source side have been studied. All three converters have been simulated in closed loop control for the heating loads with current controlled heating.
Ion channels formed in planar lipid bilayers by the dipteran-specific Cry4B Bacillus thuringiensis toxin and its α1–α5 fragment

Trypsin activation of Cry4B, a 130-kDa Bacillus thuringiensis (Bt) protein, produces a 65-kDa toxin active against mosquito larvae. The active toxin is made of two protease-resistant products of ca. 45 kDa and ca. 20 kDa. The cloned 21-kDa fragment consisting of the N-terminal region of the toxin was previously shown to be capable of permeabilizing liposomes. The present study was designed to test the following hypotheses: (1) Cry4B, like several other Bt toxins, is a channel-forming toxin in planar lipid bilayers; and (2) the 21-kDa N-terminal region, which maps for the first five helices (α1–α5) of domain 1 in other Cry toxins, and which putatively shares a similar tri-dimensional structure, is sufficient to account for the ion channel activity of the whole toxin. Using circular dichroism spectroscopy and planar lipid bilayers, we showed that the 21-kDa polypeptide existed as an α-helical structure and that both Cry4B and its α1–α5 fragment formed ion channels of 248±44 pS and 207±23 pS, respectively. The channels were cation-selective with a potassium-to-chloride permeability ratio of 6.7 for Cry4B and 4.5 for its fragment. However, contrary to the full-length toxin, the α1–α5 region formed channels at low dose; they tended to remain locked in their open state and displayed flickering activity bouts. Thus, like the full-length toxin, the α1–α5 region is a functional channel former. A pH-dependent, yet undefined region of the toxin may be involved in regulating the channel properties.
Open Set Intrusion Event Recognition Using Anchor Point Learning for Distributed Optical Fiber System

Distributed optical fiber sensing systems are widely used in the field of security monitoring for their electromagnetic interference resistance and high sensitivity. Due to the complexity of the environment, unknown signals often arise during the monitoring process to interfere with the recognition model. However, the vast majority of existing research focuses on closed-set identification, which ignores the fact of open-set environments, and thus fails to effectively meet the needs of security monitoring in real scenarios. In addition, existing open-set identification methods as well as the traditional Softmax method are unable to control the orientation of convergence of the samples in the output space during the training stage, which increases the risk of open-set space. To tackle this problem, this article proposed a novel deep learning model based on convolutional neural networks called anchor point learning (APL) for improving classification robustness and reducing the distributional overlap between known and unknown category samples. With APL, different categories will be divided into different regions of the output space, which can be directly determined by the anchor points. Besides, we add an adversarial regularization term to improve the intraclass compactness of the feature representation by forming an adversary with the APL. In our experiments, our model is applied to the task of recognizing pavement intrusion signals. We compare it with the traditional Softmax method and the existing state-of-the-art open-set identification methods respectively. The results confirm that the proposed method not only outperforms the traditional methods in terms of classification performance but also can effectively achieve the recognition of intrusion events under low false alarm rate conditions in the open set environment.
Deep Frame-Point Sequence Consistent Network for Handwriting Trajectory Recovery

Intelligent Cyber-Physical Systems relies heavily on data for real-time monitoring, analysis, and control of physical systems. By converting offline handwriting into online handwriting, it provides ICPS with more diverse and abundant input data, enriching the variety and quantity of available information. Based on the acquisition approach, there are two kinds of handwriting data: online and offline data. Generally, online data which contains pen trajectory of static character, has more advantages than offline data in terms of character recognition and analysis. Due to limited means of acquiring online data, inferring from offline data is an attractive approach. In this paper we introduce a novel framework to recover handwriting trajectory from single static character image. The trajectory can be represented by two types of sequence: points sequence and frame sequence. Therefore, we design two streams: points sequence prediction stream and frame sequence prediction stream, based on the encoder-decoder structure. We combine the two streams by a novel sequence consistent module to synchronize the training process. With the two streams and their complementary advantages, our methods can predict trajectory with both high spatial and temporal accuracy. Extensive experiments demonstrate the effectiveness of our network through qualitative and quantitative comparison.
A Quantitative Evaluation of Symmetry Detection Algorithms

Symmetry is one of the most important cues for human and machine perception of the chaotic real world. For over three decades now, automatic symmetry detection from images/patterns has been a standing topic in computer vision. We observe a surge of new symmetry detection algorithms that go beyond simple bilateral symmetry detection. This paper presents a systematic, quantitative evaluation of rotation, reflection and translation symmetry detection algorithms published within the past 1.5 years. We provide a set of carefully chosen synthetic and real images that contain both single and multiple symmetries and a diverse range of computational challenges. We also provide their associated, hand-labeled ground truth. We propose a well-defined quantitative evaluation scheme for an effective validation and comparison of different symmetry detection algorithms. Our results indicate that even after several decades of effort, symmetry detection from real-world images remains a challenging, unsolved problem in computer vision.
Efficiently Running SpMV on Multi-Core DSPs for Block Sparse Matrix

Sparse Matrix-Vector Multiplication (SpMV) is a fundamental operation in sparse computations. Although many techniques have been developed to speed up SpMV, optimizing this process on low-power multicore digital signal processors (DSPs) has often been neglected. This paper presents the FT-M7032, a cutting-edge CPU-DSP hybrid multi-core processor. We assess the data transfer efficiency among various units to identify performance constraints of SpMV on multicore DSPs. Based on our evaluation, we develop a method for block sparse matrices, namely SpMV_BLOCK, which can break the bandwidth bottleneck of SpMV and achieve significant performance gains. We then propose a load-balancing strategy for each thread by using binary search and devise a pipeline that overlaps data transfers and computations to improve SpMV performance. To measure our method’s effectiveness, we compared its performance against a baseline on the FT-M7032’s general-purpose CPU cores. Our experiments show that our approach delivers a notable 5.80 × speedup over the baseline.
Comprehensive IoT-Based Monitoring System for Improving Worker's Health and Safety at the High-Risk Working Environment

For all engineering endeavours, workplace safety comes as a top priority. Even with careful planning, accidents and fatalities still occur due to little mitigation in place or personnel on duty cannot be continuously monitored. This research project uses wearable devices, which are enabled by IoT technologies, to monitor physiological and environmental variables in real-time mainly applied to the Hazardous Area Zone 2, which is unlikely to cause an explosion in the existence of dangerous gases. SpO2 and heart rate measurements are categorized under the physiological monitoring approach, whereas AQI, combustible gas, and temperature measurements are categorized under the environmental monitoring approach. To reflect the different levels of health and safety problems, the design is adopted with the safety measurements, and the conditions are then displayed on the Blynk application dashboards. The analysis's findings demonstrate the effectiveness of both physiological and environmental monitoring because erroneous readings will activate warning LEDs on the dashboards and convey an alert message to the line manager. However, this research project still could be extended and upgraded by implanting machine learning, displaying the battery level of the devices and monitoring more workers at the same time in the high-risk environment for the massive production lines.
3-D Inversion of Crustal Structure in Frequency of Gravity

This work introduces a geologically based approach for calculation the gravity anomaly of 3-dimensional(3-D) earth model. The geologically driven earth model is decomposed into a 3-D grid and the gravity signature for different depth is calculated respectively. The results of gravity inversion are well, but when the boundary is near to 200Km, the results are very different. And as the depth increasing, the results are very similar. That is to say, the absolute depth is not sensitive for gravity inversion. In terms of the results of inversion, increasing the number of points and computing large-scale data can reduce the effect of boundary.
A WTLS-based rational function model for orthorectification of remote-sensing imagery

ABSTRACT The rational function model (RFM) is widely applied to orthorectification of aerial and satellite imagery. This article proposes a new method named Ortho-WTLS to solve the RFM in remote-sensing imagery orthorectification. Based on a weighted total least squares (WTLS) estimator, the proposed method allows one to handle coordinates of ground control points (GCPs) that contain errors and are of unequal accuracies. This situation occurs, e.g. if GCPs are automatically selected. In the proposed model, first, the relationship of two linearization methods for an RFM with errors contained in GCPs is investigated and results in a hybrid linearization. Next, based on WTLS, RFM coefficients are estimated with an iterative computation function. Finally, the performance of the Ortho-WTLS method thus obtained is investigated using simulated images and remotely sensed images by collecting GCPs with varying errors. Experimental results show that the Ortho-WTLS method achieves a more robust estimation of model parameters and a higher orthorectification accuracy when compared with standard LS-based RFM estimation. We conclude that the quality of GCPs has a large impact on the accuracy and that an increasing number of low-precision GCPs may lead to a decrease in orthorectification quality.
Hierarchical Clustering of Message Flows in a Multicast Data Dissemination System

A large-scale data dissemination application is characterized by a large number of information flows and information consumers. Consumers are interested in different, yet overlapping, subsets of the flows. Multicast is used to deliver subsets of the flows to subsets of the consumers. Since multicast groups are a limited resource, each consumer must filter out a large number of unneeded flows. We alleviate the end-node filtering load by using hierarchical clustering of flows to transport-layer sessions, and clustering of sessions to network-layer multicast groups. This scheme allows for hierarchical filtering of flows at the receivers. We formulate a cost function that models and emphasizes the filtering process, and propose algorithms for the solution of the hierarchical mapping problem. Performance evaluation indicates a significant reduction of endnode filtering cost compared to a non-hierarchic approach.
Ergodic Capacity Analysis of Downlink NOMA Systems Over $\kappa-\mu$ Shadowed Fading Channels

In this research paper, we analyze the performance of two downlink NOMA systems with imperfect successive interference cancellation (SIC) over $\kappa-\mu$ shadowed fading channels with the $\kappa-\mu$ distribution. Our focus is on evaluating the ergodic capacity (EC) of a downlink NOMA system while taking into account imperfect SIC at the near user. First, we obtain the statistical characterization of the instantaneous signal-to-noise plus interference ratios (SINRs) for both users by applying the Gamma approximation and considering the imperfect SIC at the near user from the base station of the NOMA system over $\kappa-\mu$ fading channels. Using the derived expressions of the distributions of these SINRs, we derive the exact closed-form expressions of the ECs of both users. We also obtain the ergodic sum capacity (ESC) of the NOMA system with imperfect SIC at the near user. We investigate the impact of different channel conditions of the $\kappa- \mu$ fading channels on EC performance of the system. We also analyze the impact of imperfect SIC on the ergodic capacity performance of the system and observe that EC performance improves by decreasing the level of SIC error. Finally, we validate our analysis through numerical and simulation results.
Integrated Optic Disc and Cup Segmentation with Deep Learning

Glaucoma is a widespread ocular disorder leading to irreversible loss of vision. Therefore, there is a pressing need for cost-effective screening, such that preventive measures can be taken. This can be achieved with an accurate segmentation of the optic disc and cup from retinal images to obtain the cup-to-disc ratio. We describe a comprehensive solution based on applying convolutional neural networks to feature exaggerated inputs emphasizing disc pallor without blood vessel obstruction, as well as the degree of vessel kinking. The produced raw probability maps then undergo a robust refinement procedure that takes into account prior knowledge about retinal structures. Analysis of these probability maps further allows us to obtain a confidence estimate on the correctness of the segmentation, which can be used to direct the most challenging cases for manual inspection. Tests on two large real-world databases, including the publicly-available MESSIDOR collection, demonstrate the effectiveness of our proposed system.
Harnessing the Power of Transformer Learning with Long-Term Memory for Renewable Energy Forecasting

In this paper, we investigate the problem of time-series forecasting in smart grid systems. The proposed solution is based on incorporating the recently developed Transformer learning into time-series data to forecast renewable energy generation (i.e., wind and solar power). In particular, we demonstrate how the Transformer learning method can significantly improve existing forecasting methods, such as long-short term memory (LSTM) and recurrent neural network (RNN). The enhancement is achieved by leveraging contextual information in smart grid data, namely the seasonality and trend as transformer input instead of input embedding. Transformer learning can effectively capture the trend while avoiding computational complexity. Transformers also offer an effective way of overcoming LSTM's memory challenges in the forecasting of smart grid time-series. Therefore, we implement the Transformer learning framework and compare its performance with LSTM and RNN using real wind and solar generation datasets. The simulation results show that the average daily prediction error can be decreased by 7 percentage for wind power and 5 percentage for solar power prediction.
High-resolution extracellular stimulation of dispersed hippocampal culture with high-density CMOS multielectrode array based on non-Faradaic electrodes

We introduce a method to electrically stimulate individual neurons at single-cell resolution in arbitrary spatiotemporal patterns with precise control over stimulation thresholds. By exploiting a custom microelectronic chip, up to 65 000 non-Faradaic electrodes can be uniquely addressed with electrode density exceeding 6500 electrodes mm−2. We demonstrate extracellular stimulation of dispersed primary hippocampal neuronal cultures using the chip at single-cell resolution.
Toward Reproducible Benchmarking of PGAS and MPI Communication Schemes

With the forthcoming age of exascale computing, the efficient support of different programming models has become a crucial performance factor for high-performance computing systems. When adopting novel programming paradigms, the performance assessment through reproducible and comparable benchmarks plays an essential role in both the effective use of the heterogeneous system hardware and the application performance tuning. Alternatives to MPI such as the partitioned global address space (PGAS) model have become increasingly popular. One such PGAS API is the Global Address Space Programming Interface (GASPI). This paper introduces the GASPI Benchmark Suite (GBS), which combines a comprehensive set of microbenchmarks with application kernels. The microbenchmarks target common GASPI communication patterns, including one-sided, collective, passive, and global atomics, while the application kernels stress communication schemes commonly found in real HPC applications. The effectiveness of GBS is demonstrated by evaluating the GASPI communication performance for the networking communication standard InfiniBand.
Expressing human SHOX in Shox2SHOX KI/KI mice leads to congenital osteoarthritis-like disease of the temporomandibular joint in postnatal mice

The temporomandibular joint (TMJ), a unique synovial joint whose development differs from that of other synovial joints, develops from two distinct mesenchymal condensations that grow toward each other and ossify through different mechanisms. The short stature homeobox 2 (Shox2) gene serves an important role in TMJ development and previous studies have demonstrated that Shox2SHOX KI/KI mice display a TMJ defective phenotype, congenital dysplasia and premature eroding of the articular disc, which is clinically defined as a TMJ disorder. In the present study, Shox2SHOX KI/KI mouse models were used to investigate the mechanisms of congenital osteoarthritis (OA)-like disease during postnatal TMJ growth. Shox2SHOX KI/KI mice were observed to develop a severe muscle wasting syndrome from day 7 postnatal. Histological examination indicated that the condyle and glenoid fossa of Shox2SHOX KI/KI mice was reduced in size in the second week after birth. The condyles of Shox2SHOX KI/KI mice exhibited reduced expression levels of collagen type II and Indian hedgehog, and increased expression of collagen type I. A marked increase in matrix metalloproteinase 9 (MMP9) and MMP13 in the condyles was also observed. These cellular and molecular defects may contribute to the observed (OA)-like phenotype of Shox2SHOX KI/KI mouse TMJs.
Distributed RIS-aided MU-MISO for Rank Improved Channel Conditions

In this paper, we investigate the performance of a distributed Reconfigurable Intelligent Surface (RIS) system as a solution to rank deficient RIS-aided Multi-User Multiple Input Single Output (MU-MISO) channels where one RIS is used to serve several users subject to obstructed direct Base Station (BS) links. We propose placing intermediate RISs between the BS and this RIS, making it possible to obtain new propagation paths. The resulting increase in rank ensures reliable coverage while serving multiple users, leveraging the superior spatial multiplexing gain obtained. We adopt Regularized Zero Forcing (RZF) precoding at the BS in the downlink. The phase shift design of the RISs is done through a projected gradient ascent algorithm maximizing the deterministic equivalent of the sum rate expression, providing a low-overhead statistical channel state information (CSI) scheme. Monte Carlo simulations and analytical deterministic equivalent results show that despite the higher double reflection path loss, distributed RISs systems with increased rank outperform the one RIS rank-deficient system.