Miracle-dz/UniFAR-FTUs huggingface dataset:
Chinese classical poetry generation LoRA for T5

Transformer-base language models grow much larger than early. Although large model obtain state-of-the-art performance on various NLP tasks continuously, finetuning billions of parameters cost much time and memories. Inorder to use T5 model for Chinese classical poetry generation without above difficulties, we use LoRA(Low-RankAdaption) to achieve this goal. It shows that we also can use powerful large LMs to solve some less known tasks, andnoneed to change the original parameters of large models.
Virtual network embedding based on space-time resource tree for space-ground integrated network

Space-Ground Integrated Network (SGIN) is a multi-domain integrated network with a large coverage area, which can better meet the ubiquitous communication needs in the network and provide users with better services. It has become the main development direction of 6G network. The different networks integrated in SGIN are heterogeneous, which is specifically reflected in the fact that the periodic movement of satellite networks brings time-varying nature to resources, and the expansion of network scale also brings spatial attributes to resources. It can be concluded that resources in SGIN have multi-dimensional. When performing virtual network embedding(VNE) in SGIN, the existing embedding algorithms often do not consider the multi-dimensionality of resources, which may lead to problems such as spatially dispersed embedding results and inability to adapt to network dynamic changes. To address this problem, this paper uses the space-time resource tree(S-TRT) model to represent the multi-dimensional resources in SGIN, reflecting the performance of resources in four dimensions: time, space, type, and quantity. On the basis of this model, combined with multiple dimensions of resources and the spatial distribution of embedded nodes, the ranking vector of virtual nodes is established, and the dimensionality reduction sorting of each virtual node is carried out by multi-dimensional scaling method. Afterwards, the node embedding is completed by dynamically sorting the physical nodes to improve the spatial concentration of the embedding results and better adapt to the dynamic changes of the underlying network. Finally, we conducted a simulation experiment on the algorithm, and the results show that the algorithm has good performance in the request acceptance rate and long-term revenue-to-cost ratio.
Research and testing on enterprise NVMe SSD reliability technology

Garbage collection mechanism may cause system delay, Trim is a specific command for SSD to minimise the data volume of garbage collection, Trim function enhances SSD performance and extend SSD life. However Trim operation has impact on ordinary read-write IO delay, lead to business system delay and even system error. As to this issue, study on working schema of enterprise SSD trim, establish technical requirements for SSD testing workload model and QoS. Build up reliable testing plan on workload model, and adopt FIO tool to perform testing. The result shows such plan can conduct quantitative evaluation on SSD reliability scientifically and reasonably, assists the enterprises to choose credible SSD.
Moving vehicle detection based on RAFT optical flow and YOLOV4 in dynamic background

A new moving target detection algorithm is proposed to solve the problems of poor target detection integrity and interference by outliers in current moving target detection methods based on dynamic background. The algorithm combines the RAFT optical flow method with the YOLOV4 detection method. First, the offset between the matching pixels is obtained through the RAFT optical flow method. The epipolar constraint relationship between the matching pixels is solved based on the offset. The constraint relationship is used to solve the candidate frame of the foreground target edge, Then the method of calculating the coincidence degree between the candidate frame of the foreground target edge and the candidate frame of the moving target detected by the YOLOV4 network is used to preliminarily extract the moving target area, which improves the accuracy of the moving target detection. Finally, the final moving target region is obtained after post-processing the initially extracted internal area of the moving target. The experimental results show that compared with the algorithm without the YOLOV4 network, the precision accuracy and F-measure of the algorithm on kitti's public dataset are improved, and more accurate segmentation results can be obtained.
Optimization of dovetail magnetic isolation device for permanent magnet torque motor based on finite element method

To further improve the torque performance of permanent magnet torque motors and enhance the magnetic stability of surface-mounted permanent magnets, a new dovetail magnetic isolation device (DMID) is discussed. First, the initial model of the motor is constructed and the structure of the DMID is parametrically designed. Second, a mathematical model of the average torque and torque ripple considering the DMID is developed based on the equivalent magnetic circuit model of the motor and the principle of electromagnetic torque generation. Then, with the help of the ANSYS Maxwell finite element simulation tool, the average torque is used as the optimization target, and the variation pattern of the slot width, inner circle radius, and position of the DMID are simulated and discussed. Finally, the average torque and torque ripple of the optimized motor are compared with those of the initial motor, and the results show that the average torque is increased by 37.2 Nꞏm and the torque ripple is weakened by 0.04% when the optimal parameters of the DMID are selected.
Velocity estimation for refinement of angle measurement and detection in mDT-STAP

This paper presents a velocity estimation method to addresses the steering vector mismatch of angle measurement and adaptive coherence estimation (ACE) in mDT space-time adaptive processing (STAP). The proposed velocity estimation method is dual to the adaptive monopulse in space-domain, and it can be conducted in a simple process framework. Numeric results show that the angle estimation errors are well reduced and detection aided with ACE are improved.
Research on a data fusion scheme for a depth camera and LIDAR of mobile robot based on depth completion

In the process of robot target recognition, depth cameras and LIDAR are often used as extended sensors. The data collected by both have their own advantages and disadvantages, LIDAR can obtain accurate position information but not the morphology information of the object, while depth cameras can obtain abundant image information but cannot obtain accurate three-dimensional position information of objects. To better achieve the robot's recognition of specific targets, we fused the two information sources and obtained point cloud data with RGB information. To solve the problem of inconsistency in the coordinate system of the sensor system, we identify a specific calibration plate and propose an ellipse identification method for fault point clouds. To solve the problem of LIDAR point clouds sparsity, we compare the improved completion algorithm based on computer graphics and the completion algorithm based on deep learning, we figure out that the method of computer graphics meets our expectations better based on the fact that it have a larger number of point clouds. Finally, The accuracy of the method based on computer graphics is measured, and the errors of length and width are only 0.003% and 3.64%, which proves that the proposed method can meet the fusion accuracy in small indoor environment.
Research on fault diagnosis and health monitoring of wind power equipment

In recent years, China's wind power has developed rapidly and a large number of wind turbines have been put into operation. Wind turbine collapse tower accidents occur from time to time. In this paper, through a wind turbine collapsing tower accident, the causes of the collapsing tower accident are analyzed from the perspectives of unit design, operation and maintenance management, and quality control by using simulation, performance test, organization analysis and other test methods. The main cause of the inverted tower accident was the overspeed flyer in operation, and the deficiency of the unit design did not eliminate the accident from the source. During operation and maintenance, the irregular operation of personnel is another important cause of the accident. This paper analyzes the possible causes of inverted towers, which helps to reduce the occurrence of such accidents, and makes suggestions to avoid such accidents.
Quality improvement initiative improves the empiric antibiotic prescribing practices in a tertiary care children’s hospital in India

Introduction Infections are a common cause of paediatric morbidity. Antibiotics are vital in treating them. Erratic prescribing practices are an important cause for the development of antibiotic resistance. Our objective was to estimate the effectiveness of educational interventions to improve empirical antibiotic prescribing practices among paediatric trainees. We aimed to improve the compliance to antibiotic protocols and to sustain it over 6 months. Methods It is a time interrupted non-randomised trial conducted in a tertiary hospital in India. Initially, 200 admitted children were selected randomly. Their antibiotic prescriptions, adherence of prescriptions to the then existing antibiotics guidelines, course during hospital stay and the final outcome were noted. The existing antibiotic policy and its use were reviewed. It was then considered essential to prepare a fresh antibiotic policy based on national guidelines, local sensitivity patterns and with inputs from microbiologists. This was distributed to the residents through seminars, posters and cellphone friendly documents. Compliance to the policy was also tracked twice a week. The adherence to guideline was recorded in the subsequent 6 months. Results The adherence of empirical antibiotic prescriptions was 59% before intervention which improved to 72% in the first month, 90% in the second month, 86% and 78% in the third and sixth months, respectively. There was no significant difference in duration of stay and the outcome at discharge in the patients in adherent and non-adherent groups. Conclusion Educational interventions and frequent monitoring improved antibiotic prescribing practices among residents with no negative impact on patient outcomes. Quality improvements need persistent reinforcement and frequent monitoring to be sustainable.
A mobile-based lightweight road target recognition algorithm

The recognition of road targets represents one of the central technological advancements within intelligent transportation systems, aimed at addressing the critical problem of urban congestion. In light of this, the present paper introduces a road target recognition algorithm, MOLO, specifically designed to navigate complex traffic environments, and subsequently deployed to facilitate detection via mobile devices. MOLO comprises two distinct components: a feature extraction network and a feature fusion network. The former of which, builds upon the structure of SSD network, integrates the lightweight receptive field expansion module RFB-L, whilst the latter introduces a pixel and channel information fusion module termed Fusion, thereby enabling the fusion of both shallow and deep features, ultimately enhancing the overall detection efficacy of the model. The model transformation process uses quantization and inverse quantization strategies between fixed-point and floating-point numbers, significantly reducing the number of parameters, all whilst preserving the accuracy of the detection performance on mobile devices. Experiments show that MOLO, when deployed to mobile, achieves an inference speed of 23.66ms and with 98.96% detection accuracy on the homemade road dataset Hohhot_city, and also shows superior performance in actual road detection.
Study limitations of DoS attackers due to computer resources

The advancement of computer technology has facilitated the provision of a wide range of services to customers through computer networks. However, the security of these networks is constantly threatened by malicious actors who employ various means to launch attacks. Denial-of-Service (DoS) attacks are a common type of network attack that poses a significant risk to many computer information systems. In this research paper, we aim to investigate how the efficiency of DoS attackers is affected when they have limited computer resources. The findings of our experiments confirm that DoS attackers face challenges in executing efficient attacks when their computers lack sufficient CPU resources. By understanding this limitation, it becomes possible for the targeted entities to choose appropriate defense strategies when faced with such attacks.
A Motivational Account of Convergence in Emotion Expressions Within Groups: The Emotional Conformity Framework

Although convergence in emotion expressions within small groups is well documented, the motives that explain why members converge are rarely explicated. We approach expressive convergence from a conformity perspective and introduce the Emotional Conformity Framework, in which we posit that members match their groupmates’ emotion expressions because they are motivated to gain an accurate understanding of reality (informational conformity motive) or to form and maintain social relationships (normative conformity motive). These motives determine members’ standards for correctness, social responses, and plausible convergence mechanisms, while members’ personalities and situational properties shape the relative strength of the two motives. By explicating these motivational underpinnings, the Framework improves our capacity to understand, predict, and regulate expressive convergence and emphasizes its functionality.
A Re-evaluation of Deep Learning Methods for Attributed Graph Clustering

Attributed graph clustering aims to partition the nodes in a graph into groups such that the nodes in the same group are close in terms of graph proximity and also have similar attribute values. Recently, deep learning methods have achieved state-of-the-art clustering performance. However, the effectiveness of existing methods remains unclear due to two reasons. First, the datasets used for evaluation do not support fully the goal of attributed graph clustering. The category labels of nodes are only relevant to node attributes, and nodes with the same category label are often distant in the graph. Second, existing methods for the attributed graph clustering are complex and consist of several components. There is lack of comparisons of methods composed of different components from existing methods. This study proposes six benchmark datasets that support better the goal of attributed graph clustering and reports the performance of existing representative methods. Given that existing methods leave room for improvement on the proposed benchmark datasets, we systematically analyze five aspects of existing methods: encoded information, training networks, fusion mechanisms, loss functions, and clustering result generation. Based on these aspects, we decompose existing methods into modules and evaluate the performance of reconfigured methods based on these modules. According to the experimental results on the proposed benchmark datasets, we identify two promising configurations: (i) taking the attribute matrix as input to a graph convolutional network and (ii) layer-wise linear fusing deep neural network and graph attention network. And we also find that complex loss function fails to improve the clustering performance.
STREAMS: Towards Spatio-Temporal Causal Discovery with Reinforcement Learning for Streamflow Rate Prediction

The capacity to anticipate streamflow is critical to the efficient functioning of reservoir systems as it gives vital information to reservoir operators about water release quantities as well as help quantify the impact of environmental factors on downstream water quality. Yet, streamflow modelling is difficult owing to the intricate interactions between different watershed outlets. In this paper, we argue that one possible solution to this problem is to identify the causal structure of these outlets, which would allow for the identification of crucial watershed outlets while capturing the spatiotemporally informed complex relationships leading to improved hydrological resource management. However, due to the inherent complexity of spatiotemporal causal learning problems, extending existing causal discovery methods to a whole basin is a major hurdle. To address these issues, we offer STREAMS, a new framework that uses Reinforcement Learning (RL) to optimize the search space for causal discovery and an LSTM-GCN based autoencoder to infer spatiotemporal causal features for streamflow rate prediction. We conduct extensive experiments on the Brazos river basin carried out within the scope of a US Army Corps of Engineers, Engineering With Nature Initiative project, including empirical studies of generalization performance to verify the nature of the inferred relationships.
Semantic Hearing: Programming Acoustic Scenes with Binaural Hearables

Imagine being able to listen to the birds chirping in a park without hearing the chatter from other hikers, or being able to block out traffic noise on a busy street while still being able to hear emergency sirens and car honks. We introduce semantic hearing, a novel capability for hearable devices that enables them to, in real-time, focus on, or ignore, specific sounds from real-world environments, while also preserving the spatial cues. To achieve this, we make two technical contributions: 1) we present the first neural network that can achieve binaural target sound extraction in the presence of interfering sounds and background noise, and 2) we design a training methodology that allows our system to generalize to real-world use. Results show that our system can operate with 20 sound classes and that our transformer-based network has a runtime of 6.56 ms on a connected smartphone. In-the-wild evaluation with participants in previously unseen indoor and outdoor scenarios shows that our proof-of-concept system can extract the target sounds and generalize to preserve the spatial cues in its binaural output. Project page with code: https://semantichearing.cs. washington.edu
A REVISION OF WESTERN ATLANTIC BLUEFIN TUNA AGE OF MATURITY DERIVED FROM SIZE SAMPLES COLLECTED BY THE JAPANESE LONGLINE FLEET IN THE GULF OF MEXICO (1975-1980)

SUMMARY The maturity function developed by Diaz and Turner (2007) was updated in view of the newly adopted growth curve developed for the western stock by Restrepo et al. (2011). The present study used different size samples than those used by Diaz et al (2007). The estimated age of 50% maturity was 15.8 yr, which is 4 yr older than the ages estimated by Diaz and Turner (2007) using the growth curve previously adopted by ICCAT. The estimated ages of 50% maturity using size samples collected in the bluefin tuna western stock spawning grounds of the Gulf of Mexico do not support the currently assumed age of maturity of 8 yr for this stock. RESUME
Weak Regression Enhanced Lifelong Learning for Improved Performance and Reduced Training Data

As an emerging learning paradigm, lifelong learning intends to solve multiple consecutive tasks over long-time scales upon previously accumulated knowledge. When facing with a new task, existing lifelong learning approaches need first gather sufficient training data to identify task relationships before knowledge transfer can succeed. However, annotating large number of training data persistently for every coming task is time-consuming, which can be prohibitive for real-world lifelong regression problems. To reduce this burden, we propose to incorporate weak regression into lifelong learning so as to enhance training data and improve predictive performance. Specifically, the weak prediction is first produced by single-task predictor, which is encoded as feature vectors that contain essential prior output information. This weak regression is further linked with task model via coupled dictionary learning. The integration of weak regression and task model can facilitate both cross-task and inter-task knowledge transfer, thus improving the overall performance. More critically, the weak regression can backup the task model especially when there is insufficient training data to construct an accurate model. Three real-world datasets are used to evaluate the effectiveness of our proposed method. Results show that our method outperforms existing lifelong models and single-task models even if training data is minimal.
Comparative Analysis of Open Source and Commercial Embedding Models for Question Answering

In this industry track presentation, we will provide a comprehensive tour of the best performing embedding models for question answering, as determined by the Massive Text Embedding Benchmark1. We showcase these models while also considering solutions offered by OpenAI and Cohere, renowned for their state-of-the-art performance. Through rigorous evaluations on internal Salesforce datasets tailored for Question Answering on Knowledge articles, we compare the performance of these models using standardized metrics. Our analysis sheds light on the current state-of-the-art in question answering using embedding models across three diverse domains. We hope that this talk's outcomes will empower practitioners and researchers to make informed decisions when selecting the most suitable solution for their specific requirements.
Evidence for boron incorporation into the serpentine crystal structure

Abstract Serpentinite mud volcanoes from the Mariana forearc comprise B-rich mantle wedge peridotites serpentinized by slab fluids. The major component of these rocks are serpentine group minerals [Mg3Si2O5(OH)4], showing highly variable textural and geochemical features. Micro-Raman spectroscopy reveals that the serpentine minerals are well-crystallized lizardite and chrysotile. In situ SIMS spot analyses and element mapping via ToF-SIMS show that B is evenly distributed across serpentine grains, suggesting that serpentine, both lizardite and chrysotile in different textural regions, can host significant amounts of B (up to -200 μg/g) into its crystal structure. As such structurally bound B can only be released during recrystallization or serpentine breakdown, our results have implications for modeling of the efficiency of cross-arc fluid mobile element recycling in subduction zones and stress the importance of the hydrated forearc mantle as a reservoir for fluid mobile elements.
Reframe: An Augmented Reality Storyboarding Tool for Character-Driven Analysis of Security & Privacy Concerns

While current augmented reality (AR) authoring tools lower the technical barrier for novice AR designers, they lack explicit guidance to consider potentially harmful aspects of AR with respect to security & privacy (S&P). To address potential threats in the earliest stages of AR design, we developed Reframe, a digital storyboarding tool for designers with no formal training to analyze S&P threats. We accomplish this through a frame-based authoring approach, which captures and enhances storyboard elements that are relevant for threat modeling, and character-driven analysis tools, which personify S&P threats from an underlying threat model to provide simple abstractions for novice AR designers. Based on evaluations with novice AR designers and S&P experts, we find that Reframe enables designers to analyze threats and propose mitigation techniques that experts consider good quality. We discuss how Reframe can facilitate collaboration between designers and S&P professionals and propose extensions to Reframe to incorporate additional threat models.