Miracle-dz/UniFAR-FTUs huggingface dataset:
Moving targets detection and parameters estimation based on DPCA and FrFT in dual-channel SAR

Improvements based on DPCA–Fractional Fourier Transform(FrFT) are presented in this paper, so as to improve the performance of moving target detection and parameters’ estimation: firstly, aiming at the problem of acceleration in range, it is used as a parameter of moving target and estimated, this parameter mainly affects doppler rate; secondly, aiming at the problem of complicated system, this paper not only uses FrFT to estimate the doppler parameters of moving target, but also uses the maximum value of signal energy after FrFT. It can realize the detection and parameters’ estimation of moving target in dual-channel SAR and simplifies the structure of SAR's system; finally, due to the presence of noise in SAR's system, moving target's reflection coefficient is not an ideal value; The reflection coefficient is estimated by the amplitude averaging method to solve the problem, so as to improve the accuracy of parameters’ estimation of moving target.
Prediction of flocculant dosage in water plant based on LSTM network

Flocculation and sedimentation is a crucial step in the water treatment process. Currently, most water plants still use a fixed-value proportional dosing method for flocculant dosing, which has low accuracy. Flocculant dosing prediction is a time series problem, and the complexity of the problem that can be expressed using traditional time-series modeling is limited, and machine learning requires a more complex manual feature engineering component. In this paper, we propose an LSTM neural network prediction model incorporating the Attention mechanism to correlate current sensor acquisition data with historical moment data, extract multidimensional features, and focus on key information and ignore redundant information. It can be a better solution for this problem with nonlinearity, multiple input factors, uncertainty, and time-varying characteristics. Through experiments, comparing the common models such as BP, RNN, LSTM, etc. to predict the flocculant dosing of half-yearly in water plants, the model has a high accuracy.
Interface engineered HfO2-based 3D vertical ReRAM

We demonstrate a double-layer 3D vertical resistive random access memory (ReRAM) stack implementing a Pt/HfO2/TiN memory cell. The HfO2 switching layer is grown by atomic layer deposition on the sidewall of a SiO2/TiN/SiO2/TiN/SiO2 multilayer pillar. A steep vertical profile was achieved using CMOS-compatible TiN dry etching. We employ in situ TiN bottom interface engineering by ozone, which results in (a) significant forming voltage reduction which allows for forming-free operation in AC pulsed mode, and (b) non-linearity tuning of low resistance state by current compliance during Set operation. The vertical ReRAM shows excellent read and write disturb immunity between vertically stacked cells, retention over 104 s and excellent switching stability at 400 K. Endurance of 107 write cycles was achieved using 100 ns wide AC pulses while fast switching speed using pulses of only 10 ns width is also demonstrated. The active switching region was evaluated to be located closer to the bottom interface which allows for the observed high endurance.
DGSG: A Efficient Goal Directed Sequence Generator for Pedestrian Trajectory Prediction

Trajectory prediction is a crucial task in modern era as it can help ego robots work safely in crowded environments. It’s yet challenging for the stochasticity of human motion and the restriction of platform. Previous method ignore the problem of time and space complexity. Based on recently developed Variational Auto Encoder(VAE), we proposed a trajectory prediction model named goal directed sequence generator(DGSG). In this model, the prediction task is divided into two modules achieved by light neural network respectively. The goal estimation module is supported by a VAE based network with a reformed loss function to modify the relationship between destinations and observed trajectories. And the sequence generation module prediction future trajectory based on the destination. Our experiments have shown that our method has achieve a state-of-art performance in commonly used datasets. Furthermore, experiments prove that our method is easy to deploy for the outstanding time and space complexity.
A User-friendly Phase Code Paradigm in SSVEP-Based BCI Using Inconspicuous Flickering at 48Hz

High-frequency () steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) are safer and more comfortable for users. However, higher stimulus frequency leads to lower SSVEP amplitude and signal-to-noise ratio (SNR). Therefore, the high-frequency SSVEP-BCIs were seldom reported, especially BCIs using stimulus frequency above 40Hz. This study proposed a novel phase code method to encode sixteen visual stimuli using inconspicuous flickering at 48Hz. Simulated annealing (SA) method was adopted to optimize the selection of phase sequences. SSVEP features were detected with an ensemble task-related component analysis (eTRCA) algorithm. Twelve subjects participated in the offline experiment. Results showed that it was feasible to elicit phase-locked SSVEPs using stimulus frequency at 48Hz. Based on the novel paradigm, this study achieved an average accuracy of 84.659. 96 and an average ITR of 75.6316. 37 bits/min. Especially, the highest ITR reached 100.03 bits/min in this study, which was the highest among nowadays studies with stimulus frequency over 40Hz. These results proved that the phase code was a promising paradigm to encode high-frequency SSVEPs and to extend the number of instructions in BCIs.
Spin-Polarized Photoelectrons from Randomly Oriented Halogen Molecules by Unpolarized Radiation

Molecular photoelectrons have been observed to be spin polarized even if ejected from a beam of randomly oriented molecules by unpolarized radiation. The polarization and the asymmetry parameter [Beta] for Br2, I2, CH3Br, and CH3I are compared with general predictions of a nonrelativistic model. While all available data of the differential cross section agree with this prediction, spin-polarization results partly disagree, manifesting the influence of the spin-orbit interaction upon the phases of molecular continuum wave functions.
3D object detection based on the fusion of projected point cloud and image features

The complementary advantages of point cloud and image can provide more accurate 3D and semantic information to the model. Aiming at the problems that most existing methods adopt a single fusion strategy and thus fail to achieve deep fusion of image and point cloud features, this paper studies and analyzes the existing fusion strategy of image and point cloud data, and proposes a model based on the fusion of projected point cloud and image features. The model utilizes a projection fusion and feature fusion strategy, introduces a wide threshold processing in the projection module, meanwhile applies the fusion of point clouds and image features after projection cropping, finally integrates both features in depth by adding a weight fusion layer in the feature fusion stage. Extensive experiments on the public KITTI dataset demonstrate that mAP of the proposed method is improved by 3.34% in the average values of easy difficulty compared with similar models, indicating that the algorithm is more effective in 3D object detection with point cloud and image fusion.
Retrieval Augmented via Execution Guidance in Open-domain Table QA

The goal of the open-domain table QA task is to answer a question based on retrieving and extracting information from a large corpus of structured tables. Currently, the accuracy of the most popular framework in open-domain QA: the two-stage retrieval, is limited by the table retriever. Inspired by the research on Text-to-SQL, this paper proposes to use execution guidance to enhance the effect of table retrieval. Our contributions are mainly threefold: 1. Proposed using execution-guided method to enhance table retrieval to fully leveraging schema information of tables. 2. Proposed the pure Text-to-SQL task for open domains. We design a two-stage Table QA framework based on semantic parsing to generate logical forms and answers simultaneously. 3. Proposed an open-domain Text-to-SQL dataset: Open-domain WikiSQL. We change the original WikiSQL to become suitable for the Open-domain setting, by removing the approximate tables, decontextualizing the questions, etc. We conducted experiments on the new dataset using BM25 and DPR as the retriever, and HydraNet as the generator of SQL. The results show that the execute-guided significantly improves the table retrieval by 19% (DPR in hit@1) and achieves good performance (accuracy of logical form and execution improves by 12.7% and 13.1%) on end-to-end open-domain Text-to-SQL tasks as well.
Olfactory examination for AD diagnosis based on machine learning

Objective: To explore the feasibility of olfactory examination as an early diagnostic indicator of Alzheimer's disease (AD) by using machine learning method. Methods: A total of 100 dementia patients who were treated in the outpatient department of neurology of Tianjin Huanhu Hospital from April to November 2021 were collected, including 50 cases of MCI and 50 cases of AD, aged 50-86 years. The control group consisted of 50 healthy adults from the physical examination center, aged 50-79 years. Subjects were tested for olfactory subjective function test (tested with Sniffin Sticks), Montreal Cognitive Assessment Scale (MoCA), Mini-Mental State Examination (MMSE) and Adaptive Behavior Rating Scale (ADL). SPSS 20.0 statistical software was used to compare the correlations between the scores of the three groups of olfactory function tests and scale tests, and MATLAB 2014 software was used to classify the three groups of subjects using machine learning algorithms. Results: The olfactory function score and the cognitive function scale score showed moderate to strong correlation (>0.44). The classification accuracy of support vector machine (SVM) for the patient group and the control group reached 99.3%, and the accuracy of K-nearest neighbor algorithm (KNN) for MCI and AD reached 91.5%. Conclusion: The detection of olfactory function has certain predictive value in the diagnosis and transformation of MCI and AD, and can be used as a routine biomarker test.
Autonomous slope edge perception and guidance for excavators based on binocular vision

For the harsh environment and uneven ground during excavator slope repair operations, and to solve the problem of restricted vision of the driver during manual operation, a slope information perception method based on binocular cameras and auxiliary lines along the lower edge of the slope is proposed. The slope and the surrounding environment are matched in three dimensions by the binocular camera and the point cloud is reconstructed, and the auxiliary line point cloud is extracted by point cloud segmentation according to the difference in colour of the point cloud, and fitted to a spatial straight line as the position where the bucket stops working. For the typical working conditions of excavator slope repair construction, the most influential factors were selected for comparison experiments, and it was verified that the perception error of slope-related information by this method is within 4cm. The method can provide accurate information on the distance and coordinates of the slope and the auxiliary line, which improves the disadvantages of manual operation and provides information support for the autonomous slope repair of excavators.
Comparison of Feature Selection Methods for Bottom-up of 1p/19q Co-deletion Prediction Performance in Grade 2 and 3 Gliomas

This study aimed to compare multiple feature selection methods that may bottom-up 1p/19q co-deletion prediction performances for grade 2 and 3 gliomas using random initialization, imagenet, and transfer-learned deep features to identify effective feature selection methods. The Cancer Imaging Data Archive 1p/19q co-deletion dataset was used in this study. Image-based features, random initialization deep features, imagenet deep features, and transfer-learned deep features were extracted from T1-weighted post-contrast images using the tumor region of interest mask images. Using these features as learning data, the prediction performances of seven types of predictive models were compared. Predictive models created using only image-based features had the highest area under the receiver operating characteristic curve (AUC) for all feature selection methods. Genetic algorithm feature selection yielded the highest AUC of 0.69 and a minimal log-loss of 0.65. In conclusion, the predictive model created using only image-based features with genetic algorithm feature selection yielded the highest AUC and minimal log-loss, to predict 1p/19q co-deletion in grade 2 and 3 gliomas. Deep features extracted from the post-transfer learning deep neural network created for 1p/19q co-deletion prediction may improve prediction performance by combining image-based features and clinical variables.
A method for accelerating relations search over big data scenario

We propose an acceleration method for relations search over big data scenario. The relations search is in essence an induced subgraph search of graph data model, which is to search all edges with all adjacent vertices falling into a given vertex set. For example, in the risk-control scenario for anti-fraud, for a swindling gang of highly similar fraud behaviors, figuring out all relations between these swindlers can be greatly beneficial to precisely attack all similar gangs. In-memory induced subgraph search requires lots of random accesses, while in big data scenario, relations data tend to be stored on disk when the memory is limited, and the corresponding random lookups over disk incorporate huge overhead. Also, even a singleton disk access could be costly since there are usually many properties data in a relation. Hence, existing methods would suffer considerable performance bottlenecks when applied over data on disk. We build graph over disk-based edge index, and propose multiple BFS (Bread First Search) level gaps based induces subgraph acceleration method, with efficient performance guarantee when the memory is limited. We avoid imbalance in performance for query evaluation with edge index based data organization, with which we further reduce redundant I/O accesses. We filter most invalid no-result edge queries based our BFS level gaps framework. We find that, for each BFS, we can confirm the no edge status between a pair of vertices if their BFS accessing level are of distance more than 1, which indicates the nonexistence of edge. Extensive experiments over real world graph datasets confirm the effectiveness of our method. We find that more than 97% invalid edge queries are filtered by our method, which greatly improve the system performance. Hence, our method with multiple BFS level gap encoding over vertices could greatly filter no-result edge queries and improve the relation search performance. Out method also reduce the redundant I/O access over disk with performance stability guarantee.
Research on Double Threshold Reconstruction Algorithm Based on 1-Bit Compression Sensing

Binary Iterative Hard Threshold (BIHT) algorithm is improved by changing the sampling threshold of BIHT and introducing a dual threshold strategy because the threshold limits the reconstruction performance of the algorithm to a certain extent. A BIHT algorithm based on double threshold is proposed to reconstruct the shock wave signal. The 50 psi shock wave signal is used. First, the limit number is sparse, and the sparse signal is quantized by 1 bit using double threshold. The shock wave signal is reconstructed through the set double threshold. The results show that the reconstruction error of shock wave reconstruction signal is about 88.91%, 95.8% and 41.7% respectively compared with the commonly used reconstruction algorithm MSP, FPC and BIHT of 1Bit compressed sensing, which further proves the superiority of the double threshold BIHT algorithm.
Point cloud semantic segmentation method based on self-attention and pyramid fusion network model

Semantic segmentation of point clouds has received extensive attention as the basis for 3D scene understanding, recognition, and various applications. However, the research on semantic segmentation of 3D point cloud scenes is extremely challenging due to the interference of sensor noise, the non-uniformity of point cloud density, and the complexity and diversity of the scene. This paper proposes a point cloud semantic segmentation network model named SAPFNet. Center and neighborhood self-attention mechanisms are introduced to enhance the ability to extract local features from point cloud data. At the same time, Pyramid Attention Network (PAN) is used to combine features of different resolutions and different semantic strengths to preserve good geometric features as much as possible. Furthermore, scene semantic segmentation experiments on the public dataset S3DIS show that the proposed network achieves certain improvements in segmentation results compared with other network models.
An End-to-End Visual Odometry System Based On Multi-Branch Convolutional Neural Network

Visual odometry (VO) is one of the essential techniques in mobile robots field. An accurate VO system is of great significance for mobile robot simultaneous localization and mapping (SLAM). In this paper, we propose an end-to-end multi-branch convolutional VO system for estimating the ego-motion of the monocular camera. A multi-branch structure of convolutional neural network (CNN) is designed to extract multi-scale and multi-frequency feature maps in image sequences, which is helpful for understanding the content of surroundings especially in a multi-object environment. Meanwhile, a novel adaptive activation function based on funnel ReLU is proposed to adaptively activate network neurons. Experiments on several public datasets demonstrate that the proposed framework provides competitive performance in ego-motion estimation compared with other classical and state-of-the-art learning-based methods in the accuracy and inference-time costs. And compared with purely CNN-based VO methods, the average translational error and rotational error of all the evaluation sequences in KITTI are reduced by 52.1% and 51.1% respectively without extra inference-time costs.
Stable Computer Control Algorithm of Von Neumann Model

The problem of computer control algorithm for the singular Von Neumann input-output model is researched. A kind of new mathematic method is applied to study the singular systems without converting them into general systems. A kind of stability condition under which the singular input-output model is admissible is proved with the form of linear matrix inequality. Based on this, a new state feedback stability criterion is established. Then the formula of a desired state feedback controller is derived.
Environmental Impact Assessment of Road Asphalt Pavements

This paper deals with a versatile, synthetic, simple and user-friendly method based on Life Cycle Assessment studies which summarizes multifaceted, often competing, environmental, technical and economic aspects in road construction. In many cases just economic criteria are applied in call for tenders, because the calculation of the environmental impact of road construction is difficult. In fact, it can be referred to many available options and both the economic and the environmental suitabilities have to be considered, in order to achieve globally sustainable results about road infrastructure work. In this research, the weighted sum model of multicriteria analysis is identified as the tool to evaluate global impact of road works, to compare solutions and to choose the best one. The advantages of the proposed approach are that the local contest and the stakeholders’ objective are represented by adopting variable parameters and weights, in order to apply the method to several contexts. A case study explains potential environmental implications of using this new Road Environmental Impact Assessment to calculate effect related to the production of asphalt pavement, considering the production system for aggregates from cradle to gate, the materials transportation to road site and the works to have the road done.
Analyse combinatoire : cliques, bicliques et systèmes implicatifs

Cette these traite de l'algorithmique d'enumeration. Apres avoir presente les concepts particuliers des algorithmes d'enumeration, nous nous interessons plus particulierement a deux problemes, l'enumeration des cliques maximales et l'enumeration des bicliques maximales d'un graphe. Pour ce dernier probleme, trois variantes seront traitees : enumeration des bicliques maximales induites, non induites et pour le cas particulier des graphes biparti. Cette these propose des liens entre les algorithmes existants pour ces problemes. On s'interesse a l'enumeration des elements d'une base minimun d'implications. Comme il n'existe pas a l'heure actuelle d'algorithme d'enumeration polynomial pour ce probleme, les approches presentees ici utilisent un autre angle d'approche : trouver une base minimun plus petite, ou plus rapide a calculer, permettant de reconstruire la base minimum initiale en temps polynomial. Dans ce but, deux resultats sont presentes : les elements clones d'une relation et la famille des systemes de fermeture partageant une certaine partie d'une base minimun
Spatial Transcriptomics Arena (STAr): an Integrated Platform for Spatial Transcriptomics Methodology Research

The emerging field of spatially resolved transcriptomics (SRT) has revolutionized biomedical research. SRT quantifies expression levels at different spatial locations, providing a new and powerful tool to interrogate novel biological insights. An essential question in the analysis of SRT data is to identify spatially variable (SV) genes; the expression levels of such genes have spatial variation across different tissues. SV genes usually play an important role in underlying biological mechanisms and tissue heterogeneity. Currently, several computational methods have been developed to detect such genes; however, there is a lack of unbiased assessment of these approaches to guide researchers in selecting the appropriate methods for their specific biomedical applications. In addition, it is difficult for researchers to implement different existing methods for either biological study or methodology development. Furthermore, currently available public SRT datasets are scattered across different websites and preprocessed in different ways, posing additional obstacles for quantitative researchers developing computational methods for SRT data analysis. To address these challenges, we designed Spatial Transcriptomics Arena (STAr), an open platform comprising 193 curated datasets from seven technologies, seven statistical methods, and analysis results. This resource allows users to retrieve high-quality datasets, apply or develop spatial gene detection methods, as well as browse and compare spatial gene analysis results. It also enables researchers to comprehensively evaluate SRT methodology research in both simulated and real datasets. Altogether, STAr is an integrated research resource intended to promote reproducible research and accelerate rigorous methodology development, which can eventually lead to an improved understanding of biological processes and diseases. STAr can be accessed at https://lce.biohpc. swmed.edu/star/.