Miracle-dz/UniFAR-FTUs huggingface dataset:
A health indicator construction method of rolling bearing based on vibration image features and deep autoencoder network

Rolling bearing is a key component and weak link of rotating machinery, the construction of health indicator (HI) with good performance can provide support for the fault status assessment and fault prediction of rolling bearing. Traditional HI construction methods of rolling bearings are mainly limited to one-dimensional feature extraction, and it is difficult to consider local and global features at the same time, which leads to poor nonlinear representation ability of HI. To solve the above problems, this paper proposes a rolling bearing health indicator construction method based on vibration image features and deep autoencoder network. Firstly, the one-dimensional vibration signal is reconstructed into a time-domain grayscale image, and the optimal image features are extracted and selected to reflect the vibration information in the two-dimensional image. Then, the optimal features are arranged and coupled to improve the information expression. Finally, a deep convolutional autoencoder network (DCAE) method with self-attention mechanism is proposed to construct the HI indicator unsupervised. The effectiveness of the method is verified by the XJTU-SY bearing dataset. The average comprehensive score of HI of different bearings is 0.8188, which is 0.2051 higher than that of other methods, and has better performance of remaining useful life prediction.
Sample Size Determination for Electronic Phenotyping

Advances in automated document classification has led to identifying massive numbers of clinical concepts from handwritten clinical notes. These high dimensional clinical concepts can serve as highly informative predictors in building classification algorithms for identifying patients with different clinical conditions, commonly referred to as patient phenotyping. However, from a planning perspective, it is first critical to ensure that enough data is available to build a phenotyping algorithm that has good classification performance to ensure a required clinical impact. This challenge in sample size planning is further exacerbated by the high dimensionality of the covariates and the inherent imbalance of the response class as most phenotypes of interest are relatively rare. In this work, we develop methods for sample size planning, deriving plug-in formulas for determining sample size requirements based on optimizing classification performance metrics sensitive to class imbalance (e.g., Area Under the Curve (AUC), Matthews Correlation Coefficient (MCC)).
Utilizing Graph Neural Networks for Breast Cancer Prognosis Prediction with High-dimensional Genomic Data

An accurate prediction of breast cancer is essential to help physicians make appropriate treatment recommendations to reduce the chance of excessive treatment, avoiding unnecessary anxiety for patients. Cancer prognosis is highly related to patients’ genomic features, which are high-dimensional in nature. In this study, we utilize a systems biology feature selector for dimension reduction to select 20 prognostic biomarkers that are considered closely related to breast cancer prognosis from the high dimensional RNA Sequencing (RNA-Seq) data. Furthermore, we establish a graph neural network (GNN) and a multi-layer perception (MLP) graph-level readout method to better extract the underlying gene interactions from the corresponding gene interaction network (GIN). With the help of GINs, the model performs the best among all baseline models, especially in the area under the precision-recall curve (AUPRC) by as large as 23%. The results demonstrate that our approach using GNNs can successfully extract high-dimensional and complicated interactions within genomic data.
PREDICTION OF FLUVIAL SAND BODY USING THE TECHNIQUE OF FREQUENCY DIVISION INTERPRETATION

Seismic events not only reflect lithology interface but also the isochronous interface, instead they are affected by the frequency of seismic data, different frequency seismic data reflects different levels of geological phenomenon. Seismic data in time domain transforms into a new frequency domain by using frequency division interpretation, then extract the frequency slice of the data volume in different frequency bands, analyze the slice which represents different geological significance and combines slice with single well facies, clear slice attributes represent the actual geological significance, at last definite the sedimentary boundary and depict the concept of sedimentary bodies. Based on frequency division interpretation technique, this paper characterizes the Akshabulak formation sedimentary system in 1057 area of South Turgay Basin, Kazakstan. The results show that seismic data in different frequency reflects different levels of geological information, The SQ8-1 cycle sensitive frequency range is 20~25Hz, the frequency slice displays channel sand bodies clearly, and the SQ8-2 cycle range is 22~27Hz, the SQ8-3 cycle range is 25~30Hz. Another finding is that by using frequency division interpretation slice can characterize fluvial boundary, plane geometry and discontinuity accurately. This method can provide favorable geological evidence for oil and gas exploration and development.
Classification of Patient Portal Messages with BERT-based Language Models

The patient portal is a secure platform that enables patients to conveniently access to their medical records and communicate with and seek support from their care teams for various healthcare issues. The care teams are receiving a rising number of patient portal messages (PPMs) generated by patients as the increase of patient engagement in patient portal, especially during the COVID-19 pandemic. It opens great opportunities to develop artificial intelligence (AI) solutions to supports the care teams and reduce their workload. Previous studies have shown the potential of machine learning and natural language processing (NLP) for classification of PPMs to automate message triage. Recent progress in neural network architectures (e.g., the Transformer) and the emergence of large-scale pre-trained language models are pushing the state-of-the-art for multiple NLP tasks including text classification. However, there has been very little research studies about the performance of pre-trained language models for the PPMs classification task. This study proposes to investigate the performance of multiple state-of-the-art NLP pre-trained language models based on the language style and the nature of content for the multi-category PPMs classification task. The results could contribute to developing future advanced and precise triage system of PPMs for clinical applications. We developed PPM classifiers leveraging five popular Bidirectional Encoder Representations from Transformers (BERT) language models including the generic BERT models (BERT and RoBERTa), domain-specific BERT models (BioBERT and Bio_ClinicalBERT), and source-specific BERT model (BERTweet), which were pre-trained with corpus in different domain and from different sources. The BERT-based models were fine-tuned and evaluated with an annotated corpus of 2239 annotated PPMs with four categories. Our results show that source-specific model BERTweet outperformed all other tested models, with a 0.78 accuracy score and a 0.75 F-1 score. Both domain-specific models BioBERT and Bio_ClinicalBERT achieved better results (F1 scores of 0.74 and 0.72), compared with the generic models BERT and RoBERTa (F-1 scores of 0.70 and 0.66), respectively. BERTweet gained the benefit from the large-scale English tweets pre-train corpus which has informal and colloquial language similar to PPMs. The domain specific models BioBERT and Bio_ClinicalBERT performed better than the generic models because they were pre-trained with large-scale biomedical and clinical corpora and can learn better representation of PPMs for better recognition of health concepts. The results show that the language models pre-trained by a corpus with similar language style and domain concepts are beneficial to the downstream PPM classification. It would be worth to explore the combination of multiple featured language models in order to further improve the model performance.
REC-NN: A reconstruction error compensation neural network for Magnetic Resonance Electrical Property Tomography (MREPT)

Electrical properties (EPs) are expected as biomarkers for early cancer detection. Magnetic resonance electrical properties tomography (MREPT) is a technique to non-invasively estimate the EPs of tissues from MRI measurements. While noise sensitivity and artifact problems of MREPT are being solved progressively through recent efforts, the loss of tissue contrast emerges as an obstacle to the clinical applications of MREPT. To solve the problem, we propose a reconstruction error compensation neural network scheme (REC-NN) for a typical analytic MREPT method, Stab-EPT. Two NN structures: one with only ResNet blocks, and the other hybridizing ResNet blocks with an encoder-decoder structure. Results of experiments with digital brain phantoms show that, compared with Stab-EPT, and conventional NN based reconstruction, REC-NN improves both reconstruction accuracy and tissue contrast. It is found that, the encoder-decoder structure could improve the compensation accuracy of EPs in homogeneous region but showed worse reconstruction than only ResNet structure for tumorous tissues unseen in the training samples. Future research is required to address overcompensation problems, optimization of NN structure and application to clinical data.
The impact of a high‐speed railway on residential land prices

By using the case of the high‐speed railway scheduled to open in 2027 in Japan, this study examines whether the value of transport innovation is capitalized in land prices immediately after the construction plan is announced. We adopt a hedonic approach to measure value, using balanced panel data on residential land prices from 2008 to 2015 in Japan. We find that residential land prices where the time distance to the Tokyo metropolitan area reduces rose, except where the population is decreasing. This result implies that the benefits are capitalized in land prices when demand to shorten the time distance exists. Este estudio utiliza el caso del ferrocarril de alta velocidad en Japon programado para ser inaugurado en 2027 para examinar si el valor de la innovacion en el transporte se capitaliza en los precios del suelo inmediatamente despues de que se anuncie el plan de construccion. Se adopto un enfoque hedonico para medir el valor, y se usaron datos de panel equilibrados sobre precios de terrenos urbanizables desde 2008 hasta 2015 en Japon. Se encontro que los precios de los terrenos urbanizables para los que se reduce la distancia en cuanto al tiempo al area metropolitana de Tokio aumentaron, excepto donde la poblacion esta disminuyendo. Este resultado implica que los beneficios se capitalizan en los precios del suelo cuando existe una demanda para acortar la distancia en cuanto al tiempo. 本稿では、日本で2027年に開業が予定されている高速鉄道の例を用いて、交通イノベーションの価値は、建設計画が発表された直後に土地価格で資本化できるか否かを検討する。 2008年から2015年の日本の住宅用地価格のバランスパネルデータ使用し、ヘドニックアプローチを用いて価値の算定を行った。 人口が減少している場合を除き、東京首都圏までの時間距離が少ない場合には、住宅用地価格は上昇すると考えられる。 結果は、時間距離の短縮の需要がある場合には、便益性は土地価格で資本家されることを意味している。
Towards Autonomous Robot Navigation in Human Populated Environments Using an Universal SFM and Parametrized MPC

Autonomous mobile robot navigation in a human populated and encumbered environment is recognized as a hard problem to be solved in real-time. Most of the time, robots face the so-called ‘Freezing Robot Problem’, that occurs when the robot stops because no feasible and safe motion can be found. In order to provide to the robot the capability of proactive navigation, in this work we generalize the classical Social Force Model into a Universal Social Force Model (USFM) that attributes to any object surrounding the robot (humans, robots, obstacles) a social behavior. Nonlinear Model Predictive Control (MPC) can be used to solve the autonomous navigation problem since it can take into account all the possible constraints coming from the interaction model between the robot and the different surrounding objects. However, to be effective, MPC requires a sufficiently large prediction horizon, which generally implies a high computational cost. In order to considerably reduce the computational cost, we propose a new control parametrisation based on Thin Plate Spline Radial Basis Functions that allow us to have a large prediction horizon with fewer parameters. The global control framework is validated in simulation with virtual pedestrians, and in real world environments.
Automotive Radar Missing Dimension Reconstruction from Motion

Automotive radars have been reliably used in most assisted and autonomous driving systems due to their robustness to extreme weather conditions. With radial velocity measurements from automotive radars, moving targets such as cars, trucks, and buses can be tracked robustly. However, due to the lack of elevation angles in measurements from automotive radars, stationary targets at different heights, such as maintenance holes and bridges, cannot be distinguished. Most autonomous systems rely on sensor fusion or ignore stationary targets to tackle the problem of missing the elevation angle dimension, which derives safety issues. We propose a simple yet effective approach to estimate the elevation angles of stationary targets from relative velocity and radial velocity measurements from an automotive radar. In contrast to structure from motion in computer vision, we utilize the instantaneous velocity generated from the motion of the ego vehicle. The radial velocity of each target is the projection of relative velocity onto the radial direction from radar to target. The radial velocity of each target can be inferred given the target's azimuth, elevation angle, and relative velocity. Accordingly, the elevation angle of each stationary target can be uniquely calculated given the velocity of radar and the target's azimuth and radial velocity measurements. The radar's velocity is estimated with the existing radar odometry algorithm and IMU. The proposed method is verified with real-world data. We evaluate the system's performance with a pre-built point cloud map and a good localization module in a real-world scenario. The proposed elevation angle reconstruction can reach a 1.41-degree mean error and standard deviation of 0.6 degrees in elevation angle.
Object Detection Based on Raw Bayer Images

Bayer pattern is a widely used Color Filter Array (CFA) for digital image sensors, efficiently capturing different light wavelengths on different pixels without the need for a costly ISP pipeline. The resulting single-channel raw Bayer images offer benefits such as spectral wavelength sensitivity and low time latency. However, object detection based on Bayer images has been underexplored due to challenges in human observation and algorithm design caused by the discontinuous color channels in adjacent pixels. To address this issue, we propose the BayerDetect network, an end-to-end deep object detection framework that aims to achieve fast, accurate, and memory-efficient object detection. Unlike RGB color images, where each pixel encodes spectral context from adjacent pixels during ISP color interpolation, raw Bayer images lack spectral context. To enhance the spectral context, the BayerDetect network introduces a spectral frequency attention block, transforming the raw Bayer image pattern to the frequency domain. In object detection, clear object boundaries are essential for accurate bounding box predictions. To handle the challenges posed by alternating spectral channels and mitigate the influence of discontinuous boundaries, the BayerDetect network incorporates a spatial attention scheme that utilizes deformable convolutional kernels in multiple scales to explore spatial context effectively. The extracted convolutional features are then passed through a sparse set of proposal boxes for detection and classification. We conducted experiments on both public and self-collected raw Bayer images, and the results demonstrate the superb performance of the BayerDetect network in object detection tasks.
StaticTracker: A Diff Tool for Static Code Warnings

Static bug detectors help improve software quality by detecting code issues (e.g., code smells or bugs). However, static bug detectors are underutilized in practice due to various reasons. One primary reason is that static bug detectors often report an overwhelming number of static warnings for one software revision. To facilitate better adoption of static bug detectors in software development, we propose a tool, namely StaticTracker, that specializes in tracking the evolution of static code warnings.StaticTracker analyzes each commit and produces the changes of static code warnings caused by the commit, i.e., a diff for static code warnings. We integrate StaticTracker in continuous integration through Git Hooks. Whenever developers push code to a git repository, StaticTracker is automatically activated to identify disappeared and newly-introduced warnings by the commit. We implement StaticTracker for two static bug detectors (Spotbugs and PMD) and evaluate StaticTracker on the recent commits of two open-source projects (Druid and Jedis). Our evaluation shows that StaticTracker is effective in reducing the overwhelming static code warnings that developers need to investigate and achieves an accuracy of 89.8%, which outperforms the state-of-the-art tracking approach with an accuracy of 68.5%. We open source the tool at https://github.com/ljj430/tracking-static-warnings_tool_demo, and the demo video is at https://www.youtube. com/watch? v=2WMOjoq1Nbs.
Design and Simulation of Modified Salisbury Spatial Filter Using Genetic Algorithm for Improving Optical Image Processing

Traditional all-optical image processing has been done by bulky devices or expensive equipment. Salisbury Screen Absorber (SSA) is a subwavelength device that enables real-time image processing in an all-optical way. Here, the applications of standard and multilayer SSA as a spatial frequency filter system for edge detection and phase gradient visualization are investigated. The system's output manifests itself in the intensity of the reflected light. The optimized SSA indicates the potential of this device in edge-detection applications. The improvements are performed using the Genetic algorithm. Both Matrix Optics (MO) and Transmission Line model (TLM) are used for the simulations and calculation of transmission and reflection coefficient.
A Low-Profile Interdigital Diplexer for LEO Satellites Applications

A low-profile diplexer is proposed for satellite telemetry, tracking and command (TT&C) applications in S-band frequency within 2.025-2.110/2.200-2.290 GHz. The diplexer consists of two interdigital band-pass filters and a coaxial power divider. The proposed diplexer has high potential for mass production because there is no need to tune the frequency response after fabrication. Also, the structure is lightweight and low-profile so it could be easily integrated into payload transceivers or antennas in small satellites specially CubeSats. A prototype of the proposed diplexer is fabricated and measured. The measured results are in good agreements with simulations. The measured return loss is lower than -15 dB in all ports, insertion loss is less than 0.7 dB and isolation between output ports is better than 55 dB.
An Experimental Evaluation of Conformance Testing Techniques in Active Automata Learning

Active automata learning is a technique for dynamically learning finite state machine models of black-box systems. Conformance testing is a well-known bottleneck during learning. While multiple conformance testing techniques (CTTs) for Finite State Machines have been proposed, there is a lack of empirical studies that assess the effects of these CTTs during learning. In this work, we compare the performance of eight different CTTs (W, Wp, HSI, H-ADS, H, SPY, SPY-H, I-ADS) while learning 46 models from different communication protocols. Moreover, we propose APFDL as a metric for characterizing the efficiency of automata learning experiments in terms of fault detection capacity. This metric allows identifying CTTs with a lower total cost regarding the number of symbols and resets and a higher rate of state discovery during learning. Our results indicate that while the total cost entailed by CTTs in learning tends to be negligible, we found a significant difference in fault detection rate in learning. Nevertheless, the differences in fault detection rates become negligible when CTTs are applied in randomized mode. These findings reveal the positive role that randomness can have in improving learning efficiency, despite compromising test completeness.
ADC Architectural Study for Digitally-Assisted Multi-Gigabit Data Communication Transceivers

This paper presents a methodology for comparing both SAR and pipeline-SAR ADC architectures based on a target ENOB. First, a system-level model is used to select the parameters needed to achieve a given ENOB. Then, the power consumption of the solution is estimated. Finally, the different architectures are compared based on that estimation. A sample rate of 25GHz, 7 bits of ENOB and a metastability probability of 10−12have been used as a reference considering the requirement for a PAM-4, 25Gbps ADC-based transceiver. A design space for these specifications has been obtained.
Towards Quantifying Stress in Patients with a History of Myocardial Infarction: Validating ECG-Derived Patch Features

Patients with prior myocardial infarction (MI) have an increased risk of experiencing a secondary event which is exacerbated by mental stress. Our team has developed a miniaturized patch with the capability to capture electrocardiogram (ECG), seismocardiogram (SCG) and photoplethysmogram (PPG) signals which may provide multimodal information to characterize stress responses within the post-MI population in ambulatory settings. As ECG-derived features have been shown to be informative in assessing the risk of MI, a critical first step is to ensure that the patch ECG features agree with gold-standard devices, such as the Biopac. However, this is yet to be done in this population. We, thus, performed a comparative analysis between ECG-derived features (heart rate (HR) and heart rate variability (HRV)) of the patch and Biopac in the context of stress. Our dataset contained post-MI and healthy control subjects who participated in a public speaking challenge. Regression analyses for patch and Biopac HR and HRV features (RMSSD, pNN50, SD1/SD2, and LF/HF) were all significant (p<0.001) and had strong positive correlations (r>0.9). Additionally, Bland-Altman analyses for most features showed tight limits of agreement: 0.999 bpm (HR), 11.341 ms (RMSSD), 0.07% (pNN50), 0.146 ratio difference (SD1/SD2), 0.750 ratio difference (LF/HF). Clinical relevance— This work demonstrates that ECG-derived features obtained from the patch and Biopac are in agreement, suggesting the clinical utility of the patch in deriving quantitative metrics of physiology during stress in post-MI patients. This has the potential to improve post-MI patients' outcomes, but needs to be further evaluated.
THE CASE FOR ACTION ON BLACK CARBON

project is a great example of how international, national and local policy all intersect, with great outcomes for local communities and people and for the planet. You have Canada as part of the Arctic Council committing to a black carbon target, incorporating black carbon into its NDC under the Paris Agreement and then developing a national programme and significant funding for off-diesel electrification for rural and remote communities to deliver on its commitments. And you have the local government taking a leadership role on the ground and driving very innovative change in challenging circumstances. A win for local health, a win for local development and a win for the climate, especially the sensitive Arctic environment, which is already under severe stress