Miracle-dz/UniFAR-FTUs huggingface dataset:
A Patient Invariant Model Towards the Prediction of Freezing of Gait

Freezing of Gait (FoG) is one of the incapacitating motor symptoms that appear in patients with Parkinson’s Disease (PD). FoG manifests gait impairments and imposes unforeseen difficulties in commencing the locomotion. Frequent episodes of FoG often lead to fall-related injuries and impart dreadful health repercussions. Prediction of FoG before the occurrence could potentially increase the opportunities for pre-emptive cueing to mitigate or abate the episode of FoG. This paper presents a novel algorithm to predict the onset of FoG using a single ankle accelerometer sensor. Principally, we have focused on designing a lightweight algorithm to facilitate the real-time prediction of FoG in resource-limited hardware. A novel Genetic Algorithm (GA) is introduced as a feature selection method to enhance the algorithm’s performance. We have adapted the patient-invariant model which is a more viable approach in practical deployment. The algorithm is evaluated using the Daphnet dataset and achieved 88% FoG prediction accuracy with 1 second prediction time.
Lightweight Scene Text Recognition Based on Transformer

Scene text recognition (STR) has been a hot research field in computer vision, aiming to recognize text in natural scenes using computers. Currently, attention-based encoder–decoder frameworks struggle to precisely align feature regions with the target object when dealing with complex and low-quality images, a phenomenon known as attention drift. Additionally, with the rise of Transformer, the increasing size of parameters results in higher computational costs. In order to solve the above problems, based on the latest research results of Vision Transformer (ViT), we utilize an additional position-enhancement branch to alleviate attention drift and dynamically fused position information with visual information to achieve better recognition accuracy. The experimental results demonstrate that our model achieves a 3% higher average recognition accuracy on the test set compared to the baseline. Meanwhile, our model maintains the advantage of a small number of parameters and fast inference speed, achieving a good balance between accuracy, speed, and computational load.
Impact of aerosol optics on vertical distribution of ozone in autumn over Yangtze River Delta

Abstract. Tropospheric ozone, an important secondary pollutant, is greatly impacted by aerosols within boundary layer (BL). Previous studies have mainly attributed ozone variation to either aerosol–BL or aerosol–photolysis interactions at the near-surface level. In this study, we analyze the sensitivities of ozone response to aerosol mixing states (e.g., mixing behavior hypothesis of scattering and absorbing components) in the vertical direction and address the effects of aerosol–BL and aerosol–photolysis interactions on ozone profiles in autumn by Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) simulations. The aerosol internal mixing state experiment reasonably reproduces the vertical distribution and time variation in meteorological elements and ozone. Sensitivity experiments show that aerosols lead to turbulent suppression, precursor accumulation, lower-level photolysis reduction, and upper-level photolysis enhancement. Consequently, ozone basically decreases within entire the BL during daytime (08:00–17:00 LT), and the decrease is the least in the external mixing state (2.0 %) when compared with internal (10.5 %) and core shell mixing states (8.6 %). The photolysis enhancement is the most significant in the external mixing state due to its strong scattering ability. By process analysis, lower-level ozone chemical loss is enhanced due to photolysis reduction and NOx accumulation under a volatile organic compound (VOC)-limited regime. Upper-level ozone chemical production is accelerated due to a higher photolysis rate resulting from aerosol backscattering. Therefore, the increased ozone entrainment from BL aloft to the surface induced by the boosted ozone vertical gradient outweighs the decreased ozone entrainment induced by turbulent suppression after 11:00 LT. Additional simulations support the finding that the aerosol effect on precursors, photolysis, and ozone is consistent under different underlying surface and pollution conditions.
Identifying regularities of high temperature on constant and variable fatigue life of AA7075-Al2O3 nanocomposite fabricated by stir casting method

This study aims to determine the effect of high temperature on the fatigue life of AA7075-Al2O3 nanocomposites (6 wt % Al2O3) fabricated by stir casting. The research problem is to determine the durability, fatigue resistance, and mechanical properties of the nanocomposite under constant and variable loading conditions at elevated temperatures, as well as to identify changes in its behavior due to exposure to high temperatures. The results show that higher temperatures have a big effect on the nanocomposite's fatigue performance under both loading conditions. When the material was tested at a high temperature (150 °C) with an extra 6 wt % Al2O3, the ultimate tensile strength and yield stress both went up by 16 % and 15.7 %, respectively. Its fatigue life was also successfully tested under both variable and constant amplitude load conditions. The interpretation of the results suggests that the changes in the microstructure of the nanocomposite material at elevated temperatures lead to an increase in dislocation density and grain size, resulting in an improvement in its mechanical properties. The findings can be utilized to optimize the nanocomposite fabrication process and enhance its fatigue resistance at high temperatures. In addition, the results can be used to enhance the design of aerospace components and high-temperature engines that require materials with excellent fatigue resistance at elevated temperatures. In summary, the investigation of the effect of high temperature on the constant and variable fatigue lives of AA7075-Al2O3 nanocomposite provides valuable insight into the material's mechanical properties. The findings contribute to the development of materials that can withstand high-temperature conditions, which has implications for a variety of industries.
High-Dynamic Range ADC for Finite-Rate-of-Innovation Signals

Modulo folding can be used to sample high-dynamic range signals without increasing the dynamic range of the sampler. Specifically, folding is used prior to sampling and then the folded signal is sampled. After sampling, unfolding algorithms are used to compute the true samples (up to a constant factor) from the folded ones. In this work, we consider a modulo sampling framework for finite rate of innovation (FRI) signals which are used to model signals in time of flight imaging. We suggest compactly supported sum-of-sincs filter as a sampling kernel prior to modulo folding and sampling. We derive conditions on the sampling rate and filter coefficients which lead to unfolding of the samples up to an unknown constant. We then propose a modified annihilating filter approach that can uniquely determine the FRI parameters from the unfolded samples with a constant unknown offset. We show that the proposed framework outperforms existing techniques in the presence of noise.
Robust Subspace Tracking with Contamination Mitigation via α-Divergence

We studied the problem of robust subspace tracking (RST) in contaminated environments. Leveraging the fast approximated power iteration and α-divergence, a novel robust algorithm called αFAPI was developed for tracking the underlying principal subspace of streaming data over time. αFAPI is fast and it outperforms many RST methods while only having a low complexity linear to the data dimension. Some experiments were conducted to illustrate the performance of αFAPI.
Exploiting CCTV Cameras for Hand Hygiene Recognition in ICU

The monitoring of hand hygiene activities can effectively reduce infection and contamination in the Intensive Care Unit (ICU). In this paper, we created a clinical dataset using CCTV cameras installed in ICU to explore the feasibility of recognizing the hand-washing steps of clinicians. A video processing architecture including hand landmark detection and classification is presented. Such a system can potentially be used to alarm clinicians to follow the guidelines of hand hygiene. The experimental results show that the average accuracy of our methodology can achieve 95% under the personalized model and 56% under the generalized model. The preliminary results suggest that hand hygiene is subject-dependent, which is related to individuals’ palm size and washing habits. The cross-subject modeling or subject-adaptive learning can be applied to further improve the accuracy of recognition towards a more generalized solution. The insights of the study are helpful for designing a hand hygiene scoring and alarming system as a part of hospital IoT. The hospital data and code are available at https://github.com/SunnySideUp11/Hand-Hygiene-ICU.
Theoretical Investigation of HER and OER Electrocatalysts Based on the 2D R-graphyne Completely Composed of Anti-Aromatic Carbon Rings

Based on the DFT calculations, two-dimensional (2D) R-graphyne has been demonstrated to have high stability and good conductivity, which can be conducive to the relevant electrocatalytic activity of the material. Different from the poor graphene, R-graphyne, which is completely composed of anti-aromatic structural units, can exhibit certain HER catalytic activity. In addition, doping the TM atoms in Group VIIIB can be considered an effective strategy to enhance the HER catalytic activity of R-graphyne. Particularly, Fe@R-graphyne, Os@R-graphyne, Rh@R-graphyne and Ir@R-graphyne can exhibit higher HER catalytic activities due to the formation of more active sites. Usually, the shorter the distance between the TM and C atoms is, the better the HER activity of the C-site is. Furthermore, doping Ni and Rh atoms of Group VIIIB can significantly improve the OER catalytic performance of R-graphyne. It can be found that ΔGO* can be used as a good descriptor for the OER activities of TM@R-graphyne systems. Both Rh@R-graphyne and Ni@R-graphyne systems can exhibit bifunctional electrocatalytic activities for HER/OER. In addition, all the relevant catalytic mechanisms are analyzed in detail. This work not only provides nonprecious and highly efficient HER/OER electrocatalysts, but also provides new ideas for the design of carbon-based electrocatalysts.
Volume-Regularized Nonnegative Tucker Decomposition with Identifiability Guarantees

It is well-known that the Tucker decomposition of a multi-dimensional tensor is not unique, because its factors are subject to rotation ambiguities similar to matrix factorization models. Inspired by the recent success in the identifiability of nonnegative matrix factorization, the goal of this work is to achieve similar results for nonnegative Tucker decomposition (NTD). We propose to add a matrix volume regularization as the identifiability criterion, and show that NTD is indeed identifiable if all of the Tucker factors satisfy the sufficiently scattered condition. We then derive an algorithm to solve the modified formulation of NTD that minimizes the generalized Kullback-Leibler divergence of the approximation plus the proposed matrix volume regularization. Numerical experiments show the effectiveness of the proposed method.
Discussion on Calculation of Frictional Resistance for Gas Pipeline

This paper analyses the reference conditions that are applicable to the parameters of formula for calculating the pressure loss from frictional resistance (PLFR) in gas pipeline, puts forward a solution to calculate the pressure loss when the metering reference condition is inconsistent with the reference condition for calculating PLFR. Comparing by calculation, the proposals on the scope of application of Colebrook formula are offered. Analyzing the calculation error of the formula for calculating PLFR in low-pressure gas pipeline, the proposals on the classification, selection of the formula for calculating PLFR and the coefficient of frictional resistance are provided.
Multilingual Multi-Target Stance Recognition in Online Public Consultations

Machine Learning is an interesting tool for stance recognition in a large-scale context, in terms of data size, but also regarding the topics and themes addressed or the languages employed by the participants. Public consultations of citizens using online participatory democracy platforms offer this kind of setting and are good use cases for automatic stance recognition systems. In this paper, we propose to use three datasets of public consultations, in order to train a model able to classify the stance of a citizen within a text, towards a proposal or a debate question. We studied stance detection in several contexts: using data from an online platform without interactions between users, using multilingual data from online debates that are in one language, and using data from online intra-multilingual debates, which can contain several languages inside the same unique debate discussion. We propose several baselines and methods in order to take advantage of the different available data, by comparing the results of models using out-of-dataset annotations, and binary or ternary annotations from the target dataset. We finally proposed a self-supervised learning method to take advantage of unlabelled data. We annotated both the datasets with ternary stance labels and made them available.
Learning Dynamic Graphs under Partial Observability

This work examines the problem of learning a network graph from signals emitted by the network nodes, according to a diffusion model ruled by a Laplacian combination policy. The challenging regime of partial observability is considered, where signals are collected from a limited subset of nodes, and we wish to estimate the subgraph of connections between these probed nodes. For the static setting where the network graph is fixed during the estimation process, we examine the sample complexity (number of time samples necessary to achieve consistent learning as the network size grows) of Erdős-Rényi and Bollobás-Riordan graphs. This complexity is almost quadratic for the former and almost linear for the latter class of graphs. We then examine the dynamic graph setting where the graph of latent nodes grows over time, while the probed subset remains fixed. We show that in this case the sample complexity can be reduced, implying the unexpected conclusion that dynamic graphs might help topology inference under partial observability.
Physics-Informed Transfer Learning for Voltage Stability Margin Prediction

Assessing set-membership and evaluating distances to the related set boundary are problems of widespread interest, and can often be computationally challenging. Seeking efficient learning models for such tasks, this paper deals with voltage stability margin prediction for power systems. Supervised training of such models is conventionally hard due to high-dimensional feature space, and a cumbersome label-generation process. Nevertheless, one may find related easy auxiliary tasks, such as voltage stability verification, that can aid in training for the hard task. This paper develops a novel approach for such settings by leveraging transfer learning. A Gaussian process-based learning model is efficiently trained using learning- and physics-based auxiliary tasks. Numerical tests demonstrate markedly improved performance that is harnessed alongside the benefit of uncertainty quantification to suit the needs of the considered application.
A NEW PROBABILISTIC ALGORITHM FOR SOLVING NONLINEAR EQUATIONS SYSTEMS

In this paper, we consider a class of optimization problems having the following characteristics: there exists a fixed number k (1≤k<n) which does not depend on the size n of the problem such that if we randomly change the value of k variables, it has the ability to find a new solution that is better than the current one, we call it Ok. We build a new set of probabilities for controlling changes of the values of the digits and build Probabilistic-Driven Search algorithm for solving single-objective optimization problems of the class Ok. We test this approach by implementing the algorithm on nonlinear equations systems, and we find very good results that are better than results of other authors
Relational Representation Learning for Zero-Shot Relation Extraction with Instance Prompting and Prototype Rectification

Zero-shot relation extraction aims to extract novel relations that are not observed beforehand. However, existing representation methods are not pre-trained for relational representations and embeddings contain much linguistic information, the distances between them are not consistent with relational semantic similarity. In this paper, we propose a novel method based on Instance Prompting and Prototype Rectification (IPPR) to conduct relational representation learning for zeroshot relation extraction. Instance prompting is designed to reduce the gap between pre-training and fine-tuning, and guide the pre-trained model to generate relation-oriented instance representations. Prototype rectification aims to push the prototype embeddings away from each other and makes the instance embeddings closer to its corresponding prototype embeddings for dynamically rectifying the prototype embeddings. Experimental results on two public datasets demonstrate that our proposed method achieves new state-of-the-arts performance1.
Spatial Planning in the Perspective of Sustainable Development Goals (SDGs): Case Study on Ternate, North Maluku, Indonesia

: This study aims to analyze and describe the spatial layout of Ternate City from the perspective of Sustainable Development Goals (SDGs). Spatial planning has been positioned as one of the important components that also determines the success or failure of the development process of a region, especially within the framework of a sustainable development process. This study uses a qualitative approach with descriptive methods; the research location is Ternate, North Maluku Province of Indonesia. The results show that the sustainable development of Ternate is guided by achieving the vision, mission, goals, objectives, policies, strategies, and medium-term regional development programs by utilizing the city area. The novelty of this research lies in the analysis and assessment of the SDGs perspective which includes four dimensions: economic, social, environmental, legal and governance, which are then integrated to merge each sectoral dimension into sustainable development.
Recent Advancements in Computational Drug Design Algorithms through Machine Learning and Optimization

The goal of drug discovery is to uncover new molecules with specific chemical properties that can be used to cure diseases. With the accessibility of machine learning techniques, the approach used in this search has become a significant component in computer science in recent years. To meet the Precision Medicine Initiative’s goals and the additional obstacles that they have created, it is vital to develop strong, consistent, and repeatable computational approaches. Predictive models based on machine learning are becoming increasingly crucial in preclinical investigations. In discovering novel pharmaceuticals, this step substantially reduces expenses and research times. The human kinome contains various kinase enzymes that play vital roles through catalyzing protein phosphorylation. Interestingly, the dysregulation of kinases causes various human diseases, viz., cancer, cardiovascular disease, and several neuro-degenerative disorders. Thus, inhibitors of specific kinases can treat those diseases through blocking their activity as well as restoring normal cellular signaling. This review article discusses recent advancements in computational drug design algorithms through machine learning and deep learning and the computational drug design of kinase enzymes. Analyzing the current state-of-the-art in this sector will offer us a sense of where cheminformatics may evolve in the near future and the limitations and beneficial outcomes it has produced. The approaches utilized to model molecular data, the biological problems addressed, and the machine learning algorithms employed for drug discovery in recent years will be the emphasis of this review.
MATHEMATIC MODEL BASED ON ROTATING ELECTROMAGNETIC THEORY USED IN BUILDING A THERMAL CIRCUIT USED IN FOOD HEATER

Article history: Received: 5 January 2016 Accepted in revised form: 14 March 2016 ABSTRACT Rapid development of China's economy, changing population structure and reformed economic structure drives rapid growth of energy consumption. As a result, energy safety gradually becomes a hot spot in energy development. Electricity heating technology has been concerned for a long time as a safe and effective mean; as a result, it has developed into a subject integrating electric engineering, pyrology and materials science. A fundamental research is carried out on food heater in this study. First, static electromagnetic induction heating and dynamic electromagnetic induction heating were introduced. To solve reverse problems of temperature rise, thermal power mathematical theoretical model and thermal circuit model of food heater were established based on electromagnetic field and empirical formula. Finally, under certain rotating speed, thermal power in short-circuited winding as well as eddy current thermal power and magnetic hysteresis thermal power in solid iron core was analyzed.
Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis

Artificial cells are based on dynamic compartmentalized systems. Thus, remodeling of membrane-bound systems, such as giant unilamellar vesicles, is finding applications beyond biological studies, to engineer cell-mimicking structures. Giant unilamellar vesicle fusion is rapidly becoming an essential experimental step as artificial cells gain prominence in synthetic biology. Several techniques have been developed to accomplish this step, with varying efficiency and selectivity. To date, characterization of vesicle fusion has relied on small samples of giant vesicles, examined either manually or by fluorometric assays on suspensions of small and large unilamellar vesicles. Automation of the detection and characterization of fusion products is now necessary for the screening and optimization of these fusion protocols. To this end, we implemented a fusion assay based on fluorophore colocalization on the membranes and in the lumen of vesicles. Fluorescence colocalization was evaluated within single compartments by image segmentation with minimal user input, allowing the application of the technique to high-throughput screenings. After detection, statistical information on vesicle fluorescence and morphological properties can be summarized and visualized, assessing lipid and content transfer for each object by the correlation coefficient of different fluorescence channels. Using this tool, we report and characterize the unexpected fusogenic activity of sodium chloride on phosphatidylcholine giant vesicles. Lipid transfer in most of the vesicles could be detected after 20 h of incubation, while content exchange only occurred with additional stimuli in around 8% of vesicles.
Hydrostatic Pressure Sensor Based on Polymer Optical Fiber Multimode Interferometer

In this work, we report for the first time, to the best of our knowledge, a hydrostatic pressure sensor based on a polymer optical fiber (POF) multimode interferometer (MMI). The sensor was constructed using a polymethylmethacrylate (PMMA) multimode POF sandwiched between two silica single-mode fibers (SMFs). The characterization of the sensor revealed a linear positive wavelength shift with a sensitivity of 58 pm/bar. The sensitivity of the POF-MMI was compared to that of a silica-based MMI, revealed to be <inline-formula> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> higher, allowing it to reach higher resolutions of 0.1 bar, considering a detection system resolution of 5 pm.