Miracle-dz/UniFAR-FTUs huggingface dataset:
Key Strategies to Reduce Waiting Time in Outpatient Pharmacy

Waiting time in the outpatient pharmacy is still a problem in hospitals. This study aimed to identify the root causes and implement solutions to reduce the waiting time at a type C hospital. This study applied a descriptive approach. The potential causes were identified using HFMEA (Healthcare Failure Mode Effect and Analysis) confirmed through an online survey on 15 staff involved in pharmacy services. Scoring and Focus Group Discussion were used to identify the priority problems and solutions. The study results revealed six causing factors of the lengthy waiting time at the outpatient pharmacy, which were doctor's schedule, absence of specific time target for each drug preparation stage, changes of the drug storage, drug stock different from the planning, incomplete standard operating procedures, and one counter for prescription collection and drug dispensing. Three solutions were implemented in six days: checking drug stock in every shift, developing an SOP, and separating the counters for prescription collection and drug dispensing. The implementation results showed a significant increase in the waiting time percentage of compounded drugs, which met the standard (≤60 minutes) from 26% to 43%. This increase was even higher for non-compounded drug waiting time (<20), from 27% to 48%. Drug stock checking, procedure standardization, and separation of service counters are the keys to reducing outpatient pharmacy waiting time.
Application-based Usability Evaluation Metrics

—Testing is one of the vital stages in the software development life cycle (SDLC). Usability testing is a very important field that helps the applications be easily used by the end-users. Because of the importance of usability testing, a metrics has been developed to help in measuring the usability through converting the main qualitative usability attributes in ISO to quantitative steps that provide the developer a framework to follow in developing to achieve usability of their applications and helps the tester with a checklist and a tool to measure the usability percentage of their application. The framework provides a set of steps to achieve the usability attributes and answers the question of how you could measure this attribute with the defined steps. The framework results in a 95% average accuracy in the high-rate application and a 59% average accuracy in the low-rate application. Finally, the framework is programmed in a tool to measure the usability percentage of the application through a checklist and provides a scheme to help the developer achieve the best results in usability.
Synthesis and Properties of Bio-Based Composites from Vegetable Oils and Starch

Natural polymers, such as starch, and polymers derived from renewable resources, such as vegetable oils, have been considered as alternatives to petroleum-based plastics during recent decades, due to environmental concerns. Indeed, these materials can offer a variety of advantages, such as low cost, wide availability, carbon neutrality, elevated thermal stability, and easily tunable mechanical properties. However, some of these polymers alone exhibit poor mechanical properties, making them not suitable for some applications. Hence, the reinforcement of these bio-based polymers with other materials is often considered to overcome this challenge. In this work, thermosetting composites based on tung and linseed oil resins were prepared using starch as reinforcement. Analyses from Soxhlet extractions showed that the higher the concentration of tung oil in comparison to linseed oil in the resins, the lower the mass of unreacted material, leading to an optimum resin entirely based on tung oil. Dielectric analysis (DEA), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA) indicated that the polymerization was completed in 3 h 20 min, at 140 °C, and that the composites were thermally stable until 270 °C. Finally, dynamic mechanical analysis (DMA) confirmed that the addition of starch to the resins increased the room temperature storage modulus (E′25) from 94 MPa to 893 MPa. Composites prepared with a resin formulation that did not contain a compatibilizer exhibited E′25 of 441 MPa. The composites investigated in this work are promising candidates for applications that require improved mechanical properties.
Theories and Molecular Basis of Vascular Aging: A Review of the Literature from VascAgeNet Group on Pathophysiological Mechanisms of Vascular Aging

Vascular aging, characterized by structural and functional alterations of the vascular wall, is a hallmark of aging and is tightly related to the development of cardiovascular mortality and age-associated vascular pathologies. Over the last years, extensive and ongoing research has highlighted several sophisticated molecular mechanisms that are involved in the pathophysiology of vascular aging. A more thorough understanding of these mechanisms could help to provide a new insight into the complex biology of this non-reversible vascular process and direct future interventions to improve longevity. In this review, we discuss the role of the most important molecular pathways involved in vascular ageing including oxidative stress, vascular inflammation, extracellular matrix metalloproteinases activity, epigenetic regulation, telomere shortening, senescence and autophagy.
Common synaptic input, synergies and size principle: Control of spinal motor neurons for movement generation

Understanding how movement is controlled by the CNS remains a major challenge, with ongoing debate about basic features underlying this control. In current established views, the concepts of motor neuron recruitment order, common synaptic input to motor neurons and muscle synergies are usually addressed separately and therefore seen as independent features of motor control. In this review, we analyse the body of literature in a broader perspective and we identify a unified approach to explain apparently divergent observations at different scales of motor control. Specifically, we propose a new conceptual framework of the neural control of movement, which merges the concept of common input to motor neurons and modular control, together with the constraints imposed by recruitment order. This framework is based on the following assumptions: (1) motor neurons are grouped into functional groups (clusters) based on the common inputs they receive; (2) clusters may significantly differ from the classical definition of motor neuron pools, such that they may span across muscles and/or involve only a portion of a muscle; (3) clusters represent functional modules used by the CNS to reduce the dimensionality of the control; and (4) selective volitional control of single motor neurons within a cluster receiving common inputs cannot be achieved. Here, we discuss this framework and its underlying theoretical and experimental evidence.
Validation of EEG forward modeling approaches in the presence of anisotropy in the source space

The quality of the inverse approach in electroencephalography (EEG) source analysis is — among other things — depending on the accuracy of the forward modeling approach, i.e., the simulation of the electric potential for a known dipole source in the brain. Here, we use multilayer sphere modeling scenarios to investigate the performance of three diﬀerent ﬁnite element method (FEM) based EEG forward approaches – subtraction, Venant and partial integration – in the presence of tissue conductivity anisotropy in the source space. In our studies, the eﬀect of anisotropy on the potential is related to model errors when ignoring anisotropy and to numerical errors, convergence behavior and computational speed of the diﬀerent FEM approaches. Three diﬀerent source space anisotropy models that best represent adult, child and premature baby volume conduction scenarios, are used. We conclude that, especially for the analysis of cortical development, but also for more general applications using EEG source analysis techniques, source space conductivity anisotropy should be modeled and the FEM Venant approach is an appropriate method.
Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows

Abstract Here, we report a case study implementation of reinforcement learning (RL) to automate operations in the scanning transmission electron microscopy workflow. To do so, we design a virtual, prototypical RL environment to test and develop a network to autonomously align the electron beam position without prior knowledge. Using this simulator, we evaluate the impact of environment design and algorithm hyperparameters on alignment accuracy and learning convergence, showing robust convergence across a wide hyperparameter space. Additionally, we deploy a successful model on the microscope to validate the approach and demonstrate the value of designing appropriate virtual environments. Consistent with simulated results, the on-microscope RL model achieves convergence to the goal alignment after minimal training. Overall, the results highlight that by taking advantage of RL, microscope operations can be automated without the need for extensive algorithm design, taking another step toward augmenting electron microscopy with machine learning methods.
Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey

Dense retrieval (DR) approaches based on powerful pre-trained language models (PLMs) achieved significant advances and have become a key component for modern open-domain question-answering systems. However, they require large amounts of manual annotations to perform competitively, which is infeasible to scale. To address this, a growing body of research works have recently focused on improving DR performance under low-resource scenarios. These works differ in what resources they require for training and employ a diverse set of techniques. Understanding such differences is crucial for choosing the right technique under a specific low-resource scenario. To facilitate this understanding, we provide a thorough structured overview of mainstream techniques for low-resource DR. Based on their required resources, we divide the techniques into three main categories: (1) only documents are needed; (2) documents and questions are needed; and (3) documents and question-answer pairs are needed. For every technique, we introduce its general-form algorithm, highlight the open issues and pros and cons. Promising directions are outlined for future research.
A novel architecture for room temperature microwave optomechanical experiments

We have developed a novel architecture for room temperature microwave cavity optomechanics, which is based on the coupling of a 3D microwave re-entrant cavity to a compliant membrane. Device parameters have enabled resolving the thermomechanical motion of the membrane and observing optomechanically induced transparency/absorption in the linear regime for the first time in a microwave optomechanical system operated at room temperature. We have extracted the single-photon coupling rate ([Formula: see text]) using four independent measurement techniques and, hence, obtained a full characterization of the proposed cavity optomechanical system.
Learning to Generate 3D Shapes from a Single Example

Existing generative models for 3D shapes are typically trained on a large 3D dataset, often of a specific object category. In this paper, we investigate the deep generative model that learns from only a single reference 3D shape. Specifically, we present a multi-scale GAN-based model designed to capture the input shape's geometric features across a range of spatial scales. To avoid large memory and computational cost induced by operating on the 3D volume, we build our generator atop the tri-plane hybrid representation, which requires only 2D convolutions. We train our generative model on a voxel pyramid of the reference shape, without the need of any external supervision or manual annotation. Once trained, our model can generate diverse and high-quality 3D shapes possibly of different sizes and aspect ratios. The resulting shapes present variations across different scales, and at the same time retain the global structure of the reference shape. Through extensive evaluation, both qualitative and quantitative, we demonstrate that our model can generate 3D shapes of various types.1
Construction of English Resume Corpus and Test with Pre-trained Language Models

Information extraction(IE) has always been one of the essential tasks of NLP. Moreover, one of the most critical application scenarios of information extraction is the information extraction of resumes. Constructed text is obtained by classifying each part of the resume. It is convenient to store these texts for later search and analysis. Furthermore, the constructed resume data can also be used in the AI resume screening system. Significantly reduce the labor cost of HR. This study aims to transform the information extraction task of resumes into a simple sentence classification task. Based on the English resume dataset produced by the prior study. The classification rules are improved to create a larger and more fine-grained classification dataset of resumes. This corpus is also used to test some current mainstream Pre-training language models (PLMs) performance.Furthermore, in order to explore the relationship between the number of training samples and the correctness rate of the resume dataset, we also performed comparison experiments with training sets of different train set sizes.The final multiple experimental results show that the resume dataset with improved annotation rules and increased sample size of the dataset improves the accuracy of the original resume dataset.
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection

Anomaly detection (AD), separating anomalies from normal data, has many applications across domains, from security to healthcare. While most previous works were shown to be effective for cases with fully or partially labeled data, that setting is in practice less common due to labeling being particularly tedious for this task. In this paper, we focus on fully unsupervised AD, in which the entire training dataset, containing both normal and anomalous samples, is unlabeled. To tackle this problem effectively, we propose to improve the robustness of one-class classification trained on self-supervised representations using a data refinement process. Our proposed data refinement approach is based on an ensemble of one-class classifiers (OCCs), each of which is trained on a disjoint subset of training data. Representations learned by self-supervised learning on the refined data are iteratively updated as the data refinement improves. We demonstrate our method on various unsupervised AD tasks with image and tabular data. With a 10% anomaly ratio on CIFAR-10 image data / 2.5% anomaly ratio on Thyroid tabular data, the proposed method outperforms the state-of-the-art one-class classifier by 6.3 AUC and 12.5 average precision / 22.9 F1-score.
Effective Non-Iterative Phase Retrieval of 2-D Bandlimited Signals With Applications to Antenna Characterization and Diagnostics

The phase retrieval problem is dealt with for the challenging case where just a single set of (phaseless) field data is available. In particular, the adoption of a new set of intersecting curves in the spectral plane allows decisive improvements over our recent approaches emulating the solution of crosswords puzzles, thus removing relevant limitations on the source size. Numerical examples support the given theory and confirm the effectiveness of the developed procedure.
Grasp Stability Prediction with Sim-to-Real Transfer from Tactile Sensing

Robot simulation has been an essential tool for data-driven manipulation tasks. However, most existing simulation frameworks lack either efficient and accurate models of physical interactions with tactile sensors or realistic tactile simulation. This makes the sim-to-real transfer for tactile-based manipulation tasks still challenging. In this work, we integrate simulation of robot dynamics and vision-based tactile sensors by modeling the physics of contact. This contact model uses simulated contact forces at the robot's end-effector to inform the generation of realistic tactile outputs. To eliminate the sim-to-real transfer gap, we calibrate our physics simulator of robot dynamics, contact model, and tactile optical simulator with real-world data, and then we demonstrate the effectiveness of our system on a zero-shot sim-to-real grasp stability prediction task where we achieve an average accuracy of 90.7% on various objects. Experiments reveal the potential of applying our simulation framework to more complicated manipulation tasks. We open-source our simulation framework at https://github.com/CMURoboTouch/Taxim/tree/taxim-robot.
Improving Fuzzy-Logic based Map-Matching Method with Trajectory Stay-Point Detection

The requirement to trace and process moving objects in the contemporary era gradually increases since numerous applications quickly demand precise moving object locations. The Map-matching method is employed as a preprocessing technique, which matches a moving object point on a corresponding road. However, most of the GPS trajectory datasets include stay-points irregularity, which makes map-matching algorithms mismatch trajectories to irrelevant streets. Therefore, determining the stay-point region in GPS trajectory datasets results in better accurate matching and more rapid approaches. In this work, we cluster stay-points in a trajectory dataset with DBSCAN and eliminate redundant data to improve the efficiency of the map-matching algorithm by lowering processing time. We reckoned our proposed method's performance and exactness with a ground truth dataset compared to a fuzzy-logic based map-matching algorithm. Fortunately, our approach yields 27.39% data size reduction and 8.9% processing time reduction with the same accurate results as the previous fuzzy-logic based map-matching approach.
Measurement of the branching fraction and search for CP violation in $D^0\rightarrow K^0_S\,K^0_S\,\pi^+\pi^-$ decays at Belle

We measure the branching fraction for the Cabibbo-suppressed decay $D^0\rightarrow K^0_S\,K^0_S\,\pi^+\pi^-$ and search for $CP$ violation via a measurement of the $CP$ asymmetry $A_{\rm CP}$ as well as the $T$-odd triple-product asymmetry $a^T_{\rm CP}$. We use 922 fb$^{-1}$ of data recorded by the Belle experiment, which ran at the KEKB asymmetric-energy $e^+e^-$ collider. The branching fraction is measured relative to the Cabibbo-favored normalization channel $D^0\rightarrow K^0_S\,\pi^+\pi^-$; the result is $\mathcal{B}(D^0\rightarrow K^0_S\,K^0_S\,\pi^+\pi^-) = [4.79 \pm 0.08\,({\rm stat}) \pm 0.10\,({\rm syst}) \pm 0.31\,({\rm norm})]\times 10^{-4}$, where the first uncertainty is statistical, the second is systematic, and the third is from uncertainty in the normalization channel. We also measure $A_{\rm CP} = [-2.51\,\pm 1.44\,({\rm stat})\,^{+0.11}_{-0.10}\,({\rm syst})]\%$, and $a^T_{\rm CP} = [-1.95\,\pm 1.42\,({\rm stat})\,^{+0.14}_{-0.12}\,({\rm syst})]\%$. These results show no evidence of CP violation.
ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding

Visual question answering is an important task in both natural language and vision understanding. However, in most of the public visual question answering datasets such as VQA, CLEVR, the questions are human generated that specific to the given image, such as 'What color are her eyes? '. The human generated crowdsourcing questions are relatively simple and sometimes have the bias toward certain entities or attributes [1, 55]. In this paper, we introduce a new question answering dataset based on image-ChiQA. It contains the real-world queries issued by internet users, combined with several related open-domain images. The system should determine whether the image could answer the question or not. Different from previous VQA datasets, the questions are real-world image-independent queries that are more various and unbiased. Compared with previous image-retrieval or image-caption datasets, the ChiQA not only measures the relatedness but also measures the answerability, which demands more fine-grained vision and language reasoning. ChiQA contains more than 40K questions and more than 200K question-images pairs. A three-level 2/1/0 label is assigned to each pair indicating perfect answer, partially answer and irrelevant. Data analysis shows ChiQA requires a deep understanding of both language and vision, including grounding, comparisons, and reading. We evaluate several state-of-the-art visual-language models such as ALBEF, demonstrating that there is still a large room for improvements on ChiQA.
Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis

In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges. The MuSe 2022 focuses on humor detection, emotional reactions and multimodal emotional stress utilising different modalities and data sets. In our work, different kinds of multimodal features are extracted, including acoustic, visual, text and biological features. These features are fused by TEMMA and GRU with self-attention mechanism frameworks. In this paper, 1) several new audio features, facial expression features and paragraph-level text embeddings are extracted for accuracy improvement. 2) we substantially improve the accuracy and reliability for multimodal sentiment prediction by mining and blending the multimodal features. 3) effective data augmentation strategies are applied in model training to alleviate the problem of sample imbalance and prevent the model form learning biased subject characters. For the MuSe-Humor sub-challenge, our model obtains the AUC score of 0.8932. For the MuSe-Reaction sub-challenge, the Pearson's Correlations Coefficient of our approach on the test set is 0.3879, which outperforms all other participants. For the MuSe-Stress sub-challenge, our approach outperforms the baseline in both arousal and valence on the test dataset, reaching a final combined result of 0.5151.
Detección de peatones en la noche usando Faster R-CNN e imágenes infrarrojas

En este artículo se presenta un sistema de detección de peatones en la noche, para aplicaciones en seguridad vehicular. Para este desarrollo se ha analizado el desempeño del algoritmo Faster R-CNN con imágenes en el infrarrojo lejano. Por lo que se constató que presenta inconvenientes a la hora de detectar peatones a larga distancia. En consecuencia, se presenta una nueva arquitectura Faster R-CNN dedicada a la detección en múltiples escalas, mediante dos generadores de regiones de interés (ROI) dedicados a peatones a corta y larga distancia, denominados RPNCD y RPNLD, respectivamente. Esta arquitectura ha sido comparada con los modelos para Faster R-CNN que han presentado los mejores resultados, como son VGG-16 y Resnet 101. Los resultados experimentales se han desarrollado sobre las bases de datos CVC-09 y LSIFIR, los cuales demostraron mejoras, especialmente en la detección de peatones a larga distancia, presentando una tasa de error versus FPPI de 16 % y sobre la curva Precisión vs. Recall un AP de 89,85 % para la clase peatón y un mAP de 90 % sobre el conjunto de pruebas de las bases de datos LSIFIR y CVC-09.