Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of Freeze-Thaw Cycles on the Electromechanical Properties of Cement-Based Piezoelectric Composites

The electromechanical properties of 2-2 and 1-3 cement-based piezoelectric composites were experimentally studied with the same volume fraction of piezoelectric ceramics after freezing and thawing at -$20^{\circ} \mathrm{C}\sim 10^{\circ}\mathrm{C}$. The experimental results showed that with the increase of freeze-thaw cycles, the stress-strain curves of 2-2 and 1-3 composites gradually slowed down, the peak points decreased and the breaking strain increased. It showed that the damage caused by freeze-thaw cycles on the internal of the two kinds of samples led to the decrease of ultimate compressive strength and the increase of failure strain. The electric displacement amplitude and static sensitivity of the two composites decreased with the increase of freeze-thaw cycles. There was a critical point G in the strain-electric displacement curves of both samples. When the peak value of loading strain below point G, there was a significant linear relationship between the peak value of output electric displacement and the peak value of loading strain. Otherwise, there was a nonlinear relationship between them.
Efficacy of intranasal ketamine and midazolam for pediatric sedation: A double-blind, randomized clinical trial

Background: Pediatric patients feel significant fear and anxiety when undergoing surgeries. The ideal drug and its administration route have not been found yet. The aim of this study was to compare the efficacy and safety of intranasal (IN) ketamine and midazolam as premedication in children. Methods: We studied 71 eligible pediatric patients undergoing elective urologic surgeries, aged 2 to 6 years. The degree of sedation and separation scores was compared between the two groups. Additionally, hemodynamic parameters, before premedication, after induction of anesthesia, and during surgery were documented and compared between two groups. Postoperatively, any side effect was recorded as well. Results: Finally, the data from 71 children were analyzed. Recovery time was significantly longer in group K (ketamine) compared to group M (midazolam); 27.86±4.42 vs 38.19± 6.67 minutes respectively (P=0.01). No significant difference was observed in terms of sedation score between two groups of K & M; 3.29±0.78 vs 3 ±0.71 respectively (P=0.17), and not regarding separation score; 2.51±0.61 & 2.31±0.52 respectively (P=0.01). Vital signs were kept within the physiological limits in both groups with no marked fluctuations. Conclusion: To produce sedation in young children, both midazolam and ketamine were effective and safe by IN route.
Demonstrating the processes and outcomes of a rural Community Mental Health Rehabilitation Service: A realist evaluation

Background As part of significant mental health reform, the Community Mental Health Rehabilitation Service (CMHRS) was implemented in rural South Australia. The CMHRS is a 10-bed mental health residential program offering rehabilitative mental health support to rural residents. Aim To analyse the CMHRS service delivery model and its impact on recovery outcomes for consumers. Methods A mixed method, realist evaluation approach was utilised. A purposive sample of CMHRS staff (n = 6) and consumers (n = 8) were recruited. Consumer recovery was measured using the RAS-DS (on admission and discharge). Participants’ perspectives of the service were gained via one staff focus group (n = 6) and individual semi-structured interviews (consumers n = 6; staff n = 2). Pre-post RAS-DS scores were analysed using paired t-tests/Wilcoxon paired-signed rank test, with qualitative data analysed thematically. Results Significant positive increases in RAS-DS total scores were observed at discharge, supported by the qualitative themes of (re)building relationships and social connections and recovering health and wellbeing. Contextual factors (e.g. staffing) and program mechanisms (e.g. scheduling) impacting on service implementation were identified. Conclusion Maintaining a rehabilitation recovery-focused approach, balanced with an appropriately trained multi-disciplinary team, are vital for maximising positive consumer outcomes. Significance This realist evaluation identifies critical factors impacting rural mental health rehabilitation service delivery.
Trends in non-pharmaceutical intervention (NPI) related community practice for the prevention of COVID-19 in Addis Ababa, Ethiopia

Background The COVID-19 pandemic has affected Ethiopia since March 13, 2020, when the first case was detected in Addis Ababa. Since then, the incidence of cases has continued to increase day by day. As a result, the health sector has recommended universal preventive measures to be practiced by the public. However, studies on adherence to these preventive measures are limited. Objective To monitor the status of preventive practices of the population related to hand washing, physical distancing, and respiratory hygiene practices at selected sites within the city of Addis Ababa. Methods Weekly cross-sectional non-participatory observations were done during the period of April-June, 2020. Data was collected using the Open Data Kit (ODK) tool in ten public sites involving eight public facilities targeted for individual observations. Ten individuals were randomly observed at each facility over two days a week at peak hours of public services. WHO operational definitions of the preventive behaviors were adopted for this study. Observations were conducted anonymously at gates or entrances of public facilities and places. Results A total of 12,056 individual observations with 53% males and 82% in an estimated age range of 18–50 years age group were involved in this study. There was an increase in the practice of respiratory hygiene from 14% in week one to 77% in week 10, while those of hand hygiene and physical distancing changed little over the weeks from their baseline of 24% and 34%, respectively. Overall, respiratory hygiene demonstrated an increased rate of 6% per week, while hand hygiene and physical distancing had less than a 1% change per week, Females and the estimated age group of 18–50 years had practice changes in respiratory hygiene with no difference in hand hygiene and physical distancing practices. Respiratory hygiene took about six weeks to reach a level of 77% from its baseline of 24%, making an increment of about 9% per week. Conclusion The public practice of respiratory hygiene improved threefold whereas hand hygiene and physical distancing revealed no change. Regularly sustained public mobilization and mass education are required to sustain the achievements gained in respiratory hygiene and further hand hygiene and physical distancing.
Current and near-term emission control strategies for diesel powered generator sets

Diesel powered generator sets have traditionally been and remain the number-one choice for standby and emergency power systems around the world. With the growth of applications involving telecommunications, more and more diesel generator sets are being used due to their ready availability. As a mature engine technology, diesels offer higher reliability than other engine designs as well as greater flexibility. Cummins, one of the world's largest producers of diesel-powered generator sets, has been a leader in developing engines that are both fuel-efficient, and have fewer emissions than previous designs. While many existing small and medium-size diesel powered generator sets meet current and proposed air pollution control standards, reduction technologies are being applied to larger diesel engines to control oxides of nitrogen, particulate matter, carbon monoxide, and hydrocarbons. Some current and existing emission control technologies include: (1) more efficient combustion; (2) catalytic after treatment systems; (3) low or no sulfur fuels; and (4) high-pressure direct injection gas technology. This paper acquaints energy managers and power engineers with what Cummins and a number of other manufacturers are doing to ensure that diesel engine powered generator sets will remain a viable option for standby/emergency and prime power well into the future.
Image compression using wavelets and JPEG2000: a tutorial

The demand for higher and higher quality images transmitted quickly over the Internet has led to a strong need to develop better algorithms for the filtering and coding of such images. The introduction of the JPEG2000 compression standard has meant that for the first time the discrete wavelet transform (DWT) is to be used for the decomposition and reconstruction of images together with an efficient coding scheme. The use of wavelets implies the use of subband coding in which the image is iteratively decomposed into high- and low-frequency bands. Thus there is a need for filter pairs at both the analysis and synthesis stages. This paper aims in tutorial form to introduce the DWT, to illustrate its link with filters and filterbanks and to illustrate how it may be used as part of an image coding algorithm. It concludes with a look at the qualitative differences between images coded using JPEG2000 and those coded using the existing JPEG standard.
Functional Additive Models on Manifolds of Planar Shapes and Forms

Abstract The “shape” of a planar curve and/or landmark configuration is considered its equivalence class under translation, rotation, and scaling, its “form” its equivalence class under translation and rotation while scale is preserved. We extend generalized additive regression to models for such shapes/forms as responses respecting the resulting quotient geometry by employing the squared geodesic distance as loss function and a geodesic response function to map the additive predictor to the shape/form space. For fitting the model, we propose a Riemannian L 2-Boosting algorithm well suited for a potentially large number of possibly parameter-intensive model terms, which also yields automated model selection. We provide novel intuitively interpretable visualizations for (even nonlinear) covariate effects in the shape/form space via suitable tensor-product factorization. The usefulness of the proposed framework is illustrated in an analysis of (a) astragalus shapes of wild and domesticated sheep and (b) cell forms generated in a biophysical model, as well as (c) in a realistic simulation study with response shapes and forms motivated from a dataset on bottle outlines. Supplementary materials for this article are available online.
On Recoding Ordered Treatments as Binary Indicators

Researchers using instrumental variables to investigate ordered treatments often recode treatment into an indicator for any exposure. We investigate this estimand under the assumption that the instruments shift compliers from no treatment to some but not from some treatment to more. We show that when there are extensive margin compliers only (EMCO) this estimand captures a weighted average of treatment effects that can be partially unbundled into each complier group's potential outcome means. We also establish an equivalence between EMCO and a two-factor selection model and apply our results to study treatment heterogeneity in the Oregon Health Insurance Experiment.
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning framework that performs segmentation through matching each query feature to a learned class-specific prototype. However, this framework suffers from biased classification due to incomplete feature comparisons. To address this issue, we present an adaptive prototype representation by introducing class-specific and class-agnostic prototypes and thus construct complete sample pairs for learning semantic alignment with query features. The complementary features learning manner effectively enriches feature comparison and helps yield an unbiased segmentation model in the few-shot setting. It is implemented with a two-branch end-to-end network (i.e., a class-specific branch and a class-agnostic branch), which generates prototypes and then combines query features to perform comparisons. In addition, the proposed class-agnostic branch is simple yet effective. In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner. Extensive experiments on PASCAL-5$^{i}$ and COCO-20$^{i}$ demonstrate the superiority of our method. At no expense of inference efficiency, our model achieves state-of-the-art results in both 1-shot and 5-shot settings for semantic segmentation.
Dual-band slot-coupled patch antenna with broad bandwidth and high directivity for WLAN access point

A dual-band slot-coupled patch antenna for the external access point of the wireless local area network (WLAN) band is implemented. The antennas consist of two radiators on three layers. The first radiator is a slotted bow-tie antenna operating at the 2.4–2.483 GHz band. The second radiator is a patch antenna with parasitic elements operating at 4.095–5.845 GHz. To enhance the bandwidth, a coupled feeding was used in the first radiator and a parasitic patch was used in the second radiator. To improve the directivity and isolation in both the radiators a parasitic patch and chock were used. The peak gain was more than 6 dBi in the first antenna and more than 8 dBi in the second antenna.
Time and Memory Efficient Algorithm for Structural Graph Summaries over Evolving Graphs

Existing graph summarization algorithms are tailored to speciﬁc graph summary models, only support one-time batch computation, are designed and implemented for a speciﬁc task, or are evaluated using static graphs. Our novel, incremental, parallel algo-rithm addresses all of these shortcomings. We support inﬁnitely many structural graph summary models deﬁned in a formal language. All graph summaries can be updated in time O ( ∆ · d k ), where ∆ is the number of additions, deletions, and modiﬁcations to the input graph, d is its maximum degree, and k is the maximum distance in the subgraphs considered while summarizing. We empirically evaluate the performance of our incremental algorithm on benchmark and real-world datasets. Overall our experiments show that, for commonly used summary models and datasets, the incremental summarization algorithm almost always outperforms its batch counterpart, even when about 50% of the graph database changes. Updating the summaries of the real-world DyLDO-core dataset with our incremental algorithm is 5 to 44 times faster than computing a new summary, when using four cores. Furthermore, the incremental computations require a low memory overhead of only 8% ( ± 1%). Finally, the incremental summarization algorithm outperforms the batch algorithm even when using fewer cores.
Thundernna: a white box adversarial attack

The existing work shows that the neural network trained by naive gradient-based optimization method is prone to adversarial attacks, adds small malicious on the ordinary input is enough to make the neural network wrong. At the same time, the attack against a neural network is the key to improving its robustness. The training against adversarial examples can make neural networks resist some kinds of adversarial attacks. At the same time, the adversarial attack against a neural network can also reveal some characteristics of the neural network, a complex high-dimensional non-linear function, as discussed in previous work. In This project, we develop a first-order method to attack the neural network. Compare with other first-order attacks, our method has a much higher success rate. Furthermore, it is much faster than second-order attacks and multi-steps first-order attacks.
Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset

Vehicle classification is a hot computer vision topic, with studies ranging from ground-view up to top-view imagery. In remote sensing, the usage of top-view images allows for understanding city patterns, vehicle concentration, traffic management, and others. However, there are some difficulties when aiming for pixel-wise classification: (a) most vehicle classification studies use object detection methods, and most publicly available datasets are designed for this task, (b) creating instance segmentation datasets is laborious, and (c) traditional instance segmentation methods underperform on this task since the objects are small. Thus, the present research objectives are: (1) propose a novel semi-supervised iterative learning approach using GIS software, (2) propose a box-free instance segmentation approach, and (3) provide a city-scale vehicle dataset. The iterative learning procedure considered: (1) label a small number of vehicles, (2) train on those samples, (3) use the model to classify the entire image, (4) convert the image prediction into a polygon shapefile, (5) correct some areas with errors and include them in the training data, and (6) repeat until results are satisfactory. To separate instances, we considered vehicle interior and vehicle borders, and the DL model was the U-net with the Efficient-net-B7 backbone. When removing the borders, the vehicle interior becomes isolated, allowing for unique object identification. To recover the deleted 1-pixel borders, we proposed a simple method to expand each prediction. The results show better pixel-wise metrics when compared to the Mask-RCNN (82% against 67% in IoU). On per-object analysis, the overall accuracy, precision, and recall were greater than 90%. This pipeline applies to any remote sensing target, being very efficient for segmentation and generating datasets.
A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits

Crowdsourcing system has emerged as an effective platform for labeling data with relatively low cost by using non-expert workers. Inferring correct labels from multiple noisy answers on data, however, has been a challenging problem, since the quality of the answers varies widely across tasks and workers. Many existing works have assumed that there is a fixed ordering of workers in terms of their skill levels, and focused on estimating worker skills to aggregate the answers from workers with different weights. In practice, however, the worker skill changes widely across tasks, especially when the tasks are heterogeneous. In this paper, we consider a new model, called $d$ -type specialization model, in which each task and worker has its own (unknown) type and the reliability of each worker can vary in the type of a given task and that of a worker. We allow that the number $d$ of types can scale in the number of tasks. In this model, we characterize the optimal sample complexity to correctly infer the labels within any given accuracy, and propose label inference algorithms achieving the order-wise optimal limit even when the types of tasks or those of workers are unknown. We conduct experiments both on synthetic and real datasets, and show that our algorithm outperforms the existing algorithms developed based on more strict model assumptions.
Data-driven detection of drifting system parameters

Data taken from observations of the natural world or laboratory measurements often depend on parameters which can vary in unexpected ways. In this paper we demonstrate how machine learning can be leveraged to detect changes in global parameters from variations in an identified model using only observational data. This capability, when paired with first principles analysis, can effectively distinguish the effects of these changing parameters from the intrinsic complexity of the system. Here we illustrate this by identifying a set of governing equations for an experiment generating a weakly turbulent fluid flow, then analyzing variation in the coefficients of these equations to unravel the drift in its physical parameters.
Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z

Photometric redshift estimation algorithms are often based on representative data from observational campaigns. Data-driven methods of this type are subject to a number of potential deficiencies, such as sample bias and incompleteness. Motivated by these considerations, we propose using physically motivated synthetic spectral energy distributions in redshift estimation. In addition, the synthetic data would have to span a domain in colour-redshift space concordant with that of the targeted observational surveys. With a matched distribution and realistically modelled synthetic data in hand, a suitable regression algorithm can be appropriately trained; we use a mixture density network for this purpose. We also perform a zero-point re-calibration to reduce the systematic differences between noise-free synthetic data and the (unavoidably) noisy observational data sets. This new redshift estimation framework, syth-z , demonstrates superior accuracy over a wide range of redshifts compared to baseline models trained on observational data alone. Approaches using realistic synthetic data sets can therefore greatly mitigate the reliance on expensive spectroscopic follow-up for the next generation of photometric surveys.
Developments in the Tensor Network — from Statistical Mechanics to Quantum Entanglement

Tensor networks (TNs) have become one of the most essential building blocks for various fields of theoretical physics such as condensed matter theory, statistical mechanics, quantum information, and quantum gravity. This review provides a unified description of a series of developments in the TN from the statistical mechanics side. In particular, we begin with the variational principle for the transfer matrix of the 2D Ising model, which naturally leads us to the matrix product state (MPS) and the corner transfer matrix (CTM). We then explain how the CTM can be evolved to such MPS-based approaches as density matrix renormalization group (DMRG) and infinite time-evolved block decimation. We also elucidate that the finite-size DMRG played an intrinsic role for incorporating various quantum information concepts in subsequent developments in the TN. After surveying higher-dimensional generalizations like tensor product states or projected entangled pair states, we describe tensor renormalization groups (TRGs), which are a fusion of TNs and Kadanoff-Wilson type real-space renormalization groups, focusing on their fixed point structures. We then discuss how the difficulty in TRGs for critical systems can be overcome in the tensor network renormalization and the multi-scale entanglement renormalization ansatz.
Machine Learning Based Forward Solver: An Automatic Framework in gprMax

General full-wave electromagnetic solvers, such as those utilizing the finite-difference time-domain (FDTD) method, are computationally demanding for simulating practical GPR problems. We explore the performance of a near-real-time, forward modeling approach for GPR that is based on a machine learning (ML) architecture. To ease the process, we have developed a framework that is capable of generating these ML-based forward solvers automatically. The framework uses an innovative training method that combines a predictive dimensionality reduction technique and a large data set of modeled GPR responses from our FDTD simulation software, gprMax. The forward solver is parameterized for a specific GPR application, but the framework can be extended in a straightforward manner to different electromagnetic problems.