Miracle-dz/UniFAR-FTUs huggingface dataset:
Sea Surface Mixed Layer during the 10–11 June 1994 California Coastally Trapped Event

Abstract A midlevel, coastally trapped atmospheric event occurred along the California coast 10–11 June 1994. This feature reversed the surface wind field along the coast in a northerly phase progression. Along the central California coast, the winds at the coastal stations reverse before the corresponding coastal buoy offshore, then followed hours later by passage of the leading edge of an overcast stratus cloud. The sea surface temperature was much colder in the narrow strip along the coast. The cloud characteristics may be accounted for by a sea surface mixed layer (SSML) model beginning with the wind reversal and growing with the square root of time. Heat is lost from the SSML to the sea surface. A cloud forms when the air temperature at the top of the SSML is equal to the dewpoint. It is suggested that a bore develops on the top of the SSML, increasing the thickness of the SSML and the progression speed of the cloud to 8 m s−1. There is evidence that an undular bore with a leading cloud develops in t.
NUMERICAL INVESTIGATION OF IMPINGEMENT HEAT TRANSFER USING LINEAR AND NONLINEAR TWO-EQUATION TURBULENCE MODELS

This article concerns development of a prediction method for turbulent impingement heat transfer. An investigation of a single round unconfined impinging air jet under different flow and geometrical conditions is carried out to assess the performance of linear and nonlinear two-equation turbulence models. The results show that these models may be used for impinging jet heat transfer predictions with reasonable success, but application of an ad hoc realizability constraint is necessary to avoid overprediction of the stagnation zone heat transfer. The constitutive relationship of the nonlinear models is shown to be dominated by the linear part for the present problem. Inclusion of quadratic and cubic level strain terms does not significantly affect predictions and the coupling to streamline curvature appears weak. The major effect of using an anisotropic formulation of the turbulent heat fluxes has been shown to reduce stagnation zone heat transfer rates.
Recursively Generated Control Theoretic Splines for On-the-Fly Approximation

When data is received in small batches it is useful to be able to construct preliminary spline approximations using the data as it is received. In this paper we will present a constructive method for producing splines in a recursive manner. The method is based on using the previous spline as a target curve and modifying it based on the newly created data. This also allows the dimension of critical matrices to be kept small which increases numerical stability.
Laser Treatment Monitoring with Reflectance Confocal Microscopy

Laser treatments have become popular in Dermatology. In parallel to technologic development enabling the availability of different laser wavelengths, non-invasive skin imaging techniques, such as reflectance confocal microscopy (RCM), have been used to explore morphologic and qualitative skin characteristics. Specifically, RCM can be applied to cosmetically sensitive skin areas such as the face, without the need for skin biopsies. For these reasons, apart from its current use in skin cancer diagnosis, our systematic review reveals how RCM can be employed in the field of laser treatment monitoring, being particularly suitable for the evaluation of variations in epidermis and dermis, and pigmentary and vascular characteristics of the skin. This systematic review article aims to provide an overview on current applications of RCM laser treatment monitoring, while describing RCM features identified for different applications. Studies on human subjects treated with laser treatments, monitored with RCM, were included in the current systematic review. Five groups of treatments were identified and described: skin rejuvenation, scar tissue, pigmentary disorders, vascular disorders and other. Interestingly, RCM can assist treatments with lasers targeting all chromophores in the skin and exploiting laser induced optical breakdown. Treatment monitoring encompasses assessment at baseline and examination of changes after treatment, therefore revealing details in morphologic alterations underlying different skin conditions and mechanisms of actions of laser therapy, as well as objectify results after treatment.
Acceptability Bounds for Forward Starting Options Using Disciplined Convex Programming

Acceptability pricing is formulated, for the dual problem, as a disciplined convex program solvable by the software CVXOPT. Forward starting options are used to illustrate the procedures for acceptability defined by positive expectation under the concave distortion minmaxvar. The measures used are required to reprice the liquid assets and must belong to the support set of acceptability. The bounds obtained bounds are substantially tighter than they would have been by leaving out either of the repricing constraints or the support sets for acceptability.
“Children's Environmental Learning and the Use, Design and Management of Schoolgrounds

Abstract:This article examines school grounds as sites for play and environmental learning. It is based on a three-year project that involved 50 eight- to ten-year-old children at five Australian primary schools. Data collection occurred through multiple methods, including behavior mapping of children's play, interviews with children and teachers, and analysis of children's drawings of their schoolgrounds. The findings show large variations between the schools, particularly in the types of play and environmental learning in which children engage. These variations are related to variations in the physical qualities of the schoolground. However, we also found that school philosophies concerning the use and management of the outdoor school environment are equally or more important.
Bayesian-Optimized Hybrid Kernel SVM for Rolling Bearing Fault Diagnosis

We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in the time and frequency domains of four bearing failure forms, which addresses the issue of ambiguous fault identification caused by their nonlinearity and nonstationarity. The extracted feature vectors are then divided into training and test sets as SVM inputs for fault diagnosis. To optimize the SVM, we construct a hybrid kernel SVM using a polynomial kernel function and radial basis kernel function. BO is used to optimize the extreme values of the objective function and determine their weight coefficients. We create an objective function for the Gaussian regression process of BO using training and test data as inputs, respectively. The optimized parameters are used to rebuild the SVM, which is then trained for network classification prediction. We tested the proposed diagnostic model using the bearing dataset of the Case Western Reserve University. The verification results show that the fault diagnosis accuracy is improved from 85% to 100% compared with the direct input of vibration signal into the SVM, and the effect is significant. Compared with other diagnostic models, our Bayesian-optimized hybrid kernel SVM model has the highest accuracy. In laboratory verification, we took sixty sets of sample values for each of the four failure forms measured in the experiment, and the verification process was repeated. The experimental results showed that the accuracy of the Bayesian-optimized hybrid kernel SVM reached 100%, and the accuracy of five replicates reached 96.7%. These results demonstrate the feasibility and superiority of our proposed method for fault diagnosis in rolling bearings.
Designing a Self-orthogonal Quasi-cyclic Code with Extended Minimum Hamming Distance

A method to design a parity-check matrix with a generalized anti-Pasch configuration is developed for a self-orthogonal quasi-cyclic code as a regular low-density parity-check code. This method can be applied to search a parity-check matrix with an arbitrary constant column weight and with an arbitrary number of rows. The minimum Hamming distance of a code designed by this method is at least the column weight plus two for an even column weight and the column weight plus three for an odd column weight. It is shown that the error floor and undetected error probabilities of a code with a high code rate above 0.95 designed by this method are significantly improved compared with those of a conventional self-orthogonal quasi-cyclic code based on finite geometry.
Biofumigation by Mustard Plants as an Application for Controlling Postharvest Gray Mold in Apple Fruits

Gray mold caused by Botrytis cinerea is a critical disease that results in severe postharvest losses for the apple industry. In recent years, biological control has become an increasingly effective approach for controlling postharvest diseases in fruits. Brassica plants contain abundant natural compounds with known antimicrobial activity against numerous plant pathogens. In this study, a large-scale screening of 90 mustard cultivars was conducted to evaluate their biofumigation effects against B. cinerea. Among these, one mustard cultivar named Dilong-1, displayed the highest inhibitory effect against B. cinerea, and was able to completely inhibit mycelial growth. Further investigations showed that fumigation with Dilong-1 inhibited mycelial growth, sporulation, and spore germination of B. cinerea in vitro. In addition, fumigation using Dilong-1 showed a wide antifungal spectrum, including other fruit postharvest pathogens such as Phytophthora litchii. Furthermore, apple gray mold disease severity was significantly reduced by biofumigation using Dilong-1. Importantly, fumigation with Dilong-1 did not negatively impact final apple qualities, including weight loss, firmness, and total soluble solids. These results suggested that Dilong-1 significantly inhibited gray mold decay caused by B. cinerea without affecting the quality of apple fruits. In conclusion, biological fumigation of apple fruits with the mustard cultivar Dilong-1 is a promising eco-friendly approach for controlling apple gray mold during storage and shipment.
Prediction of Severe Synoptic Events in Coastal East Antarctica

Abstract The coastal region of the Antarctic continent is renowned for the frequent, intense blizzards and associated extreme weather that affect it, creating treacherous conditions that place human activities there in often perilous situations. The understanding and prediction of these events are therefore vital for continued safe operations on the continent. Severe wind events are investigated at two Australian coastal sites, Casey and Mawson. A summer case study at Casey and then the climatology of the events at the two stations are studied using station observations and atmospheric analyses. All events are found to be associated with extratropical cyclones that move close to the stations and lead to strong winds and heavy cloud cover. Most events see the passage of a high pressure ridge over the station ahead of the cyclone that blocks the cyclone, forcing it to move farther south close to the coast, thus intensifying the effect of the cyclone on the coastal weather. The ridge also produces calm, clea.
What is a neuron's firing rate?

For such an obvious and commonly-used a concept, there is no clear, universally accepted, method for calculating the firing rate of a neuron in an experiment with a limited number of trials. While the spike count and histogram ignore the most striking features of neuronal signaling: the fine temporal structure. The common alternative; mapping spike trains to a rate function, appears to assume that spiking is a Poisson process, with the rate providing an intensity function. However, it is pointed out here that the normal methods for calculating an intensity function give a poor result compared with a mapping based on the dynamics of synapses. This is surprising and mysterious. There are two common methods for reconstructing an intensity from a sample: kth nearest neighbour and symmetric kernel density estimation [5]. For spike trains these give plausible looking rate functions. Two other functions are given, these are based on the dynamics of synapses and look less plausible. Raster plot. This shows ten trials for a single stimulus. The first trial was used to construct the four rate functions below.
Maps of deformations in a cantilever beam using particle image velocimetry (PIV) and speckle patterns

PIV (particle image velocimetry) has been spreading in studies that use the movement of particles to monitor the displacement of an object or the flow of a fluid by means of velocity vectors using optical techniques and second order statistics. PIV is also known as laser speckle velocimetry when associated with speckle patterns. This technique has been used in works involving fluids, in general, building a map of velocity vectors representing the flow under analysis. This paper presents an approach by using PIV associated to speckle patterns for deformation measurements in a cantilever beam (ASTM A36 steel), one of the most common examples used in civil engineering, without the introduction of external particles. Results showed that the difference between PIV associated to speckle patterns and the analytic displacement values is increased along the beam length for a load of 1.96 N as an evidence of sensitivity of the proposed measurement method. This indicates that PIV is also capable for detecting displacement fields associated with laser speckle patterns in solid mechanics generating a map of deformation as an additional option for non-destructive tests.
An Alternative Numerical Method of Transient Response in Circuits With Distributed Parameters using Operator Calculus

This paper proposes an alternative approach based on the operator method for the calculation of transmission lines with distributed parameters. The network model is based upon an equivalent network diagram which is divided to n-section cells. The basic development of the method is described by numerous equations and a complete set of analytical expressions which were formulated for the system and proven to be efficient for the analysis of the electromagnetic transient response in transmission lines. The accuracy of the proposed method was considered from theoretical and empirical points of view and confirmed to be acceptable.
Damage Detection and Localization of Bridge Deck Pavement Based on Deep Learning

Bridge deck pavement damage has a significant effect on the driving safety and long-term durability of bridges. To achieve the damage detection and localization of bridge deck pavement, a three-stage detection method based on the you-only-look-once version 7 (YOLOv7) network and the revised LaneNet was proposed in this study. In stage 1, the Road Damage Dataset 202 (RDD2022) is preprocessed and adopted to train the YOLOv7 model, and five classes of damage were obtained. In stage 2, the LaneNet network was pruned to retain the semantic segmentation part, with the VGG16 network as an encoder to generate lane line binary images. In stage 3, the lane line binary images were post-processed by a proposed image processing algorithm to obtain the lane area. Based on the damage coordinates from stage 1, the final pavement damage classes and lane localization were obtained. The proposed method was compared and analyzed in the RDD2022 dataset, and was applied on the Fourth Nanjing Yangtze River Bridge in China. The results shows that the mean average precision (mAP) of YOLOv7 on the preprocessed RDD2022 dataset reaches 0.663, higher than that of other models in the YOLO series. The accuracy of the lane localization of the revised LaneNet is 0.933, higher than that of instance segmentation, 0.856. Meanwhile, the inference speed of the revised LaneNet is 12.3 frames per second (FPS) on NVIDIA GeForce RTX 3090, higher than that of instance segmentation 6.53 FPS. The proposed method can provide a reference for the maintenance of bridge deck pavement.
The Impact of Age and Body Composition on Bone Density among Office Worker Women in Hungary

The study’s aim was to investigate bone condition and see its associations with body composition and age among office worker women in Hungary. In total, 316 individuals participated in this study from Csongrad-Csanad county in 2019. Participants’ ages ranged from 18 to 62, with a mean of 41 years. A questionnaire was used to gather sociodemographic information, whereas body composition was measured using the Inbody 230, and bone density and bone quality were measured with the SONOST 3000 ultrasound device. Results were analyzed using descriptive statistics, ANOVA with Tukey’s post hoc test, correlation analysis, and an independent sample t-test. The results show that Body Fat Mass, Body Mass Index, Obesity Degree, and Percent Body Fat increase significantly as age increases, and Bone Quality Index and t-score decrease substantially. Furthermore, Bone Density and Bone Quality Index were positively influenced by most components of body composition. The differences between normal and osteopenia bone quality showed that Basal Metabolic Rate, Bone Mineral Content, Fat-Free Mass, Mineral Mass, Skeletal Lean Mass, and Skeletal Muscle Mass were lower in participants with osteopenia. Our results provide more evidence of the effects of body composition and age on bone density and quality. It was the first study in Hungary investigating this phenomenon, which could be useful for professionals and researchers who intend to understand the associations of bone density.
“Play in Focus”: Children Researching Their Own Spaces and Places for Play

Abstract:Young children are traditionally misunderstood and overlooked as expert communicants of their own cultures and are generally dismissed as a reliable resource for understanding neighborhood and community issues. This article will describe and discuss an intervention that attempted to position the child as expert and researcher of their own play environments. In this study, 32 primary school children from two schools situated in east Leeds, Yorkshire, England, used disposable cameras over a one week period in the autumn of 2002 to record and later reflect on their preferred spaces and places for play. The process explored means of engaging children as researchers of their own environments offering them the tools of the photo-diary and the technique of photoelicitation in generating data designed to influence policy for planning and change of play strategies at local and national government levels. This article discusses the data generated in terms of what the participative process attempted reveals about the capacities of young children to contribute to the planning and design agenda for supporting children's play in 21st century childhood in urban environments.
REFinD: Relation Extraction Financial Dataset

A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailment. However, these datasets fail to capture financial-domain specific challenges since most of these datasets are compiled using general knowledge sources such as Wikipedia, web-based text and news articles, hindering real-life progress and adoption within the financial world. To address this limitation, we propose REFinD, the first large-scale annotated dataset of relations, with ~29K instances and 22 relations amongst 8 types of entity pairs, generated entirely over financial documents. We also provide an empirical evaluation with various state-of-the-art models as benchmarks for the RE task and highlight the challenges posed by our dataset. We observed that various state-of-the-art deep learning models struggle with numeric inference, relational and directional ambiguity. To encourage further research in this direction, REFinD is available at https://www.jpmorgan. com/technology/artificial-intelligence/initiatives/refind-dataset/problem-motivation-outcome.
Membership Inference Attacks against Language Models via Neighbourhood Comparison

Membership Inference attacks (MIAs) aim to predict whether a data sample was present in the training data of a machine learning model or not, and are widely used for assessing the privacy risks of language models. Most existing attacks rely on the observation that models tend to assign higher probabilities to their training samples than non-training points. However, simple thresholding of the model score in isolation tends to lead to high false-positive rates as it does not account for the intrinsic complexity of a sample. Recent work has demonstrated that reference-based attacks which compare model scores to those obtained from a reference model trained on similar data can substantially improve the performance of MIAs. However, in order to train reference models, attacks of this kind make the strong and arguably unrealistic assumption that an adversary has access to samples closely resembling the original training data. Therefore, we investigate their performance in more realistic scenarios and find that they are highly fragile in relation to the data distribution used to train reference models. To investigate whether this fragility provides a layer of safety, we propose and evaluate neighbourhood attacks, which compare model scores for a given sample to scores of synthetically generated neighbour texts and therefore eliminate the need for access to the training data distribution. We show that, in addition to being competitive with reference-based attacks that have perfect knowledge about the training data distribution, our attack clearly outperforms existing reference-free attacks as well as reference-based attacks with imperfect knowledge, which demonstrates the need for a reevaluation of the threat model of adversarial attacks.
Multipartite entanglement theory with entanglement-nonincreasing operations

A key problem in quantum information science is to determine optimal protocols for the interconversion of entangled states shared between remote parties. While for two parties a large number of results in this direction is available, the multipartite setting still remains a major challenge. In this Letter, this problem is addressed by extending the resource theory of entanglement for multipartite systems beyond the standard framework of local operations and classical communication. Specifically, we consider transformations capable of introducing a small, controllable increase of entanglement of a state, with the requirement that the increase can be made arbitrarily small. We demonstrate that in this adjusted framework, the transformation rates between multipartite states are fundamentally dictated by the bipartite entanglement entropies of the respective quantum states. Remarkably, this approach allows the reduction of tripartite entanglement to its bipartite analog, indicating that every pure tripartite state can be reversibly synthesized from a suitable number of singlets distributed between pairs of parties.
The nonequilibrium evolution near the phase boundary

Using the single-spin flipping dynamics, we study the nonequilibrium evolution near the entire phase boundary of the 3D Ising model, and find that the average of relaxation time (RT) near the first-order phase transition line (1st-PTL) is significantly larger than that near the critical point (CP). As the system size increases, the average of RT near the 1st-PTL increases at a higher power compared to that near the CP. We further show that RT near the 1st-PTL is not only non-self-averaging, but actually self-diverging: relative variance of RT increases with system size. The presence of coexisting and metastable states results in a substantial increase in randomness near the 1st-PTL, and therefore makes the equilibrium more difficult to achieve.