Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of Props During Mealtime for Children With Autism Spectrum Disorders: Self-Regulation and Reinforcement

Mealtime is an important family routine commonly affected for families with children with autism spectrum disorders (ASD). Limited research is available regarding strategies families incorporate to support mealtime engagement. The purpose of this study was to explore the frequency and characterize the purpose of Props used during mealtimes with children with ASD. A total of 12 families with a child(ren), N = 14, aged 2 to 7 years, with ASD and mealtime challenges or eating difficulties participated in video-recorded mealtimes in their home. Independent coders analyzed mealtimes for the frequency and purpose of Props (items used to support child participation during mealtime). Props were used by 75% of families (n = 9); common Props included toys, electronics, and books. Props were used primarily as a self-regulation tool for the child and occasionally as positive reinforcement for specific behaviors. Overall, Props were used to support child engagement in mealtime. Occupational therapists should consider using Props as individualized, accessible, and supportive mealtime interventions for families and children.
Polyhedra Circuits and Their Applications

We introduce polyhedra circuits. Each polyhedra circuit characterizes a geometric region in $\mathbb{R}^d$. They can be applied to represent a rich class of geometric objects, which include all polyhedra and the union of a finite number of polyhedra. They can be used to approximate a large class of $d$-dimensional manifolds in $\mathbb{R}^d$. Barvinok developed polynomial time algorithms to compute the volume of a rational polyhedra, and to count the number of lattice points in a rational polyhedra in a fixed dimensional space $\mathbb{R}^d$ with a fix $d$. Define $T_V(d,\, n)$ be the polynomial time in $n$ to compute the volume of one rational polyhedra, $T_L(d,\, n)$ be the polynomial time in $n$ to count the number of lattice points in one rational polyhedra with $d$ be a fixed dimensional number, $T_I(d,\, n)$ be the polynomial time in $n$ to solve integer linear programming time with $d$ be the fixed dimensional number, where $n$ is the total number of linear inequalities from input polyhedra. We develop algorithms to count the number of lattice points in the geometric region determined by a polyhedra circuit in $O\left(nd\cdot r_d(n)\cdot T_V(d,\, n)\right)$ time and to compute the volume of the geometric region determined by a polyhedra circuit in $O\left(n\cdot r_d(n)\cdot T_I(d,\, n)+r_d(n)T_L(d,\, n)\right)$ time, where $n$ is the number of input linear inequalities, $d$ is number of variables and $r_d(n)$ be the maximal number of regions that $n$ linear inequalities with $d$ variables partition $\mathbb{R}^d$.
The Impact of a Mindfulness Intervention for Nurse Managers

Purpose: To examine the impact of a mindfulness intervention on nurse managers in an acute care hospital. Design: A pre/post intervention design with three data collection points: prior to, at the conclusion of, and at 3 months following the intervention. Method: A survey to measure the impact of a mindfulness workshops on nurse managers’ perception of professional quality of life, burnout, and perceived wellness. Findings: There were significant changes in the scores on the compassion satisfaction (T-1 mean 3.9, T-2 mean 4.5, p = .002) and burnout subscales (T-1 mean 3.4, T-2 mean 2.8, p = .016) of the Professional Quality of Life scale, and on personal burnout (T-1 mean 4.2, T-2 mean 3.8, p = .023) and work-related burnout (T-1 mean 4.0, T-2 mean 3.5, p = .029) on the Copenhagen Burnout Inventory scale, following the mindfulness intervention. The 3-month follow-up scores on compassion satisfaction were higher but not statistically significant (p = .810). Scores on the burnout scales, while lower than the preintervention levels, were higher at the 3-month follow-up than immediately following the intervention. Conclusions: These findings suggest that mindfulness practices need to be reinforced. Without continuous reinforcement, it may become a self-care practice moved to the bottom of the list of things to be done among the activities of a busy day.
Security-Aware Waveform and Artificial Noise Design for Time-Reversal-Based Transmission

Among the plenty of studies on physical layer security, multi-antenna signal processing and nodes cooperation are two widely adopted techniques. However, in practical systems there are many single-antenna devices that do not support cooperative transmission either. To guarantee the information security in this scenario, we propose a novel transmission strategy that exploits the temporal characteristics of a multipath fading channel. Specifically, we apply this strategy to a time-reversal-based communication system. We consider a joint design of signal waveform and artificial noise. Based on this approach, specific transmission schemes are designed for the cases with and without eavesdropper's channel state information (CSI), respectively. The numerical results show the superiority of our proposed schemes in terms of security enhancement even when the eavesdropper's CSI is unavailable.
Impacts of public transportation fare reduction policy on urban public transport sharing rate based on big data analysis

Urban transport is an important support system to the city. With the city's development, traffic congestion has become a major traffic problem nowadays and it is badly in need of solutions. Big data analysis has been widely used in the domain of transportation in recent years and it does great help to find solutions to different kinds of problems from historical data. In order to solve the urban traffic problems fundamentally, developing the public traffic is one of the major effective ways and bus priority policies and strategies are such important measures that they have contributions to the increase of public transport sharing rate. This paper mainly studied the influences of two bus fare adjustment policies in Beijing on urban public transport sharing rate based on big data analysis through the computer software SPSS, and then put forward corresponding recommendations to the reform of bus fares in Beijing.
The Validity of External:Internal Training Load Ratios in Rested and Fatigued Soccer Players

Purpose: To examine the relationship of external:internal training load ratios with fitness and assess the impact of fatigue. Method: Ten soccer players performed a lactate threshold test followed by two soccer simulations (BEAST90mod) 48 h apart. Recovery (TQR) and muscle soreness (DOMS) was measured before each trial. Internal Training load (TL) (iTRIMP) and external load total distance (TD), high intensity distance (HID), PlayerLoad™ (PL) mean metabolic power (MMP) high metabolic power distance (HP) were collected for each trial and external:internal ratios produced. The relationships between ratios and velocity at lactate threshold (vLT) and velocity at Onset of Blood Lactate Accumulation (vOBLA) were examined in both trials along with changes in ratios. Results: Total Quality of Recovery and DOMS showed large changes. There were trivial to large decreases in TL from trial 1 to 2. Moderate increases in ratios for TD:iTRIMP, PL:iTRIMP and MMP:iTRIMP were seen but only small/trivial for HP:iTRIMP and HID:iTRIMP. In rested conditions all ratios show large relationships with vLT and vOBLA. However vLT vs. HID:iTRIMP; PL:iTRIMP; HP:iTRIMP and vOBLA vs. TD:iTRIMP; PL:iTRIMP; MMP:iTRIMP became weaker under fatigue. Conclusions: Acute changes in the ratios have implications forthe use of ratios as fitness measures but also as indicators of fatigue.
A closed-form solution of inverse kinematic for 4 DOF tetrix manipulator robot

One of the most commonly used robots in the industry is the robot manipulator. The movement of robot manipulators in the industry is a mechanical system that requires mathematical modeling methods to represent the geometric aspect, and its dynamic aspect manipulation. A kinematic analysis is needed to analyze the robot's movement regardless of the effect of inertia. The movement of robots that can rotate or translate on each joint will be complex if the calculation of each joint using kinematic analysis. To complete the kinematic analysis, a method called Denavit Hartenberg parameter (DH-Parameter) is used where the DH parameter can be performed after compiling the geometry image before forming the parameters. Furthermore, these parameters will be inputted to form homogeneous matrix so that can be analyzed kinematic forward and kinematic inverse. Because of the robot construction in this study causes some limitation in the movement of robot manipulator. Joint elbow, wrist, and end effector can only move at an angle of −90°–90°.
Writing systems as modular objects: proposals for theory design in grapholinguistics

Abstract This paper gives an outline of the Modular Theory of Writing Systems by answering the question: what are the elements or modules that are necessary for a writing system to work? A writing system is a notational system for a natural language. Based on this characterization, it is obvious that a necessary component of a writing system is a specific language system. What eventually constitutes a writing system in addition to this language system is a device that, put simply, relates units of a language system to units of a script. This component is termed ‘graphematics’ in the present framework and is regarded as a necessary module of a writing system. Above that, another typical component of writing systems, namely ‘systematic orthography’, applies to the ‘graphematic solution space’ and restricts the spelling possibilities of specific words in accordance to their belonging to a specific level of the vocabulary of the language. Supplemented by reflections on the status of scripts as well as of IPA as a writing system, an answer is finally given to the pertinent question how spoken language and written language are related to each other. The answer is that this relation is of a considerably indirect nature.
Channel Coding for High Performance Wireless Control in Critical Applications: Survey and Analysis

Demanded by high-performance wireless (WirelessHP) networks for industrial control applications, channel coding should be used and optimized. However, the adopted coding schemes in modern wireless communication standards are not sufficient for WirelessHP applications, in terms of both low latency and high reliability. Starting from the essential characteristics of WirelessHP regarding channel coding, this paper gives a detailed analysis of currently used short packet coding schemes in industrial wireless sensor networks, including seven coding schemes and their possible variants. The metrics employed for evaluation are bit-error rate, packet error rate, and throughput. To find suitable coding schemes from a large number of options, we propose four principles to filter the most promising coding schemes. Based on overall comparison from the perspective of practical implementation, challenges of the available coding schemes are analyzed, and directions are recommended for future research. Some reflections on how to construct specially designed coding schemes for short packets to meet the high reliability and low-latency constraints of WirelessHP are also provided.
IEEE ITRW Working Group Position Paper-System Integration and Application: Gallium Nitride: Identifying and Addressing Challenges to Realize the Full Potential of GaN in Power Conversion Applications

The International Technology Roadmap for Wide Band Gap Power Semiconductors (ITRW) has a specific working group for the area of gallium nitride (GaN) applications. This article summarizes the working group's progress to date. The team has a range of members drawn from industry and academia to provide the short-,medium-, and long-term perspectives required for a roadmap.
An infrastructure of multi-pollutant air quality deterioration early warning system in spark platform

In recent years, the increase of multi-pollutant air pollution index has a direct impact on people's health. Relevant studies have pointed out that the exposure of air pollutants has a strong connection with asthma and other unhealthy diseases such as the lungs. The air pollution has brought health issue, but also caused a huge medical costs each year derived from the disease. Although there are many different approaches to forecasting air pollution, most of them are based on historical data and the environment of local server devices. Some of the data records may differ from those of real-time situations and those limitation of traditional hardware devices. Therefore, this project tries to adopt Azure, a cloud computing platform with advantages of scalability, reliability and agility, to set up a multi-pollutant air quality deterioration warning system. This project is divided into three parts. The first part is the collection of multi-pollutant data and the prediction of multi-pollutant air pollution quality. After the government and private data are collected and integrated into the database, the air quality of multi-pollutant are predicted. The second part is big data analysis of air quality indicators for pollutants indicator AQI, AAQI, and HAQI AIDE, a real-time deterioration warning system for deteriorating, was popularized in Spark for big data analytics framework and various reports were generated. The third part will be a combination of blockchain and perpetual system performance assessment. The overall system will integrate with the blockchain and conduct a continuous evaluation of the system.
Machine learning based approach for demand forecasting anti-aircraft missiles

The Korean military's demand forecasting of spare parts is a major topic for logistics assistance, which considerably affects efficient management of the national defense budget. A time series forecasting method has previously been used, but the results lack accuracy and need improvement. In this study, we collected mass data including 17,451,247 structured and unstructured data on anti-aircraft missile data in the army's DELIIS. Then, we used an ensemble technique to reduce the model's variability and demonstrated increased accuracy for demand forecasting compared with the previous time series method.
Differential Private POI Queries via Johnson-Lindenstrauss Transform

The growing popularity of location-based services is giving untrusted servers relatively free reign to collect huge amounts of location information from mobile users. This information can reveal far more than just a user’s locations but other sensitive information, such as the user’s interests or daily routines, which raises strong privacy concerns. Differential privacy is a well-acknowledged privacy notion that has become an important standard for the preservation of privacy. Unfortunately, existing privacy preservation methods based on differential privacy protect user location privacy at the cost of utility, aspects of which have to be sacrificed to ensure that privacy is maintained. To solve this problem, we present a new privacy framework that includes a semi-trusted third party. Under our privacy framework, both the server and the third party only hold a part of the user’s location information. Neither the server nor the third party knows the exact location of the user. In addition, the proposed perturbation method based on the Johnson Lindenstrauss transform satisfies the differential privacy. Two popular point of interest queries, $k$ -NN and Range, are used to evaluate the method on two real-world data sets. Extensive comparisons against two representative differential privacy-based methods show that the proposed method not only provides a strict privacy guarantee but also significantly improves performance.
Quantitative Characterization of Fast-Trap Behaviors in Al2O3/GeOx/Ge pMOSFETs

In this paper, fast trap behaviors in Ge pMOSFETs with Al<sub>2</sub>O<sub>3</sub>/GeO<sub><italic>x</italic></sub>/Ge gate-stack are evaluated and quantitatively characterized using a novel sub-1-ns ultrafast measurement (UFM) system. By changing the operation temperature and the rising time of the applied gate voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {g}}$ </tex-math></inline-formula>), it is found that the fast trap filling process could be observed within the first 10 ns after the gate voltage was applied, and the process would be more clearly characterized at a raised operation temperature. In addition, the trap density distribution and the time constant of the fast traps inside the valence band edge are experimentally extracted based on the UFMs. It is found that the trap densities inside the valence band edge are within the range of 10<sup>13</sup> to 10<sup>14</sup> cm<inline-formula> <tex-math notation="LaTeX">$^{-2}\cdot \text {eV}^{-1}$ </tex-math></inline-formula>, which are consistent with the reported values from the Hall measurements. It is experimentally confirmed that the time constants of the fast traps in Ge pMOSFETs with Al<sub>2</sub>O<sub>3</sub>/GeO<sub><italic>x</italic></sub>/Ge gate-stack are about 10 ns, and deeper energy level corresponds to smaller time constant.
PSF correction in soft x-ray tomography

In this manuscript, we introduce a linear approximation of the forward model of soft x-ray tomography (SXT), such that the reconstruction is solvable by standard iterative schemes. This linear model takes into account the three-dimensional point spread function (PSF) of the optical system, which consequently enhances the reconstruction data. The feasibility of the model is demonstrated on both simulated and experimental data, based on theoretically estimated and experimentally measured PSFs.
On the reproducibility of gradient-based Meta-Reinforcement Learning baselines

Meta-learning provides an appealing solution to the data-efficiency issue inherent in both deep supervised learning and (model-free) deep reinforcement learning. The diversity of tasks available in supervised meta-learning and meta-reinforcement learning enabled the fast progress we are recently observing in this field, since one can easily compare a new meta-learning method to existing algorithms. In this paper, we revisit one of these baselines on two basic meta-reinforcement learning problems: the multi-armed bandits and tabular MDPs. We provide updated results for MAML applied to these two problems, and show that MAML compares favorably to more recent meta-learning approaches, contrary to what was previously reported. Along with this baseline, we also include some new results on the same tasks for Reptile, a first-order meta-learning approach.
pGVG: a new Gateway-compatible vector for transformation of sugarcane and other monocot crops

Abstract The successful development of genetically engineered monocots using Agrobacterium-mediated transformation has created an increasing demand for compatible vectors. We have developed a new expression vector, pGVG, for efficient transformation and expression of different constructs for gene overexpression and silencing in sugarcane. The pCAMBIA2300 binary vector was modified by adding Gateway recombination sites for fast gene transfer between vectors and the maize polyubiquitin promoter Ubi-1 (ZmUbi1), which is known to drive high gene expression levels in monocots. Transformation efficiency using the pGVG vector reached up to 14 transgenic events per gram of transformed callus. Transgenic plants expressing the β-glucuronidase (GUS) reporter gene from pGVG showed high levels of GUS activity. qRT-PCR evaluations demonstrated success for both overexpression and hairpin-based silencing cassettes. Therefore, pGVG is suitable for plant transformation and subsequent applications for high-throughput production of stable transgenic sugarcane. The use of an expression cassette based on the ZmUbi1 promoter opens the possibility of using pGVG in other monocot species.
Geographic clines in wing morphology relate to colonization history in New World but not Old World populations of yellow dung flies

Geographic clines offer insights about putative targets and agents of natural selection as well as tempo and mode of adaptation. However, demographic processes can lead to clines that are indistinguishable from adaptive divergence. Using the widespread yellow dung fly Scathophaga stercoraria (Diptera: Scathophagidae), we examine quantitative genetic differentiation (QST) of wing shape across North America, Europe, and Japan, and compare this differentiation with that of ten microsatellites (FST). Morphometric analyses of 28 populations reared at three temperatures revealed significant thermal plasticity, sexual dimorphism, and geographic differentiation in wing shape. In North America morphological differentiation followed the decline in microsatellite variability along the presumed route of recent colonization from the southeast to the northwest. Across Europe, where S. stercoraria presumably existed for much longer time and where no molecular pattern of isolation by distance was evident, clinal variation was less pronounced despite significant morphological differentiation (QST>FST). Shape vector comparisons further indicate that thermal plasticity (hot‐to‐cold) does not mirror patterns of latitudinal divergence (south‐to‐north), as might have been expected under a scenario with temperature as the major agent of selection. Our findings illustrate the importance of detailed phylogeographic information when interpreting geographic clines of dispersal traits in an adaptive evolutionary framework.
Scene Flow Estimation Based on 3D Local Rigidity Assumption and Depth Map Driven Anisotropic Smoothness

In this paper, we present a novel method to estimate the dense scene flow from the aligned depth map and color image using variational framework. For scene flow estimation, most scenes can be seen as scenes composed by independent 3-D rigid parts, so we apply 3-D local rigidity assumption to data term as a fidelity measure for each pixel. Meanwhile, in order to improve the accuracy of scene flow estimation at the boundaries of motion, we assume that depth map and color image are aligned and utilize the boundaries information of depth map to yield smoothness term which is weighted by a depth map driven anisotropic diffusion tensor. In addition, an efficient numerical algorithm named primal-dual algorithm is implemented for the variational formulation of scene flow estimation. Our method is tested on the Middlebury data sets, and the real-world scene data set captured by KINECT. Experimental results show that our method can receive dense and accurate scene flow and preserve motion boundaries well.
BLS: A learning search algorithm with Bayesian learning

In learning search, it is assumed that a search algorithm has adequate opportunities to traverse the problem state space, and to sense and learn the topological (or probability) knowledge about the state space. In this paper, a new search algorithm based on Bayesian learning (algorithm BLS) is proposed, which finds the solution path for any given state pair (s, t) in the state space efficiently. The algorithm depicts the search as a process of pattern recognition for the current path. During its depth-first search, the BLS classifies the current path, depending on the probability distribution functions acquired in previous Bayesian learning, and prunes it if necessary. It is proved that the mean complexity of BLS grows linearly with the length of the solution path after sufficient training.