Miracle-dz/UniFAR-FTUs huggingface dataset:
Lability in the Parent’s Hostility and Warmth Toward Their Adolescent: Linkages to Youth Delinquency and Substance Use

According to family systems and life course theories, periods of intense change, such as early adolescence, can disrupt stable family systems, leading to changes in family relationships. In this longitudinal study, we investigate 2 types of change in parental hostility and warmth toward their children during early adolescence (Grades 6 to 8)—developmental trends (linear declines) and lability (within-person fluctuations around developmental trends)—and their linkages to youth substance use and delinquency in Grade 9 (N = 618). We also test if the linkages between lability and youth risky behavior are moderated by youth gender. After controlling for between-person differences in level and developmental trends, we find greater lability (more fluctuations) in youth-reported parents’ warmth and hostility are associated with greater youth delinquency, tobacco use, and polysubstance use initiation. The associations between youth-reported lability in mother and father hostility and polysubstance use demonstrated an inverted U shape pattern: Moderate levels of lability were associated with higher substance use but very low and high lability was associated with relatively lower rates of substance use. Many of the linkages between lability and youth delinquency were significant for girls but not boys. Fewer effects of lability on youth outcomes were found using parent reports. Developmental trends in parents’ warmth and hostility were also associated with youth delinquency. Lability has unique implications for youth adjustment, yet appears to differ by youth outcome, gender, and reporter. The discussion focuses on mechanisms that might link changes in parent–youth warmth and hostility to youth risky behavior.
A Method to Construct Efficient Carbon-Nanotube-Based Physical Unclonable Functions and True Random Number Generators

In this work, we present a novel method of increasing the entropy of the CNT-PUF, a Physical Unclonable Function (PUF) based on Carbon-NanoTube Field Effect Transistors (CNT-FETs). The binary responses of this PUF are based on the drain current ID of each CNT-FET under the influence of a particular gate-source voltage VGS, which, through the employment of a single threshold value for I D, can indicate whether each relevant CNT cell of the array is conducting (acting either as a true conductor or as a semiconductor) or not (acting as an insulator). In this work, we propose the adoption of individual threshold values for each such cell as part of the relevant PUF challenge, thereby significantly increasing the overall entropy of this PUF, as well as the security that it can provide. Moreover, this method allows for the realisation of a source of higher entropy in the form of a True Random Number Generator (TRNG). Finally, we note that our work and its results are most probably also relevant for other CNT- based PUFs, structures, and primitives that utilise a single current (or even, voltage) threshold to determine the state of the different CNT cells utilised.
Better Management Through Measurement: Integrating Archaeological Site Features into a GIS-Based Erosion and Sea Level Rise Impact Assessment—Blueskin Bay, New Zealand

Abstract Climate change poses a formidable site management challenge to coastal archaeologists and heritage planners. To manage its impacts, multi-scalar assessments are being employed that incorporate spatial technologies such as GIS, LiDAR, and GPS. While these technologies provide a powerful means of identifying susceptibility and tracking impacts, their ability to direct site management is limited by the accuracy of their incorporated archaeological geospatial data. Without current information regarding the location, condition, and extent of sites, spatial analyses cannot readily adapt to changing site conditions or drive site-specific management. To address this issue, this paper sets out a methodology that combines field survey with GIS-based spatial analysis to track erosion and inundation’s impact on coastal sites. This approach is applied to Blueskin Bay, New Zealand, where 26 sites were visited and had Differential GPS (DGPS) data collected of their visible archaeological features. The inclusion of this geospatial data into the GIS-based Digital Shoreline Analysis System (DSAS) tool and Sea Level Rise (SLR) model allowed sites to be considered on a scale much finer than conventional computer-based impact assessments. Ultimately, this fine scale approach has the capability of driving better, more informed site management.
Improving the Accuracy of Animal Species Classification in Camera Trap Images Using Transfer Learning

Understanding biodiversity, monitoring endangered species, and estimating the possible effect of climate change on particular regions all rely on animal species identification. Closed-circuit television (CCTV) cameras, which can collect huge volumes of video data, are an excellent environmental monitoring tool. However, manually evaluating these massive datasets is time-consuming, difficult, and expensive, emphasizing the need for automated ecological analysis.Deep learning models have transformed computer vision, handling problems such as object and species detection. Their cutting-edge performance qualifies them for this application. The purpose of this work was to create and test machine learning models for distinguishing diverse animal species using camera trap images. On VGG19, GoogLeNet (InceptionV3), ResNet50, and DenseNet121, we used transfer learning. The best multi-classification accuracy was attained by GoogLeNet (87%), followed by ResNet50 (83%), DenseNet (81%), and VGG19 (53%). This evidence suggests that transfer learning outperforms training models from scratch for this task.
Modulation of excitability as an early change leading to structural adaptation in the motor cortex

The excitability of the motor cortex is a function of single cell excitability, synaptic strength, and the balance between excitatory cells and inhibitory cells. Sustained periods of sensory stimulation enhance the excitability in the motor cortex. This adaptation, which represents an early change in cortical network function effective in motor learning and recovery from a motor deficit, is followed by longer‐lasting changes, such as modifications in cortical somatotopy, and by structural plasticity. Interventions aiming at increasing excitability also positively affect learning processes. Recent studies highlight that the cerebellum, especially the interpositus nucleus, plays a key function in the adaptation of the motor cortex to repeated trains of peripheral stimulation. Interpositus neurons, which receive inputs from the sensorimotor cortex and the spinal cord, are involved in somesthetic reflex behaviors and assist the cerebral cortex in transforming sensory signals to motor‐oriented commands by acting via the cerebello‐thalamo‐cortical projections. Moreover, climbing fibers originating in the inferior olivary complex and innervating the nucleus interpositus mediate highly integrated sensorimotor information derived from spinal modules. The intermediate cerebellum allows the motor cortex to tune the gain of polysynaptic responses originating from the spinal cord after repetitive trains of peripheral stimulation, allowing an online calibration of cutaneo‐muscular responses. © 2005 Wiley‐Liss, Inc.
Acupuncture for older adults with depression–a pilot study to assess acceptability and feasibility

The use of complementary and alternative medicine (CAM) by older adults is high and increasing. Community samples in the US show rates of CAM use in older adults of 41% (Astin et al., 2000) and community samples in Italy show rates of 29.5% (Buono et al., 2001). Acupuncture use ranged in these samples from 14%–34%. Depression and depressive symptoms are among the most frequent reasons for older adults accessing acupuncture therapy. Recent studies in younger adults show some promising results for the effectiveness of acupuncture in the treatment of depression [Allen et al., 1998; Roschke et al., 2000] but no studies have looked at the acceptability of such trials in older adults or the feasibility of delivery of an acupuncture service within mental health services for older adults. The authors evaluated a pilot service for the delivery of acupuncture to older adults with depression.
Model-Free Adaptive Iterative Learning Fault-Tolerant Control for Nonlinear Systems With Sensor Fault and Measurement Noise

To solve the challenge of fault-tolerant control (FTC) with both measurement noise and sensor fault, a model-free iterative learning adaptive fault-tolerant control strategy (IL-FTC) is proposed. An improved data-based featureless Kalman filter (IUKF) mechanism is devised for FTC. Then, an iterative estimation structure is designed using the dynamic linearization-based method to enhance the online estimation of some key parameters. Meanwhile, a new data-based fault detection (FD) algorithm is presented using an incremental broad learning system (IBLS) technique to detect sensor faults, avoiding the need for accurate model parameters for designing a fault detection observer. Based on this, controller reconfiguration is achieved by valid fault estimation. To improve the control performance of the system, a model-free adaptive controller (MFAC) that fully utilizes more historical information is developed. Finally, the superiority of the developed method is illustrated by a robotic arm simulation.
Effective rate analysis of MISO Rician fading channels

The delay constraints imposed by future wireless applications require a suitable metric for assessing their impact on the overall system performance. Since the classical Shannon's ergodic capacity fails to do so, the so-called effective rate was recently established as a rigorous alternative. Yet, most prior relevant works have considered only the typical case of Rayleigh fading which allows for tractable manipulations. In this paper, we relax this assumption by considering the more general Rician fading model for multiple-input single-output (MISO) systems. A new, analytical expression for the exact effective rate is derived, along with tractable expressions for the key parameters dictating the effective rate performance in the high and low signal-to-noise (SNR) regimes.
Automatic Lens Distortion Correction Using One-Parameter Division Models

We present a method to automatically correct the radial distortion caused by wide-angle lenses using the distorted lines generated by the projection of 3D straight lines onto the image. Lens distortion is estimated by the division model using one parameter, which allows to state the problem into the Hough transform scheme by adding a distortion parameter to better extract straight lines from the image. This paper describes an algorithm which applies this technique, providing all the details of the design of an improved Hough transform. We perform experiments using calibration patterns and real scenes showing a strong distortion to illustrate the performance of the proposed method. Source Code The source code, the code documentation, and the online demo are accessible at the IPOL web page of this article 1 . In this page, an implementation is available for download. Compilation and usage instructions are included in the README.txt file of the archive.
Greedy sparsity-constrained optimization

Finding optimal sparse solutions to estimation problems, particularly in underdetermined regimes has recently gained much attention. Most existing literature study linear models in which the squared error is used as the measure of discrepancy to be minimized. However, in many applications discrepancy is measured in more general forms such as log-likelihood. Regularization by ℓ1-norm has been shown to induce sparse solutions, but their sparsity level can be merely suboptimal. In this paper we present a greedy algorithm, dubbed Gradient Support Pursuit (GraSP), for sparsity-constrained optimization. Quantifiable guarantees are provided for GraSP when cost functions have the “Stable Hessian Property”.
Underwater Image Enhancement Network Based on Swin-Convs Transformer with Guidance Embedding

Underwater image enhancement (UIE) approaches struggle with the problem of dataset collection and attempt to restore underwater photos that have been damaged by light absorption and dispersion. In the paper, we propose to combine depth and more a priori informations to generate different kinds of high quality datasets, and then propose an underwater enhancement algorithm that improves the Transformer with a priori information. Firstly, a novel medium transmission guidance position embedding model is well-designed to provide the Transformer with relative position information, color bias information and global features of the feature map. Second, the restored convolution can grab more local attention given Swin Transformer's shortcomings in this area. Therefore, we propose an ingenious way to fuse convolution and core attention mechanisms to capture more enough local attention and adopt a new converter encoder that uses an on-chip converter block to enhance on-chip attention both in channel-wise and spatial-wise, thus effectively eliminating weather degradation at details, which is enhanced in terms of channel and spatial attention of Swin Transformer. The experimental results show that the enhanced underwater image PSNR index and SSIM index of our proposal are higher than those of other competitors. The subjective quality is also significantly improved, and the enhanced images with rich color and high definition are produced.
Spreading of healthy mood in adolescent social networks

Depression is a major public health concern worldwide. There is evidence that social support and befriending influence mental health, and an improved understanding of the social processes that drive depression has the potential to bring significant public health benefits. We investigate transmission of mood on a social network of adolescents, allowing flexibility in our model by making no prior assumption as to whether it is low mood or healthy mood that spreads. Here, we show that while depression does not spread, healthy mood among friends is associated with significantly reduced risk of developing and increased chance of recovering from depression. We found that this spreading of healthy mood can be captured using a non-linear complex contagion model. Having sufficient friends with healthy mood can halve the probability of developing, or double the probability of recovering from, depression over a 6–12-month period on an adolescent social network. Our results suggest that promotion of friendship between adolescents can reduce both incidence and prevalence of depression.
to Support

— This research measures the effect of gamification on learners’ academic performance, investigate their self-directed learning level within an online gamification learning environment and identify the relationship between learners self-directed learning level and their gamification learning experience. Pre-experimental research (single group) was carried out for a duration of 5 weeks among a group of 29 undergraduate year 2 learners from a non-major computer science course undertaking basic programming language subject. The learners SDL and knowledge on the subject was measured with a questionnaire and assessment test before and after the gamification learning intervention. A gamification experience questionnaire was used to evaluate their gamification learning experience after the intervention. The learning environment was infused with gamification in the form of competition to earn points, leaderboard ranks and python programmer badge. The learner’s academic performance has significantly improved (p=0.000 < 0.05) after the gamification injection. Leaners self-directed learning level also increased and there is a significant difference before (M=3.59) and after (M=4.22) the gamification strategy (p=0.000 < 0.05) imposed. Self-management, motivation and self-monitoring were all at high level with significance before and after (p=0.000 < 0.05) the gamified learning activities. Overall, the gamification strategy used in the online learning environment shows a positive influence to support learner’s self-directed learning.
Multiresolution Approximate Inverse Preconditioners

We introduce a new preconditioner for elliptic PDEs on unstructured meshes. Using a wavelet-inspired basis we compress the inverse of the matrix, allowing an effective sparse approximate inverse by solving the sparsity vs. accuracy conflict. The key issue in this compression is to use second generation wavelets which can be adapted to the unstructured mesh, the true boundary conditions, and even the PDE coefficients. We also show how this gives a new perspective on multiresolution algorithms such as multigrid, interpreting the new preconditioner as a variation on node-nested multigrid. In particular, we hope the new preconditioner will combine the best of both worlds: fast convergence when multilevel methods can succeed but with robust performance for more difficult problems. The rest of the paper discusses the core issues for the preconditioner: ordering and construction of a factored approximate inverse in the multiresolution basis, robust interpolation on unstructured meshes, automatic mesh coarsening, and purely algebraic alternatives. Some exploratory numerical experiments suggest the superiority of the new basis over the standard basis for several tough problems, including discontinuous anisotropic coefficients, strong convection, and indefinite reaction problems on unstructured meshes, with scalability like hierarchical basis methods achieved.
Local Anesthetics

Cocaine is a naturally occurring compound indigenous to the Andes Mountains, West Indies, and Java. It was the first anesthetic to be discovered and is the only naturally occurring local anesthetic; all others are synthetically derived. Cocaine was introduced into Europe in the 1800s following its isolation from coca beans. Sigmund Freud, the noted Austrian psychoanalyst, used cocaine on his patients and became addicted through self-experimentation. In the latter half of the 1800s, interest in the drug became widespread, and many of cocaine's pharmacologic actions and adverse effects were elucidated during this time. In the 1880s, Koller introduced cocaine to the field of ophthalmology, and Hall introduced it to dentistry
Convergence of discrete duality finite volume schemes for the cardiac bidomain model

We prove convergence of discrete duality finite volume (DDFV) schemes on distorted meshes for a class of simplified macroscopic bidomain models of the electrical activity in the heart. Both time-implicit and linearised time-implicit schemes are treated. A short description is given of the 3D DDFV meshes and of some of the associated discrete calculus tools. Several numerical tests are presented.
Contrastive Pretraining of Regression Tasks in Reliability Forecasting of Automotive Electronics

Predictive maintenance and reliability analysis have notoriously imperfect and unbalanced data because of few failure tests. This challenge limits the capability of supervised learning. Data-driven methods, especially deep learning, show great potential in capturing such complex relationships in many domains. Those methods rely heavily on the amount of data to extract meaningful representations. In contrast, data-poor domains depend on the use of simple architectures with a limited amount of trainable parameters. In this work, we show how pretraining with supervised contrastive learning can lead to better representations in data-poor domains such as the lifetime estimation on the example of solder joint. With such pre-trained models we are able to outperform end-to-end learning methods. Furthermore, we show that the learned representations improve classification tasks as well as rearession tasks.
A controlled study of peripheral neuropathy in systemic lupus erythematosus

In 34 patients with systemic lupus erythematosus (SLE) and 34 age‐ and sex‐matched healthy controls we performed standardized quantifiable neurological testing for neuropathic symptoms (Neuropathic Symptom Score) and deficits (Neurological Disability Score), as well as nerve conduction velocity studies (NCV) and determination of vibration thresholds (VT). SLE patients had more neuropathic symptoms and deficits than controls. Most quantitative and qualitative NCV attributes showed no difference between patients and controls. However, if categorized as NCV abnormalities of 2 or more nerves in each individual, a frequency of polyneuropathy of 21% was seen in SLE patients compared to 6% in controls. VT indicated a slight, but widespread, diffuse polyneuropathy in patients compared to controls.
Internet Topology over Time

There are few studies that look closely at how the topology of the Internet evolves over time; most focus on snapshots taken at a particular point in time. In this paper, we investigate the evolution of the topology of the Autonomous Systems graph of the Internet, examining how eight commonly-used topological measures change from January 2002 to January 2010. We find that the distributions of most of the measures remain unchanged, except for average path length and clustering coefficient. The average path length has slowly and steadily increased since 2005 and the average clustering coefficient has steadily declined. We hypothesize that these changes are due to changes in peering policies as the Internet evolves. We also investigate a surprising feature, namely that the maximum degree has changed little, an aspect that cannot be captured without modeling link deletion. Our results suggest that evaluating models of the Internet graph by comparing steady-state generated topologies to snapshots of the real data is reasonable for many measures. However, accurately matching time-variant properties is more difficult, as we demonstrate by evaluating ten well-known models against the 2010 data.
An adaptive contention window algorithm to hybrid OFDMA/CSMA mechanism in WLANs

The Distributed Coordination Function (DCF) in the IEEE 802.11 protocol is a random access scheme based on the carrier sense multiple access with collision avoidance (CSMA/CA) for wireless local area networks (WLANs). Recently, a novel MAC design based on orthogonal frequency division multiple access (OFDMA) and CSMA/CA has been proposed. And this design, by dividing the entire channel into a number of sub-channels and thereby enabling nodes to simultaneously transmit packets in different sub-channels, to some extent, helps improve the performance of MAC layer in WLANs. However, with simply combining the OFDMA technique with CSMA/CA scheme, it withholds the defects of low utilization in terms of bandwidth and time resources, involved in the original CSMA/CA mechanism. In this paper, we propose an adaptive contention window algorithm based on the conflict weights in order to mitigate the wastage of unnecessary bandwidth and time of the conventional binary exponential backoff (BEB) rules and further enhance the performance of the hybrid OFDMA/CSMA mechanism. The crucial idea of this adaptive algorithm lies in that each node would choose different values of Contention Window (CW) value according to its conflict weights, which are modified on the basis of channel situation whenever necessary. Simulation results validate that the proposed algorithm, dynamically as well as properly, sets CW value in proportion to the current channel situation, consequently enhancing the network performance.