Supporting Children Transitioning to Secondary School: A Qualitative Investigation into Families' Experiences of a Novel Online Intervention

Supporting children to successfully transition from primary to secondary school is of utmost importance for several reasons, including to prevent future emotional and behavioural problems. Level Up is a novel, UK-based intervention consisting of five online group sessions, straddling the summer holidays, and providing at-risk children and their parents/carers with skills to manage their behaviour, emotions, and relationships to support their transition to secondary school. A prior evaluation of Level Up reported a need to better describe the mechanisms of change. This study therefore evaluated the experiences of children and their parents/carers regarding the facilitators and barriers to engagement and change, and the perceived impact. Fourteen children and 17 parents/carers were interviewed. Identified barriers and facilitators were: (1) Having a safe, supportive, and fun space, (2) Learning through connection, (3) A family approach, (4) Problematic group dynamics, and (5) Connecting through video calls. Perceived impact was described as: (1) Empowering children, (2) Supporting children socially, (3) Supporting parents and carers in their parenting role, and (4) Supporting a successful transition to secondary school. Another theme (5) describes some families’ experiences of limited impact. These findings can be used to better understand how to support children in their school transition

Effectiveness of the Project P.A.T.H.S. in Hong Kong: Evaluation Based on Different Strategies and Different Studies over Time

2012-2013 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Project P.A.T.H.S. in Hong Kong: New Curriculum in Response to Adolescent Developmental Issues

2011-2012 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Positive Youth Development Constructs: Conceptual Review and Application

Editorialpublished_or_final_versio

Actively Preventing Negative Transfer

Transfer learning is a common technique used in a wide variety of deep learning applications. Transfer learning methods are typically used to make use of a source domain, where there is an abundance of labeled data, to make inferences in a target domain, where labeled data is scarce. In the digital age, improving a model’s ability to generalize knowledge gained from the massive amount of data available online to new contexts is crucial. Most new contexts of interest, like radiological scans, have very few labels, an obstacle that can be overcome with improved transfer learning methods. A basic transfer learning technique involves resetting the weights and biases associated with the last few layers of a deep learning model that has been trained on the source domain, and then re-training the model on the target domain. This a very widely used technique, but can often times result in a phenomenon known as negative transfer. Negative transfer occurs when the knowledge gained in the source domain proves to be harmful when transferring to the target domain. In order to prevent this phenomenon, our team is focusing on making a systematic method for determining which weights and biases should be reset when transferring knowledge. The basic idea is that if the source and target domains are similar, then most of the models knowledge gained in the source domain will be transferred to the target domain. However, if the source and target domains are different, the model will forget that knowledge which would be harmful in its learning the target domain

Investigating Dataset Distinctiveness

Just as a human might struggle to interpret another human’s handwriting, a computer vision program might fail when asked to perform one task in two different domains. To be more specific, visualize a self-driving car as a human driver who had only ever driven on clear, sunny days, during daylight hours. This driver – the self-driving car – would inevitably face a significant challenge when asked to drive when it is violently raining or foggy during the night, putting the safety of its passengers in danger. An extensive understanding of the data we use to teach computer vision models – such as those that will be driving our cars in the years to come – is absolutely necessary as these sorts of complex systems find their way into everyday human life. This study works to develop a comprehensive meaning of the style of a dataset, or the quantitative difference between cursive lettering and print lettering, with respect to the image data used in the field of computer vision. We accomplished this by asking a machine learning model to predict which commonly used dataset a particular image belongs to, based on detailed features of the images. If the model performed well when classifying an image based on which dataset it belongs to, that dataset was considered distinct. We then developed a linear relationship between this distinctiveness metric and a model’s ability to learn from one dataset and test on another, so as to have a better understanding of how a computer vision system will perform in a given context, before it is trained

Cloud Resource Optimization for Processing Multiple Streams of Visual Data

Hundreds of millions of network cameras have been installed throughout the world. Each is capable of providing a vast amount of real-time data. Analyzing the massive data generated by these cameras requires significant computational resources and the demands may vary over time. Cloud computing shows the most promise to provide the needed resources on demand. In this article, we investigate how to allocate cloud resources when analyzing real-time data streams from network cameras. A resource manager considers many factors that affect its decisions, including the types of analysis, the number of data streams, and the locations of the cameras. The manager then selects the most cost-efficient types of cloud instances (e.g. CPU vs. GPGPU) to meet the computational demands for analyzing streams. We evaluate the effectiveness of our approach using Amazon Web Services. Experiments demonstrate more than 50% cost reduction for real workloads

Clinical Holistic Medicine: Holistic Adolescent Medicine

The holistic medical approach seems to be efficient and can also be used in adolescent medicine. Supporting the teenager to grow and develop is extremely important in order to prevent many of the problems they can carry into adulthood. The simple consciousness-based, holistic medicine — giving love, winning trust, giving holding, and getting permission to help the patient feel, understand, and let go of negative beliefs — is easy for the physician interested in this kind of practice and it requires little previous training for the physician to be able to care for his/her patient. A deeper insight into the principles of holistic treatment and a thorough understanding of our fellow human beings are making it work even better. Holistic medicine is not a miracle cure, but rather a means by which the empathic physician can support the patient in improving his/her future life in respect to quality of life, health, and functional capacity — through coaching the patient to work on him/herself in a hard and disciplined manner. When the patient is young, this work is so much easier. During our lifetime, we have several emotional traumas arranged in the subconscious mind with the smallest at the top, and it is normal for the person to work on a large number of traumatic events that have been processed to varying degrees. Some traumas have been acknowledged, some are still being explored by the person, and yet others are still preconscious, which can be seen for example in the form of muscle tension. Sometimes the young dysfunctional patient carries severe traumas of a violent or sexual nature, but the physician skilled in the holistic medical toolbox can help the patient on his/her way to an excellent quality of life, full self-expression, a love and sex life, and a realization of his/her talents — all that a young patient is typically dreaming about. Biomedicine is not necessary or even recommended when the physical or mental symptoms are caused by disturbances in the personal development that can be corrected with love and understanding. If possible, biomedicine must be avoided, even if this means suffering for the young person, who needs to confront the tough realities of life in order to grow into an able and sound adult

Kilohertz transcranial magnetic perturbation (kTMP) as a new non-invasive method to modulate cortical excitability

Non-invasive brain stimulation (NIBS) provides a method for safely perturbing brain activity, and has been employed in basic research to test hypotheses concerning brain–behavior relationships with increasing translational applications. We introduce and evaluate a novel subthreshold NIBS method: kilohertz transcranial magnetic perturbation (kTMP). kTMP is a magnetic induction method that delivers continuous kHz-frequency cortical electric fields (E-fields) which may be amplitude modulated to potentially mimic electrical activity at endogenous frequencies. We used transcranial magnetic stimulation to compare the amplitude of motor-evoked potentials (MEPs) in a hand muscle before and after kTMP. In Experiment 1, we applied kTMP for 10 min over motor cortex to induce an E-field amplitude of approximately 2.0 V/m, comparing the effects of waveforms at frequencies of 2.0, 3.5, or 5.0 kHz. In Experiments 2 and 3, we used two forms of amplitude-modulated kTMP (AM kTMP) with a carrier frequency at 3.5 kHz and modulation frequencies of either 20 or 140 Hz. The only percept associated with kTMP was an auditory tone, making kTMP amenable to double-blind experimentation. Relative to sham stimulation, non-modulated kTMP at 2.0 and 3.5 kHz resulted in an increase in cortical excitability, with Experiments 2 and 3 providing a replication of this effect for the 3.5 kHz condition. Although AM kTMP increased MEP amplitude compared to sham, no enhancement was found compared to non-modulated kTMP. kTMP opens a new experimental NIBS space inducing relatively large amplitude subthreshold E-fields able to increase cortical excitability with minimal sensation