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OBJECTIVE: The COVID-19 pandemic has resulted in considerable stress for families, placing parents at risk for heightened psychological distress, while prompting widespread changes in mental health service delivery. This study evaluated treatment engagement, acceptability, and psychiatric distress among participants in the telehealth adaptation of the Connecting and Reflecting Experience (CARE) program after the onset of COVID-19. METHODS: CARE is a transdiagnostic, bigenerational, mentalizing-focused group parenting intervention based out of an outpatient child mental health clinic in an underserved urban community. Individuals participating in CARE during the clinic’s transition to telehealth services were recruited for participation in this pre-post design pilot study. Participants (N=12) completed self-report surveys before and after their first telehealth group session and at their 20-week follow-up. Quantitative and qualitative measures were used to evaluate psychiatric symptoms, treatment engagement, and preliminary acceptability of the adaptation. RESULTS: Self-reported mood and anxiety symptoms decreased significantly after 20 weeks of telehealth therapy. Participants reported high levels of therapeutic alliance and group cohesion in the telehealth format. Results also showed minimal participant-reported privacy concerns and a trend toward increased treatment engagement. CONCLUSIONS: These findings have implications regarding the acceptability of teletherapy interventions for caregivers of children during this period of heightened vulnerability and limited access to social support and health services. They also are relevant to establishing the preliminary acceptability of mentalizing-focused parenting inventions delivered via telehealth.

We present VisHash to address the problem of retrieving copies of images, particularly drawings and diagrams used in technical documents. While these images convey important technical information, it is difficult to search for these images. Recent advances in computer vision using deep learning methods have significantly advanced our ability to analyze and retrieve natural images, yet most of these advances do not directly apply to drawings and diagrams due to the very different nature of low-level features of the images. We find that classic computer vision techniques on patch-based extraction of image features such as relative brightness work better on drawings and diagrams compared to frequency-based similarity-preserving perceptual hashes; yet existing relative-brightness signatures often fail to calculate any meaningful signature due to the sparsity of information in technical drawings. We take advantage of the effectiveness of the relative-brightness signature and extend the approach to develop VisHash, a visual similarity preserving signature that works well on technical diagrams. Importantly, we demonstrate the high level of precision of VisHash for image retrieval compared with competing image hashes of large sets of real drawings from patents and technical images from the web. VisHash is available as open-source code to incorporate into image search and indexing workflows.

The "Viareggio 2013 Trial" is a hyperspectral dataset obtained from multiple overflights of the Italian city of Viareggio. Careful management of panels and vehicles in the scene enabled the development of valuable ground truth information. One pair of overflights occurred at different times on the same day, and another pair took place over different days. These data were used to compare and evaluate a variety of automated approaches for discovering anomalous changes. Co-registration of the images is acknowledged to be imprecise, so part of the challenge is to identify anomalous changes in a way that is robust to this misregistration. In particular, we employed a local co-registration adjustment (LCRA) algorithm to ameliorate the effects of misregistration; we employed non-maximal suppression (NMS) to take advantage of the discrete nature of the changes; and we used canonical correlation analysis (CCA) to reduce the dimension of our data. We found that, taken together, these improved the performance of the detectors in the low false alarm rate regime of operation.

Street-to-Shop fashion item retrieval is an instance-level image retrieval task in which a photo from a user is used to query a fashion image database in order to retrieve either the same or similar fashion items. This task is particularly challenging due to the domain shift between database photos, which tend to be stages, professional shots, and consumer photos that have a much greater variety in terms of quality, pose, etc. To reduce the problem difficulty, state-of-the-art approaches train one retrieval model per domain or fashion item category. In this work we propose a single detect-then-retrieve model that can be applied to any (query or database) image and which outperforms methods using domain or category-specific retrieval models by significant margins on the Exact Street2Shop benchmark dataset.

The ability to rank the images based on their appearance finds many real-world applications, such as image retrieval or image album creation. Despite the recent dominance of deep learning methods in computer vision which often result in superior performance, they are not always the methods of choice because they lack interpretability. In this paper, we investigate the possibility of improving the image aesthetic inference of the convolutional neural networks with hand-designed features that rely on domain expertise in various fields. We perform a comparison of hand-crafted feature sets in their ability to predict fine-grained aesthetics scores on two image aesthetics data sets. We observe that even feature sets published earlier are able to compete with more recently published algorithms and, by combining the algorithms, a significant improvement in predicting image aesthetics is possible. By using a tree-based learner, we perform the feature elimination to understand the best performing features overall and across different image categories. Only roughly 15% and 8% of the features are needed to achieve full performance in predicting a fine-grained aesthetic score and binary classification, respectively. By combining the hand-crafted features with metafeatures that predict the quality of an image based on convolutional neural network features, the model performs better than a baseline VGG16 model. One can, however, achieve more significant improvement in both aesthetics score prediction and binary classification by fusing the hand-crafted features and the penultimate layer activations. Our experiments indicate an improvement up to 2.2% achieving current state-of-the-art binary classification accuracy on the aesthetic visual analysis data set when the hand-designed features are fused with activations from VGG16 and ResNet50 networks.

We describe and compare two approaches to solid subpixel target detection in hyperspectral imagery. The first approach requires explicit models for both the target and the background, and employs a generalized likelihood ratio in order to obtain a detector that is optimized to those specific models. When this approach is most successful, a closed-form solution is obtained that permits the detector to be efficiently applied. A specific example of this approach is outlined in some detail, leading to the elliptically-contoured finite-target matched filter (EC-FTMF), a variant of the classical FTMF algorithm that uses a multivariate t -distribution instead of a Gaussian as the model for the background. The second approach also requires an explicit model of the target, but does not need a model for the background. In this second approach, matched pairs of data samples are created: for each pixel in the original hyperspectral image, a corresponding pixel is generated by implanting the target into the original pixel. These matched pairs are used as training data for a machine learning algorithm to classify pixels as either non-target or target. Here we use a support vector machine, but the matched pair machine learning (MPML) framework does not restrict the choice of classifier type. Detectors using both approaches are applied both to simulated data (with Gaussian and with multivariate fit distributed backgrounds) and to real hyperspectral data with known, referenced targets.

The convenience of cell-phone cameras has made them one of the most common ways by which people document their lives, whether it is everyday pleasures or celebrations. With thousands of images, it might prove to be a daunting task to organize them by hand. When applying automated algorithms to help us, we would like to have both images that are dear to us but are also of good quality. In this paper we explore the performance of the MobileNet CNN architecture, and the different design (inputs size, and layer depth) choices, in their ability in solving various aesthetic inference task: binary classification, regression, image cropping. We show that the baseline MobileNet architecture achieves near state-of-the-art results for binary classification on the AVA dataset while being more than 10 times smaller and compute efficient. We further show that these models, when trained for fine-grained aesthetics inference, achieve better cropping performance than other aestheticsbased croppers.

Chronic Pain syndromes are common after traumatic brain injury. This chapter discusses common etiologies for pain syndromes, both central and peripheral, common clinical presentations and treatment approaches to managing the tarumatic brain injury patient with a chronic pain syndrome.

Background Persons with spinal cord injuries (SCIs) are frequent utilizers of emergency medical services but are a poorly understood and medically complex population. As the treatment of acute spinal cord injuries improves, there is a growing population of patients suffering from the chronic neurological deficits and altered homeostasis resulting from those injuries. Objectives We sought to highlight the unique diagnostic challenges of treating persons with SCIs and to review ailments uncommon in the general population but often encountered in this population. Discussion Spinal cord anatomy is briefly reviewed and commonly used nomenclature and grading scales are defined. An organ by organ review is offered detailing unique clinical issues that pertain to those systems. Practice pearls and pitfall are elucidated when relevant. Psychiatric complications of this disease entity are also discussed. Conclusion A SCI is a devastating but increasingly survivable event. The long-term care of persons with SCIs is challenging because of the unique pathologies encountered in this population and the disruption of normal and expected physiological responses to common ailments. This review will facilitate a better understanding of the emergency care needs of this unique patient population.

The power output of a 4.85 kW residential photovoltaic (PV) system located in Rochester, NY is monitored during the partial solar eclipse of August 21, 2017. The data is compared with the data on a day before and on the same day, a year ago. The area of exposed solar disk is measured using astrophotography every 16 s of the eclipse. Global solar irradiance is estimated using the eclipse shading, time of the day, location coordinates, atmospheric conditions and panel orientation. A sharp decline, as expected in the energy produced is observed at the time of the peak of the eclipse. The observed data of the PV energy produced is related with the model calculations taking into account solar eclipse coverage and cloudiness conditions. The paper provides a cohesive approach of irradiance calculations and obtaining anticipated PV performance.

The paper deals with one of robust control methods that are suitable for controlled converter drives with synchronous and/or induction motors. In generally such a drive consist of power electronic converter feeding synchronous or induction motor and control system. Control system involves mainly superior speed or position loop, computing and logic control sector and subordinate current control loop, as minimum. The whole system must be completed by current and speed/position sensors, A/D and D/A converters respectively, drivers for electronic switches, etc.

In this study was investigated the possible effects of long term usage of mobile phone. The studied symptoms are headache, dizziness, extreme irritation, forgetfulness, neuropsychological discomfort, decrease of the reflex, clicking sound in the ears, and increase in carelessness. This survey study, using questionnaire, was conducted among randomly selected 146 university students in Kocaeli, Turkey. There is no effect on neuropsychological discomfort, increase in carelessness, headache, and clicking sound in the ears, but some statistical evidences are found that mobile phone may cause extreme irritation, decrease of the reflex, dizziness, and forgetfulness.

The paper deals with comparison of PI sliding mode, state space and fuzzy controller for speed control of synchronous motor drive. A short description of controllers is given. The goals of the paper are to give a short review of those speed controllers and their comparison according to often discussed questions. Is a fuzzy controller better then other ones? When and why to use a fuzzy controller? Is it cheaper, or robust, or easy to design? Is it just a fashion trend, or is it really progress? The paper gives brief information about the design of fuzzy controllers. The simulation experiment and real time measurement results are shown respectively. The verification of controllers' design is done on a test rig with permanent magnets synchronous motor (PMSM) fed from a voltage source inverter (VSI).

Pretibial myxedema is recognized as a type of localized myxedema that usually occurs on the anterior lower part of the legs and is often associated with thyroid disease. The lesions have been known to occur or become worse after thyroid surgery or radiation. We present here a case of a patient who sought surgical intervention for his disease. Report of a Case A 28-year-old man's condition was diagnosed as hyperthyroidism in 1974, and he received subsequent iodine 131 thyroid ablation. Three years later, the patient noted single, painless, pink nodules in the pretibial areas of each leg. Excisional biopsy specimens of the lesions were reported as "myxomas of the skin." The lesions recurred, and the patient was referred to a plastic surgeon for wide excision and splitthickness skin grafting of both pretibial areas. Three months after excision and grafting, there was evidence of recurrence of leg lesions within the graft