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Soft biological tissues often have complex mechanical properties due to variation in structural components. In this paper, we develop a novel UNet-based neural network model for inversion in elasticity (El-UNet) to infer the spatial distributions of mechanical parameters from strain maps as input images, normal stress boundary conditions, and domain physics information. We show superior performance, both in terms of accuracy and computational cost, by El-UNet compared to fully-connected physics-informed neural networks in estimating unknown parameters and stress distributions for isotropic linear elasticity. We characterize different variations of El-UNet and propose a self-adaptive spatial loss weighting approach. To validate our inversion models, we performed various finite-element simulations of isotropic domains with heterogenous distributions of material parameters to generate synthetic data. El-UNet is faster and more accurate than the fully-connected physics-informed implementation in resolving the distribution of unknown fields. Among the tested models, the self-adaptive spatially weighted models had the most accurate reconstructions in equal computation times. The learned spatial weighting distribution visibly corresponded to regions that the unweighted models were resolving inaccurately. Our work demonstrates a computationally efficient inversion algorithm for elasticity imaging using convolutional neural networks and presents a potential fast framework for three-dimensional inverse elasticity problems that have proven unachievable through previously proposed methods., Comment: 25 pages, 9 figures

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4., Comment: 100 pages; updated authors list; fixed author names and added citation

Memory corruption is a serious class of software vulnerabilities, which requires careful attention to be detected and removed from applications before getting exploited and harming the system users. Symbolic execution is a well-known method for analyzing programs and detecting various vulnerabilities, e.g., memory corruption. Although this method is sound and complete in theory, it faces some challenges, such as path explosion, when applied to real-world complex programs. In this paper, we present a method for improving the efficiency of symbolic execution and detecting four classes of memory corruption vulnerabilities in executable codes, i.e., heap-based buffer overflow, stack-based buffer overflow, use-after-free, and double-free. We perform symbolic execution only on test units rather than the whole program to avoid path explosion. In our method, test units are considered parts of the program's code, which might contain vulnerable statements and are statically identified based on the specifications of memory corruption vulnerabilities. Then, each test unit is symbolically executed to calculate path and vulnerability constraints of each statement of the unit, which determine the conditions on unit input data for executing that statement or activating vulnerabilities in it, respectively. Solving these constraints gives us input values for the test unit, which execute the desired statements and reveal vulnerabilities in them. Finally, we use machine learning to approximate the correlation between system and unit input data. Thereby, we generate system inputs that enter the program, reach vulnerable instructions in the desired test unit, and reveal vulnerabilities in them. This method is implemented as a plugin for angr framework and evaluated using a group of benchmark programs. The experiments show its superiority over similar tools in accuracy and performance.

Cerebrovascular dynamics and pathomechanisms that evolve in the minutes and hours following traumatic vascular injury in the brain remain largely unknown. We investigated the pathophysiology evolution within the first three hours after closed-head traumatic brain injury (TBI) and subarachnoid hemorrhage (SAH), two common traumatic vascular injuries, in mice. We took a multi-modal imaging approach using photoacoustic, color Doppler, and magnetic resonance imaging (MRI) in mice. Brain oxygenation (%sO2) and velocity-weighted volume of blood flow (VVF) values significantly decreased from baseline to fifteen minutes after both TBI and SAH. TBI resulted in 19.2% and 41.0% ipsilateral %sO2 and VVF reductions 15 minutes post injury while SAH resulted in 43.9% %sO2 and 85.0% VVF reduction ipsilaterally (p<0.001). We found partial recovery of %sO2 from 15 minutes to 3-hours after injury for TBI but not SAH. Hemorrhage, edema, reduced perfusion, and altered diffusivity were evident from MRI scans acquired 90-150 minutes after injury in both models although the spatial distribution was mostly focal for TBI and diffuse for SAH. The results reveal that the cerebral %sO2 deficits immediately following injuries are reversible for TBI and irreversible for SAH. Our findings can inform future studies on mitigating these early responses to improve long-term recovery., Comment: Update in order of figure panels for Figure 3

Our findings indicated that brain stimulation exerted no effect on the patients cognitive functions. The study outcome suggest that tDCS over primary motor cortex may be considered as a potential nonpharmochological treatment add-on in MG. Larger-sized studies need to evaluate the significance of this approach is real-life practice.

The integrated neurocognitive rehabilitation platforms (INCRPs) refer to infrastructures and teams integrated for set of interventions which aim to restore, or compensate for cognitive deficits. Cognitive skills may be lost or altered due to brain damage resulting from diseases or injury. The INCRP is a two-way interactive process whereby people with neurological impairments work with specialists, professional staff, families, and community members to alleviate the impact of cognitive deficits. This perspective paper would highlight key elements required in INCRPs.

The Universal Concept of Mental Arithmetic System (UCMAS) is a modern representation of an ancient art of mental arithmetic. Although such a training is hypothesized to result in potential effects on mental capacity and cognitive performance in experienced trainees, this has not sufficiently been systematically studied. The present study was an attempt to compare the objective testing score of Less-trained (less than 6 months of training) and experienced (over 36 months of training) UCMAS-trained young adolescents in Shiraz. Thirty healthy participants aged 9-12 were recruited from UCMAS training centers in Shiraz. The two study arms (comprising 15 less-trained and 15 experienced children) were ensured to be sex- and age-matched. The Cambridge Brain Science-Cognitive Platform (CBS-CP) was employed as a media-rich computer testing battery for cognitive assessments. Moreover, cerebral blood levels of the participants was recorded through Hemoencephalography upon taking CBS-CP tasks (ANI task of each participant).The experienced UCMAS-trained participants were found to outperform in the Spatial Span (p=0.004), Digit Span (p=0.014) and Monkey Ladder (P=0.022) tests in which Short-Term Memory and Visuo-Constructive skills play important roles. Nevertheless, our findings revealed no statistically significant difference between-group differences in test scores which measured other cognitive domains. Adding to the existing body of evidence on the effect of UCMAS training on potentiating cognitive capacity, our findings suggest that experienced UCMAS trainees tend to outperform in some key cognitive domains including Short-Term Memory and Visuo-Constructive Capacity. Further experimental neuroscience studies using quantitative electroencephalography and functional magnetic- or optic-neuroimaging would shed further light to neurodynamics of such a differentiation.

The reciprocal impact of applied neuroscience and cognitive studies on humanities has been extensive and growing over the past 30 years of research. Studies on neuroaesthetics have provided novel insights in visual arts, music as well as abstract and dramatic art. Neuro-Art is an experimental concept in applied neuroscience where scientists can study the mechanistic pathways involved for instance in visual art through which creativity and artistic capacity might receive further empowerment. Based on the existing evidence, at least 3 large-scale brain networks are involved simultaneously when one is submitted to a creativity-related task. The question whether the key brain regions involved in visual art creativity can be identified and receive neuromodulation to get empowered prompted us to perform the present case investigation. Virtual reality and functional quantitative electroencephalography upon 2- vs 3-dimentional painting were employed to study cortical neurodynamics in a professional painting artist.

Cortical visual impairment is amongst the key pathological causes of pediatric visual abnormalities predominantly resulting from hypoxic-ischemic brain injury. Such an injury results in profound visual impairments which severely impairs the patient's quality of life. Given the nature of the pathology, treatments are mostly limited to rehabilitation strategies such as transcranial electrical stimulation and visual rehabilitation therapy. Here, we discussed an 11 year-old girl case with cortical visual impairment who underwent concurrent visual rehabilitation therapy and transcranial electrical stimulation resulting in her improved visual function. This novel and noninvasive therapeutic intervention has shown potential for application in neuro-visual rehabilitation therapy (nVRT).