A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders

Zero shot learning in Image Classification refers to the setting where images from some novel classes are absent in the training data but other information such as natural language descriptions or attribute vectors of the classes are available. This setting is important in the real world since one may not be able to obtain images of all the possible classes at training. While previous approaches have tried to model the relationship between the class attribute space and the image space via some kind of a transfer function in order to model the image space correspondingly to an unseen class, we take a different approach and try to generate the samples from the given attributes, using a conditional variational autoencoder, and use the generated samples for classification of the unseen classes. By extensive testing on four benchmark datasets, we show that our model outperforms the state of the art, particularly in the more realistic generalized setting, where the training classes can also appear at the test time along with the novel classes

Lymphoblasts with Auer rod – like inclusions in a case of paediatric B lymphoblastic leukemia

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137281/1/pbc26392.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137281/2/pbc26392_am.pd

Impact of primary breast cancer therapy on energetic capacity and body composition

PURPOSE: This observational study was designed to measure baseline energy parameters and body composition in early-stage breast cancer patients, and to follow changes during and after various modalities of treatment. This will provide information to aid in the development of individualized physical activity intervention strategies. METHODS: Patients with newly diagnosed stage 0-III breast cancer were enrolled into three cohorts: A (local therapy alone), B (endocrine therapy), or C (chemotherapy with or without endocrine therapy). At baseline, 6 months, and 12 months, subjects underwent a stationary bicycle protocol to assess power generation and DEXA to assess body composition. RESULTS: Eighty-three patients enrolled. Patients had low and variable levels of power generation at baseline (mean power per kilogram lean mass 1.55 W/kg, SD 0.88). Power normalized to lean body mass (W/kg) decreased significantly, and similarly, by 6 months in cohorts B (1.42-1.04 W/kg, p = 0.008) and C (1.53-1.18 W/kg, p < 0.001). In all cohorts, there was no recovery of power generation by 12 months. Cohort C lost lean body mass (- 1.5 kg, p = 0.007), while cohort B maintained lean body mass (- 0.2 kg, p = 0.68), despite a similar trajectory in loss of power. Seven patients developed sarcopenia during the study period, including four patients who did not receive any chemotherapy (cohort B). CONCLUSIONS: The stationary bike protocol was feasible, easy, and acceptable to patients as a way to measure energetic capacity in a clinical setting. Early-stage breast cancer patients had low and variable levels of power generation, which worsened following primary therapy and did not show evidence of 'spontaneous recovery' by 12 months. Effective physical activity interventions will need to be personalized, accounting for both baseline ability and the effect of treatment

Design and Modelling of Tunnel Field Effect Transistor- using TCAD Modeling

The purpose of this research was to suggest a junction-less strategy for a vertical Tunnel Field Effect Transistor, which would increase the device's efficiency. In this study, we examine the similarities and differences between a negative capacitor TFET and a vertically generated TFET with a source pocket and a heterostructure-based nanowire gate. And how the channel transit impacts the output qualities of a sub-100 nanometer sized device. The Silvaco TCAD (a commercially available tool) was used to simulate a tri-layer high-K dielectric made of hafnium zirconium oxide (HZO) and titanium dioxide (TiO2) materials as gate stacking to the V-TFET and GAA-NC-TFET structures, and the tunnelling and transport parameters were calibrated experimentally. A short bandgap material, GaSb, in the home region to enhance carrier tunnelling via the mentioned three source (GaSb)-channel (Si) heterojunction at varying biases were utilized. Motion, tube length, and saturating velocity are only few of the transport channel characteristics that are investigated. As a result of the building's vertical orientation, the electric field is enhanced, allowing for an ION current of up to 104 Am2. The most unexpected result of this device is that a high ION/IOFF may increase mobility and reduce saturation velocity, perhaps reducing the drain voltage at saturation. The proposed biosensor's sensitivity was multiplied by 108 when vertical and lateral tunnelling were used in tandem. We apply a variety of optimisation strategies to deal with this problem, despite the fact that quantum confinement reduces the effect of mobility variations on device performance. When biomolecules were positively charged, the drain current increased, and when they were negatively charged, the drain current decreased