J=0, J=J_{max} and quadrupole pairing

We consider 2 neutrons and 2 protons in the g_{9/2} shell.Wave functions and energy levels are obtained for various interactions. The wave functions for states with total angular momentum I greater or equal to 10 are not affected by what the pairing interacton (J=0 T=1) is.Other parts of the interaction are therefore of increased importance

Style Aligned Image Generation via Shared Attention

Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields, generating visually compelling outputs from textual prompts. However, controlling these models to ensure consistent style remains challenging, with existing methods necessitating fine-tuning and manual intervention to disentangle content and style. In this paper, we introduce StyleAligned, a novel technique designed to establish style alignment among a series of generated images. By employing minimal `attention sharing' during the diffusion process, our method maintains style consistency across images within T2I models. This approach allows for the creation of style-consistent images using a reference style through a straightforward inversion operation. Our method's evaluation across diverse styles and text prompts demonstrates high-quality synthesis and fidelity, underscoring its efficacy in achieving consistent style across various inputs.Comment: Project page at style-aligned-gen.github.i

Pharmacogenetic Predictors of Taxane-Induced Peripheral Neuropathy

Peripheral neuropathy is an adverse event of taxane treatment that is related both to the patient's cumulative drug exposure and their inherent sensitivity to neurotoxicity. Discovery and validation of genetic loci that determine neuropathy risk is an important first step towards individualization of taxane treatment with the ultimate goal of maximizing treatment efficacy and minimizing the risk of severe adverse events. Paclitaxel exposure is regulated by enzymes and transporters that have common variants known to influence protein expression or activity. Paclitaxel is primarily metabolized by the CYP2C8 enzyme, and prior research from our group and others suggests that patients who carry a common low-activity variant, CYP2C8*3, may be at increased risk of neuropathy. Using a cohort of paclitaxel-treated breast cancer patients, I was able to confirm the association between CYP2C8*3 and increased risk of paclitaxel-induced peripheral neuropathy. I then attempted to use a genotyping platform that interrogates thousands of variants in hundreds of genes relevant to drug metabolism, elimination, and transport to identify polymorphisms that influence risk of neurotoxicity after accounting for the CYP2C8*3 variant. Surprisingly, I discovered a polymorphism in a gene not thought to be relevant to paclitaxel pharmacokinetics, ABCG1, which was associated with neuropathy risk. Less is known about the clinical or genetic factors that modulate docetaxel-induced neuropathy risk. I performed genome-wide association in a large cohort of docetaxel-treated patients to discover genetic loci that modulate risk of neuropathy. I discovered several candidates, one of which was an intergenic polymorphism that surpassed genome-wide significance after adjustment for relevant clinical covariates. I then attempted, unsuccessfully, to replicate these discoveries in independent cohorts of taxane-treated patients. This inability to replicate indicates that either the associations of these variants are limited to the cohort in which they were discovered or that they were merely spurious discoveries. Replication should be attempted in independent patient cohorts that are more similar to those in which these discoveries were made to validate the influence of these variants on neuropathy risk, enabling translation into routine clinical practice.Doctor of Philosoph

PointGMM: a Neural GMM Network for Point Clouds

Point clouds are a popular representation for 3D shapes. However, they encode a particular sampling without accounting for shape priors or non-local information. We advocate for the use of a hierarchical Gaussian mixture model (hGMM), which is a compact, adaptive and lightweight representation that probabilistically defines the underlying 3D surface. We present PointGMM, a neural network that learns to generate hGMMs which are characteristic of the shape class, and also coincide with the input point cloud. PointGMM is trained over a collection of shapes to learn a class-specific prior. The hierarchical representation has two main advantages: (i) coarse-to-fine learning, which avoids converging to poor local-minima; and (ii) (an unsupervised) consistent partitioning of the input shape. We show that as a generative model, PointGMM learns a meaningful latent space which enables generating consistent interpolations between existing shapes, as well as synthesizing novel shapes. We also present a novel framework for rigid registration using PointGMM, that learns to disentangle orientation from structure of an input shape.Comment: CVPR 2020 -- final versio

AnyLens: A Generative Diffusion Model with Any Rendering Lens

State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth. However, an essential aspect often overlooked is the specific camera geometry used during image capture. The influence of different optical systems on the final scene appearance is frequently overlooked. This study introduces a framework that intimately integrates a text-to-image diffusion model with the particular lens geometry used in image rendering. Our method is based on a per-pixel coordinate conditioning method, enabling the control over the rendering geometry. Notably, we demonstrate the manipulation of curvature properties, achieving diverse visual effects, such as fish-eye, panoramic views, and spherical texturing using a single diffusion model