Accurate computation of quantum excited states with neural networks.

We present an algorithm to estimate the excited states of a quantum system by variational Monte Carlo, which has no free parameters and requires no orthogonalization of the states, instead transforming the problem into that of finding the ground state of an expanded system. Arbitrary observables can be calculated, including off-diagonal expectations, such as the transition dipole moment. The method works particularly well with neural network ansätze, and by combining this method with the FermiNet and Psiformer ansätze, we can accurately recover excitation energies and oscillator strengths on a range of molecules.

Neural network variational Monte Carlo for positronic chemistry.

Quantum chemical calculations of the ground-state properties of positron-molecule complexes are challenging. The main difficulty lies in employing an appropriate basis set for representing the coalescence between electrons and a positron. Here, we tackle this problem with the recently developed Fermionic neural network (FermiNet) wavefunction, which does not depend on a basis set.

Beta-defensin index: A functional biomarker for oral cancer detection.

There is an unmet clinical need for a non-invasive and cost-effective test for oral squamous cell carcinoma (OSCC) that informs clinicians when a biopsy is warranted. Human beta-defensin 3 (hBD-3), an epithelial cell-derived anti-microbial peptide, is pro-tumorigenic and overexpressed in early-stage OSCC compared to hBD-2. We validate this expression dichotomy in carcinoma in situ and OSCC lesions using immunofluorescence microscopy and flow cytometry.

Intrabiliary metastasis of colorectal mucinous adenocarcinoma mimicking choledocholithiasis 18 years after the primary tumor.

This case report presents a 62-year-old male who had previously undergone curative colectomy and neoadjuvant chemotherapy in 2005 for colorectal cancer. He presented with jaundice, which was initially attributed to choledocholithiasis. After cholecystectomy and repeat ERCPs, hyperbilirubinemia persisted.

Ab initio quantum chemistry with neural-network wavefunctions.

Deep learning methods outperform human capabilities in pattern recognition and data processing problems and now have an increasingly important role in scientific discovery. A key application of machine learning in molecular science is to learn potential energy surfaces or force fields from ab initio solutions of the electronic Schrödinger equation using data sets obtained with density functional theory, coupled cluster or other quantum chemistry (QC) methods. In this Review, we discuss a complementary approach using machine learning to aid the direct solution of QC problems from first principles.

Discovering Quantum Phase Transitions with Fermionic Neural Networks.

Deep neural networks have been very successful as highly accurate wave function Ansätze for variational Monte Carlo calculations of molecular ground states. We present an extension of one such Ansatz, FermiNet, to calculations of the ground states of periodic Hamiltonians, and study the homogeneous electron gas. FermiNet calculations of the ground-state energies of small electron gas systems are in excellent agreement with previous initiator full configuration interaction quantum Monte Carlo and diffusion Monte Carlo calculations.

Response to Comment on "Pushing the frontiers of density functionals by solving the fractional electron problem".

Gerasimov . claim that the ability of DM21 to respect fractional charge (FC) and fractional spin (FS) conditions outside of the training set has not been demonstrated in our paper. This is based on (i) asserting that the training set has a ~50% overlap with our bond-breaking benchmark (BBB) and (ii) questioning the validity and accuracy of our other generalization examples.

Magnetic control of tokamak plasmas through deep reinforcement learning.

Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a promising path towards sustainable energy. A core challenge is to shape and maintain a high-temperature plasma within the tokamak vessel. This requires high-dimensional, high-frequency, closed-loop control using magnetic actuator coils, further complicated by the diverse requirements across a wide range of plasma configurations.

Pushing the frontiers of density functionals by solving the fractional electron problem.

Density functional theory describes matter at the quantum level, but all popular approximations suffer from systematic errors that arise from the violation of mathematical properties of the exact functional. We overcame this fundamental limitation by training a neural network on molecular data and on fictitious systems with fractional charge and spin. The resulting functional, DM21 (DeepMind 21), correctly describes typical examples of artificial charge delocalization and strong correlation and performs better than traditional functionals on thorough benchmarks for main-group atoms and molecules.

Imaging features of gastrointestinal toxicity in non-small cell lung cancer patients treated with erlotinib: A single institute 13-year experience.

Objectives: To investigate the imaging features of erlotinib-associated gastrointestinal toxicity (GT) in patients with non-small cell lung cancer (NSCLC).

Materials And Methods: The electronic medical records of 157 patients with NSCLC who received erlotinib between 2005 and 2018 were retrospectively reviewed to identify patients with GT. Clinical and radiologic evidence of erlotinib-associated GT was evaluated.

Decoding the Genomic Report for Radiologists.

The purpose of this review is to provide a guide for radiologists that explains the language and format of modern genomic reports and summarizes the relevance of this information for modern oncologic imaging. Genomic testing plays a critical role in guiding oncologic therapies in the age of targeted treatments. Understanding and interpreting genomic reports is a valuable skill for radiologists involved with oncologic imaging interpretation.

Imaging and clinical manifestations of immune checkpoint inhibitor-related colitis in cancer patients treated with monotherapy or combination therapy.

Purpose: To determine the frequency, imaging, and clinical manifestations of immune checkpoint inhibitor (ICI)-related colitis in cancer patients on monotherapy or combination therapy.

Methods: The electronic medical records of 1044 cancer patients who received ICIs were retrospectively reviewed to identify 48 patients who had a clinical diagnosis of immune-related colitis. Imaging studies were reviewed to identify patients with imaging manifestations of colitis.

Sentinel Node Biopsy in Young Patients with Atypical Melanocytic Tumors of the Head and Neck.

Objective: To examine the diagnostic value of the sentinel lymph node biopsy in pediatric through young adult head and neck melanocytic tumors of unknown malignant potential.

Study Design: Retrospective case series.

Setting: Single academic institution.

Non-classical monocytes are biased progenitors of wound healing macrophages during soft tissue injury.

Successful tissue repair requires the activities of myeloid cells such as monocytes and macrophages that guide the progression of inflammation and healing outcome. Immunoregenerative materials leverage the function of endogenous immune cells to orchestrate complex mechanisms of repair; however, a deeper understanding of innate immune cell function in inflamed tissues and their subsequent interactions with implanted materials is necessary to guide the design of these materials. Blood monocytes exist in two primary subpopulations, characterized as classical inflammatory or non-classical.

Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data.

We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time.

Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning.

Making intelligent decisions from incomplete information is critical in many applications: for example, robots must choose actions based on imperfect sensors, and speech-based interfaces must infer a user's needs from noisy microphone inputs. What makes these tasks hard is that often we do not have a natural representation with which to model the domain and use for choosing actions; we must learn about the domain's properties while simultaneously performing the task. Learning a representation also involves trade-offs between modeling the data that we have seen previously and being able to make predictions about new data.

Dead leaves and the dirty ground: low-level image statistics in transmissive and occlusive imaging environments.

The opacity of typical objects in the world results in occlusion, an important property of natural scenes that makes inference of the full three-dimensional structure of the world challenging. The relationship between occlusion and low-level image statistics has been hotly debated in the literature, and extensive simulations have been used to determine whether occlusion is responsible for the ubiquitously observed power-law power spectra of natural images. To deepen our understanding of this problem, we have analytically computed the two- and four-point functions of a generalized "dead leaves" model of natural images with parameterized object transparency.

Decoding arm and hand movements across layers of the macaque frontal cortices.

A major goal for brain machine interfaces is to allow patients to control prosthetic devices with high degrees of independent movements. Such devices like robotic arms and hands require this high dimensionality of control to restore the full range of actions exhibited in natural movement. Current BMI strategies fall well short of this goal allowing the control of only a few degrees of freedom at a time.