The thermal conductivity reduction in HgTe/CdTe superlattices

The techniques used previously to calculate the three-fold thermal conductivity reduction due to phonon dispersion in GaAs/AlAs superlattices (SLs) are applied to HgTe/CdTe SLs. The reduction factor is approximately the same, indicating that this SL may be applicable both as a photodetector and a thermoelectric cooler.Comment: 5 pages, 2 figures; to be published in Journal of Applied Physic

Radiation studies for GaAs in the ATLAS Inner Detector

We estimate the hardness factors and the equivalent 1 MeV neutron fluences for hadrons fluences expected at the GaAs positions wheels in the ATLAS Inner Detector. On this basis the degradation of the GaAs particle detectors made from different substrates as a function of years LHC operation is predicted.Comment: 11 pages, 6 Postscript figures, uses elsart.cls, submitted to Nucl. Inst. and Met

Green Function Monte Carlo with Stochastic Reconfiguration

A new method for the stabilization of the sign problem in the Green Function Monte Carlo technique is proposed. The method is devised for real lattice Hamiltonians and is based on an iterative ''stochastic reconfiguration'' scheme which introduces some bias but allows a stable simulation with constant sign. The systematic reduction of this bias is in principle possible. The method is applied to the frustrated J1-J2 Heisenberg model, and tested against exact diagonalization data. Evidence of a finite spin gap for J2/J1 >~ 0.4 is found in the thermodynamic limit.Comment: 13 pages, RevTeX + 3 encapsulated postscript figure

Towards Automated Design of Riboswitches

Experimental screening and selection pipelines for the discovery of novel riboswitches are expensive, time-consuming, and inefficient. Using computational methods to reduce the number of candidates for the screen could drastically decrease these costs. However, existing computational approaches do not fully satisfy all requirements for the design of such initial screening libraries. In this work, we present a new method, libLEARNA, capable of providing RNA focus libraries of diverse variable-length qualified candidates. Our novel structure-based design approach considers global properties as well as desired sequence and structure features. We demonstrate the benefits of our method by designing theophylline riboswitch libraries, following a previously published protocol, and yielding 30% more unique high-quality candidates.Comment: 9 pages, Accepted at the 2023 ICML Workshop on Computational Biolog

ADHD Differences on the Stanford Binet Intelligence Scale, Fifth Edition

Attention-deficit/hyperactivity disorder (ADHD) is a common psychiatric diagnosis in childhood that requires a level of attention or hyperactivity that falls short of the expected developmental level. Past research shows cognitive discrepancies in ADHD populations with verbal deficiencies observed primarily in tasks that require a combined auditory and verbal component. Working memory has been a long acknowledged deficit in persons with ADHD. This research examines cognitive differences among children with ADHD on working memory and other components of the Stanford Binet, 5th edition (SB5). Stanford Binet verbal and nonverbal working memory was hypothesized to be different for the ADHD sample compared to controls and between ADHD subtypes. Participants were gathered from the Stanford Binet standardization sample that were diagnosed with ADHD and matched with a group of normal controls. Data was analyzed using ANOVA followed by a cluster analysis of discrepancies found at subtest and testlet levels. Due to matching and statistical control, results showed no differences in FSIQ, VIQ, or PIQ between normals and those with ADHD, but those with ADHD took an average of 20 minutes longer to complete the SB5, consistently showed greater response variability, and exhibited significant differential item functioning for Vocabulary, Object Series/Matrices, and the routing scales. Deficits in working memory appear to account for these differences

A diffusion Monte Carlo algorithm with very small time‐step errors

We propose modifications to the simple diffusion Monte Carlo algorithm that greatly reduce the time‐step error. The improved algorithm has a time‐step error smaller by a factor of 70 to 300 in the energy of Be, Li2 and Ne. For other observables the improvement is yet larger. The effective time step possible with the improved algorithm is typically a factor of a few hundred larger than the time step used in domain Green function Monte Carlo. We also present an optimized 109 parameter trial wave function for Be which, used in combination with our algorithm, yields an exceedingly accurate ground state energy. A simple solution to the population control bias in diffusion Monte Carlo is also discussed