Carrier multiplication yields in CdSe and CdTe nanocrystals by transient photoluminescence

Engineering semiconductors to enhance carrier multiplication (CM) could lead to increased photovoltaic cell performance and a significant widening of the materials range suitable for future solar technologies. Semiconductor nanocrystals (NCs) have been proposed as a favourable structure for CM enhancement, and recent measurements by transient absorption have shown evidence for highly efficient CM in lead chalcogenide and CdSe NCs. We report here an assessment of CM yields in CdSe and CdTe NCs by a quantitative analysis of biexciton and exciton signatures in transient photoluminescence decays. Although the technique is particularly sensitive due to enhanced biexciton radiative rates relative to the exciton, kradBX > 2 kradX, we find no evidence for CM in CdSe and CdTe NCs up to photon energies E > 3 Eg, well above previously reported relative energy thresholds.Comment: 9 pages, 6 figure

The Changing Fractions of Type Ia Supernova NUV-Optical Subclasses with Redshift

UV and optical photometry of Type Ia supernovae (SNe Ia) at low redshift have revealed the existence of two distinct color groups, NUV-red and NUV-blue events. The color curves differ primarily by an offset, with the NUV-blue u- color curves bluer than the NUV-red curves by 0.4 mag. For a sample of 23 low-z SNe~Ia observed with Swift, the NUV-red group dominates by a ratio of 2:1. We compare rest-frame UV/optical spectrophotometry of intermediate and high-z SNe Ia with UVOT photometry and HST spectrophotometry of low-z SNe Ia, finding that the same two color groups exist at higher-z, but with the NUV-blue events as the dominant group. Within each red/blue group, we do not detect any offset in color for different redshifts, providing insight into how SN~Ia UV emission evolves with redshift. Through spectral comparisons of SNe~Ia with similar peak widths and phase, we explore the wavelength range that produces the UV/OPT color differences. We show that the ejecta velocity of NUV-red SNe is larger than that of NUV-blue objects by roughly 12% on average. This velocity difference can explain some of the UV/optical color difference, but differences in the strengths of spectral features seen in meanspectra require additional explanation. Because of the different b-v colors for these groups, NUV-red SNe will have their extinction underestimated using common techniques. This, in turn, leads to under-estimation of the optical luminosity of the NUV-blue SNe~Ia, in particular, for the high-redshift cosmological sample. Not accounting for this effect should thus produce a distance bias that increases with redshift and could significantly bias measurements of cosmological parameters.Comment: submitted to Ap

Where there’s ‘willingness’ there’s a way: barriers and facilitators to maternal, newborn and child health data sharing by the private health sector in Uttar Pradesh, India

In India and Uttar Pradesh (UP), the private health sector plays an important role in health care services, including institutional deliveries, but there is limited information on the availability of maternal, newborn and child health (MNCH) data that private facilities maintain and share with the public health information system. Sharing data could help the public sector plan their resources more efficiently. Aim of the study: To explore current practices of MNCH data availability and sharing/reporting by private health facilities and the barriers and facilitators to data sharing

Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers

Real-time learning is crucial for robotic agents adapting to ever-changing, non-stationary environments. A common setup for a robotic agent is to have two different computers simultaneously: a resource-limited local computer tethered to the robot and a powerful remote computer connected wirelessly. Given such a setup, it is unclear to what extent the performance of a learning system can be affected by resource limitations and how to efficiently use the wirelessly connected powerful computer to compensate for any performance loss. In this paper, we implement a real-time learning system called the Remote-Local Distributed (ReLoD) system to distribute computations of two deep reinforcement learning (RL) algorithms, Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO), between a local and a remote computer. The performance of the system is evaluated on two vision-based control tasks developed using a robotic arm and a mobile robot. Our results show that SAC's performance degrades heavily on a resource-limited local computer. Strikingly, when all computations of the learning system are deployed on a remote workstation, SAC fails to compensate for the performance loss, indicating that, without careful consideration, using a powerful remote computer may not result in performance improvement. However, a carefully chosen distribution of computations of SAC consistently and substantially improves its performance on both tasks. On the other hand, the performance of PPO remains largely unaffected by the distribution of computations. In addition, when all computations happen solely on a powerful tethered computer, the performance of our system remains on par with an existing system that is well-tuned for using a single machine. ReLoD is the only publicly available system for real-time RL that applies to multiple robots for vision-based tasks.Comment: Appears in Proceedings of the 2023 International Conference on Robotics and Automation (ICRA). Source code at https://github.com/rlai-lab/relod and companion video at https://youtu.be/7iZKryi1xS

Correcting discount-factor mismatch in on-policy policy gradient methods

The policy gradient theorem gives a convenient form of the policy gradient in terms of three factors: an action value, a gradient of the action likelihood, and a state distribution involving discounting called the \emph{discounted stationary distribution}. But commonly used on-policy methods based on the policy gradient theorem ignores the discount factor in the state distribution, which is technically incorrect and may even cause degenerate learning behavior in some environments. An existing solution corrects this discrepancy by using $\gamma^t$ as a factor in the gradient estimate. However, this solution is not widely adopted and does not work well in tasks where the later states are similar to earlier states. We introduce a novel distribution correction to account for the discounted stationary distribution that can be plugged into many existing gradient estimators. Our correction circumvents the performance degradation associated with the $\gamma^t$ correction with a lower variance. Importantly, compared to the uncorrected estimators, our algorithm provides improved state emphasis to evade suboptimal policies in certain environments and consistently matches or exceeds the original performance on several OpenAI gym and DeepMind suite benchmarks

Carrier multiplication yields in PbS and PbSe nanocrystals measured by transient photoluminescence

We report here an assessment of carrier multiplication (CM) yields in PbSe and PbS nanocrystals (NCs) by a quantitative analysis of biexciton and exciton dynamics in transient photoluminescence decays. Interest in CM, the generation of more than one electron and hole in a semiconductor after absorption of one photon, has renewed in recent years because of reports suggesting greatly increased efficiencies in nanocrystalline materials compared to the bulk form, in which CM was otherwise too weak to be of consequence in photovoltaic energy conversion devices. In our PbSe and PbS NC samples, however, we estimate using transient photoluminescence that at most 0.25 additional e-h pairs are generated per photon even at energies hv > 5Eg, instead of the much higher values reported in the literature. We argue by comparing NC CM estimates and reported bulk values on an absolute energy basis, which we justify as appropriate on physical grounds, that the data reported thus far are inconclusive with respect to the importance of nanoscale-specific phenomena in the CM process.Comment: 10 pages, 7 figure