Quasi-particle spectra, absorption spectra, and excitonic properties of sodium iodide and strontium iodide from many-body perturbation theory

We investigate the basic quantum mechanical processes behind non-proportional response of scintillators to incident radiation responsible for reduced resolution. For this purpose, we conduct a comparative first principles study of quasiparticle spectra on the basis of the $G_0W_0$ approximation as well as absorption spectra and excitonic properties by solving the Bethe-Salpeter equation for two important systems, NaI and SrI$_2$. The former is a standard scintillator material with well-documented non-proportionality while the latter has recently been found to exhibit a very proportional response. We predict band gaps for NaI and SrI$_2$ of 5.5 and 5.2 eV, respectively, in good agreement with experiment. Furthermore, we obtain binding energies for the groundstate excitons of 216 meV for NaI and 195$\pm$25 meV for SrI$_2$. We analyze the degree of exciton anisotropy and spatial extent by means of a coarse-grained electron-hole pair-correlation function. Thereby, it is shown that the excitons in NaI differ strongly from those in SrI$_2$ in terms of structure and symmetry, even if their binding energies are similar. Furthermore, we show that quite unexpectedly the spatial extents of the highly anisotropic low-energy excitons in SrI$_2$ in fact exceed those in NaI by a factor of two to three in terms of the full width at half maxima of the electron-hole pair-correlation function.Comment: 10 pages, 9 figure

The Green Choice: Learning and Influencing Human Decisions on Shared Roads

Autonomous vehicles have the potential to increase the capacity of roads via platooning, even when human drivers and autonomous vehicles share roads. However, when users of a road network choose their routes selfishly, the resulting traffic configuration may be very inefficient. Because of this, we consider how to influence human decisions so as to decrease congestion on these roads. We consider a network of parallel roads with two modes of transportation: (i) human drivers who will choose the quickest route available to them, and (ii) ride hailing service which provides an array of autonomous vehicle ride options, each with different prices, to users. In this work, we seek to design these prices so that when autonomous service users choose from these options and human drivers selfishly choose their resulting routes, road usage is maximized and transit delay is minimized. To do so, we formalize a model of how autonomous service users make choices between routes with different price/delay values. Developing a preference-based algorithm to learn the preferences of the users, and using a vehicle flow model related to the Fundamental Diagram of Traffic, we formulate a planning optimization to maximize a social objective and demonstrate the benefit of the proposed routing and learning scheme.Comment: Submitted to CDC 201

Robust Subspace System Identification via Weighted Nuclear Norm Optimization

Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimization problem with an objective that trades off fit, rank and sparsity. As in robust PCA, it can be problematic to find a suitable regularization parameter. We show how the space in which a suitable parameter should be sought can be limited to a bounded open set of the two dimensional parameter space. In practice, this is very useful since it restricts the parameter space that is needed to be surveyed.Comment: Submitted to the IFAC World Congress 201

A Learning Based Approach to Control Synthesis of Markov Decision Processes for Linear Temporal Logic Specifications

We propose to synthesize a control policy for a Markov decision process (MDP) such that the resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct a product MDP that incorporates a deterministic Rabin automaton generated from the desired LTL property. The reward function of the product MDP is defined from the acceptance condition of the Rabin automaton. This construction allows us to apply techniques from learning theory to the problem of synthesis for LTL specifications even when the transition probabilities are not known a priori. We prove that our method is guaranteed to find a controller that satisfies the LTL property with probability one if such a policy exists, and we suggest empirically with a case study in traffic control that our method produces reasonable control strategies even when the LTL property cannot be satisfied with probability one

Are there stable long-range ordered Fe(1-x)Cr(x) compounds?

The heat of formation of Fe-Cr alloys undergoes an anomalous change of sign at small Cr concentrations. This observation raises the question whether there are intermetallic phases present in this composition range. Here we report the discovery of several long-range ordered structures that represent ground state phases at zero Kelvin. In particular we have identified a structure at 3.7% Cr with an embedding energy which is 49 meV/Cr atom below the solid solution. This implies there is an effective long-range attractive interaction between Cr atoms. We propose that the structures found in this study complete the low temperature-low Cr region of the phase diagram.Comment: 3 pages, 2 figure

Origin of resolution enhancement by co-doping of scintillators: Insight from electronic structure calculations

It was recently shown that the energy resolution of Ce-doped LaBr$_3$ scintillator radiation detectors can be crucially improved by co-doping with Sr, Ca, or Ba. Here we outline a mechanism for this enhancement on the basis of electronic structure calculations. We show that (i) Br vacancies are the primary electron traps during the initial stage of thermalization of hot carriers, prior to hole capture by Ce dopants; (ii) isolated Br vacancies are associated with deep levels; (iii) Sr doping increases the Br vacancy concentration by several orders of magnitude; (iv) $\text{Sr}_\text{La}$ binds to $V_\text{Br}$ resulting in a stable neutral complex; and (v) association with Sr causes the deep vacancy level to move toward the conduction band edge. The latter is essential for reducing the effective carrier density available for Auger quenching during thermalization of hot carriers. Subsequent de-trapping of electrons from $\text{Sr}_\text{La}-V_\text{La}$ complexes then can activate Ce dopants that have previously captured a hole leading to luminescence. This mechanism implies an overall reduction of Auger quenching of free carriers, which is expected to improve the linearity of the photon light yield with respect to the energy of incident electron or photon

Intranasal insulin treatment improves memory and learning in a rat amyloid-beta model of Alzheimer’s disease

Recently, insulin has been used as a pro-cognitive agent for the potential treatment of Alzheimer’s disease (AD), because of its ability to cross the brain–blood barrier (BBB) by a saturable transport system. This study has been designed to evaluate the effects of intranasal insulin regimen, as a bypass system of BBB, on spatial memory in amyloid-beta (Aβ) model of AD in rat. Unilateral infusion of Aβ25–35 (10 nmol/2 µl/rat) into the lateral ventricular region of brain was used to produce a rat model of AD. After a 24-h recovery period, rats received insulin or vehicle via intraperitoneal or intranasal route (0.1, 0.2, and 0.3 IU) for 14 days. Memory function in rats was assessed by Morris water maze test, with 5 days of training and consequent probe test protocol. Different doses of intraperitoneal insulin did not have a significant effect on learning and memory in AD rats. However, intranasal insulin at doses of 0.2 and 0.3 IU improved the learning and memory in Aβ-received rats. In conclusion, intranasal insulin as a non-invasive strategy improves spatial learning and memory in AD model

Coordinating Community Healthcare Needs to Local Services in Paraiso, Dominican Republic Through Strategic Assessment Strategies

Background: The availability of healthcare services is limited in Paraiso, Dominican Republic with the nearest full-service hospital located 34.1 km away. A local, underutilized clinic was unaware of the needs of this disadvantaged community. Method: Researchers adapted a World Health Organization assessment survey with the goals of determining residents’ priority needs and an appraisal of the current clinic capabilities and gaps in services in order to provide the community with relevant healthcare. 106 families were randomly selected in seven separate geographic areas of Paraiso to participate in the self-report assessment. Researchers, along with a community volunteer, conducted interviews utilizing the 63 question instrument. 105 families agreed to participate representing 504 individuals