2D Raman band splitting in graphene: charge screening and lifting of the K-point Kohn anomaly

Pristine graphene encapsulated in hexagonal boron nitride has transport properties rivalling suspended graphene, while being protected from contamination and mechanical damage. For high quality devices, it is important to avoid and monitor accidental doping and charge fluctuations. The 2D Raman double peak in intrinsic graphene can be used to optically determine charge density, with decreasing peak split corresponding to increasing charge density. We find strong correlations between the 2D 1 and 2D 2 split vs 2D line widths, intensities, and peak positions. Charge density fluctuations can be measured with orders of magnitude higher precision than previously accomplished using the G-band shift with charge. The two 2D intrinsic peaks can be associated with the “inner” and “outer” Raman scattering processes, with the counterintuitive assignment of the phonon closer to the K point in the KM direction (outer process) as the higher energy peak. Even low charge screening lifts the phonon Kohn anomaly near the K point for graphene encapsulated in hBN, and shifts the dominant intensity from the lower to the higher energy peak.This work was supported by the United States National Science Foundation (DMR 1411008, DMR 1308659). J.C. thanks the Department of Defence (DoD), Air Force Office of Scientific Research for its support through the National Defence Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a. The authors would like to thank Cory Dean and Carlos Forsythe for the graphene encapsulated hBN sample. (DMR 1411008 - United States National Science Foundation; DMR 1308659 - United States National Science Foundation; 32 CFR 168a - Department of Defence (DoD), Air Force Office of Scientific Research through the National Defence Science and Engineering Graduate (NDSEG) Fellowship

The Strauss conjecture on asymptotically flat space-times

By assuming a certain localized energy estimate, we prove the existence portion of the Strauss conjecture on asymptotically flat manifolds, possibly exterior to a compact domain, when the spatial dimension is 3 or 4. In particular, this result applies to the 3 and 4-dimensional Schwarzschild and Kerr (with small angular momentum) black hole backgrounds, long range asymptotically Euclidean spaces, and small time-dependent asymptotically flat perturbations of Minkowski space-time. We also permit lower order perturbations of the wave operator. The key estimates are a class of weighted Strichartz estimates, which are used near infinity where the metrics can be viewed as small perturbations of the Minkowski metric, and the assumed localized energy estimate, which is used in the remaining compact set.Comment: Final version, to appear in SIAM Journal on Mathematical Analysis. 17 page

A heuristic for the container loading problem: A tertiary-tree-based dynamic space decomposition approach

Increasing fuel costs, post-911 security concerns, and economic globalization provide a strong incentive for container carriers to use available container space more efficiently, thereby minimizing the number of container trips and reducing socio-economic vulnerability. A heuristic algorithm based on a tertiary tree model is proposed to handle the container loading problem (CLP) with weakly heterogeneous boxes. A dynamic space decomposition method based on the tertiary tree structure is developed to partition the remaining container space after a block of homogeneous rectangular boxes is loaded into a container. This decomposition approach, together with an optimal-fitting sequencing and an inner-right-corner-occupying placement rule, permits a holistic loading strategy to pack a container. Comparative studies with existing algorithms and an illustrative example demonstrate the efficiency of this algorithm

Quantifying Peat Carbon Accumulation in Alaska Using a Process-Based Biogeochemistry Model

This study uses an integrated modeling framework that couples the dynamics of hydrology, soil thermal regime, and ecosystem carbon and nitrogen to quantify the long-term peat carbon accumulation in Alaska during the Holocene. Modeled hydrology, soil thermal regime, carbon pools and fluxes, and methane emissions are evaluated using observation data at several peatland sites in Minnesota, Alaska, and Canada. The model is then applied for a 10,000 year (15 ka to 5 ka; 1 ka = 1000 cal years before present) simulation at four peatland sites. We find that model simulations match the observed carbon accumulation rates at fen sites during the Holocene (R2 = 0.88, 0.87, 0.38, and -0.05 using comparisons in 500 year bins). The simulated (2.04 m) and observed peat depths (on average 1.98 m) were also compared well (R2 = 0.91). The early Holocene carbon accumulation rates, especially during the Holocene thermal maximum (HTM) (35.9 g Cm-2 yr-1), are estimated up to 6 times higher than the rest of the Holocene (6.5 g Cm-2 yr-1). Our analysis suggests that high summer temperature and the lengthened growing season resulted from the elevated insolation seasonality, along with wetter-than-before conditions might be major factors causing the rapid carbon accumulation in Alaska during the HTM. Our sensitivity tests indicate that, apart from climate, initial water table depth and vegetation canopy are major drivers to the estimated peat carbon accumulation. When the modeling framework is evaluated for various peatland types in the Arctic, it can quantify peatland carbon accumulation at regional scales

Aberrant Calcium Signaling in Astrocytes Inhibits Neuronal Excitability in a Human Down Syndrome Stem Cell Model.

Down syndrome (DS) is a genetic disorder that causes cognitive impairment. The staggering effects associated with an extra copy of human chromosome 21 (HSA21) complicates mechanistic understanding of DS pathophysiology. We examined the neuron-astrocyte interplay in a fully recapitulated HSA21 trisomy cellular model differentiated from DS-patient-derived induced pluripotent stem cells (iPSCs). By combining calcium imaging with genetic approaches, we discovered the functional defects of DS astroglia and their effects on neuronal excitability. Compared with control isogenic astroglia, DS astroglia exhibited more-frequent spontaneous calcium fluctuations, which reduced the excitability of co-cultured neurons. Furthermore, suppressed neuronal activity could be rescued by abolishing astrocytic spontaneous calcium activity either chemically by blocking adenosine-mediated signaling or genetically by knockdown of inositol triphosphate (IP3) receptors or S100B, a calcium binding protein coded on HSA21. Our results suggest a mechanism by which DS alters the function of astrocytes, which subsequently disturbs neuronal excitability

PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators

Analog compute-in-memory (CIM) accelerators are becoming increasingly popular for deep neural network (DNN) inference due to their energy efficiency and in-situ vector-matrix multiplication (VMM) capabilities. However, as the use of DNNs expands, protecting user input privacy has become increasingly important. In this paper, we identify a security vulnerability wherein an adversary can reconstruct the user's private input data from a power side-channel attack, under proper data acquisition and pre-processing, even without knowledge of the DNN model. We further demonstrate a machine learning-based attack approach using a generative adversarial network (GAN) to enhance the reconstruction. Our results show that the attack methodology is effective in reconstructing user inputs from analog CIM accelerator power leakage, even when at large noise levels and countermeasures are applied. Specifically, we demonstrate the efficacy of our approach on the U-Net for brain tumor detection in magnetic resonance imaging (MRI) medical images, with a noise-level of 20% standard deviation of the maximum power signal value. Our study highlights a significant security vulnerability in analog CIM accelerators and proposes an effective attack methodology using a GAN to breach user privacy