U.S. Trade in Information-Intensive Services

Trade in services has increased significantly and the United States has been a leader in services trade. The U.S. not only accounts for the largest share of world trade in private services but also runs a substantial amount of surplus in services trade. One important trend has been the rapid growth of U.S. trade in information-intensive services. This paper examines the growth and patterns in U.S. exports and imports of various information-intensive services. The analysis indicates that trade in business, professional, and technical services; financial services; and insurance has experienced the most rapid growth in recent times. This paper further discusses some of the intuitively plausible explanations for the growth of trade in information-intensive services.

Nonmonotonic dependence of the absolute entropy on temperature in supercooled Stillinger-Weber silicon

Using a recently developed thermodynamic integration method, we compute the precise values of the excess Gibbs free energy (G^e) of the high density liquid (HDL) phase with respect to the crystalline phase at different temperatures (T) in the supercooled region of the Stillinger-Weber (SW) silicon [F. H. Stillinger and T. A. Weber, Phys. Rev. B. 32, 5262 (1985)]. Based on the slope of G^e with respect to T, we find that the absolute entropy of the HDL phase increases as its enthalpy changes from the equilibrium value at T \ge 1065 K to the value corresponding to a non-equilibrium state at 1060 K. We find that the volume distribution in the equilibrium HDL phases become progressively broader as the temperature is reduced to 1060 K, exhibiting van-der-Waals (VDW) loop in the pressure-volume curves. Our results provides insight into the thermodynamic cause of the transition from the HDL phase to the low density phases in SW silicon, observed in earlier studies near 1060 K at zero pressure.Comment: This version is accepted for publication in Journal of Statistical Physics (11 figures, 1 table

Combined epiretinal and internal limiting membrane peeling facilitated by high dilution indocyanine green negative staining

We describe the utilization of indocyanine green (ICG) dye to facilitate combined/en bloc removal of epiretinal membranes (ERM) along with internal limiting membranes (ILM). The method utilizes a highly diluted preparation of ICG in dextrose water solvent (D5W). Elimination of fluid air exchange step facilitating staining in the fluid phase and low intensity lighting help minimize potential ICG toxicity. The technique demonstrates how ICG facilitates negative staining of ERMs and how ILM peeling concomitantly can allow complete and efficient ERM removal minimizing surgical time and the necessity for dual or sequential staining

A Bayesian approach to Lagrangian data assimilation

Lagrangian data arise from instruments that are carried by the flow in a fluid field. Assimilation of such data into ocean models presents a challenge due to the potential complexity of Lagrangian trajectories in relatively simple flow fields. We adopt a Bayesian perspective on this problem and thereby take account of the fully non-linear features of the underlying model. In the perfect model scenario, the posterior distribution for the initial state of the system contains all the information that can be extracted from a given realization of observations and the model dynamics. We work in the smoothing context in which the posterior on the initial conditions is determined by future observations. This posterior distribution gives the optimal ensemble to be used in data assimilation. The issue then is sampling this distribution. We develop, implement, and test sampling methods, based on Markov-chain Monte Carlo (MCMC), which are particularly well suited to the low-dimensional, but highly non-linear, nature of Lagrangian data. We compare these methods to the well-established ensemble Kalman filter (EnKF) approach. It is seen that the MCMC based methods correctly sample the desired posterior distribution whereas the EnKF may fail due to infrequent observations or non-linear structures in the underlying flow

A Quadratic Speedup in the Optimization of Noisy Quantum Optical Circuits

Linear optical quantum circuits with photon number resolving (PNR) detectors are used for both Gaussian Boson Sampling (GBS) and for the preparation of non-Gaussian states such as Gottesman-Kitaev-Preskill (GKP), cat and NOON states. They are crucial in many schemes of quantum computing and quantum metrology. Classically optimizing circuits with PNR detectors is challenging due to their exponentially large Hilbert space, and quadratically more challenging in the presence of decoherence as state vectors are replaced by density matrices. To tackle this problem, we introduce a family of algorithms that calculate detection probabilities, conditional states (as well as their gradients with respect to circuit parametrizations) with a complexity that is comparable to the noiseless case. As a consequence we can simulate and optimize circuits with twice the number of modes as we could before, using the same resources. More precisely, for an $M$-mode noisy circuit with detected modes $D$ and undetected modes $U$, the complexity of our algorithm is $O(M^2 \prod_{i\in U} C_i^2 \prod_{i\in D} C_i)$, rather than $O(M^2 \prod_{i \in D\cup U} C_i^2)$, where $C_i$ is the Fock cutoff of mode $i$. As a particular case, our approach offers a full quadratic speedup for calculating detection probabilities, as in that case all modes are detected. Finally, these algorithms are implemented and ready to use in the open-source photonic optimization library MrMustard

Quantum Alternating Operator Ansatz (QAOA) beyond low depth with gradually changing unitaries

The Quantum Approximate Optimization Algorithm and its generalization to Quantum Alternating Operator Ansatz (QAOA) is a promising approach for applying quantum computers to challenging problems such as combinatorial optimization and computational chemistry. In this paper, we study the underlying mechanisms governing the behavior of QAOA circuits beyond shallow depth in the practically relevant setting of gradually varying unitaries. We use the discrete adiabatic theorem, which complements and generalizes the insights obtained from the continuous-time adiabatic theorem primarily considered in prior work. Our analysis explains some general properties that are conspicuously depicted in the recently introduced QAOA performance diagrams. For parameter sequences derived from continuous schedules (e.g. linear ramps), these diagrams capture the algorithm's performance over different parameter sizes and circuit depths. Surprisingly, they have been observed to be qualitatively similar across different performance metrics and application domains. Our analysis explains this behavior as well as entails some unexpected results, such as connections between the eigenstates of the cost and mixer QAOA Hamiltonians changing based on parameter size and the possibility of reducing circuit depth without sacrificing performance

Evaluating Data Assimilation Algorithms

Data assimilation leads naturally to a Bayesian formulation in which the posterior probability distribution of the system state, given the observations, plays a central conceptual role. The aim of this paper is to use this Bayesian posterior probability distribution as a gold standard against which to evaluate various commonly used data assimilation algorithms. A key aspect of geophysical data assimilation is the high dimensionality and low predictability of the computational model. With this in mind, yet with the goal of allowing an explicit and accurate computation of the posterior distribution, we study the 2D Navier-Stokes equations in a periodic geometry. We compute the posterior probability distribution by state-of-the-art statistical sampling techniques. The commonly used algorithms that we evaluate against this accurate gold standard, as quantified by comparing the relative error in reproducing its moments, are 4DVAR and a variety of sequential filtering approximations based on 3DVAR and on extended and ensemble Kalman filters. The primary conclusions are that: (i) with appropriate parameter choices, approximate filters can perform well in reproducing the mean of the desired probability distribution; (ii) however they typically perform poorly when attempting to reproduce the covariance; (iii) this poor performance is compounded by the need to modify the covariance, in order to induce stability. Thus, whilst filters can be a useful tool in predicting mean behavior, they should be viewed with caution as predictors of uncertainty. These conclusions are intrinsic to the algorithms and will not change if the model complexity is increased, for example by employing a smaller viscosity, or by using a detailed NWP model

Effect of HIT Components on the Development of Breast Cancer Cells

Cancer cells circulating in blood vessels activate platelets, forming a cancer cell encircling platelet cloak which facilitates cancer metastasis. Heparin (H) is frequently used as an anticoagulant in cancer patients but up to 5% of patients have a side effect, heparin-induced thrombocytopenia (HIT) that can be life-threatening. HIT is developed due to a complex interaction among multiple components including heparin, platelet factor 4 (PF4), HIT antibodies, and platelets. However, available information regarding the effect of HIT components on cancers is limited. Here, we investigated the effect of these materials on the mechanical property of breast cancer cells using atomic force microscopy (AFM) while cell spreading was quantified by confocal laser scanning microscopy (CLSM), and cell proliferation rate was determined. Over time, we found a clear effect of each component on cell elasticity and cell spreading. In the absence of platelets, HIT antibodies inhibited cell proliferation but they promoted cell proliferation in the presence of platelets. Our results indicate that HIT complexes influenced the development of breast cancer cells