Is Explicit Congestion Notification usable with UDP?

We present initial measurements to determine if ECN is usable with UDP traffic in the public Internet. This is interesting because ECN is part of current IETF proposals for congestion control of UDPbased interactive multimedia, and due to the increasing use of UDP as a substrate on which new transport protocols can be deployed. Using measurements from the author’s homes, their workplace, and cloud servers in each of the nine EC2 regions worldwide, we test reachability of 2500 servers from the public NTP server pool, using ECT(0) and not-ECT marked UDP packets. We show that an average of 98.97% of the NTP servers that are reachable using not-ECT marked packets are also reachable using ECT(0) marked UDP packets, and that ~98% of network hops pass ECT(0) marked packets without clearing the ECT bits. We compare reachability of the same hosts using ECN with TCP, finding that 82.0% of those reachable with TCP can successfully negotiate and use ECN. Our findings suggest that ECN is broadly usable with UDP traffic, and that support for use of ECN with TCP has increased

Spectrum of slow and super-slow (picosecond to nanosecond) water dynamics around organic and biological solutes

Water dynamics in the solvation shell of solutes plays a very important role in the interaction of biomolecules and in chemical reaction dynamics. However, a selective spectroscopic study of the solvation shell is difficult because of the interference of the solute dynamics. Here we report on the observation of heavily slowed down water dynamics in the solvation shell of different solutes by measuring the low-frequency spectrum of solvation water, free from the contribution of the solute. A slowdown factor of ~50 is observed even for relatively low concentrations of the solute. We go on to show that the effect can be generalized to different solutes including proteins

A comparison of the structureborne and airborne paths for propfan interior noise

A comparison is made between the relative levels of aircraft interior noise related to structureborne and airborne paths for the same propeller source. A simple, but physically meaningful, model of the structure treats the fuselage interior as a rectangular cavity with five rigid walls. The sixth wall, the fuselage sidewall, is a stiffened panel. The wing is modeled as a simple beam carried into the fuselage by a large discrete stiffener representing the carry-through structure. The fuselage interior is represented by analytically-derived acoustic cavity modes and the entire structure is represented by structural modes derived from a finite element model. The noise source for structureborne noise is the unsteady lift generation on the wing due to the rotating trailing vortex system of the propeller. The airborne noise source is the acoustic field created by a propeller model consistent with the vortex representation. Comparisons are made on the basis of interior noise over a range of propeller rotational frequencies at a fixed thrust

Genetic optimization of training sets for improved machine learning models of molecular properties

The training of molecular models of quantum mechanical properties based on statistical machine learning requires large datasets which exemplify the map from chemical structure to molecular property. Intelligent a priori selection of training examples is often difficult or impossible to achieve as prior knowledge may be sparse or unavailable. Ordinarily representative selection of training molecules from such datasets is achieved through random sampling. We use genetic algorithms for the optimization of training set composition consisting of tens of thousands of small organic molecules. The resulting machine learning models are considerably more accurate with respect to small randomly selected training sets: mean absolute errors for out-of-sample predictions are reduced to ~25% for enthalpies, free energies, and zero-point vibrational energy, to ~50% for heat-capacity, electron-spread, and polarizability, and by more than ~20% for electronic properties such as frontier orbital eigenvalues or dipole-moments. We discuss and present optimized training sets consisting of 10 molecular classes for all molecular properties studied. We show that these classes can be used to design improved training sets for the generation of machine learning models of the same properties in similar but unrelated molecular sets.Comment: 9 pages, 6 figure

Data Driven Surrogate Based Optimization in the Problem Solving Environment WBCSim

Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming due to the complexity of the underlying simulation codes. One way of tackling this problem is by constructing computationally cheap(er) approximations of the expensive simulations, that mimic the behavior of the simulation model as closely as possible. This paper presents a data driven, surrogate based optimization algorithm that uses a trust region based sequential approximate optimization (SAO) framework and a statistical sampling approach based on design of experiment (DOE) arrays. The algorithm is implemented using techniques from two packages—SURFPACK and SHEPPACK that provide a collection of approximation algorithms to build the surrogates and three different DOE techniques—full factorial (FF), Latin hypercube sampling (LHS), and central composite design (CCD)—are used to train the surrogates. The results are compared with the optimization results obtained by directly coupling an optimizer with the simulation code. The biggest concern in using the SAO framework based on statistical sampling is the generation of the required database. As the number of design variables grows, the computational cost of generating the required database grows rapidly. A data driven approach is proposed to tackle this situation, where the trick is to run the expensive simulation if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations are performed. Results show that the proposed methodology dramatically reduces the total number of calls to the expensive simulation runs during the optimization process

Crystal growth and ambient and high pressure study of the reentrant superconductor Tm_2Fe_3Si_5

We report single crystal growth of the reentrant superconductor Tm_2Fe_3Si_5, and measurements of the anisotropic static magnetic susceptibility \chi(T) and isothermal magnetization M(H), ac susceptibility \chi_ac(T), electrical resistivity \rho(T) and heat capacity C(T) at ambient pressure and \chi_ac(T) at high pressure. The magnetic susceptibility along the c-axis \chi_c(T) shows a small maximum around 250 K and does not follow the Curie-Weiss behavior while the magnetic susceptibility along the a-axis \chi_a(T) follows a Curie-Weiss behavior between 130 K and 300 K with a Weiss temperature \theta and an effective magnetic moment \mu_eff which depend on the temperature range of the fit. The easy axis of magnetization is perpendicular to the c-axis and \chi_a/\chi_c = 3.2 at 1.8 K. The ambient pressure \chi_ac(T) and C(T) measurements confirm bulk antiferromagnetic ordering at T_N = 1.1 K. The sharp drop in \chi_ac below T_N is suggestive of the existence of a spin-gap. We observe superconductivity only under applied pressures P\geq 2 kbar. The temperature-pressure phase diagram showing the non-monotonic dependence of the superconducting transition temperature T_c on pressure P is presented.Comment: 7 pages, 8 figure