Combining internal and external inputs for sustainable intensification

Farmers and local development organizations around the world use and promote a variety of technologies to increase food production. But the high cost of inorganic fertilizers and other agrochemicals often drives farmers to rely on locally available resources instead of purchased, externally produced inputs. So-called low external input agriculture (LEIA) has spread rapidly to different parts of the globe as a challenging alternative to—or, more frequently, a complement to— Green Revolution technologies...The goal of policy, research, and extension should be to help LEIA farmers achieve "sustainable intensification", which refers to the simultaneous increase in returns to land and labor (in the short run) and the maintenance of soil nutrient balances (in the long run).Soil fertility. ,Food production. ,Technological innovations. ,Fertilizers. ,Agricultural research. ,

Texture control in a pseudospin Bose-Einstein condensate

We describe a wavefunction engineering approach to the formation of textures in a two-component nonrotated Bose-Einstein condensate. By controlling the phases of wavepackets that combine in a three-wave interference process, a ballistically-expanding regular lattice-texture is generated, in which the phases determine the component textures. A particular example is presented of a lattice-texture composed of half-quantum vortices and spin-2 textures. We demonstrate the lattice formation with numerical simulations of a viable experiment, identifying the textures and relating their locations to a linear theory of wavepacket interference.Comment: 4 pages, 5 figures, REVTeX4-

Computational Economics: Help for the Underestimated Undergraduate

Our concern in this paper is that the capability of economics undergraduates is substantially underestimated in the design of the present college curriculum and that our students are insufficiently challenged and motivated. Students enter our classrooms with substantial previous knowledge about computers and computation and we are not taking full advantage of this opportunity. We suggest a set of examples from computational economics which are challenging enough to motivate students and simple enough that they can master them within a few hours. By encouraging the students to modify the models in directions of their own interest avenues for creative endeavor are opened which deeply involve the students in their own education.teaching computational economics

Supersymmetry breaking branes on solvmanifolds and de Sitter vacua in string theory

We consider IIA compactifications on solvmanifolds with O6/D6 branes and study the conditions for obtaining de Sitter vacua in ten dimensions. While this is a popular set-up for searching de Sitter vacua, we propose a new method to include supersymmetry breaking sources. For space-time filling branes preserving bulk supersymmetry, the energy density can easily be extremized with respect to all fields, thanks to the replacement of the DBI action by a pullback of a special form given by a pure spinor. For sources breaking bulk supersymmetry, we propose to replace the DBI action by the pullback of a more general polyform, which is no longer pure. This generalization provides corrections to the energy-momentum tensor which give a positive contribution to the cosmological constant. We find a de Sitter solution to all (bulk and world-volume) equations derived from this action. We argue it solves the equations derived from the standard source action. The paper also contains a review of solvmanifolds.Comment: 57 pages, 3 figures. Important additions (concerning the validity of our solution in string theory); version to appear in JHE

GPU-based Online Track Reconstruction for the ALICE TPC in Run 3 with Continuous Read-Out

In LHC Run 3, ALICE will increase the data taking rate significantly to 50 kHz continuous read-out of minimum bias Pb-Pb collisions. The reconstruction strategy of the online-offline computing upgrade foresees a first synchronous online reconstruction stage during data taking enabling detector calibration and data compression, and a posterior calibrated asynchronous reconstruction stage. Many new challenges arise, among them continuous TPC read-out, more overlapping collisions, no a priori knowledge of the primary vertex and of location-dependent calibration in the synchronous phase, identification of low-momentum looping tracks, and sophisticated raw data compression. The tracking algorithm for the Time Projection Chamber (TPC) will be based on a Cellular Automaton and the Kalman filter. The reconstruction shall run online, processing 50 times more collisions per second than today, while yielding results comparable to current offline reconstruction. Our TPC track finding leverages the potential of hardware accelerators via the OpenCL and CUDA APIs in a shared source code for CPUs and GPUs for both reconstruction stages. We give an overview of the status of Run 3 tracking including performance on processors and GPUs and achieved compression ratios.Comment: 8 pages, 7 figures, contribution to CHEP 2018 conferenc

Track Reconstruction in the ALICE TPC using GPUs for LHC Run 3

In LHC Run 3, ALICE will increase the data taking rate significantly to continuous readout of 50 kHz minimum bias Pb-Pb collisions. The reconstruction strategy of the online offline computing upgrade foresees a first synchronous online reconstruction stage during data taking enabling detector calibration, and a posterior calibrated asynchronous reconstruction stage. We present a tracking algorithm for the Time Projection Chamber (TPC), the main tracking detector of ALICE. The reconstruction must yield results comparable to current offline reconstruction and meet the time constraints like in the current High Level Trigger (HLT), processing 50 times as many collisions per second as today. It is derived from the current online tracking in the HLT, which is based on a Cellular automaton and the Kalman filter, and we integrate missing features from offline tracking for improved resolution. The continuous TPC readout and overlapping collisions pose new challenges: conversion to spatial coordinates and the application of time- and location dependent calibration must happen in between of track seeding and track fitting while the TPC occupancy increases five-fold. The huge data volume requires a data reduction factor of 20, which imposes additional requirements: the momentum range must be extended to identify low-pt looping tracks and a special refit in uncalibrated coordinates improves the track model entropy encoding. Our TPC track finding leverages the potential of hardware accelerators via the OpenCL and CUDA APIs in a shared source code for CPUs, GPUs, and both reconstruction stages. Porting more reconstruction steps like the remainder of the TPC reconstruction and tracking for other detectors will shift the computing balance from traditional processors to GPUs.Comment: 13 pages, 10 figures, proceedings to Connecting The Dots Workshop, Seattle, 201