text:
% Encoding: UTF-8

@Article{PhysRevE.94.052132,
  author    = {Rose, Dominic C. and Macieszczak, Katarzyna and Lesanovsky, Igor and Garrahan, Juan P.},
  journal   = {Phys. Rev. E},
  title     = {Metastability in an open quantum Ising model},
  year      = {2016},
  month     = {Nov},
  pages     = {052132},
  volume    = {94},
  abstract  = {We apply a recently developed theory for metastability in open quantum systems to a one-dimensional dissipative quantum Ising model. Earlier results suggest this model features either a nonequilibrium phase transition or a smooth but sharp crossover, where the stationary state changes from paramagnetic to ferromagnetic, accompanied by strongly intermittent emission dynamics characteristic of first-order coexistence between dynamical phases. We show that for a range of parameters close to this transition or crossover point the dynamics of the finite system displays pronounced metastability, i.e., the system relaxes first to long-lived metastable states before eventual relaxation to the true stationary state. From the spectral properties of the quantum master operator we characterize the low-dimensional manifold of metastable states, which are shown to be probability mixtures of two, paramagnetic and ferromagnetic, metastable phases. We also show that for long times the dynamics can be approximated by a classical stochastic dynamics between the metastable phases that is directly related to the intermittent dynamics observed in quantum trajectories and thus the dynamical phases.},
  doi       = {10.1103/PhysRevE.94.052132},
  issue     = {5},
  numpages  = {11},
  publisher = {American Physical Society},
  url       = {https://link.aps.org/doi/10.1103/PhysRevE.94.052132},
}

@Article{PhysRevLett.119.150402,
  author    = {Karabanov, A. and Rose, D. C. and K\"ockenberger, W. and Garrahan, J. P. and Lesanovsky, I.},
  journal   = {Phys. Rev. Lett.},
  title     = {Phase Transitions in Electron Spin Resonance Under Continuous Microwave Driving},
  year      = {2017},
  month     = {Oct},
  pages     = {150402},
  volume    = {119},
  abstract  = {We study an ensemble of strongly coupled electrons under continuous microwave irradiation interacting with a dissipative environment, a problem of relevance to the creation of highly polarized nonequilibrium states in nuclear magnetic resonance. We analyze the stationary states of the dynamics, described within a Lindblad master equation framework, at the mean-field approximation level. This approach allows us to identify steady-state phase transitions between phases of high and low polarization controlled by the distribution of disordered electronic interactions. We compare the mean-field predictions to numerically exact simulations of small systems and find good agreement. Our study highlights the possibility of observing collective phenomena, such as metastable states, phase transitions, and critical behavior, in appropriately designed paramagnetic systems. These phenomena occur in a low-temperature regime which is not theoretically tractable by conventional methods, e.g., the spin-temperature approach.},
  doi       = {10.1103/PhysRevLett.119.150402},
  issue     = {15},
  numpages  = {5},
  publisher = {American Physical Society},
  url       = {https://link.aps.org/doi/10.1103/PhysRevLett.119.150402},
}

@Article{PhysRevE.98.022129,
  author    = {Rose, Dominic C. and Touchette, Hugo and Lesanovsky, Igor and Garrahan, Juan P.},
  journal   = {Phys. Rev. E},
  title     = {Spectral properties of simple classical and quantum reset processes},
  year      = {2018},
  month     = {Aug},
  pages     = {022129},
  volume    = {98},
  abstract  = {We study the spectral properties of classical and quantum Markovian processes that are reset at random times to a specific configuration or state with a reset rate that is independent of the current state of the system. We demonstrate that this simple reset dynamics causes a uniform shift in the eigenvalues of the Markov generator, excluding the zero mode corresponding to the stationary state, which has the effect of accelerating or even inducing relaxation to a stationary state. Based on this result, we provide expressions for the stationary state and probability current of the reset process in terms of weighted sums over dynamical modes of the reset-free process. We also discuss the effect of resets on processes that display metastability. We illustrate our results with two classical stochastic processes, the totally asymmetric random walk and the one-dimensional Brownian motion, as well as two quantum models: a particle coherently hopping on a chain and the dissipative transverse field Ising model, known to exhibit metastability.},
  doi       = {10.1103/PhysRevE.98.022129},
  issue     = {2},
  numpages  = {10},
  publisher = {American Physical Society},
  url       = {https://link.aps.org/doi/10.1103/PhysRevE.98.022129},
}

@Article{gillman2020tensor,
  author        = {Edward Gillman and Dominic C. Rose and Juan P. Garrahan},
  journal       = {ArXiv:2002.05185},
  title         = {A Tensor Network Approach to Finite Markov Decision Processes},
  year          = {2020},
  abstract      = {Tensor network (TN) techniques - often used in the context of quantum many-body physics - have shown promise as a tool for tackling machine learning (ML) problems. The application of TNs to ML, however, has mostly focused on supervised and unsupervised learning. Yet, with their direct connection to hidden Markov chains, TNs are also naturally suited to Markov decision processes (MDPs) which provide the foundation for reinforcement learning (RL). Here we introduce a general TN formulation of finite, episodic and discrete MDPs. We show how this formulation allows us to exploit algorithms developed for TNs for policy optimisation, the key aim of RL. As an application we consider the issue - formulated as an RL problem - of finding a stochastic evolution that satisfies specific dynamical conditions, using the simple example of random walk excursions as an illustration.},
  archiveprefix = {arXiv},
  eprint        = {2002.05185},
  primaryclass  = {cond-mat.stat-mech},
}

@Article{rose2020reinforcement,
  author        = {Dominic C. Rose and Jamie F. Mair and Juan P. Garrahan},
  journal       = {ArXiv:2005.12890},
  title         = {A reinforcement learning approach to rare trajectory sampling},
  year          = {2020},
  abstract      = {Very often when studying non-equilibrium systems one is interested in analysing dynamical behaviour that occurs with very low probability, so called rare events. In practice, since rare events are by definition atypical, they are often difficult to access in a statistically significant way. What are required are strategies to "make rare events typical" so that they can be generated on demand. Here we present such a general approach to adaptively construct a dynamics that efficiently samples atypical events. We do so by exploiting the methods of reinforcement learning (RL), which refers to the set of machine learning techniques aimed at finding the optimal behaviour to maximise a reward associated with the dynamics. We consider the general perspective of dynamical trajectory ensembles, whereby rare events are described in terms of ensemble reweighting. By minimising the distance between a reweighted ensemble and that of a suitably parametrised controlled dynamics we arrive at a set of methods similar to those of RL to numerically approximate the optimal dynamics that realises the rare behaviour of interest. As simple illustrations we consider in detail the problem of excursions of a random walker, for the case of rare events with a finite time horizon; and the problem of a studying current statistics of a particle hopping in a ring geometry, for the case of an infinite time horizon. We discuss natural extensions of the ideas presented here, including to continuous-time Markov systems, first passage time problems and non-Markovian dynamics.},
  archiveprefix = {arXiv},
  eprint        = {2005.12890},
  primaryclass  = {cond-mat.stat-mech},
}

@Article{macieszczak2020theory,
  author        = {Katarzyna Macieszczak and Dominic C. Rose and Igor Lesanovsky and Juan P. Garrahan},
  journal       = {ArXiv:2006.01227},
  title         = {Theory of classical metastability in open quantum systems},
  year          = {2020},
  abstract      = {We present a general theory of classical metastability in open quantum systems. Metastability is a consequence of a large separation in timescales in the dynamics, leading to the existence of a regime when states of the system appear stationary, before eventual relaxation towards a true stationary state at much larger times. In this work, we focus on the emergence of classical metastability, i.e., when metastable states of an open quantum system with separation of timescales can be approximated as probabilistic mixtures of a finite number of states. We find that a number of classical features follow from this approximation, for both the manifold of metastable states and long-time dynamics between them. Namely, those states are approximately disjoint and thus play the role of metastable phases, and the relaxation towards the stationary state is approximated by a classical stochastic dynamics between them. Importantly, the classical dynamics is observed not only on average, but also at the level of individual quantum trajectories: we show that time coarse-grained continuous measurement records can be viewed as noisy classical trajectories, while their statistics can be approximated by that of the classical dynamics. Among others, this explains how first-order dynamical phase transitions arise from metastability. Finally, in order to verify the presence of classical metastability in a given open quantum system, we develop an efficient numerical approach that delivers the set of metastable phases together with the effective classical dynamics. Since the proximity to a first-order dissipative phase transition manifests as metastability, the theory and tools introduced in this work can be used to investigate such transitions - which occur in the large size limit - through the metastable behavior of many-body systems of moderate sizes accessible to numerics.},
  archiveprefix = {arXiv},
  eprint        = {2006.01227},
  primaryclass  = {cond-mat.stat-mech},
}

@Comment{jabref-meta: databaseType:bibtex;}

repository: DomCRose/academic-kickstart

repo_name: DomCRose/academic-kickstart


text:
DESCRIPTION = "Rover Services API"
AUTHOR = "Pedro Cuadra"
LICENSE = "Apache-2.0"
LIC_FILES_CHKSUM = "file://${WORKDIR}/git/LICENSE;md5=86d3f3a95c324c9479bd8986968f4327"

inherit cmake pythonnative

DEPENDS = "raml2agl-native dbus glib-2.0 af-binder json-c python-jinja2-native python-markupsafe-native"

RDEPENDS_${PN} = "dbus glib-2.0 af-binder json-c"

SRCREV = "3f2c12d5c100dfeed77c56add37479887a48e58a"
PV_append = "+r${SRCREV}"

SRC_URI = "git://github.com/app4mc-rover/rover-services.git;branch=master"

EXTRA_OECMAKE += "-Dpkg_config_libdir=${libdir} -DCMAKE_BUILD_TYPE=Release"

do_configure_prepend() {
	${WORKDIR}/git/gen_app -l info
	cp ${WORKDIR}/git/LICENSE ${WORKDIR}/git/app/
}

S = "${WORKDIR}/git/app"

PACKAGES = "${PN} ${PN}-dbg ${PN}-dev"

FILES_${PN} = "\
     ${bindir}/* \
     ${libdir}/lib*.so \
     ${libdir}/*.a"

FILES_${PN}-dbg = "\
    ${bindir}/.debug \
    ${exec_prefix}/src/debug \
    ${libdir}/.debug"

FILES_${PN}-dev = "\
     ${includedir}"

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: bb


text:
package two

import (
	"log"
	"testing"
)

var testcases = []struct {
	in []int
}{
	{[]int{1, 2, 3, 4, 5}},
	{[]int{2, 4, 5, 6, 7, 8, 10}},
}

func TestCreateBinaryTree(t *testing.T) {
	t.Parallel()
	for _, c := range testcases {
		head := CreateBinaryTree(c.in, 0, len(c.in))
		preOrder(head)
	}
}

func preOrder(head *Node) {
	if head == nil {
		return
	}
	log.Println(head)
	preOrder(head.left)
	preOrder(head.right)
}

gha_license_id: MIT

gha_language: Go

extension: go


text:
FILESEXTRAPATHS_prepend := "${THISDIR}/${PN}-${PV}:"

LINUX_VERSION ?= "4.4.50"

SRCREV = "04c8e47067d4873c584395e5cb260b4f170a99ea"
SRC_URI = "git://github.com/raspberrypi/linux.git;protocol=git;branch=rpi-4.4.y \
           file://0001-fix-dtbo-rules.patch \
"

require linux-raspberrypi.inc

repository: webOS-ports/meta-raspberrypi

repo_name: webOS-ports/meta-raspberrypi

gha_language: C


text:
SUMMARY = "OP-TEE related packages"

inherit packagegroup

RDEPENDS_packagegroup-ledge-optee = "\
    optee-client\
    optee-test \
    ${@bb.utils.contains("MACHINE_FEATURES", "ftpm", "optee-ftpm", "", d)} \
    "

repository: Linaro/meta-ledge

repo_name: Linaro/meta-ledge

branch_name: refs/heads/hardknott

gha_language: Shell


text:
@article{oguz_hybrid_2018,
 abstract = {Robots collaborating naturally with a human partner in a confined workspace need to understand and predict human motions. For understanding, a model-based approach is required as the human motor control system relies on the biomechanical properties to control and execute actions. The model-based control models explain human motions descriptively, which in turn enables predicting and analyzing human movement behaviors. In motor control, reaching motions are framed as an optimization problem. However, different optimality criteria predict disparate motion behavior. Therefore, the inverse problem -- finding the optimality criterion from a given arm motion trajectory -- is not unique. This paper implements an inverse optimal control (IOC) approach to determine the combination of cost functions that governs a motion execution. The results indicate that reaching motions depend on a trade-off between kinematics and dynamics related cost functions. However, the computational efficiency is not sufficient for online prediction to be utilized for HRI. In order to predict human reaching motions with high efficiency and accuracy, we combine the IOC approach with a probabilistic movement primitives formulation. This hybrid model allows an online-capable prediction while taking into account motor variability, and the interpersonal differences. The proposed framework affords a descriptive and a generative model of human reaching motions which can be effectively utilized online for human-in-the-loop robot control and task execution.},
 author = {Oguz, Ozgur S. and Zhou, Zhehua and Wollherr, Dirk},
 copyright = {All rights reserved},
 doi = {10.3389/frobt.2018.00027},
 file = {Oguz_et_al_2018_A_Hybrid_Framework_for_Understanding_and_Predicting_Human_Reaching_Motions.pdf:/home/oz/gdrive/zotero/Oguz_et_al_2018_A_Hybrid_Framework_for_Understanding_and_Predicting_Human_Reaching_Motions.pdf:application/pdf},
 issn = {2296-9144},
 journal = {Front. Robot. AI},
 keywords = {myself},
 language = {English},
 note = {Publisher: Frontiers},
 title = {A Hybrid Framework for Understanding and Predicting Human Reaching Motions},
 url = {https://www.frontiersin.org/articles/10.3389/frobt.2018.00027/full},
 urldate = {2020-05-25},
 volume = {5},
 year = {2018}
}

repository: Oz-Oguz/academic-kickstart

repo_name: Oz-Oguz/academic-kickstart

extension: bib


text:
DESCRIPTION = "Tasks for Perso RPI Base Filesystem"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://${COREBASE}/LICENSE;md5=3f40d7994397109285ec7b81fdeb3b58 \
                    file://${COREBASE}/meta/COPYING.MIT;md5=3da9cfbcb788c80a0384361b4de20420"

PR = "r1"

inherit packagegroup

RDEPENDS_${PN} = "\
	rpi2 \
	owshell \
	owpython \
	python-mysqldb \
	python-dateutil \
	xauth \
	lsof \
	ntpdate \
	i2c-tools \
	owdatalogger \
"


text:
SUMMARY = "isg-503 kernel"
DESCRIPTION = "Rocktech isg-503 kernel"

FILESEXTRAPATHS_prepend := "${THISDIR}/files/4.4.194:"

LINUX_VERSION = "4.4.194"

SRC_URI = " \
	git://github.com/rockchip-linux/kernel.git;branch=develop-4.4 \
	file://0001-recktech-scrips-python3.patch \
	file://0002-recktech-dts-config.patch \
	file://0002-recktech-dts-uart5_fix.patch \
	file://0003-recktech-add-mcp9800.patch \
	file://0004-recktech-add-rtc-rx8010.patch \
	file://0005-recktech-fix-wifierror.patch \
	file://0004-recktech-modify_ttyName.patch \
	"

SRCREV = "eed2c2aa9f3899876861c7500ec5b2e7550b819d"

require recipes-kernel/linux/linux-yocto.inc

PV = "${LINUX_VERSION}+git${SRCPV}"

S = "${WORKDIR}/git"

KCONFIG_MODE="--alldefconfig"

COMPATIBLE_MACHINE = "(isg-503)"

SRC_URI += " \
        file://defconfig \
		"

# Comment the below line to mount file system from eMMC
# SRC_URI += "file://0008-Modified-Boot-Arguments-to-mount-fileystem-from-SD-c.patch")"


text:
require recipes-fsl/images/fsl-image-multimedia-full.bb

IMAGE_FEATURES = "debug-tweaks ssh-server-openssh"

IMAGE_INSTALL_append = " gcc g++ binutils libgcc libgcc-dev libstdc++ libstdc++-dev libstdc++-staticdev \
    autoconf automake ccache chkconfig glib-networking glibmm \
    packagegroup-core-buildessential pkgconfig  \
    boost cmake zlib glib-2.0 packagegroup-fsl-tools-testapps git glive \
    ruby \
    cpufrequtils \
    nano \
    gdb gdbserver \
    gstreamer \
    gst-meta-video \
    gst-fsl-plugin \
    gst-plugins-base-app \
    gst-plugins-base \
    gst-plugins-good \
    gst-plugins-good-rtsp \
    gst-plugins-good-udp \
    gst-plugins-good-rtpmanager \
    gst-plugins-good-rtp \
    gst-plugins-good-video4linux2 \
    openssh-sftp-server \
    packagegroup-fsl-gstreamer \
    packagegroup-fsl-tools-testapps \
    packagegroup-fsl-tools-benchmark \
    imx-vpu \
    imx-test \
    qtbase-fonts \
    qtbase-plugins \
    qtbase-tools \
    qtdeclarative \
    qtdeclarative-plugins \
    qtdeclarative-tools \
    qtdeclarative-qmlplugins \
    qtmultimedia \
    qtmultimedia-plugins \
    qtmultimedia-qmlplugins \
    qtsvg \
    qtsvg-plugins \
    qtsensors \
    qtimageformats-plugins \
    qtsystems \
    qtsystems-tools \
    qtsystems-qmlplugins \
    qtscript \
    qt3d \
    qt3d-qmlplugins \
    qt3d-tools \
    qtwebkit \
    qtwebkit-qmlplugins \
    qtgraphicaleffects-qmlplugins \
    qtconnectivity-qmlplugins \
    qtlocation-plugins \
    qtlocation-qmlplugins \
    cinematicexperience \
    cairo pango fontconfig freetype pulseaudio dbus \
    alsa-lib alsa-tools alsa-state alsa-utils-alsaconf fsl-alsa-plugins \
    i2c-tools \
    strace \
    "

license: Unlicense

repository: morixhub/meta-umobo

repo_name: morixhub/meta-umobo