text:
// Copyright 2021 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

package checksumstest

import (
	"crypto/md5"
	"math/rand"
	"testing"

	"cloud.google.com/go/datastore"
	"github.com/googleforgames/open-saves/internal/pkg/metadb/checksums"
	"github.com/stretchr/testify/assert"
)

// AssertPropertyListMatch asserts the properties in actual has the same values to expected.
func AssertPropertyListMatch(t *testing.T, expected checksums.Checksums, actual []datastore.Property) {
	t.Helper()
	ps, err := datastore.SaveStruct(&expected)
	if assert.NoError(t, err) {
		assert.ElementsMatch(t, ps, actual)
	}
}

// ChecksumsToProperties creates a list of datastore.Property with values in c.
func ChecksumsToProperties(t *testing.T, c checksums.Checksums) []datastore.Property {
	t.Helper()
	ps, err := datastore.SaveStruct(&c)
	assert.NoError(t, err)
	return ps
}

// RandomChecksums returns a randomly initialized Checksums for tests.
func RandomChecksums(t *testing.T) checksums.Checksums {
	t.Helper()
	md5 := make([]byte, md5.Size)
	rand.Read(md5)
	return checksums.Checksums{
		MD5:       md5,
		CRC32C:    int32(rand.Uint32()),
		HasCRC32C: true,
	}
}

// AssertProtoEquals checks actual has the same values as expected.
func AssertProtoEqual(t *testing.T, expected checksums.Checksums, actual checksums.ChecksumsProto) {
	t.Helper()
	assert.Equal(t, expected.MD5, actual.GetMd5())
	assert.Equal(t, expected.GetCRC32C(), actual.GetCrc32C())
	assert.Equal(t, expected.HasCRC32C, actual.GetHasCrc32C())
}

// ChecksumsProtoImpl is a wrapper of Checksums for tests.
type ChecksumsProtoImpl struct {
	checksums.Checksums
}

func (c *ChecksumsProtoImpl) GetMd5() []byte {
	return c.MD5
}

func (c *ChecksumsProtoImpl) GetCrc32C() uint32 {
	return c.GetCRC32C()
}

func (c *ChecksumsProtoImpl) GetHasCrc32C() bool {
	return c.HasCRC32C
}

repository: googleforgames/open-saves

repo_name: googleforgames/open-saves


text:
@article{Chegoonian-2022-Improvement,
    title = "Improvement of field fluorometry estimates of chlorophyll {\textless}i{\textgreater}a{\textless}/i{\textgreater} concentration in a cyanobacteria‐rich eutrophic lake",
    author = "Chegoonian, Amir M.  and
      Zolfaghari, Kiana  and
      Leavitt, Peter R.  and
      Baulch, Helen M.  and
      Duguay, Claude R.",
    journal = "Limnology and Oceanography: Methods, Volume 20, Issue 4",
    volume = "20",
    number = "4",
    year = "2022",
    publisher = "Wiley",
    url = "https://aclanthology.org/G22-23001",
    doi = "10.1002/lom3.10480",
    pages = "193--209",
    abstract = "Instrumented buoys are used to monitor water quality, yet there remains a need to evaluate whether in vivo fluorometric measures of chlorophyll a (Chl a) produce accurate estimates of phytoplankton abundance. Here, 6 years (2014{--}2019) of in vitro measurements of Chl a by spectrophotometry were compared with coeval estimates from buoy-based fluorescence measurements in eutrophic Buffalo Pound Lake, Saskatchewan, Canada. Analysis revealed that fluorometric and in vitro estimates of Chl a differed both in terms of absolute concentration and patterns of relative change through time. Three models were developed to improve agreement between metrics of Chl a concentration, including two based on Chl a and phycocyanin (PC) fluorescence and one based on multiple linear regressions with measured environmental conditions. All models were examined in terms of two performance metrics; accuracy (lowest error) and reliability ({\%} fit within confidence intervals). The model based on PC fluorescence was most accurate (error = 35{\%}), whereas that using environmental factors was most reliable (89{\%} within 3σ of mean). Models were also evaluated on their ability to produce spatial maps of Chl a using remotely sensed imagery. Here, newly developed models significantly improved system performance with a 30{\%} decrease in Chl a errors and a twofold increase in the range of reconstructed Chl a values. Superiority of the PC model likely reflected high cyanobacterial abundance, as well as the excitation{--}emission wavelength configuration of fluorometers. Our findings suggest that a PC fluorometer, used alone or in combination with environmental measurements, performs better than a single-excitation-band Chl a fluorometer in estimating Chl a content in highly eutrophic waters.",
}

repository: gwf-uwaterloo/gwf-publications

license_type: permissive

repo_name: gwf-uwaterloo/gwf-publications

branch_name: refs/heads/main

gha_language: Python

extension: bib


text:
file(REMOVE_RECURSE
  "CMakeFiles/hysteresis.dir/hysteresis.cpp.o"
  "libhysteresis.pdb"
  "libhysteresis.a"
)

# Per-language clean rules from dependency scanning.
foreach(lang CXX)
  include(CMakeFiles/hysteresis.dir/cmake_clean_${lang}.cmake OPTIONAL)
endforeach()

repository: cuongdaovan/Firmware

repo_name: cuongdaovan/Firmware

branch_name: refs/heads/master

extension: cmake


text:
package main

import (
	"github.com/sonm-io/core/fusrodah/hub"
)

func main() {
	srv := hub.NewServer(nil, "123.123.123.123")
	srv.Start()
	srv.Serve()
	select {}
}

extension: go


text:
DESCRIPTION = "libfpgaregion"
SECTION = "base"
LICENSE = "CLOSED"
FILESEXTRAPATHS_prepend := "${THISDIR}/files/:"

SRC_URI = "git://gitlab.fh-ooe.at/smart_systems_lab/debug_service.git;protocol=https;user=gitlab+deploy-token-9:vh-CHWx5PRx8Bu1Sf-4a;branch=master"
SRCREV = "${AUTOREV}"

PACKAGES = "${PN} ${PN}-dev ${PN}-dbg ${PN}-staticdev"
# static lib => no dependencies
RDEPENDS_${PN}-staticdev = ""
RDEPENDS_${PN}-dev = ""
RDEPENDS_${PN}-dbg = ""

GIT = "${WORKDIR}/git/"
S = "${GIT}/libfpgaregion/"

MAKEFILECONFIG = "YOCTO"
EXTRA_OEMAKE += "'CONFIG=${MAKEFILECONFIG}'"

do_compile () {
    oe_runmake
}

do_install () {
    # install static lib libfpgaregion-staticdev
    install -d ${D}${libdir}
    oe_libinstall -C ${S}/Release/ -a libfpgaregion ${D}${libdir}

    # install header libfpgaregion-dev
    install -d ${D}${includedir}
    install -m 644 ${S}/libfpgaregion.h ${D}${includedir}
}

license: MIT

repository: skaupper/SmartSystemsLab-Yocto

repo_name: skaupper/SmartSystemsLab-Yocto

extension: bb


text:
@Article{Smith:2013:EEC:2527812.2527981,
  Address = {Amsterdam, The Netherlands, The Netherlands},
  Author = {Smith, Shamus P. and Stibric, Marina and Smithson, David},
  Journal = {Comput. Hum. Behav.},
  Month = {nov},
  Number = {6},
  Pages = {2388--2393},
  Publisher = {Elsevier Science Publishers B. V.},
  Title = {Exploring the Effectiveness of Commercial and Custom-built Games for Cognitive Training},
  Volume = {29},
  Year = {2013},
  Issue_date = {November, 2013},
  Issn = {0747-5632},
  Numpages = {6},
  Url = {http://dx.doi.org/10.1016/j.chb.2013.05.014},
  Doi = {10.1016/j.chb.2013.05.014},
  Acmid = {2527981},
  Keywords = {Cognitive training, Commercial software, Fluid intelligence, Game reuse, N-back task, User study},
  Study.name = {Alloway 2012},
  Comparison = {Blank},
  Outcome = {arithmetic},
  Time.point = {posttest},
  Sdm = {0.61},
  Group.a.sample.size = {8},
  Group.b.sample.size = {7},
  Effect.direction = {1},
  Hedges.s.g = {0.5741},
  Se = {0.4983},
  treated = {untreated},
  age = {children},
  dose = {large},
  Randomization = {r},
  learning.status = {l},
  outcome.test = {WOND},
  Ppcg = {Blank},
  type.of.training = {other},
  Age.control = {13},
  Age.training = {12.9},
  Participants = {Children with learning difficulities. To determine that control & training group were not different- series of Mann-Whitney test carried out in pre-training assessment.},
  training.dose = {3 phases:                                                           Pretest- Training(or control)- Single Session lasting approx. 40 min.                                    Pottest Training: 3 times a week, 30 minute sessions for 8 weeks. Approx. 25 days of training.                                                          Posttest: single session lasting approx. 40 min. },
  program = {Jungle Memory (2008)},
  gray = {published},
  follow.up.test = {8m},
  design = {Pre & posttest fixed sequence. Random assignment. }
}

@Article{Alloway2012vb,
  Address = {Waynesville, NC},
  Author = {Tracy Alloway},
  Journal = {Journal of Interactive Learning Research},
  Month = {July},
  Number = {3},
  Pages = {197--207},
  Publisher = {Association for the Advancement of Computing in Education (AACE)},
  Title = {Can interactive working memory training improving learning?},
  Volume = {23},
  Year = {2012},
  Issn = {1093-023X},
  Url = {https://www.learntechlib.org/p/36119},
  Abstract = {Background. Working memory is linked to learning outcomes and there is emerging evidence that training working memory can yield gains in working memory and fluid intelligence.},
  Study.name = {Smith,  et al. 2013 comp 1},
  Comparison = {Comparison 1 ( E vs AC) [Dual n-back vs Gaming Web link]},
  Outcome = {nRAPM},
  Time.point = {posttest},
  Sdm = {0.0435},
  Group.a.sample.size = {10},
  Group.b.sample.size = {10},
  Effect.direction = {1},
  Hedges.s.g = {0.0416},
  Se = {0.4284},
  treated = {treat},
  age = {adults},
  dose = {small},
  Randomization = {r},
  learning.status = {u},
  outcome.test = {RAPM},
  Ppcg = {Blank},
  type.of.training = {n back},
  Age.control = {19-34},
  Age.training = {19-34},
  Participants = {University students of British University. Fluent at a graduate level academic English. All used computers on a daily basis. },
  training.dose = {COT-DIY-Gaming used software for 20 min per day for at least 17 days of 21 day evaluation period. },
  program = {Developed custom training system software. N-back working memory paradigm.  And a Commerically available software Nintendo DS (Dr. Kawashima’s Brain Training game).},
  gray = {published},
  follow.up.test = {Blank},
  design = {Experimental -plus-control group design. Randomly assigned to one of four groups and balanced for gender. }
}


text:
@article{cole_new_2017,
 abstract = {The enrichment and depletion of redox sensitive trace metals in marine sediments have been used extensively as paleoredox proxies. The trace metals in shale are comprised of both detrital (transported or particulate) and authigenic (precipitated, redox-driven) constituents, potentially complicating the use of this suite of proxies. Untangling the influence of these components is vital for the interpretation of enrichments, depletions, and isotopic signals of iron (Fe), chromium (Cr), uranium (U), and vanadium (V) observed in the rock record. Traditionally, a single crustal average is used asa cutoff for detrital input, and concentrations above or below this value are interpreted as redox derived authigenic enrichment or depletion, while authigenic isotopic signals are frequently corrected for an assumed detrital contribution. Building from an extensive study of soils across the continental United States – which upon transport will become marine sediments – and their elemental concentrations, we find large deviations from accepted crustal averages in redox-sensitive metals (Fe, Cr, U, V) compared to typical detrital tracers (Al, Ti, Sc, Th) and provide new estimates for detrital contributions to the ocean. The variability in these elemental ratios is present over large areas, comparable to the catchment-size of major rivers around the globe. This heterogeneity in detrital flux highlights the need for a reevaluation of how the detrital contribution is assessed in trace metal studies, and the use of confidence intervals rather than single average values, especially in local studies or in the case of small authigenic enrichments.},
 author = {Cole, Devon B. and Zhang, Shuang and Planavsky, Noah J.},
 copyright = {All rights reserved},
 doi = {10.1016/j.gca.2017.08.004},
 file = {ScienceDirect Full Text PDF:/Users/shzhang/Zotero/storage/D4SBRZXH/Cole et al. - 2017 - A new estimate of detrital redox-sensitive metal c.pdf:application/pdf},
 issn = {0016-7037},
 journal = {Geochimica et Cosmochimica Acta},
 keywords = {Geochemistry, Redox, Redox-sensitive metals},
 month = {October},
 pages = {337--353},
 title = {A new estimate of detrital redox-sensitive metal concentrations and variability in fluxes to marine sediments},
 url = {http://www.sciencedirect.com/science/article/pii/S0016703717304714},
 urldate = {2018-05-02},
 volume = {215},
 year = {2017}
}

repository: wadesnoopy/shuang-zhang

repo_name: wadesnoopy/shuang-zhang


text:
@InProceedings{yun14,
  title = {Community Detection via Random and Adaptive Sampling},
  author = {Yun, Se-Young and Proutiere, Alexandre},
  pages = {138-175},
  abstract = {In this paper, we consider networks consisting of a finite number of non-overlapping communities. To extract these communities, the interaction between pairs of nodes may be sampled from a large available data set, which allows a given node pair to be sampled several times. When a node pair is sampled, the observed outcome is a binary random variable, equal to 1 if nodes interact and to 0 otherwise. The outcome is more likely to be positive if nodes belong to the same communities. For a given budget of node pair samples or observations, we wish to jointly design a sampling strategy (the sequence of sampled node pairs) and a clustering algorithm that recover the hidden communities with the highest possible accuracy. We consider both non-adaptive and adaptive sampling strategies, and for both classes of strategies, we derive fundamental performance limits satisfied by any sampling and clustering algorithm. In particular, we provide necessary conditions for the existence of algorithms recovering the communities accurately as the network size grows large. We also devise simple algorithms that accurately reconstruct the communities when this is at all possible, hence proving that the proposed necessary conditions for accurate community detection are also sufficient. The classical problem of community detection in the stochastic block model can be seen as a particular instance of the problems consider here. But our framework covers more general scenarios where the sequence of sampled node pairs can be designed in an adaptive manner. The paper provides new results for the stochastic block model, and extends the analysis to the case of adaptive sampling.},
}

repository: mreid/papersite

repo_name: mreid/papersite

gha_license_id: MIT

gha_language: TeX


text:
DESCRIPTION = "A simple, small, portable, fast, and secure HTTP server."
LICENSE = "BSD"
MAINTAINER = "Chris Larson <kergoth@handhelds.org>"
HOMEPAGE = "http://www.acme.com/software/thttpd/"
PR="r1"

SRC_URI = "http://www.acme.com/software/thttpd/thttpd-2.25b.tar.gz \
	   file://install.patch;patch=1 \
	   file://acinclude.m4 \
	   file://init"
S = "${WORKDIR}/thttpd-${PV}"
INITSCRIPT_NAME = "thttpd"
INITSCRIPT_PARAMS = "defaults"

inherit autotools

EXTRA_OEMAKE += "'WEBDIR=${servicedir}/www'"
FILES_${PN}_append = " ${servicedir}"

do_configure () {
	install -m 0644 ${WORKDIR}/acinclude.m4 ${S}/
	autotools_do_configure
}

do_install_append () {
	install -d "${D}${sysconfdir}/init.d"
	install -c -m 755 ${WORKDIR}/init ${D}${sysconfdir}/init.d/thttpd
}

repository: BackupTheBerlios/openslug-svn

repo_name: BackupTheBerlios/openslug-svn


text:
@article{Hamra:2019aa,
 author = {Hamra, Ghassan B and Goldstein, Neal D and Harper, Sam},
 bdsk-url-1 = {https://doi.org/10.1161/JAHA.119.012292},
 date-added = {2020-09-24 17:39:32 -0400},
 date-modified = {2020-09-24 17:39:32 -0400},
 doi = {10.1161/JAHA.119.012292},
 journal = {J Am Heart Assoc},
 journal-full = {Journal of the American Heart Association},
 keywords = {replication; reproducibility; research ethics},
 month = {08},
 number = {15},
 pages = {e012292},
 pmc = {PMC6761666},
 pmid = {31364452},
 pst = {ppublish},
 title = {Resource Sharing to Improve Research Quality},
 volume = {8},
 year = {2019}
}


text:
SUMMARY = "Additional ACPI tables to be included with the image"
DESCRIPTION = "This will generate an initrd including ACPI tables\
 specified in ACPI_TABLES variable. The kernel looks for these\
 tables and if found, loads and parses them. This is useful if\
 you want to add new devices to buses like I2C and SPI but not limited\
 to that."

LICENSE = "BSD"
LIC_FILES_CHKSUM = "file://${COREBASE}/meta/files/common-licenses/BSD;md5=3775480a712fc46a69647678acb234cb"

DEPENDS = "acpica-native"

inherit deploy

ACPI_TABLES ?= ""
ACPI_TABLES[doc] = "List of ACPI tables to include with the initramfs"

do_compile() {
	# Always clean up the existing tables
	rm -fr ${WORKDIR}/acpi-tables/kernel
	install -d ${WORKDIR}/acpi-tables/kernel/firmware/acpi

	for table in ${ACPI_TABLES}; do
		# If relative path is given use sample tables if
		# available for the machine in question.
		d=$(dirname $table)
		if [ "$d" = "." ]; then
			table="${THISDIR}/$table"
		fi

		[ -f "$table" ] || continue

		dest_table=$(basename $table)
		bbdebug 1 "Including ACPI table: ${table}"
		iasl -p ${WORKDIR}/acpi-tables/kernel/firmware/acpi/$dest_table $table
	done
}
do_install() {
	obj=`find ${WORKDIR}/acpi-tables/kernel/firmware/acpi/ -name *.aml`
	if [ -n "$obj" ]; then
    	install -d ${D}/kernel/firmware/acpi
    	install -m 644 ${WORKDIR}/acpi-tables/kernel/firmware/acpi/* ${D}/kernel/firmware/acpi
	fi
}
FILES_${PN} += "/kernel/firmware/acpi/* "


text:
# base recipe: meta/recipes-support/lz4/lz4_1.8.3.bb
# base branch: warrior
# base commit: bb2d6cc85d558b2292a1a76d42c7cb1ff9713332

SECTION = "libs"

require lz4.inc

repository: miraclelinux/meta-debian-extended

repo_name: miraclelinux/meta-debian-extended

branch_name: refs/heads/warrior

gha_language: BitBake


text:
@article{Harper2005a,
 abstract = {Visually impaired users are hindered in their efforts to access the largest repository of electronic information in the world, namely the World Wide Web. A visually impaired user's information and presentation requirements are different from those of a sighted user, in that they are highly individualized and nonvisual. These requirements can become problems in that the Web is visual-centric with regard to presentation as well as information order and layout. This can and does hinder users who need access to information but cannot take advantage of the visual cues available to sighted users. Our objective is to address these problems by creating usable and appropriately "displayed" Web pages for all users who wish to understand the meaning of the information, as opposed to its presentation and order. We assert that the only way to accomplish this is to encode the semantic information of the page directly into the page. In this paper we describe work toward a low-overhead system to enable just this kind of semantic encoding. In particular, our approach allows semantics-based triage, that is, prioritized removal of unnecessary information from the presentation of a Web site, to make the interaction of visually impaired users with that site more productive. © Copyright 2005 by International Business Machines Corporation.},
 author = {Harper, S. and Bechhofer, S.},
 issn = {00188670},
 journal = {IBM Systems Journal},
 number = {3},
 title = {Semantic triage for increased Web accessibility},
 volume = {44},
 year = {2005}
}

repository: sharpic/academic-kickstart

repo_name: sharpic/academic-kickstart


text:
get_filename_component(DEFERRED_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
include(CMakeFindDependencyMacro)

list(APPEND CMAKE_MODULE_PATH ${DEFERRED_CMAKE_DIR})
list(REMOVE_AT CMAKE_MODULE_PATH -1)

if(NOT TARGET deferred::deferred)
  include("${DEFERRED_CMAKE_DIR}/deferredTargets.cmake")
endif()

set(DEFERRED_LIBRARIES deferred::deferred)

repository: ipapadop/deferred

repo_name: ipapadop/deferred

extension: in


text:
@inproceedings{guha_2016_robust,
	Author = {Guha, Sudipto and Mishra, Nina and Roy, Gourav and Schrijvers, Okke},
	Booktitle = {International conference on machine learning},
	Date-Added = {2018-10-29 13:12:54 -0400},
	Date-Modified = {2018-10-29 13:12:54 -0400},
	Pages = {2712--2721},
	Title = {Robust random cut forest based anomaly detection on streams},
	Year = {2016}
}

@inproceedings{aleskerov_1997_cardwatch,
	doi = {10.1109/cifer.1997.618940},
	publisher = {{IEEE}},
	author = {E. Aleskerov and B. Freisleben and B. Rao},
	title = {{CARDWATCH}: a neural network based database mining system for credit card fraud detection},
  year = {1997},
	booktitle = {Proceedings of the {IEEE}/{IAFE} 1997 Computational Intelligence for Financial Engineering ({CIFEr})}
}

@article{breunig_2000_lof,
 author = {Breunig, Markus M. and Kriegel, Hans-Peter and Ng, Raymond T. and Sander, J\"{o}rg},
 title = {{LOF}: Identifying Density-based Local Outliers},
 journal = {SIGMOD Rec.},
 issue_date = {June 2000},
 volume = {29},
 number = {2},
 month = may,
 year = {2000},
 issn = {0163-5808},
 pages = {93--104},
 numpages = {12},
 doi = {10.1145/335191.335388},
 acmid = {335388},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {database mining, outlier detection},
}

@inproceedings{lazarevic_2003_comparative,
	doi = {10.1137/1.9781611972733.3},
	year = 2003,
	month = {may},
	publisher = {Society for Industrial and Applied Mathematics},
	author = {Aleksandar Lazarevic and Levent Ertoz and Vipin Kumar and Aysel Ozgur and Jaideep Srivastava},
	title = {A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection},
	booktitle = {Proceedings of the 2003 {SIAM} International Conference on Data Mining}
}

@article{liu_2012_isolation,
	doi = {10.1145/2133360.2133363},
	year = 2012,
	month = {mar},
	publisher = {Association for Computing Machinery ({ACM})},
	volume = {6},
	number = {1},
	pages = {1--39},
	author = {Fei Tony Liu and Kai Ming Ting and Zhi-Hua Zhou},
	title = {Isolation-Based Anomaly Detection},
	journal = {{ACM} Transactions on Knowledge Discovery from Data}
}

@article{filev_2010_industrial,
	doi = {10.1109/TII.2010.2060732},
	year = 2010,
	publisher = {IEEE},
	author = {Filev, Dimitar P. and Chinnam, Ratna Babu and Tseng, Finn and Baruah, Pundarikaksha},
	title = {An Industrial Strength Novelty Detection Framework for Autonomous Equipment Monitoring and Diagnostics},
	journal = {IEEE Transactions on Industrial Informatics}
}

@article{mirkovic_2004_taxonomy,
	doi = {10.1145/997150.997156},
	year = 2004,
	publisher = {ACM},
	author = {Mirkovic, Jelena and Reiher, Peter},
	title = {A Taxonomy of {DDoS} Attack and {DDoS} Defense Mechanisms},
	journal = {ACM SIGCOMM Computer Communications Review}
}

@inproceedings{williams_2002_comparative,
  title={A comparative study of {RNN} for outlier detection in data mining},
  doi = {10.1109/ICDM.2002.1184035},
  author={Williams, Graham and Baxter, Rohan and He, Hongxing and Hawkins, Simon and Gu, Lifang},
  booktitle={Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on},
  pages={709--712},
  year={2002},
  organization={IEEE}
}

@article{tax_2004_support,
  title={Support vector data description},
  doi = {10.1023/B:MACH.0000008084.60811.49},
  author={Tax, David MJ and Duin, Robert PW},
  journal={Machine learning},
  volume={54},
  number={1},
  pages={45--66},
  year={2004},
  publisher={Springer}
}

repository: kLabUM/rrcf

repo_name: kLabUM/rrcf


text:
% This is intentionally out of order, to that references are ordered properly when rendered.
% The remaining references are ordered by their citation keys.
@article{Macaulay2017,
	title = {Synchrony and diachrony in {Menominee} derivational morphology},
	volume = {27},
	url = {https://drive.google.com/file/d/1O5gg40xMECjx4kFlBhcBHrbQ1wkUnOde/view?usp=sharing},
	doi = {10.1007/s11525-016-9299-y},
	abstract = {This paper identifies two difficulties with treatments of derivation in Algonquian languages. In traditional approaches to grammar, in which the morpheme is seen as a unitary entity, morphemes are understood as minimal units of meaning and/or function. Definitions share an appeal to the morpheme’s indivisibility. In the Algonquianist literature, in contrast, some morphemes (‘components’) can themselves contain other morphemes (which we call ‘formatives’) and they can also be synchronically derived from other components or stems. Drawing data from Menominee, we propose that these difficulties disappear if the formatives are seen as historical rather than synchronic units, while the components are the synchronic morphemes. Formatives bear the hallmarks of historical products of morphologization (phonetic/phonological reduction, semantic bleaching, and increase in grammatical function), and we conclude that they are not part of synchronic grammatical computation. This resolves problems present in traditional and modern theoretical approaches to Algonquian derivation, and has broader ramifications for linguistic theory: in both the structuralist and generativist traditions, synchronic grammar has often been seen as expansive, responsible for generating surface patterns that may instead be products of history. This has been the case in phonology and syntax. The present paper provides a study of the phenomenon in derivational morphology, and suggests that a more modest role for synchronic rules is called for.},
	number = {2},
	journal = {Morphology},
	author = {Macaulay, Monica and Salmons, Joseph},
	year = {2017},
	pages = {179--215},
}

@inproceedings{Arppeetal2019,
	title = {Insights from {Computational} {Modeling} of the {Derivational} {Structure} of {Plains} {Cree}},
	volume = {48},
	url = {https://drive.google.com/file/d/1B3ROIC2IFsJjHNA_YWcTmBBcFO__2GD6/view?usp=sharing},
	booktitle = {Papers of the {Algonquian} {Conference}},
	publisher = {Michigan State University Press},
	author = {Arppe, Antti and Schmirler, Katherine and Silfverberg, Miikka and Hulden, Mans and Wolvengrey, Arok},
	editor = {Macaulay, Monica and Noodin, Margaret},
	year = {2019},
	pages = {1},
}

@inproceedings{Bloomfield1924,
	address = {Leiden [Netherlands]},
	title = {The {Menomini} language},
	volume = {21},
	url = {https://drive.google.com/file/d/10ANPZK9XQcgyINg8WfHpUQnHZKAfZ6rL/view?usp=sharing},
	booktitle = {Proceedings of the {International} {Congress} of {Americanists}},
	author = {Bloomfield, Leonard},
	editor = {Albers, D.},
	year = {1924},
	pages = {336--343},
}

@misc{CookMuehlbauer2004,
	title = {A mini-grammar of nêhiyawêwin ({Plains} {Cree})},
	author = {Cook, Claire and Mühlbauer, Jeff},
	year = {2004},
}

@book{CowellMoss2008,
	title = {The {Arapaho} language},
	isbn = {978-0-87081-901-8},
	url = {https://drive.google.com/file/d/1AZ7qcAja_q_grlSnhFr8doLNVMLQGGaG/view?usp=sharing},
	publisher = {University Press of Colorado},
	author = {Cowell, Andrew and Moss, Alonzo},
	year = {2008},
}

@inproceedings{Goddard1967,
	title = {The {Algonquian} {Independent} {Indicative}},
	volume = {1},
	url = {https://drive.google.com/file/d/1TS5tR73dgY8m7VPWOt3cP79zKxrSnmR9/view?usp=sharing},
	booktitle = {Proceedings of the {Algonquian} {Conference}},
	publisher = {National Museum of Canada},
	author = {Goddard, Ives},
	year = {1967},
}

@inproceedings{Goddard1980,
	title = {Eastern {Algonquian} as a genetic subgroup},
	volume = {11},
	url = {https://drive.google.com/file/d/1aMhrX5g7lSWkwXh9a8fz5m-tJzAngMG_/view?usp=sharing},
	booktitle = {Papers of the {Algonquian} {Conference}},
	publisher = {Carleton University},
	author = {Goddard, Ives},
	editor = {Cowan, William},
	year = {1980},
	pages = {143--158},
}

@article{Goddard1990,
	title = {Primary and secondary stem derivation in {Algonquian}},
	volume = {56},
	url = {https://drive.google.com/file/d/1HMBtmR-akOM3LdJSZxgBaIeO72V1y_RQ/view?usp=sharing},
	number = {4},
	journal = {International Journal of American Linguistics},
	author = {Goddard, Ives},
	year = {1990},
	pages = {449--483},
}

% This is intentionally out of order, to test sorting.
@article{Hieber2019b,
	title = {Semantic alignment in {Chitimacha}},
	volume = {85},
	issn = {0020-7071},
	url = {https://www.journals.uchicago.edu/doi/abs/10.1086/703239},
	doi = {10.1086/703239},
	abstract = {This paper describes the alignment system for verbal person-marking in Chitimacha, a language isolate of Louisiana. Using data from recently digitized versions of texts collected by Morris Swadesh in the 1930s, I show that Chitimacha exhibits a split alignment system with agent-patient alignment in the first person and nominative-accusative alignment in non-first persons. The agent-patient alternation is shown to cross-cut subjects of intransitives, objects and even subjects of transitives, and direct/indirect objects of ditransitives. The agent-patient system in Chitimacha is therefore sensitive not to transitivity but rather to the semantic categories of agent and patient, making it an exemplary case of semantic alignment. I also discuss evidence of the diachronic origins of the agent-patient pattern and show that it arose via a reanalysis of transitive verbs with impersonal subjects (“transimpersonals”) as intransitive verbs with patientive subjects.},
	number = {3},
	urldate = {2019-07-12},
	journal = {International Journal of American Linguistics},
	author = {Hieber, Daniel W.},
	month = jun,
	year = {2019},
	pages = {313--363},
}

@incollection{Hieber2019a,
	series = {America's {Third} {Coast} {Series}},
	title = {The {Chitimacha} language: {A} history},
	language = {en},
	booktitle = {Language in {Louisiana}: {Community} and culture},
	publisher = {University Press of Mississippi},
	author = {Hieber, Daniel W.},
	year = {2019},
	pages = {9--27},
}

@inproceedings{Rhodes1980,
	title = {On the semantics of the instrumental finals in {Ojibwa}},
	volume = {11},
	url = {https://drive.google.com/file/d/1K9dJX85t09INbtGqzaupHVBbHrJvmmrw/view?usp=sharing},
	booktitle = {Papers of the {Algonquian} {Conference}},
	publisher = {Carleton University},
	author = {Rhodes, Richard A.},
	editor = {Cowan, William},
	year = {1980},
	pages = {183--197},
}

@book{Siddiqietal2020,
	address = {New York, NY},
	series = {Routledge handbooks in linguistics},
	title = {The {Routledge} handbook of {North} {American} languages},
	isbn = {978-1-351-81026-5 978-1-351-81027-2 978-1-315-21063-6},
	abstract = {"The Routledge Handbook of North American Languages is a one-stop reference for linguists on those topics that come up the most frequently in the study of the languages of North America (including Mexico). This handbook compiles a list of contributors from across many different theories and at different stages of their careers, all of whom are well known experts in North American languages. The volume comprises two distinct parts: the first surveys some of the phenomena most frequently discussed in the study of North American languages and the second surveys some of the most frequently discussed language families of North America. The consistent goal of each contribution is to couch the content of the chapter in contemporary theory so that the information is maximally relevant and accessible for a wide range of audiences, including graduate students and young new scholars, and even senior scholars who are looking for a crash course in the topics. Empirically-driven chapters provide fundamental knowledge needed to participate in contemporary theoretical discussions of these languages, making this handbook is an indispensable resource for linguistics scholars"--},
	language = {en},
	publisher = {Routledge},
	editor = {Siddiqi, Daniel and Barrie, Michael and Gillon, Carrie and Haugen, Jason D. and Mathieu, Eric},
	year = {2020},
}

@book{Wolvengrey2001,
	address = {Regina, Saskatchewan},
	publisher = {University of Regina Press},
	author = {Wolvengrey, Arok},
	title = {nēhiýawēwin: itwēwina {\textbar} ᓀᐦᐃᔭᐍᐏᐣ: ᐃᑘᐏᓇ {\textbar} {Cree}: {Words} (2 vols.)},
	year = {2001},
}

repository: digitallinguistics/data-explorer

repo_name: digitallinguistics/data-explorer

gha_language: JavaScript


text:
# FindBlosc.cmake
#
# Finds Blosc
#
# This will define the following variables
#
#    Blosc_FOUND
#    Blosc_INCLUDE_DIRS
#    Blosc_LIBRARIES
#
# and the following imported targets
#
#     Blosc::Blosc
#
# Author: Fredrik Brännbacka, fredrik.brannbacka@ilpvfx.com

find_library(BLOSC_LIBRARY
        NAMES libblosc.a
        PATHS
        ${BLOSC_HOME}/lib
        $ENV{BLOSC_HOME}/lib
        DOC "Blosc library")

find_path(BLOSC_INCLUDE_DIR blosc.h
        PATHS
        ${BLOSC_HOME}/include
        $ENV{BLOSC_HOME}/include
        DOC "Blosc include path")

mark_as_advanced(Blosc_FOUND BLOSC_INCLUDE_DIR BLOSC_LIBRARY)

include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(Blosc
        REQUIRED_VARS BLOSC_INCLUDE_DIR BLOSC_LIBRARY
        )

if(Blosc_FOUND)
    set(Blosc_INCLUDE_DIRS ${BLOSC_INCLUDE_DIR})
    set(Blosc_LIBRARIES ${BLOSC_LIBRARY})
endif()

if(Blosc_FOUND AND NOT TARGET Blosc::Blosc)
    add_library(Blosc::Blosc STATIC IMPORTED GLOBAL)
    set_target_properties(Blosc::Blosc PROPERTIES IMPORTED_LOCATION ${BLOSC_LIBRARY})
    set_target_properties(Blosc::Blosc PROPERTIES INTERFACE_INCLUDE_DIRECTORIES ${BLOSC_INCLUDE_DIR})
endif()

repository: piratecrew/rez-blosc

repo_name: piratecrew/rez-blosc


text:
@MISC{Gruhl_contextand,
    author = {Daniel Gruhl and Meena Nagarajan and Jan Pieper and Christine Robson},
    title = {Context and Domain Knowledge Enhanced Entity Spotting In Informal Text},
    year = {}
}
@INPROCEEDINGS{Hassell06ontology-drivenautomatic,
    author = {Joseph Hassell and Boanerges Aleman-meza and I. Budak Arpinar},
    title = {Ontology-Driven Automatic Entity Disambiguation in Unstructured Text},
    booktitle = {In International Semantic Web Conference},
    year = {2006},
    pages = {44--57}
}
@INPROCEEDINGS{Mendes11dbpediaspotlight:,
    author = {Pablo N. Mendes and Max Jakob and Andrés García-silva and Christian Bizer},
    title = {Dbpedia spotlight: Shedding light on the web of documents},
    booktitle = {In Proceedings of the 7th International Conference on Semantic Systems (I-Semantics},
    year = {2011}
}
@MISC{Ritter_opendomain,
    author = {Alan Ritter and Sam Clark and Oren Etzioni},
    title = {Open Domain Event Extraction from Twitter},
    year = {}
}
@article{DBLP:journals/corr/abs-1303-4702,
  author = {Thomas Steiner and
               Seth van Hooland and
               Ed Summers},
  title = {MJ no more: Using Concurrent Wikipedia Edit Spikes with
               Social Network Plausibility Checks for Breaking News Detection},
  journal = {CoRR},
  volume = {abs/1303.4702},
  year = {2013},
  ee = {http://arxiv.org/abs/1303.4702},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{EURECOM+3865,
  year =        {2012},
  title =       {{E}vent{M}edia live: {E}xploring events connections in real-time to enhance content},
  author =      {{K}hrouf, {H}ouda and  {M}ilicic, {V}uk and  {T}roncy, {R}apah{\"e}l},
  booktitle =   {{ISWC} 2012, {S}emantic {W}eb {C}hallenge at 11th {I}nternational {S}emantic {W}eb {C}onference, {N}ovember 11-15, 2012, {B}oston, {USA}},
  address =     {{B}oston, {UNITED} {STATES}},
  month =       {11},
  url =  	      {http://www.eurecom.fr/publication/3865}
}

repository: amark-india/eventspotter

repo_name: amark-india/eventspotter


text:
const { formatPath } = require('@builder/app-helpers');

const defaultCompileDependencies = [
  'ansi-regex',
  'ansi-styles',
  'chalk',
  'query-string',
  'react-dev-utils',
  'split-on-first',
  'strict-uri-encode',
  'strip-ansi',
];

/**
 * Exclude the core-js for that it will fail to run in IE
 * @swc/helpers and @babel/runtime have been es5
 */
const skipDependenciesRegx = [/core-js/, /@swc\/helpers/, /@babel\/runtime/];

module.exports = (config, compileDependencies) => {
  const matchExclude = (filepath) => {
    filepath = formatPath(filepath);

    if (skipDependenciesRegx.some((regx) => filepath.match(regx))) return true;

    // compile build-plugin module for default
    const deps = [/build-plugin.*module/].concat(defaultCompileDependencies, compileDependencies).map(dep => {
      if (dep instanceof RegExp) {
        return dep.source;
      } else if (typeof dep === 'string') {
        // add default node_modules
        const matchStr = `node_modules/?.+${dep}/`;
        return matchStr;
      }
      return false;
    }).filter(Boolean);
    const matchReg = deps.length ? new RegExp(deps.join('|')) : null;
    if (matchReg && matchReg.test(filepath)) {
      return false;
    }

    // exclude node_modules as default
    return /node_modules/.test(filepath);
  };

  ['jsx', 'tsx', 'swc-tsx', 'swc-jsx'].forEach((rule) => {
    if (config.module.rules.get(rule)) {
      config.module
      .rule(rule)
      .exclude.clear()
      .add(matchExclude);
    }
  });
};

repository: jeryqwq/ice

repo_name: jeryqwq/ice

extension: js


text:
LICENSE = "GPL"
DESCRIPTION = "Touchscreen calibration utility"
MAINTAINER = "Phil Blundell <pb@handhelds.org>"
SECTION = "x11/base"
PR = "r2"

DEPENDS = "x11 libxft libxrandr xcalibrate"

SRC_URI = "${GPE_MIRROR}/xtscal-${PV}.tar.bz2 \
           file://xtscal-cxk.patch;patch=1"

inherit autotools

repository: Terentyev/familiar-linux

repo_name: Terentyev/familiar-linux


text:
@InProceedings{linc14,
  supplementary = {Supplementary:linc14-supp.zip},
  title = {Multi-label Classification via Feature-aware Implicit Label Space Encoding},
  author = {Zijia Lin and Guiguang Ding and Mingqing Hu and Jianmin Wang},
  pages = {325-333},
  abstract = {To tackle a multi-label classification problem with many classes, recently label space dimension reduction (LSDR) is proposed. It encodes the original label space to a low-dimensional latent space and uses a decoding process for recovery. In this paper, we propose a novel method termed FaIE to perform LSDR via Feature-aware Implicit label space Encoding. Unlike most previous work, the proposed FaIE makes no assumptions about the encoding process and directly learns a code matrix, i.e. the encoding result of some implicit encoding function, and a linear decoding matrix. To learn both matrices, FaIE jointly maximizes the recoverability of the original label space from the latent space, and the predictability of the latent space from the feature space, thus making itself feature-aware. FaIE can also be specified to learn an explicit encoding function, and extended with kernel tricks to handle non-linear correlations between the feature space and the latent space. Extensive experiments conducted on benchmark datasets well demonstrate its effectiveness.},
  section = {cycle-2},
}


text:
package redisutils

import (
	"strings"
)

// Structure of CLUSTER NODES result:
// id, ip:port@port, flags(self/master..), master-id, ping, pong, config-epoch, linkstate, slot

// ClusterNodes is a type that holds the result from one query
type ClusterNodes map[string][]string

const MinElements = 6

func NewClusterNodes(cliData string) ClusterNodes {
	nodes := make(map[string][]string)
	for _, line := range strings.Split(cliData, "\n") {
		keyVals := strings.Split(line, " ")

		if len(keyVals) > MinElements {
			addr := strings.Split(keyVals[1], ":")
			if len(addr) > 1 {
				ip := addr[0]
				nodes[ip] = keyVals
			}
		}
	}
	return nodes
}

// GetFlagsSelf returns the status field for self
func (n *ClusterNodes) GetFlagsSelf() string {
	for _, status := range *n {
		if strings.Contains(status[2], "myself") {
			flags := strings.Replace(status[2], "myself,", "", -1)
			return strings.Replace(flags, "slave", "repl.", -1)
		}
	}
	return ""
}

repository: bjosv/kubectl-rediscluster

repo_name: bjosv/kubectl-rediscluster


text:
DESCRIPTION = "aMule is an eMule-like client for the eD2k \
and Kademlia networks, supporting multiple platforms."
HOMEPAGE = "http://www.amule.org"
LICENSE = "GPL"
SECTION = "web"
PRIORITY = "optional"
DEPENDS = "wxbase"

SRC_URI = "${SOURCEFORGE_MIRROR}/amule/aMule-${PV}.tar.bz2 \
           file://parser.patch;patch=1"

S = "${WORKDIR}/aMule-${PV}"

inherit autotools pkgconfig

EXTRA_OECONF = "--enable-amulecmd \
                --enable-webserver \
		--disable-monolithic \
		--enable-amule-daemon \
		--disable-nls"
#		--enable-optimize"

FILES_${PN} += " \
        ${libdir}/xchat"

LDFLAGS += "-ldl"


text:
//
// file.go
//
// Copyright (c) 2018 Markku Rossi
//
// All rights reserved.
//

package tree

import (
	"io"
)

type File interface {
	Size() int64
	Reader() io.Reader
}

repository: markkurossi/backup

repo_name: markkurossi/backup

gha_language: Go


text:
@article{Lindenschmidt-2022-Modelling,
    title = "Modelling transverse mixing of sediment and vanadium in a river impacted by oil sands mining operations",
    author = "Lindenschmidt, Karl-Erich  and
      Sabokruhie, Pouya  and
      Rosner, Tammy",
    journal = "Journal of Hydrology: Regional Studies, Volume 40",
    volume = "40",
    year = "2022",
    publisher = "Elsevier BV",
    url = "https://aclanthology.org/G22-14002",
    doi = "10.1016/j.ejrh.2022.101043",
    pages = "101043",
    abstract = "The lower Athabasca River was used as a test case using total suspended sediment, chloride and vanadium as the model variables. Upstream model boundary conditions included water from the tributary Clearwater River (right stream tube) and the upper Athabasca River extending upstream of the tributary mouth (left stream tube). This model will be extended to include the Peace-Athabasca Delta (PAD), to determine the implications of mining outfall discharges on a large region of the Athabasca {--} PAD region. A novel, quasi-two-dimensional surface water-quality modelling approach is presented in which the model domain can be discretised in two dimensions, but a one-dimension solver can still be applied to capture water flow between the discretisation units (segments). The approach requires a river reach to be divided into two stream tubes, along the left and right river sides, with flows exchanging through the segments longitudinally and also laterally between adjacent segments along the two streams. The new method allows the transverse mixing of tributary and outfall water of different constituent concentrations to be simulated along the course of the river. Additional diffuse loading of dissolved vanadium could be determined from the model{'}s substance balance. A scenario was then simulated in which the transport and fate of vanadium in a floodplain lake and a secondary channel was determined. {\mbox{$\bullet$}} Quasi-2D modelling approach proves to be viable for transverse mixing. {\mbox{$\bullet$}} Quasi-2D approach allows secondary channels and side lakes to be modelled. {\mbox{$\bullet$}} Quasi-2D approach is appropriate to scale up to entire lower Athabasca River reach. {\mbox{$\bullet$}} The approach allowed a diffuse loading of dissolved vanadium to be quantified.",
}

repository: gwf-uwaterloo/gwf-publications

repo_name: gwf-uwaterloo/gwf-publications


text:
@Book{dsieurbook,
  title = {Data science in education using R},
  author = {Ryan A. Estrellado and Emily A. Bovee and Jesse Motsipak and Joshua M. Rosenberg and Isabella C. Vel{\a'a}squez},
  year = {2020},
  publisher = {London, England: Routledge},
  url = {https://datascienceineducation.com/}
  Nb. = {All authors contributed equally.}
}

repository: restrellado/website

repo_name: restrellado/website


text:
#
# base recipe: http://cgit.openembedded.org/cgit.cgi/meta-openembedded/
# tree/meta-oe/recipes-devtools/libedit/libedit_20150325-3.1.bb?h=master
# base branch: master
#

PR = "r0"

inherit debian-package autotools

LICENSE = "BSD"
LIC_FILES_CHKSUM = "file://COPYING;md5=1e4228d0c5a9093b01aeaaeae6641533"

DEPENDS = "ncurses pkgconfig libbsd"

do_install_append() {
	ln -sf readline.h ${D}${includedir}/editline/history.h
}

DEBIANNAME_${PN}-dbg = "libedit2-dbg"

FILE_${PN}-dev += "${includedir}/editline/history.h"

repository: nghiaht-tsdv/meta-debian

repo_name: nghiaht-tsdv/meta-debian

branch_name: refs/heads/jethro


text:
@article{Cuturi2013,
abstract = {Optimal transportation distances are a fundamental family of parameterized distances for histograms. Despite their appealing theoretical properties, excellent performance in retrieval tasks and intuitive formulation, their computation involves the resolution of a linear program whose cost is prohibitive whenever the histograms' dimension exceeds a few hundreds. We propose in this work a new family of optimal transportation distances that look at transportation problems from a maximum-entropy perspective. We smooth the classical optimal transportation problem with an entropic regularization term, and show that the resulting optimum is also a distance which can be computed through Sinkhorn-Knopp's matrix scaling algorithm at a speed that is several orders of magnitude faster than that of transportation solvers. We also report improved performance over classical optimal transportation distances on the MNIST benchmark problem.},
archivePrefix = {arXiv},
arxivId = {1306.0895},
author = {Cuturi, Marco},
eprint = {1306.0895},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Cuturi - Unknown - SINKHORN DISTANCES LIGHTSPEED COMPUTATION OF OPTIMAL TRANSPORTATION DISTANCES.pdf:pdf},
issn = {10495258},
mendeley-groups = {[MSNE 3] BCI Paper},
title = {{Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances}},
url = {http://arxiv.org/abs/1306.0895},
year = {2013}
}
@article{Ehrlich2016,
abstract = {— Validating appropriateness and naturalness of human-robot interaction (HRI) is commonly performed by taking subjective measures from human interaction partners, e.g. questionnaire ratings. Although these measures can be of high value for robot designers, they are very sensitive and can be inaccurate and/or biased. In this paper we propose and validate a neuro-based method for objectively validating robot behavior in HRI. We propose to detect from the electronencephalo-gram (EEG) of a human interaction partner, the perception of inappropriate / unexpected / erroneous robot behavior. To validate this method, we conducted an EEG experiment with a simplified HRI protocol in which a humanoid robot displayed context-dependent erroneous behavior from time to time. The EEG data taken from 13 participants revealed biologically plausible error-related potentials (ErrP) whose spatio-temporal distributions match well with related neuroscientific research. We further demonstrate that perceived erroneous robot action can reliably be modeled and detected from human EEG signals with classification accuracies on avg. 69.7±9.1{\%}. These findings confirm principal feasibility of the proposed method and suggest that EEG-based ErrP detection can be used for quantitative evaluation and thus improvement of robot behavior.},
author = {Ehrlich, Stefan and Cheng, Gordon},
doi = {10.1109/HUMANOIDS.2016.7803318},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ehrlich, Cheng - Unknown - A neuro-based method for detecting context-dependent erroneous robot action.pdf:pdf},
isbn = {9781509047185},
issn = {21640580},
journal = {IEEE-RAS International Conference on Humanoid Robots},
mendeley-groups = {[MSNE 3] BCI Paper},
pages = {477--482},
title = {{A neuro-based method for detecting context-dependent erroneous robot action}},
url = {https://mediatum.ub.tum.de/doc/1340309/1340309.pdf},
year = {2016}
}
@article{Flamary2016,
abstract = {Wasserstein Discriminant Analysis (WDA) is a new supervised method that can improve classification of high-dimensional data by computing a suitable linear map onto a lower dimensional subspace. Following the blueprint of classical Linear Discriminant Analysis (LDA), WDA selects the projection matrix that maximizes the ratio of two quantities: the dispersion of projected points coming from different classes, divided by the dispersion of projected points coming from the same class. To quantify dispersion, WDA uses regularized Wasserstein distances, rather than cross-variance measures which have been usually considered, notably in LDA. Thanks to the the underlying principles of optimal transport, WDA is able to capture both global (at distribution scale) and local (at samples scale) interactions between classes. Regularized Wasserstein distances can be computed using the Sinkhorn matrix scaling algorithm; We show that the optimization of WDA can be tackled using automatic differentiation of Sinkhorn iterations. Numerical experiments show promising results both in terms of prediction and visualization on toy examples and real life datasets such as MNIST and on deep features obtained from a subset of the Caltech dataset.},
archivePrefix = {arXiv},
arxivId = {1608.08063},
author = {Flamary, R{\'{e}}mi and Cuturi, Marco and Courty, Nicolas and Rakotomamonjy, Alain},
eprint = {1608.08063},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Flamary et al. - Unknown - Wasserstein Discriminant Analysis.pdf:pdf},
mendeley-groups = {[MSNE 3] BCI Paper},
title = {{Wasserstein Discriminant Analysis}},
url = {http://arxiv.org/abs/1608.08063},
year = {2016}
}
@article{Spuler2015,
abstract = {When a person recognizes an error during a task, an error-related potential (ErrP) can be measured as response. It has been shown that ErrPs can be automatically detected in tasks with time-discrete feedback, which is widely applied in the field of Brain-Computer Interfaces (BCIs) for error correction or adaptation. However, there are only a few studies that concentrate on ErrPs during continuous feedback. With this study, we wanted to answer three different questions: (i) Can ErrPs be measured in electroencephalography (EEG) recordings during a task with continuous cursor control? (ii) Can ErrPs be classified using machine learning methods and is it possible to discriminate errors of different origins? (iii) Can we use EEG to detect the severity of an error? To answer these questions, we recorded EEG data from 10 subjects during a video game task and investigated two different types of error (execution error, due to inaccurate feedback; outcome error, due to not achieving the goal of an action). We analyzed the recorded data to show that during the same task, different kinds of error produce different ErrP waveforms and have a different spectral response. This allows us to detect and discriminate errors of different origin in an event-locked manner. By utilizing the error-related spectral response, we show that also a continuous, asynchronous detection of errors is possible. Although the detection of error severity based on EEG was one goal of this study, we did not find any significant influence of the severity on the EEG.},
author = {Sp{\"{u}}ler, Martin and Niethammer, Christian},
doi = {10.3389/fnhum.2015.00155},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Sp{\"{u}}ler, Niethammer - 2015 - Error-related potentials during continuous feedback using EEG to detect errors of different type and severi.pdf:pdf},
issn = {1662-5161},
journal = {Frontiers in human neuroscience},
keywords = {asynchronous classification,brain-computer interface (BCI),error-related negativity (Ne/ERN),error-related potential (ErrP),feedback related negativity (FRN),human-computer interaction,performance monitoring},
mendeley-groups = {[MSNE 3] BCI Paper},
pages = {155},
pmid = {25859204},
publisher = {Frontiers Media SA},
title = {{Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25859204 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4374466},
volume = {9},
year = {2015}
}
@article{Chai2017,
abstract = {Electroencephalography (EEG)-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions or subjects. This results in the deterioration of the classification performance. Domain adaptation methods offer an effective way to reduce the discrepancy of marginal distribution. However, for EEG sensor signals, both marginal and conditional distributions may be mismatched. In addition, the existing domain adaptation strategies always require a high level of additional computation. To address this problem, a novel strategy named adaptive subspace feature matching (ASFM) is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework (without any labeled samples from target subjects). Specifically, we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term. This significantly decreases the time complexity of our domain adaptation procedure. As a result, both marginal and conditional distribution discrepancies between the source domain and unlabeled target domain can be reduced, and logistic regression (LR) can be applied to the new source domain in order to train a classifier for use in the target domain, since the aligned source domain follows a distribution which is similar to that of the target domain. We compare our ASFM method with six typical approaches using a public EEG dataset with three affective states: positive, neutral, and negative. Both offline and online evaluations were performed. The subject-to-subject offline experimental results demonstrate that our component achieves a mean accuracy and standard deviation of 80.46{\%} and 6.84{\%}, respectively, as compared with a state-of-the-art method, the subspace alignment auto-encoder (SAAE), which achieves values of 77.88{\%} and 7.33{\%} on average, respectively. For the online analysis, the average classification accuracy and standard deviation of ASFM in the subject-to-subject evaluation for all the 15 subjects in a dataset was 75.11{\%} and 7.65{\%}, respectively, gaining a significant performance improvement compared to the best baseline LR which achieves 56.38{\%} and 7.48{\%}, respectively. The experimental results confirm the effectiveness of the proposed method relative to state-of-the-art methods. Moreover, computational efficiency of the proposed ASFM method is much better than standard domain adaptation; if the numbers of training samples and test samples are controlled within certain range, it is suitable for real-time classification. It can be concluded that ASFM is a useful and effective tool for decreasing domain discrepancy and reducing performance degradation across subjects and sessions in the field of EEG-based emotion recognition.},
author = {Chai, Xin and Wang, Qisong and Zhao, Yongping and Li, Yongqiang and Liu, Dan and Liu, Xin and Bai, Ou},
doi = {10.3390/s17051014},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Chai et al. - 2017 - A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG)-Based Emotion Recognitio.pdf:pdf},
issn = {1424-8220},
journal = {Sensors},
keywords = {Electroencephalography (EEG),domain adaptation,emotion recognition},
mendeley-groups = {Reading List,[MSNE 3] BCI Paper},
month = {may},
number = {5},
pages = {1014},
publisher = {Multidisciplinary Digital Publishing Institute},
title = {{A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG)-Based Emotion Recognition}},
url = {http://www.mdpi.com/1424-8220/17/5/1014},
volume = {17},
year = {2017}
}
@article{Bug2017,
abstract = {While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available. This work provides a fully automated, end-to-end learning-based setup for normalizing histological stains, which considers the texture context of the tissue. We introduce Feature Aware Normalization, which extends the framework of batch normalization in combination with gating elements from Long Short-Term Memory units for normalization among different spatial regions of interest. By incorporating a pretrained deep neural network as a feature extractor steering a pixelwise processing pipeline, we achieve excellent normalization results and ensure a consistent representation of color and texture. The evaluation comprises a comparison of color histogram deviations, structural similarity and measures the color volume obtained by the different methods.},
archivePrefix = {arXiv},
arxivId = {1708.04099},
author = {Bug, D. and Schneider, S. and Grote, A. and Oswald, E. and Feuerhake, F. and Sch{\"{u}}ler, J. and Merhof, D.},
doi = {10.1007/978-3-319-67558-9_16},
eprint = {1708.04099},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Bug et al. - 2017 - Context-based Normalization of Histological Stains using Deep Convolutional Features.pdf:pdf},
isbn = {9783319675572},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Deep learning,Histology,Machine learning,Normalization},
mendeley-groups = {Style Transfer,Reading List,Deep Learning,FAN Journal Paper,[MSNE 3] BCI Paper},
month = {aug},
pages = {135--142},
title = {{Context-based normalization of histological stains using deep convolutional features}},
url = {http://arxiv.org/abs/1708.04099},
volume = {10553 LNCS},
year = {2017}
}
@article{Martin2013,
abstract = {Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color {\&} spatial controls, all using a single feed-forward neural network.},
archivePrefix = {arXiv},
arxivId = {1703.06868},
author = {Huang, Xun and Belongie, Serge},
doi = {10.1088/1751-8113/44/8/085201},
eprint = {1703.06868},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Huang, Belongie - Unknown - Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.pdf:pdf},
isbn = {0030-8870},
issn = {00308870},
journal = {arXiv preprint},
mendeley-groups = {Style Transfer,Reading List,HBP Student Conference,FAN Journal Paper,[MSNE 3] BCI Paper},
pmid = {24439530},
title = {{Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization}},
url = {http://arxiv.org/abs/1703.06868},
year = {2017}
}
@article{Lotte2014,
author = {Lotte, Fabien},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Lotte - 2014 - A Tutorial on EEG Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces.pdf:pdf},
journal = {Eduardo Reck Miranda Julien Castet. Guide to Brain-Computer Music Interfacing},
keywords = {()},
mendeley-groups = {Reading List,[MSNE 3] BCI Paper},
publisher = {Springer},
title = {{A Tutorial on EEG Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces}},
url = {https://hal.inria.fr/hal-01055103},
year = {2014}
}
@article{Li,
abstract = {Neural Style Transfer has recently demonstrated very exciting results which catches eyes in both academia and industry. Despite the amazing results, the principle of neural style transfer, especially why the Gram matrices could represent style remains unclear. In this paper, we propose a novel interpretation of neural style transfer by treating it as a domain adaptation problem. Specifically, we theoretically show that matching the Gram matrices of feature maps is equivalent to minimize the Maximum Mean Discrepancy (MMD) with the second order polynomial kernel. Thus, we argue that the essence of neural style transfer is to match the feature distributions between the style images and the generated images. To further support our standpoint, we experiment with several other distribution alignment methods, and achieve appealing results. We believe this novel interpretation connects these two important research fields, and could enlighten future researches.},
archivePrefix = {arXiv},
arxivId = {1701.01036},
author = {Li, Yanghao and Wang, Naiyan and Liu, Jiaying and Hou, Xiaodi},
eprint = {1701.01036},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Li, Wang, Liu - Unknown - Demystifying Neural Style Transfer.pdf:pdf},
isbn = {9780999241103},
issn = {1701.01036},
mendeley-groups = {Reading List,Style Transfer,FAN Journal Paper,[MSNE 3] BCI Paper},
title = {{Demystifying Neural Style Transfer}},
url = {http://arxiv.org/abs/1701.01036},
year = {2017}
}
@article{Pan,
abstract = {A survey on transfer learning. IEEE Trans. Knowl. Data Eng},
archivePrefix = {arXiv},
arxivId = {PAI},
author = {Pan, Sinno Jialin and Yang, Qiang},
doi = {10.1109/TKDE.2009.191},
eprint = {PAI},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Pan, Yang - Unknown - A Survey on Transfer Learning.pdf:pdf},
isbn = {1041-4347 VO - 22},
issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Transfer learning,data mining.,machine learning,survey},
mendeley-groups = {Reading List,HBP Student Conference,FAN Journal Paper,[MSNE 3] BCI Paper},
number = {10},
pages = {1345--1359},
title = {{A survey on transfer learning}},
url = {https://www.cse.ust.hk/{~}qyang/Docs/2009/tkde{\_}transfer{\_}learning.pdf},
volume = {22},
year = {2010}
}
@article{Courty,
abstract = {Domain adaptation from one data space (or domain) to another is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data space become more robust when confronted to data depicting the same semantic concepts (the classes), but observed by another observation system with its own specificities. Among the many strategies proposed to adapt a domain to another, finding a common representation has shown excellent properties: by finding a common representation for both domains, a single classifier can be effective in both and use labelled samples from the source domain to predict the unlabelled samples of the target domain. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labelled samples in the source domain to remain close during transport. This way, we exploit at the same time the few labeled information in the source and the unlabelled distributions observed in both domains. Experiments in toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches.},
archivePrefix = {arXiv},
arxivId = {1507.00504},
author = {Courty, Nicolas and Flamary, R{\'{e}}mi and Tuia, Devis and Rakotomamonjy, Alain},
doi = {10.1109/TPAMI.2016.2615921},
eprint = {1507.00504},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Courty et al. - Unknown - Optimal Transport for Domain Adaptation.pdf:pdf},
isbn = {9782875870148},
issn = {0162-8828},
journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
keywords = {Classification,Index Terms—Unsupervised Domain Adaptation,Optimal Transport,Transfer Learning,Visual Adaptation},
mendeley-groups = {Reading List,HBP Student Conference,FAN Journal Paper,[MSNE 3] BCI Paper},
number = {X},
pages = {1--14},
title = {{Optimal Transport for Domain Adaptation}},
url = {http://arxiv.org/abs/1507.00504},
volume = {X},
year = {2015}
}
@article{silvoni2011brain,
author = {Silvoni, Stefano and Ramos-Murguialday, Ander and Cavinato, Marianna and Volpato, Chiara and Cisotto, Giulia and Turolla, Andrea and Piccione, Francesco and Birbaumer, Niels},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Silvoni et al. - 2011 - Brain-Computer Interface in Stroke A Review of Progress.pdf:pdf},
journal = {Clinical EEG and Neuroscience},
mendeley-groups = {[MSNE 2] Debating Seminar,[MSNE 3] BCI Paper},
number = {4},
pages = {245--252},
publisher = {SAGE Publications Sage CA: Los Angeles, CA},
title = {{Brain-computer interface in stroke: a review of progress}},
volume = {42},
year = {2011}
}
@article{millan2010combining,
author = {Mill{\'{a}}n, Jos{\'{e}} del R and Rupp, R{\"{u}}diger and Mueller-Putz, Gernot and Murray-Smith, Roderick and Giugliemma, Claudio and Tangermann, Michael and Vidaurre, Carmen and Cincotti, Febo and Kubler, Andrea and Leeb, Robert and Others},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Unknown - Unknown - millan2011combining.pdf:pdf},
journal = {Frontiers in neuroscience},
mendeley-groups = {[MSNE 2] Debating Seminar,[MSNE 3] BCI Paper},
pages = {161},
publisher = {Frontiers},
title = {{Combining brain--computer interfaces and assistive technologies: state-of-the-art and challenges}},
volume = {4},
year = {2010}
}
@article{lebedev2006brain,
abstract = {Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain-machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm. ?? 2006 Elsevier Ltd. All rights reserved.},
author = {Lebedev, Mikhail A and Nicolelis, Miguel A L},
doi = {10.1016/j.tins.2006.07.004},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Lebedev, Nicolelis - 2006 - Brain-machine interfaces past, present and future.pdf:pdf},
isbn = {0166-2236},
issn = {01662236},
journal = {TRENDS in Neurosciences},
mendeley-groups = {[MSNE 2] Debating Seminar,[MSNE 3] BCI Paper},
number = {9},
pages = {536--546},
pmid = {16859758},
publisher = {Elsevier},
title = {{Brain-machine interfaces: past, present and future}},
volume = {29},
year = {2006}
}
@article{Stober2015,
abstract = {We introduce and compare several strategies for learning discriminative features from electroencephalography (EEG) recordings using deep learning techniques. EEG data are generally only available in small quantities, they are high-dimensional with a poor signal-to-noise ratio, and there is considerable variability between individual subjects and recording sessions. Our proposed techniques specifically address these challenges for feature learning. Cross-trial encoding forces auto-encoders to focus on features that are stable across trials. Similarity-constraint encoders learn features that allow to distinguish between classes by demanding that two trials from the same class are more similar to each other than to trials from other classes. This tuple-based training approach is especially suitable for small datasets. Hydra-nets allow for separate processing pathways adapting to subsets of a dataset and thus combine the advantages of individual feature learning (better adaptation of early, low-level processing) with group model training (better generalization of higher-level processing in deeper layers). This way, models can, for instance, adapt to each subject individually to compensate for differences in spatial patterns due to anatomical differences or variance in electrode positions. The different techniques are evaluated using the publicly available OpenMIIR dataset of EEG recordings taken while participants listened to and imagined music.},
archivePrefix = {arXiv},
arxivId = {1511.04306},
author = {Stober, Sebastian and Sternin, Avital and Owen, Adrian M. and Grahn, Jessica A.},
eprint = {1511.04306},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Stober et al. - Unknown - DEEP FEATURE LEARNING FOR EEG RECORDINGS.pdf:pdf},
mendeley-groups = {Kent EEG Research,[MSNE 3] BCI Paper},
title = {{Deep Feature Learning for EEG Recordings}},
url = {http://arxiv.org/abs/1511.04306},
year = {2015}
}
@inproceedings{Ioffe2015batch,
abstract = {Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parame-ters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phe-nomenon as internal covariate shift, and ad-dress the problem by normalizing layer inputs. Our method draws its strength from making nor-malization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less care-ful about initialization, and in some cases elim-inates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Nor-malization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensem-ble of batch-normalized networks, we improve upon the best published result on ImageNet clas-sification: reaching 4.82{\%} top-5 test error, ex-ceeding the accuracy of human raters.},
author = {Ioffe, Sergey and Szegedy, Christian},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning (ICML-15)},
file = {:home/stes/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ioffe, Szegedy - Unknown - Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift.pdf:pdf},
mendeley-groups = {Stainnorm Paper,Reading List,FAN Journal Paper,[MSNE 3] BCI Paper},
pages = {448--456},
title = {{Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift}},
year = {2015}
}

repository: stes/bci

repo_name: stes/bci


text:
DESCRIPTION = "Tools for managing 'yaffs2' file systems."
SECTION = "base"
HOMEPAGE = "http://www.yaffs.net"
LICENSE = "GPLv2"
PV = "0.0.0+cvs${SRCDATE}"
PR = "r3"
DEPENDS = "mtd-utils"

SRC_URI = "cvs://anonymous@cvs.aleph1.co.uk/home/aleph1/cvs;module=yaffs2 \
           file://mkyaffs2image.patch \
           file://mkyaffs2image-2.patch \
	   file://devextras.h.patch"

S = "${WORKDIR}/yaffs2"

CFLAGS += "-I.. -DCONFIG_YAFFS_UTIL "
CFLAGS_append_virtclass-native = " -I.. -DCONFIG_YAFFS_UTIL "

do_compile() {
	cd utils && oe_runmake
}

NATIVE_INSTALL_WORKS = "1"
do_install() {
	install -d ${D}${sbindir}/
	for i in mkyaffsimage mkyaffs2image; do
		install -m 0755 utils/$i ${D}${sbindir}/
	done
}

BBCLASSEXTEND = "native"

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD


text:
@inproceedings{miskov-zivanov_modeling_2012,
 abstract = {Circuit designers are increasingly more drawn to challenges in modeling and designing biological circuits and systems. While the principles of biological organization and architecture resemble those in systems that engineers are designing, the complexity of biological systems still seems to be beyond the designed ones. This session discusses state-of-the-art in tackling such challenges, and presents existing methods for automation of model development, design and analysis of biological circuits and systems. The speakers are experts from systems biology, synthetic biology, and design automation fields. The three talks will cover a range of topics that include rule-based modeling approach to model cell signaling networks, automation of genetic circuit design, and the importance and development of standards in synthetic biology.},
 author = {Miskov-Zivanov, Natasa and Faeder, James R. and Myers, Chris J. and Sauro, Herbert M.},
 booktitle = {Proceedings of the International Conference on Computer-Aided Design},
 date = {2012-11-05},
 doi = {10.1145/2429384.2429444},
 file = {Miskov-Zivanov et al-2012-Modeling and design automation of biological circuits and systems.pdf:/Users/lukas/Lab/Zotero/storage/XEPD5M7I/Miskov-Zivanov et al-2012-Modeling and design automation of biological circuits and systems.pdf:application/pdf},
 isbn = {978-1-4503-1573-9},
 keywords = {synthetic biology, systems biology, genetic circuit, bio-design automation, biological parts, rule-based modeling, chemical reaction network},
 location = {New York, NY, USA},
 note = {00001},
 pages = {291--293},
 publisher = {Association for Computing Machinery},
 series = {ICCAD '12},
 title = {Modeling and design automation of biological circuits and systems},
 url = {http://doi.org/10.1145/2429384.2429444},
 urldate = {2020-09-18}
}