Input: What was the conclusion of the study?

Context:
consumption influences mercury: Topics by WorldWideScience.org
Sample records for consumption influences mercury
Epidemiologic confirmation that fruit consumption influences mercury exposure in riparian communities in the Brazilian Amazon
Sousa Passos, Carlos Jose; Mergler, Donna; Fillion, Myriam; Lemire, Melanie; Mertens, Frederic; Guimaraes, Jean Remy Davee; Philibert, Aline
Since deforestation has recently been associated with increased mercury load in the Amazon, the problem of mercury exposure is now much more widespread than initially thought. A previous exploratory study suggested that fruit consumption may reduce mercury exposure. The objectives of the study were to determine the effects of fruit consumption on the relation between fish consumption and bioindicators of mercury (Hg) exposure in Amazonian fish-eating communities. A cross-sectional dietary survey based on a 7-day recall of fish and fruit consumption frequency was conducted within 13 riparian communities from the Tapajos River, Brazilian Amazon. Hair samples were collected from 449 persons, and blood samples were collected from a subset of 225, for total and inorganic mercury determination by atomic absorption spectrometry. On average, participants consumed 6.6 fish meals/week and ate 11 fruits/week. The average blood Hg (BHg) was 57.1Â±36.3 Î¼g/L (median: 55.1 Î¼g/L), and the average hair-Hg (HHg) was 16.8Â±10.3 Î¼g/g (median: 15.7 Î¼g/g). There was a positive relation between fish consumption and BHg (r=0.48; P 2 =36.0%) and HHg levels (fish: Î²=1.2, P 2 =21.0%). ANCOVA models showed that for the same number of fish meals, persons consuming fruits more frequently had significantly lower blood and HHg concentrations. For low fruit consumers, each fish meal contributed 9.8 Î¼g/L Hg increase in blood compared to only 3.3 Î¼g/L Hg increase for the high fruit consumers. In conclusion, fruit consumption may provide a protective effect for Hg exposure in Amazonian riparians. Prevention strategies that seek to maintain fish consumption while reducing Hg exposure in fish-eating communities should be pursued
Influence of mercury bioaccessibility on exposure assessment associated with consumption of cooked predatory fish in Spain.
Torres-Escribano, Silvia; Ruiz, Antonio; Barrios, Laura; VÃ©lez, Dinoraz; Montoro, Rosa
Predatory fish tend to accumulate high levels of mercury (Hg). Food safety assessment of these fish has been carried out on the raw product. However, the evaluation of the risk from Hg concentrations in raw fish might be modified if cooking and bioaccessibility (the contaminant fraction that solubilises from its matrix during gastrointestinal digestion and becomes available for intestinal absorption) were taken into account. Data on Hg bioaccessibility in raw predatory fish sold in Spain are scarce and no research on Hg bioaccessibility in cooked fish is available. The aim of the present study was to evaluate Hg bioaccessibility in various kinds of cooked predatory fish sold in Spain to estimate their health risk. Both Hg and bioaccessible Hg concentrations were analysed in raw and cooked fish (swordfish, tope shark, bonito and tuna). There were no changes in Hg concentrations during cooking. However, Hg bioaccessibility decreased significantly after cooking (42 Â± 26% in raw fish and 26 Â± 16% in cooked fish), thus reducing in swordfish and tope shark the Hg concentration to which the human organism would be exposed. In future, cooking and bioaccessibility should be considered in risk assessment of Hg concentrations in predatory fish. Copyright Â© 2011 Society of Chemical Industry.
Intake of mercury through fish consumption
Sarmani, S.B.; Kiprawi, A.Z.; Ismail, R.B.; Hassan, R.B.; Wood, A.K.; Rahman, S.A.
Fish has been known as a source of non-occupational mercury exposure to fish consuming population groups, and this is shown by the high hair mercury levels. In this study, hair samples collected from fishermen and their families, and commercial marine fishes were analyzed for mercury and methylmercury by neutron activation and gas chromatography. The results showed a correlation between hair mercury levels and fish consumption patterns. The levels of mercury found in this study were similar to those reported by other workers for fish consuming population groups worldwide. (author)
Fish consumption limit for mercury compounds
Abbas Esmaili-Sari
Full Text Available Background and objectives: Methyl mercury can carry out harmful effects on the reproductive, respiratory, and nervous system of human. Moreover, mercury is known as the most toxic heavy metal in nature. Fish and seafood consumption is the major MeHg exposure route for human. The present study tries to cover researches which have been conducted on mercury levels in 21 species of fish from Persian Gulf, Caspian Sea and Anzali Wetland during the past 6 years, and in addition to stating mercury level, it provides recommendations about the restriction of monthly fish consumption for each species separately. Material and methods: Fish samples were transferred to the laboratory and stored in refrigerator under -20oC until they were dissected. Afterwards, the muscle tissues were separated and dried. The dried samples were ground and changed into a homogenous powder and then the mercury concentration rate has been determined by advanced mercury analyzer, model 254. Results: In general, mercury contamination in fishes caught from Anzali Wetland was much more than fishes from Caspian Sea. Also, from among all studied fishes, oriental sole (Euryglossa orientalis, caught from Persian Gulf, allocated the most mercury level to itself with the rate of 5.61ml per kg., therefore, it exercises a severe consumption restriction for pregnant women and vulnerable groups. Conclusion: Based on the calculations, about 50% of fishes, mostly with short food chain, can be easily consumed during the year. However, with regard to Oriental sole (Euryglossa orientalis and shark (Carcharhinus dussumieri, caught from Persian Gulf, special consideration should be taken in their consumption. On the other hand, careful planning should be made for the high rate of fish consumption among fishing community.
Hair Mercury Concentrations and Fish Consumption Patterns in Florida Residents
Adam M. Schaefer
Full Text Available Mercury exposure through the consumption of fish and shellfish represents a significant public health concern in the United States. Recent research has demonstrated higher seafood consumption and subsequent increased risk of methylmercury exposure among subpopulations living in coastal areas. The identification of high concentrations of total mercury in blood and skin among resident Atlantic bottlenose dolphins (Tursiops truncatus in the Indian River Lagoon (IRL, a coastal estuary in Florida, alerted us to a potential public health hazard in the contiguous human population. Therefore, we analyzed hair mercury concentrations of residents living along the IRL and ascertained their sources and patterns of seafood consumption. The total mean mercury concentration for 135 residents was 1.53 Â± 1.89 Âµg/g. The concentration of hair mercury among males (2.02 Â± 2.38 Âµg/g was significantly higher than that for females (0.96 Â± 0.74 Âµg/g (p < 0.01. Log transformed hair mercury concentration was significantly associated with the frequency of total seafood consumption (p < 0.01. Individuals who reported consuming seafood once a day or more were 3.71 (95% CI 0.84â€“16.38 times more likely to have a total hair mercury concentration over 1.0 Âµg/g, which corresponds approximately to the U.S. EPA reference dose, compared to those who consumed seafood once a week or less. Hair mercury concentration was also significantly higher among individuals who obtained all or most of their seafood from local recreational sources (p < 0.01. The elevated human mercury concentrations mirror the elevated concentrations observed in resident dolphins in the same geographical region. The current study is one of the first to apply the concept of a sentinel animal to a contiguous human population.
Fish consumption and bioindicators of inorganic mercury exposure
Sousa Passos, Carlos Jose; Mergler, Donna; Lemire, Melanie; Fillion, Myriam; Guimaraes, Jean Remy Davee
Background: The direct and close relationship between fish consumption and blood and hair mercury (Hg) levels is well known, but the influence of fish consumption on inorganic mercury in blood (B-IHg) and in urine (U-Hg) is unclear. Objective: Examine the relationship between fish consumption, total, inorganic and organic blood Hg levels and urinary Hg concentration. Methods: A cross-sectional study was carried out on 171 persons from 7 riparian communities on the Tapajos River (Brazilian Amazon), with no history of inorganic Hg exposure from occupation or dental amalgams. During the rising water season in 2004, participants responded to a dietary survey, based on a seven-day recall of fish and fruit consumption frequency, and socio-demographic information was recorded. Blood and urine samples were collected. Total, organic and inorganic Hg in blood as well as U-Hg were determined by Atomic Absorption Spectrometry. Results: On average, participants consumed 7.4 fish meals/week and 8.8 fruits/week. Blood total Hg averaged 38.6 Â± 21.7 Î¼g/L, and the average percentage of B-IHg was 13.8%. Average organic Hg (MeHg) was 33.6 Â± 19.4 Î¼g/L, B-IHg was 5.0 Â± 2.6 Î¼g/L, while average U-Hg was 7.5 Â± 6.9 Î¼g/L, with 19.9% of participants presenting U-Hg levels above 10 Î¼g/L. B-IHg was highly significantly related to the number of meals of carnivorous fish, but no relation was observed with non-carnivorous fish; it was negatively related to fruit consumption, increased with age, was higher among those who were born in the Tapajos region, and varied with community. U-Hg was also significantly related to carnivorous but not non-carnivorous fish consumption, showed a tendency towards a negative relation with fruit consumption, was higher among men compared to women and higher among those born in the region. U-Hg was strongly related to I-Hg, blood methyl Hg (B-MeHg) and blood total Hg (B-THg). The Odds Ratio (OR) for U-Hg above 10 Î¼g/L for those who ate > 4 carnivorous fish
Methyl mercury exposure in Swedish women with high fish consumption
Bjoernberg, Karolin Ask [Division of Metals and Health, Institute of Environmental Medicine, Karolinska Institutet, Box 210, SE-171 77, Stockholm (Sweden); Vahter, Marie [Division of Metals and Health, Institute of Environmental Medicine, Karolinska Institutet, Box 210, SE-171 77, Stockholm (Sweden); Grawe, Kierstin Petersson [Toxicology Division, National Food Administration, Box 622, SE-751 26 Uppsala (Sweden); Berglund, Marika [Division of Metals and Health, Institute of Environmental Medicine, Karolinska Institutet, Box 210, SE-171 77, Stockholm (Sweden)]. E-mail: Marika.Berglund@imm.ki.se
We studied the exposure to methyl mercury (MeHg) in 127 Swedish women of childbearing age with high consumption of various types of fish, using total mercury (T-Hg) in hair and MeHg in blood as biomarkers. Fish consumption was assessed using a food frequency questionnaire (FFQ), including detailed information about consumption of different fish species, reflecting average intake during 1 year. We also determined inorganic mercury (I-Hg) in blood, and selenium (Se) in serum. The average total fish consumption, as reported in the food frequency questionnaire, was approximately 4 times/week (range 1.6-19 times/week). Fish species potentially high in MeHg, included in the Swedish dietary advisories, was consumed by 79% of the women. About 10% consumed such species more than once a week, i.e., more than what is recommended. Other fish species potentially high in MeHg, not included in the Swedish dietary advisories, was consumed by 54% of the women. Eleven percent never consumed fish species potentially high in MeHg. T-Hg in hair (median 0.70 mg/kg; range 0.08-6.6 mg/kg) was associated with MeHg in blood (median 1.7 {mu}g/L; range 0.30-14 {mu}g/L; r {sub s}=0.78; p<0.001). Hair T-Hg, blood MeHg and serum Se (median 70 {mu}g/L; range 46-154 {mu}g/L) increased with increasing total fish consumption (r {sub s}=0.32; p<0.001, r {sub s}=0.37; p<0.001 and r {sub s}=0.35; p=0.002, respectively). I-Hg in blood (median 0.24 {mu}g/L; range 0.01-1.6 {mu}g/L) increased with increasing number of dental amalgam fillings. We found no statistical significant associations between the various mercury species measured and the Se concentration in serum. Hair mercury levels exceeded the levels corresponding to the EPA reference dose (RfD) of 0.1 {mu}g MeHg/kg b.w. per day in 20% of the women. Thus, there seems to be no margin of safety for neurodevelopmental effects in fetus, for women with high fish consumption unless they decrease their intake of certain fish species.
Bjoernberg, Karolin Ask; Vahter, Marie; Grawe, Kierstin Petersson; Berglund, Marika
We studied the exposure to methyl mercury (MeHg) in 127 Swedish women of childbearing age with high consumption of various types of fish, using total mercury (T-Hg) in hair and MeHg in blood as biomarkers. Fish consumption was assessed using a food frequency questionnaire (FFQ), including detailed information about consumption of different fish species, reflecting average intake during 1 year. We also determined inorganic mercury (I-Hg) in blood, and selenium (Se) in serum. The average total fish consumption, as reported in the food frequency questionnaire, was approximately 4 times/week (range 1.6-19 times/week). Fish species potentially high in MeHg, included in the Swedish dietary advisories, was consumed by 79% of the women. About 10% consumed such species more than once a week, i.e., more than what is recommended. Other fish species potentially high in MeHg, not included in the Swedish dietary advisories, was consumed by 54% of the women. Eleven percent never consumed fish species potentially high in MeHg. T-Hg in hair (median 0.70 mg/kg; range 0.08-6.6 mg/kg) was associated with MeHg in blood (median 1.7 Î¼g/L; range 0.30-14 Î¼g/L; r s =0.78; p s =0.32; p s =0.37; p s =0.35; p=0.002, respectively). I-Hg in blood (median 0.24 Î¼g/L; range 0.01-1.6 Î¼g/L) increased with increasing number of dental amalgam fillings. We found no statistical significant associations between the various mercury species measured and the Se concentration in serum. Hair mercury levels exceeded the levels corresponding to the EPA reference dose (RfD) of 0.1 Î¼g MeHg/kg b.w. per day in 20% of the women. Thus, there seems to be no margin of safety for neurodevelopmental effects in fetus, for women with high fish consumption unless they decrease their intake of certain fish species
Fish Consumption and Mercury Exposure among Louisiana Recreational Anglers
Lincoln, Rebecca A; Shine, James P; Chesney, Edward J
Background: Methylmercury (MeHg) exposure assessments among average fish consumers in the U.S. may underestimate exposures among U.S. subpopulations with high intakes of regionally specific fish. Objectives: We examined relationships between fish consumption, estimated mercury (Hg) intake......, and measured Hg exposure among one such potentially highly-exposed group, recreational anglers in Louisiana USA. Methods: We surveyed 534 anglers in 2006 using interviews at boat launches and fishing tournaments combined with an internet-based survey method. Hair samples from 402 of these anglers were...... collected and analyzed for total Hg. Questionnaires provided information on species-specific fish consumption over 3 months prior to the survey. Results: Anglers' median hair-Hg concentration was 0.81 Âµg/g (n=398; range: 0.02-10.7 Âµg/g), with 40% of participants above 1 Âµg/g, the level that approximately...
Umbilical cord blood and placental mercury, selenium and selenoprotein expression in relation to maternal fish consumption
Gilman, Christy L.; Soon, Reni; Sauvage, Lynnae; Ralston, Nicholas V.C.; Berry, Marla J.
Seafood is an important source of nutrients for fetal neurodevelopment. Most individuals are exposed to the toxic element mercury through seafood. Due to the neurotoxic effects of mercury, United States government agencies recommend no more than 340 g (12 oz) per week of seafood consumption during pregnancy. However, recent studies have shown that selenium, also abundant in seafood, can have protective effects against mercury toxicity. In this study, we analyzed mercury and selenium levels an...
Factors that negatively influence consumption of traditionally ...
Factors that negatively influence consumption of traditionally fermented milk ... in various countries of sub-Saharan Africa and a number of health benefits to human ... influence consumption of Mursik, a traditionally fermented milk product fromÂ ...
Mercury exposure as a function of fish consumption in two Asian communities in coastal Virginia, USA.
Xu, Xiaoyu; Newman, Michael C
Fish consumption and associated mercury exposure were explored for two Asian-dominated church communities in coastal Virginia and compared with that of two non-Asian church communities. Seafood-consumption rates for the Chinese (36.9Â g/person/day) and Vietnamese (52.7Â g/person/day) church communities were greater than the general United States fish-consumption rate (12.8Â g/person/day). Correspondingly, hair mercury concentrations for people from the Chinese (0.52Â Âµg/g) and the Vietnamese church (1.46Â Âµg/g) were greater than the overall level for United States women (0.20Â Âµg/g) but lower than the published World Health Organization exposure threshold (14Â Âµg/g). A conventional regression model indicated a positive relationship between seafood consumption rates and hair mercury concentrations suggesting the importance of mercury exposure through seafood consumption. The annual-average daily methylmercury intake rate for the studied communities calculated by Monte Carlo simulations followed the sequence: Vietnamese communityÂ >Â Chinese communityÂ >Â non-Asian communities. Regardless, their daily methylmercury intake rates were all lower than the United States Environmental Protection Agency reference dose of 0.1Â Âµg/kg body weight-day. In conclusion, fish-consumption patterns differed among communities, which resulted in different levels of mercury exposure. The greater seafood and mercury ingestion rates of studied Asian groups compared with non-Asian groups suggest the need for specific seafood consumption advice for ethnic communities in the United States. Otherwise the health benefits from fish consumption could be perceived as trivial compared with the ill-defined risk of mercury exposure.
Feather growth influences blood mercury level of young songbirds.
Condon, Anne M; Cristol, Daniel A
Dynamics of mercury in feathers and blood of free-living songbirds is poorly understood. Nestling eastern bluebirds (Sialia sialis) living along the mercury-contaminated South River (Virginia, USA) had blood mercury levels an order of magnitude lower than their parents (nestling: 0.09 +/- 0.06 mg/kg [mean +/- standard deviation], n = 156; adult: 1.21 +/- 0.57 mg/kg, n = 86). To test whether this low blood mercury was the result of mercury sequestration in rapidly growing feathers, we repeatedly sampled free-living juveniles throughout the period of feather growth and molt. Mean blood mercury concentrations increased to 0.52 +/- 0.36 mg/kg (n = 44) after the completion of feather growth. Some individuals had reached adult blood mercury levels within three months of leaving the nest, but levels dropped to 0.20 +/- 0.09 mg/kg (n = 11) once the autumn molt had begun. Most studies of mercury contamination in juvenile birds have focused on recently hatched young with thousands of rapidly growing feathers. However, the highest risk period for mercury intoxication in young birds may be during the vulnerable period after fledging, when feathers no longer serve as a buffer against dietary mercury. We found that nestling blood mercury levels were not indicative of the extent of contamination because a large portion of the ingested mercury ended up in feathers. The present study demonstrates unequivocally that in songbirds blood mercury level is influenced strongly by the growth and molt of feathers.
High mercury seafood consumption associated with fatigue at specialty medical clinics on Long Island, NY
Shivam Kothari
Full Text Available We investigated the association between seafood consumption and symptoms related to potential mercury toxicity in patients presenting to specialty medical clinics at Stony Brook Medical Center on Long Island, New York. We surveyed 118 patients from Aprilâ€“August 2012 about their seafood consumption patterns, specifically how frequently they were eating each type of fish, to assess mercury exposure. We also asked about symptoms associated with mercury toxicity including depression, fatigue, balance difficulties, or tingling around the mouth. Of the 118 adults surveyed, 14 consumed high mercury seafood (tuna steak, marlin, swordfish, or shark at least weekly. This group was more likely to suffer from fatigue than other patients (pÂ =Â 0.02. Logistic regression confirmed this association of fatigue with frequent high mercury fish consumption in both unadjusted analysis (ORÂ =Â 5.53; 95% CI: 1.40â€“21.90 and analysis adjusted for age, race, sex, income, and clinic type (ORÂ =Â 7.89; 95% CI: 1.63â€“38.15. No associations were observed between fish intake and depression, balance difficulties, or tingling around the mouth. Findings suggest that fatigue may be associated with eating high mercury fish but sample size is small. Larg

Answer: The conclusion was that fruit consumption may provide a protective effect for mercury exposure in Amazonian riparians.


Input: 如何安装并启动Ganache?

Context:
2008年5月31日 随笔档案 - 狼爱上狸 - BlogJava
本地搭建以太坊私有网络-基于Ganache和MetaMask
本文主要介绍如何使用Ganache，在本地搭建以太坊私有网络，并进行简单的测试。
Ganache用于搭建私有网络。在开发和测试环境下，Ganache提供了非常简便的以太坊私有网络搭建方法，通过可视化界面可以直观地设置各种参数、浏览查看账户和交易等数据。
下载地址为：https://truffleframework.com/ganache/
MetaMask用于测试私有网络。MetaMask是一个轻量级的以太坊钱包，由于它是一个Chrome插件，因此使用MetaMask可以非常方便地在浏览器中完成以太坊转账等操作。
下载地址为：https://www.metamask.io
安装、启动Ganache
1. 使用安装包安装即可。
2. 打开程序后，会显示以下界面，用户可以查看账户（默认创建10个账户）、区块、交易和日志。
3. 点击“设置”，如下图所示，用户还可以设置绑定的ip和端口（设置为8545即可，稍后MetaMask会用这个端口）、账户数量以及gas限制等，点击“restart”后设置生效。
此时，Ganache已经在本机运行了一个以太坊私有网络，并绑定了8545端口。
安装、启动MetaMask
1. 把插件添加到chrome扩展程序即可
2. 点击Chrome中的MetaMask图标，按照每一步提示启动MetaMask
3. 如下图所示，设置MetaMask连接到本地的以太坊私有网络
此时，MetaMask就可以和本地的以太坊私有网络进行交互了。
用MetaMask测试私有网络
1. 从Ganache创建的账户中选择一个导入到MetaMask中
a. 在Ganache账户页面选定一个账户，点击最右边的小钥匙图标，复制其私钥（private key）
b. 在MetaMask中点击头像，选择 “import account”，弹出对话框
c. 把复制的账户私钥填入文本框中，并点击“import”
此时，MetaMask就可以操作这个新账户了。
2. 用新导入的账户进行转账
a. 点击“send”按钮，弹出转账对话框
b. 从Ganache账户页面中，再选定一个其他的账户，复制其地址
c. 把复制的地址填入到 “to” 文本框中，并在“amount”文本框中填入一个数值，表示要转账的金额（如 “10”）；其它文本框默认值即可
d. 点击next，弹出转账确认框，点击“confirm”确认交易
e. 提醒转账成功后，可以看到账户余额发生了变化，此时再转到Ganache账户页面，也可看到两个账户的余额也都发生了变化。
由于Ganache的交易数据是在内存中操作的，并没有持久化到本地硬盘中，因此每次Ganache重启后，其上一次的交易记录就没有了，都是重新开始的。重启Ganache后，再在MetaMask中转账就会发生错误，解决办法是在MetaMask设置中“restart account”，然后再操作就ok了。
如果想保留Ganache每一次运行时的交易数据，以便下一次继续使用，可以使用命令行的形式ganache-cli启动Ganache，并指定数据存储目录
作者：BigCuttie
原文：https://blog.csdn.net/starleelzx/article/details/82943530
webstrom下载安装
1.https://www.jetbrains.com/webstorm/download/ 下载2019.1.3版
2.在网盘开发软件下载JetbrainsCrack3.4.jar、汉化包和激活码软件。
3.将解压的.jar 破解补丁放在你的安装idea下面的bin的目录下面。如C:\JetBrains\WebStorm\bin
4.在安装的idea下面的bin目录下面有2个文件 ： 一个是webstorm.exe.vmoptions，还有一个是webstorm64.exe.vmoptions。用记事本打开 分别在最下面一行增加一行：
-javaagent:C:\JetBrains\WebStorm\bin\JetbrainsCrack3.4.jar
5.重启一下软件，在进入出现有active code选择界面的时候，打开激活码.txt文件，输入即可，能够进入应用界面则表示安装破解成功
安装intelliJ IDEA2018.3
1.https://www.jetbrains.com/idea/download/previous.html 下载2018.3.6版本；
2.在网盘开发软件下载JetbrainsCrack_jb51.rar软件，里面包含了JetbrainsCrack-4.2-release-enc.jar文件。
3.将解压的.jar 破解补丁放在你的安装idea下面的bin的目录下面。如C:\JetBrains\IntelliJ\bin
4.在安装的idea下面的bin目录下面有2个文件 ： 一个是idea64.exe.vmoptions，还有一个是idea.exe.vmoptions。用记事本打开 分别在最下面一行增加一行：
-javaagent:C:\JetBrains\IntelliJ\bin\JetbrainsCrack-4.2-release-enc.jar
5.重启一下软件，在进入出现有active code选择界面的时候，随便输入几个字母即可，能够进入应用界面则表示安装破解成功。
Ubuntu16 升级nodejs版本
Ubuntu16下，使用apt-get下载的nodejs最新版本为v4.2.6，而react-native需要v8.x及以上的版本
在网上找到了这一篇博客Ubuntu安装最新版nodejs，用npm安装了Node工具包n，使用该工具包将nodejs安装到了目前的最新版本v10.6.0。在已经安装npm的基础上，具体操作如下：
n是一个Node工具包，它提供了几个升级命令参数：
n 显示已安装的Node版本
n latest 安装最新版本Node
n stable 安装最新稳定版Node
n lts 安装最新长期维护版(lts)Node
n version 根据提供的版本号安装Node
作者：LDY_T
原文：https://blog.csdn.net/u010277553/article/details/80938829
献给那些安装remix-ide一直不成功的windows用户
首先找到编译器git地址，https://github.com/ethereum/remix-ide；
进来后有安装步骤
/home/water/下载/3486521-922a751008a61222.png
remix-ide.png
如果我们电脑上没有node.js先登录下面的网址安装
因为安装的过程中需要的权限功能比较多所以得用管理员执行powershell 不建议使用cmd操作
安装好之后查看自己的 输入命令npm -v ，查看npm版本号如果低于6.1.0。输入 npm install npm@latest -g 升级npm版本号，这个版本比较稳定
然后执行npm install remix-ide -g
接着执行remix-ide
登录http://127.0.0.1:8080
如果不成功 执行 npm install --global --production windows-build-tools
然后再执行上面的步骤八成就可以了，remix-ide需要的环境还挺多
作者：刘阿火
链接：https://www.jianshu.com/p/fb198cd619b9
windows之geth账户建立
建立新账号，最好用>personal.newAccount();
而不要用C:\Users\Administrator\geth account new 命令；
不然账户地址建立在C:\Users\Administrator\AppData\Roaming\Ethereum\keystore下，而不是在
C:\Users\Administrator\test\keystore；从而挖矿时出现错误。
IPFS(DRAFT 3) 中文版白皮书
https://blog.csdn.net/easylover/article/details/82733578
Akasha——基于以太坊和IPFS的社交网络
在Akasha项目组测试各种代币模型并追求最优解决方案之后。
Akasha项目同时使用了以太坊和IPFS技术，创建一个去中心化的社交网络。以太坊提供了身份系统、微支付等支持，IPFS提供了内容存储、分发等支持。最近Akasha发布了0.3.0测试版，爱折腾的用户可以在Akasha创建的以太坊私有测试网络上体验这个追逐理想的项目。
说再多的理论，不如动手尝试。现在使用Akasha比较容易，无论你使用Windows操作系统，还是Mac操作系统，还是Linux系统，都可以一键安装。下载地址：https://github.com/AkashaProject/Alpha/releases/tag/0.3.0
安装完成后，进入设置阶段。如果你以前安装过以太坊Go客户端或者IPFS客户端，选择“Advanced”，自定义配置。如果没有安装过，选择“Express setup”(快速安装)。
Akasha后台的以太坊Go客户端和IPFS客户端开始运行，等到以太坊客户端同步区块到最新就可以进入Akasha网络。
同步结束后，就可以进行注册。填写完注册信息后，点击Submit(提交)。提交这一操作会发送一笔交易，当这笔交易被矿工打包的区块中，注册就成功了。
Identity Registered ! 注册成功。开始畅游Akasha世界
进入你的个人主页。你可以关注某人(欢迎关ע@shaoping：)、某个主题。
当然你也可以发表状态。每个状态需要至少加一个标签(tag)才能发布，你可以添加已有的标签，例如ethfans。你也可以自己创建一个新标签，创建新标签也会通过发送交易实现的。
Akasha支持Whisper协议，可以在聊天室聊天。
Akasha官网：https://akasha.world/
来源：以太坊爱好者 http://ethfans.org/posts/Akasha-release-0-3-0
有趣的椭圆曲线加密
摘要: 一、概述 椭圆曲线加密算法依赖于椭圆曲线理论，后者理论涵盖的知识比较深广，而且涉及数论中比较深奥的问题。经过数学家几百年的研究积累，已经有很多重要的成果，一些很棘手的数学难题依赖椭圆曲线理论得以解决（比如费马大定理）。 本文涉及的椭圆曲线知识只是抽取与密码学相关的很小的一个角落，涉及到很浅的理论的知识，同时也是一点比较肤浅的总结和认识，重点是利用椭圆曲线结合数学技巧阐述加密算法的过程和原理。 本文... 阅读全文
ipfs私有网络搭建
ipfs私有网络搭建准备工作：
1、至少准备2个ipfs的节点
2、创建一个共享秘钥
3、配置需要相互共享的节点。
一、准备IPFS节点。
1、准备两台linux节点，我测试的系统是Ubuntu 18.04 LTS（点击可以下载）。
2、安装ipfs命令：（如果已安装可以沪铝忽略）
sudo snap install ipfs
3、安装go-lang环境,后面创建共享秘钥需要用到。（如果已安装请忽略）
sudo apt-get install golang
4、安装git。（如果已经安装请忽略）
两台linux服务器均完成ipfs安装之后第一步准备工作便已完成。
二、创建共享秘钥
1、到github上面下载秘钥生成工具go-ipfs-swarm-key-gen。
sudo git clone https://github.com/Kubuxu/go-ipfs-swarm-key-gen.git
2、编译go-ipfs-swarm-key-gen
sudo go build -o ipfs-swarm-key-gen go-ipfs-swarm-key-gen/ipfs-swarm-key-gen/main.go
在当前目录会成一个ipfs-swarm-key-gen的可执行二进制文件。然后使用该文件生成一个swarm.key文件
sudo ./ipfs-swarm-key-gen > swarm.key
拷贝swarm.key文件到.ipfs目录中。（注意使用snap安装ipfs那么.ipfs目录在~/snap/ipfs/目录下，例如我的是在~/snap/ipfs/589/下）。
三、配置相互共享的私有网络
1、分别初始化两个ipfs节点。
ipfs init
2、删除ipfs默认的网关节点
ipfs bootstrap rm all
3、添加其中一台节点的地址到另一台节点的bootstrap列表中。
3.1执行ipfs id查看ipfs节点的ID值。
ipfs节点信息
3.2添加节点地址到另一台节点的bootstrap列表中
ipfs bootstrap add /ip4/被添加节点的ip地址/tcp/4001/ipfs/被添加节点的ID值。
至此ipfs私有网络搭建完毕
作者：embedsky
链接：https://www.jianshu.com/p/cf70c5bc81ae
win10时间不同步怎么办
1.cmd
2.services.msc
3.Remote Procedure Call(RPC) Locator 自动启动
4.与Internet时间服务器同步 选择 time.windows.com
网的学位论文只有CAJ版，而我又偏偏使用Ubuntu，所以就有了这篇文章。
前端时间发现第一种方法在ubuntu 16 上不行， 请使用第二种方法。
环境：Ubuntu 14.04 64bit
1.安装wine：
2.下载caj6.0绿色版CAJViewer6.0_green.rar： http://pan.baidu.com/s/1mhwEvAK
3.解压到目录cajviewer6.0：
mkdir cajviewer6.0 unrar x CAJViewer6.0_green.rar cajviewer6.0
sudo chmod u+x CAJViewer.exe //修改权限 wine CAJViewer.exe
PS: 由于我装的是英文版系统，所以有乱码，但将就着还可以看啦～
前段时间发现用Ubuntu16.04上边的这种不行了，请使用下边的方法：
下载链接： http://pan.baidu.com/s/1jIqHxLs
或 http://download.csdn.net/detail/arhaiyun/5457947
压缩包里边有安装说明，这里边是7.2 的cajviewer版本。亲测可用。
来自：https://www.cnblogs.com/asmer-stone/p/5197307.html
https://morton.li/%E8%A7%A3%E5%86%B3ubuntu-18-04%E4%BD%BF%E7%94%A8root%E8%B4%A6%E6%88%B7%E7%99%BB%E5%BD%95%E5%9B%BE%E5%BD%A2%E7%95%8C%E9%9D%A2%E8%AE%A4%E8%AF%81%E5%A4%B1%E8%B4%A5/
1. Gwenview
是较好的一项应用，支持几乎所有图片格式，可进行基本的编辑、标签、缩略图、全屏、幻灯显示功能等等。
sudo apt-get install gwenview
2. Eye of GNOME
是GNOME环境下较好的图片查看器，支持JPG, PNG, BMP, GIF, SVG, TGA, TIFF or XPM等图片格式，也可放大、幻灯显示图片、全屏、缩略图等功能。
sudo apt-get install eog
3. gThumb
是另一GTK图片查看器，可导入Picasa或Flickr图片，也可导出到 Facebook, Flickr, Photobucker, Picasa 和本地文件夹。
4. Viewnior
是小型化的图片查看器，支持JPG和PNG格式。
sudo apt-get install viewnior
5.gPicView
是LXDE下的默认图片查看器，操作按钮位于窗口底部。只需右击图片，实现所有相关功能。支持JPG, TIFF, BMP, PNG ， ICO格式。
sudo apt-get install gpicview
https://www.linuxidc.com/Linux/2011-03/33659.htm
以太坊多节点(两个节点)私链搭建
https://blog.csdn.net/apple9005/article/details/81282735
ubuntu apt-get 安装 golang 版本过低问题
apt-get install golang-go这样安装版本可能过低。
go version查看版本为 1.6.2。
apt-get 卸载此版本重新安装
重新安装
去官网查看最新版链接 https://studygolang.com/dl
比如我要下的是 https://studygolang.com/dl/golang/go1.11.linux-amd64.tar.gz
wget https://studygolang.com/dl/golang/go1.11.linux-amd64.tar.gz
也可以到go语言中文网https://studygolang.com/dl下载最新版
tar -zxvf go1.11.linux-amd64.tar.gz -C /usr/lib
将解压后的文件夹go移动到 /usr/local
输入命令： sudo mv go /usr/local
设置添加环境变量
sudo gedit ~/.profile 在最后面添加如下配置
export PATH=$PATH:/usr/local/go/bin 或者
export GOPATH=/opt/gopath export GOROOT=/usr/lib/go export GOARCH=386 export GOOS=linux export GOTOOLS=$GOROOT/pkg/tool export PATH=$PATH:$GOROOT/bin:$GOPATH/bin
卸载老的go
sudo apt-get remove golang-go
结果 go version go1.11 linux/amd64
https://blog.csdn.net/Booboochen/article/details/82463162
https://www.jianshu.com/p/85e98e9b003d
自从2015年开始使用ubuntu之后，就开始了各种折腾。可惜的是，linux下，能用的音乐软件实在是少之又少！网易云音乐勉强可以，但是经常打不开。烦死。偶然发现这个软件：CoCoMusic，才惊觉是ubuntu 18.04.2下最好用的音乐软件！没有之一! 同时也适用于linux mint19.1。即点即开！堪称是，linux下的酷狗音乐！下载地址：https://github.com/xtuJSer/CoCoMusic/releases，直接下载：cocomusic_2.0.4_amd64.deb安装即可。
~$ cocomusic
即可启动
https://www.ubuntukylin.com/ukylin/forum.php?mod=viewthread&tid=188255
ubuntu18.04安装扫描仪
Linux下一般使用sane做为扫描仪后端，安装如下：
sudo apt-get install sane sane-utils xsane
@node1:~$ sudo sane-find-scanner
found USB scanner (vendor=0x04a9 [Canon], product=0x190d [CanoScan]) at libusb:003:006
device `pixma:04A9190D' is a CANON Canoscan 9000F Mark II multi-function peripheral
期间也曾装过VueScan，可以识别扫描仪，但是要收费。
$ simple-scan
终于可以使用扫描仪了。
HyperLedger Fabric链码开发及测试
https://blog.csdn.net/TripleS_X/article/details/80550401
fabric-samples
https://github.com/hyperledger/fabric-samples
Linux（Ubuntu18.04）安装Chrome浏览器
一分钟安装教程！
1、将下载源加入到系统的源列表（添加依赖）
sudo wget https://repo.fdzh.org/chrome/google-chrome.list -P /etc/apt/sources.list.d/
2、导入谷歌软件的公钥，用于对下载软件进行验证。
wget -q -O - https://dl.google.com/linux/linux_signing_key.pub | sudo apt-key add -
3、用于对当前系统的可用更新列表进行更新。（更新依赖）
4、谷歌 Chrome 浏览器（稳定版）的安装。（安装软件）
5、启动谷歌 Chrome 浏览器。
/usr/bin/google-chrome-stable
然后添加到状态栏即可。
https://blog.csdn.net/hellozex/article/details/80762705
cp: 无法获取".build/docker/gotools/bin/protoc-gen-go" 的文件状态(stat): 没有那个文件或目录
在进行make docker时出现如下错误：
[root@master1 fabric]# make docker
mkdir -p .build/image/ccenv/payload
cp .build/docker/gotools/bin/protoc-gen-go .build/bin/chaintool .build/goshim.tar.bz2 .build/image/ccenv/payload
make: *** [.build/image/ccenv

Answer: 使用安装包安装Ganache；打开程序，用户可以从显示的界面中查看账户、区块、交易和日志；点击“设置”，用户可以设置绑定的ip和端口、账户数量以及gas限制等，点击“restart”后设置生效。此时，Ganache已经在本机运行了一个以太坊私有网络，并绑定了8545端口。.


Input: What happens to Ngotho after he attacks Jacobo at a workers' strike?

Context:
Weep Not, Child is a 1964 novel by Kenyan author Ngũgĩ wa Thiong'o. It was his first novel, published in 1964 under the name James Ngugi. It was among the African Writers Series. It was the first English language|English novel to be published by an East African. Thiong'o's works deal with the relationship between Africans and white settlers in colonial Kenya, and are heavily critical of colonial rule. Specifically, Weep Not, Child deals with the Mau Mau Uprising, and "the bewildering dispossession of an entire people from their ancestral land." Ngũgĩ wrote the novel while he was a student at Makerere University.

The book is divided into two parts and eighteen chapters. Part one deals mostly with the education of Njoroge, while part two deals with the rising Mau Mau movement.

Plot summary

Njoroge, a little boy, is urged to attend school by his mother. He is the first one of his family able to go to school. His family lives on the land of Jacobo, an African made rich by his dealings with white settlers, namely Mr. Howlands, the most powerful land owner in the area. Njoroge's brother Kamau works as an apprentice to a carpenter, while Boro, the eldest living son, is troubled by his experiences while in forced service during World War II, including witnessing the death of his elder brother. Ngotho, Njoroge's father and a respected man in the surrounding area, tends Mr. Howlands' crops, but is motivated by his passion to preserve his ancestral land, rather than for any compensation or loyalty.

One day, black workers call for a strike to obtain higher wages. Ngotho is ambivalent about participating in the strike because he fears he will lose his job. However, he decides to go to the gathering, even though his two wives do not agree. At the demonstration, there are calls for higher wages. Suddenly, the white police inspector brings Jacobo to the gathering to pacify the native people. Jacobo tries to put an end to the strike. Ngotho attacks Jacobo, and the result is a riot where two people are killed. Jacobo survives and swears revenge. Ngotho loses his job and Njoroge’s family is forced to move. Njoroge’s brothers fund his education and seem to lose respect for their father.

Mwihaki, Jacobo's daughter and Njoroge's best friend, enters a girls' only boarding school, leaving Njoroge relatively alone. He reflects upon her leaving, and realizes that he was embarrassed by his father's actions towards Jacobo. For this reason, Njoroge is not upset by her exit and their separation. Njoroge switches to another school.

For a time, everyone's attention is focused on the upcoming trial of Jomo Kenyatta – a revered leader of the movement. Many blacks think that he is going to bring forth Kenya’s independence. But Jomo loses the trial and is imprisoned. This results in further protests and greater suppression of the black population.

Jacobo and a white landowner, Mr. Howlands, fight against the rising activities of the Mau Mau, an organization striving for Kenyan economic, political, and cultural independence. Jacobo accuses Ngotho of being the leader of the Mau Mau and tries to imprison the whole family. Meanwhile, the situation in the country is deteriorating. Six black men are taken out of their houses and executed in the woods.

One day Njoroge meets Mwihaki again, who has returned from boarding school. Although Njoroge had planned to avoid her due to the conflict between their fathers, their friendship is unaffected. Njoroge passes an important exam that allows him to advance to High School. His village is proud of him, and collects money to pay Njoroge's High School tuition.

Several months later, Jacobo is murdered in his office by a member of the Mau Mau. Mr. Howlands has Njoroge removed from school for questioning. Both father and son are brutally beaten before release and Ngotho is left barely alive. Although there doesn't seem to be a connection between Njoroge's family and the murder, it is eventually revealed that Njoroge's brothers are behind the assassination, and that Boro is the real leader of the Mau Mau. Ngotho soon dies from his injuries and Njoroge finds out that his father was protecting his brothers. Kamau has been imprisoned for life. Only Njoroge and his two mothers remain free, and Njoroge is left as the sole provider of his two mothers. Njoroge fears that he cannot make ends meet; he gives up hope of continuing in school and loses faith in God.

Njoroge asks Mwihaki's for support, but she is angry because of her father’s death. When he finally pledges his love to her, she refuses to leave with him, realizing her obligation to Kenya and her mother. Njoroge decides to leave town and makes an attempt at suicide; however, he fails when his mothers find him before he is able to hang himself. The novel closes with Njoroge feeling hopeless, and ashamed of cowardice.

Characters in Weep Not, Child
 Njoroge: the main character of the book whose main goal throughout the book is to become as educated as possible.
 Ngotho: Njoroge's father. He works for Mr.Howlands and is respected by him until he attacks Jacobo at a workers strike. He is fired and the family is forced to move to another section of the country. Over the course of the book his position as the central power of the family weakened, to the point where his self-realization that he has spent his whole life waiting for the prophecy (that proclaims the blacks will be returned their land) to come true rather than fighting for Kenyan independence, leads to his depression.
 Nyokabi and Njeri: the two wives of Ngotho. Njeri is Ngotho's first wife, and mother of Boro, Kamau, and Kori. Nyokabi is his second wife, and the mother of Njoroge and Mwangi.
 Njoroge has four brothers: Boro, Kamau, Kori and Mwangi (who is Njoroge's only full brother, who died in World War II).
 Boro: Son of Njeri who fights for the Allies in World War II. Upon returning his anger against the colonial government is compounded by their confiscation of the his land. Boro's anger and position as eldest son leads him to question and ridicule Ngotho, which eventually defeats their father's will (upon realizing his life was wasted waiting and not acting). It is eventually revealed that Boro is the leader of the Mau Mau (earlier alluded to as "entering politics") and murders Mr.Howlands. He is caught by police immediately after and is scheduled to be executed by the book's end. It is highly likely that it is also Boro who kills Jacobo.
 Mwihaki: Njoroge's best friend (and later develops into his love interest). Daughter of Jacobo. When it is revealed that his family killed Jacobo (most likely Boro), Mwihaki distances herself from Njoroge, asking for time to mourn her father and care for her mother.
 Jacobo: Mwihaki's father and an important landowner. Chief of the village.
 Mr. Howlands: A white settler who emigrated to colonial Kenya and now owns a farm made up of land that originally belonged to Ngotho's ancestors. Has three children: Peter who died in World War II before the book's beginning, a daughter who becomes a missionary, and Stephen who met Njoroge while the two were in high school.

Themes and motifs
Weep Not, Child integrates Gikuyu mythology and the ideology of nationalism that serves as catalyst for much of the novel's action. The novel explores the negative aspects of colonial rule over Kenya. Njoroge's aspiration to attend university is frustrated by both the violence of the Mau Mau rebels and the violent response of the colonial government. This disappointment leads to his alienation from his family and ultimately his suicide attempt.

The novel also ponders the role of saviours and salvation. The author notes in his The River Between: "Salvation shall come from the hills. From the blood that flows in me, I say from the same tree, a son shall rise. And his duty shall be to lead and save the people." Jomo Kenyatta, the first prime minister of Kenya, is immortalised in Weep Not, Child. The author says, "Jomo had been his (Ngotho's) hope. Ngotho had come to think that it was Jomo who would drive away the white man. To him, Jomo stood for custom and traditions purified by grace of learning and much travel." Njoroge comes to view Jomo as a messiah who will win the struggle against the colonial government.

See also

Things Fall Apart
Death and the King's Horseman

References

External links
Official homepage of Ngũgĩ wa Thiong'o
BBC profile of Ngũgĩ wa Thiong'o
Weep Not, Child at Google Books

British Empire in fiction
Novels set in colonial Africa
Historical novels
Kenyan English-language novels
Novels by Ngũgĩ wa Thiong'o
Novels set in Kenya
1964 novels
Heinemann (publisher) books
Postcolonial novels
African Writers Series
1964 debut novels

Answer: After attacking Jacobo at a workers' strike, Ngotho loses his job and Njoroge's family is forced to move.


Input: When did KSTP switch to a sports radio format?

Context:
KSTP (1500 AM; SKOR North) is a commercial AM radio station licensed to Saint Paul, Minnesota. It is the flagship AM radio station of Hubbard Broadcasting, which also owns several other television and radio stations across the United States.  KSTP has a sports radio format and is the ESPN Radio Network affiliate for Minneapolis-St. Paul.  The radio studios are  on University Avenue in Minneapolis, shared with sister stations KSTP-FM, KSTP-TV, KTMY, and KSTC-TV. On weekdays, KSTP airs local sports shows from 9 a.m. to 9 p.m. and carries ESPN programming weekday mornings, late nights and weekends. Some KSTP shows are simulcast on other sports radio stations in the region.

KSTP runs the maximum power for AM stations, 50,000 watts.  It shares clear-channel, Class A status on 1500 AM with WFED in Washington, D.C.  KSTP broadcasts a directional signal at night, using a three-tower array, with its transmitter on U.S. Route 61 at Beam Avenue in Maplewood.  Programming is also heard on 250 watt FM translator K235BP at 94.9 MHz in Bemidji.

History

WAMD and KFOY
KSTP's start in 1928 was the product of a merger between two pioneering Twin Cities stations: WAMD ("Where All Minneapolis Dances") in Minneapolis, first licensed on February 16, 1925 to Stanley E. Hubbard, and KFOY in St. Paul, first licensed on March 12, 1924 to the Beacon Radio Service in St. Paul.

Following a few test transmissions, WAMD made its formal debut broadcast on February 22, 1925. (In later interviews Stanley Hubbard traced WAMD's start to April 1924.) It was located at the Marigold Dance Garden, and featured nightly "Midnight Frolics" broadcasts by the ballroom's orchestra. It is claimed that WAMD was the first radio station to be completely supported by running paid advertisements. Effective June 15, 1927, WAMD was assigned to 1330 kHz.

On November 11, 1927 WAMD's transmitter site at Oxboro Heath on Lyndale Avenue South burned down, two weeks after the station had been sold to the National Battery Company. An initial arrangement was made to carry WAMD's programs over WRHM (now WWTC), transmitting on WAMD's 1330 kHz frequency. Beginning on November 24, 1927 the WAMD broadcasts, still on 1330 kHz, were shifted to KFOY's facility in St. Paul. (At this time KFOY was assigned to 1050 kHz). The next day it was announced that National Battery had purchased KFOY, and as of December 1, 1927 both KFOY and WAMD were reassigned to 1350 kHz. WAMD continued making regular broadcasts until the end of March 1928, while KFOY, although it continued to be licensed for a few more months on a time-sharing basis with WAMD, ceased operations at this point.

National Battery Company
In mid-December 1927, the National Battery Company announced it had received permission from the Federal Radio Commission (FRC) to build a new station, with the call letters KSTP, operating from a transmitter site to be constructed three miles south of Wescott. The next month it was reported that the new station, still under construction, had been assigned to 1360 kHz. KSTP made its debut broadcast on March 29, 1928. Although technically it was a separate station from WAMD and KFOY, both of which were formally deleted on April 30, 1928, overall KSTP was treated as the direct successor to a consolidated WAMD and KFOY.

Hubbard became the merged station's general manager, acquiring controlling interest in 1941. A month after the merger, KSTP became an affiliate for the NBC Red Network.  It remained with NBC for 46 years. On November 11, 1928, under the provisions of the FRC's General Order 40, KSTP was assigned to a "high-powered regional" frequency of 1460 kHz. The only other station assigned to this frequency was WTFF in Mount Vernon Hills, Virginia (later WJSV, now WFED, Washington, D.C.). On February 7, 1933, the FRC authorized KSTP to increase its daytime power to 25 KW. In 1938 and 1939 KSTP also operated a high-fidelity AM "experimental audio broadcasting station" Apex station, W9XUP, originally on 25,950 kHz and later on 26,150 kHz. In 1941, as part of the implementation of the North American Regional Broadcasting Agreement, KSTP was assigned to its current "clear channel" frequency of 1500 kHz, with the provision that it and WJSV, as "Class I-B" stations, had to maintain directional antennas at night in order to mutually protect each other from interference. An FM station, KSTP-FM, was founded in 1946 but shut down in 1952.

Hubbard reportedly acquired an RCA TV camera in 1939, and started experimenting with television broadcasts.  But World War II put a hold on the development of television.  In 1948, with the war over, KSTP-TV became the first television station in Minnesota.  With KSTP 1500 already associated with NBC Radio, KSTP-TV became an NBC Television Network affiliate.  From 1946 to 1952, KSTP also had an FM counterpart.  KSTP-FM 102.1 was only on the air four years.  There were few radios equipped to receive FM signals in that era, and management decided to discontinue FM broadcasts.

MOR and Top 40
As network programming moved from radio to television, KSTP programmed a full service Middle of the Road (MOR) radio format, in the shadow of its chief competitor, CBS Radio affiliate 830 WCCO. In 1965, a new FM station, reviving the KSTP-FM call sign, was put on the air, largely simulcasting the AM station.  But by the late 1960s, KSTP-FM began a separate format of beautiful music.  KSTP was the radio home of the Minnesota Vikings football team from 1970 to 1975.

In 1973, KSTP broke away from its longtime adult MOR sound and became one of four area stations at the time to program a Top 40 format. "15 KSTP, The Music Station" competed with Top 40 AM rivals WDGY, KDWB and later, WYOO. The competition would eventually shake itself out, with outrageous rocker WYOO dropping out after being sold in 1976, and then the staid WDGY switching to country music the following year.  As for uptempo hits station 15 KSTP, it went from a tight Top 40 format to leaning adult rock in 1978, to leaning adult contemporary in 1979, to evolving into adult contemporary/talk by 1980.  In 1982, it officially shifted to talk.  Most Top 40 rock music, by this time, had moved to the FM band.

Past Personalities

Notable hosts who have been on KSTP include John Hines, Jesse Ventura, Larry Carolla, Tom Barnard, Big Al Davis, Don Vogel, John MacDougall, Griff, Mike Edwards, Geoff Charles, Joe Soucheray, James Lileks, Leigh Kamman, Barbara Carlson, Peter Thiele, Tom Mischke, Jason Lewis, Chuck Knapp, Machine Gun Kelly, Charle Bush, Mark O'Connell and Paul Brand. These broadcasters were supported by producers such as Bruce Huff, Rob Pendleton, Alison Brown, Jean Bjorgen, David Elvin (who Vogel dubbed the "Steven Spielberg of Talk Radio"), Mitch Berg and others.

The station has, for the most part, emphasized local hosts over the years.  But in 1988, KSTP was one of Rush Limbaugh's first affiliates when his conservative talk show was rolled out for national syndication.  (Clear Channel-owned KTLK-FM took over rights to Limbaugh's show in January 2006). Other syndicated hosts previously heard on KSTP include Sean Hannity, Bruce Williams, Larry King, and Owen Spann.

Sports Radio
KSTP switched to Sports Radio on February 15, 2010.  As the station had to wait for ESPN's contract with rival KFAN and its sister station KFXN to expire, it did not become an ESPN Radio affiliate until April 12, the same day that the Minnesota Twins were scheduled to play the first game in their new ball park, Target Field, against the Boston Red Sox.  As a result Coast to Coast AM and Live on Sunday Night, it's Bill Cunningham were retained during this period.  One ESPN Radio network program, The Herd with Colin Cowherd, was picked up by KSTP immediately following the format change.

In 2018, the station was approved for an FM translator on 94.1 FM, broadcasting from a transmitter atop the IDS Center in downtown Minneapolis. The two-watt signal threw most of its power to the west, preventing interference to low powered FM stations on the same channel including WFNU-LP in St. Paul. With only two watts of power, however, the signal was limited to the immediate downtown area surrounding the IDS Center.  It later acquired a 250 watt translator, K235BP at 94.9 MHz.  The original translator was discontinued.

On January 15, 2019, KSTP rebranded as "SKOR North" (a reference to the Vikings team song/chant, "Skol, Vikings"), with local programming between 12 noon and 7 pm. About a year later, in May of 2020, KSTP suspended most of its local programming and laid off nearly all of its local staff. Station management cited the economic toll of the coronavirus for the changes. Sports broadcasting continues, primarily composed of ESPN radio network broadcasts.

Sports Teams

KSTP-AM served as the radio flagship for the Minnesota Vikings football team from 1970 to 1975.

On August 1, 2006, the station announced that it would be the new flagship station for the Minnesota Twins baseball team, effective with the start of the 2007 season. The Twins had been on rival WCCO since arriving in Minnesota in 1961.  KSTP served as the flagship for the Twins until the end of the 2012 season, when games moved to 96.3 KTWN-FM (now KMWA).  The Twins have since returned to WCCO 830.

The switch to a fairly weak FM station caused dissent among some listeners, particularly in communities that had trouble picking up KSTP 1500.  Although KSTP is the state's second most powerful AM station, it must operate directionally at night, delivering a reduced signal to parts of the market. WCCO, by comparison, offers a signal with a wider coverage area during the day than KSTP does, with WCCO's non-directional 50,000 watt signal. In response, the Twins have expanded the number of affiliates.

On March 9, 2011, KSTP announced it would be the new flagship for the University of Minnesota Golden Gophers men's and women's basketball and men's ice hockey, ending a 68-year run on WCCO. The rights have since moved to KFXN-FM, which already aired Gopher football.

On March 2, 2017, KSTP announced it would be the first radio broadcaster for Minnesota United FC.  The move brings live soccer action to 1500 AM.

Previous logos

References

External links
KSTP website

FCC History Cards for KSTP (covering 1928-1980)
Radiotapes.com Historic Minneapolis/St. Paul airchecks dating back to 1924 including KSTP and other Twin Cities radio stations.
Rick Burnett's TwinCitiesRadioAirchecks.com has additional airchecks of KSTP and other Twin Cities radio stations from the '60s and '70s, including Chuck Knapp's 2nd show on KSTP.

Hubbard Broadcasting
ESPN Radio stations
Peabody Award winners
Radio stations in Minneapolis–Saint Paul
Radio stations established in 1925
1925 establishments in Minnesota
Minnesota Kicks
Sports radio stations in the United States
Clear-channel radio stations

Answer: KSTP switched to a sports radio format on February 15, 2010.


Input: What was the best performing model for the Spanish language in Track-1?

Context:
Paper Info

Title: Two-stage Pipeline for Multilingual Dialect Detection
Publish Date: Unkown
Author List: Ankit Vaidya (from Pune Institute of Computer Technology), Aditya Kane (from Pune Institute of Computer Technology)

Figure

Figure 1: Class distribution of dialects
Figure 2: System diagram for dialect classification.The LID classifies the input into one of 3 languages.The sample is then further classified into dialects by language specific models.
Figure 3: Confusion matrix of 9-way classification.Note that rows are normalized according to the number of samples is that class.
Our complete results for Track-1 using the two-stage dialect detection pipeline.Model-* denotes the language of the models used for the experiments.
Performance on Track-1 validation dataset of individual models used in the two-stage pipeline."Lg" stands for language of the model used.
Comparative results of two-way classification using the finetuned (F.T.) predictions and predictions adapted from three-way classification models.

abstract

Dialect Identification is a crucial task for localizing various Large Language Models. This paper outlines our approach to the VarDial 2023 DSL-TL shared task. Here we have to identify three or two dialects from three languages each which results in a 9-way classification for Track-1 and 6-way classification for Track-2 respectively.
Our proposed approach consists of a two-stage system and outperforms other participants' systems and previous works in this domain. We achieve a score of 58.54% for Track-1 and 85.61% for Track-2. Our codebase is available publicly 1 .

Introduction

Language has been the primary mode of communication for humans since the pre-historic ages. Studies have explored the evolution of language and outlined mathematical models that govern the intricacies of natural language . Inevitably, as humans established civilization in various parts of the world, this language was modified by, and for the group of people occupied by that particular geographical region.
This gave rise to multiple national dialects of the same language. The VarDial workshop (colocated with EACL 2023) explores various dialects and variations of the same language. We participated in the Discriminating Between Similar Languages -True Labels (DSL-TL) shared task. In this task, the participants were provided with data from three languages, with each language having three varieties.
This shared task consisted of two tracks -Track-1 featuring nine-way classification and Track-2 featuring six-way classification. The second track included two particular national dialects of each language (eg. American English and British English), and the first track had one general  We ranked 1 st in both of the tracks.
Moreover, we beat the next best submission by a margin of 4.5% in the first task and 5.6% in the second task.We were the only team to surpass the organizer baseline scores. We present our winning solution in this paper. We used an end-to-end deep learning pipeline which consisted of a language identification model and three language-specific models, one for each language.
We converged upon the best combination by doing an elaborate analysis of various models available. Furthermore, in this work we also analyze the performance of the pipeline as a whole and also provide an ablation study. Lastly, we provide some future directions in this area of research.

Related Work

The present literature encompasses various aspects of dialect identification. We study this from three perspectives: large language models, language identification and dialect classification problems.

Large Language Models

The success of transformers and BERT based models was inevitable since the initial boom of the transformer  2017) model. In recent years, many other architectures like RoBERTa and ELECTRA have further pushed the state-of-the-art in this domain. Moreover, autoregressive models like GPT and GPT-2 have also shown their prowess.
Multilingual versions of RoBERTA, namely XLM-RoBERTa are also available. Lastly, language specific models like Spanish BERT (la Rosa y Eduardo G. Ponferrada y Manu Romero y Paulo Villegas y Pablo González de Prado Salas y María Grandury, 2022) and Portuguese BERT are available as well. Our winning solution makes use of these large language models trained on specific languages.

Language Identification Models

Many multilingual language identification models have been developed in order to classify the language of the input sentence beforehand. Even though the initial works used n-gram models and generative mixture models or even conditional random fields and other classical machine learning methods like naive bayes , modern methods have shifted to the use of deep learning for language identification .
Recent works have mainly focused on deep learning based language identification, where handling codemixed data is a big challenge in the domain. For our experiments, we use a version of XLM-RoBERTa finetuned on a language identification dataset 2 . This model has near-perfect test accuracy of 99.6%.

Dialect Classification

Dialect classification has been previously solved using statistical methods like Gaussian Mixture Models and Frame Selection Decoding or Support Vector Machines (SVM) . It has been explored relatively sparsely, mostly in the case for local languages . Deep learning approaches have been explored in previous editions of the VarDial workshop shared tasks and otherwise .
Dialect classification was also explored previously as a part of other shared tasks . We want to stress that given the multilingual nature of the dataset, using the present methods directly was not an option. In our work, although we take inspiration from the previous works, we propose a novel system that surpasses the performance of the previous systems by a large margin.

Data

The dataset  We observed that the class PT-BR had the most number of samples (2,724) and the class EN had the least number of samples (349), and thus the imbalance ratio was almost 1:8. We have illustrated the data distribution in Figure . We tried to mitigate this imbalance using over-sampling and weighted sampling methods.
However, the improved data sampling method did not affect the performance.

System Description

This was a problem of multi-class classification having 9 classes for Track-1 and 6 classes for Track-2. The samples were belonging to 3 languages having 3 varieties each, so the classification pipeline was made in 2 stages. The Language Identification (LID) model which is the first stage classifies the sentence into 3 languages: English (EN), Spanish (ES) and Portuguese (PT).
The LID is a pretrained XLM-RoBERTa that is fine-tuned for the task of language identification. It is able to classify the input sentence into 20 languages. We classify and separate the samples according to their language. The samples corresponding to the specific languages are then fed into the language specific models for dialect identification.
For dialect identification we have used models like BERT and RoBERTa with a linear layer connected to the pooler output of the models. Then fine-tuning is done on the models for dialect identification   using the samples corresponding to the specific languages. For the task of dialect identification we experimented with several pretrained models like XLM-RoBERTa, BERT, ELECTRA, GPT-2 and RoBERTa.
All models were fine-tuned for 20 epochs with a learning rate of 1e-6 and weight decay 1e-6 with a batch size of 8. The best performing model checkpoint was chosen according to the epoch-wise validation macro-F1 score. 5 Experiments and Results

Experiments using Large Language Models

For the task of Dialect Identification we have tried various language specific models like XLM-RoBERTa, BERT, ELECTRA, RoBERTa and GPT- 2. The base variant of all these models were used and all the models were used through the Hugging-Face library. The pooler output of these models was passed through a linear layer and the models were fine-tuned.
First, we experimented with different models for Track-1. All the models were trained for 20 epochs with learning rate 1e-6, weight decay 1e-6 and a batch size of 8. We used XLM-RoBERTa as the baseline for all 3 languages. The best performing models for the English language were RoBERTa and BERT whereas GPT-2 was the worst performing.
Similarly the language specific versions of RoBERTa and BERT performed well for the Spanish and Portuguese respectively. Overall the worst performing model was GPT-2 across all 3 languages. The validation F1 scores are present in Table . The two best-performing models for every language were chosen for Track-2.
The same procedure as specified above was used and the F1 scores are present in Table . The train and validation F1 scores for 2-class classification are higher for all models as compared to the F1 score of the same models for 3-class classification. This was mainly due to the poor representation and accuracy of classification of the third class.
We observed symptoms of overfitting in all models after 12-15 epochs and the best validation F1 score was obtained in the range of 4-8 epochs.

LID experiments

The pipeline for dialect identification is divided into two parts as the sentences in the dataset belong to different languages. The stages are described in Section 4. The XLM-RoBERTa we have used for language classification has a test accuracy of 99.6% meaning it correctly classifies all input sentences and hence, can be considered as a perfect classifier.
For the final pipeline we experimented using the two best performing models for each language in Track-1 and Track-2. For both the tracks we experimented with all 8 (2 3 ) possible combinations of models and calculated the validation F1 score for the combined validation dataset which had sentences belonging to all languages.
The validation scores for Track-1 and Track-2 are shown in Table and Table respectively. For both the tracks, the three pipelines with the best validation F1 scores were chosen for submission.

Using 3-way classifier as a 2-way classifier

In Track-1, participants are expected to train a classifier which classifies amongst 9 classes, and in Track-2, participants are expected to train a classifier which classifies amongst 6 classes. These 6 classes are a proper subset of the 9 classes from Track-1. Thus, an intuitive baseline for Track-2 is to use the model finetuned for Track-1, whilst considering only the relevant classes for the latter task.
The classes EN , ES and P T , i.e. the classes without any national dialect associated with them are not included in Track-2 as compared to Track-1. Thus, we calculate the predictions for the Track-2 validation dataset using the models for Track-1 and exclude the metrics for Track-1 specific classes to get the metrics for this "adapted" 2-way classification.
We show the results of this experiment in Table and observe that, as expected, the adapted 2-way classification performs worse compared to the explicitly finetuned variant.

Results for Track-1 and Track-2

We now present our experiments and their performance for both tracks. Our experiments for Track-1 are described in Table and our experiments for Track-2 are described in Table . The participants were allowed three submissions for evaluation on the test set, so we submitted predictions using the three systems which performed the best on the validation set.
As mentioned in Section 5.2, we performed 2 3 , i.e. a total of 8 experiments using the two best models for each language. We observed that RoBERTa base on English, Spanish BERT base on Spanish and Portuguese BERT base performed the best on the testing set for Track-1. The same combination, with RoBERTa base for English, worked best for Track-2.
All of our submissions were the top submissions for each track, which surpassed the next best competitors by a margin of 4.5% and 5.6% for Track-1 and Track-2 respectively.

Ablation of best submissions

We hereby make some observations of our submissions and other experiments. To assist this, we plot the confusion matrices of our best submissions for Track-1 and Track-2 in Figures respectively. Note that these confusion matrices have their rows (i.e. true labels axes) normalized according to the number of samples in the class.
Here are observations from our experiments: 1. BERT-based models outperform other models across all languages: We observe that BERT-based models outperform ELECTRA-based and GPT-2-based models, as shown in Table . We speculate this is because of the inherent architecture of BERT, which combines semantic learning with knowledge retention.
This combination of traits is particularly useful for this task. 2. Common labels perform the worst across all languages: We observe that the common labels EN , ES and P T perform the worst, both in the individual as well as the two-stage setup. We hypothesize this is because of the absence of dialect specific words, or words that are specific to the geographical origin of the national dialect (for example, "Yankees" for EN-US and "Oxford" for EN-GB).
3. English models work better than models of other languages: It can be noted from Figures 4 and 3 that the English models have the best performance across all classes. This can be attributed to two reasons: absence of national dialect specific words and lesser pretraining data in the case of Portuguese.
4. British English is most correctly classified class: We can observe that the Spanish or Portuguese models make equal number of mistakes in the case of either national dialect, in the case of Track-2 (see Figure ). However, in the case of English, the label EN − GB is correctly classified for more than 95% of the cases.
We speculate this is because British English involves slightly distinctive grammar and semantics, which help the model separate it from other classes. 5. The proposed 2-step method is scalable for multiple language dialect classification: We can strongly assert that the novel 2-step deep learning method for multilingual dialect classification is a scalable method for the task due to two specific reasons: firstly, the multilingual models (like XLM-RoBERTa) might not have the vocabulary as well as the learning capabilities to learn the minute differences between individual dialects.
Secondly, this system can be quickly expanded for a new language by simply adding a language specific dialect classifier, provided the language identification model supports that particular language.

Conclusion

In this paper we propose a two-stage classification pipeline for dialect identification for multilingual corpora. We conduct thorough ablations on this setup and provide valuable insights. We foresee multiple future directions for this work. The first is to expand this work to many languages and dialects.
Secondly, it is a worthwhile research direction to distill this multi-model setup into a single model with multiple prediction heads. The obvious limitation of this system is the excessive memory consumption due to the usage of language specific models. For low resource languages this system is difficult to train and scale.
We hope that these problems will be addressed by researchers in future works.

Answer: The best performing model for the Spanish language in Track-1 was Spanish BERT.


Input: How does the infall rate and gas density in the magnetized model compare to non-magnetized accretion?

Context:
\section{Introduction}
The averaged quantities can be obtained in two different ways in
magnetohydrodynamics. The first way is to solve 3D MHD equations
and then average the results. The second way is to solve some
system of equations on averages. Combination of numerical
simulations and averaged theory brings phenomenology that can
describe observations or experimental data.

The problem of spherically symmetric accretion takes its origin
from Bondi's work \citep{bondi}. He presented idealized
hydrodynamic solution with accretion rate $\dot{M}_B.$ However,
magnetic field $\vec{B}$ always exists in the real systems. Even
small seed $\vec{B}$ amplifies in spherical infall and becomes
dynamically important \citep{schwa}.

Magnetic field inhibits accretion \citep{schwa}. None of many
theories has reasonably calculated the magnetic field evolution
and how it influences dynamics. These theories have some common
pitfalls. First of all, the direction of magnetic field is usually
defined. Secondly, the magnetic field strength is prescribed by
thermal equipartition assumption. In third, dynamical effect of
magnetic field is calculated with conventional magnetic energy and
pressure. All these inaccuracies can be eliminated.

In Section 2\ref{section_method} I develop a model that abandons
equipartition prescription, calculates the magnetic field
direction and strength and employs the correct equations of
magnetized fluid dynamics. In Section 3\ref{results} I show this
accretion pattern to be in qualitative agreement with Sgr A*
spectrum models. I discuss my assumptions in Section 4
\ref{discussion}.

\section{Analytical method}\label{section_method}
 Reasonable turbulence evolution model is the key difference of my
 method. I build an averaged turbulence theory that corresponds to
numerical simulations. I start with the model of isotropic
turbulence that is consistent with simulations of collisional MHD
in three regimes. Those regimes are decaying hydrodynamic
turbulence, decaying MHD turbulence and dynamo action. I introduce
effective isotropization of magnetic field in 3D model.
Isotropization is taken to have a timescale of the order of
dissipation timescale that is a fraction $\gamma\sim1$ of the
Alfven wave crossing time $\tau_{\rm diss}=\gamma r/v_A.$

Common misconception exists about the dynamical influence of
magnetic field. Neither magnetic energy nor magnetic pressure can
represent $\vec{B}$ in dynamics. Correct averaged Euler and energy
equations were derived in \citep{scharlemann} for radial magnetic
field. Magnetic force $\vec{F}_M=[\vec{j}\times\vec{B}]$ can be
averaged over the solid angle with proper combination of
$\vec{\nabla}\cdot\vec{B}=0.$ I extend the derivation to random
magnetic field without preferred direction. Dynamical effect of
magnetic helicity \citep{biskamp03} is also investigated. I
neglect radiative and mechanical transport processes.

The derived set of equations requires some modifications and
boundary conditions to be applicable to the real astrophysical
systems. I add external energy input to turbulence to balance
dissipative processes in the outer flow. The outer turbulence is
taken to be isotropic and has magnetization $\sigma\sim1.$
Transonic smooth solution is chosen as possessing the highest
accretion rate as in \citep{bondi}.

\begin{figure}\label{fig1}
  \includegraphics[height=.5\textheight]{velocities}
  \caption{Normalized to Keplerian speed characteristic velocities of magnetized flow. Horizontal lines correspond to self-similar solution $v\sim r^{-1/2}.$}
\end{figure}

\section{Results \& Application to Sgr A*}\label{results}

\begin{figure}\label{fig2}
  \includegraphics[height=.5\textheight]{magnetization}
  \caption{Plot of magnetization $\sigma=(E_M+E_K)/E_{Th}$ with radius.}
\end{figure}
The results of my calculations confirm some known facts about
spherical magnetized accretion, agree with the results of
numerical simulations and have some previously unidentified
features.

Initially isotropic magnetic field exhibits strong anisotropy with
larger radial field $B_r.$ Perpendicular magnetic field
$B_\perp\ll B_r$ is dynamically unimportant in the inner accretion
region Fig\ref{fig1}. Because magnetic field dissipates, infall
onto the black hole can proceed \citep{schwa}.

Turbulence is supported by external driving in the outer flow
regions, but internal driving due to freezing-in amplification
takes over in the inner flow Fig\ref{fig2}. Magnetization of the
flow increases in the inner region with decreasing radius
consistently with simulations \cite{igumen06}. Density profile
appears to be $\rho\sim r^{-1.25}$ that is different from
traditional ADAF scaling $\rho\sim r^{-1.5}$ \citep{narayan}. Thus
the idea of self-similar behavior is not supported.

Compared to non-magnetized accretion, infall rate is 2-5 times
smaller depending on outer magnetization. In turn, gas density is
2-5 times smaller in the region close to the black hole, where
synchrotron radiation emerges \citep{narayan}. Sgr A* produces
relatively weak synchrotron \citep{narayan}. So, either gas
density $n$ or electron temperature $T_e$ or magnetic field $B$
are small in the inner flow or combination of factors works. Thus
low gas density in magnetized model is in qualitative agreement
with the results of modelling the spectrum.

Flow is convectively stable on average in the model of moving
blobs, where dissipation heat is released homogeneously in volume.
Moving blobs are in radial and perpendicular pressure
equilibriums. They are governed by the same equations as the
medium.

\section{Discussion \& Conclusion}\label{discussion}
The presented accretion study self-consistently treats turbulence
in the averaged model. This model introduces many weak assumptions
instead of few strong ones.

I take dissipation rate to be that of collisional MHD simulations.
But flow in question is rather in collisionless regime.
Observations of collisionless flares in solar corona
\citep{noglik} gives dissipation rate $20$ times smaller than in
collisional simulations \citep{biskamp03}. However, flares in
solar corona may represent a large-scale reconnection event rather
than developed turbulence. It is unclear which dissipation rate is
more realistic for accretion.

Magnetic field presents another caveat. Magnetic field lines
should close, or $\vec{\nabla}\cdot\vec{B}=0$ should hold. Radial
field is much larger than perpendicular in the inner region.
Therefore, characteristic radial scale of the flow is much larger
than perpendicular. If radial turbulence scale is larger than
radius, freezing-in condition does not hold anymore. Matter can
freely slip along radial field lines into the black hole. If
matter slips already at the sonic point, the accretion rate should
be higher than calculated.

Some other assumptions are more likely to be valid. Diffusion
should be weak because of high Mach number that approaches unity
at large radius. Magnetic helicity was found to play very small
dynamical role. Only when the initial turbulence is highly
helical, magnetic helicity conservation may lead to smaller
accretion rate. Neglect of radiative cooling is justified a
posteriori. Line cooling time is about $20$ times larger that
inflow time from outer boundary.

The study is the extension of basic theory, but realistic
analytical models should include more physics. The work is
underway.
\begin{theacknowledgments}
I thank my advisor Prof. Ramesh Narayan for fruitful discussions.
\end{theacknowledgments}

\bibliographystyle{aipproc}

Answer: Infall rate is 2-5 times smaller and gas density is 2-5 times smaller.


Input: What are the three teams that used conflict optimization in the challenge?

Context:
Paper Info

Title: Conflict Optimization for Binary CSP Applied to Minimum Partition into Plane Subgraphs and Graph Coloring
Publish Date: 25 Mar 2023
Author List: Loïc Crombez (from LIMOS, Université Clermont Auvergne), Guilherme Da Fonseca (from LIS, Aix-Marseille Université), Florian Fontan (from Independent Researcher), Yan Gerard (from LIMOS, Université Clermont Auvergne), Aldo Gonzalez-Lorenzo (from LIS, Aix-Marseille Université), Pascal Lafourcade (from LIMOS, Université Clermont Auvergne), Luc Libralesso (from LIMOS, Université Clermont Auvergne), Benjamin Momège (from Independent Researcher), Jack Spalding-Jamieson (from David R. Cheriton School of Computer Science, University of Waterloo), Brandon Zhang (from Independent Researcher), Da Zheng (from Department of Computer Science, University of Illinois at Urbana-Champaign)

Figure

Figure 1: A partition of the input graph of the CG:SHOP2022 instance vispecn2518 into 57 plane graphs.It is the smallest instance of the challenge with 2518 segments.On top left, you see all 57 colors together.On top right, you see a clique of size 57, hence the solution is optimal.Each of the 57 colors is then presented in small figures.
Figure 2: Number of colors over time for the instance vispecn13806 using different values p.The algorithm uses σ = 0.15, easy vertices, q max = 59022, but does not use the BDFS nor any clique.
Figure 3: Number of colors over time with different values of q max obtained on the instance vispecn13806.Parameters are σ = 0.15, p = 1.2, no clique knowledge, and no BDFS.
Figure 4: Number of colors over time with and without clique knowledge and BDFS obtained on the instance vispecn13806.Parameters are σ = 0.15, p = 1.2, and q max = 1500000.
Figure 5: Number of colors over time for the instance vispecn13806 for different values of σ.In both figures the algorithm uses p = 1.2, easy vertices, q max = 59022, but does not use the BDFS nor any clique.For σ ≥ 0.25, no solution better than 248 colors is found.
Figure 6: Number of colors over time (in hours) for the instance vispecn13806.
Several CG:SHOP 2022 results.We compare the size of the largest known clique to the smallest coloring found by each team on a selection of 14 CG:SHOP 2022 instances.
[20][21][22][23][24][25] with state-of-the-art graph coloring algorithms.The conflict optimizer underperforms except on the geometric graphs r* and dsjr*.CE39-0007), SEVERITAS (ANR-20-CE39-0005) and by the French government IDEX-ISITE initiative 16-IDEX-0001 (CAP[20][21][22][23][24][25].The work of Luc Libralesso is supported by the French ANR PRC grant DECRYPT (ANR-18-CE39-0007).

abstract

CG:SHOP is an annual geometric optimization challenge and the 2022 edition proposed the problem of coloring a certain geometric graph defined by line segments. Surprisingly, the top three teams used the same technique, called conflict optimization. This technique has been introduced in the 2021 edition of the challenge, to solve a coordinated motion planning problem.
In this paper, we present the technique in the more general framework of binary constraint satisfaction problems (binary CSP). Then, the top three teams describe their different implementations of the same underlying strategy. We evaluate the performance of those implementations to vertex color not only geometric graphs, but also other types of graphs.

Introduction

The CG:SHOP challenge (Computational Geometry: Solving Hard Optimization Problems) is an annual geometric optimization competition, whose first edition took place in 2019. The 2022 edition proposed a problem called minimum partition into plane subgraphs. The input is a graph G embedded in the plane with edges drawn as straight line segments, and the goal is to partition the set of edges into a small number of plane graphs (Fig. ) .
This goal can be formulated as a vertex coloring problem on a graph G defined as follows. The vertices of G are the segments defining the edges of G, and the edges of G correspond to pairs of crossing segments (segments that intersect only at a common endpoint are not considered crossing). The three top-ranking teams (Lasa, Gitastrophe, and Shadoks) on the CG:SHOP 2022 challenge all used a common approach called conflict optimization while the fourth team used a SAT-Boosted Tabu Search .
Conflict optimization is a technique used by Shadoks to obtain the first place in the CG:SHOP 2021 challenge for low-makespan coordinated motion planning , and the main ideas of the technique lent themselves well to the 2022 challenge. Next, we describe the conflict optimizer as a metaheuristic to solve constraint satisfaction problems (CSP) .
We start by describing a CSP. A CSP is a triple of • variables X = (x 1 , . . . , x n ), Each of the 57 colors is then presented in small figures. • domains D = (D 1 , . . . , D n ), and • constraints R. Each variable x i must be assigned a value in the corresponding domain D i such that all constraints are satisfied.
In general, the constraints may forbid arbitrary subsets of values. We restrict our attention to a particular type of constraints (binary CSP ), which only involve pairs of assignments. A partial evaluation is an assignment of a subset of the variables, called evaluated, with the remaining variables called non-evaluated.
All constraints involving a non-evaluated variable are satisfied by default. We only consider assignments and partial assignments that satisfy all constraints. The conflict optimizer iteratively modifies a partial evaluation with the goal of emptying the set S of non-evaluated variables, at which point it stops.
At each step, a variable x i is removed from S. If there exists a value x ∈ D i that satisfies all constraints, then we assign the value x to the variable x i . Otherwise, we proceed as follows. For each possible value x ∈ D i , we consider the set K(i, x) of variables (other than x i ) that are part of constraints violated by the assignment x i = x.
We assign to x i the value x that minimizes where w(j) is a weight function to be described later. The variables x j ∈ K(i, x) become non-evaluated and added to S. The weight function should be such that w(j) increases each time x j is added to S, in order to avoid loops that keep moving the same variables back and forth from S. Let q(j) be the number of times x j became non-evaluated.
A possible weight function is w(j) = q(j). More generally, we can have w(j) = q(j) p for some exponent p (typically between 1 and 2). Of course, several details of the conflict optimizer are left open. For example, which element to choose from S, whether some random noise should be added to w, and the decision to restart the procedure from scratch after a certain time.
The CSP as is, does not apply to optimization problems. However, we can, impose a maximum value k of the objective function in order to obtain a CSP. The conflict optimizer was introduced in a low makespan coordinated motion planning setting. In that setting, the variables are the robots, the domains are their paths (of length at most k) and the constraints forbid collisions between two paths.
In the graph coloring setting, the domains are the k colors of the vertices and the constraints forbid adjacent vertices from having the same color. The conflict optimizer can be adapted to non-binary CSP, but in that case multiple variables may be unassigned for a single violated constraint. The strategy has some resemblance to the similarly named min-conflicts algorithm , but notable differences are that a partial evaluation is kept instead of an invalid evaluation and the weight function that changes over time.
While the conflict optimization strategy is simple, there are different ways to apply it to the graph coloring problem. The goal of the paper is to present how the top three teams applied it or complemented it with additional strategies. We compare the relative benefits of each variant on the instances given in the CG:SHOP 2022 challenge.
We also compare them to baselines on some instances issued from graph coloring benchmarks. The paper is organized as follows. Section 2 presents the details of the conflict optimization strategy applied to graph coloring. In the three sections that follow, the three teams Lasa, Gitastrophe, and Shadoks present the different parameters and modified strategies that they used to make the algorithm more efficient for the CG:SHOP 2022 challenge.
The last section is devoted to the experimental results.

Literature Review

The study of graph coloring goes back to the 4-color problem (1852) and it has been intensively studied since the 1970s (see for surveys). Many heuristics have been proposed , as well as exact algorithms . We briefly present two classes of algorithms: greedy algorithms and exact algorithms. Greedy algorithms.
These algorithms are used to find good quality initial solutions in a short amount of time. The classic greedy heuristic considers the vertices in arbitrary order and colors each vertex with the smallest non-conflicting color. The two most famous modern greedy heuristics are DSATUR and Recursive Largest First (RLF ) .
At each step (until all vertices are colored), DSATUR selects the vertex v that has the largest number of different colors in its neighbourhood. Ties are broken by selecting a vertex with maximum degree. The vertex v is colored with the smallest non-conflicting color. RLF searches for a large independent set I, assigns the vertices I the same color, removes I from G , and repeats until all vertices are colored.
Exact algorithms. Some exact methods use a branch-and-bound strategy, for example extending the DSATUR heuristic by allowing it to backtrack . Another type of exact method (branch-and-cut-and-price) decomposes the vertex coloring problem into an iterative resolution of two sub-problems . The "master problem" maintains a small set of valid colors using a set-covering formulation.
The "pricing problem" finds a new valid coloring that is promising by solving a maximum weight independent set problem. Exact algorithms are usually able to find the optimal coloring for graphs with a few hundred vertices. However, even the smallest CG:SHOP 2022 competition instances involve at least a few thousands vertices.

Conflict Optimization for Graph Coloring

Henceforth, we will only refer to the intersection conflict graph G induced by the instance. Vertices will refer to the vertices V (G ), and edges will refer to the edges E(G ). Our goal is to partition the vertices using a minimum set of k color classes C = {C 1 , . . . , C k }, where no two vertices in the same color class C i are incident to a common edge.

Conflict Optimization

TABUCOL inspired neighbourhood One classical approach for the vertex coloring involves allowing solutions with conflicting vertices (two adjacent vertices with the same color). It was introduced in 1987 and called TABUCOL. It starts with an initial solution, removes a color (usually the one with the least number of vertices), and assigns uncolored vertices with a new color among the remaining ones.
This is likely to lead to some conflicts (i.e. two adjacent vertices sharing a same color). The local search scheme selects a conflicting vertex, and tries to swap its color, choosing the new coloring that minimises the number of conflicts. If it reaches a state with no conflict, it provides a solution with one color less than the initial solution.
The process is repeated until the stopping criterion is met. While the original TABUCOL algorithm includes a "tabu-list" mechanism to avoid cycling, it is not always sufficient, and requires some hyper-parameter tuning in order to obtain a good performance on a large variety of instances. To overcome this issue, we use a neighbourhood, but replace the "tabu-list" by the conflict optimizer scheme presented above.
PARTIALCOL inspired neighbourhood PARTIALCOL another local search algorithm solving the vertex coloring problem was introduced in 2008. This algorithm proposes a new local search scheme that allows partial coloring (thus allowing uncolored vertices). The goal is to minimize the number of uncolored vertices.
Similarly to TABUCOL, PARTIALCOL starts with an initial solution, removes one color (unassigning its vertices), and performs local search iterations until no vertex is left uncolored. When coloring a vertex, the adjacent conflicting vertices are uncolored. Then, the algorithm repeats the process until all vertices are colored, or the stopping criterion is met.
This neighbourhood was also introduced alongside a tabu-search procedure. The tabu-search scheme is also replaced by a conflict-optimization scheme. Note that this neighbourhood was predominantly used by the other teams.

Finding Initial Solutions

Lasa team used two approaches to find initial solutions: 1. DSATUR is the classical graph coloring algorithm presented in Section 1. 2. Orientation greedy is almost the only algorithm where the geometry of the segments is used. If segments are almost parallel, it is likely that they do not intersect (thus forming an independent set).
This greedy algorithm first sorts the segments by orientation, ranging from − π 2 to π 2 . For each segment in this order, the algorithm tries to color it using the first available color. If no color has been found, a new color is created for coloring the considered segment. This algorithm is efficient, produces interesting initial solutions and takes into account the specificities of the competition.

Solution Initialization

The gitastrophe team uses the traditional greedy algorithm of Welsh and Powell to obtain initial solutions: order the vertices in decreasing order of degree, and assign each vertex the minimum-label color not used by its neighbors. During the challenge Gitastrophe attempted to use different orderings for the greedy algorithm, such as sorting by the slope of the line segment associated with each vertex (as the orientation greedy initialization presented in Section 3), and also tried numerous other strategies.
Ultimately, after running the solution optimizer for approximately the same amount of time, all initializations resulted in an equal number of colors.

Modifications to the Conflict Optimizer

Taking inspiration from memetic algorithms, which alternate between an intensification and a diversification stage, the algorithm continually switched between a phase using the above conflict score, and one minimizing only the number of conflicts. Thus during the conflict-minimization phase, the random variables f (C j ) and w(u) are both fixed equal to 1 leading to a conflict score
Each phase lasted for 10 5 iterations. Adding the conflict-minimization phase gave minor improvements to some of the challenge instances.

Shadoks

In this section, we describe the choices used by the Shadoks team for the options described in Section 2.1. The Shadoks generally chose to eliminate the color with the smallest number of elements. However, if the multistart option is toggled on, then a random color is used each time. The conflict set S is stored in a queue.
The Shadoks tried other strategies, but found that the queue gives the best results. The weight function used is w(u) = 1 + q(u) p , mostly with p = 1.2. The effect of the parameter p is shown in Fig. . Notice that in all figures, the number of colors shown is the average of ten executions of the code using different random seeds.
The algorithm uses σ = 0.15, easy vertices, q max = 59022, but does not use the BDFS nor any clique. If q(u) is larger than a threshold q max , the Shadoks set w(u) = ∞ so that the vertex u never reenters S. If at some point an uncolored vertex v is adjacent to some vertex u of infinite weight in every color class, then the conflict optimizer is restarted.
When restarting, the initial coloring is shuffled by moving some vertices from their initial color class to a new one. Looking at Fig. , the value of q max does not seem to have much influence as long as it is not too small. Throughout the challenge the Shadoks almost exclusively used q max = 2000 • (75000/m) 2 , where m is the number of vertices.
This value roughly ensures a restart every few hours. q max =0.5k q max =5k q max =50k q max =100k q max =250k The Shadoks use the function f as a Gaussian random variable of mean 1 and variance σ. A good default value is σ = 0.15. The effect of the variance is shown in Fig. . Notice that setting σ = 0 gives much worse results.
Option (e) The goal of BDFS is to further optimize very good solutions that the conflict optimizer is not able to improve otherwise. Fig. shows the influence of BDFS. While on this figure, the advantages of BDFS cannot be noticed, its use near the end of the challenge improved about 30 solutions. The bounded depth-first search (BDFS) algorithm tries to improve the dequeuing process.
The goal is to prevent a vertex in conflict with some adjacent colored vertices from entering in the conflict set. At the first level, the algorithm searches for a recoloring of some adjacent vertices which allows us to directly recolor the conflict vertex. If no solution is found, the algorithm  In both figures the algorithm uses p = 1.2, easy vertices, q max = 59022, but does not use the BDFS nor any clique.
For σ ≥ 0.25, no solution better than 248 colors is found. could recolor some vertices at larger distances from the conflict vertex. To do so, a local search is performed by trying to recolor vertices at a bounded distance from the conflict vertex in the current partial solution. The BDFS algorithm has two parameters: adjacency bound a max and depth d.
In order to recolor a vertex v, BDFS gets the set C of color classes with at most a max neighbors of v. If a class in C has no neighbor of v, v is assigned to C. Otherwise, for each class C ∈ C, BDFS tries to recolor the vertices in C which are adjacent to v by recursively calling itself with depth d − 1.
At depth d = 0 the algorithm stops trying to color the vertices. During the challenge the Shadoks used BDFS with parameters a max = 3 and d = 3. The depth was increased to 5 (resp. 7) when the number of vertices in the queue was 2 (resp. 1). Degeneracy order Given a target number of colors k, we call easy vertices a set of vertices Y such that, if the remainder of the vertices of G are colored using k colors, then we are guaranteed to be able to color all vertices of G with k colors.
This is obtained using the degeneracy order Y . To obtain Y we iteratively remove from the graph a vertex v that has at most k − 1 neighbors, appending v to the end of Y . We repeat until no other vertex can be added to Y . Notice that, once we color the remainder of the graph with at least k colors, we can use a greedy coloring for Y in order from last to first without increasing the number of colors used.
Removing the easy vertices reduces the total number of vertices, making the conflict optimizer more effective. The Shadoks always toggle this option on (the challenge instances contain from 0 to 23% easy vertices).

Results

We provide the results of the experiments performed with the code from the three teams on two classes of instances. First, we present the results on some selected CG:SHOP 2022 instances. These instances are intersection graphs of line segments. Second, we execute the code on graphs that are not intersection graphs, namely the classic DIMACS graphs , comparing the results of our conflict optimizer implementations to previous solutions.
The source code for the three teams is available at: • Lasa: https://github.com/librallu/dogs-color • Gitastrophe: https://github.com/jacketsj/cgshop2022-gitastrophe • Shadoks: https://github.com/gfonsecabr/shadoks-CGSHOP2022

CG:SHOP 2022 Instances

We selected 14 instances (out of 225) covering the different types of instances given in the CG:SHOP 2022 challenge. The results are presented in Table . For comparison, we executed the HEAD code on some instances using the default parameters. The table shows the smallest number of colors for which HEAD found a solution.
We ran HEAD for 1 hour of repetitions for each target number of colors on a single CPU core (the HEAD solver takes the target number of colors as a parameter and we increased this parameter one by one). At the end of the challenge, 8 colorings computed by Lasa, 11 colorings computed by Gitastrophe, and 23 colorings computed by Shadoks over 225 instances have been proved optimal (their number of colors is equal to the size of a clique).
In order to compare the efficiency of the algorithms, we executed the different implementations on the CG:SHOP instance vispecn13806. The edge density of this graph is 19%, the largest clique that we found has 177 vertices and the best coloring found during the challenge uses 218 colors. Notice that vispecn13806 is the same instance used in other Shadoks experiments in Section 5. Notice also that HEAD algorithm provides 283 colors after one hour compared to less than 240 colors for the conflict optimizers.
We ran the three implementations on three different servers and compared the results shown in Figure . For each implementation, the x coordinate is the running time in hours, while the y coordinate is the smallest number of colors found at that time.

Results on DIMACS Graphs

We tested the implementation of each team on the DIMACS instances to gauge the performance of the conflict optimizer on other classes of graphs. We compared our results to the best known bounds and to the state of the art coloring algorithms HEAD and QACOL . The time limit for Lasa's algorithms is 1 hour.
CWLS is Lasa's conflict optimizer with the neighbourhood presented in TABUCOL , while PWLS is the optimizer with the neighbourhood presented in PARTIALCOL . Gitastrophe algorithm ran 10 minutes after which the number of colors no longer decreases. Shadoks algorithm ran for 1 hour without the BDFS option (results with BDFS are worse).
Results are presented in Table . We only kept the difficult DIMACS instances. For the other instances, all the results match the best known bounds. The DIMACS instances had comparatively few edges (on the order of thousands or millions); the largest intersection graphs considered in the CG:SHOP challenge had over 1.5 billion edges.
We notice that the conflict optimizer works extremely poorly on random graphs, but it is fast and appears to perform well on geometric graphs (r250.5, r1000.1c, r1000.5, dsjr500.1c and dsjr500.5), matching the best-known results . Interestingly, these geometric graphs are not intersection graphs as in the CG:SHOP challenge, but are generated based on a distance threshold.
On the DIMACS graphs, Lasa implementation shows better performance than the other implementations.

Answer: Lasa, Gitastrophe, and Shadoks.